Welcome, humans.

The #1 question we get in our “AI Skill of the Day Section” is how do I use AI agents?

Well, later today at 10 AM PT / 1 PM ET, agent builder James McAulay is joining us for a beginner-friendly crash course on doing exactly that: he’ll show you how to choose a task worth automating, break it into steps, and turn it into a repeatable workflow that can be “agentified” (and what tools he recommends to do so!).

Why James? Because he used to work at ElevenLabs, and now runs a fully AI-native business with no employees, and his Agent Accelerator students report automating an average of five hours of manual work every week. That’s happy hour time, bb!

Here’s what happened in AI today:

• 🙀 Google reshuffled DeepMind as Jeff Dean launched Discovery Loop.

• 📰 Meta launched Muse Code to challenge Claude Code and Codex.

• 📰 A 4B open model matched GPT-5.6 Sol at 1/100 the cost.

• 🍪 OpenWorker turns your desktop into a local AI coworker.

• 🎓️ Turn one successful AI task into a reusable skill.

…and a whole lot more that you can read about here.

🙀 Google Musical Chairs: Demis Moves Up, Jeff Dean Moves Out

Google just played the highest-IQ game of musical chairs in corporate history.

Demis Hassabis is stepping away from Google DeepMind’s daily operations to become Alphabet’s chief scientist. Koray Kavukcuoglu will run the model-and-product machine. Meanwhile, Jeff Dean and three researchers behind huge chunks of modern computing are leaving to build their own company.

As in, a startup, with a pitch deck. Imagine the VCs in that meeting just salivating over the opportunity to fund these legends. It’s giving “shut up and take my money”

Here's what happened:

• Demis Hassabis became chair of Google DeepMind and chief scientist of Alphabet, focusing on artificial general intelligence, science, and strategy.

• Koray Kavukcuoglu took operational control of DeepMind, including Gemini models, frontier research, the Gemini app, and developer products.

• Jeff Dean, Sanjay Ghemawat, Quoc Le, and Oriol Vinyals launched Discovery Loop, an independent company designed to automate scientific and engineering experiments.

• Google will remain involved as a founding investor, Cloud partner, and research collaborator.

So what’s the big deal here? Well, the departing team helped create MapReduce, BigTable, TensorFlow, TPUs, sequence-to-sequence learning, and Gemini. In normal-person language: they built major machinery Google uses to process the internet and train modern AI.

Discovery Loop wants AI systems to repeatedly propose an experiment, run it, inspect the result, and try again. It will start with machine-learning research, improve its own technology, then expand into medicine, clean energy, water, and cybersecurity.

Why this matters: Google is separating three jobs that had become one giant mandate: shipping Gemini quickly, deciding where advanced AI should go, and using AI to accelerate science. Koray owns execution. Demis gets the long-range steering wheel. Dean’s group gets startup freedom.

That could help each team move faster, but Google is losing four people whose knowledge is difficult to replace. Partnerships preserve financial upside. They do not instantly recreate decades of shared instincts about how Google’s systems work.

Our take: The galaxy brain read on this is that it looks less like Google breaking apart and more like Alphabet building an AI solar system. DeepMind develops frontier models, Isomorphic Labs pursues drug discovery, and Discovery Loop automates research, while Google supplies capital and compute and… gets out of the way (fingers crossed, if you’re team Google)?.

The gamble is whether keeping brilliant alumni in orbit works as well as keeping them in the building. The next test is Gemini 4: Google now has clearer leadership, but fewer legends in the room when something breaks. Pre-Gemini 4, the rumor is something’s coming later today…

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Save Your Spot

🎓 AI Skill of the Day: Turn a Good AI Result Into a Reusable Skill

Most people treat every successful AI task like a lucky answer: useful once, then lost in the chat history. Instead, turn every verified win into a reusable skill.

Microsoft’s Web Skill Factory used this idea to convert solved website tasks into reusable programs. Reusing the skill library raised held-out accuracy from 55% to 70% while reducing the number of steps.

After the AI completes a task correctly, ask it to document the inputs, exact steps, tools, checks, failure modes, and approval points. Separate the reusable process from one-off details such as names, dates, files, and destinations.

Turn the task we just completed into a reusable skill.

Include:
1. The goal and required inputs.
2. The exact steps and tools used.
3. A verification checklist with pass/fail criteria.
4. Common failure modes and recovery steps.
5. Actions that require human approval.
6. A short template I can reuse next time.

Only include steps supported by the work we actually completed. Do not invent missing details.

Our favorite insight: Save only verified successes. A bad workflow preserved perfectly is still a bad workflow.

Have a specific skill you want to learn? Request it here.

🍪 Treats to Try

*Asterisk = from our partners (only the first one!). Advertise to 700K+ readers here!

1. *Why We Love It: AI agents need context to deliver. Slack provides it at scale.

2. Wondering turns any subject into a personalized course of three-minute visual lessons, exercises, and review built around your goals —free plan, then $14.99/mo.

3. Osmo turns Figma frames or visual references into editable motion graphics you can prompt, hand-tune, and export with transparent backgrounds.

4. OpenWorker takes an outcome like prepping a sales call or triaging an incident, works across your files, Slack, email, calendar, and more, then asks before it acts —free/open-source, plus model costs.

5. Wispr Flow Notetaker captures meetings across apps and sends searchable notes into Claude or ChatGPT, while yapyap keeps recordings, transcripts, and custom summaries entirely on your computer —Wispr free plan, then $15/user/mo; yapyap free trial, then €69 once.

6. Cloudflare OS lets your team build sandboxed internal apps today, while Wallets lets you claim an agent identity now and will add controlled API spending next —OS is free/open-source; Wallets pricing not public.

7. Muse Code runs long coding jobs through persistent background agents that can plan changes, edit large repositories, and verify the result —beta pricing not public.

8. FlowIn predicts and rewrites text at your cursor across Gmail, Slack, Notion, Terminal, and other Mac apps —free to download.

9. Neon and Castform post-trained a 4B open model that matched GPT-5.6 Sol on search-result retrieval while costing 100 times less.

10. Prime Agent gives coding agents persistent subagents, memory, and self-editing skills so they can improve their own workflow during long jobs —free/open-source, plus model costs.

11. Sapiom builds, runs, and monitors agents through one system that routes model calls, controls spending, and connects paid tools without separate vendor accounts (raised $35M) —free plan with 50 runs/day, then $1/additional run.

12. Tasklet rolled out a new agent-first workspace that lets you manage each persistent agent’s knowledge, connections, permissions, automations, usage, and project-specific threads from one page; no pricing details.

13. LFM2.5-2.6B is an AI you can use to run private agents locally that plan, call tools, and complete multi-step tasks at up to 220 tokens per second while using under 2.5 GB; free for companies under $10M in annual revenue.

📰 Around the Horn

• Anthropic confirmed it is building an in-house chip team to co-design custom hardware around Claude while keeping its existing suppliers.

• Google entered talks for a $1.5B-plus Mechanize deal that would hire its team and license its coding-agent technology.

• Jamie Dimon rallied leaders at more than 40 companies to join an alliance addressing AI, cyber, and critical-infrastructure threats.

• MIT researchers found AI automation is spreading broadly across thousands of workplace tasks, while a separate MIT-Stanford study found financial advice improved most users but created 4% to 5% retirement-wealth gaps.

• MIT engineers built an adaptive therapy robot that learns from physical therapists, while Vanderbilt began developing an EHR agent to speed Alzheimer’s patients into treatment.

• Meta’s ad systems ran more than 50 ads containing AI-generated CSAM imagery before removing them after WIRED’s inquiry.

• A University of Washington study found female animals made up just 2% of nearly 24,000 children’s stories generated by leading AI models

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New from The Neuron: We could have Fable level AI on our laptops “within two years”

Intel’s Dr. Olena Zhu showed us the version of local AI that feels useful right now. They just released SuperClaw’s public beta, which includes email, coding, and deep-research agents that can combine outside research with private company data while keeping the sensitive material local. Parts of the stack are open, so teams can inspect the architecture and build on it.

The bigger idea behind Intel’s approach is wild: local models have been inheriting frontier-level capability after an average of roughly 24.8 months. If that trend holds, a Fable-class intelligence model could run on a high-end laptop by 2028. See above re: problems with datacenters for why that’s actually really important.  

Watch / listen: YouTube | Spotify | Apple Podcasts

Shout out to Nerdio who sponsored this video!

A Cat’s Commentary

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