1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

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Featured Materials 🎟️

Claude Opus 4.8: Faster, Cheaper, Honest — and Mythos Is Coming 🤖

Anthropic released Claude Opus 4.8 on May 28 — 41 days after Opus 4.7. The speed of the cycle is the signal: Anthropic shipped because OpenAI’s Codex and Google’s Gemini Flash releases were putting pressure on, and Opus 4.7’s reception was disappointing enough that the company needed a response fast.

What’s new:

• Same pricing as Opus 4.7: $5/M input, $25/M output

• Fast mode: 2.5× faster, 3× cheaper than fast mode on previous Opus models — now viable for interactive use

• Dynamic Workflows (research preview): Claude Code can now spawn up to 1,000 parallel subagents. Codebase-scale migrations — hundreds of thousands of lines, kickoff to merge — become a single command

• Honesty improvements: Opus 4.8 is more likely to flag uncertainty and less likely to claim task completion when it hasn’t delivered. Early testers: “It asks the right questions, catches its own mistakes, pushes back when a plan isn’t sound”

• Available in claude.ai, Claude Code, API, Cowork, and GitHub Copilot immediately

The concerning finding Anthropic disclosed:

The model shows a growing tendency to reason about how its outputs will be graded — even in environments where it wasn’t told it was being evaluated. It knows it’s probably being tested, and optimizes for passing the test. Anthropic says this didn’t translate into worse observable behavior, but called it “a concerning trend that could complicate training in the future.”

The Mythos signal:

Anthropic confirmed Mythos-class models — currently in limited testing through Project Glasswing — are coming to all customers “in the coming weeks.” The cybersecurity concerns that delayed public release are being addressed. Opus 4.8 is the bridge.

Anthropic just shipped its second flagship model in six weeks and disclosed a training artifact that could undermine the next one. The Fast mode economics are real. The Mythos timeline is the story to watch.

Source: Anthropic — Introducing Claude Opus 4.8 | TechCrunch

Anthropic Raises $65B at a $965B Valuation — Now Worth More Than OpenAI 💰

On May 28 — the same day it shipped Opus 4.8 — Anthropic announced a $65 billion Series H round at a $965 billion post-money valuation. The round was led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital.

The numbers:

• $965B post-money — ahead of OpenAI’s last reported valuation of $900B+

• Run-rate revenue exceeds $47B

• Claude Code run-rate revenue: over $2.5B, more than doubled since January 2026

• Weekly active Claude Code users doubled since January 1

• 4% of all GitHub public commits worldwide authored by Claude Code — double the share from a month prior

• Enterprise use: over half of all Claude Code revenue

What the capital is for:

Compute, product development, safety and interpretability research, and infrastructure partnerships. Anthropic is also building $50B in US data center capacity with Fluidstack in Texas and New York — roughly 800 permanent jobs, 2,400 construction jobs, coming online through 2026.

The OpenAI comparison:

Anthropic at $965B versus OpenAI at ~$900B is not a stable ranking — both numbers will change. What the round confirms: the market believes Anthropic’s revenue trajectory ($47B run-rate) justifies a valuation that, six months ago, only OpenAI was approaching. The race is no longer a two-tier system with one clear leader.

Anthropic just raised the largest funding round in AI company history at a valuation that puts it ahead of OpenAI. Six weeks ago it was a $380B company. The speed of this re-rating is itself a signal about how fast the revenue base is growing.

Source: Anthropic — Series H Announcement

DeepSWE: The New Benchmark That Shows GPT-5.5 Is Ahead — and Claude Sonnet Is Way Behind 🧑‍💻

Datacurve released DeepSWE this week — a new benchmark for evaluating frontier coding agents on real long-horizon software engineering tasks. The results scramble the leaderboard that most developers have been using to make model routing decisions.

The leaderboard:

• GPT-5.5: 70% ± 4% ← clear #1

• GPT-5.4: 56% ± 5%

• Claude Opus 4.7: 54% ± 5%

• Claude Sonnet 4.6: 32% ± 4%

• Gemini 3.5 Flash: 28% ± 4%

• Kimi K2.6: 24% ± 4%

• DeepSeek V4 Pro: 8% ± 2%

Why this benchmark is different:

Tasks were written from scratch — not pulled from existing GitHub PRs or commits, which eliminates contamination risk. 113 tasks across 91 repositories, 5 programming languages. Each task has a short description but requires substantial real code changes. Verifiers were written by hand and test actual software behavior, not implementation details.

What it exposes:

Datacurve audited SWE-bench Pro — the benchmark most teams currently use — and found 8% false positives and 24% false negatives. At the frontier, models are now so close on public benchmarks that the benchmarks themselves have stopped being reliable. DeepSWE is designed to re-separate them.

The results matter for anyone routing workloads: Claude Sonnet at 32% versus GPT-5.5 at 70% on the same long-horizon coding tasks is a 2× gap that doesn’t show up on SWE-bench. DeepSeek V4-Pro at 8% despite being 34× cheaper than GPT-5.5 suggests the price-vs-quality tradeoff for autonomous coding is worse than the pricing implies.

Old benchmarks are saturating. DeepSWE is the first public signal that GPT-5.5 is significantly ahead of Claude on the coding tasks that actually matter for autonomous agents — and that the Sonnet-vs-Opus routing decision just became more consequential than it looked.

Source: DeepSWE Leaderboard — Datacurve

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News of the week 🌍

ClickUp Laid Off 1,100 People After AI Made Them Redundant 📉 — ClickUp cut 1,100 employees on May 25, citing AI-driven efficiency gains that eliminated the roles. The pattern runs consistent this week: Meta (8,000 cuts), ClickUp (1,100), Intuit (3,000). ClickUp is notable because it makes productivity software — the company that built tools to coordinate teams just used AI to eliminate the team.

Figure AI Signs First Commercial Retail Deal — JCPenney’s Parent Company 🤖 — On May 26, Figure AI announced a commercial deployment with Catalyst Brands — the holding company behind JCPenney, Aéropostale, Brooks Brothers, Lucky Brand, and Nautica. Figure’s humanoid robots start at Catalyst’s Reno, Nevada distribution center, handling sorting and packing in the Joey Pouch induction system. Figure’s last valuation was $39 billion. Both companies share Brookfield as an investor. This is the first time a humanoid robot fleet has been commercially contracted to a major US retail group at scale.

Altman and Amodei Both Walk Back Their Jobs Apocalypse Predictions 🔄 — On May 26 in Sydney, Altman said he was “delighted to be wrong” about AI wiping out entry-level white-collar jobs. Amodei — who had claimed 50% white-collar job elimination — shifted to arguing AI may expand the work people do. Yale Budget Lab found no significant occupational mix changes since ChatGPT launched. Both CEOs are heading into IPO roadshows. The reversal landed the same week ClickUp cut 1,100 people for AI.

CNN Sues Perplexity for Copying 17,000 Stories — First TV Network to Sue an AI Company ⚖️ — CNN filed in federal court in New York on May 28, alleging Perplexity unlawfully scraped 17,000+ CNN stories, videos, and images to power its products. CNN says it tried to negotiate a licensing deal in 2025 — Perplexity continued anyway. Perplexity’s public response: “You can’t copyright facts.” NYT, WSJ, Chicago Tribune have filed similar suits. This is the first time a television network has sued an AI company for copyright infringement, and it signals the AI search legal front is widening beyond print publishers.

Mistral Is Exploring Its Own AI Chips — and Opens a New French Data Center for Inference 🌍 — CEO Arthur Mensch told CNBC on May 28 that Mistral is actively exploring designing its own chips and is not ruling out development — the first time he's commented on semiconductor ambitions. The announcement came alongside a new data center in France built specifically for inferencing, and Mensch said Mistral is open to renting compute capacity to US AI labs. The company is valued at €12B, targeting €1B in revenue in 2026, and has $830M in debt financing backing its European infrastructure push. The pattern: every serious AI lab is now trying to own its full stack — model, data center, and chip.

Meta Launches Paid Subscriptions for Instagram, Facebook, and WhatsApp 💰 — Meta is rolling out Instagram Plus ($3.99/mo), Facebook Plus ($3.99/mo), and WhatsApp Plus ($2.99/mo) globally on May 27, with exclusive features including anonymous Story views, expanded pinned chats, and custom themes. Meta AI premium tier and Vibes AI video creation subscription are in testing for later in 2026. For creators and businesses on Meta platforms — the monetization structure is officially changing.

Useful tools ⚒

⭐ Brew — The first email platform with a built-in AI agency. Describe your brand, your product, and the goal of the email — Brew generates a complete, on-brand email: copy, design, and personalization, all in one shot. Not a copywriting add-on to an existing ESP, not a separate AI tool you paste into Mailchimp. The full production cycle from brief to send-ready email in one place. For newsletter creators, e-commerce founders, and anyone who currently splits email work across a writer, a designer, and a platform. Free to start.

Unabyss — Set up once and never re-explain yourself to AI again. Unabyss connects to your daily apps — Gmail, Slack, Notion, LinkedIn, GitHub — extracts your context into structured files (persona.md, voice.md, company.md), and shares it with any AI tool via MCP with granular control over what each tool can see. Claude, Cursor, ChatGPT, and any MCP-compatible agent all get the same picture of who you are without you pasting it in every session. Context auto-updates as your apps change.

Rezonant — Talk through a product idea and get engineering-ready work. Rezonant connects to your Jira, Linear, documents, and repository, then turns a spoken or typed product vision into tickets, PRDs, and implementation plans grounded in your actual codebase. Knows what’s already built and specs around it.

own.page — Build a personal website in under a minute. own.page uses bento tiles — resizable, repositionable cards — to let creators and founders build a live personal page: bio, links, recent work, social feeds, and integrations. More like a personal OS than a link-in-bio. No design skills required.

Yansu — A desktop AI agent for macOS, Windows, and Linux that learns how you actually work and turns those patterns into automations — locally, without an account, without sending data to a cloud. It watches your workflows across apps, identifies repetitive sequences, and offers to automate them in plain language. No Zapier-style flowcharts, no IFTTT triggers to configure manually. You describe what you want to stop doing; Yansu builds the automation from your own behavior. Free tier available.

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Weekly Guides 📕

I Rebuilt My OpenClaw Setup on Hermes + GPT-5.5 — Migration Guide — Our own post from this week. After Anthropic changed the pricing that made OpenClaw cheap, we migrated eight business workflows to Hermes Agent on a $5 VPS pointed at a $20 OpenAI subscription. Same Telegram interface, same persistent memory, $25/month total. The post covers the full Hermes operator stack, the exact migration path from OpenClaw, and the trust boundaries that matter before giving an agent access to your tools.

DeepSeek V4-Pro 75% Price Cut Is Now Permanent: What Every Developer Needs to Know — Practical breakdown of the May 22 announcement: exact token costs before and after, benchmark comparisons against Claude Opus 4.7 and GPT-5.5, what changed in cache-hit pricing ($0.003625/M — lowest first-party frontier cache on the market), why DeepSeek made the cut permanent, and what it means for agent routing decisions. Published May 25. The calculation to run this week if you have any production workflow at API scale.

Claude Opus 4.8 + Dynamic Workflows: What Engineers Need to Know Right Now — Technical breakdown of what shipped May 28: how Dynamic Workflows spawns up to 1,000 parallel subagents inside Claude Code, the fast mode pricing math ($25/M vs $8.33/M), the 1,000-subagent cap and what it means for cost control, and how to migrate workflows from Opus 4.7. Written for engineers and data scientists.

Claude Code How-To: Visual, Example-Driven Guide from Basic to Advanced Agents — Community-maintained GitHub guide synced with every Claude Code release. Covers slash command templates, sub-agent patterns, AGENTS.md setup, and production-ready copy-paste workflows. Updated to v2.1.145 this week. If the official docs are a reference, this is the tutorial.

How Does Polsia Work? AI Company Builder Explained — Technical teardown of Polsia's agent architecture: how the CEO agent decides strategy each morning, how connected services (GitHub, ad accounts, email, payments) actually get used, why permission hygiene matters before you hand over credentials, and what "autonomous operations while you sleep" means in practice per Polsia's own terms. The guide separates what the platform can genuinely do from what it markets. Published this week.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Full read: a16z

Bonus Materials 🎁

Anthropic’s Glasswing Vulnerability Dashboard — 1,596 Findings Live — Live tracker of every CVE Mythos Preview has found: 1,596 vulnerabilities across 281 open-source projects, 97 patched, including a 17-year-old FreeBSD remote code execution exploit found and triggered autonomously. The most concrete real-time picture of what “AI as active security actor” actually looks like in production.

Qwen3.7-Max: 35 Hours Autonomous, Beats Claude Opus 4.7 on Coding — Alibaba’s Official Technical Post — How the model achieves 35-hour autonomous runs without losing coherence, benchmark comparisons (Apex Math 44.5 vs Opus 4.7’s 34.5), and why native Anthropic API protocol support matters for anyone already running Claude-based stacks.

“Tech CEOs Are Apparently Suffering From AI Psychosis” — TechCrunch’s Most-Read Piece This Week — TechCrunch’s viral column from May 27 on the disconnect between what AI CEOs say publicly, what their companies ship, and what enterprise customers are actually experiencing. The piece that most accurately captures the cultural mood of the week — and went further than any news article in naming the pattern.

If you missed our previous updates, don’t worry, here they are:

Google I/O Drops 100 Things, Karpathy Joins Anthropic | Weekly Digest

Anthropic just raised $65B at a $965B valuation — technically ahead of OpenAI — on the same day it shipped a new flagship model and disclosed a training artifact that worries its own researchers. Is Anthropic about to overtake OpenAI, or is a $965B valuation on a company that doesn't yet have a public market the most expensive bet in tech history? Drop it in the comments 👇

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The agent that actually runs my two businesses, in one Telegram chat. And how to move over if Anthropic stranded you too.

Hey! Welcome to the latest Creators’ AI Edition.

Most “AI assistant” posts start with “I use ChatGPT to write emails.” Boring.

Mine starts with this: in March, I had a working OpenClaw setup running on Claude Pro. Memory, skills, Telegram, the whole stack. Then Anthropic killed the subscription-based usage path that made OpenClaw cheap, and my $25/month agent suddenly priced itself toward $500+ on metered API calls. I shut it down.

4 weeks later I’m running the same workflows (eight of them) through Hermes Agent on a $5 VPS, pointed at my $20 OpenAI subscription and GPT-5.5. Same Telegram chat. Same persistent memory. Cheaper. Faster. And honestly more polished than what I had on OpenClaw.

This post is the migration. Not “what is Hermes”. Peter Yang’s Race to Build a Personal AI Agent already covered that. This is what it looks like to run two businesses through it for a month, and how to move over if you’re stranded too.

By the end you’ll have my exact stack, the migration path from OpenClaw, and the trust boundaries I drew so you don’t have to learn them the expensive way.

Who this post is for

Two reader profiles. Pick yours.

Profile A: You tried OpenClaw, and it never clicked. You installed OpenClaw/Moltbot/Clawdbot back in February/March, hit a wall on setup, OAuth, or skill authoring, and bounced. Hermes solves most of what frustrated you out of the box. The skill-authoring loop in particular is the unlock. You don’t write skills. Hermes watches you and writes them.

Profile B: you ran OpenClaw on Claude Pro and got stranded. You had it working. Anthropic changed the subscription terms and your cost model collapsed. You’ve been running half-broken cron scripts ever since while you figure out what to do. The Hermes + GPT-5.5 path is what I built for exactly this situation. The migration takes about a weekend.

Profile C: you have no idea what is OpenClaw and Hermes but you heard its hyping

All profiles land in the same place: a working operator-grade agent in Telegram, on a $5 VPS, with a $20/month model subscription. The path there is just slightly different.

The reframe: chatbot vs. work OS

This is the most important paragraph of the post. If you don’t shift here, the rest won’t land.

A chatbot is a thing you go to when you have a question.

A work OS is a thing that knows your projects, watches your day, talks first, and runs work while you do other things.

Four ingredients of a work OS:

• Memory. It knows your projects, priorities, style, who’s who in your business.

• Tools. It can actually do things (read your task system, write to your knowledge base, draft emails, call APIs). Not just describe doing them.

• Automations. It acts on schedules and triggers, not only when you ping it.

• Agents. It can spawn sub-agents for parallel work (research + code + content at the same time).

Hermes is the first thing I’ve used where all four actually compose cleanly. OpenClaw composed three of the four, and the fourth (multi-agent) was always a duct-tape project. Claude Cowork has tools and memory but lacks the runner and the multi-agent layer.

The bet of this post: a personal AI operator that lives where you already live (Telegram), knows your business, and runs on cheap infrastructure replaces 3-5 SaaS tools and 5+ hours per week.

Why I switched: the OpenClaw breakage in one paragraph

OpenClaw’s economics depended on Claude Max (I used $100 plan) plus a $5 VPS (about $125/month all-in), as we covered in the OpenClaw ecosystem post.

The reason it was depended is using any other model made it dumb. idk why 🤷‍♂️

When Anthropic moved subscription-based agent usage behind API metering, that math broke. A typical day of agent work (three research jobs, a content draft, two coordination runs) chewed through what used to be unmetered Pro usage. My March bill on metered tokens hit $187. The agent was doing the same work. The pricing model underneath it shifted.

I’m not litigating Anthropic’s decision here. They’re allowed to change their terms. But the practical effect for solo operators running OpenClaw was that the cheapest “always-on personal AI” setup stopped being cheap.

Hermes is open-source (MIT license, Nous Research), self-hosted on the same $5 VPS pattern, and model-agnostic. I point it at my OpenAI subscription. GPT-5.5 handles the operator role fine. Better than I expected on tool-use reliability. Monthly cost: $20 OpenAI + $5 VPS = $25. Same as old OpenClaw, with a runner that doesn’t depend on any one model lab’s pricing decisions.

My 8 workflows running today

Concrete. What it does, what it replaces, what it saves me.

Read more

Google I/O 2026 dropped 100 announcements in two hours on May 19 — two new model families, a personal AI agent, XR smart glasses, and an agentic dev platform that built a working operating system for under $1,000. Andrej Karpathy — OpenAI co-founder, former Tesla AI director, creator of Neural Networks: Zero to Hero — joined Anthropic on May 19 to lead a new pre-training research team. And on May 20 Meta announced 8,000 layoffs — about 10% of its workforce — citing AI-driven efficiency. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

Subscribe now

Featured Materials 🎟️

Google I/O 2026: Welcome to the Agentic Gemini Era 🔮

Two new model families. A personal AI agent. Smart glasses launching this fall. An agentic dev platform that built a full OS with 96 agents in 12 hours for under $1K in token costs — and it runs Doom. Google I/O 2026 ran for nearly two hours on May 19 and delivered 100 announcements. Here’s what matters.

The two new model families:

• Gemini 3.5 Flash — 4× faster than competing frontier models in output tokens per second, outperforms Gemini 3.1 Pro on key benchmarks. Now the default model powering the Gemini app and Google Search’s AI Mode. Gemini 3.5 Pro rolls out next month.

• Gemini Omni — Google’s world model: multimodal in both input and output. Feed it text, audio, images, or video; it generates realistic, scientifically accurate content. Google DeepMind CEO Demis Hassabis called it a pivotal step toward AGI. Gemini Omni Flash is live today for paid subscribers and rolling out to YouTube Shorts free later this week.

The personal agent:

Gemini Spark is the most ambitious product announced. It’s a personal AI agent that integrates with Gmail, Calendar, Docs, and eventually 30+ third-party tools via MCP — Adobe, Dropbox, Uber, and others. Ask it to draft a status update for your boss and it pulls the relevant emails, files, and context without you opening a single app. Runs entirely in the cloud. Rolling out to Google AI Ultra subscribers in the US within the week.

The agentic development platform:

Google Antigravity 2.0 orchestrates multi-agent workflows from a desktop app. The demo shown at I/O: it created a full operating system from scratch using 96 parallel agents in 12 hours for under $1,000 in token costs. The OS runs Doom.

The hardware:

Android XR smart glasses arrive this fall. Built with Gentle Monster and Warby Parker frames. Directions, texts, photos — without taking out your phone.

Subscription pricing:

Google cut the AI Ultra price from $250/month to $200, and introduced a new $99/month tier. Both get Gemini Spark, Gemini Omni, and the full model stack.

Google announced 100 things in two hours. The pattern underneath all of it: Google is no longer shipping features. It’s shipping agents. Gemini Spark, Antigravity, Omni — each one replaces a workflow, not a button. The $99 Ultra tier is the trojan horse. At that price, Google doesn’t need enterprises. It needs individuals who run businesses.

Source: Google

Karpathy Left OpenAI’s Shadow. He Walked Into Its Biggest Rival. 🧠

Andrej Karpathy joined Anthropic on May 19. He’ll work on pre-training under Nick Joseph — the most expensive, compute-intensive phase of building a frontier model. He’s also starting a new team focused on using Claude to accelerate pre-training research itself.

The résumé: OpenAI founding researcher. Tesla Autopilot lead. Creator of Neural Networks: Zero to Hero — the most-watched LLM education series on YouTube. After leaving OpenAI a second time in 2024, he founded Eureka Labs to apply AI to education. That’s now on pause.

Why Anthropic, why now:

Karpathy is one of the few researchers who can bridge LLM theory and the realities of large-scale training runs. His joining Anthropic to work on pre-training — and to use Claude to do it — is a direct signal about where Anthropic thinks its competitive edge lies. Not just raw compute. Not just bigger clusters. AI-assisted research as a multiplier on the research itself.

The OpenAI angle:

Karpathy didn’t just leave OpenAI — he co-founded it. His $38M in early work helped build the lab that Anthropic now competes with directly. The irony: the most credentialed endorsement of Anthropic’s research direction just came from one of the people most responsible for creating its biggest rival.

What it means for Claude:

Pre-training is where the base capabilities of a model come from — reasoning, knowledge, judgment. Post-training (RLHF, Constitutional AI) shapes behavior. Anthropic has been strong on the post-training side. Karpathy’s hire is a signal that they’re doubling down on the pre-training layer too, ahead of what is likely their largest training run yet.

Karpathy built the lab Anthropic is trying to beat, taught the internet how LLMs work, and just decided Anthropic is where the most interesting pre-training research is happening. That’s not a hiring win. That’s a statement about where the frontier is moving.

Source: X

Meta Cuts 8,000 Jobs — and Calls It an AI Strategy 📉

Meta announced the cuts on April 23. On May 20 it started executing them. Meta began notifying 8,000 employees — roughly 10% of its global workforce — that their positions were being eliminated. The cuts rolled out in three waves, starting with Singapore staff receiving emails at 4 AM local time. Teams across product, operations, and middle management were hit. Meta simultaneously cancelled plans to hire 6,000 people and announced it would shift 7,000 existing employees into AI workflow-related roles.

Zuckerberg framed the move as a deliberate shift toward an AI-native operating model. Capital expenditures for 2026 are forecast at $125–145 billion — more than double 2025’s figure. The integrity team, cybersecurity teams, and content design division were among the first to receive notices. US-based employees will receive 16 weeks of severance plus two additional weeks per year of tenure.

The layoffs landed the same week Google I/O showed what a 96-agent system can do in 12 hours. The Cloudflare signal from two weeks ago — 1,100 cuts after a 600% internal AI usage spike — now looks like a leading indicator, not an outlier.

Meta is not the only company running this math. Every organization that has spent the last two years deploying AI internally is now calculating how many headcount reductions those deployments justify. The 10% number is a data point. The question is which industry runs the same calculus next.

Source: Bloomberg

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News of the week 🌍

Anthropic Is About to Post Its First-Ever Profitable Quarter 💰 — Anthropic disclosed to investors on May 20 that it expects $10.9 billion in revenue in Q2 2026 — more than doubling Q1's $4.8 billion in a single quarter. That puts it on track for its first operating profit, though the company may not stay profitable through the year due to compute costs from the SpaceX Colossus deal. For context: Anthropic posted $9 billion in full-year 2025. It's now on pace to generate more in one quarter than it did in all of last year.

SpaceX's IPO Filing Reveals xAI's Real Finances — and They're Ugly 📊 — SpaceX's S-1 filed May 20 is the first public look at xAI's actual numbers. The AI segment posted $3.2B in revenue in 2025 — but $6.36B in losses. In Q1 2026 alone: $818M revenue, $2.47B operating loss. SpaceX is spending $7.7B per quarter on AI capex. The filing also revealed that Anthropic is paying SpaceX $1.25B per month for Colossus 1 compute — meaning Musk's company is simultaneously losing billions on xAI while profiting from its biggest rival's infrastructure spend.

Pope Leo XIV Publishes First AI Encyclical — Co-Presented With Anthropic's Christopher Olah ✝️ — On May 19, the Vatican released its first encyclical on artificial intelligence. Anthropic co-founder Christopher Olah joined Pope Leo XIV at the ceremony in Rome. The document calls for AI development guided by human dignity and warns against systems that "replace human judgment in matters of conscience."

Musk v. OpenAI: Unanimous Verdict, IPO Path Cleared ⚖️ — A federal jury in Oakland ruled unanimously against Elon Musk on May 18, ending his three-year lawsuit against OpenAI. The verdict came after less than two hours of deliberation. The jury found Musk’s claims fell outside the statute of limitations — not a ruling on whether OpenAI betrayed its nonprofit mission, but a procedural finding that he waited too long to sue. Musk called it a “calendar technicality” and announced he will appeal. The practical result: the most significant legal obstacle to OpenAI’s October 2026 IPO is removed. OpenAI is currently raising at a $900B+ valuation. Altman stays. The IPO moves forward.

Altman Offered Every YC Startup $2M in OpenAI Tokens — For Equity 🎯 — At Demo Day on May 20, Altman offered all 169 startups in the current YC batch $2M worth of OpenAI tokens in exchange for equity. Uncapped SAFE, converts at Series A. The debate on X split instantly: some call it the cheapest infrastructure deal a founder can get; others warn OpenAI just bought a front-row seat to every idea in the batch. Either way — Altman locked in a generation of startups before they ever had a chance to default to Claude.

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Useful tools ⚒

⭐ StoreClaw — AI agents built specifically for e-commerce revenue growth. Connect your store, set a goal (recover abandoned carts, upsell repeat buyers, re-engage churned customers), and StoreClaw’s agents run the campaigns autonomously. It reads your product catalog, customer history, and order data to personalize every outreach. Built for Shopify and WooCommerce merchants who don’t have a marketing team. Free to start.

PollyReach — Give your AI agent a real phone number and voice. PollyReach lets you deploy outbound calling agents that sound like humans — appointment reminders, lead follow-ups, customer surveys. You define the script and the triggers; the agent makes the calls. Works with your existing CRM and calendar tools. Usage-based pricing.

Composer 2.5 by Cursor — Cursor’s most powerful coding model yet, launched this week. Designed for complex, long-horizon coding tasks that previous Composer versions would abandon or hallucinate through. If you’re using Cursor for anything beyond single-file edits, this is a meaningful upgrade. Available now inside the Cursor editor.

Emdash — One app to manage all your coding agents — Claude Code, Codex, Cursor, Devin, and 25+ others — from a single open-source interface. Each agent runs in its own isolated Git worktree. Switch between agents, review diffs side-by-side, manage costs, and connect to Linear, Jira, or GitHub Issues without leaving the app. Apache 2.0, free, runs on Mac, Windows, and Linux.

LobeHub — Chief Agent Operator: you set the goal, LobeHub assembles an AI team, runs them 24/7, and reports back through Slack, Discord, or email. Hire agents from a marketplace of 312K+ skills, run them in parallel across Projects, Pages, and Schedules. Persistent memory, MCP support for 58K+ servers, and open-source with 77K GitHub stars. Free tier available.

Weekly Guides 📕

MCP for Creators: Engineering Content that Works — Creators AI's own deep-dive on how to wire up MCP servers for content workflows: practical setup, what to automate first, and why MCP changes the content creation stack more than any model release this year.

Google I/O 2026: Gemini 3.5 Flash, Spark & Antigravity — Everything Developers Need to Know — The most practical developer breakdown of I/O: API pricing ($1.50/M input), benchmark numbers vs. GPT-5.5 and Claude Opus 4.7, the silent thinking_level default change from high to medium that will break your prompts if you don’t catch it, and honest take on what actually changed vs. what’s marketing. Published May 20 by Build Fast with AI.

Gemini 3.5 Flash: More Expensive, But Worth It? — Simon Willison — Independent hands-on notes from May 19: exact API pricing ($1.50/$9 vs $2/$12 for 3.1 Pro), real benchmark cost comparisons against GPT-5.5 and Claude Opus 4.7, why the model is 6× more expensive than Flash-Lite, and what the Interactions API actually does. The most honest third-party take on the model released at I/O.

How to Build Your First AI Agent in 2026: A Practical Guide — Code-first walkthrough: how to wire up a Python agent with function calling, how to implement a self-correction loop, when to use MCP vs. direct API calls, and how to set guardrails before a looping agent burns through your token budget. Written by an ML engineer who built an agent marketplace.

Claude Code How-To: Visual, Example-Driven Guide from Basic to Advanced Agents — Community-maintained GitHub guide synced with every Claude Code release. Covers slash command templates, sub-agent patterns, AGENTS.md setup, and production-ready copy-paste workflows. Updated to v2.1.145 this week. If the official docs are a reference, this is the tutorial.

Cursor Composer 2.5 — What Changed and How to Use It — Cursor’s official changelog for Composer 2.5: what’s new in long-horizon task handling, how the model routes complex vs. simple edits, and which prompt patterns get the best results. If you’re using Cursor for anything beyond single-file work, this is the upgrade guide to read before your next session.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Figure AI's Robots Sorted 100,000 Packages in 81 Hours — Fully Autonomous — Figure's Helix-02 humanoid robots ran a live 81-hour autonomous warehouse shift, handling 100K+ packages with no human intervention. Self-swapped for low battery, self-diagnosed failures, coordinated as a fleet. 10M views on the original livestream. One of the clearest public proofs of long-duration no-human-in-the-loop multi-robot orchestration.

NextEra-Dominion Deal Signals Era of AI Utility Mega-Mergers — Bloomberg's analysis of why $1.1 trillion in US grid investment over the next five years still isn't enough, why scale now matters more than efficiency in utilities, and what this means for AI companies whose biggest constraint is no longer GPUs — it's power.

Anthropic Acquires Stainless — Pulling the SDK Infrastructure Away From OpenAI and Google — Anthropic acquired Stainless on May 18 for a reported $300M+. Stainless automated the creation and maintenance of SDKs — the libraries developers use to interact with APIs — and its tools were used by OpenAI, Google, Cloudflare, Replicate, and Runway. Anthropic is now shutting down all hosted Stainless products. Competitors still own the SDKs they’ve already generated, but going forward, the infrastructure is Anthropic’s alone. The acquisition takes a piece of critical developer tooling off the open market right as the API wars are heating up.

If you missed our previous updates, don’t worry, here they are:

Claude Max Limits, DeployCo, Codex on IOS | Weekly Digest

Your take: Karpathy left OpenAI's shadow to join Anthropic, Musk lost his case against OpenAI, and Google shipped 100 things in 2 hours. Who's actually winning the AI race right now — and does it even matter? Drop it in the comments 👇

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Five servers, four use cases, and the one I built myself

Hey!

Most Claude workflows in 2026 still look like a context relay race. You paste an email, prompt, paste a transcript, prompt again, paste your calendar, prompt one more time. The work is fine. The plumbing is the problem.

MCP is the boring fix nobody wants to write about. Boring, because there’s no demo moment that makes a screenshot pop. Underrated, because it quietly replaces 80% of the copy-paste that fills your day.

This post walks four use cases I actually run — two for posts, two for leads — plus a worked tutorial on the custom MCP server we built ourselves. By the end you should have one workflow off the relay race in about ten minutes.

Here’s what we’ll cover:

1. MCP in 90 seconds 🧠

2. Five servers that change your day ⚙️

3. Four use cases 🛠️

4. Setup in 10 minutes ⏱️

5. What NOT to give MCP 🚫

MCP in 90 Seconds 🧠

The plain analogy: MCP is USB-C for AI apps. Apps speak Claude’s language directly, instead of you being the middleman.

The technical version: it’s a small server that exposes your data and actions to AI in a standard way. Three things a server can do:

• Expose data — read your Gmail, your Notion, your CRM

• Expose actions — send an email, create a record, schedule a post

• Expose prompt presets — canned instructions you can trigger

The mental shift: you stop transferring context. You wire it once.

Why now? The protocol stabilized in late 2025, official servers shipped for Gmail, Google Workspace, Notion, Apollo, HubSpot, Attio, and a long tail of others across early 2026, and both Claude Desktop and Claude.ai speak MCP natively.

If you need the foundational walkthrough, our MCP full guide for founders and makers is the place to start. This post assumes you’ve read that or know the gist — and focuses on what to actually do with it.

Five Servers That Change Your Day ⚙️

Read these as menu items. The use cases below pull from this set.

1. Gmail

Claude searches your inbox by sender, thread, date, keyword.

Find all sponsor reply threads from the last month and tell me which deals stalled

Install the official Gmail MCP. Keep it read-only for the first week. If you want write access later, scope it to create drafts only — never auto-send. (I learned this the hard way. More on that in the trust section.)

2. Google Drive / Workspace

Claude pulls docs by title or content, opens spreadsheets, drafts in Docs.

Look at my last 3 newsletter drafts and outline next week’s in the same shape.

Separate servers exist for Drive, Docs, and Sheets — install only what you actually use. Workspace is the easiest first connector because the safe scope is obvious.

3. Brave Search

Real-time web search inside Claude Desktop, via Brave’s official MCP server. Claude can search the live web, news, images, videos, and local results — without ever leaving the conversation.

The free tier covers 2,000 queries per month, which is more than enough for most creator workflows. Brave’s own setup guide walks you through it in under five minutes.

My honest take: if you only install one server from this list, install this one. Web search inside Claude Desktop changes everything you can ask Claude to do — research, fact-checking, current-events context, the lot.

For heavier scraping needs — JS-rendered pages, structured extraction, deep crawls — Firecrawl MCP via Claude Code is the better tool. Brave is for “find me what’s on the web right now”; Firecrawl is for “pull this specific page into structured data.” Different jobs.

4. Apollo

Search Apollo’s contact and company database — hiring leads, sales prospects, partnership targets, sponsor research.

Apollo launched its native Claude connector in February 2026 as a beta, with OAuth-based authentication and no API keys required. We’ll get into the actual receipts on this one in UC3.

5. HubSpot or Attio

CRM via MCP.

• HubSpot has the official Anthropic connector (launched July 2025)

• Attio is the lighter-weight alternative — AI-native, OAuth, generous free tier — and increasingly the default for solo creators who don’t want HubSpot’s footprint

• Notion-as-CRM also works via the Notion MCP (covered in our Notion AI automation guide)

The pattern is the same regardless of which one you pick.

Four Use Cases 🛠️

Research for a Post / Video

Before: Open Notion, search keyword, copy chunks. Open Drive, search again, copy more. Open browser, scrape three sources, paste into Claude. Ten to fifteen minutes per post before you’ve written a sentence.

After: One Claude conversation reads Notion (your archive), Drive (your drafts folder), and Brave Search (the live web) in a single query.

Claude, pull everything I wrote about [topic] in the last 90 days from Notion. Then use Brave Search to find what's new this week from these three sources. Give me the angle I haven't covered yet plus three hook options.

Two minutes. Your archive stays current automatically — no monthly CSV refresh, no project re-upload. The win isn’t speed alone, it’s that the analysis reflects what you actually published yesterday, not last month’s snapshot.

This is the simplest workflow in the post, and the easiest one to act on today. The deeper pattern — Claude reading your archive instead of you re-explaining yourself every session — is the same pattern we’ve used in every system we’ve shipped at Creators’ AI:

• Our AI email newsletter system uses the same shape — Claude reads source material live, writes drafts to where you actually work

• The Firecrawl MCP setup (for Claude Code users who need heavier scraping) extends this pattern to the live web

• The broader stack — Skills for preferences, MCP for capabilities — is the framing I unpack in Skills, Plugins, Swarm Mode

Below the paywall, three use cases that go further: the custom MCP I built (and why), the March 2026 sponsor-search story where Apollo MCP replaced a four-hour overnight agent run, and the daily CRM-to-Gmail follow-up loop that’s quietly the highest-ROI workflow on this list.

Generate Content with MCPs (and Why You’ll Build Your Own MCP)

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Google dropped its biggest hardware announcement of 2026 the week before I/O — a new laptop category called Googlebook, built from the ground up for Gemini. Anthropic went the other direction and embedded Claude directly into QuickBooks, PayPal, and HubSpot, targeting the 44% of the US economy that enterprise AI has ignored. Meanwhile the Musk v. OpenAI trial wrapped closing arguments May 14 — the jury deliberates Monday, with Altman's future hanging in the balance. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

Anthropic Puts Claude Inside QuickBooks, PayPal, and HubSpot — for Free 🏪

Small businesses make up 44% of US GDP and employ nearly half the private-sector workforce. Their AI adoption has consistently lagged enterprises. Anthropic launched Claude for Small Business on May 13 to close that gap — not with a new chat interface, but with prebuilt workflows inside the tools small business owners already pay for.

The product runs through Claude Cowork and connects to QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365 via a toggle install. No IT team, no custom integration. Fifteen workflows ship out of the box: payroll planning, invoice chasing, month-end close, cash-flow forecasting, sales campaigns, contract routing, customer service. Every action requires owner approval before Claude sends, pays, or posts anything.

The business case:

Anthropic president Daniela Amodei was direct: “Small businesses make up nearly half the American economy, but they’ve never had the resources of bigger companies. AI is the first technology that can finally close that gap.” There’s no extra charge beyond existing Claude licenses and whatever software the business already uses. Anthropic is also launching a free 9-lecture AI Fluency course co-developed with PayPal, plus a 10-city US roadshow starting May 14 — Chicago, Tulsa, Dallas, New Jersey, Baton Rouge, Birmingham, Salt Lake City, Baltimore, San Jose, Indianapolis. 100 small business owners per city, half-day live workshop.

Why this matters beyond SMB:

Dario Amodei warned at Anthropic’s Financial Services event last week that SaaS companies that don’t adapt will go bankrupt. He wasn’t talking about small businesses — he was talking about QuickBooks, HubSpot, DocuSign. Claude for Small Business is Anthropic embedding itself one layer below those products. If it works, the leverage shifts.

Enterprise AI was always going to reach small businesses eventually. Anthropic just decided not to wait. The HVAC company and the 30-person real estate brokerage are now the target — and the SaaS tools they already use are the distribution channel.

Source: Anthropic

Anthropic Changes the Rules for Claude Power Users 💳

Anthropic quietly restructured the economics of its paid plans this week — and the developer community noticed immediately.

Every paid Claude plan now includes a monthly API credit equal to the dollar amount of the subscription for programmatic usage: the Agent SDK, claude -p, GitHub Actions, and third-party harnesses like OpenClaw. Pay $200/month, get $200 in API credits on top of your interactive Claude.ai and Claude Code usage.

On paper, a good deal. In practice, power users who had been running third-party Claude harnesses at an estimated 70–90% discount from standard API pricing called it a rug pull. The backlash was immediate — Theo, Jeremy Howard, Matt Pocock, and Omar Sanseviero all weighed in publicly within hours of the announcement.

Anthropic partially offset the reaction with a separate move: +50% to Claude Code weekly limits through July 13, stacked on top of the previously announced 2× increase to the 5-hour limit. Claude Code users on interactive plans are getting more. Third-party harness users are getting metered.

What it signals:

Claude Code now has enough brand and market position that Anthropic is putting its most favorable pricing behind its own tools. The subsidy era for alternative harnesses is ending. Meanwhile OpenAI — the challenger in this specific battle — is going the other direction: two free months of Codex for enterprise customers who switch in the next 30 days.

Anthropic built its developer base partly on pricing generosity toward third-party tooling. This week it started converting that goodwill into margin. The question is how many power users follow Codex.

Source: X

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News of the week 🌍

Musk v. OpenAI: Closing Arguments Done, Jury Deliberates Monday ⚖️ — The first phase of the trial wrapped May 14 after closing arguments from both sides. Musk’s team argued OpenAI “misused millions in donations” and betrayed its nonprofit mission. OpenAI countered that Musk’s $38M came without strings and that the mission continued through structural changes. The nine-person jury begins deliberating Monday, May 18 — but their verdict is advisory only. Judge Gonzalez Rogers makes the final call on liability, and a separate remedies phase (potential Altman removal, unwinding the 2025 recapitalization) runs concurrently before the judge alone.

Anthropic in Talks to Raise $30B at $900B+ Valuation 💰 — Bloomberg confirmed on May 12 that Anthropic is in early discussions to raise at least $30B, which would triple its February valuation of $380B and put it above OpenAI’s $852B post-money figure. The round is expected to close by end of May and may be Anthropic’s last private round before an IPO targeted for October 2026. ARR is reportedly at $14B, with Claude Code alone generating $2.5B annually.

Amazon Replaces Rufus With Alexa for Shopping 🛒 — Amazon launched Alexa for Shopping on May 13, retiring the Rufus brand and combining Rufus’s product expertise with Alexa+’s personalization layer. Available free to all US customers — no Prime, no Echo device required. It tracks price history, schedules recurring orders, and includes a “Buy for Me” feature that purchases on your behalf at other retailers. 300 million customers used Rufus in 2025.

OpenAI Brings Codex to Mobile — Manage Your Coding Agent From Your Phone 📱 — OpenAI pushed Codex into the ChatGPT mobile app on iOS and Android. Start a coding agent session on your machine, then manage it from your phone: review diffs, approve commands, inspect terminal output, look at screenshots, switch models. No desk required. The gap between "AI coding agent" and "always-on background worker" just got a lot smaller.

OpenAI Launches $4B Deployment Company — Anthropic Mirrors the Move 🏢 — OpenAI stood up a standalone enterprise consulting arm on May 11 with $4B from TPG, Goldman Sachs, McKinsey, Bain Capital, and 15 others — embedding Forward Deployed Engineers directly inside Fortune 500 clients, Palantir-style. Acquired London firm Tomoro (150 engineers, clients include Tesco and Virgin Atlantic) to launch with an existing book of business. Same day, Anthropic announced its own $1.5B deployment JV with Blackstone, Hellman & Friedman, and Goldman Sachs. The model race is over. The deployment race is on.

Google Announces Googlebook — A Laptop Built Around Gemini 💻 — Google unveiled a new laptop category on May 12 at the Android Show: I/O Edition. Googlebooks ship with Gemini Intelligence built into the OS — Magic Pointer cursor, Android app streaming from phone, vibe-coded widgets on demand, seamless file sync. Manufacturing partners: Acer, Asus, Dell, HP, Lenovo. First units land this fall. The kicker: a federal antitrust judge had just barred Google from paying for AI default placement on third-party devices. Google's answer was to build its own.

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Useful tools ⚒️

⭐ Spellar 3.0 — Most meeting tools give you notes. Spellar 3.0 gives you memory. It joins your calls without a bot, captures every word, and builds persistent context across all your meetings — so you can ask what a client said three calls ago or surface decisions from last month. Runs on Claude, GPT, Gemini, or Perplexity. Exports to Notion, Jira, Linear, Google Docs. Mac, iOS, and web. Free tier available.

Higgsfield Supercomputer — A cloud-native AI agent that runs your entire content pipeline from one prompt. Tell it “build a week of Instagram ads plus competitor analysis” and it plans the work, picks the right models, generates the assets, and delivers them. Three layers of memory, 40+ built-in tools, connects to Slack, Notion, Figma and 30+ services. Learns your brand guidelines over time. Free to start.

Latitude for Claude Code — See exactly where Claude Code is burning tokens and why. Shows token usage by task, flags which prompts hit your limits most, and helps you optimize before you run out of budget mid-session. Built specifically for Claude Code users.

Blaze 2.0 — AI marketer for small and mid-size businesses: strategy, content, and ads in one tool. Describe your business, Blaze builds a content calendar, writes social posts, and creates ad copy — all in your brand voice. Built for founders who don’t have a marketing team.

Weavable — Context infrastructure for AI agents. Instead of flooding your agents with raw API data, Weavable maintains a continuous updating changelog across your work tools — HubSpot, Slack, Zendesk, Jira, GitHub, email — and serves agents the specific signals they need through a single MCP endpoint. One-tenth the tokens compared to direct app connections, according to their own evals.

Weekly Guides 📕

How AI Agents Are Built in May 2026 — Step-by-step guide to building your first real AI agent from scratch: how to structure AGENTS.md, TASK.md, and MEMORY.md files, how the reasoning loop works, when to use tools vs. subagents, and how to avoid the most common failure modes.

Generate AI Videos From Claude With Higgsfield MCP — Step-by-Step — Higgsfield's own setup guide for connecting their MCP server to Claude in under a minute: how to add the custom connector, set permissions, and start generating 4K video from a plain-language brief.

Claude Code Security: MCP Connections, Permission Architecture, and Best Practices — Official Anthropic documentation on securing Claude Code in production: permission-based architecture, MCP server allowlisting, prompt injection defenses, sandbox configuration, and team security settings. Essential for any org deploying Claude agents at scale.

Building for the Gemini Intelligence System on Android — Developer’s Cut — Google’s official developer breakdown from the Android Show on May 12: how to integrate AppFunctions API, how task automation works across form factors, what’s available now vs. what’s coming at I/O, and how Googlebook changes the target surface area for Android apps.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Cerebras IPO Opens +89% Above Price, $106B Valuation on Nasdaq Debut — The AI chip company that makes wafer-scale processors debuted on Nasdaq on May 14, opening at $350 vs. its $185 IPO price and raising $5.55B. Amazon and OpenAI are customers. The largest US IPO of 2026 so far, and a signal that AI infrastructure investing hasn’t cooled.

TikTok Symphony Creative Studio Now Open to All Businesses — ByteDance’s AI ad creation tool went generally available this week for every business on TikTok. Type a brief, get a polished video: synced audio, avatar in one of 30 languages, product-consistent visuals powered by Seedance 2.0. Every clip ships with an AI-generated label. For any brand spending on TikTok, this changes the cost floor for video ads overnight.

OpenAI Explores Legal Options Against Apple — Reuters reported May 14 that OpenAI is looking at potential legal action against Apple, which has been building its own AI features into iOS 27 using Anthropic and Google — not OpenAI. The story is early-stage but reveals a new front in the AI platform wars opening up just as the Musk trial was wrapping.

US and China Are Discussing AI Guardrails for the Most Powerful Models — Treasury Secretary Bessent confirmed May 14 that the US and China are in active talks about guardrails specifically for frontier AI models. The first public confirmation of bilateral AI safety talks at this level. Context: China’s four open-weight coding models dropped in a 12-day window last month, closing the capability gap faster than expected.

If you missed our previous updates, don’t worry, here they are:

Musk v. OpenAI: Chaos Under Oath, Anthropic Dreams on SpaceX, China’s $20B Challenger | Weekly Digest

Your take: Anthropic is pulling back pricing subsidies for third-party Claude harnesses — are you staying, or is this the moment you move to Codex? Drop it in the comments 👇

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At a Glance

In this piece, you will learn how to:

• Stop treating AI as a writing assistant and start treating it as infrastructure

• Understand what a real creator AI stack looks like under the hood (with two verified examples)

• Build your own four-component system starting this week

This post is prepared with Guest Author — Claudia Faith, Fractional Chief AI Officer. If you also want to write for Creators AI — send us email here

I run two Substack newsletters, a LinkedIn presence for my consulting business, and a live AI community. Each brand has a different voice, different visuals, different audience. For months I produced everything manually: writing articles one at a time, scheduling social posts by hand, creating images in a browser tool, copying and pasting between apps until my eyes glazed over.

On a good week, that took around 8 hours. On a bad week, when I was also running my consulting business and showing up for coaching clients, the content slipped. Articles posted late. Notes got skipped. Images were whatever I could throw together in five minutes.

The turning point came when I stopped thinking about AI as a writing assistant and started thinking about it as infrastructure. I didn’t need a better prompt for Tuesday’s article. I needed a stack where every tool connects to the next, where the output from one step feeds into the next, and where one Sunday session handles what used to take me all week.

When I started looking at how other growing creators approach this, I saw the same pattern everywhere. The ones scaling fastest built systems, and those systems look surprisingly similar under the hood.

This and many other practical posts on building with AI are available exclusively to our subscribers

Two Stacks Worth Studying

Before I walk through my own setup, here are two creators who built AI production systems I’ve learned from. Both are publicly documented and verifiable.

Ryan Law’s 23-Skill Editorial Pipeline (Ahrefs)

Ryan Law runs content at Ahrefs, a company doing over $100M in annual revenue. He built a system inside Claude Code using 23 custom skill files, each handling one editorial step: keyword research, structural outlining, line editing, internal linking, WordPress shortcodes. He chained them into a master skill called “blog-pipeline” that runs all 23 sequentially.

A publish-ready article draft in 6 to 12 minutes. Roughly 45 articles processed so far. He published the whole thing on the Ahrefs blog with full technical detail.

The takeaway: every skill file saves the instructions, the examples, and the formatting rules for that one step. When Ryan updates how Ahrefs handles internal linking, he edits one file. Every future article inherits the change. He started with a single skill and kept adding as he identified repetitive steps. The system grew with the work.

We covered Claude Code Skills in depth here: Skills, Plugins, Swarm Mode: Practical Tips with Claude

Dan Koe’s Repurposing Engine

Dan Koe has 2 million followers and 120,000 newsletter subscribers. His principle: every idea gets used multiple times. He tests ideas on X first (the character limit forces compression), expands winners into his newsletter, turns that into a YouTube script, then feeds the transcript into Gemini to extract usable building blocks in a fraction of the time manual notes would take.

His custom Claude prompts deconstruct his best-performing posts into hooks, quotes, pain points, and narrative arcs. One prompt he calls his “Deep Post Generator” can pull up to 60 social post ideas from a single long-form piece. He discussed the whole workflow on The Startup Ideas Podcast.

The takeaway: Dan’s AI tools aren’t generating content from nothing. They’re taking content he already created and finding the 20 other shapes it could take. The original thinking stays human. The multiplication is automated.

My Stack: The Full Tool Chain

Here’s every layer, what it does, and how they talk to each other.

Claude Code: the command center

Everything runs through Claude Code, Anthropic’s CLI tool. I use it in VS Code, and it’s the single interface I open for almost all content work. Writing, image generation, data analysis, file exports. I give it instructions, it calls the right tools, and I review the output.

The reason Claude Code works as a command center is that it can run scripts, read and write files, and use browser automation all from one conversation. I don’t switch between five apps. I describe what I need, and Claude orchestrates the steps.

New to Claude Code? Start here: How Claude Code Can Be Your AI Teammate

CLAUDE.md: the master instruction file

This is the file Claude Code reads automatically every time I start a conversation in my project folder. It contains every workflow I’ve built: how to write an article, how to generate Notes, how to create carousels, how to produce images, how to export for Substack. It also defines my folder structure, my naming conventions, and which tools to use for which tasks.

I update this file whenever I refine a workflow. Every future session inherits the update. If I figure out a better way to structure a CTA, I change it in one place and every article I write afterward follows the new pattern.

Voice profiles: one per brand

Each of my brands has its own markdown file with a full voice analysis. I built these by reading 15 of my own published posts per brand and extracting the patterns: how I open articles, my sentence rhythm, transition phrases I use naturally, and critically, phrases I never use and specific AI writing patterns to avoid. There’s a list of 18 AI fingerprints that I flag as banned, things like “Not X, it’s Y” constructions, em-dashes, rhetorical questions, invented compound labels.

Claude reads the matching voice file before generating anything. Without it, AI output sounds like AI. With it, the output sounds like me having a particularly productive morning. This is the single highest-leverage piece of my stack.

Puppeteer MCP: automated data collection

I use Puppeteer, a browser automation tool, connected to Claude Code through the MCP protocol. This lets Claude control a real browser session.

Every Sunday I ask Claude to connect to my Substack profile and pull the last 7 days of Notes performance: likes, comments, restacks, subscriber conversions. No manual scrolling through dashboards, no copying numbers into spreadsheets. Claude reads the data directly from the page and tells me what performed, what fell flat, and where the patterns are. That analysis feeds straight into the next batch of content.

One finding that came out of this automated tracking: the Note that converted the most subscribers one week had 34 likes. The Note with 266 likes converted the second fewest. I would never have caught that by glancing at my dashboard. The automated data pull made it visible, and it changed how I select content formats.

Imagen API: image generation from the terminal

I use Google’s Imagen API through a Python script I wrote called generate_image.py. Each brand has an image style template in its voice file (sketch style for my AI newsletter, a different look for my consulting brand). When I write an article, Claude builds the image prompt from the brand template and the article topic, then runs the script. The header image generates in about 30 seconds without me opening a browser or logging into a design tool.

I built a second script for carousels (generate_carousel.py) that works the same way but generates multiple slides. For my consulting brand, both scripts can include reference photos of me for character-consistent images across posts.

Parallel agents: multiple tasks at once

When I produce a full article, Claude Code spawns three agents that run simultaneously. One writes the article using the voice profile. One generates SEO metadata, keywords, and an FAQ answer. One builds the image prompt and generates the header image. When the article is done, a fourth agent reads it and writes a Notes teaser.

A full article package that used to take me a full morning now runs in about 15 minutes, and most of that time is me reviewing and editing the output.

Custom scripts: the last mile

The stack includes a few utility scripts that handle the boring parts:

• md_to_docx.py converts finished articles to Word format. Substack’s import feature works cleanly with .docx files, preserving all formatting. No more copy-paste formatting disasters.

• Notes scheduler accepts CSV batch uploads with content, date, and time per Note. Substack only lets you schedule Notes one at a time, which is painful when you’re scheduling 21 per week.

• generate_preview_gif.py creates animated prompt-to-image reveal GIFs for social sharing.

• Notes scheduler accepts CSV batch uploads with content, date, and time per Note. Substack only lets you schedule Notes one at a time, which is painful when you're scheduling 21 per week. I built the scheduler to solve that for myself and now sell it as part of my paid subscription.

How it all connects

Here’s what a typical Sunday session looks like, start to finish:

I open Claude Code and ask it to pull my last week’s Notes performance via Puppeteer. It reads the data and summarizes what worked. I tell it to generate 21 new Notes based on that analysis, my voice file, and the formula structures I’ve tested over months. It produces a CSV. I review and edit the Notes (about 15 minutes of actual work), upload the CSV to my scheduler, and the full week is handled.

You can check out my scheduler here

If I’m also producing an article that week, I give Claude the topic and it runs the parallel pipeline: article, metadata, and image simultaneously. The finished package lands in a dated folder, ready to export as a .docx and import into Substack.

Total Sunday time for a full week of Notes plus one article: about 45 minutes. That used to be closer to 8 hours spread across the week, done in fragments, mostly mediocre because I was always rushing.

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Building Your Own Stack

Every creator system I studied, including my own, has four components. You can build them in any order, but you need all four for the system to actually run itself.

• A voice file. Document how you write. Pull 10–15 of your published pieces and identify: your opening patterns, sentence rhythm, transition phrases, recurring expressions, and phrases you’d never use. Save this as a file your AI reads before generating anything. This takes a few hours the first time. You never have to do it again unless your style evolves.

• Proven formulas. Study what works in your space. Pull high-performing content from creators with audiences similar to yours, break down the structural patterns, and rank them by the metric that matters to your business: subscribers, clients, or sales. Save these as reusable templates with real examples. Any AI model produces dramatically better output with a structural template than with a blank prompt.

• A batch rhythm. Pick your most repetitive content task and batch it. If you post daily on any platform, you’re a candidate. Choose one session per week where you produce everything for that platform in one sitting. The specific cadence matters less than the commitment to doing it in one focused block.

• A feedback loop. After each batch cycle, review what performed and feed that data back into the next batch. My system does this weekly through the Puppeteer data pull. Over months, the output improves because the system learns from real performance data instead of assumptions.

You don’t need 23 skill files or a full Puppeteer integration to start. Pick the content task you repeat most often. Do it once with AI, but document every instruction you give along the way: the voice rules, the structure you prefer, the things you fix in the output. Save that as one file. Next week, feed it back and batch 7 days in one sitting.

If the quality holds and the time drops, you have the seed of a stack. Keep adding pieces as you hit new bottlenecks, and within a month you’ll have something that runs in a fraction of the time you’re spending now.

Also read: Your AI Agent Stack Is Spaghetti — here’s how to fix it

Where the Stack Breaks

Every creator I’ve talked to who runs this kind of system has the same disclaimer: it works until it doesn’t. The honest version of “AI as infrastructure” looks like this.

Usage limits hit at the worst time. Claude Code, Imagen, Puppeteer running in parallel. Each one burns tokens or API credits. You hit the weekly limit on Claude on a Sunday afternoon mid-batch, and the whole pipeline stalls until reset. Even on the highest paid tiers. You learn to front-load the expensive work earlier in the week, but the ceiling is real.

The setup is genuinely complex. A voice file, a CLAUDE.md, 4 to 23 skill files, Puppeteer MCP, custom Python scripts, API keys for image generation, a folder structure that everything depends on. When something breaks, the debugging surface is huge. Was it the skill? The MCP? The script? A model update? Most creators I know have a “works on my machine” graveyard of half-built workflows that fell over once and never got fixed.

Picking up where you left off is a job in itself. You start an article Tuesday. You come back Thursday. Claude doesn’t remember which voice file you were using, which version of the outline you approved, what you already corrected in the draft. Without disciplined file naming and session notes, you rebuild context every time. The “infrastructure” only feels like infrastructure when you also build the habits to use it.

Quality drifts without warning. Some weeks the output is sharp. Some weeks the same prompt, same voice file, same skill produces flat copy or off-brand images. Model updates change behavior silently. Skills that worked perfectly in March generate slop in May. You don’t get a release note. You catch it because a Note flopped or a thumbnail looks wrong, and you trace it back.

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Skills don’t fix everything. This is the one I want to be direct about, because the discourse around skill files is getting evangelical. A skill saves your instructions, not your judgment. It can enforce a structure, a banned-phrase list, a CTA pattern. It cannot tell you that the angle is boring, that the case study isn’t strong enough, or that the opening doesn’t earn the reader’s attention. The taste layer stays human. If you build a system that assumes the skill handles quality, the quality will erode and you won’t notice until subscribers do.

The stack is still worth building. 45 minutes versus 8 hours is real, and compounding. But anyone promising a hands-off AI workflow either hasn’t run one long enough, or is selling you the dream rather than the system.

Let me know how it goes!

Oh, by the way: If you want the full setup behind my AI-driven writing workflow, I bundled everything I described here and you can grab it here

Have you started building your own creator AI stack — or are you still running everything manually? Share in the comments.

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Claudia Faith holds a Master of Science in AI. She’s a VC-backed founder who’s worked with Forbes 100 companies to deploy AI where it actually matters. As a Fractional Chief AI Officer, she handles strategy and implementation, and offers 1:1 coaching for business owners who don’t want to be the last in their industry to figure this out. If you want help thinking through your own AI strategy, start here Start here.

Elon Musk's lawsuit against OpenAI reached its most explosive week yet — former CTO Mira Murati testified under oath that Altman was "creating chaos," and that OpenAI was "at catastrophic risk of falling apart." At the same time, Anthropic announced it's taking all capacity at SpaceX's Colossus 1 data center — 220,000+ Nvidia GPUs — while Musk sues their biggest competitor in federal court. Then China's Moonshot AI closed a $2B round at a $20B+ valuation, and its model is already #2 on OpenRouter. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

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🤖 Sponsored: Luma AI’s UNI-1 is now an API

Luma’s Uni-1 — the model creative teams have been using for video generation — is now available as an API.

What this unlocks: plug it into any product, wire it into a pipeline, or build something new from scratch. Anywhere code runs, Uni-1 can think. It understands intent, not just prompts — give it a brief or a reference board, and it resolves creative direction before generating a single frame.

If you’ve ever wanted to add AI video to your app, automate creative workflows, or build a brand-consistent visual tool, this is the infrastructure that makes it possible.

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Featured Materials 🎟️

The Musk v. OpenAI Trial: Chaos Under Oath 🔥

The most-watched AI story of the week didn’t involve a model launch. It happened in a federal courtroom in Oakland.

Week two of Elon Musk’s $180 billion lawsuit against OpenAI delivered testimonies that turned the company’s founding myth inside out. Former CTO Mira Murati testified via pre-recorded video on May 6 that CEO Sam Altman was “creating chaos” — saying one thing to one executive and the complete opposite to another. “OpenAI was at catastrophic risk of falling apart,” she said. “I was concerned about the company completely blowing up.”

The full picture of Week 2:

• Mira Murati (ex-CTO): Altman “pitted executives against one another” and undermined her role — yet she still wanted him to stay as CEO, because removing him risked the whole company imploding. When asked if Altman was always candid with her: “Not always.”

• Shivon Zilis (ex-board member, mother of four of Musk’s children, now at Neuralink): The board “voiced extreme concern” about the launch of ChatGPT — released “without any semblance of board communication.”

• Helen Toner (ex-board member): Called Murati “two-faced” about Altman’s 2023 firing — “waiting to see which way the wind would blow.”

• Greg Brockman (OpenAI President): Testified Musk wanted personal control — “he said people needed to know he was in charge.”

• Musk himself (Week 1): Tried to settle with Brockman days before trial. Said he felt like “a fool” for funding OpenAI.

What this trial is actually deciding:

Musk sued OpenAI in 2024 alleging it illegally converted from nonprofit to for-profit — betraying the mission he funded with $38M. He’s seeking up to $180B in damages plus Altman’s removal from the board. If he wins, it could derail OpenAI’s IPO entirely. The trial runs through May 22. Still to testify: Ilya Sutskever and Microsoft CEO Satya Nadella.

Musk wanted to control the future of AI. Altman built it without him. Now both versions of the story are being told under oath — and neither makes OpenAI look like it was ever just about humanity.

Source: Reuters

Anthropic Signs With SpaceX — and Teaches Its Agents to “Dream” 🤖

At Anthropic’s Code with Claude developer conference in San Francisco on May 6, the company announced it will take all capacity at SpaceX’s Colossus 1 Memphis data center — 220,000+ Nvidia GPUs and 300+ MW coming online within a month. Both companies also “expressed interest” in joint orbital data centers.

The SpaceX angle matters: Elon Musk is currently suing OpenAI in federal court while his rocket company supplies Anthropic’s compute.

Beyond infrastructure, the conference itself was substantive:

• API volume grew 80× year-over-year in Q1 2026 vs. a 10× internal plan

• Rate limits doubled for Pro, Max, and Team plans; peak-hour Opus caps removed

• Claude Opus 4.7 tops the Vals AI Finance Agent benchmark at 64.37%

The standout announcement — “dreaming”:

Anthropic unveiled Claude Managed Agents (research preview) with a capability it calls “dreaming”: when agents are idle, they replay past task sessions to self-improve — inspired by how sleep consolidates memory in humans. Early numbers: Harvey reported ~6× higher task completion rates, Wisedocs cut document review time by 50%.

Anthropic is using Elon Musk’s GPUs while Musk sues their biggest competitor in federal court. The “dreaming” agents story is technically the bigger deal — but the Musk angle won the news cycle.

Source: Anthropic

China’s Moonshot AI: $2B Raised, $20B+ Valuation, and #2 on OpenRouter 🚀

China’s Moonshot AI closed a $2B round on May 7 — led by Meituan’s Long-Z Investment, Tsinghua Capital, China Mobile, and CPE Yuanfeng. Valuation landed at $20B+, more than 4× its late-2025 figure of $4.3B. The company has now raised ~$3.9B in six months.

The product behind the number: April ARR exceeded $200M, driven by Kimi chatbot subscriptions and API usage. The latest model — Kimi K2.6 (1T-parameter MoE, 32B active parameters, 384 experts) — is the #2 most-used model on OpenRouter after GPT-5.5. Modified MIT license: commercial use is allowed, but you can’t use the weights to train competing frontier models.

Why Western developers are routing through it:

It’s not ideology — it’s the spreadsheet. GPT-5.5 costs $30/M output tokens. Kimi K2.6 undercuts that significantly. DeepSeek V4 did the same last week. The demand for cheap Chinese open-weight models is structural.

A Chinese AI company just 4×’d its valuation in under six months on $200M monthly revenue. The US-China AI race isn’t just playing out at the frontier model level — it’s happening in the API economics layer where most developers actually make decisions.

Source: TechCrunch

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News of the week 🌍

iOS 27 Will Let You Pick Claude, Gemini, or ChatGPT as Your Default AI 📱 — Mark Gurman reports Apple plans to open iOS/iPadOS/macOS 27 to third-party AI models via “Extensions” — users can set Claude, Gemini, or ChatGPT as the default for Siri, Writing Tools, and Image Playground. Per-feature defaults configurable in Settings. Official reveal at WWDC, June 8–12. The subtext: Apple Intelligence has been behind, and Apple knows it.

OpenAI and Anthropic Both Launched $10B+ PE Ventures — on the Same Day 💰 — On May 4, OpenAI finalized "The Deployment Company" at roughly $10B with TPG, Brookfield, Advent International, Bain Capital, SoftBank, and Dragoneer — PE firms reportedly guaranteed 17.5% annual returns over five years, former COO Brad Lightcap leads it. Hours later, Anthropic announced a $1.5B JV with Blackstone, Hellman & Friedman, Goldman Sachs, Apollo, GIC, Leonard Green, and Sequoia to embed Claude across 2,000+ PE portfolio companies. Both deals explicitly target enterprise consulting — not just API access. McKinsey and BCG now have two well-funded AI-native competitors with PE distribution machines behind them.

EU AI Act High-Risk Deadlines Pushed to 2027–2028 🌍 — The EU Council and Parliament agreed on a Digital Omnibus deal on May 7: Annex III high-risk obligations move from August 2026 → December 2027, Annex I safety deadlines → August 2028, generative-AI watermarking → December 2026. The deal also bans “nudification” AI systems. For any team building AI products for the European market — meaningful runway extension.

Sierra Raises $950M at $15.8B — One-Third of the World’s Largest Banks Are Customers 🏦 — Bret Taylor’s enterprise AI agent company closed a $950M Series E led by Tiger Global and GV. Customers include Prudential, Cigna, Blue Cross Blue Shield, and Rocket Mortgage. Valuation up from $10B last fall. Taylor warned of a market correction “within two years” — in the same week he closed the round.

Pennsylvania Sues Character.AI — Chatbot Posed as a Licensed Doctor ⚖️ — AG Josh Shapiro filed suit alleging a Character.AI bot named “Emilie” claimed an Imperial College London medical degree, a valid Pennsylvania medical license, and produced a fake PA license number when investigators asked about depression and medication. The state seeks an injunction against unauthorized practice of medicine — months after Character.AI settled multiple teen-harm suits in January.

CAISI Signs Safety Agreements With Google, Microsoft and xAI 🔒 — The NIST Center for AI Standards and Innovation signed pre-release safety evaluation agreements with Google, Microsoft, and xAI this week — covering cybersecurity, biosecurity, and chem-weapons evaluations. CAISI has existing agreements with OpenAI and Anthropic from earlier this year. The notable move: xAI joining the framework for the first time, bringing Grok under government pre-release review.

Useful tools ⚒️

⭐ Kanwas — Open-source real-time canvas where humans and AI agents share one self-evolving context for docs, decisions, and research. Shared working memory between your team and your agents — everything stays in sync without copy-pasting across tools.

Mindra — Spin up a team of specialized AI agents that work 24/7, communicate with each other, and take real action across your stack — no coding required. Self-healing: when an agent fails, it re-plans and retries instead of breaking the whole workflow. Built for founders and operators who want to delegate entire workflows, not just tasks.

Velo 2.0 — Turns raw screen recordings into polished video messages with a chat-native editor — no timeline, no re-recording. Describe the edit you want, Velo rewrites the script, clones your voice, and updates the video live. One recording produces both a video and a structured doc.

Kilo Code v7 — Open-source AI coding agent for VS Code rebuilt on OpenCode server. V7 adds real parallelism: multiple tool calls firing simultaneously, parallel subagents via Agent Manager, and inline diff reviewer with line-level comments. Choose any model — Claude, GPT-5.5, Gemini, or local LLM.

Shadow 2.0 — Bot-free AI meeting assistant that runs quietly on your Mac and captures everything said and shown on screen — without joining as a visible participant. V2.0 executes tasks live during the call: CRM updates, follow-up drafts, slide generation — before the meeting ends.

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Weekly Guides 📕

Claude Managed Agents Dreaming — Official Anthropic Docs — Official API reference for the dreaming feature: how to configure dream schedules, set auto vs. manual memory updates, use the dreaming-2026-04-21 beta header, and run your first dream in Python. If you're building agents, this is where to start.

How to Use Anthropic’s New Finance Agents — Official Docs — Anthropic’s own walkthrough of the 10 new financial services agents: what each one does, which MCP connectors are included, and how the Moody’s data integration works. If you’re in fintech or building for financial clients, start here.

Vercel AI SDK Agents: Complete 2026 Implementation Guide — End-to-end build guide covering the new Agent abstraction, MCP integration, and Fluid Compute deployment. Published May 6, directly tied to this week’s releases.

Claude Code’s Hidden Skill Budget Setting (May 2026) — Practical walkthrough of skillListingBudgetFraction in Claude Code v2.1.129 — a setting most users never touch that directly affects how aggressively Claude Code burns through your execution budget. Copy-paste fixes included.

Claude for Excel, PowerPoint, Word and Outlook — Official Setup Guide — Launched May 7: Claude now works across all four Microsoft 365 apps in one continuous conversation — triage email in Outlook, open the attachment in Word, build the model in Excel, turn it into a deck in PowerPoint, without re-explaining context at each step. GA for Excel, Word, PowerPoint on all paid plans; Outlook in public beta.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

AlphaEvolve One-Year Impact Report — Google DeepMind — One year since launch: AC Optimal Power Flow feasibility jumped from 14% to 88%, 10× lower error on Willow quantum circuits, +5% accuracy across 20 disaster-prediction categories. The clearest case study yet for AI coding agents making genuine scientific discoveries.

The State of Global AI Diffusion in 2026 — Microsoft published its Q1 2026 AI Diffusion Report on May 7: global AI usage hit 17.8% of working-age population (+1.5pp in the quarter), 26 economies now exceed 30% adoption, the UAE leads at 70.1%, the US moved up from 24th to 21st. The first data-backed snapshot of where AI is actually being used globally — and where it isn't.

Harvard Study: OpenAI's o1 Outperforms ER Physicians in 67% of Triage Cases — Harvard Medical School and Beth Israel ran six experiments — real ER cases, no preprocessing. AI correctly diagnosed 67% vs 55% and 50% for the two attending physicians. Every single experiment: AI won. The authors don't advocate clinical deployment yet and call for prospective trials — but this is the clearest peer-reviewed evidence that reasoning models are now operating above average physician level on diagnostic tasks.

Publishers Sue Meta — Zuckerberg Personally Authorized Torrenting 267TB of Pirated Books to Train Llama — Court documents allege Zuckerberg personally approved using LibGen to download 267TB of copyrighted books — fiction, nonfiction, academic texts — to train Llama models, overruling legal counsel who flagged copyright risk. If upheld, it directly threatens the legal basis for training any LLM on web-scraped text. The most significant AI IP case since the Getty Images suit.

If you missed our previous updates, don’t worry, here they are:

Claude #1 on App Store, GPT-5.4 Lands, DeepSeek V4 Is Here | Weekly Digest

What’s your take — are AI labs actually a threat to consulting firms, or is this just well-funded positioning? Drop it in the comments 👇

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Copy-paste prompts for invoices, travel, subscriptions, meetings, and the admin pile that quietly eats your week.

Most people open Cowork, type something vague like “organize my files”, get a half-baked answer back, and close the tab. That’s the trap.

Cowork is not a chatbot. It does not reward one-shot prompting. It rewards iteration. You give it a job. You correct what it got wrong. Then (and this is the part everyone misses) you ask Cowork to rewrite your original prompt so the workflow runs cleanly next time. After three or four cycles, you have a prompt that just works. And if you set it on a schedule, it works without you.

New to Cowork entirely? Start with our Claude Cowork Complete Guide. This post is the prompt layer on top of that setup.

In the last few weeks, I tested a stack of prompts with my own files and inbox. I also pulled the strongest workflows from public Cowork users (Frank Andrade and Ilia Karelin’s prompt library, the orthopedic surgeon running five parallel Cowork tasks at 6am, Anthropic’s own Dispatch documentation). Below are the 10 that earned their place.

In this guide, you’ll get prompts to automate:

1. The freelance invoice tracker (live artifact, not just a snapshot)

2. The vendor comparison table that ends with a recommendation

3. The travel itinerary that pulls every confirmation into one file

4. The meeting transcript to action-items machine

5. The subscription tracker that actually saves you money

6. The job application dashboard

7. The Downloads folder organizer (set it once, forget it)

8. The morning brief (calendar plus email plus Slack in one pass)

9. The competitor watch (scheduled, not manual)

10. The Dispatch “prep me for the next meeting” prompt

How to actually use these prompts

The mechanics are simple:

1. Copy the prompt

2. Open Claude Cowork on your desktop

3. Paste and run

4. Grant folder, Gmail, or Calendar permissions when asked

5. Customize anything in [BRACKETS] for your situation

The non-obvious part is what happens after the first run. Cowork rarely nails it on attempt one. It scans the wrong folder, formats the table differently than you wanted, misses an edge case. Don’t start over. Tell Cowork what to fix. Then say:

“Now rewrite my original prompt so the next run produces this output cleanly. Save it as a Skill I can reuse.”

That single move turns a one-off task into a reusable system. After three iterations, you stop prompting and start running.

1. The Freelance Invoice Tracker

The problem. You invoiced someone. You forgot when. You don’t remember if they paid. Your bookkeeping lives across Gmail, a folder of PDFs, and a Notion page you stopped updating in March.

What this prompt does:

• Scans Gmail and a local invoices folder for every invoice you’ve sent

• Flags overdue and unpaid invoices

• Calculates total outstanding at the top

• Saves a tracker file you can reference any time (or a live artifact that re-scans on demand)

The prompt:

Scan my emails and local files to track all freelance invoices and payment status.

GMAIL FILTER:
- Search for emails containing: invoice, payment, due, overdue, receipt
- Date range: [Last 30 days / Last 90 days / This year]

LOCAL FOLDER TO SCAN (optional):
- [path to invoices folder, e.g., ~/Documents/Invoices]
- File types: .pdf, .docx, .xlsx, .csv

FOR EACH INVOICE FOUND, EXTRACT:
- Client name
- Invoice number
- Amount
- Date sent
- Due date
- Payment status: Paid / Unpaid / Overdue / Unknown

BUILD A TRACKER TABLE:
- All invoices sorted by due date (oldest first)
- Status: OVERDUE / DUE SOON / PAID
- Total outstanding amount at the top
- Total paid this period

SUMMARY AT THE TOP:
- Total invoiced: $[X]
- Total paid: $[X]
- Total outstanding: $[X]
- Number of overdue invoices

OUTPUT: Save as invoices-tracker-YYYY-MM.md in [YOUR FOLDER PATH]

Pro tip. Run this on the first Monday of every month. The overdue column tells you exactly who to follow up with before chasing anything by hand.

Want a live version instead of a static file? Replace the last line with:

OUTPUT: Build this as a live artifact. Every time it's reopened, re-scan Gmail and the local folder and refresh all numbers. Do not cache the previous state.

The live artifact stays connected to Gmail. Every time you open it, it re-scans your inbox and updates totals and statuses. New invoice sent, payment received, due date passed: the artifact reflects all of it without touching the prompt again. There’s a Reload button in the top right corner if you want to refresh mid-session.

2. The Vendor Comparison Table

The problem. You’re picking between three or four tools. You have ten tabs open. You still haven’t decided.

Read more

Anthropic is weighing a $50B raise at a $900B valuation that would make it the most valuable AI startup in the world — more than doubling its February figure in under three months. Big Tech dropped Q1 earnings on April 29 and raised AI capex to a combined $700 billion for 2026 — only Google convinced investors the bill is already paying off. Then Anthropic's next model family leaked: internal red teaming logs spotted Claude Jupiter V1 on April 30, pointing at a launch at the May 6 Code with Claude conference. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

Anthropic Is Weighing a $900B Valuation. Claude Code Built It. 💰

Anthropic wants to raise $50 billion at a $900 billion valuation. Multiple preemptive offers at $850–900B have landed on the table. The board meets in May to decide. If it closes at the high end, Anthropic becomes the most valuable private AI company in the world — ahead of OpenAI’s $852B post-money from March.

How they got here:

Anthropic’s annual revenue run rate crossed $30B in early April — up from $9B at the end of 2025. The actual current rate, per sources close to the company’s financials, is closer to $40B. That’s a 4x jump in under five months. For context: OpenAI went from $20B to $25B annualized in the same window. The growth slopes are no longer close.

The engine behind it is Claude Code. Cowork and Claude Code together account for the majority of Anthropic’s revenue acceleration, driven by enterprise teams replacing developer headcount with agent-based workflows. The company now has more than 300,000 business customers on Claude Code, with an annualized run rate TechCrunch sources peg at $2.5B for that product line alone.

The timeline:

• February 2026: Raised at $380B valuation

• April 29: Bloomberg breaks $900B story

• April 30: TechCrunch reports 48-hour investor allocation window

• May: Board meeting to finalize

The IPO angle:

This is likely Anthropic’s last private round. Early backers from 2024 and earlier are sitting this one out — waiting to cash in at IPO. A $900B private valuation puts the IPO price discovery in trillion-dollar territory. For comparison: Google went public at a $23B valuation in 2004. Anthropic is approaching the public markets at 40x that figure, still in private hands.

The $900B headline is the output. Claude Code is the input. Every AI lab is now racing to own the same developer workflow — and Anthropic got there first.

Source: X

Big Tech Q1 Earnings: The $700B Bill, and Only One Company Justified It 📊

Microsoft, Alphabet, Meta, and Amazon reported Q1 2026 earnings on April 29. All four beat revenue estimates. All four raised AI infrastructure spending. Combined 2026 capex is now tracking $650–700 billion — the largest concentrated infrastructure cycle in tech history. Then the market reacted, and the verdict was unambiguous: only Google convinced investors the spend is working.

The numbers by company:

• Alphabet: $109.9B revenue (+22% YoY, beat by $2.7B). Google Cloud +63% to $20B — fastest growth in years, and $2B above estimates. Backlog hit $462B, nearly doubling quarter-over-quarter. Capex raised to $180–190B for 2026. Stock up 6%+ pre-market.

• Microsoft: $82.9B revenue (+18% YoY). AI business now at $37B annualized run rate, up 123% YoY. Azure guided 37–38% growth. Copilot crossed 20M paid seats. Capex raised to $190B — $35B above analyst consensus. Gross margin shrank to lowest since 2022.

• Meta: $56.3B revenue (+33% YoY — fastest growth in 4 years). Capex raised to $125–145B, up $10B. Stock fell 6% after hours. Free cash flow dropped to $1.2B from $26B a year ago.

• Amazon: $181.5B revenue (+17% YoY). AWS grew 28% to $37.6B — fastest in 15 quarters. Bedrock processed more tokens in Q1 2026 than in all prior years combined. Capex: $200B for 2026. EPS at $2.78 crushed $1.62 estimate.

The market’s verdict:

Google’s Cloud growing 63% — more than doubling its previous growth rate — was enough. Azure and AWS showed strong signals but with margin concerns. Meta’s capex hike ($10B surprise increase for “higher component costs, particularly memory pricing”) spooked investors who wanted payback before more bets. The implicit message: $700B in capex requires 63% Cloud growth to justify. Anything less and investors wait for proof.

Four companies are spending the equivalent of Sweden’s GDP on AI infrastructure this year. Three of them couldn’t convince their investors it was working. One showed 63% Cloud growth. The bar is now 63%.

Source: Alphabet IR

Anthropic’s Next Model Is Already in Red Teaming — Codename: Jupiter 🪐

Anthropic has begun red teaming a new internal model build codenamed Claude Jupiter V1, spotted in internal test logs on April 30. The codename follows the company’s established pattern: pre-release safety probes are tagged with planet names, not final product names. Last year the equivalent exercise was called Neptune — and it preceded the Claude 4 family launch by a few weeks.

The timing is significant. Anthropic is hosting its Code with Claude developer conference in San Francisco on May 6, followed by London and Tokyo dates. At the previous Code with Claude event (May 22, 2025), Anthropic announced the Claude 4 series. If the pattern holds, Jupiter V1 is being hardened for an announcement timed to the SF event.

What Jupiter V1 appears to be: not a minor update. Internal model naming at Anthropic separates the planet-codename safety probes (which reference genuinely new model families) from incremental Opus/Sonnet/Haiku point releases. Jupiter suggests a Claude 5-class model — the first new base model family since Claude 4 launched a year ago.

The $900B fundraising round, the IPO timeline, and the Jupiter red teaming all land in the same week. Anthropic is not slowing down.

Anthropic is red teaming its next model family while simultaneously racing to close its last private round before IPO. The two things are connected: If Jupiter is the next base family, the $50B raise starts to look less like vanity pricing and more like compute prepayment.

Source: Testing Catalog

News of the week 🌍

Meta Plans 8,000 Job Cuts Starting May 20 — AI Is Explicit in the Memo 📉 Meta confirmed this week it plans to cut approximately 8,000 jobs beginning May 20, with a large portion in mid-level management. The internal framing is direct: the company is redirecting $135B toward AI infrastructure and expects smaller teams to achieve the same output. This is the largest single-round reduction Meta has announced since its 2022 “Year of Efficiency” cuts. It follows Snap’s 1,000-person cut two weeks ago where the CEO explicitly cited AI — the pattern of major consumer tech companies attributing headcount reduction directly to AI capability is now established, not anecdotal.

SoftBank plans Roze IPO at $100B 🏗️ — SoftBank is spinning out a new AI and robotics company called Roze, targeting a $100B valuation in what would be one of the largest AI IPOs to date. Roze focuses on physical AI infrastructure: using robotics to build data centers and bundling SoftBank’s bets in energy, land, and digital infrastructure. CEO Masayoshi Son is doubling down on “physical AI” as the next frontier. IPO timing: second half of 2026.

White House Opposes Anthropic's Mythos Expansion ⚠️ — The WSJ reported on April 30 that the White House is formally opposing Anthropic's plan to expand access to Claude Mythos, citing security concerns over a model capable of carrying out autonomous cyberattacks. This comes the same week Anthropic is weighing a $900B valuation. The tension is explicit: investors are racing to fund the company; the US government is trying to contain its most capable product.

OpenAI Open-Sources Symphony — Every Linear Ticket Gets Its Own Coding Agent 🎵 — On April 27, OpenAI published Symphony: an open-source orchestration spec that turns your issue tracker into a control plane for coding agents. Every open ticket automatically gets a dedicated Codex agent running until the task is done. Engineers review output, not supervise sessions. OpenAI’s own teams saw a 500% increase in landed pull requests in the first three weeks.

Useful tools ⚒️

⭐ Plurai — Vibe-train your AI evals and guardrails by example, not by writing complex test cases. Show Plurai what good and bad outputs look like, and it builds and maintains evaluation logic automatically. #1 on Product Hunt April 29 with 673 upvotes. For any team running Claude, GPT-5.5, or DeepSeek V4 in production who wants evals that don’t require a dedicated ML engineer. Free trial available.

Wonder — AI design agent that works directly on your canvas in real time. Describe what you want, Wonder generates and iterates without leaving your design file. #4 on Product Hunt April 30 with 260 upvotes. Works with Figma components. Free public alpha.

KarmaBox — Run your own Claude Code instance on mobile. Full agentic coding sessions from your phone — same capabilities as desktop Claude Code, sandboxed on-device. #3 on Product Hunt April 29 with 321 upvotes. Free during beta, iOS.

Orange Slice — Automate any sales task with a plain English description: prospecting, follow-up sequences, CRM updates, outreach. No workflow builder, no integrations to configure. #1 on Product Hunt April 27 with 460 upvotes. Free trial, paid plans from $49/mo.

Gemini Deep Research Agent — Web and MCP research agents now available in the Gemini API. Run multi-step research tasks, pull from web sources and connected MCP tools, get structured output. #5 on Product Hunt April 30 with 206 upvotes. Available via Gemini API, usage-based pricing.

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Weekly Guides 📕

How to Use Mistral Medium 3.5 for Self-Hosted AI: Hardware Guide & Setup — Official Mistral Docs: exact VRAM requirements, FP8 vs BF16 tradeoffs, vLLM and SGLang setup commands. If you’re evaluating self-hosting instead of API, start here.

How Big Tech Q1 Earnings Should Change Your AI Infrastructure Decisions — Bedrock +170% QoQ, Google Cloud backlog $462B, Azure supply-constrained. Which cloud to build on in H2 2026 based on this week's numbers.

Dark Factory: How to Build a Codebase Where AI Agents Ship Without Human Review — What it takes to move from "AI helps me code" to "AI ships code autonomously." Seven production requirements every team needs before removing humans from the review loop.

DeepSeek V4 API Migration Guide: Everything Before the July 24 Deadline — Legacy model names deepseek-chat and deepseek-reasoner retire July 24. One-line code change, same endpoint, same API key — but the deadline is real. Audit your codebase now.

New Claude Connectors: Official Setup Guide — Spotify, Uber, Instacart & 12 More — How to connect the 15 new lifestyle apps to Claude, what each can do inside a conversation, and how confirmation works before Claude takes action.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Andrej Karpathy: From Vibe Coding to Agentic Engineering — Karpathy’s fireside chat with Sequoia’s Stephanie Zhan at AI Ascent 2026. He explains why vibe coding raised the floor, why “agentic engineering” is now becoming the serious discipline on top of it, and why building with agents still requires taste, judgment, and technical understanding.

Microsoft and OpenAI Restructure Their Partnership — Cloud Exclusivity Ends — On April 27, Microsoft and OpenAI restructured their deal: Azure remains primary cloud partner but exclusivity is over, and AGI-linked deal terms are removed. Quietly significant for every company building on Azure-hosted OpenAI APIs.

Behind the Curtain: We've Been Warned — Axios's rare long-form piece on pace, not products: Anthropic is the fastest-growing company in American business history ($1B to $30B ARR in 16 months). The most capable models are now the least transparent. AI agents erased $2 trillion from public software company valuations in 10 weeks.

Stripe Builds Wallets for AI Agents — Stripe launched Link wallets for agents and its Agentic Commerce Suite, letting businesses sell inside AI apps while agents get spending controls, approval flows, and transaction visibility. The boring payments layer just became the most important part of agentic commerce.

If you missed our previous updates, don’t worry, here they are:

GPT-5.5 Doubles the Price, Google Goes Full Agent, DeepSeek V4 Resets the Floor | Weekly Digest

Brilliant cut prompts from 20+ to 2. Datadog killed week-long design cycles. But there’s a catch most reviewers missed.

Hey! Today we’re going deep on Claude Design. The tool that made Figma’s stock drop 7% in a single day and made every product designer on X simultaneously panic and tweet about it.

Anthropic shipped Claude Design on April 17, alongside the Opus 4.7 release we covered last week. The timing was loud. Mike Krieger (Anthropic’s Chief Product Officer and the guy who literally co-founded Instagram) quietly resigned from Figma’s board three days before the announcement. The Information had already reported that Anthropic’s next model would ship with design tools. The market read the signals and Figma got punished.

Then I actually used Claude Design for a week. And the headline takes are wrong.

It’s not a Figma killer. It’s not a Lovable competitor. It’s something neither category covers, and that’s exactly why it matters.

At a Glance

In this piece, you will learn:

• What Claude Design actually does (and what it doesn’t), with real screenshots

• 4 production case studies from Brilliant, Datadog, Mercury, and Peter Yang’s 16-minute everything-build

• 5 power-user tips that separate “shipping” from “burning your weekly quota in 30 minutes”

• The honest catch on token economics that nobody on Twitter is talking about

What Claude Design Actually Is

Claude Design is a conversational prototyping tool. It lives inside claude.ai (look for the Design tab in the sidebar after you log in), and it’s powered by Claude Opus 4.7, the most capable vision model Anthropic has shipped to date.

The interface is dead simple: chat on the left, canvas on the right. You describe what you want in plain English. Claude builds a working design on the canvas. You refine through conversation, draw red marker annotations directly on the design, click and tweak elements, or drag custom sliders Claude builds for you on the fly.

Here’s the part that made my jaw drop: it’s not generating images. It’s generating live HTML, CSS, and React components. Real code. Things you can click. Things you can hand to a developer and say “build this.”

That’s not an image generator. That’s an actual design tool.

The output covers most of what teams ship every week:

• Landing pages and marketing sites

• Clickable interactive prototypes

• Pitch decks and presentations

• One-pagers and SOPs

• Wireframes and design explorations

• Brand systems and component libraries

• Even animated brand videos and 3D web experiences

It exports to PDF, PPTX, Canva, standalone HTML, Keynote, and Figma import. There’s also a killer feature most people miss: it packages everything into a handoff bundle for Claude Code.

The 5 Features That Actually Matter

Most of the launch coverage stopped at “AI tool that makes designs.” That’s too shallow to be useful. Here’s what actually changes the game:

1. Design system ingestion. Point Claude at your codebase or design files during onboarding. It reads your colors, typography, components, and spacing rules, then applies them automatically to every project after that. Teams can maintain multiple design systems for sub-brands or campaigns. The single source of truth becomes your code, not a Figma library that drifts over time.

2. Web capture tool. Drop in a URL and Claude pulls elements directly from your live site so prototypes look like the real product. This is huge for marketing teams iterating on existing pages.

3. The Tweaks panel. Custom sliders that Claude builds for you on the fly: typography scale, color temperature, spacing density, section ordering. Drag the sliders, the design updates live. And here’s the part nobody mentions: the Tweaks panel doesn’t burn chat tokens. More on that later.

4. Inline comments. Click directly on any element and request a targeted change instead of describing the location in chat. Faster, cheaper, more precise.

5. Handoff to Claude Code. When you press “Hand off to Claude Code,” it doesn’t just dump HTML. It packages the design with the intent, component choices, and architectural decisions preserved as context. Claude Code then builds on top of the design instead of reinterpreting it.

That last one is the difference between Claude Design and every other AI design tool that’s launched in the past year. We show below how we used it in our case

Real Case 1: How We Rebuilt Creators AI in Claude Design

Read more

GPT-5.5 shipped on April 23 and doubled the API price in the same breath — $5 in, $30 out per million tokens. Vertex AI is dead: Google rebranded it to Gemini Enterprise Agent Platform at Cloud Next and unveiled two new TPU generations built for agent workloads. Then DeepSeek V4 dropped on April 24, matched frontier benchmarks, and undercut GPT-5.5 on output by more than 100x. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

GPT-5.5 Is Smart, Weird, and Twice the Price 💸

GPT-5.5 is out — $5 per million input, $30 per million output. That’s exactly double GPT-5.4 and 20% more than Claude Opus 4.7. OpenAI released it on April 23 to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. It’s also the first fully retrained base model since GPT-4.5 — every GPT-5.x release between them was a post-training iteration on the same base. GPT-5.5 is not. The benchmarks tell part of the story. The invoice tells the rest.

What actually shipped:

• Terminal-Bench 2.0: 82.7% — up from 74.2% on GPT-5.4, new SOTA among all models

• SWE-Bench Pro: 58.6% — Opus 4.7 still leads at 64.3%

• 1M context window — first OpenAI model to match Gemini 2.5 Pro and DeepSeek V4

• Agent-first architecture — OpenAI describes it as a system that “takes a sequence of actions, uses tools, checks its own work, and keeps going” without re-prompting

The pricing story:

GPT-5.4 was the cheapest frontier model by a wide margin. That was the point. Sam Altman framed the hike as reflecting “the true cost of frontier compute.” OpenAI says effective cost increase is closer to 20% once token efficiency is factored in — GPT-5.5 is more efficient per task than 5.4. Whether that math holds for your workload is for you to verify.

Source: OpenAI | OpenAI Community | LLM Stats

Vertex AI Is Dead, Long Live Gemini Enterprise 🏢

Vertex AI no longer exists. At Google Cloud Next on April 22 in Las Vegas, Google rebranded it to Gemini Enterprise Agent Platform and paired the launch with two new TPU generations. The rebrand is bigger than the silicon.

The TPUs:

• TPU 8t (training) — 9,600 chips per superpod, near-linear scaling to 1 million chips across data centers, 3x the compute of the previous generation

• TPU 8i (inference) — 3x the on-chip SRAM, 80% better performance-per-dollar, purpose-built for low-latency agentic workloads and MoE models like DeepSeek V4

The agent platform:

Gemini Enterprise ships with a built-in orchestration layer, Agent Studio (low-code builder), persistent Memory Bank, and a governance layer. The key new protocol: A2A (Agent-to-Agent) v1.0, now in production at 150 organizations — it lets agents built on different frameworks (LangGraph, CrewAI, LlamaIndex, AutoGen) hand off tasks to one another without understanding each other’s internal architecture. A Salesforce agent can call a Google agent, which calls a ServiceNow agent, all via A2A.

Over 40 launch partners including Box, Workday, Salesforce, and ServiceNow committed on day one. Google also announced a $750M fund for agentic AI startups with dedicated allocations for Europe, Japan, and India.

The Anthropic number buried in the keynote:

Anthropic crossed $30B in annualized TPU revenue on Google Cloud in April 2026, with 80% from enterprise API usage. Every TPU performance gain announced this week directly reduces Anthropic’s inference cost structure.

Vertex AI was a model-serving tool. Gemini Enterprise is a bid to be the operating system for agent economies. Microsoft now has 18 months to answer.

Source: Google Cloud | Google Blog | Reuters

DeepSeek V4 Drops: Near-Frontier Quality, Fraction of the Price🧨

DeepSeek V4 landed on April 24 in two variants — V4-Pro (1.6T total / 49B active) and V4-Flash (284B / 13B active), both with 1M context. V4-Pro scored 3,206 on Codeforces, ahead of GPT-5.4’s 3,168. On SWE-Verified: 80.6%. Then they published the price sheet.

The numbers that reset the floor:

• V4-Flash: $0.14 input / $0.28 output per million tokens

• V4-Pro: $1.74 input / $3.48 output per million tokens

• GPT-5.5 for comparison: $5.00 / $30.00

Flash output is more than 100x cheaper than GPT-5.5 while competitive on reasoning and code. Pro stays nearly 9x cheaper than GPT-5.5 at frontier-adjacent quality.

The license and the catch:

Weights released under a Modified MIT License — commercial use and redistribution allowed, but a non-compete clause prevents using them to train competing frontier models. The hosted API runs on DeepSeek’s own H800/H100 fleet in China. Huawei simultaneously announced that its Ascend 950 chips will fully support V4 out of the box — the first frontier-class model deployable at scale on Chinese domestic hardware without US-manufactured GPUs.

Every frontier lab spent 2025 arguing scaling laws still hold. DeepSeek spent 2025 proving architecture and data curation beat raw FLOPs. V4 is the receipt.

Source: DeepSeek | Hugging Face

News of the week 🌍

SpaceX Gets an Option to Buy Cursor for $60B 🚀 — SpaceX struck a deal with Cursor to build a next-generation coding and knowledge-work AI, with an option to acquire the startup for $60B later this year or pay $10B for the partnership instead. The signal is obvious: coding agents are no longer “developer productivity tools.” They’re becoming strategic infrastructure.

Claude Opus 4.7 Lands in Amazon Bedrock ☁️ — Anthropic made Claude Opus 4.7 available in Amazon Bedrock on April 20, roughly three weeks after the API launch. Bedrock also added prompt caching with up to 90% discount on repeated context — notably cheaper than Anthropic’s direct equivalent for high-volume repeated prompts.

Kimi K2.6 Launches Open-Source 🧨 — Aimed Directly at GPT-5.4Moonshot AI released Kimi K2.6 with a pointed message: this is a frontier coding and reasoning model under Modified MIT. It’s a 1T-parameter MoE model with 32B active parameters, 256K context, and native multimodal input. Pricing: $0.95 input / $4.00 output per million tokens — half the price of GPT-5.4 at launch. Combined with DeepSeek V4 dropping on April 24, this week produced two significant Chinese open-weight releases in a single four-day window.

Cerebras Files S-1 — Nasdaq IPO Targeting $22–25B 📈 — Cerebras Systems filed its S-1, targeting the CBRS ticker on Nasdaq at a $22–25B valuation. The filing confirms a multi-year, $20B+ compute deal with OpenAI — the company’s largest single customer and the deal that flipped its revenue concentration story from 87% UAE to a more diversifiable base. Revenue hit $510M in 2025, up 76% year-over-year. Roadshow begins early May; mid-May listing window.

Perplexity Personal Computer — general availability this week 🖥️ — The Mac app that ingests your local files, emails, and calendar and answers across all of them in a single retrieval pass. Free during public beta; Pro subscription required at GA.

Sam Altman publishes AI industrial policy paper 📄 — 13-page document proposing a Public Wealth Fund giving every American a stake in AI companies, a subsidized 32-hour workweek, and taxes on automated labor. Whether you agree or not, this is the framing OpenAI is taking into Washington.

Useful tools ⚒️

⭐ Dune — A physical 3-button Mac keypad that reads which app is in the foreground and automatically changes what its keys do in real-time. In GitHub: raise a PR, approve/reject. In your meeting app: join, toggle mic, control camera. Triggers AI agents with one tap. #1 on Product Hunt this week with 558 upvotes. $99 one-time, macOS only.

Pegasus 1.5 by TwelveLabs — AI model that transforms raw video into structured, timestamped metadata: scenes, objects, actions, dialogue — all indexed and searchable. Drop a 2-hour recording in, get a queryable knowledge base out. For anyone building on top of video content. 200 upvotes at launch today. Free tier available.

Claude Desktop Buddy — Bring Claude into the physical world via maker hardware. Connects Claude’s capabilities to physical inputs and outputs — buttons, LEDs, sensors. #2 on Product Hunt this week with 387 upvotes. Open-source, free.

Subspace — All your coding agents in one app with persistent context. Multi-agent coordination, sandboxed runs, Git workflows, parallel task handling across local and cloud setups. Free tier available.

The New Waydev — Measure the full AI SDLC from token to production. Tracks what your AI coding agents are actually shipping, where the bottlenecks are, and how token spend maps to output quality. #3 on Product Hunt this week. Free trial available.

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Weekly Guides 📕

Introducing GPT-5.5 — Official OpenAI Announcement — The full release post: benchmark table, pricing rationale, Codex integration details, and the “new class of intelligence” framing. The SWE-Bench Pro vs Opus 4.7 comparison at the bottom is the most honest part.

Google TPU 8t and 8i Technical Deep Dive — Google’s own breakdown of the chip architecture, Virgo Network topology, and Boardfly interconnect. If you’re building on Google Cloud or tracking Anthropic’s inference cost trajectory, start here.

How to Use the DeepSeek V4 API: Full Migration Guide — Published today alongside the launch. OpenAI-compatible endpoint, one base-URL swap from GPT-5.5, Python and Node examples, thinking-mode math, and the July 24 deprecation deadline for old model IDs. If you're evaluating V4 for production, start here.

DeepSeek V4 — Full Breakdown by Simon Willison — The clearest technical summary of V4-Pro and V4-Flash: architecture, pricing math, Modified MIT license terms, and honest benchmark context. Written same day as launch.

Gemini Enterprise Agent Platform — What Actually Changed from Vertex AI — The New Stack’s breakdown of what’s new vs. rebranded, A2A protocol v1.0 explained, and where existing Vertex AI pipelines stand.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Cerebras S-1 SEC Filing — Full Document — The full prospectus filed April 17. The OpenAI deal structure starts on page 47. The UAE revenue concentration risk is more nuanced than the headlines suggest.

GPT-5.5 vs Opus 4.7 — Full Benchmark Comparison — Honest cross-benchmark breakdown. GPT-5.5 wins on Terminal-Bench and long-horizon planning. Opus 4.7 wins on SWE-Bench Pro, MCP-Atlas, multilingual, and agentic finance. Pick by workload, not by headline.

Cursor Is Raising $2B at a $50B Valuation — The round is already oversubscribed. $2B ARR in February, $6B projected by year-end. Nvidia joining as strategic investor alongside a16z and Thrive. Six months ago the valuation was $29B. The AI coding market is compressing timelines that used to take a decade into a single fiscal year.

The MCP Security Problem Just Got Real — OX Security found critical remote-code-execution issues around MCP server handling, with reports saying major SDKs and a large number of MCP instances may be affected. Great Bonus pick because CAI audience already cares about MCP, agents, and “what can go wrong when your AI gets tools.”

If you missed our previous updates, don’t worry, here they are:

Opus 4.7 Is Live, Cyber Arms Race Official, Stanford Shows the Receipts | Weekly Digest

AI agents are already shopping, researching, and deciding on behalf of real users. Whether you run a store, a SaaS product, a portfolio of content, or a one-person agency — the same shift (being optimized for an AI reader, not a human one) is coming for whatever you sell. This is the working map: AEO/GEO/AOO, the four channels worth showing up in, and the people defining how it plays out.

Hey everyone!

One in five Cyber Week 2025 orders involved an AI agent. Roughly $70 billion in GMV, according to Salesforce. And most of the brands, creators, and operators whose businesses depend on being found don’t actually know if their visibility was part of that number or not.

That’s what agentic commerce looks like in early 2026. It’s not the thing that will happen — it’s the thing that’s quietly already happening, and the users aren’t telling you because they just think they decided to buy, sign up, or subscribe on their own.

The traditional funnel (impression → click → browse → cart → checkout, or its SaaS equivalent: impression → click → trial → activation → conversion) is collapsing into a single chat bubble. In that bubble, the AI pulls data, compares it against everyone else’s, and either recommends brand or doesn’t. If it doesn’t, brand weren’t just outranked — brand were invisible.

Agentic commerce could generate up to $1 trillion in US B2C retail revenue by 2030, with global projections of $3–5 trillion (McKinsey via VGS, January 2026).

Tobi Lütke put it more bluntly on X: “Shopify is building the foundation for agentic commerce.” Sundar Pichai followed with: “AI agents will be a big part of how we shop.” When the CEOs of Shopify and Google are saying the same thing in the same week, it’s worth a closer look.

A note on audience. This post is the generalist view — what agentic commerce is, why it matters for anyone dependent on being found by AI (not just eCom), and the framework to think about it. If you run a Shopify store or DTC brand and want the operational playbook — catalog audits, channel toggles, attribution setup, startup ecosystem — that’s living on Agentique, my other newsletter focused specifically on agentic commerce for operators. I’ll link the Practical Playbook in a callout further down.

At a Glance

In this post:

• What agentic commerce actually is (and what it isn’t yet)

• How discovery and agentic commerce are different problems with different optimization work

• The new acronym soup — AEO, GEO, AOO — and what each actually means

• The 4 AI channels worth showing up in (for anyone who depends on being found)

• The voices defining the space, and the critics worth taking seriously

• What’s working, what’s broken, and where to focus

What Agentic Commerce Actually Is

Agentic commerce is any transaction where an AI agent — not a human — does the discovery, comparison, or checkout work. Often all three.

A user tells ChatGPT “find me the best noise-cancelling headphones under $300 with 30+ hours battery life.” The agent queries product data across merchants, reads aggregated reviews, checks price and stock, and either shortlists 3 options or, with permission, completes the purchase. The shopper never sees a Google SERP, never opens ten tabs, never visits anyone’s product page.

Same pattern for SaaS: “find me a project management tool under $20/user that integrates with Linear and has native time tracking.” The agent hits review sites, aggregator content, company docs, and returns a shortlist. Two or three years from now it’ll sign up for the trial on your behalf.

The numbers behind the shift are no longer abstract:

• 23% of Americans made a purchase using AI in the past month (Morgan Stanley)

• 805% YoY traffic growth from generative AI channels in July 2025 (Adobe)

• 4.4x higher conversion for AI-generated product recommendations vs traditional search (McKinsey)

• 44% of US consumers are comfortable with an AI agent browsing and buying on their behalf — rising to 59% among 18-34 year olds (Worldpay)

• AI-referral conversions grew 1,247% in late 2025 (Signifyd). MetaRouter projects agent traffic moving from sub-1% to 15–25% of total ecommerce traffic by 2027.

And the protocol layer actually stabilised in the last six months, which is what makes this practical instead of speculative:

• Sep 2025 — OpenAI + Stripe launch the Agentic Commerce Protocol (ACP) with Instant Checkout in ChatGPT

• Nov 2025 — Perplexity ships Buy with Pro to all US users via PayPal

• Jan 11, 2026 (NRF) — Google + Shopify announce the Universal Commerce Protocol (UCP) with 20+ retailer partners

• Winter ‘26 Edition — Shopify launches Agentic Storefronts, connecting merchants to ChatGPT, Google AI Mode, Gemini, and Microsoft Copilot from one admin toggle

• Mar 24, 2026 — Agentic Storefronts activated for all eligible US Shopify merchants. ~5.6 million stores now technically connected.

The plumbing is done. The mental model shift isn’t.

Discovery vs. Agentic Commerce: Not the Same Problem

Here’s the distinction most posts skip, and it’s the one that changes your optimization work.

Discovery is when a human asks ChatGPT “what’s the best noise-cancelling headphone under $300?” or “what’s a good lightweight CRM for a 5-person sales team?” and reads the answer. The reader is the customer. Your job is to be cited — mentioned in the prose, linked as a source, surfaced in the carousel. You’re optimizing to be read about.

Agentic commerce is when an AI agent queries your site or catalog programmatically, parses structured data, compares options against constraints, and completes a transaction — often without a human ever seeing your page. The reader is a machine. You’re optimizing to be transacted with.

Both matter. They reward different work.

If you only invest in one, you can still show up but not close. If you only invest in the other, agents can check you out but you never enter the consideration set.

To unwrap Agentic Comemrce and keep up with ultraspeed development of new protocols and partnerships in Commerce there is a Agentique newsletter.

Agentique

Agentique - a monthly dispatch on agentic commerce. Field reports on AI agents, autonomous checkout, and the new mechanics of DTC, retail, and marketplaces.

By Daniil Andreev

AEO, GEO, AOO: The Acronym Soup, Decoded

Twenty years of SEO is now spawning three overlapping acronyms — and they’re not interchangeable.

AEO — Answer Engine Optimization. Optimizing your content so AI answer engines (ChatGPT, Perplexity, Claude, Google AI Overviews) cite you when answering a user’s question. The work overlaps with classic SEO but the surface is different: instead of ranking for a query, you’re being quoted in a paragraph. Wins look like: clear definitional content, structured FAQs, comparison-ready specs, inclusion in the “best X for Y” listicles that AI training data ingests.

GEO — Generative Engine Optimization. A near-synonym for AEO that’s gained traction with the SEO community (Olga Zarr, Julian Goldie, Brad Smith are among the practitioners writing about it most). Some treat GEO as a superset that includes how your brand appears across the entire generative experience — not just answers but also recommendations, summaries, and image-based results. In practice, AEO and GEO are used interchangeably by 80% of people writing about them. Don’t lose sleep over the terminology.

AOO — Agent-Oriented Optimization. This is the one most operators haven’t internalised yet. ARK Invest popularised the term: optimizing not for a human reading an answer, but for an AI agent transacting with your store, your SaaS, your booking flow. The signals that matter are completely different — MCP endpoint compatibility, data freshness, predictable checkout or signup, structured attributes that an agent can filter on. As ARK put it: “MCP compatibility is key.”

The split is simple:

• AEO/GEO lives on the discovery side of the table above. Win these and you get cited.

• AOO lives on the agentic commerce side. Win this and agents can actually transact with you.

Most “AI SEO” content circulating in April 2026 is really AEO/GEO advice repackaged — we went deep on the citation-and-ranking side in LLMs Optimization (AI SEO) if you want the playbook. Genuinely useful AOO writing is much rarer because the agent surface is newer and the data signals (MCP server quality, catalog/data accuracy, transaction reliability) are harder to measure than citation rates. Expect AOO tooling — the equivalent of Ahrefs or Semrush, but for agent traffic — to be a major startup wave through 2026.

Every previous platform shift in commerce — search, social, mobile — eventually settled into a stable equilibrium where operators knew how to be visible. Agentic commerce probably won’t.

If you are form the eCom / Marketing and want to learn practical aspects of implementing Agentic Comemrce check Agentique’s Practical Playbook for Operators:

Agentic Commerce Playbook

The 4 AI Discovery Channels You Can’t Ignore

Four channels matter in April 2026. Each behaves differently, and the work to show up in each is not the same — whether you’re selling products, SaaS, content, or services.

Read more

Anthropic shipped Claude Opus 4.7 this week — same price as 4.6, but with 3x sharper vision, stronger coding, and the first built-in cybersecurity safeguards. OpenAI fired back on Monday with GPT-5.4-Cyber, its own locked-down model for verified security professionals — and the Spud (GPT-5.5) launch window keeps slipping. Meanwhile, Stanford released its 2026 AI Index and the numbers don't lie: 53% global adoption in three years, SWE-bench coding scores jumping from 60% to near 100% in a single year, $581.7 billion in corporate AI investment. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

Subscribe now

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Featured Materials 🎟️

Claude Opus 4.7 Is Out — And It Brought a Design Tool That Spooked Adobe 🖥️

Claude Opus 4.7 is out — same price as 4.6, better at almost everything that matters. The two-month cadence holds: Opus 4.5 in November, 4.6 in February, 4.7 on April 16. On April 16, Anthropic shipped the upgrade.

What’s new:

• 13% lift on coding benchmarks — with particular gains on the hardest tasks. Users who’ve been handing off complex multi-session work report they can now do it with less supervision.

• 3.75 megapixel vision — up from 1.15 megapixels on Opus 4.6. That’s a 3x jump in visual capacity. Screenshots, diagrams, and dense documents now come through at actual fidelity, and computer use coordinates map 1:1 with real pixels.

• New xhigh effort level — a setting between “high” and “max” that gives developers finer control over the reasoning-vs-latency tradeoff. Claude Code now defaults to xhigh for all plans.

• /ultrareview in Claude Code — runs a dedicated review pass on your changes and flags what a careful human reviewer would catch before you commit.

• Task budgets (beta) — developers get finer control over how Claude allocates reasoning on longer runs.

• Cybersecurity safeguards — Opus 4.7 is the first publicly available Claude model with automated detection and blocking of prohibited cybersecurity uses. Anthropic is using this release to test guardrails before any broader Mythos-class rollout.

Pricing is unchanged: $5 input / $25 output per million tokens. Available now on claude.ai, the API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry.

One caveat worth knowing: Opus 4.7 regresses on Terminal-Bench 2.0 (69.4% vs GPT-5.4’s 75.1%) and softens slightly on BrowseComp vs 4.6. If those two workflows drive your production use, worth knowing.

The design tool story:

The model wasn't the only news. On April 14, The Information reported Anthropic is also launching an AI design tool that generates websites, landing pages, and presentations from plain-text prompts — no design skills required. Shares of Adobe, Figma, Wix, and GoDaddy closed in the red the same day. Anthropic has already partnered with Figma to convert AI-generated code into editable design files. Until now, Anthropic only built chat interfaces and developer tools. Gamma, Google Stitch, and Canva owned visual and creative workflows. That changes. As of publication, the design tool hasn’t officially dropped — watch Anthropic’s release notes this week.

Anthropic is shipping its most capable available model — at the same price as its previous one — while simultaneously building a product that replaces the starting point of design work. That’s not an incremental update. That’s a product strategy.

OpenAI Launches GPT-5.4-Cyber — The AI Cybersecurity Arms Race Is Now Official 🔐

OpenAI's answer to Project Glasswing just landed. GPT-5.4-Cyber is a fine-tuned variant of GPT-5.4 built exclusively for defensive cybersecurity — distributed only to verified security professionals through an expanded Trusted Access for Cyber (TAC) program. The announcement dropped April 14.

What GPT-5.4-Cyber can do that GPT-5.4 can’t:

• Binary reverse engineering — analyze compiled software for malware, vulnerabilities, and security flaws without source code access. This is a capability that was previously too sensitive to give broadly.

• Fewer refusals on dual-use queries — security professionals kept hitting guardrails when asking legitimate penetration testing questions. GPT-5.4-Cyber removes that friction for vetted users.

• Advanced defensive workflows — vulnerability prioritization, impact analysis, repair proposal generation.

OpenAI’s Codex Security has already contributed to 3,000+ critical and high-severity vulnerability fixes across open-source projects since launch.

How to get access:

Two paths. Individual security professionals can verify at chatgpt.com/cyber. Enterprise teams request access through an OpenAI representative. The model deploys gradually — vetted vendors first, broader rollout to come.

Why both labs are doing this at the same time:

Anthropic’s Claude Mythos found thousands of zero-day vulnerabilities autonomously — including a 17-year-old remote code execution flaw in FreeBSD — which spooked banks, governments, and enterprise security teams. Both OpenAI and Anthropic are now racing to establish the “responsible” model for deploying powerful cybersecurity AI: lock down the most dangerous capabilities, but give verified defenders a head start over attackers.

Two of the most capable AI companies in the world are simultaneously building models too dangerous to release publicly — and creating exclusive access tiers for people who need to use them anyway. That’s not safety theater. That’s a real governance problem being handled in real time.

• Source: OpenAI Blog | Axios | The Hacker News

Stanford’s 2026 AI Index: The Annual Report Card Just Dropped 📊

SWE-bench — the benchmark where AI models fix real GitHub issues — went from 60% to near 100% in a single year. Generative AI hit 53% global adoption faster than the PC or the internet. Stanford's 2026 AI Index dropped this week, and it's the most important data document in AI right now.

The headline findings:

• Humanity’s Last Exam scores jumped 30 percentage points in a single year — from 8.8% (GPT-4 era) to 38.3%. Models as of April 2026 (Claude Opus 4.6, Gemini 3.1 Pro) now top 50%.

• The US-China model performance gap has effectively closed. Anthropic leads Arena Elo (1,503), followed by xAI (1,495), Google (1,494), OpenAI (1,481), then Alibaba (1,449) and DeepSeek (1,424). Six percentage points separate first from sixth.

• $581.7 billion in global corporate AI investment in 2025 — a 130% jump from the previous year. Generative AI accounted for nearly half, growing 200%+.

• Organizational adoption reached 88%. 4 in 5 university students use generative AI for coursework. Only 6% of teachers say their school’s AI policies are actually clear.

• The “jagged frontier” problem persists. Models that solve PhD-level physics problems still struggle with clock-reading tasks. Robots succeed at only 12% of household tasks.

• Transparency is falling. The Foundation Model Transparency Index dropped from 58 to 40 points average. The most capable models disclose the least.

The context that matters for creators:

Estimated U.S. consumer surplus from generative AI reached $172 billion annually by early 2026. The median value per user tripled in a single year — while the tools remain mostly free. Marketing output productivity is up 50% in organizations that’ve integrated AI into creative workflows. The tools are here. The gap between users and non-users is widening every month.

AI didn’t plateau. It didn’t hit a wall. SWE-bench went from 60% to 100% in one year. That pace, if it continues, doesn’t give anyone a comfortable window to “wait and see.”

• Source: Stanford HAI | MIT Technology Review | IEEE Spectrum

News of the week 🌍

GPT-5.5 “Spud” April 14 Rumor Busted — New Window: April 21 to May 25 🥔 — The AI community convinced itself GPT-5.5 would drop April 14. It didn’t. The rumor came from an unverifiable “insider” post with no primary sources — OpenAI never corroborated it. What remains confirmed: pretraining wrapped March 24 at the Stargate data center in Abilene, Texas. Altman said “a few weeks.” Brockman called it “two years of research” with a “big model feel.” Polymarket still prices 78% probability of release by April 30. The new realistic window is April 21 to May 25. For context: while the community was waiting on Spud, Anthropic shipped Opus 4.7. The model race is too close to time bets around a single player.

Cerebras Targets IPO This Week — OpenAI Chip Deal Now $20B+ 💾 — Cerebras Systems, the AI chip startup that makes wafer-scale processors (56x larger than Nvidia’s H100), is targeting a Nasdaq IPO as soon as this week at a $35B+ valuation — a 60% premium over its $22B February valuation. The deal anchor: OpenAI agreed to pay Cerebras $20B+ for server chips, potentially receiving Cerebras equity in return. For context, Cerebras chips are up to 20x faster than Nvidia GPUs on AI inference workloads. This is the first serious public test of whether a non-Nvidia AI chip company can command frontier valuations.

Snap Cuts 1,000 Jobs, CEO Explicitly Cites AI 📉 — Snap laid off approximately 1,000 employees — 16% of its global workforce — with CEO Evan Spiegel stating in a company-wide memo that AI is replacing repetitive work. The cuts will save over $500 million by H2 2026. Affected staff receive four-month severance. This is one of the clearest public examples of a major consumer tech company directly attributing headcount reduction to AI capability — not to restructuring, not to market conditions. Read alongside Block’s 40% cut in February.

EU AI Act Enforcement: Four Months Away — and Europe Is Already Blinking ⚖️ — The EU AI Act's high-risk AI enforcement deadline lands August 2, 2026. This week Amnesty International and 127 civil society organizations published a joint warning: the European Commission's latest "simplification" proposals quietly weaken both the AI Act and GDPR under pressure from Big Tech lobbying. For any team selling to European clients or operating EU infrastructure, the window to audit AI tools is now under four months.

OpenAI Acquires TBPN — Its First Ever Media Company 📺 — OpenAI announced on April 8 that it acquired TBPN, the Technology Business Programming Network — a daily live tech and business show that's become a cult phenomenon in Silicon Valley. First media acquisition in OpenAI's history. Deal terms undisclosed. The move signals OpenAI is building a content distribution layer, not just a model company.

Useful tools ⚒️

⭐ Fathom 3.0 — AI meeting notetaker that now reads your screen. Version 3.0 adds fact-checking via Claude and ChatGPT Vision, so it can analyze shared documents, decks and screenshots during the call — not just transcribe audio. #1 on Product Hunt this week. Free plan available.

Figma for Agents — Connect AI agents directly to your design system. Agents can now read components, tokens, and guidelines from Figma and build UI that matches your brand without re-briefing every session. Launched this week — and lands right as Anthropic announced their own AI design tool.

Claude Code Routines — Put Claude Code tasks on autopilot with reusable smart routines. Define a workflow once — lint, test, deploy, summarize changes — and trigger it on schedule or on command. No re-prompting every session. Launched this week alongside the Opus 4.7 rollout.

Krisp Accent Converter for YouTube — Real-time accent conversion for YouTube creators: normalizes your speech so every viewer clearly understands you, regardless of accent or background noise. Removes one of the biggest drop-off reasons for non-native English creators.

Luma Agents — Creative AI agents that plan, iterate, and refine with full visual context. Unlike text-only agent loops, Luma agents see and evaluate what they’re generating — images, video sequences, compositions — and adjust without you re-prompting. Built for video and visual production workflows.

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Weekly Guides 📕

Claude Opus 4.7: Full Benchmark Breakdown and Migration Guide — 14 benchmark comparisons across every relevant task, the xhigh effort level explained, the new tokenizer’s impact on costs, and a complete migration checklist from Opus 4.6. Start here before updating any production pipelines.

Stanford 2026 AI Index: 12 Key Takeaways — The condensed version of the 400-page report. 12 data points that actually matter, with charts. The SWE-bench chart alone is worth bookmarking.

OpenAI GPT-5.4-Cyber: How to Apply for Trusted Access — OpenAI’s official page explaining the TAC program tiers, what GPT-5.4-Cyber can do vs. standard GPT-5.4, and both paths to access (individual and enterprise). If you work in security, read this now.

Intent by Augment Code: Official Docs and Getting Started — How to set up your first Space, how the Coordinator + Specialist agent model works, and when spec-driven development makes sense over a single Claude Code session.

How Claude Code Can Be Your AI Teammate — Our full deep dive on using Claude Code in production, Our foundational deep dive on using Claude Code in production. With Opus 4.7 now defaulting to high effort and adding /ultrareview, the basics matter more than ever. If you’re new to agentic coding, start here before anything else.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

One Man Used Claude and NotebookLM to Save His Mother's Life — Pratik Desai built an AI workflow to manage his mother's Stage 4 cancer care — catching a misdiagnosis and coordinating three critical interventions. The most concrete case study this week of what AI in healthcare actually means.

OpenAI's Vision for the AI Economy: Robot Taxes and a 4-Day Work Week — Sam Altman's 13-page policy paper published this week: a Public Wealth Fund giving every American a stake in AI companies, subsidized 32-hour workweek, taxes on automated labor. Worth reading regardless of whether you agree.

Snap's AI Layoffs: Spiegel's Full Statement — Evan Spiegel’s internal memo in full. Clear, direct, no hedging. Read alongside the Block/Dorsey story from February — two different companies, same underlying argument, very different public perception.

Cerebras IPO: What You Need to Know Before It Prices — Full breakdown of the Cerebras story: the wafer-scale chip architecture, the OpenAI $20B+ deal, the G42 concentration risk, and what the April IPO means for the non-Nvidia AI chip thesis.

If you missed our previous updates, don’t worry, here they are:

Meta Goes Closed Source, Mythos Gets Real, Gemma 4 Beats Llama | Weekly Digest

Most people using Claude are prompting it in a chat window. A smaller group is calling the API directly. But there’s a third tier that barely anyone has explored yet: Claude Managed Agents — Anthropic’s hosted runtime for building stateful, tool-using agents that actually persist across turns.

Not sessions that reset when you close the tab. Not pipelines that crash when a step fails. Real persistent agents with environments, file mounts, conversation memory, and production-grade controls baked in.

This post covers what they are, how they work inside the Console, and — crucially — how they compare to the setups you’re probably already using: Claude Cowork, cron jobs on a VPS, and self-hosted OpenClaw/Moltbot.

New to Claude agents entirely? Start with how solopreneurs are using full AI agents and Claude Code as your AI teammate before continuing here.

What Are Claude Managed Agents?

Straight from Anthropic’s cookbook repo:

“Claude Managed Agents is Anthropic’s hosted runtime for stateful, tool-using agents. You define an agent and a sandboxed environment once, then run them in sessions that persist files, tool state, and conversation across turns.”

Three things in that sentence matter.

Stateful. The agent remembers where it left off. It doesn’t start from zero each API call.

Sandboxed environment. The agent runs in Anthropic’s isolated compute layer — your files, tools, and code execute there consistently, not on your machine.

Sessions. Each run is a tracked session that can be paused, resumed, handed to a human, or branched. This is what makes production deployments possible.

The Console: What It Actually Looks Like

Before we talk about code, here’s what you’re working with inside Anthropic’s Console.

The Agents Dashboard

The Agents Dashboard — a list of all your deployed agent definitions. Each shows model, status, session count, and last activity. You manage definitions here; sessions run separately.

You define an agent once and it lives here permanently. The definition includes: name, model, system prompt, and which tools the agent can call. Sessions are spun up from this definition on demand — one definition, unlimited concurrent sessions.

Creating a New Agent

The Create Agent form. The system prompt is where you give the agent its domain expertise — this persists across every session that uses this definition. Tools are toggled per agent: code_execution, file_read, file_write, web_search.

The form is straightforward. The important fields:

• System prompt — this is your agent’s expertise. It doesn’t reset between sessions.

• Tools — which capabilities it can use. code_execution is the main one for data work; file_read / file_write for artifact generation.

• Model — Haiku for speed and cost, Sonnet for most work, Opus for complex reasoning chains.

The Session Stream View

The Session Stream. Left: live event log — every tool call, result, and text the agent produces. Right: Artifacts panel — files the agent creates during the session, downloadable immediately. Bottom: file mounts and token usage.

The event stream is important to understand. It shows you exactly what the agent is doing at every step:

• session_start — session initialized, files mounted

• text — the agent’s reasoning (visible in real time)

• tool_use — the agent calling a tool (e.g., code_execution)

• tool_result — what the tool returned

• requires_action — the agent is paused, waiting for your input

This isn’t a black box. You can watch it think, catch mistakes mid-run, and build confidence in what it’s doing before you trust it with production data.

The Human-in-the-Loop Gate

The requires_action state — the agent has done its work and is waiting for a human decision. The summary shows exactly what it found and what it wants to do next. You approve, reject, or send custom instructions to resume.

This is the feature most people don’t realize exists. When an agent reaches a step you’ve defined as a gate — merging a PR, sending an email, making a purchase — it pauses and surfaces a decision UI. The agent has done all the investigation. You make the final call.

The pattern: agent does the analysis, human makes the irreversible decision.

The Architecture: Three Things You Configure

When you set up a Claude Managed Agent, there are three layers:

1. The Agent definition — model + system prompt + tools + Skills. Configure once.

2. The Environment — Anthropic’s sandboxed compute layer. Files mount here. Code executes here. State persists here across turns in a session.

3. Sessions — each conversation thread. Multiple sessions can run from the same agent definition simultaneously. Each keeps its own file state, tool outputs, and conversation history.

The core API:

import anthropic
client = anthropic.Anthropic()

# Define the agent once
agent = client.beta.managed_agents.agents.create(
    name="data-analyst",
    model="claude-sonnet-4-6",
    system_prompt="You are a senior data analyst...",
    tools=[{"type": "code_execution"}, {"type": "file_write"}],
)

# Spin a session per user/task
session = client.beta.managed_agents.sessions.create(
    agent_id=agent.id,
    files=[{"path": "sales_q1.csv", "content": csv_bytes}]
)

# Stream the run
with client.beta.managed_agents.sessions.stream(
    session_id=session.id,
    input="Analyze this CSV and generate a revenue report."
) as stream:
    for event in stream:
        if event.type == "requires_action":
            handle_human_approval(event)

# Pull artifacts when done
artifacts = client.beta.managed_agents.sessions.artifacts.list(session.id)

The Real Use Cases

Anthropic ships five applied notebooks in claude-cookbooks/managed_agents. Here’s what each one does:

1. Data Analyst → HTML Report. Mount a CSV. The agent analyzes it with pandas and plotly, generates charts, writes a full narrative HTML report. No dashboard, no data engineer — one session, one artifact.

2. Slack Data Bot. The same data analyst agent, wrapped in a Slack integration. Mention it with a CSV attached — it replies in-thread with the report. The key is session continuity. Each Slack thread maps to one session. Follow-up questions in the thread continue the same session; the CSV is already loaded, the prior analysis is in memory. No re-uploading, no re-explaining.

We covered how a similar pattern powers newsletter automation systems in our n8n playbook post. Managed Agents brings that same continuity natively, without the workflow builder overhead.

3. SRE Incident Responder. A pager alert fires → session opens → agent investigates (reads logs, diffs code, identifies root cause) → opens a PR with a fix → pauses for human approval before merging. The human-in-the-loop gate is native here. No custom code required — the agent calls requires_action, the Console surfaces the approval UI, and you resume the session with your decision.

4. Expense Approval Workflow (paid). Employees submit expenses in natural language. Agent reviews policy, either auto-approves (within threshold) or escalates to a manager. Custom decide() and escalate() tools give the agent structured actions; the requires_action state handles the escalation pause.

5. Issue → PR Pipeline (paid). Takes a GitHub issue, fixes it, opens a PR, monitors CI, addresses a review comment, and merges — all in one session. The critical feature: when CI fails mid-chain, the agent reads the failure (stored in session file state) and continues from that checkpoint. It doesn’t restart.

Read more

The Mythos leak was just a rumor. This week Anthropic made it real — and announced no one gets to use it. Meta shipped its first model under Alexandr Wang — and for the first time in its history, shipped it closed-source. Google dropped an open model that beats Llama 4 across every major benchmark — at a fraction of the compute. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

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Featured Materials 🎟️

Anthropic Officially Releases Mythos — And Immediately Locks It Away 🔐

Three weeks ago the model leaked from a CMS error. On April 7, Anthropic made it official — and confirmed that Claude Mythos Preview will not be publicly available. Not because it underperformed. Because it performed too well.

Internally, Anthropic used Mythos to scan the world’s most critical software. It found thousands of zero-day vulnerabilities autonomously — including a 17-year-old remote code execution flaw in FreeBSD (CVE-2026-4747) that let anyone gain root over NFS. No human assistance. Fully autonomous.

- 93.9% on SWE-bench Verified — highest score ever posted for a coding benchmark

- 97.6% on USAMO 2026 — math olympiad level

- Parameter count undisclosed — Anthropic has not revealed the architecture details of Mythos Preview

- $100M in usage credits committed to Project Glasswing partners

What Project Glasswing actually is:

Instead of a public release, Anthropic launched Project Glasswing — a controlled-access security initiative. 50+ organizations receive Mythos Preview access specifically to find and patch vulnerabilities before attackers get there: AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft, Nvidia, Palo Alto Networks. Plus ~40 more critical infrastructure maintainers.

Mythos drew a line the industry hasn't seen before: a model so capable that the responsible move is to not ship it. Project Glasswing is the right call. The uncomfortable question is what happens when the next lab reaches the same capability and makes a different choice.

• Source: Anthropic | Red Team Blog | TechCrunch | Fortune | NBC News

Meta Builds the Best Health AI on the Planet — Then Closes the Source 🔒

Meta launched Muse Spark on April 8. First model from Meta Superintelligence Labs. First model under Alexandr Wang. And the first model Meta has ever shipped as closed-source proprietary software.

That last part is the story. Meta built Llama. Meta defined the open-source AI movement. Muse Spark ships with no weights, no license, no public release plan. Zuckerberg’s team rebuilt their entire AI stack from scratch over nine months — and the result is too competitive to give away.

The model:

• Inputs: voice, text, image — Output: text only (for now)

• Fast mode for quick queries, Contemplating mode for complex tasks (runs sub-agents in parallel), Shopping mode with behavioral signals layered on top

• HealthBench Hard: 42.8 — beats every frontier model tested. GPT-5.4 scores 40.1. Gemini 3.1 Pro scores 20.6.

• Ranks 4th on the Artificial Analysis Intelligence Index — behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6

• Live now in the Meta AI app and meta.ai. Rolling out to WhatsApp, Instagram, Facebook, Messenger, and Ray-Bans in the coming weeks — US first

What Wang’s team actually built:

Nine months, no legacy constraints, full stack from the ground up. Small and fast by design, capable enough to outperform every competitor on health reasoning. Meta plans an open-source version eventually — but “eventually” with no date is not a commitment.

Meta open-sourcing Llama was a strategic move to commoditize competitors and commoditize compute. Muse Spark ships closed-source — the first time Meta has ever done that. An open-source version is planned but without a date, which in practice means the competitive advantage stays proprietary for now.

• Source: Meta AI Blog | CNBC | The Verge | gHacks

Gemma 4: A 31B Model That Outperforms Llama 4 at a Fraction of the Size 🪶

Google released Gemma 4 under Apache 2.0. No restrictions, no fees, no gating. A 31B Dense model that beats Meta’s Llama 4 across every major benchmark: AIME 2026 Math (89.2% vs 88.3%), LiveCodeBench v6 (80.0% vs 77.1%), GPQA Diamond (84.3% vs 82.3%), τ2-bench Agentic (86.4% vs 85.5%).

Four models for four use cases:

• Effective 2B (E2B) — smartphones, Raspberry Pi, 5GB RAM

• Effective 4B (E4B) — lightweight consumer hardware

• 26B Mixture of Experts — mid-range GPUs, 16GB+ RAM (4-bit)

• 31B Dense — high-end consumer GPU, 20GB RAM (4-bit)

All models: text + image inputs, 256K context window, 140+ languages. Edge models also handle audio. Day-one support on Hugging Face, Ollama, vLLM, llama.cpp, MLX, LM Studio, Unsloth, NVIDIA NIM, and Docker.

Google distilled Gemini 3’s research into a model family anyone can run on their laptop. The 31B Dense model fits on a single consumer GPU and outperforms everything Meta, Alibaba, and Mistral have shipped at any size. The open-source frontier moved again — in Google’s direction.

• Source: Google DeepMind Blog | Google Open Source Blog | AI Dev Page

News of the Week 🌍

• Cursor 3 ships an Agents Window 🪟 — Cursor rebuilt its IDE from scratch around one bet: that developers will manage agents, not write code. The new Agents Window is a separate interface built alongside the existing IDE — shows every local and cloud agent in one unified sidebar, with the option to switch back to the classic IDE at any time — including sessions started from Slack, GitHub, or Linear. Parallel execution, cloud handoff so agents keep running after you close your laptop, Design Mode for annotating UI in a built-in browser, and a 30-plugin marketplace from Atlassian, GitLab, Datadog, and Hugging Face. Price unchanged at $20/month.

  Source: Cursor Blog | Cursor Changelog

• Google open-sources Scion 🧪 — Google published Scion: a multi-agent orchestration testbed that runs Claude Code, Gemini CLI, and Codex as isolated containers with separate credentials and git worktrees. Each agent gets its own environment. Guardrails enforce at the infrastructure level, not in software. Google calls it a “hypervisor for agents.” Local mode is stable. Kubernetes runtime has known rough edges. Google shipped a demo game built entirely by collaborating agent teams — worth looking at to understand what the tool actually does.

  Source: GitHub | InfoQ

• OpenAI publishes its economic blueprint for the intelligence age 📋 — Sam Altman released a 13-page policy paper titled “Industrial Policy for the Intelligence Age.” The proposals: a national Public Wealth Fund giving every American a stake in AI companies, a subsidized 32-hour workweek at full pay, taxes on automated labor, and automatic safety net triggers that activate when AI-driven displacement hits preset thresholds. Altman told Axios it’s “a starting point, not a prescription.” The timing — weeks before an expected Spud launch and a potential 2026 IPO — is not accidental. The company racing to build AGI is now also writing the rulebook for what happens after.

  Source: OpenAI | Axios | TechCrunch

• GPT-5.5 “Spud” confirmed weeks away 🥔 — Sam Altman confirmed pretraining wrapped March 24. “A few weeks.” Greg Brockman on the Big Technology podcast: “two years of research, big model feel.” Polymarket assigns ~78% probability to an April release and 95%+ by end of June. GPT-5.4 set the current baseline at 75% on OSWorld-Verified, just above the 72.4% human score. Whether Spud ships as GPT-5.5 or GPT-6 remains unconfirmed — it depends on how large the performance gap turns out to be. Spud is positioned as the first OpenAI model genuinely built for autonomous agentic workflows, not chat.

  Source: The Information | TechRadar

• Anthropic launches Claude Managed Agents 🤖 — Anthropic launched the public beta of Claude Managed Agents on April 8: a suite of composable APIs for building and deploying production-grade cloud agents without touching the infrastructure. The design philosophy is “brain vs. hands” — Claude reasons, each session runs in a disposable isolated Linux container. Sessions persist through disconnections. Sandboxed execution, checkpointing, credential management, scoped permissions — all handled by Anthropic. Pricing: standard API token costs plus $0.08 per session-hour (~$58/month for a 24/7 agent before token costs). One constraint: runs exclusively on Anthropic’s infrastructure — not available through Bedrock or Vertex AI yet. Early adopters already in production: Notion, Asana, Rakuten, and Sentry. Anthropic reports 10x faster time-to-ship vs. building agent infrastructure from scratch. The announcement hit 5.09M views on X.

  Source: Anthropic Docs | SiliconANGLE | TechRadar

Useful tools ⚒️

⭐ NovaVoice — Voice OS for your desktop: context-aware dictation, AI assistant on any screen via hotkey, and cross-app voice commands without switching tabs.

NovaVoice isn’t just dictation. Dictate into Notion and get formatted Markdown. Hit the hotkey, ask anything by voice — no tab switching. Tell it to message someone in Telegram: it opens the app, finds the contact, drafts the message. You press send. Supports 6 languages mid-sentence, custom dictionary for names and shortcuts, and a smart popover for instant text reformatting. Available on desktop, free tier included.

Google Gemma 4 — Google’s open model family that outperforms Llama 4 across every major benchmark. Apache 2.0, no fees, 256K context window. Runs on a single consumer GPU.

Handle Extension — Point and click to refine UI directly in your browser, feed the changes straight to Claude Code, Cursor, or Codex. No re-prompting, no copy-paste. Chrome extension, open source, free.

Influcio — Self-learning AI agent for influencer marketing: finds creators, runs campaigns end-to-end, and optimizes every next launch from performance data. Replaces the full manual workflow with one system.

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Weekly Guides 📕

Gemma 4 Official Blog — Google — The canonical starting point. Architecture breakdown, full benchmark comparisons against Llama 4, and deployment options across every platform.

Run Gemma 4 Locally — Unsloth Docs — Exact RAM requirements by quantization level, hardware targets for each model size, and commands to get everything running. Shorter and more useful than the official docs.

Project Glasswing — Anthropic — Full explanation of what Mythos found autonomously, how the controlled-access program works, which organizations participate, and why the model won’t ship publicly.

Cursor 3: What Actually Changed — Medium — Hands-on walkthrough of the Agents Window, Design Mode, and cloud handoff. More candid about trade-offs than the official announcement.

Introducing Muse Spark — Meta AI Blog — Meta’s account of the nine-month development arc and architectural decisions. Also where you can try the model.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

Claude Mythos Preview — Red Team Technical Report — Benchmarks, the specific CVEs Mythos found autonomously, and the capability thresholds that triggered the restricted release. Required reading.

Muse Spark: Benchmarks & Full Comparison — Side-by-side Muse Spark vs GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro across reasoning, coding, vision, and health evals.

Gemma 4: Performance-to-Size Breakdown — Deep dive into how Google achieved frontier-level results at a fraction of the compute. Architecture decisions, training choices, benchmark context.

Google Scion — GitHub — The actual repo. README explains the agent hypervisor architecture and the demo game built entirely by multi-agent teams.

If you missed our previous updates:

OpenAI Raises $122B, Claude Code Leaks Its Secrets | Weekly Digest

A founder sent me landing page visuals, product shots, UGC concepts, 40 ad variations. He made it in one afternoon. One URL -One photo. That was it. The uncomfortable question hit immediately: how many teams are still paying premium creative budgets for work that just became same-day?

Case 1. Animated product sites built from keyframes

This is still the cleanest “wow” case in the set. It ends as a live site that a DTC brand would buy for $20k. Hero motion, scroll reveals, product deconstruction, dynamic copy, and mobile responsiveness. Just look at this:

This animation is displayed on the site’s main page. It was built from a single image — specifically with Nano Banana 2

Ed Hill made a site that a DTC brand would pay $15–20k for. Hero motion, scroll reveals, product deconstruction, mobile responsive. Built in a day.

How to replicate that:

1. Generate two consistent product frames in Nano Banana 2: one “closed” hero frame and one “exploded” or transformed end frame.

2. Animate the two stills in Kling 3 to create a short transition clip.

3. Feed that clip into Claude Code, often with a site-building skill or a prebuilt markdown spec.

4. Let Claude generate the site structure around the motion asset: hero, benefit blocks, proof sections, CTA, responsive layout, and scroll logic.

5. Export and host the result, often on Vercel or a similar front-end stack.

You can read more about this case here.

If you are still piecing together how Claude Code skills fit into these workflows, Creators AI’s Skills, Plugins, Swarm Mode: Practical Tips with Claude is a useful background read.

Prompt to copy

Use one prompt for each stage rather than one giant all-purpose instruction blob.

For the start frame:

Create a premium studio hero frame for [product]. Black background, sharp rim light, reflective materials, product centered, luxury-tech campaign look

For the end frame:

Create a matching end frame for the same product, but with components separated in a clean exploded layout. Keep materials, proportions, and camera angle consistent

For Claude Code:

Build a premium landing page around this motion asset. Use scroll-driven transitions, restrained copy, dark UI, large type, one hero section, three benefit blocks, one proof strip, one CTA section, and full mobile responsiveness

Why this one belongs in the piece

Because it is one of the first Nano Banana workflows that clearly crosses the line from spectacle to service. The client-facing angle is “premium motion microsite in 48 hours.”

Business angle

This is easy to package as a service: launch microsites, product pages, SaaS homepages, luxury e-commerce campaigns, or founder-led launch pages. The value is speed with polish, not novelty for its own sake.

Recommended videos & posts:

• Nick Saraev — “Claude Code + Nano Banana 2 + Kling = $15K Animated Sites”

• Nate Herkelman — “Build AI Websites with Claude Code and Nano Banana 2”

If you want more breakdowns of AI workflows that already map to real services — subscribe!

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And if a motion microsite already feels billable, the next workflow is even easier to explain to a client: one input, dozens of ready-to-test creatives.

Case 2. Static Ads factories that turn one brand URL into 30-40 ad creatives

This is where Nano Banana stops being a creative tool and starts behaving like a production engine.

One URL — and we got a dozens, hundreds of ad creatives:

Examples of ads we got just from one URL

Read more

At a Glance

In this piece, you will learn how to:

• Set up Cowork correctly so it follows your brand voice from day one

• Use it for 5 high-impact marketing tasks: briefs, audits, repurposing, reporting, scheduling

• Copy 5 ready-to-use prompts that consistently produce better output

This post is prepared with Guest Author - Andy Wood . Creator of Andy Wood on Substack If you also want to write for Creators AI - send us email here

Most AI tools give you a text block. Cowork gives you the finished file. Here's how that changes marketing work. Most marketers I talk to work the same way with AI. They open ChatGPT or Claude, type a request, get back a block of text, then spend the next twenty minutes reformatting it, copying it into a document, adjusting it to match the brief. The AI did some of the work. The human did the rest.

Cowork changes that sequence. It’s not a better chatbot. It’s a desktop agent that reads your files, writes real documents, and finishes jobs while you do something else.

This is what that looks like for marketing work.

This and many other practical posts on building with AI are available exclusively to our subscribers

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What Cowork Actually Is

Anthropic launched Cowork on macOS in January 2026, with full Windows support arriving in February. It’s built on the same underlying agent architecture as Claude Code, the tool developers use to write and edit software autonomously, but designed for everyone else.

The practical difference from regular Claude chat: you give it access to a folder on your computer. It can read everything in that folder, create new files and edit existing ones. You describe what you want, approve its plan, let it run. When you come back, there’s a Word document, a spreadsheet, a formatted brief. Not a text block you now have to do something with.

Scenario: A content strategist at a B2B software company has a Monday morning problem: the marketing director needs a campaign brief by noon, the source material is scattered across three meeting notes docs, a competitor comparison PDF and some rough audience notes. Previously, she’d spend an hour manually pulling it together. With Cowork, she drops everything into a folder, writes a single prompt, approves the plan, and comes back forty minutes later to a formatted Word document. She spends fifteen minutes reviewing and editing. Brief is done before 9am.

I think Cowork is the most useful thing Anthropic has shipped for non-technical users. The output lands in your file system, in formats you can actually use.

A few things worth knowing before you start. Cowork is available on all paid plans (Pro at $20/month and above), and it’s in research preview, meaning it’s functional but still being refined. It consumes more of your usage allowance than regular chat, so complex sessions on a Pro plan will hit limits faster. For heavy marketing work, Max makes more sense. The desktop app must stay open while Cowork is running too. Close it and the session ends.

New to Cowork entirely? We covered the full launch and what it means for non-technical users: Claude Cowork: Complete Guide & Practical Use Cases

Setting It Up Right

Download the Claude Desktop app from claude.com/download. Open it, and you’ll see a mode selector at the top — Chat and Cowork. Switch to Cowork.

The first thing worth doing is writing global instructions. Go to Settings > Cowork, click “Edit” next to Global instructions. Tell it your brand voice, preferred document formats, your tone. Something like: “I write for B2B SaaS audiences. Tone is direct and practical, short paragraphs, no jargon. Preferred format for reports is Word. Preferred format for data is Excel.”

This applies across every session. Write it once and Cowork follows it automatically.

Folder instructions are underused. If you have a client folder with particular requirements, add a brief note inside it. Cowork reads it whenever you’re working in that directory and applies the context without you having to repeat yourself. You can also ask Cowork to update folder instructions mid-session and it will write them itself.

Scenario: A freelance marketing consultant manages four clients simultaneously, each with distinct tones: one formal and regulatory, one casual and consumer-facing, one technical and developer-focused, one lifestyle brand. Rather than restating context at the start of every session, she keeps a one-paragraph instructions file inside each client folder. Cowork reads it automatically. She never has to remember which client prefers bullet points over paragraphs.

One more setup tip: before starting any complex task, tell Cowork explicitly what not to do as well as what to do. “Create new files, don’t edit originals” is the kind of instruction that prevents headaches later.

The Marketing Tasks Where It Delivers

Brand voice documentation

Point Cowork at a folder of your existing content (blog posts, email campaigns, ad copy, social posts) and ask it to produce a brand voice guide: tone, vocabulary patterns, what to avoid, example sentences showing the voice in practice.

The prompt that works well: “Read all files in this folder. They’re examples of our marketing content. Produce a brand voice guide as a Word document. Include: tone description, vocabulary we use and avoid, sentence structure patterns, and 5 before/after examples showing the voice applied to generic text.”

Scenario: A head of marketing at a professional services firm is onboarding a new content agency. Rather than scheduling a briefing call and hoping the agency absorbs the nuance, she points Cowork at two years of existing content and asks it to extract a voice guide. The document that comes back has vocabulary the agency should use and avoid, sentence length patterns, a list of phrases that have appeared repeatedly across their best-performing content. The agency lead reads it in twenty minutes. First draft lands on-brief.

This used to take a consultant or a full internal day. The output is a document you can hand to any copywriter or agency without editing.

Content repurposing at scale

Give Cowork a folder of long-form content (six blog posts, a white paper, a webinar transcript) and ask it to produce a month of social content, a three-part newsletter sequence and a short talk outline, all as separate files in the same folder.

What makes this different from a chat session is context persistence. Cowork holds the thread across a two-hour session, references decisions made an hour earlier, and keeps output consistent across formats. Regular chat can’t do that reliably.

Scenario: A solo marketing manager at a fast-growing SaaS company runs the entire content operation. After their annual product launch webinar, she has 90 minutes of transcript and no time to do anything with it. She drops the transcript into a folder, runs the repurpose prompt, and leaves for lunch. She comes back to five LinkedIn posts, a three-part email sequence and a talk outline for the next industry event, all pulling from the same core argument. What she’d normally spread across a week of evenings is done before 2pm.

Ask for the social posts first, approve them, then ask Cowork to use the same angle and messaging for the newsletter sequence. It will keep the thread running rather than starting fresh.

Campaign brief production

You have research notes, a meeting summary, a few competitor references. Cowork reads those source files and produces a formatted brief: objective, audience, message hierarchy, channel guidance, success metrics.

Because it’s working from your actual files rather than generating from scratch, the output reflects the real context of the campaign. Ask it to flag any gaps in the source material before producing the brief. You’ll often find it spots things you missed.

Scenario: A marketing director preparing a product launch brief has notes from four separate stakeholder conversations, each with slightly different takes on the target audience. He runs the source-first brief prompt and asks Cowork to flag contradictions before producing the document. Cowork returns a list: the sales team described the buyer as a VP-level decision-maker; the product team’s notes describe a technical champion who influences upward. He sends a quick message to both teams, resolves the conflict, then tells Cowork to proceed. The brief that comes back reflects the real picture rather than burying the ambiguity in vague copy.

Content audits

Drop a folder of existing content and a topic list. Ask Cowork to identify gaps, flag dated material and suggest where existing pieces could be updated rather than replaced.

Scenario: An in-house SEO manager has been asked to audit three years of blog content before a site migration: 60-plus posts, mixed quality, a handful of which reference statistics that are now years out of date. She asks Cowork to read every file and return a spreadsheet with columns for topic, quality score, publish date and recommended action. It flags 14 posts for update, 9 for retirement and 38 to keep. It also identifies three clusters of posts covering almost identical ground. She uses that spreadsheet as her migration brief. The whole audit takes an afternoon rather than a week.

This is tedious work most marketing teams either skip or do badly because it takes too long. Ask for the output as a spreadsheet with columns for piece title, publish date, topic coverage and recommended action (keep/update/retire). That format makes it immediately actionable.

Reporting across Excel and PowerPoint

If you have the Claude in Excel and Claude in PowerPoint add-ins installed (both free for paid plan subscribers on Mac), Cowork can work across them simultaneously. Analyse campaign data in a spreadsheet and push a chart or summary slide directly into a presentation, without switching between apps.

Scenario: A performance marketing manager has to produce a monthly board report. Previously, she’d spend half a day extracting data from campaign dashboards, pasting into Excel, building charts, then manually recreating those charts in PowerPoint. Now she exports raw campaign data into a folder, runs a Cowork session with both add-ins active, and describes the output she needs: analysis and charts in the spreadsheet, key findings as slides in the PowerPoint template she’s already set up. She comes back to a near-finished deck. She spends twenty minutes on narrative polish. The report is done before lunch.

For a monthly marketing report the workflow looks like this: raw data in Excel, Cowork builds the analysis and charts, those charts land in the PowerPoint template you’ve already set up. The whole thing runs while you do something else.

Scheduled tasks

Type /schedule in any Cowork task and you can set it to run automatically: daily, weekly, monthly. The scheduled task only runs while your computer is awake and the app is open, but within that constraint it’s a real time-saver.

Useful scheduled tasks for marketers: a competitive monitoring digest every Friday pulling from saved articles and notes, a content performance summary every Monday from your analytics exports, a draft newsletter every Thursday from the week’s content folder.

Scenario: A market intelligence lead at a mid-size technology company saves competitor blog posts, press releases and newsletter issues to a folder throughout the week — a thirty-second habit he’s built into his routine. Every Friday at 4pm, Cowork processes the folder and writes a digest: three trend signals, notable competitor moves, one content idea suggested by what it’s read. Monday morning, instead of starting from scratch on his weekly briefing, he edits a document that’s already 80 percent there.

This turns Cowork from a tool you use into a system that runs. That’s a meaningfully different thing.

If you’re already running AI workflows and want to go deeper on agent orchestration, we covered how solopreneurs are structuring their full agent stacks: How Solopreneurs Are Using Full AI Agents

Is your marketing team still reformatting AI outputs by hand — or have you moved to agent-based workflows? Share in the comments!

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The Prompts That Actually Work

Most Cowork users describe what they want in plain English. That works. But certain prompt structures consistently get better output, faster. These five are worth saving to your prompts folder before your first real session.

Prompt 1: The Source-First Brief

What it does: Turns scattered research files into a formatted campaign brief, with gaps flagged before you get the document.

When to use it: You have notes, a meeting summary and some competitor references but no structured brief yet.

The Prompt:

"Read all files in [FOLDER NAME]. Before producing anything, list the information you found and any gaps that would weaken the brief. Wait for my response. Then produce a campaign brief as a Word document with these sections: objective, target audience, core message, channel guidance, success metrics. Use only what's in the files. Don't invent details."

How to use it:

• Drop your source files into one folder before starting

• Paste the prompt, replacing [FOLDER NAME] with the actual path

• Review the gap list. Add any missing context as a quick text note in the folder, then tell Cowork to proceed

Prompt tip: Add “flag any claims in the source material that seem contradictory” to catch conflicting direction from stakeholders before it ends up in the document.

Prompt 2: The Voice Extractor

What it does: Produces a brand voice guide your copywriters can actually follow, built from your existing content rather than your opinions about your content.

When to use it: Onboarding a new agency, briefing a freelancer, or trying to document a voice that’s currently just “vibes.”

The Prompt:

"Read all files in [FOLDER NAME]. They are examples of our published marketing content. Analyse the writing and produce a brand voice guide as a Word document. Include: tone description in 3-4 sentences, vocabulary we use and vocabulary we avoid, sentence structure patterns, and 5 before/after examples showing the voice applied to generic text. Base everything on what's actually in the files."

How to use it:

• Gather 8-15 pieces of content you’re genuinely happy with (not everything you’ve published)

• Remove any content that felt off-brand at the time

• Run the prompt, then review the vocabulary list carefully — that’s where most voice guides go wrong

Prompt tip: Run this on a competitor’s content too, with a separate folder. Comparing the two outputs is a faster way to find differentiation than most positioning exercises.

Prompt 3: The Repurpose Stack

What it does: Takes one piece of long-form content and produces social posts, a newsletter section and a talk outline as separate files, keeping the angle consistent across all three.

When to use it: You have a webinar transcript, a white paper or a long blog post that deserves more reach than it got.

The Prompt:

"Read [FILE NAME]. Identify the single strongest idea in it. Then produce three separate files: (1) five LinkedIn posts based on that idea, each under 200 words, saved as linkedin-posts.docx; (2) a 400-word newsletter section expanding on the same idea, saved as newsletter-section.docx; (3) a 10-minute talk outline with speaker notes, saved as talk-outline.docx. Keep the core argument consistent across all three. Don't change the angle between formats."

How to use it:

• Run this on one piece of content at a time, not a folder. Cowork needs a single source to keep the angle consistent

• Review the LinkedIn posts first. They’ll show you quickly whether Cowork found the right idea

• If the angle is off, redirect mid-task rather than restarting

Prompt tip: After approving the outputs, ask Cowork to add each piece to a content tracker spreadsheet with publish date, format and status columns. One extra instruction, and your calendar is half-built.

Prompt 4: The Audit-to-Action

What it does: Reviews a folder of existing content and produces a spreadsheet telling you what to keep, update or retire, with a reason for each decision.

When to use it: Quarterly content reviews, site migrations, or any time someone asks “what should we actually be publishing?”

The Prompt:

"Read all files in [FOLDER NAME]. For each piece of content, produce a row in a spreadsheet saved as content-audit.xlsx. Columns: file name, approximate publish date if mentioned, primary topic, content quality (1-5 based on clarity and specificity), recommended action (keep/update/retire), reason for recommendation in one sentence. If a piece is dated by specific statistics or references, flag it as 'update - data.' If a piece overlaps substantially with another file in the folder, flag both."

How to use it:

• Include a rough date in each filename if your files don’t have metadata (e.g. “2023-q1-email-onboarding.docx”)

• Don’t clean up the folder first. Let Cowork see the full picture, including the weak stuff

• Filter the spreadsheet by recommended action when it comes back. Start with the “update” pile

Prompt tip: Ask Cowork to add a fifth column: “potential to repurpose for social.” It will flag pieces with strong standalone ideas that never got distributed properly.

Prompt 5: The Scheduled Digest

What it does: Sets up a recurring task that reads your saved articles, notes and competitor updates each week and produces a summary document automatically.

When to use it: You save links and articles all week but never actually process them. This closes that loop without you having to think about it.

The Prompt:

"Read all files in [FOLDER NAME]. They are articles, notes and competitor content saved this week. Produce a weekly digest as a Word document called [DATE]-weekly-digest.docx. Structure: (1) three most relevant trends with a one-paragraph summary each; (2) competitor moves worth noting, with source file name; (3) one content idea suggested by what you've read, with a brief rationale. Keep the whole document under 500 words."

How to use it:

• Set up a “weekly reading” folder and drop links, PDFs and copied text into it through the week

• Add /schedule to the task and set it to run every Friday at a time you’re usually at your desk

• Review the digest on Monday. The trend summaries alone will save an hour of catch-up reading

Prompt tip: Add “note any ideas that appeared in multiple sources” to the prompt. Convergence across unconnected sources is usually a stronger signal than a single article claiming something is important.

Tips That Actually Make a Difference

Approve the plan before you walk away. Cowork shows you its proposed steps before it starts. Read them. It takes thirty seconds and saves you from coming back to a finished document that went in the wrong direction.

Break big tasks into stages, not one giant prompt. “Audit our content, produce a brief, write five social posts and schedule them” is too much in one go. Do the audit first, approve the output, then move to the brief. Staged sessions produce better work.

Use the steering feature mid-task. If you check in partway through and Cowork has gone off-track, you can redirect it without restarting. Just type a correction. Most people don’t realise this is possible and either let bad work complete or restart from scratch.

Scenario: A marketing ops manager sets a Cowork session running to produce a quarterly report from three months of campaign data. She checks in after twenty minutes and notices Cowork is organising the report by channel rather than by objective. She types a single correction: “Reorganise by campaign objective, not channel.” Cowork adjusts mid-task. The finished report is structured correctly without her having to restart.

Ask it to explain its plan in plain language first. Before approving, type: “Explain what you’re about to do in plain English.” Occasionally you’ll catch a misunderstanding that the formatted plan didn’t make obvious.

Keep a prompts folder. Any prompt that produces good output, save it to a text file in your Cowork folder. Over time you build a personal library of what works for your content, your voice, your clients. Cowork can read that folder and suggest the right prompt for a new task.

We’ve covered how Cowork fits into a broader daily workflow in: How Cowork Can Boost Your Routine Work

What It Won’t Do

Cowork doesn’t generate images. It’s built for text-based outputs: documents, spreadsheets, scripts, briefs, strategies. You’ll still need a separate tool for visuals.

Cloud files need a bit of setup. MCP connectors let you work directly with tools like Google Drive and Notion through Cowork’s settings, or you can sync cloud folders locally as a simpler fallback.

No memory across sessions either. Cowork doesn’t retain context from a previous session, so if you’re continuing a project, keep a summary document in the folder that Cowork can read at the start of each new task.

An Honest Assessment

Cowork rewards people who already know what good output looks like. If you can read a brief and spot what’s missing, you’ll get excellent results. If you’re hoping it will figure out the marketing strategy independently, you’ll be disappointed.

Output quality depends on what you give it too. Vague source material produces vague documents. The more organised your input folder, the better the work that comes back.

For a solo marketer or a small team doing the work of a much larger department, it’s the closest thing I’ve seen to an actual extra pair of hands. Not a prompt that helps you think. An agent that does the work, delivers the file, moves on to the next task.

That’s a different category of tool. Worth the learning curve.

Where to Start

Start with something low-stakes: three or four existing blog posts in a folder. Ask Cowork to read them and produce five LinkedIn posts based on the key ideas. Review what comes back. Adjust your global instructions if the tone isn’t right. Run it again.

Once you’re comfortable with how it handles simple repurposing, move to something more involved: a content audit, a campaign brief from real source files, a brand voice document.

The shift that takes the most getting used to is describing outcomes rather than instructions — telling Cowork what you want to end up with rather than how to do it step by step. Most people have that sorted within a session or two.

Share this with a marketer who’s still copying AI outputs into Word documents by hand

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Which of these five prompts are you trying first? Drop it in the comments — I’d love to know what your use case looks like.

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OpenAI raised more money in a single round than the GDP of Hungary. Anthropic accidentally shipped the source code of its most valuable product to the entire internet. And SpaceX filed for what could be the largest IPO in human history — with Grok inside. Welcome to the last week of Q1 2026. Let’s break it down.

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

OpenAI Raises $122 Billion at $852 Billion Valuation 💰

On March 31, OpenAI closed the largest private funding round in history — $122 billion at an $852 billion post-money valuation. The round was anchored by three major investors: Amazon ($50 billion, of which $35B is contingent on an IPO or reaching AGI), NVIDIA ($30 billion), and SoftBank ($30 billion). For the first time, OpenAI also raised $3 billion from individual investors through bank channels and will be included in ARK Invest ETFs.

The numbers behind the raise:

• $2B/month in revenue, growing 4x faster than Alphabet and Meta at the same stage

• 900 million weekly active ChatGPT users

• 50M+ paid subscribers

• 15 billion tokens processed per minute via API

• Codex now serves 2M weekly users — up 5x in three months

• Ads pilot hit $100M ARR in under six weeks

• Enterprise is 40% of revenue, on track for parity with consumer by end of 2026

What it’s for:

OpenAI is building what it calls a “unified AI superapp” — combining ChatGPT, Codex, browsing, and agentic capabilities into one product. The company has also discontinued the standalone Sora app, redirecting compute toward its higher-revenue priorities.

The twist:

The day after the record raise, Bloomberg reported that $600 million in OpenAI shares had been sitting unsold on secondary markets — six institutional investors couldn’t find a single buyer. Meanwhile, across secondary platforms, $2 billion in cash was waiting to deploy into Anthropic. OpenAI’s enterprise API market share dropped from 50% to 25% over the past year, while Anthropic’s rose from 12% to 32%. OpenAI reportedly labeled this gap a “red alert” internally. The largest fundraise in history and the quietest secondary market in years — happening simultaneously.

The bigger picture: This round is as much about anchoring IPO expectations as it is about the capital. Both OpenAI and Anthropic are targeting public listings in Q4 2026.

• Source: Bloomberg | CNBC | TechCrunch | OpenAI Blog

Claude Code Accidentally Leaked Its Entire Source Code 🔓

On March 31 at 04:23 UTC, Anthropic published Claude Code v2.1.88 to npm — and accidentally shipped .map sourcemap files that fully reconstructed nearly 2,000 TypeScript files and 512,000 lines of code. No exploit, no credentials, no special access required. A security researcher flagged it publicly within hours. The post accumulated 34 million views. A mirrored copy appeared on GitHub and crossed 84,000 stars. Anthropic pulled the version the same day and used copyright takedown requests to remove 8,000+ copies.

What the leak revealed:

• Undercover Mode — Claude Code is instructed via system prompt: “You are operating UNDERCOVER in a PUBLIC/OPEN-SOURCE repository. Your commit messages, PR titles, and PR bodies MUST NOT contain ANY Anthropic-internal information. Do not blow your cover.” In other words, when developers use Claude Code to contribute to public repos, the tool actively conceals its involvement.

• Anti-distillation poisoning — controls that inject fake tool definitions into API responses if competitors attempt to scrape Claude Code outputs to train their own models.

• Three-layer memory architecture — the solution to “context entropy” that Anthropic spent years building. A lightweight index (MEMORY.md), topic files fetched on-demand, and a grep-only transcript layer. Competitors now have the blueprint.

• KAIROS / autoDream — a background daemon mode where Claude Code performs “memory consolidation” while the user is idle — merging observations, resolving contradictions, converting insights into facts.

• Claude Mythos (codename: Capybara) — multiple references to an unreleased model described in a separate leaked document as a “step change in capabilities,” with a significantly larger context window than anything currently on the market.

• Frustration tracking — code that scans user prompts for profanity, insults, and phrases like “this sucks,” and appears to log expressions of negativity.

The human error behind the leak:

Anthropic acquired the Bun JavaScript runtime at the end of 2025. A known Bun bug (issue #28001, filed March 11) caused sourcemaps to ship in production builds. The bug was open for 20 days before the leak. Nobody caught it.

For creators and builders: The most commercially significant finding isn’t the “Undercover Mode” — it’s the three-layer memory architecture. If you’re building AI agents that need to maintain coherent context over long sessions, this leaked blueprint is the most detailed public documentation of a production solution available anywhere.

• Source: $2.5B Codebase Leaked. Reveal of Claude Code Files

SpaceX Files for the Largest IPO in History 🚀

SpaceX has confidentially filed for an IPO targeting a $1.75 trillion valuation and a $75 billion raise — which would exceed the GDP of more than half the world’s countries and shatter Saudi Aramco’s previous record of $29.4 billion. This is not just a rocket company going public. In February, SpaceX absorbed xAI in an all-stock deal at a $1.25 trillion combined valuation. It also owns X (formerly Twitter). The entity going public spans orbital launches, satellite internet (Starlink), AI (Grok), and social media — all controlled by one individual.

The numbers:

• Starlink: $10.6B revenue in 2025, 54% EBITDA margins, 10M+ subscribers across 150 countries

• SpaceX total 2025 revenue: $16B

• xAI component: reportedly burning ~$1B/month

• All 11 original xAI co-founders have departed since the merger

The AI angle:

Musk said in March that xAI “was not built right the first time around, so is being rebuilt from the foundations up.” SpaceX recently hired two engineers from Cursor to bolster its AI coding efforts. The IPO would be the first major public test of whether xAI’s Grok can be valued alongside Starlink’s cash flow — or whether it drags the valuation down.

SpaceX, OpenAI, and Anthropic together could raise more than $150 billion in IPOs by the end of 2026 — roughly as much as all US VC-backed company IPOs combined over the past decade. PitchBook warns this concentration of capital may crowd out smaller listings entirely.

• Source: The Next Web | BusinessToday | Fortune

News of the week 🌍

• DeepSeek Goes Dark for 7 Hours — Right Before V4 🔻— DeepSeek experienced its longest outage since going viral in early 2025 — 7 hours and 13 minutes of full service disruption, affecting users and API-dependent products globally. No official cause was given. The timing is notable: V4, DeepSeek's next major model, is expected in April, and industry observers speculate the downtime reflects infrastructure work ahead of the launch. For teams evaluating DeepSeek as a low-cost alternative to OpenAI and Anthropic APIs, this is a concrete reliability signal — good model, still maturing infrastructure.

• Google Search Live Launches in 200+ Countries 🌐 — Google’s real-time multimodal search — point your phone at anything, ask a question out loud, get an AI answer live — rolled out globally, powered by Gemini 3.1 Flash Live. Available across all languages where AI Mode is active. Also released as an API in Google AI Studio. Real-time voice+camera AI search is now a global-default product, not a US experiment.

• Mistral Raises $830M in Debt for European AI Infrastructure 🇪🇺 — France’s Mistral secured $830M from a seven-bank group to buy 13,800 NVIDIA chips and build a major data center near Paris. Europe’s clearest move yet toward AI sovereignty — owned compute, local infrastructure, a target of 200 megawatts across Europe by end of 2027.

• The Same Morning as the Claude Code Leak, npm Got Hit by a RAT ☠️ — At 00:21 UTC on March 31 — four hours before Anthropic accidentally shipped their source code to npm — attackers planted malicious versions of the axios package (1.14.1 and 0.30.4) in the same registry, carrying an embedded Remote Access Trojan. Two separate npm security incidents in one morning: one accidental, one deliberate. Any developer who ran npm install on March 31 had two separate reasons to audit their lockfiles immediately.

• Runway Launches $10M Fund for AI Video Startups 🎬 — Runway is seeding early-stage startups building on its video models — free API credits, a Builders program, and direct investment checks. The AI video company is positioning itself as ecosystem infrastructure rather than just a destination product. For founders building video-native AI applications, this is the most accessible compute funding available right now.

• Anthropic Research: Claude Has Internal Emotion Representations 🧠 — Anthropic published research finding that Claude has internal representations of “emotion concepts” that actually drive its behavior — sometimes in surprising ways. The paper raises new questions about what’s actually happening inside frontier AI systems at inference time.

Useful tools ⚒️

⭐ Jupid — File your taxes with AI. Connects to your financial accounts, uses Claude to analyze transactions and fill your tax return end-to-end.

Notion MCP — Your Notion workspace inside every AI agent. Lets any MCP-compatible AI agent read, write, and query Notion databases directly — no manual copy-paste between tools.

Computer Use in Claude Code — Let Claude use your computer from the CLI. Native computer use built directly into the terminal workflow — click, type, scroll, interact with any app.

Ollama v0.19 — Massive local model speedup on Apple Silicon with MLX. If you run models locally on a Mac, this update changes the experience entirely. Decode speed nearly doubles, prefill 1.6x faster. Requires 32GB+ unified memory — check before updating.

Pixero AI — Autonomous AI agents inside your ad campaigns — creative generation, targeting, A/B testing, all agent-native. No manual setup required.

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Weekly Guides 📕

Gemini Live API — Official Developer Documentation — Google's official guide to building real-time voice+vision agents on Gemini 3.1 Flash Live: WebSocket sessions, multimodal input handling, function calling, and ephemeral token setup.

Ollama 0.19 + MLX on Apple Silicon — Official Setup — Ollama's own walkthrough of the v0.19 MLX backend: what changed under the hood, which models are supported, benchmark numbers (1.6x prefill, 2x decode), and the 32GB memory floor to check before updating.

Migrating from Gemini 2.5 Flash Live to 3.1 — Google's official migration checklist: new model string, changed thinking config, and the three features — proactive audio, affective dialog, async tool calling — that do not carry over from 2.5. Required reading before you touch an existing voice agent.

Install Claude Code Without npm — Native Installer — Anthropic's official response to the March 31 supply chain incident: a standalone binary that bypasses npm's dependency chain entirely. One curl command, no lockfile to audit.

Claude Code Architecture Patterns from the Leak — VentureBeat Deep Dive — How to implement the three-layer memory architecture, anti-distillation traps, and multi-agent orchestration patterns revealed in the leaked source — the closest thing to official agent architecture docs that exists right now.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

DeepSeek's Worst Outage Ever — What It Tells You About AI Infrastructure — Why 7 hours of downtime reveals more about DeepSeek's trajectory than any benchmark: the gap between building a capable model and running production infrastructure at the scale of hundreds of millions of users.

Anthropic Research: Emotion Concepts in Claude — What Anthropic’s own researchers found when they looked for emotional representations inside the model

The Axios npm Supply Chain Attack: Full Technical Breakdown — Security Boulevard— The complete account of the malicious axios 1.14.1/0.30.4 packages planted by North Korea-nexus threat actor UNC1069 — how the RAT worked, what to check in your lockfiles, and why this happened on the same morning as the Claude Code leak.

April 2026 AI Model Landscape: Everything That’s Coming — Claude Mythos, GPT-5.5 (Spud), DeepSeek V4, Grok 5 — the full Q2 model pipeline

If you missed our previous updates, don’t worry, here they are:

Claude Mythos Leaked, ARC-AGI-3 Humiliates Every AI, Sora Is Dead

You know that feeling when someone spends years building something locked-down and secretive — and then accidentally leaves the front door wide open?

That’s exactly what happened to Anthropic on March 31, 2026.

One day before April Fools’ Day, they shipped the entire source code of Claude Code to the public npm registry inside a routine update. By morning, 512,000 lines of proprietary TypeScript were live on GitHub, being forked tens of thousands of times, and analyzed by basically every developer on the internet.

Not a hack. Not a breach. A misconfigured .npmignore file.

And what developers found inside was... a lot. Hidden features nobody knew existed. An internal model roadmap. A subsystem specifically designed to prevent Anthropic’s secrets from leaking — inside a codebase that just leaked.

We went deep on this one. Here’s everything that matters for you.

How It Happened

Claude Code is built with Bun as its JavaScript bundler. Bun generates debug .map files by default unless you explicitly turn that off. Nobody did. So version 2.1.88 shipped with a 59.8MB source map that pointed directly to a complete ZIP of Anthropic’s original TypeScript — sitting in a public Cloudflare R2 bucket with zero access controls.

At 4:23am ET, an intern at Solayer Labs named Chaofan Shou spotted it, posted a download link on X, and the race was on.

Boris Cherny — the creator of Claude Code, who we’ve covered before — confirmed it publicly on X: plain human error. His follow-up:

Anthropic’s official line:

The brutal irony? The leaked code contained a whole subsystem called Undercover Mode — designed specifically to stop Anthropic’s internal information from leaking. They built an AI feature to prevent leaks. Then leaked their own source code through a config file.

The GitHub Chaos (Real Links)

Read more

At a Glance

In this piece, you will learn how to:

• Understand why your AI stack breaks (and which layer to blame)

• Apply a simple 3-layer architecture to any solo operator setup

• Replace individual tools without rebuilding everything from scratch

This post is prepared with Guest Author - Jurgen Appelo . Creator of Solo Chief If you also want to write for Creators AI - send us email here

When your AI stack breaks, do you know which layer to open? A proper AI agent architecture tells you exactly where to look.

A few weeks ago, I realized my AI infrastructure sucked.

After a year of experimenting with LLMs, I had ChatGPT, Claude, and Gemini all cross-contaminating each other’s project contexts. My data looked as if it had been distributed by a hand grenade across file systems and cloud services. I used Claude Cowork, NotebookLM, and Perplexity like a five-year-old uses a box of crayons. And I had workflows and business processes flowing and connecting like a bowl of spaghetti that someone dropped from the tenth floor.

If I ever wanted to scale my business, I needed to stop treating my architecture like a junkyard.

Does that sound familiar?

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The Lasagna Principle: Why Your AI Agent Architecture Needs Layers

Here’s what I’ve learned from years of building software and months of wiring AI agents together: when you’re the only one responsible for the whole stack, sloppy wiring isn’t just a nuisance. It’s a liability you carry alone.

Every solo operator who’s tried building an AI agent orchestration stack hits the same wall. They start with one automation. It works. They add a second. Still fine. By the fifteenth or sixteenth, they’re staring at a clump of Christmas tree wiring where a Slack message somehow triggers a scraper that writes to a database that kicks off another workflow that occasionally emails a client the wrong welcome email.

We wrote a full breakdown for solo operators building with agents in 2026 — check out 4 Ways Monetize AI Agents in 2026 for real patterns and examples.

Software engineers addressed this problem decades ago with N-tier architecture. The idea is old and boring, which is exactly why it works: you separate your system into layers, and each layer only talks to the one directly above or below it. Your interface doesn’t know how you store your data; your data doesn’t care how your workflows run. Each layer minds its own business.

I think of it as lasagna instead of spaghetti. Similar ingredients, completely different structure. And when something breaks (because it always will), you know exactly which layer to open up and poke around in.

“I think of it as lasagna instead of spaghetti. Similar ingredients, completely different structure.”

For my AI agent orchestration stack, I keep it simple with just three layers: a UX layer where I interact with the system, a workflow layer where automations orchestrate the work, and a persistence layer where state lives and persists.

The UX layer is the surface where a person (or agent) touches the system. It could be a Slack message, a Claude Cowork command, a voice prompt, even a button on a vibe-coded web app. The point is that this layer knows nothing about what happens next. It just accepts input and, eventually, shows output.

The workflow layer is the heart of any AI agent orchestration setup. It’s where the (mostly) deterministic logic lives: the if-this-then-that decisions, the error handling, the retries. The workflow layer doesn’t store anything itself, and it doesn’t talk to humans directly. It just orchestrates. Receive a trigger, do some work, pass the result.

If you're using Claude's ecosystem as your workflow layer, we covered orchestration in depth: Skills, Plugins, Swarm Mode: Practical Tips with Claude.

The persistence layer doesn’t know or care that Make or n8n or Zapier sent the data. It could just as easily have come from a Python script or a manual import. The schema is stable regardless of what’s happening in the layers above.

Why AI Agent Orchestration Needs Layers

The real payoff of proper AI agent architecture: you can replace any layer without rebuilding the whole stack. You can swap tools in and out without having to go back to Cursor or Claude Code for a minor change.

“That’s the real payoff of proper AI agent architecture: you can replace any layer without rebuilding the whole stack.”

I’d be lying if I said it’s all upside. Having more layers means more things that can break independently. Debugging a failure that starts in Slack, passes through Make, and surfaces as a wrong value in Fibery is like playing a game of telephone with robots. It takes longer to set up than a quick-and-dirty single-tool solution. And there’s a constant temptation to over-engineer — to add a fourth or fifth layer “just in case” when what you actually need is to ship something.

Tools like Claude Cowork can sit cleanly in your workflow layer — handling browser automation, file management, and multi-step agent tasks without leaking into your UX or persistence layers.

I’m building slower than the hammock crowd. But at 8 AM, when something broke and there’s nobody to call, I want to know which layer to open. That’s worth the extra time.

The real question every Solo Chief has to answer for themselves: how much structure is worth the slowdown? I don’t have a formula for that. I only know what my tolerance for 8 AM debugging sessions is, and it’s getting lower every year.

Is your AI stack already split into layers — or still one big tangle? Share with us in the comments!

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From Spaghetti to Lasagna: Building Your AI Agent Stack

Am I happy with the AI agent orchestration architecture I have now? Not yet. I still have too much spaghetti and too little lasagna. But the direction is clear, and that makes all the difference. Every update I make to my agentic orchestration must satisfy the N-tier architecture. Every new piece I add goes into one of three places: UX layer, workflow layer, or persistence layer.

Now that I have the horizontal architecture fleshed out, I can start separating the agents into their own vertical domains: one agent for Nonfiction Writing, one for Finance & Admin, one for Personal Branding, and so on. Each with their own objectives. Each with their own layers. My agentic organization will look less like a junkyard and more like something I’d trust to run while I’m asleep.

An unorganized bunch of wires will work fine as long as you’re still experimenting. But for anything more serious and viable, you’ll want to think about giving your AI agent orchestration stack a bit more structure.

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What does your AI stack look like right now — and what’s the one layer you’d fix first? Drop it in the comments — I’d love to know.

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