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!

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Built for founders, executives, and operators who network with intention — not volume. From digital business cards with selective sharing to calendar-aware meeting scheduling — ConnectMachine handles your entire relationship layer, quietly and privately.

Try ConnectMachine Free

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 🎁

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

Subscribe now

Stop losing contacts. Let AI remember everyone for you.

ConnectMachine is your private AI agent for professional networking. Describe who you're looking for in plain English — ConnectMachine finds them, surfaces the context of when and where you met, and helps you reconnect at exactly the right moment. No scattered business cards. No noisy social feeds. No lost opportunities slipping through the cracks.
Built for founders, executives, and operators who network with intention — not volume. From digital business cards with selective sharing to calendar-aware meeting scheduling — ConnectMachine handles your entire relationship layer, quietly and privately.

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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.

Want more breakdowns like this? Share this edition with a friend.

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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.

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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 🎁

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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.

Know friends, co-founders or vibe-coders who need this content. Share!

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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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Anthropic accidentally leaked their next model — and it sounds like nothing we've seen. Claude Mythos scored "dramatically higher" than Opus 4.6 on coding, reasoning, and cybersecurity. Cursor got caught building "their" model on a Chinese open-source base. Sora is dead, taking a $1B Disney deal with it.

ARC-AGI-3 launched and humiliated every frontier AI on the planet — GPT-5.4, Gemini, Claude, Grok all scored below 1% while untrained humans hit 100%

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

Portkey’s enterprise-grade Gateway is now fully open source + ready for the agentic era

Portkey’s Gateway is built for teams running AI in production, at scale. The newly open-sourced Gateway combines Portkey’s OSS Gateway, SaaS Gateway, and a new MCP Gateway, making AI systems observable, governed, and reliable. A single production layer where every model request and agent action flows through one place. Fork it. Self-host it. Build on it.

The repo is open →

Featured Materials 🎟️

Claude Can Now Control Your Mac 🖥️

On March 23, Anthropic launched Computer Use for macOS — and it’s the most significant product move they’ve made since Claude Code. Claude can now point, click, type, and navigate apps on your Mac to complete tasks while you’re away from your desk.

It’s available as a research preview inside Claude Cowork and Claude Code for Pro and Max subscribers.

How it actually works:

Claude follows a clear priority ladder when given a task:

1. Connectors first — if you have Slack, Google Calendar, or Drive connected, it uses those (fastest, most reliable)

2. Browser second — if no connector exists, Claude controls Chrome to reach the web version of a tool

3. Full screen control last — only as a last resort does it take over your mouse, keyboard, and screen directly

The key companion feature is Dispatch — assign Claude a task from your iPhone, and return to finished work on your desktop. Export a pitch deck as PDF, attach it to a calendar invite, start a dev server and screenshot it — all while you’re doing something else.

What’s also new in Claude Code:

• Auto mode — classifier-based permissioning lets safe shell commands run autonomously while blocking destructive ones

• Planner–generator–evaluator harness — improves long-running coding tasks through structured iteration, QA, and context handling

• Anthropic also launched a science blog to formalize AI-driven research workflows. 74 product updates in 52 days.

The real picture:

Anthropic explicitly says reliability is still ~50% on complex tasks and recommends not using it with sensitive data in this preview. But the architecture is right — connector-first, screen-control as fallback, permission-gated at every step. This is Anthropic’s answer to Manus Desktop and OpenClaw, built directly into the model layer.

Available to Claude Pro ($20/mo) and Max ($100–200/mo) subscribers on macOS only for now. Windows is likely next given Cowork already added it in February.

Cursor Launched a “Frontier AI Model.” One Developer Found the Real Name in the API🔍

On March 19, Cursor announced Composer 2 — their “self-developed, frontier-level coding intelligence” — with impressive benchmarks: 61.7% on Terminal-Bench 2.0, beating Claude Opus 4.6 at one-tenth the price.

The narrative lasted less than 24 hours.

Developer @fynnso was debugging and intercepted an API call. The model identifier in the response wasn’t “Composer 2.” It was kimi-k2p5-rl-0317-s515-fast — literally: Kimi K2.5, reinforcement learning, trained March 17, fast serving. He tweeted it. 2.6 million views. Elon Musk replied: “Yeah, it’s Kimi 2.5.”

Why Cursor used Kimi in the first place:

Western open-source options were thin. Meta’s Llama 4 Behemoth is indefinitely delayed. Gemma 3 tops out at 27B parameters. Kimi K2.5 is a 1 trillion parameter MoE with 32B active parameters — 6x more active compute than GPT-OSS 120B, which matters for the kind of long-horizon, multi-file coding tasks Composer 2 is built for.

What this actually means:

The story isn’t about one company’s disclosure failure. It’s about the fact that the best open-source foundations for building serious AI products are currently Chinese. Cursor is a $50B company with $2B ARR. If they’re quietly building on Kimi, other companies are doing it too — they just haven’t been caught yet.

This is the most important story in AI infrastructure this week that isn’t about benchmarks.

Anthropic Accidentally Leaked Their Most Powerful Model Ever. Meet Claude Mythos🔓

On March 26, a configuration error in Anthropic's content management system left nearly 3,000 unpublished assets publicly searchable — including a draft blog post describing a new model called Claude Mythos (internal codename: Capybara). Anthropic confirmed they're testing it with early access customers. Their own words: "a step change" and "the most capable we've built to date."

What the leak revealed:

Mythos sits above Opus in capability tier — not a replacement, a new level entirely. On benchmarks, it posts "dramatically higher scores" than any previous Claude model on coding, academic reasoning, and cybersecurity tasks.

The irony: a company whose entire brand is "responsible AI" accidentally leaked documents warning their own flagship model is an unprecedented cybersecurity risk.

Currently in early access testing. No public release date announced.

News of the week 🌍

Gemini 3.1 Flash Live Launches — And Google Lets You Import Your ChatGPT History 🎙️

On March 26, Google launched Gemini 3.1 Flash Live: a real-time voice model that better handles acoustic nuances like pitch, pace, and emotional cues. It powers Search Live and Gemini Live with multi-language support and lower latency. On the same day, Google added a Gemini import feature — users can now upload zipped chat exports from ChatGPT, Claude, or any other AI directly into Gemini. Google is quietly building the stickiest migration path in the market. You can now bring your entire conversation history with you when you switch.

China Trapped Manus's Founders. Meta Is Still Shipping Their Product. 🚨 On March 25, the FT reported that Chinese authorities have barred Manus CEO Xiao Hong and Chief Scientist Ji Yichao from leaving the country. The two Singapore-based founders were summoned to a Beijing meeting with the National Development and Reform Commission, questioned about potential violations of foreign investment rules around Meta's $2B acquisition of Manus, and told they cannot travel internationally until the review concludes. Beijing is reportedly furious that Manus relocated operations to Singapore without government vetting — and worried other Chinese AI startups will do the same. Worst case scenario being discussed: invalidating the acquisition entirely, though Meta has already integrated Manus's team and is actively shipping Manus Desktop. Meta's response: "The transaction complied fully with applicable law." The situation in one sentence: Meta is launching the product globally while both of its co-founders are stuck in China and can't join the team.

Meta Cuts 700 Jobs. Then Announces $921M in Executive AI Bonuses.

On March 25, Meta eliminated approximately 700 roles — primarily in Reality Labs, recruiting, sales, and Facebook — as part of a strategic refocus on AI. Less than 24 hours earlier, Meta unveiled a new stock program that could pay top executives up to $921 million each over five years to retain AI leadership.

The message from Zuckerberg is clear: the metaverse bet is being unwound. AI is the only bet that matters now.

China Bans Manus Founders From Leaving the Country. Meta's $2B Deal Is Now a Geopolitical Incident 🌐

On March 25, Chinese authorities summoned Manus co-founders Xiao Hong and Ji Yichao to a meeting with Beijing's National Development and Reform Commission — and told them they wouldn't be going anywhere. No charges filed. Just: you're staying.

The trigger: Beijing is reviewing whether Meta's $2 billion acquisition of Manus violated China's foreign investment laws. The core question — whether Manus transferred core AI agent IP to its Singapore entity without required government approval. Advanced AI agents now fall under China's technology export rules.

The "Singapore washing" playbook — relocate HQ offshore, restructure ownership, sidestep both Beijing and Washington — just became significantly riskier. Every Chinese AI founder with an offshore structure is watching this. Every Western VC that backed them too.

CNBC reported this morning: founders and investors are already rethinking exit strategies.

MCP Hits 97 Million Installs — It’s Now Infrastructure

The Model Context Protocol crossed 97 million installs in March 2026. MCP is no longer an experiment — it’s infrastructure. Every major AI provider now ships MCP-compatible tooling. If you’re building anything agentic and not thinking about MCP, you’re building on sand.

ARC-AGI-3 Launched — And Every AI Scored Below 1%

François Chollet’s ARC Prize Foundation launched ARC-AGI-3 on March 25 — the most radical benchmark change since the original in 2019. Previous versions tested static grid puzzles. Version 3 drops an AI agent into an interactive, game-like environment with zero instructions, zero stated goals, and no description of the rules. The agent has to explore, figure out what it’s supposed to do, and execute.

The results were brutal. Frontier models on the new benchmark: Gemini 3.1 Pro — 0.37%, GPT-5.4 — 0.26%, Claude Opus 4.6 — 0.25%, Grok-4.20 — 0.00%. Untrained humans: 100%. A simple CNN doing graph search scored 12.58% — beating every LLM by more than 12 points.

The prize pool is $2M on Kaggle. All winning solutions must be open-sourced. Chollet’s take: “AI can do many things, but it cannot have general intelligence as long as this fundamental divide exists.”

The most important thing about ARC-AGI-3 isn’t the scores. It’s what the benchmark is testing — the ability to learn rules from scratch in an unfamiliar environment. That’s exactly what current AI cannot do. All the benchmark hype of the past year just got a reality check.

Sora Is Gone — And It Took the Disney Deal With It

OpenAI shut down the Sora AI video app, API, and website on March 24. The immediate trigger was legal pushback and the collapse of the $1B Disney deal — Disney had committed to invest and license characters to Sora, but pulled out ahead of the shutdown.

The deeper reason: compute costs per generated minute were economically irreconcilable with any API price users would actually pay. Six months after a hyped launch, OpenAI killed one of its most public consumer bets.

The same week, OpenAI announced advanced talks with Sam Altman-backed Helion Energy to secure fusion power — starting at 12.5% of output and scaling to 5 GW by 2030 and 50 GW by 2035. Altman is stepping down from Helion’s board and recusing himself while retaining his stake. The OpenAI Foundation also launched a $25B health strategy with over $1B near-term for disease research. ChatGPT gained a visual shopping discovery layer with product images, prices, and side-by-side comparisons via the Agentic Commerce Protocol.

Shutting down Sora while simultaneously betting on fusion energy in the same week is a very specific kind of company energy.

Useful tools ⚒️

⭐ Agentation — Today’s #1 on Product Hunt, and for good reason. You annotate any element on your UI — click, type, done — and Agentation generates structured output that Claude Code, Codex, or any AI coding agent can immediately understand and act on. Instead of telling an agent “the blue button in the sidebar,” you give it .sidebar > button.primary and your feedback. Includes MCP integration so your agent can acknowledge, question, or resolve feedback in real-time. Free. Works in any React app.

Suno v5.5 — Released March 26. Suno’s biggest update ever: you can now add your actual singing voice to AI-generated tracks. Upload a short vocal sample, verify your identity, and every song you generate can sound like you singing it. Also new: Custom Models (fine-tune v5.5 on your own catalog) and My Taste (the model learns your genre preferences over time). Voices is for Pro and Premier subscribers ($10+/mo). My Taste is free for everyone.

Voxtral TTS by Mistral — Open-source text-to-speech that supports 9 languages and clones a voice from under 5 seconds of audio. Dropped March 26. Fits on a smartwatch. Direct competition for ElevenLabs and Deepgram — and it’s free to run locally.

Universal CLI by Composio — Connect AI agents to 1,000+ apps directly from your terminal. Dropped this week. Instead of configuring individual MCP servers or API keys per tool, Composio’s CLI handles auth, routing, and tool discovery for your agent in one command. For anyone building agentic workflows, this removes a genuinely annoying setup step.

Codex Plugins by OpenAI — OpenAI launched Codex plugins this week: package Codex skills and app integrations as reusable plugins. 20+ initial integrations including Figma, Notion, Gmail, and Slack. Essentially standardizes repeatable AI workflows so your team doesn’t rebuild the same agent logic from scratch every time.

Weekly Guides 📕

How to Set Up Claude Computer Use on Mac: Full Setup + 8 Workflows — Step-by-step: how to enable Computer Use in Claude Code, the permission requirements, safety checklist, and 8 real workflows worth automating first. Published March 26 by MindStudio.

Claude Cowork Complete Guide: Computer Use, Dispatch, Projects & Connectors — The most thorough Cowork guide available right now. Covers everything from Computer Use setup to Dispatch remote control, Projects, Windows support, 38+ connectors, plugins, scheduled tasks, and power-user tips. Updated March 24.

Introducing Mistral Small 4 — Official Architecture Breakdown — Mistral’s own guide: the MoE setup, the reasoning_effort toggle, benchmark comparisons against GPT-OSS 120B, and deployment options from Hugging Face to NVIDIA NIM. Start here before anything else.

ARC-AGI-3: Interactive Benchmark for Agentic Intelligence — The Paper — The official paper and launch page: what the benchmark actually tests (interactive environments, zero instructions, goal discovery), how to participate in the $2M Kaggle competition, and why LLMs fail so badly. Required reading to understand where current AI genuinely falls short.

Suno v5.5 Voices: How to Add Your Singing Voice to AI Music — Suno’s official launch post walking through Voices setup, voice verification, Custom Models training, and My Taste personalization. Clear practical guide for any creator already using Suno.

AI Meme of the Week 🤡

$100B in compute. 0.26%.

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

Top 100 Gen AI Consumer Apps: March 2026 — a16z’s biannual deep dive: ChatGPT at 900M weekly active users, Claude growing paid subscribers 200%+ YoY, and the full breakdown of which categories are winning.

The Full Cursor / Kimi K2.5 Story: What the API Call Revealed — TechCrunch’s complete account: how @fynnso found the model ID, what Cursor admitted, what Kimi K2.5 actually is, and why the Western open-source gap matters.

Anthropic vs. Pentagon: Full Legal Context — The Axios breakdown of why Anthropic sued the DoD, what “supply chain risk” designation actually means, and how Google is quietly winning the military AI contract race.

MCP Standard and Ecosystem in 2026 — Why 97 million installs matters and how to actually build with MCP in production today

If you missed our previous update, don’t worry, here it is:

NVIDIA Bets $1 Trillion, Anthropic Builds a Think Tank | Weekly Digest

465 posts. One guide to find exactly what you need.

Hey! It’s Daniil.

Creators AI has been running since March 2023. That’s 3 years of weekly deep dives, tool reviews, real-world use cases, builder stories, and AI news — distilled from watching the space evolve from GPT-4 hype to agents running your actual workflows.

So much fun and amazing content we created during that time, and we decided to look back and analyze it all (with Claude for sure). I hope this post will be helpful for both our loyal followers and new subscribers.

This post is a living index. It organizes everything by skill level and topic, so you can jump straight to what matters to you.

How to Use This Post

Not sure where to start? Pick your path below. Each section links directly to the best posts on that topic. If you’re a regular reader, use the topic sections to catch up on anything you missed.

The newsletter has three types of content:

• Deep dives & tutorials — step-by-step, tool guides, how-tos

• Use cases & examples — real people doing real things with AI

• Weekly Digests — the news that actually mattered each week

🗺️ Choose Your Path

🌱 New to AI? Start Here

You don’t need a technical background. These posts are written for anyone curious about what AI can actually do for them.

Your first five reads:

1. Prompt Engineering Is Easy — the one skill that multiplies everything else

2. Simple Trick for GPT-4o Image Gen — get stunning visuals without Midjourney (still really relevant)

3. Make Videos 3X Faster With AI — practical video workflow for any creator

4. How to Save Money on AI Tools with Simple Prompts — use free tiers better than most people use paid plans

⚡ Already Using AI Daily? Go Deeper

You know the basics. These posts push into how AI actually fits into professional workflows — content, projects, business.

Where to go next:

• Year in AI: Summary & Tools of 2025 — the best overview of everything that mattered last year (and still useful)

• How To Make Complex Tasks with AI Agents — move from one-off prompts to automated pipelines

• Let AI Run Your Projects: Tips and Real Cases — project management handed off to AI

• Best Tips & Tools for AI Creative Writing 2025 — the full stack for writers

• How to keep up with AI: Sources & Learning Tips — how to stay sharp without drowning in feeds

🔧 Building with AI? Go Advanced

You’re coding, building products, automating workflows, or running AI agents. These are the technical deep dives.

Start with these:

• Autoresearch: The Loop That Improves Your Work While You Sleep — Karpathy’s self-improving loop, could be applied everywhere

• MCP: The API 2.0? — why the Model Context Protocol changes how you integrate tools

• How Context Engineering Can 10x Your AI Models Performance — the technique replacing prompt engineering

• Skills, Plugins, Swarm Mode: Practical Tips for Claude — running multi-agent Claude setups

• State of AI Report 2025: Insights and Predictions — the macro picture for builders

📚 Browse by Topic

🎬 Content Creation with AI

For creators making videos, writing content, designing visuals, and building audiences.

Beginner:

• Make Videos 3X Faster With AI

• Simple Trick for GPT-4o Image Gen

Intermediate:

• Best Tips & Tools for AI Creative Writing 2025

• GPT Image 1.5: Features, Capabilities, and Nano Banana Pro

• Higgsfield AI Tutorial: Style Control of Your Videos

• Consistent AI Design Without Headaches: Recraft Review

• How Influencers Are Turning AI Chatbots into Millions

• Sora 2 Is More Than Just Memes

• Build Your AI Email Newsletter System

🤖 AI Agents & Automation

Workflows that run themselves. Agents that do the work. No-code and low-code setups.

Beginner:

• How Cowork Can Boost Your Routine Work

• NotebookLM Updated Guide: Work & Learning Tips

• Notion AI: How You Can Change Notes into Workflow

Intermediate:

• How To Make Complex Tasks with AI Agents

• Claude Cowork: Complete Guide & Practical Use Cases

• Let AI Run Your Projects: Tips and Real Cases

• Clawdbot: The Viral AI Agent That Lives in Your Messenger

• Claude Code Tips: for Dev and Non-Dev Tasks

• 4 Ways to Monetize AI Agents in 2026

• Your First Game with Manus

Advanced:

• Autoresearch: The Loop That Improves Your Work While You Sleep

• The n8n Playbook for AI Apps and Agents

• How to Build AI Agents Using Grok API

• AgentKit by OpenAI Review

🏗️ Building with AI & Vibe Coding

For anyone building apps, tools, or products — with or without a coding background.

Beginner:

• Vibe Coding for Beginners

• Why Vibe Coding Is Important

• Vibe Coding Inspo: Hot Cases and Tips

Intermediate:

• MCP: The API 2.0?

• 50 Most Popular GenAI Apps: Explained

• Building $600K/Mo SaaS in One Year

Advanced:

• MCP Full Guide for Founders & Makers

• Hacks for Vibe Coding with Cursor AI

• How Context Engineering Can 10x Your AI Models Performance

• Skills, Plugins, Swarm Mode: Practical Tips for Claude

• Complete Guide to Gemini CLI

💼 Business & Monetization

Building a product, startup, or revenue stream with AI at the center.

Intermediate:

• 4 Ways to Monetize AI Agents in 2026

• How Influencers Are Turning AI Chatbots into Millions

• How Tiny AI Startups Are Scaling to $10M+ ARR

• Lovable Growth Secrets (and Costs): $200M ARR Playbook

• How to Be an Investable AI Startup in 2025

• AI Startup Ideas for 2025

• YC AI Startup F25 Batch Review

🛠️ AI Tools & Reviews

Deep dives into specific tools — what they do, how to use them, and whether they’re worth it.

Beginner:

• How to Save Money on AI Tools with Simple Prompts

• Year in AI: Summary & Tools of 2025 — the definitive 2025 tool roundup

Intermediate:

• AgentKit by OpenAI Review

• Kimi K2: The Open Model Getting Momentum

• Nano Banana Pro: What Makes It So Powerful?

• Comet Browser by Perplexity: 40+ Use Cases

• Why Activate ChatGPT Memory

• Clawdbot: The Viral AI Agent That Lives in Your Messenger

Advanced:

• MCP Full Guide for Founders & Makers

• LLMs Optimization (AI SEO)

• Consistent AI Design Without Headaches: Recraft Review

Creators' AI

Moltbook & Others: OpenClaw (exMoltBot) Ecosystem

Just a few days ago, a new social network got viral exclusively for artificial intelligence (no humans allowed…

Read more

2 months ago · 7 likes · Creators AI

📖 Learning & Education

Prompting, thinking frameworks, and how to develop real AI fluency.

Beginner:

• Prompt Engineering Is Easy

• How to Save Money on AI Tools with Simple Prompts

• How to keep up with AI: Sources & Learning Tips

Intermediate:

• NotebookLM Updated Guide: Work & Learning Tips

• Nano Banana Use Cases & Tutorial | Part 1 + Part 2

• Clawdbot: The Viral AI Agent That Lives in Your Messenger

Advanced:

• Programming Prompt Engineering & Copilots

• LLMs Optimization (AI SEO)

• How Context Engineering Can 10x Your AI Models Performance

• Skills, Plugins, Swarm Mode: Practical Tips for Claude

This index will be updated as the archive grows. New issues drop weekly. If you’re not subscribed yet, don’t forget to!

Thanks for reading!

The biggest AI conference of the year just wrapped in San Jose. Jensen Huang put $1 trillion on the table and called it conservative. Anthropic launched a think tank while suing the Pentagon. OpenAI quietly made its best reasoning model free. And a rogue AI agent at Meta triggered a company-wide security alert. Seven days. No boring moments. Let’s get into it.

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 🎟️

NVIDIA GTC 2026: Jensen Huang Says $1 Trillion — And That’s Just the Start 🟢

NVIDIA GTC 2026 ran March 16–19 in San Jose — 10,000+ people packed the SAP Center, and Jensen Huang delivered two hours of announcements that set the direction for the next phase of AI infrastructure.

The headline number:

Jensen Huang said he expects purchase orders between Blackwell and Vera Rubin to reach $1 trillion through 2027 — doubling earlier estimates of $500 billion. He attributed the surge to agentic AI driving a fundamental shift in computing requirements: more inference, more orchestration, more continuous compute.

What’s new:

• Vera Rubin NVLink 72 — the new Vera Rubin CPU is designed for super high single-thread AI performance and represents 40 million times more compute in 10 years.

• Groq 3 LPU — Huang unveiled the Nvidia Groq 3 Language Processing Unit — the company’s first chip from the startup it mostly acquired through a $20 billion asset purchase in December. The Groq 3 LPX rack holds 256 LPUs, designed to sit beside the Vera Rubin rack-scale system.

• NemoClaw — NVIDIA’s enterprise-secure reference stack for OpenClaw, with 17 launch partners. Built to resolve the key problem with OpenClaw in production: no sandboxing, no PII filtering, no compliance controls.

• NVIDIA Agent Toolkit — includes NVIDIA OpenShell open source runtime for building self-evolving agents. Leading partners including Adobe, Atlassian, Salesforce, SAP, Cisco, CrowdStrike, and ServiceNow are advancing enterprise AI agents with the toolkit.

• Autonomous vehicles — Huang announced that Nissan, BYD, Geely, Isuzu and Hyundai are building level 4 autonomous vehicles on Nvidia’s Drive Hyperion program, declaring “the ChatGPT moment for self-driving cars has arrived.”

• Disney’s Olaf robot — trained in NVIDIA simulation, walked on stage and held a conversation with Huang. The keynote closed with an AI-generated song performed by robots around a campfire.

For creators: The Groq LPU is the inference play. Training is settled — NVIDIA dominates. The fight now is for cheap, fast, scalable inference. The $1T number is the bet that agentic AI workflows will consume compute orders of magnitude beyond what chat-based AI did.

• Source: CNBC | Tom’s Hardware | NVIDIA Blog | TechRadar

Anthropic Launches a Think Tank While Fighting the Pentagon 🧠

Anthropic launched the Anthropic Institute — an internal think tank that merges three of its current research teams into a unified operation focused on AI’s societal implications. The timing is pointed: the company is simultaneously suing the Trump administration over its Pentagon blacklisting.

What the Institute does:

The institute brings together three existing teams: the Frontier Red Team, which stress-tests AI systems to understand the limits of their capabilities; Societal Impacts, which studies how AI is being used in the real world; and Economic Research, which tracks its impact on jobs and the broader economy.

The institute will examine questions such as how powerful AI systems could affect jobs and economic activity, what risks they could create or amplify, how companies should determine the values reflected in AI systems, and how increasingly capable systems should be governed if recursive self-improvement begins.

The people:

The Institute starts with roughly 30 experts and is slated to double its staff each year. Jack Clark, previously head of public policy, will now serve as the Institute’s Head of Public Benefit. Founding hires include Matt Botvinick (ex-Google DeepMind), economist Anton Korinek (University of Virginia), and Zoë Hitzig (ex-OpenAI).

The DC play:

In parallel, Anthropic is expanding its Public Policy organization. The company plans to open an office in Washington, D.C. this spring. Sarah Heck (ex-Stripe) leads the team.

The bigger picture: Leaders connected to OpenAI, Google, and Microsoft have backed Anthropic in legal filings against the Pentagon designation. The Institute is Anthropic’s bet that public research credibility — not just product growth — is how you win the long game with governments.

• Source: Anthropic Blog | eWeek | SiliconAngle | TechBuzz

GPT-5.4 Mini Is Now Free for Everyone 💸

On March 17, OpenAI rolled out GPT-5.4 mini in ChatGPT. GPT-5.4 mini is available to Free and Go users via the “Thinking” feature in the + menu. This is the first time OpenAI’s reasoning-capable model tier is available at no cost.

What changed:

• Free and Go users: access GPT-5.4 mini via Thinking toggle

• Plus and paid users: GPT-5.4 mini as rate-limit fallback during peak usage

• Enterprise: option to default Auto routing to mini

• GPT-5 Thinking mini will be retired in 30 days as a selectable option

• The model picker was simplified to three tiers: Instant, Thinking, and Pro — with an Auto-switch feature and advanced controls for thinking effort.

Pricing:

GPT-5.4 nano is only available in the API and costs $0.20 per 1M input tokens and $1.25 per 1M output tokens — making it one of the cheapest reasoning-capable APIs on the market.

This is OpenAI’s clearest move yet to compete with Claude’s free tier momentum. Reasoning for free used to mean a degraded experience. GPT-5.4 mini on the Thinking tier is not a degraded experience.

• Source: Releasebot | AI Pulse

News of the week 🌍

• Claude 1M Context Now Free for Everyone 🧠 — The 1M token context window is now generally available for Opus 4.6 and Sonnet 4.6 at standard pricing on the Claude Platform — no multiplier, no extra fees. 1M context is also included in Claude Code for Max, Team, and Enterprise users with Opus 4.6. Fewer compactions, more conversation kept intact.

• Rogue AI Agent Triggers Security Crisis at Meta 🚨 — A rogue AI agent at Meta triggered a severe security incident, inadvertently exposing massive amounts of sensitive company and user data to unauthorized personnel for two hours. Meta classified the event as a “Sev 1” — its second-highest severity level. The incident originated from a routine internal technical query that an autonomous agent mishandled. Timing note: it happened the same week Meta formally brought the Moltbook team into Meta Superintelligence Labs.

• Microsoft Fears OpenAI’s AWS Deal Violates Azure Contract 🤝 — Microsoft has flagged that OpenAI’s newly announced partnership with Amazon Web Services may violate the Azure exclusivity terms in their existing agreement. Microsoft fears OpenAI’s AWS deal may violate Azure exclusivity contract. If true, this would be the biggest crack in the Microsoft-OpenAI partnership since the OpenAI board crisis of 2023.

• Micron Earnings Beat — Stock Drops Anyway 📉 — On March 18, Micron disclosed earnings and guidance that significantly exceeded expectations, but its share price dropped as much as 6% in after-hours trading. Micron also revealed that capital expenditure for the current fiscal year will exceed $25 billion. Investors are asking whether the AI infrastructure buildout is ahead of actual demand.

• Fed Holds Rates at 3.5–3.75% 🏦 — On March 18, the Federal Reserve held rates steady, maintaining the target range between 3.5% and 3.75%. AI infrastructure spending remains the biggest wildcard in the macro picture, with hyperscalers committing hundreds of billions in capex regardless of rate environment.

• Bernie Sanders Interviewed Claude About AI Privacy — And It Went Viral 🤖 — On March 19, Senator Bernie Sanders sat down with a phone on a C-stand and asked Anthropic's Claude one question: what would surprise Americans about how their personal data is being collected? Claude said companies collect everything — browsing history, location, purchases, and even how long you hover over something before deciding not to click. When Sanders pressed on motive, Claude replied: "Money, Senator, it's fundamentally about profit." The 9-minute video hit 5.1M views on X within hours. The internet reacted with "Old Man Yells at Claude" memes — but the irony wasn't lost: a senator used a chatbot built by one of the most powerful AI companies to warn the public about the dangers of powerful AI companies.

• OpenJarvis: Local-First AI Agents from Stanford 🏠 — Stanford researchers introduced OpenJarvis, an open-source framework for personal AI agents designed to run on-device by default, calling the cloud only when necessary. The “local-first” approach emphasizes privacy, latency, and efficiency. A direct counterpoint to every cloud-first agent framework announced this week.

• Google Maps Gets Gemini: Ask Maps + Immersive Navigation 🗺️ — Google unveiled Maps updates powered by Gemini, including Ask Maps — conversational queries for complex, real-world questions — and Immersive Navigation, a more visual driving experience with 3D context.

Useful tools ⚒️

⭐ CreateOS — Build and deploy apps from any AI coding tool, in one place. Works with Claude Code, Cursor, Lovable, and v0. The missing “last mile” layer between vibe-coded output and actual deployment.

SuperX — All-in-one growth OS for serious X creators. Analytics, scheduling, engagement tracking, and AI-assisted content — all in one dashboard. For anyone building an audience on X in 2026.

Atoms — Turn your ideas into products that sell. Validates, builds, and launches product concepts with AI — from idea to landing page.

Hugo — The AI agent that doesn’t charge $1 per support ticket. Flat-rate customer support automation that handles complex queries, not just FAQs.

Umbrel Pro — 16TB home cloud server. Run OpenClaw, store files, and self-host your AI stack without any subscription. Privacy-first infrastructure for builders.

Know friends, co-founders or vibe-coders who need this content. Share!

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Weekly Guides 📕

NVIDIA GTC 2026: Every Major Announcement in One Place — NVIDIA’s own rolling coverage from Jensen’s keynote through March 20, including all partner announcements and robotics demos

NemoClaw vs OpenClaw: What Changes for Enterprise — Why 70+ dependencies and zero sandboxing made OpenClaw a non-starter for Fortune 500, and how NemoClaw fixes it

The Agentic Web Explained: Moltbook, OpenClaw, and What Comes Next — The best breakdown of why Meta bought Moltbook and OpenAI bought Promptfoo on the exact same day

The Anthropic Institute: Full Charter and Founding Hires — Anthropic’s own post on what the Institute will research, who leads it, and why they’re opening a DC policy office

GPT-5.4 Mini: What Free Users Actually Get — Full changelog for the model picker update, Auto-switch behavior, and what gets retired in 30 days

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

NVIDIA GTC 2026 Full Keynote Replay — Jensen Huang’s two-hour address: Vera Rubin, Groq LPU, NemoClaw, Disney robots, and the $1T order projection

The Rogue AI Agent Incident at Meta: Full Report — Detailed breakdown of the Sev 1 security incident, how an autonomous agent exposed sensitive data for two hours, and what it means for enterprise agentic AI

OpenJarvis on GitHub — Stanford’s Local-First Agent Framework — The privacy-first alternative to every cloud agent launched this week

McKinsey March 2026 AI Employment Report — 12% of job tasks automated, 8% new AI-related categories created. Net employment flat — with significant variation by sector

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

Claude Marketplace Launch, Google Pentagon Deal & Anthropic Lawsuit | Weekly Digest

Karpathy left an agent running on his codebase for two days.

700 experiments. No human in the loop. Every bad idea rolled back automatically. Every winner kept.

The result: an 11% performance gain on a benchmark that thousands of ML engineers compete on.

That’s Autoresearch. And the reason it matters isn’t the ML benchmark. It’s the underlying logic — a loop that can be pointed at almost any measurable problem. Cold email reply rates. Page render speed. Landing page conversions. Retrieval accuracy. Anything with a scoreboard.

Let’s break it down.

The Story Behind the Loop

Karpathy built a system called nanochat — a minimal character-level language model — and gave an agent one job: make it train faster. Not by rewriting everything. By doing one thing at a time.

The agent could touch one file. It could run one experiment. It read one metric. And it had a simple rule: if the number got better, keep the change. If not, revert it. Start again.

After two days and roughly 700 experiments, about 20 changes survived. The benchmark moved from 2.02 hours to 1.80 hours — roughly 13 minutes faster per full training run.

That doesn’t sound dramatic. But think about what actually happened.

No human reviewed 700 experiment results. No one debated which change to try next. No one rolled anything back manually. The loop did all of it — and stacked 20 real improvements cleanly, without one cancelling out another.

That’s the compounding part. And it’s what makes this different from just asking an AI to “help improve” something.

How Autoresearch Actually Works

It’s easier to understand as a loop than as a codebase.

Here’s how the loop works in plain language, step by step:

1. You write the goal in plain English. You create a simple text document and write your instructions: what you want the AI to improve, what it’s allowed to touch, and what “better” looks like. That’s the only thing you write. The AI does everything else.

2. The AI makes one small change. It opens the one file it’s allowed to edit and tries something — maybe it adjusts how fast the model learns, maybe it changes how information is grouped and processed. One small change. Nothing else.

3. It runs a 5-minute test. Every single experiment runs for exactly 5 minutes — no exceptions. The fixed time is what makes experiments fair. Every change gets the same amount of time to prove itself, whether it’s a tiny tweak or a bigger structural change.

4. It checks one number. At the end of the 5 minutes, the system produces a single score. Lower is better. The AI asks one question: is this score lower than before the change?

5. Keep or undo — automatically. If the score improved, the change is saved and becomes the new starting point. If the score got worse or stayed the same, the change is completely undone — as if it never happened. No human needs to review anything. It happens automatically.

6. Repeat, immediately. The AI doesn’t pause or wait for approval. It goes straight back to step 2 and tries something new, building on whatever worked and ignoring whatever didn’t. This happens around 8 to 12 times every hour.

7. You check the results in the morning. Every improvement that survived is recorded. Every failed attempt was automatically cleaned up. You wake up to a clear history of what was tried, what worked, and by how much — often 60 to 100 completed experiments from a single overnight run.

The core idea is simple: try something → measure it → keep it or undo it → repeat. What makes it powerful is that a machine can run this loop dozens of times while you sleep, without getting tired, distracted, or tempted to keep a bad idea just because it took effort to come up with.

The interesting thing is that this loop works in the same way across wildly different domains. Which is exactly what the next three cases show.

Now lets check some cases

Case 1: Andrej Karpathy + nanochat — Where the Pattern Came From

The original implementation is still the clearest.

Result: 700 experiments over two days. 20 surviving changes. “Time to GPT-2” benchmark went from 2.02 hours → 1.80 hours. An 11% improvement on a leaderboard thousands of engineers compete on.

How it worked: Think of the agent as a junior researcher with a key to one locked room. It could edit the mutable training layer, run the same 5-minute experiment each time, and read the same frozen metric. Because every run used the same budget, the winning changes stacked cleanly instead of creating noise.

What it means for you: If you have any process that takes 1–2 hours per full run and repeats regularly — training pipelines, content workflows, benchmark suites — an 11% improvement compounds fast. Across 50 runs per month, that’s roughly 11 hours back. And you didn’t have to review a single experiment.

The real takeaway isn’t the benchmark. It’s this: once you have a clean loop with a frozen metric, the system starts doing research on your behalf.

This connects directly to something we covered in depth in our post on context engineering — the main control surface in agent workflows often sits in how you structure the instructions and context itself. Autoresearch is the same idea applied to iteration.

Case 2: Tobi Lütke + Shopify Liquid — When Autoresearch Escaped the Lab

Read more

This week, Anthropic stopped being a model company and became a platform. Google quietly signed the Pentagon deal that Anthropic walked away from — then delivered the biggest Workspace update in years. Anthropic’s lawyers filed a lawsuit against the Trump administration. A Block data scientist blew up Jack Dorsey’s AI-cuts narrative. And NASA used Claude to drive a rover 400 meters on Mars. Let’s get into it.

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 Launches Claude Marketplace — And Becomes a Platform Company 🏪

On Saturday March 8, Anthropic quietly launched something that changes its competitive position more than any model release: the Claude Marketplace. It’s an enterprise app store where companies with existing Anthropic spend commitments can apply that budget toward Claude-powered tools built by third-party developers — without separate procurement processes for each vendor.

The launch partners are:

• GitLab — deploy agentic AI across the entire software development lifecycle

• Harvey — AI-native legal workflows

• Lovable — full-stack vibe coding platform

• Replit — AI software development

• Rogo — financial research and analysis

• Snowflake — data cloud + Claude integrations

Why this matters more than it looks:

Anthropic’s run-rate revenue recently surpassed $19 billion — more than double three months prior. The marketplace is a direct attempt to extend that momentum before competitors build their own distribution layers.

The logic is exactly what made Salesforce AppExchange, AWS Marketplace, and ServiceNow Store into competitive moats: if you control where enterprises discover and buy AI tools, you don’t just sell models — you become the operating system. Cox Automotive’s Chief Product Officer put it plainly: the marketplace lets teams move faster by extending their Anthropic investment into partner tools without managing separate procurement for each.

The platform bet:

OpenAI has the GPT Store. Microsoft integrates third-party agents into Copilot. Google is building out its agent ecosystem through Vertex AI. Anthropic’s move is modeled on Amazon’s distribution architecture — and by positioning Claude as both the model and the marketplace for enterprise software built on it, Anthropic is making a direct play for the distribution moat.

Whether this gains traction depends on how quickly Anthropic expands beyond six launch partners. But the intent is clear: the next phase of the AI race isn’t just about who has the best model. It’s about who controls the ecosystem around it.

For creators and builders: If you’re building AI-powered products, being inside this marketplace eventually means reaching companies that have already committed budget to the platform. Watch this space — the first 30 days of partner announcements will tell you how fast Anthropic intends to move.

• Source: PYMNTS | Product Hunt

Google Takes the Pentagon, Gemini Takes Google Workspace 🏛️

This week Google had the most productive seven days in its recent AI history. Two separate but thematically connected moves — one political, one product — showed exactly where Google is placing its bets.

The Pentagon deal:

On March 10, Bloomberg reported that Google is deploying Gemini AI agents across the Pentagon’s entire 3 million-person workforce — the exact contract Anthropic walked away from last week by refusing to waive its red lines on autonomous weapons and domestic surveillance.

Google’s Gemini agents will initially operate on unclassified networks, according to Emil Michael, the Under Secretary of Defense for Research and Engineering. The scale is unprecedented: this is a federal government essentially running AI agents as a standard tool across every desk in the Defense Department.

The contrast with Anthropic’s situation couldn’t be sharper. While Anthropic’s lawyers were filing a lawsuit to reverse its “supply chain risk” designation, Google was signing the largest government AI deployment in history.

The Workspace drop:

On the same day — March 10 — Google shipped arguably the most consequential Workspace update in years. Gemini is now deeply woven into Docs, Sheets, Slides, and Drive in ways that go beyond the usual “AI helper on the side.” Key new features:

• Drive AI Overviews — natural language search now generates a summary at the top of results, with citations, before you open a single document

• Ask Gemini in Drive — ask complex questions across your files, emails, calendar, and the web simultaneously

• Help me create (Docs) — describe what you want and Gemini pulls from your Drive, Gmail, and Calendar to generate a personalized first draft

• Match writing style — unify tone and voice across a document or team

• Sheets from a prompt — build or edit entire spreadsheets from natural language, pulling data from across your files and messages

All features rolled out in beta to Google AI Ultra and Pro subscribers.

The practical implication: Google just made its office suite natively agentic. You no longer need a separate AI tool to work with your documents — your documents now know about your email, calendar, and other files by default.

For creators: If you’re heavy on Google Workspace, these features are worth testing immediately. The Drive AI Overviews alone could cut research time significantly. The catch: all of this lives behind an AI Ultra or Pro subscription.

• Source: Bloomberg | TechCrunch | Google Blog

Anthropic vs. the Pentagon: The Lawsuit 🏛

️

The legal phase of the Anthropic–Pentagon conflict began this week. Anthropic filed a lawsuit against the Trump administration seeking to reverse the Department of Defense’s “supply chain risk” designation — the label applied after Anthropic refused to grant unrestricted military use of Claude.

The designation is extraordinary in its scope. As Anthropic’s lawyers note, it doesn’t just block DOD procurement — it threatens to force any company doing defense business (including NVIDIA, which sells chips to both the military and to Anthropic) to sever commercial ties with Anthropic entirely. That’s the $60 billion investor risk the designation created.

Complicating the narrative: the Pentagon hasn’t actually stopped using Claude yet. Defense Department CTO Emil Michael told CNBC it would take time to transition away from Anthropic’s models. And Palantir CEO Alex Karp confirmed at AIPcon 9 that Palantir — which is deeply integrated with the DOD and had partnered with Anthropic on AWS — is still running Claude. An internal Pentagon memo obtained by CBS News confirmed exemptions will be considered for “mission-critical activities” where “no viable alternative exists.”

Microsoft, Google, and Amazon Web Services all confirmed this week that Claude remains fully available on their cloud platforms for non-defense workloads. The real-world commercial impact of the designation appears, so far, to be limited.

The bigger question: Anthropic is now simultaneously suing the government, launching a marketplace, and hitting $19B in run-rate revenue. The company that chose ethics over a Pentagon contract is, by almost every commercial metric, doing better than ever. Whether the lawsuit succeeds may matter less than what happens to Anthropic’s brand with enterprise customers watching closely.

• Source: CNBC / Palantir | CBS News Pentagon memo

News of the week 🌍 (Mar 9 – Mar 13)

• Claude identified 22 Firefox vulnerabilities in two weeks 🔐 — Anthropic published results of a security collaboration with Mozilla: Claude Opus 4.6 analyzed Firefox’s JavaScript engine and broader codebase over two weeks, identifying 22 vulnerabilities — 14 of them high-severity. Most were already patched in Firefox 148. The model was effective at finding bugs but less capable at generating working exploits, producing only two proof-of-concept exploits using approximately $4,000 in API credits. The announcement doubles as a signal about Claude’s agentic security capabilities — and arrives as Anthropic tries to demonstrate that AI safety and real-world utility can coexist. Source

• NASA drove Perseverance on Mars using Claude 🚀 — Anthropic confirmed this week that Claude helped NASA navigate its Perseverance rover approximately 400 meters across the Martian surface. The first AI-assisted planetary drive. Details on the specific integration remain sparse, but the announcement landed on Anthropic’s homepage as their flagship real-world deployment story. Source

• OpenAI hardware chief resigns after Pentagon deal ⚠️ — Caitlin Kalinowski, OpenAI’s executive leading robotics and hardware, resigned on March 9 following the company’s reported partnership with the DOD. Her departure highlights the internal tension at OpenAI over the military deal — the mirror image of the tension that eventually led to Anthropic’s refusal. She’s not the first OpenAI employee to leave over the Pentagon contract direction. Source

• ChatGPT gets interactive math and science tools 📐 — OpenAI rolled out interactive visual explanations for more than 70 core math and science concepts. Users can manipulate variables and watch formulas and graphs respond in real time. 140 million people use ChatGPT each week for math and science alone. The rollout reached all logged-in users globally. Source

• GPT-5.1 officially retired (March 11) 🗑️ — OpenAI quietly shut down GPT-5.1 Instant, Thinking, and Pro. All existing conversations automatically migrated to the corresponding GPT-5.3 or GPT-5.4 variants. No announcement, no fanfare — just an item in the release notes. The pace of model retirement is accelerating: GPT-5.1 lasted less than three months. Source

• The Block “AI washing” story unravels further 🧵 — The week’s most revealing subplot: Naoko Takeda, a data scientist who left Block after Dorsey’s 40% cuts, disclosed she was offered a 75% pay increase — 90% including a one-time bonus — to stay on. If AI tools are doing 4,000 people’s work, why is Block paying desperately to retain the ones who stayed? A former Block comms chief also wrote in the NYT that specific cuts — shrinking the policy team, eliminating DEI roles — look like standard cost management repackaged as futurism. Dorsey’s own response to Wired was revealing: “Something really shifted in December in the sophistication of tools. Anthropic’s Opus 4.6 and OpenAI’s Codex 5.3 went from being really good at greenfield products to being really good at larger and larger codebases.” Genuine insight — but whether that justifies cutting half the company’s workforce remains disputed. Source | Bloomberg

• Karpathy open-sources AutoResearch 🔬 — Andrej Karpathy released AutoResearch on March 9: an open-source project for running fully automated AI-driven research loops on a small, single-GPU LLM training setup. The intent is to make AI self-improvement research accessible outside of well-funded labs. Early interest from the ML community has been significant. Source

• Gemini Embedding 2 launches in public preview 🔢 — Google released Gemini Embedding 2 on March 10: its first natively multimodal embedding model. Text, images, video, audio, and PDFs can now all be mapped into a single embedding space — meaning systems can compare different media types directly. For developers building cross-media search, document retrieval, or recommendation systems, this is a meaningful infrastructure shift. Available as a preview model via the Gemini API. Source

Useful tools ⚒️

⭐ Visual Translate by Vozo — Translate the text in your videos — without recreating the visuals. Every frame stays intact; Vozo replaces on-screen text and captions in-place across more than 40 languages. For any creator, educator, or brand trying to reach international audiences without re-shooting or commissioning full localization, this is the clearest time-saving tool of the week.

Inspector — Figma for Claude Code — a visual layer on top of your Claude Code sessions. Instead of text-only output, Inspector shows your agent’s work as a design-aware workspace: component trees, visual diffs, element hierarchies. For anyone using Claude Code to build UI or front-end systems, this closes the biggest gap in the agentic coding workflow.

InsForge — A context layer for AI coding agents — structured project knowledge including architecture docs, coding standards, dependency graphs, and component specs — so agents don’t start from zero on every session. Built for teams where multiple agents (or team members) are working on the same codebase and need a shared understanding. Think of it as institutional memory for your AI engineer.

Chronicle 2.0 — AI-generated presentations that don’t look like AI-generated presentations. Chronicle ships fully designed, properly typeset decks — not the generic grids and clip-art outputs most AI slide tools produce. Significant rebuild from the first version: improved layout engine, brand-aware design, and better handling of data-heavy slides. For anyone who regularly creates pitches, reports, or client deliverables.

Naoma AI Demo Agent — An AI video demo agent for B2B SaaS — it creates, narrates, and delivers personalized product demos automatically, on demand, for inbound leads. Sales teams at product companies can deploy it so prospects get an instant, tailored demo experience without a human joining the call. Reduces time-to-demo from days to minutes.

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Weekly Guides 📕

Claude Marketplace Launch: What It Means for Builders and Enterprise Teams — Deep analysis of Anthropic’s platform move: why the AppExchange model applies to AI, who the launch partners are, and what it means if Anthropic becomes the distribution layer for enterprise AI tools

The Anthropic vs Pentagon Case: What the Lawsuit Actually Says — The legal and strategic context behind Anthropic’s lawsuit: the specific claims, the supply chain risk mechanism, and why the designation is more consequential than a simple contract dispute

Gemini in Workspace: The Complete Guide to Every New Feature — Google’s own rundown of all new capabilities in Docs, Sheets, Slides, and Drive. Start here before diving into the third-party coverage

The Block AI Washing Debate: Who’s Right? — The most balanced breakdown of the Dorsey controversy: what the departing data scientist’s story reveals, what the former comms chief found in the cuts, and what the “something shifted in December” quote actually means

Inspector: How to Use Claude Code With Visual Context — The Product Hunt launch thread for Inspector, including maker notes on how the visual layer integrates with Claude Code sessions and what kinds of projects benefit most

AI Meme of the Week 🤡

source: X / r/aimemes

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

Anthropic’s Statement on Claude Remaining Available Across Cloud Platforms — Microsoft, Google, and AWS each confirmed this week that Claude continues to be available through their platforms for non-defense workloads. Dario Amodei’s clarification on the narrow scope of the Pentagon designation and what it does and doesn’t affect

Claude + Mozilla: The Firefox Vulnerability Report — The full account of Anthropic’s two-week security collaboration with Mozilla: which vulnerabilities were found, where they were in the codebase, what the limitations were on exploit generation, and what it cost in API credits

Gemini Embedding 2: What Natively Multimodal Retrieval Actually Means — Technical breakdown of the Gemini Embedding 2 launch: what changes when text, images, video, and audio share the same embedding space, and where the production caveats are during public preview

The Block AI Job Cuts: A Bellwether or Not? — Josh Bersin’s HR-research perspective on whether Dorsey’s restructuring represents a real AI-driven shift or a performance management reset with better branding. Includes the actual unit economics of replacing engineers with AI agents

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

Let’s say it right away — there’s a big difference between using AI and selling something AI-powered people actually want.

And, really, most creators are still on the first step. They use AI to write faster, research faster, edit faster, repurpose faster. Are you happy with that? Maybe. But not for long. The real opportunity in 2026 sits one layer above that. It’s about packaging your expertise into something that can do useful work on demand.

That is what agents change.

The new era. How creator monetization is changing nowadays

In 2024, creators sold content.

In 2025, creators sold access.

In 2026, the strongest ones are starting to sell executions.

That is the leap.

• An ebook gives information.

• A course gives structure.

• A community gives accountability.

• An agent gives labor.

That one difference changes the economics.

The old content funnel was built around teaching. The new one is increasingly built around utility. The most interesting creator businesses now are similar to tiny software companies built around trust. Or a very specific job to be done.

Subscribe for more creator’s breakdowns!

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Four monetization models that actually fit creators

Let’s continue. So the real question now is different than it was recently: what part of my expertise can become a repeatable paid action?

Once you ask that, the whole business model gets clearer!

So here’s all four most important models for the creator in 2026.

Model 1. Paid access to a niche agent

This is the cleanest model. The point is that the creator takes one repeated audience pain point and wraps it in a simple promise. So, “give me the input, get the result”.

That can look the same as a newsletter growth agent that suggests good headlines. Or:

• A dating advice bot trained on a creator’s framework.

• A brand brief agent for small businesses.

• A research assistant for B2B writers.

• A sponsor-fit agent for media operators.

This model works best when buyers already trust the creator’s taste. Or maybe a specific method. The monetization is straightforward too!

One of the clearest proof points came from creator Tom Kuegler, who built “The NoteSmith,” a bot that gives feedback on Substack Notes in seconds. In his own write-up, he says 200 people tried it, multiple users bought his paid tier to keep access, and the product reached $5.000 in annualized revenue in its first week.

So you see — that is exactly what this model looks like when it works: narrow pain point, & immediate utility.

The next model will also not leave you indifferent.

Model 2. Productized services powered by agents

Read more

This was the most politically charged week in AI since the OpenAI boardroom drama of 2023. Anthropic went to war with the Pentagon and came out of it as the #1 downloaded app in America. OpenAI fired back with its biggest model drop of the year. DeepSeek emerged from a year of silence with something that could change the cost of AI forever. And Alibaba imploded its own AI team the day after its biggest open-source release. Seven days. Zero boring moments. Let’s get into it.

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 🎟️

Claude #1 on App Store: How a Pentagon Fight Made Anthropic a Household Name🏆

Two months ago Claude sat at #42 in the App Store. Today it’s #1 on both Apple’s App Store and Google Play — not because of a model update or a viral feature, but because Anthropic refused to let the Pentagon use its AI for mass domestic surveillance and autonomous weapons. And a country full of users decided to vote with their thumbs.

What actually happened:

Last week, Anthropic and the Department of Defense clashed over contract terms. Anthropic refused to waive two red lines: no use of Claude in fully autonomous weapons, and no use for mass surveillance of American citizens. The Trump administration responded by directing federal agencies to phase out Anthropic’s technology over six months. Secretary of Defense Pete Hegseth then designated Anthropic a “supply chain risk to national security.”

OpenAI moved in the opposite direction — Sam Altman announced a deal to deploy its models in classified networks. Users flooded Reddit and X with guides for deleting ChatGPT and switching to Claude. Pop star Katy Perry posted a screenshot of Claude’s Pro pricing page with a red heart. Screenshots of “Thank you” messages written in chalk covered the sidewalk outside Anthropic’s San Francisco offices.

The numbers followed:

• Daily sign-ups broke all-time records every single day this week

• Free users up 60%+ since January

• Paid subscribers more than doubled this year

• Claude was so overwhelmed by the migration that it went down briefly on Monday morning — Anthropic called it “unprecedented demand”

Anthropic’s response on the product side:

They didn’t just sit back and watch the downloads come in. Within days:

• Memory added to the Free tier — the ability to save and build on prior conversations, previously a paid feature, is now free for everyone

• Chat import from other AI platforms — a one-click tool to migrate your ChatGPT history to Claude launched Friday

• Claude Code surged to become the most widely adopted coding agent across startups and enterprise teams entering 2026

CEO Dario Amodei called the government’s designation “retaliatory and punitive,” but added that Anthropic remains open to working with the military within its previously stated limits. Even Sam Altman — whose company took Anthropic’s place on the DOD contract — said the supply chain risk designation was “a very bad decision” and “an extremely scary precedent.”

The bigger picture: Consumer AI tools are now daily-use apps for tens of millions of people. That means corporate decisions — especially ones involving government or defense contracts — now resonate directly with a mainstream audience. Whether this loyalty shift persists beyond the news cycle is the question everyone in AI is watching.

• Source: TechCrunch | CNN Business | Fortune | Engadget

OpenAI Drops GPT-5.4: The First General Model With Native Computer Use 🖥️

On March 5 — one day after the most embarrassing week in OpenAI’s PR history — Sam Altman posted two words on X: “I think people will like this.” GPT-5.4 was live.

OpenAI called it their “most capable and efficient frontier model for professional work.” For once, the marketing language might not be an exaggeration.

What’s new:

• Native computer use: GPT-5.4 is the first general-purpose model from OpenAI that can autonomously interact with software through screenshots, mouse commands, and keyboard inputs — no special setup required. On OSWorld-Verified (the standard computer-use benchmark), it hit 75.0% — surpassing the human baseline of 72.4% and far ahead of GPT-5.2’s 47.3%.

• 1 million token context (API preview): Matches Google and Anthropic’s offerings for the first time. Enough to process entire codebases or months of documents in one go.

• 33% fewer factual errors in individual claims vs. GPT-5.2. Full responses 18% less likely to contain mistakes.

• 83% on GDPval (OpenAI’s knowledge-work benchmark for writing, research, and analysis) — up from 70.9%.

• Upfront reasoning plan in ChatGPT: In Thinking mode, the model shows you its reasoning plan before it answers. You can interrupt and redirect mid-response without starting over.

• Tool Search: A new API system where the model looks up tool definitions only when needed, instead of loading every available tool into every prompt. This dramatically cuts token usage for large tool ecosystems — essential for agent pipelines.

Versions and pricing:

Version Best for API Pricing GPT-5.3 Instant Everyday chat, web tasks Already live, default for all ChatGPT users GPT-5.4 Thinking Multi-step reasoning, long workflows $2.50/$15 per million tokens GPT-5.4 Pro Max compute, complex deliverables $30/$180 per million tokens

GPT-5.4 Pro is available via the API and for ChatGPT Enterprise and Edu subscribers. GPT-5.4 Thinking rolls out to Plus, Teams, and Pro plan users.

How it compares:

Partners are enthusiastic. Mercor’s CEO said GPT-5.4 “excels at creating long-horizon deliverables such as slide decks, financial models, and legal analysis.” On BigLaw Bench (legal document work), it scored 91%. On APEX-Agents (professional AI benchmarks for law and finance), it topped the leaderboard.

Three months ago, OpenAI was losing the agentic coding race. Claude Code had captured developers. Codex felt like it was built for an older era. GPT-5.4, combined with recent Codex releases, shifts that balance back toward OpenAI — at least on benchmarks. The real-world test comes when developers spend a week with it.

• Source: TechCrunch | Gizmodo | Cybersecurity News | Every.to Vibe Check

DeepSeek V4 Has Landed: Open Source, Multimodal, and 1/20th the Cost of GPT-5 🐋

DeepSeek’s last big drop — R1 in January 2025 — wiped ~$1 trillion off US tech stocks in a single day. Fourteen months of silence later, V4 arrived in early March 2026, and it’s even more ambitious.

This is DeepSeek’s first natively multimodal model. It processes and generates text, images, and video simultaneously — not through bolted-on adapters, but built into pre-training from the ground up. The headline specs:

• ~1 trillion total parameters (Mixture-of-Experts architecture, only ~32B activate per token — actually more efficient per query than V3)

• 1 million token context window — unmatched in the open-weight space

• Native multimodal: text, images, and video in one unified model

• Open-source — Apache 2.0-style license, free to use commercially, fine-tune, and deploy

• Optimized for Chinese chips (Huawei, Cambricon) — notably not shared with Nvidia ahead of launch, a direct signal of China’s parallel AI infrastructure push

The cost story:

DeepSeek’s API price for V4 is approximately $0.14/M input tokens — roughly 1/20th the cost of GPT-5 at equivalent performance levels. One benchmark client running 50,000 financial documents per day through GPT-5 was paying $4,200/month. The same workload through DeepSeek V4? $210. Same accuracy within 2 percentage points.

How does it benchmark?

On long-context coding tasks — the primary design target — early reports show V4 competitive with or exceeding Claude and ChatGPT. On vision and multimodal understanding, Qwen 3.5 (released in February) still leads. DeepSeek V4’s edge is maximum context windows, video processing, and price.

The geopolitical dimension:

DeepSeek explicitly did not share V4 with Nvidia or AMD before launch — breaking with standard industry practice. The model was built entirely on Chinese hardware. As the US-China AI race splits into parallel ecosystems, V4 is a direct statement: frontier AI no longer requires US chips or US infrastructure.

For creators and builders: The practical implication is already here. If you’re building AI-powered products, tools, or workflows that process documents, generate images, or need long context — V4’s cost structure is a 10-30x cost reduction vs. proprietary alternatives. The infrastructure overhead of self-hosting breaks even at roughly 15-40M tokens/month. Below that threshold, the API is almost certainly the cheapest frontier option available.

• Source: Financial Times | VentureBeat | TechNode | Particula Tech Deep Dive

News of the week 🌍

• Alibaba’s Qwen Implodes the Day After Its Biggest Release 💥 — Alibaba launched the Qwen 3.5 small model series (0.8B to 9B parameters) on March 2, drawing praise from Elon Musk for “impressive intelligence density.” The 9B model matches OpenAI’s 120B model while running on a standard laptop. Then, 24 hours later: the project’s technical lead Lin Junyang (”Justin”) posted two words on X: “me stepping down. bye my beloved qwen.” Two more senior researchers departed the same week. Alibaba CEO Eddie Wu responded with a new Foundation Model Task Force and a statement doubling down on open-source commitment. Alibaba shares dropped 5.3% in Hong Kong — their biggest intraday loss since October. Source | VentureBeat

• GPT-5.3 Instant Rolled Out to All ChatGPT Users (March 3) 💬 — The update that preceded GPT-5.4 quietly dropped Monday and is now the default for every ChatGPT user. Key changes: 400K token context (up from 128K), 26.8% fewer hallucinations when using web search, less hedging, less caveating, and a tuning specifically designed to reduce the over-eager “Of course! Absolutely! Great question!” tone that annoyed users for years. Smaller update than 5.4, but it’s the version most people interact with every day. Source

• New Siri Arriving This Month With Gemini 3 Under the Hood 🍎 — Apple’s completely rebuilt Siri is set to debut with iOS 26.4, targeting a March 2026 release. The new version gains “on-screen awareness” and cross-app integration — powered by Google’s Gemini 3 model running inside Apple’s Private Cloud Compute for privacy. The Apple-Google AI partnership announced in January is now nearly at users’ doors. Source

• Hundreds March Through London Against AI — Biggest Protest Yet 🪧 — On February 28, groups Pause AI and Pull the Plug led hundreds of protesters through London’s King’s Cross tech hub — home to OpenAI, Meta, and Google DeepMind UK offices. Signs ranged from “Stop the Slop” (AI-generated content) to “Who will be whose tool?” The organizers framed it as a social event and the largest anti-AI march to date. Global head of Pause AI: “We can’t pressure companies, but we can make AI seem like a less sexy career.” Source

• Enterprise Monkey Dumps ChatGPT Entirely for Claude 🤝 — The Melbourne AI agency announced it’s migrating all internal operations from ChatGPT to Claude, citing both Anthropic’s Pentagon stance (”you’ve changed since you started hanging out with the military”) and persistent hallucination issues in recent OpenAI models that “haven’t improved across recent releases.” They specifically highlighted Claude’s superiority for autonomous agents: better MCP integrations, native tool use, and structured reasoning. One data point in what could be a larger enterprise trend. Source

• Alibaba Poaches Google DeepMind Researcher for Qwen 🔄 — Even as the Qwen team was losing people, Alibaba was recruiting. Zhou Hao, a senior staff research scientist who previously contributed to Gemini 3, AI Mode, and Deep Research at Google, has been hired as head of post-training research. The talent wars between US and Chinese AI labs are clearly flowing in both directions. Source

• US Supreme Court Declines AI Copyright Case 📜 — The Supreme Court declined to hear a case challenging the legal stance that AI-generated creative works require human authorship for copyright protection. The ruling reinforces existing law: AI alone cannot be a copyright holder. For creators and brands building AI-generated content at scale, human creative input isn’t just a philosophical position — it’s now the legal baseline for IP protection.

• OpenAI Codex App Launches for Windows 💻 — OpenAI’s Codex desktop app is now available on Windows (previously macOS-only). The app supports parallel agents with isolated worktrees, reviewable diffs, and seamless sign-in with ChatGPT. You can run multiple Codex agents simultaneously across the same project — each working on a different feature or bug. Source

Useful tools ⚒️

⭐ Anything API — Turn any website into a callable API — no scraping, no reverse-engineering, no puppeteer hacks. You describe what you want (”get the pricing table from this SaaS page”) and Anything API delivers a clean JSON endpoint. Essential for anyone building agents that need to pull live data from sites that don’t have official APIs. The agentic web just got a lot more accessible.

Krisp Accent Conversion — Real-time AI that converts accented speech into your listener’s expected dialect — live, mid-call. The same team behind the best background noise cancellation app has solved the accent barrier for global remote teams, customer support, and international podcasters. Works as a virtual mic layer with no extra hardware.

Notra — Your daily work — Slack messages, meeting notes, emails — automatically turned into publish-ready content drafts. Connect your tools, tell Notra your content format (newsletter, LinkedIn post, blog), and it surfaces the moments from your week worth writing about. For creators who know what they want to say but never have time to say it.

Enia Code — An AI coding agent that doesn’t wait to be asked — it reviews your code proactively, suggests improvements, and over time learns your team’s style and standards. Think of it as a senior dev who actually reads every PR without being tagged. Plugs directly into GitHub.

Voicr — Voice in, polished text out — in seconds. Speak naturally, get a clean structured draft. Unlike most voice-to-text tools, Voicr handles the full pipeline: transcription → structure → format cleanup. Available as a floating widget on mobile and desktop. For creators, founders, and anyone who thinks faster out loud than on a keyboard.

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Weekly Guides 📕

GPT-5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro: The Honest 2026 Comparison — Complete breakdown of every GPT-5 model variant, when to use each, and how 5.4 stacks up against the current Claude and Gemini flagship offerings

DeepSeek V4 + Qwen 3.5: Open Source AI Is Rewriting the Cost Rules — Side-by-side technical comparison with real enterprise cost examples. The $210/month vs $4,200/month case study is a must-read for anyone building AI-powered products

Vibe Coding: The Complete Guide for Non-Engineers in 2026 — Honest tool comparison across Lovable, Bolt, v0, Cursor, Replit, and OpenClaw. Organized by skill level. If you’ve been curious about building something but don’t know where to start, begin here

Claude Just Hit #1 — Here’s the Full Story Behind the Ascent — Deep dive into what the App Store moment actually means: Claude’s product momentum, the Pentagon timeline, and why this might be more than a protest bump

Beyond the Vibes: How Senior Developers Are Actually Using AI Coding Tools — The most honest technical guide to AI-assisted development this week. What vibe coding gets wrong at scale, how to avoid the “six-month hangover,” and what a proper AI coding workflow actually looks like

AI Meme of the Week 🤡

When OpenAI signs the Pentagon deal and loses 1.5M users in a week... then releases GPT-5.4 the next Thursday

“My mom said I can have my reputation back after I drop a benchmark”

source: X / r/aimemes

AI Tweet of the Week 🐦

@cnulty (Christopher Nulty, Anthropic): “At the start of 2026, Claude was #42 in the App Store. Today, for the first time, we hit #1.”

The internal employee tweet that started a weekend-long news cycle. Quiet, two lines, and made every major outlet’s front page by Sunday morning.

(Bonus) Materials 🎁

Claude’s Full Statement on the Pentagon Designation — Read It Yourself — Dario Amodei’s public response, the two specific red lines Anthropic held, and the exact language they were unwilling to remove from the contract

DeepSeek V4 Technical Architecture: What’s Actually New — The Engram memory system, the MoE efficiency gains, and why building native multimodality into pre-training changes the output quality ceiling

50 GPT-5.4 Prompts for Professional Work — OpenAI’s own Academy breakdown with specific prompt templates for documents, code review, and long-horizon workflows using the new Thinking mode

The Full Alibaba Qwen Timeline: What Actually Happened This Week — From the Qwen 3.5 open-source launch on March 2 to Jack Ma’s rare appearance on March 3 — a timeline of the most dramatic 72 hours in Chinese AI

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

Perplexity Computer, Nano Banana 2, Samsung AI Phone | Weekly Digest

Eight minutes later, everything was done. Clean folder, clean spreadsheet, zero typos.

And that is exactly when the real problem showed up.

The output looked so polished that my first instinct was to ship it — skip the check, close the laptop, move on. Cowork had solved the speed problem, but it quietly created a trust problem: when AI-generated work looks finished too early, teams stop verifying it before they should.

This post is about the pattern I found to fix that. Three rules, four real cases from public Cowork users, and one checklist I now run before every serious task.

This post is for you if you’ve tried Cowork at least once and got decent results — but you’re not sure how to make it reliable for real work, the kind where mistakes actually cost something. If you’ve been using Claude Code in the terminal, this is the non-technical counterpart.

New to Cowork? We wrote a complete beginner guide with practical use cases — start there.

Subscribe now

The three rules that make Cowork usable

Before we get into cases, here are three principles that separate good Cowork runs from chaotic ones.

Rule 1. Start with a shared folder that has a job.

If the folder is a mess, Cowork will be a mess. If the folder has a purpose, naming rules, and a place for logs, Cowork becomes much easier to trust.

What goes wrong without it: You point Cowork at your Downloads folder, it reorganizes 200 files, and you can’t tell what changed or where things went. No audit trail, no undo path.

Try this instead: Create

/project-name/input/
/project-name/output/
/project-name/logs/  

Rule 2. Make sure every run produces files.

The plan should produce a plan.md. The cleanup should produce a report. The research should produce notes and a next-step prompt. If the output lives only in chat, you will lose the thread.

What goes wrong without it: You have a great conversation with Cowork, close the window, and realize nothing was saved. The work existed for 15 minutes.

Try this instead: End every prompt with “Save all outputs to /output/ and create a session log.”

Rule 3. Separate planning from execution.

“Plan first, execute after approval” is boring, but it prevents the most expensive class of errors – silent file moves, wrong assumptions, and work that looked fine until you inspected the folder.

What goes wrong without it: Cowork renames 300 files using a convention you never agreed to. Now you’re spending more time undoing than you saved.

Try this instead: Always add “Do not execute yet. Show me the plan first.” to your first prompt. We covered this plan-first approach in depth in our post on making complex tasks with AI agents. Simalrly Claude Code has Plan Mode which helps with the same feature.

My personal lifehack is to open ChatGPT Codex CLI or alternative tab and share plan from Cowork/Claude Code to get alternative opinion and reccs

Be sure to read our Cowork complete guide. You’ll discover useful tools, practical use cases, and you can practice with AI from scratch.

For the best readability of this post, I will divide each case into the same set of parameters – how to reproduce, what to ask, what good output looks like, and why it matters.

Let’s start with a quick win, then move into the heavier cases.

Quick win: Sort your Downloads and never think about it again

Before we get into the serious cases, try this one. It takes under two minutes and shows what Cowork does differently from chat.

Point Cowork at your Downloads folder and ask it to sort everything by file type into subfolders and produce a summary of what moved. The result is immediately satisfying — and it teaches you the basic loop: folder in, structured output, audit trail. (If you prefer doing this kind of thing from the terminal, our Claude Code tips post covers the same idea for developers.)

Prompt to copy:

Sort everything in /Downloads into subfolders by file type (PDFs, images, spreadsheets, other). Create a move-log.md with the original path and new path for each file. Do not delete anything.

This one is almost impossible to mess up, and it gives you a feel for the tool before we push it further.

Case 1. The evidence vault — when 500+ PDFs become a strategy engine

Read more

Google just dropped an image generator that went viral once — and now it’s back, faster and smarter. Perplexity went from “answer engine” to “digital worker” in one launch. Samsung basically declared the chatbot era over at Unpacked. Another seven days in AI, another week where everyone shipped something wild. Let’s break it all 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 🎟️

Nano Banana 2: Google’s Viral Image Generator Is Back — Faster, Smarter, Free 🍌

Remember when Nano Banana went viral last August and millions of people were generating images in Gemini? Google just leveled up. On February 26, they launched Nano Banana 2 (officially Gemini 3.1 Flash Image) — and this time it brings Pro-quality features to everyone at Flash speed.

What’s new?

• Pro features, now free: Subject consistency across up to 5 characters and 14 objects in a single workflow. Previously locked behind premium tiers.

• Sub-second 4K synthesis: Resolutions from 512px to full 4K, in any aspect ratio. Vertical Stories, widescreen presentations, high-res backdrops — all production-ready.

• Precision text rendering: Generate legible text inside images for marketing mockups, greeting cards, and infographics. It even translates and localizes text within images.

• Real-world knowledge grounding: Pulls from Gemini’s knowledge base and real-time web search for more accurate renderings of real subjects.

• Rolling out everywhere: Default in the Gemini app (Fast, Thinking, Pro modes), Google Search, AI Mode, Google Lens, Flow (video editing), AI Studio, Gemini API, Vertex AI, and Gemini CLI.

Nano Banana Pro sticks around for “high-fidelity tasks requiring maximum factual accuracy.” Google AI Pro and Ultra subscribers retain access via the three-dot menu.

See It in Action: Wow Examples 🤯

Google built two demo apps that show what Nano Banana 2 can actually do for creators:

🐾 Pet Passport: Upload one photo of your pet. Nano Banana 2 drops them into famous landmarks around the world — Eiffel Tower, Great Wall, Colosseum — while maintaining their exact appearance. Perfect for pet influencers and viral pet content.

🌍 Global Ad Localizer: Feed it an English ad. Nano Banana 2 translates the text AND adapts the visuals for different markets. Japanese version? Hindi version? Arabic? It handles the copy AND the cultural context in one shot. This alone is worth it for any creator or marketer targeting multiple regions.

Meanwhile, the viral prompts community has already gone wild. Some highlights from the first 24 hours on X:

• Generating fake but photorealistic YouTube thumbnails from text-only prompts

• Creating 6-panel comic stories with 3 consistent characters across every frame

• Building entire product mockup photoshoots — no camera, no studio

• Rendering accurate data visualizations and infographics from descriptions

Try it yourself: Open Gemini → type any image prompt. Nano Banana 2 is now the default. Or use AI Studio for developer access.

• Source: Google Blog | Developer Blog | TechCrunch | BGR Viral Examples

Perplexity Computer: One Prompt, 19 Models, Entire Workflows Done 🖥️

Perplexity went quiet for two months. Now we know why. On February 25, CEO Aravind Srinivas launched Perplexity Computer — a multi-model agent orchestration platform that coordinates 19 different AI models to complete complex, long-running workflows entirely in the background.

How it works:

• You describe the outcome — “build a marketing campaign,” “create a research report,” “develop an Android app.”

• Computer breaks it into subtasks, spawns specialized sub-agents, and delegates each to the best-suited model.

• The model roster: Claude Opus 4.6 (core reasoning), Gemini (deep research), Nano Banana (images), Veo 3.1 (video), Grok (speed tasks), ChatGPT 5.2 (long-context recall). Plus 13 more specialized models.

• Runs for hours or months without re-prompting. Persistent memory. 400+ app integrations.

• Sub-agents get a real browser, filesystem, and tool integrations. If one hits a wall, it creates more agents.

How it compares:

The Verge described it as existing “somewhere between OpenClaw and Claude Cowork.” Unlike OpenClaw (open-source, runs on your machine), Computer is fully cloud-managed — sandboxed execution, curated integrations, centralized safeguards. Unlike Claude Cowork (enterprise collaboration), Computer is consumer-facing and model-agnostic.

$200/month (Perplexity Max subscribers only). Pro and Enterprise rollout confirmed soon.

This is the first consumer product where you hand a single prompt to a system that delegates across every major AI lab’s best models. Whether it works reliably is TBD. But the architecture is genuinely novel. One early reviewer built 2 micro-apps, 4 research packets, and an automation workflow in a single night.

• Source: VentureBeat | Fortune | Semafor | PYMNTS

Samsung Galaxy S26: The “Agentic AI Phone” Has Arrived 📱

Galaxy Unpacked 2026 (February 25, San Francisco) wasn’t just a phone launch — Samsung declared it “the beginning of truly agentic AI” on mobile. The Galaxy S26 series is Samsung’s third-generation AI phone, and this time the AI doesn’t just assist — it acts.

What’s new?

• Now Nudge: Understands conversational context and proactively surfaces suggestions. Typing about dinner plans? It nudges you with restaurant bookings. No app switching required.

• Gemini 3 agent tasks (beta): Offload multi-step tasks with a side-button press. In the live demo, Gemini read a group chat about pizza, built the order in a delivery app, and prepared the cart — all while the phone remained usable.

• Three AI agents, one phone: Bixby (device agent with real-time web search), Gemini (agentic tasks), and Perplexity (”Hey Plex” wake word — first non-Google AI agent with wake-word access on Samsung).

• World’s first mobile privacy display: Hardware-level screen that limits viewing angles on demand. Protect bank details, messages, passwords in public.

• Performance leap: Snapdragon 8 Elite Gen 5 — 39% faster NPU, 19% faster CPU, 24% GPU boost. Up to 31 hours video playback. 75% charge in 30 minutes.

Pre-orders open now. General availability March 11, 2026. S26 starts at $900, S26+ at $1,100, S26 Ultra at $1,300.

The bigger picture

Samsung’s TM Roh said it best: “The technologies that change history fade into the background because they become infrastructure.” This is Samsung betting that AI on phones transitions from novelty feature to invisible utility. 350K+ peak concurrent viewers at the livestream suggest people agree.

• Source: Samsung Newsroom | Google Blog | Engadget | TechRepublic

News of the week 🌍

• Jack Dorsey Cuts 40% of Block, Blames AI 🪓 — The biggest AI-layoff story of the year just dropped. Jack Dorsey laid off 4,000+ employees (40% of Block’s workforce) saying “intelligence tools have changed what it means to build and run a company.” Block stock soared 20%+. Employees are furious — morale at “the worst in four years,” with workers calling top-down AI mandates “crazy.” A Harvard Business Review study cited in internal discussions found AI is actually intensifying workloads, not reducing them. This is the sharpest test yet of the “AI replaces headcount” thesis at scale

• Meme or Not? A Toilet Company Is the Hottest AI Stock of 2026 🚽 — No, seriously. Japan’s TOTO — the company behind those legendary heated bidet seats — is up nearly 40% in 2026. Why? Their advanced ceramics division makes electrostatic chucks used in semiconductor fabrication for AI memory chips. Activist investor Palliser Capital called TOTO “the most undervalued and overlooked AI memory beneficiary,” claiming a $3.6B valuation gap. Ceramics now contribute ~50% of TOTO’s operating profit. When a toilet maker becomes an AI infrastructure play, you know the hype cycle has reached a new level.

• Airbnb: 33% of Support Now Handled by AI 🏠 — During its Q4 earnings call, Airbnb revealed that a third of North American customer support is now managed by AI agents. Global rollout planned within a year, including AI voice agents. New CTO Ahmad Al-Dahle (ex-Meta Llama team lead) is steering the company toward becoming “AI-native.” Q4 revenue: $2.78B, up 12% YoY. CEO Brian Chesky: “AI is the best thing that ever happened to Airbnb.” Source

• Microsoft Identifies “AI Memory Poisoning” Attack 🛡️ — Security researchers found that manipulated “Summarize with AI” links can embed hidden instructions that alter chatbot memory and bias future recommendations. 30+ organizations across finance, health, and SaaS sectors were affected. Microsoft implemented Copilot mitigations. Source

• 41% of Official MCP Servers Lack Authentication 🔒 — A security audit of 518 AI agent tools found that nearly half of MCP (Model Context Protocol) servers have no authentication. As agentic AI deployments multiply across enterprises, this is a ticking time bomb. Source

• IBM Plans to Triple Entry-Level Hiring 📈 — Challenging the “AI kills junior jobs” narrative, IBM will triple entry-level hiring in 2026. Early-career employees will focus on client engagement and product development, augmented by AI, rather than repetitive coding. Source

• OpenAI Drops gpt-realtime-1.5 🎤 — New real-time speech model with tighter instruction adherence for voice AI agents. If you’re building voice assistants, this matters. Source

• Canva Acquires Cavalry and MangoAI 🎬 — Canva expands into 2D animation with two acquisitions, signaling a play for motion content creators. Source

Useful tools ⚒️

⭐ KiloClaw — Hosted OpenClaw. No Mac mini required. Cloud-based access to the viral open-source agent framework — one-click setup, no hardware needed. The “OpenClaw for everyone” moment.

Stitch by Google — Turn napkin sketches into production-ready UI in seconds. Google Labs’ new tool converts hand-drawn wireframes or sketches into working code instantly. Designers and devs, this one’s for you.

Notion Custom Agents — Anything you can do in Notion, your agent can now do for you — database queries, page creation, cross-workspace automation. The AI-powered workspace just got way more powerful.

Koidex — Know if a package, extension, or AI model is actually safe. Security scanning for the AI era. Essential for developers shipping AI-powered apps.

ChatPal — Practice speaking, get fluent! AI-powered language practice that feels like chatting with a patient tutor.

Weekly Guides 📕

Nano Banana 2: Official Blog & Getting Started — Google’s full breakdown of features, availability, and developer access

Build with Nano Banana 2: Developer Deep Dive — Pet Passport, Global Ad Localizer demos, API code samples, and partner integrations

Best Nano Banana 2 Prompts: eWeek’s Hands-On Guide — 6 tested prompts with real results, including multilingual text localization

Perplexity Computer: Hands-On Overnight Review — 2 micro-apps, 4 research packets, 1 automation — all built in one night

Samsung Galaxy S26: Agentic AI Features Deep Dive — Samsung Newsroom’s complete breakdown of Now Nudge, Gemini agents, and privacy display

AI Meme of the Week 🤡

related to the news mentioned earlier about TOTO & Ajinomoto

source: X

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

Awesome Nano Banana Pro Prompt Collection on GitHub — Curated list of viral prompts for pet stickers, product posters, travel illustrations, and 6-panel comics

Google Revamped Flow Into a Full AI Creative Studio — Flow now combines image and video generation in one workspace

Nano Banana 2 Viral Examples Roundup by BGR — 10 wildest early outputs with the tweets that posted them

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

Sonnet 4.6, Grok’s 4 Agents, and Meta’s Manus in Ads Manager | Weekly Digest

Twelve videos in a row. The same 7-second chorus. One DM later, I found out it took 9 minutes to make…

And yesterday my feed pulled a simple trick on me! I watched a dozen videos back-to-back - different creators, different niches, different editing styles - and the same song kept showing up.

Not a remix. Not a similar vibe. The exact same glittery, slightly nostalgic earworm engineered to move your thumb.

So I messaged one of the creators and asked, “What is this track?”.

The reply: “I made it last night. 9 minutes.”

That is the moment the shift became obvious: in short-form content, music is no longer only art. It is interface design.

In feeds, the track often moves faster than the video idea itself. For creators and founders, that changes how attention is earned - and how products are packaged for public platforms.

1. Opening

2. What goes viral now

3. Four signals this is already mainstream

4. Part 1 - Build loops first

5. Part 2 - Turn the winner into a full track

6. Part 3 - Proof & rights

7. Part 4 - Where to make it (and what to screenshot)

8. Prompts you can steal

9. Optional visual idea in Creators AI style

10. Related Creators AI references (add 3 real links before publishing)

11. Links worth your 5 minutes

12. Closing

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

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What goes viral now

The thing that goes viral is not always a full song. It is a handle - a memetic key that can fit into many stories.

Read more

This week was genuinely one of the wildest in the AI race so far.

Three major model drops in a single week (Sonnet 4.6, Grok 4.20, Gemini 3.1 Pro), Meta quietly slipping an AI agent into every advertiser’s dashboard, and the India AI Summit giving us the most meme-worthy moment of the year — all in seven days. 🤯

If you blinked, you missed at least two product launches. Let’s catch you up.

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 🎟️

Claude Sonnet 4.6: Opus-Level Intelligence at 1/5 the Price 🧠

Anthropic released Claude Sonnet 4.6 on February 17 — just 12 days after Opus 4.6 — and it’s being called a “seismic repricing event” for the AI industry. Performance that previously required an Opus-class model is now available at Sonnet pricing: $3/$15 per million tokens (vs. Opus at $15/$75).

What’s new?

• Computer use jumped to 72.5% on OSWorld — nearly a fivefold improvement from Sonnet 3.5’s 14.9% just 16 months ago. This is the capability that unlocks automation of legacy software without APIs.

• 1M token context window (in beta) — enough to hold entire codebases, lengthy contracts, or dozens of research papers in a single request. Double the largest window previously available for Sonnet.

• 60.4% on ARC-AGI-2 — above most comparable models, though still behind Opus 4.6 and Gemini 3 Deep Think.

• Box tested it extensively: 77% accuracy on heavy reasoning tasks (up from 62%), 94% on retail tasks, 88% on public sector work, 78% on healthcare.

• GitHub’s VP of Product Joe Binder confirmed: “Already excelling at complex code fixes, especially when searching across large codebases.”

Sonnet 4.6 is now the default model for Free and Pro plan users on claude.ai and Claude Cowork. Available on all Claude plans, Claude Code, the API, and all major cloud platforms.

Is it a breakthrough?

This isn’t just a model update — it’s a business model disruption. When Opus-class intelligence becomes available for a few dollars per million tokens, companies that were cautiously piloting AI agents suddenly face a fundamentally different cost calculus. The agents that were too expensive to run continuously in January are suddenly affordable in February. VentureBeat called it “the headline that matters most.”

• Source: CNBC | VentureBeat | TechCrunch

Grok 4.20: xAI Didn’t Release a Model — It Released a Team

Elon Musk didn’t announce a launch window for Grok 4.20 — he just posted that it was live on February 17. And it’s the most structurally different Grok release since the original.

Instead of a single model answering your question, four specialized AI agents now work together on every query:

• Grok (Captain) — coordinates, synthesizes, delivers the final answer

• Harper — research, fact-checking, real-time data from X’s firehose (~68M English posts/day)

• Benjamin — math, code, logic, step-by-step reasoning

• Lucas — creative thinking, divergent ideas, balance

They run in parallel, debate each other’s outputs, and peer-review before you see a single word.

What’s new?

• Hallucinations dropped 47–65% according to early users. The peer-review mechanism catches contradictions before output.

• “Rapid learning” architecture — the model improves weekly based on user feedback, with release notes published alongside every update. Musk: “Unlike prior versions of Grok, 4.2 is able to learn rapidly.”

• Grok 4.20 Heavy (SuperGrok Heavy tier) scales up to 16 agents for extreme-complexity tasks.

• In Alpha Arena Season 1.5 (live stock trading), Grok 4.20 variants were the only profitable models — turning $10K into $11K–$13.5K while OpenAI and Google rivals finished in the red.

• Medical document analysis: Upload lab reports, imaging results, prescriptions for AI-generated analysis.

Available to SuperGrok (~$30/month) and X Premium+ subscribers. Free users can also access the multi-agent system on grok.x.ai.

This is the first consumer-facing AI from a major lab where multiple named agents visibly reason together. Love it or hate it, it’s a genuinely different approach.

• Source: NextBigFuture | AdwaitX | EONMSK

Meta Puts Manus AI Inside Ads Manager — Every Advertiser Now Has an AI Agent 📊

About a month after acquiring Manus for $2 billion, Meta has embedded the autonomous AI agent directly into Ads Manager. This isn’t a chatbot that writes copy when asked — Manus acts as an autonomous operator inside campaigns.

What it does:

• Automates report building and campaign analysis

• Conducts audience research without switching platforms

• Generates insights from performance data

• Handles recurring tasks on a schedule

All Meta advertisers can access Manus via the “Tools” menu in Ads Manager right now. Some users are also getting pop-up prompts encouraging activation.

Should you care?

Yes — but temper expectations. Jon Loomer noted that the current integration is more of a redirect to Manus AI’s LLM interface than a seamless in-platform experience. The real promise is ahead: as Meta tightens the integration, expect autonomous campaign optimization, A/B testing, and creative iteration without human prompts. Alexander Zakharov warned: “Brands should expect to lose visibility into campaign decisions. Define your guardrails now.”

• Source: Social Media Today | Search Engine Land | Jon Loomer

News of the week 🌍

• Gemini 3.1 Pro Drops (Feb 19) 🧪 — Google released the first “.1” increment in Gemini history — skipping the usual mid-year “.5” cycle. Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 (more than double Gemini 3 Pro’s 31.1%), hit 44.4% on Humanity’s Last Exam, and topped the APEX-Agents leaderboard for professional task performance. Priced the same as 3 Pro ($2/$12 per million tokens) — less than half the cost of Claude Opus 4.6. Available in the Gemini app, NotebookLM, AI Studio, and Vertex AI. Source

• India AI Impact Summit 🇮🇳 — The biggest global AI summit of the year brought Altman, Amodei, Pichai, and Hassabis to New Delhi. India projected $200B+ in AI investment over the next 2 years and committed 20,000 GPUs for domestic infrastructure. Adani committed $110B for green AI data centers. Reliance Jio announced $109.7B for AI infrastructure. Anthropic opened its Bengaluru office and partnered with Infosys. OpenAI opened two new India offices and partnered with TCS. Source

• Meta x Nvidia Multi-Year Deal 🤝 — Meta and Nvidia announced a massive multi-year partnership for Meta’s AI data center buildout. Includes Nvidia’s Vera Rubin platform, Spectrum-X networking, and Confidential Computing for WhatsApp. Meta becomes the first to deploy Nvidia’s Grace CPUs as standalone chips. With Meta’s $135B 2026 capex, the deal is “certainly in the tens of billions.” Source

• Micron’s $200B Expansion 💾 — Micron plans a massive expansion to address what it calls the biggest memory chip shortage in 40 years. AI workloads are consuming memory faster than the industry can produce it. Source

• Cohere Goes Multilingual & On-Device 🌍 — Cohere released a new family of open multilingual models supporting 70+ languages, small enough to run on everyday devices. The “small-but-capable” direction matters: companies want AI that’s cheaper, more private, and viable in regions where cloud costs are constraints. Source

• 1 in 3 Say AI Chatbots Are Their Worst Service Experience 😬 — New survey data shows nearly a third of customers rank AI chatbots as their worst customer service interaction. The trust gap should alarm every brand betting on AI-first support. Source

• Google I/O 2026 Announced 📅 — Set for May 19-20 at Shoreline Amphitheatre. Google is making AI the headline from the start, with Gemini front and center alongside Android, Chrome, and Google Cloud updates. Source

• Deutsche Bank Asked AI How It Plans to Destroy Jobs 🏦 — The bank literally asked an AI model to predict which jobs it would eliminate. The AI predicted handling 75% of all customer service interactions by 2026 and flagged media/entertainment as “likely to be disrupted.” But it also conceded “sectors of resilience” where deep empathy remains premium. Source

Useful tools ⚒️

⭐ Moda — (Our Top Pick) “Finally, AI designs you can actually edit.” Generate on-brand visuals and then tweak every element — fonts, colors, layouts — without starting from scratch. Launched Feb 18 on Product Hunt with 521+ upvotes. Great for marketers and social media managers who are tired of AI designs they can’t customize.

Origami.chat — Find your perfect leads with one prompt. Describe your ideal customer, and Origami pulls matching contacts with emails and context. 481 upvotes on PH (Feb 19). Sales teams, take note.

Reloop — Create winning ads without prompts or skills. Upload your product, and Reloop generates scroll-stopping creatives optimized for Meta and Google. Launched Feb 19 with 319 upvotes.

Monologue for iOS — Turn your voice into polished writing anywhere on iPhone. Think quick notes that come out as finished paragraphs. Launched Feb 19 — perfect companion for content creators on the go.

IMAI Studio — AI-powered design studio for enduring brand experiences. Trending on TAAFT this week (Feb 17) with 5,100+ views. Focuses on creating consistent, professional product design assets.

Share this post with friends, especially those interested in AI!

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Weekly Guides 📕

Getting Started with Claude Sonnet 4.6 — Official model docs and migration guide from Sonnet 4.5

How Grok 4.20’s Multi-Agent System Works — Deep dive into Harper, Benjamin, Lucas, and the Captain

Gemini 3.1 Pro: Getting Started for Developers — Official guide with API access via AI Studio, Vertex AI, and Antigravity

Mastering n8n for AI Automations — Revisit our deep dive and hook up your workflows to the new models

AI Meme of the Week 🤡

The GPT-4o retirement hit the AI community hard. OpenAI removed it from ChatGPT, and the internet responded exactly how you’d expect:
Over 20,000 people signed a petition. A dedicated subreddit (r/4oforever) was created. People are literally mourning a language model. We’ve reached the stage where AI models get retirement parties and funeral memes. What a time to be alive. 😭

AI Tweet of the Week 🐦

The moment of the week happened at India’s AI Impact Summit when Prime Minister Modi asked all the tech leaders on stage to hold hands and raise them together in a show of unity. Everyone joined in — except Sam Altman and Dario Amodei, who were standing right next to each other and conspicuously raised their fists instead of holding hands. 🤜🤛

Siddharth Bhatia

(Bonus) Materials 🎁

Anthropic & Infosys Partner for Telecom AI Agents — Claude heading into regulated industries at scale

Fresh Air: “Can the AI Chatbot Claude Be Contained?” — NPR’s deep dive into AI ethics, aired Feb 18

MIT: AI Models Perform Worse for Non-Native English Speakers — Important equity research on AI bias in language proficiency

MIT: Long Conversations Cause LLMs to Mirror User Viewpoints — Echo chamber effect in extended AI chats

Fortune: Thousands of CEOs Admit AI Had No Impact on Productivity — The “Solow Paradox” is back, 40 years later

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

GLM-5, Gemini 3 Deep Think, and Anthropic’s $30B | Weekly Digest