Hey ya’ll, Alex here, and this week I was especially giddy to record the show! Mostly because when a thing clicks for me that hasn’t clicked before, I can’t wait to tell you all about it!

This week, that thing is Agent Skills! The currently best way to customize your AI agents with domain expertise, in a simple, repeatable way that doesn’t blow up the context window! We mentioned skills when Anthropic first released them (Oct 16) and when they became an open standard but it didn’t really click until last week! So more on that below.

Also this week, Anthropic released a research preview of Claude Cowork, an agentic tool for non coders, OpenAI finally let loos GPT 5.2 Codex (in the API, it was previously available only via Codex), Apple announced a deal with Gemini to power Siri, OpenAI and Anthropic both doubled down on healthcare and much more! We had an incredible show, with an expert in Agent Skills, Eleanor Berger and the usual gang on co-hosts, strongly recommend watching the show in addition to the newsletter!

Also, I vibe coded skills support for all LLMs to Chorus, and promised folks a link to download it, so look for that in the footer, let’s dive in!

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Big Company LLMs + APIs: Cowork, Codex, and a Browser in a Week

Anthropic launches Claude Cowork: Agentic AI for Non‑Coders (research preview)

Anthropic announced Claude Cowork, which is basically Claude Code wrapped in a friendly UI for people who don’t want to touch a terminal. It’s a research preview available on the Max tier, and it gives Claude read/write access to a folder on your Mac so it can do real work without you caring about diffs, git, or command line.

The wild bit is that Cowork was built in a week and a half, and according to the Anthropic team it was 100% written using Claude Code. This feels like a “we’ve crossed a threshold” moment. If you’re wondering why this matters, it’s because coding agents are general agents. If a model can write code to do tasks, it can do taxes, clean your desktop, or orchestrate workflows, and that means non‑developers can now access the same leverage developers have been enjoying for a year.

It also isn’t just for files—it comes with a Chrome connector, meaning it can navigate the web to gather info, download receipts, or do research and it uses skills (more on those later)

Earlier this week I recorded this first reactions video about Cowork and I’ve been testing it ever since, it’s a very interesting approach of coding agents that “hide the coding” to just... do things. Will this become as big as Claude Code for anthropic (which is reportedly a 1B business for them)? Let’s see!

There are real security concerns here, especially if you’re not in the habit of backing up or using git. Cowork sandboxes a folder, but it can still delete things in that folder, so don’t let it loose on your whole drive unless you like chaos.

GPT‑5.2 Codex: Long‑Running Agents Are Here

OpenAI shipped GPT‑5.2 Codex into the API finally! After being announced as the answer for Opus 4.5 and only being available in Codex. The big headline is SOTA on SWE-Bench and long‑running agentic capability. People describe it as methodical. It takes longer, but it’s reliable on extended tasks, especially when you let it run without micromanaging.

This model is now integrated into Cursor, GitHub Copilot, VS Code, Factory, and Vercel AI Gateway within hours of launch. It’s also state‑of‑the‑art on SWE‑Bench Pro and Terminal‑Bench 2.0, and it has native context compaction. That last part matters because if you’ve ever run an agent for long sessions, the context gets bloated and the model gets dumber. Compaction is an attempt to keep it coherent by summarizing old context into fresh threads, and we debated whether it really works. I think it helps, but I also agree that the best strategy is still to run smaller, atomic tasks with clean context.

Cursor vibe-coded browser with GPT-5.2 and 3M lines of code

The most mind‑blowing thing we discussed is Cursor letting GPT‑5.2 Codex run for a full week to build a browser called FastRenderer. This is not Chromium‑based. It’s a custom HTML parser, CSS cascade, layout engine, text shaping, paint pipeline, and even a JavaScript VM, written in Rust, from scratch. The codebase is open source on GitHub, and the full story is on Cursor’s blog

It took nearly 30,000 commits and millions of lines of code. The system ran hundreds of concurrent agents with a planner‑worker architecture, and GPT‑5.2 was the best model for staying on task in that long‑running regime. That’s the real story, not just “lol a model wrote a browser.” This is a stress test for long‑horizon agentic software development, and it’s a preview of how teams will ship in 2026.

I said on the show, browsers are REALLY hard, it took two decades for the industry to settle and be able to render websites normally, and there’s a reason everyone’s using Chromium. This is VERY impressive 👏

Now as for me, I began using Codex again, but I still find Opus better? Not sure if this is just me expecting something that’s not there? I’ll keep you posted

Gemini Personal Intelligence: The Data Moat king is back!

What kind of car do you drive? Does ChatGPT know that? welp, it turns our Google does (based on your emails, Google photos) and now Gemini can tap into this personal info (if you allow it, they are stressing privacy), and give you much more personalized answers!

Flipping this Beta feature on, lets Gemini reason across Gmail, YouTube, Photos, and Search with explicit opt‑in permissions, and it’s rolling out to Pro and Ultra users in the US first.

I got to try it early, and it’s uncanny. I asked Gemini what car I drive, and it told me I likely drive a Model Y, but it noticed I recently searched for a Honda Odyssey and asked if I was thinking about switching. It was kinda... freaky because I forgot I had early access and this was turned on 😂

Pro Tip: if you’re brave enough to turn this on, ask for a complete profile on you 🙂

Now the last piece is for Gemini to become proactive, suggesting things for me based on my needs!

Apple & Google: The Partnership (and Drama Corner)

We touched on this in the intro, but it’s official: Apple Intelligence will be powered by Google Gemini for “world knowledge” tasks. Apple stated that after “careful evaluation,” Google provided the most capable foundation model for their.. apple foundation models. It’s confusing, I agree.

Honestly? I got excited about Apple Intelligence, but Siri is still... Siri. It’s 2026 and we are still struggling with basic intents. Hopefully, plugging Gemini into the backend changes that?

In other drama: The silicon valley carousel continues. 3 Co-founders (Barret Zoph, Sam Schoenholz and Luke Metz) from Thinking Machines (and former OpenAI folks) have returned to the mothership (OpenAI), amid some vague tweets about “unethical conduct.” It’s never a dull week on the timeline.

This Week’s Buzz: WeaveHacks 3 in SF

I’ve got one thing in the Buzz corner this week, and it’s a big one. WeaveHacks 3 is back in San Francisco, January 31st - February 1st. The theme is self‑improving agents, and if you’ve been itching to build in person, this is it. We’ve got an amazing judge lineup, incredible sponsors, and a ridiculous amount of agent tooling to play with.

You can sign up here: https://luma.com/weavehacks3

If you’re coming, add to the form you heard it on ThursdAI and we’ll make sure you get in!

Deep Dive: Agent Skills With Eleanor Berger

This was the core of the episode, and I’m still buzzing about it. We brought on Eleanor Berger, who has basically become the skill evangelist for the entire community, and she walked us through why skills are the missing layer in agentic AI.

Skills are simple markdown files with a tiny bit of metadata in a directory together optional scripts, references, and assets. The key idea is progressive disclosure. Instead of stuffing your entire knowledge base into the context, the model only sees a small list of skills and let it load only what it needs. That means you can have hundreds of skills without blowing your context window (and making the model dumber and slower in result)

The technical structure is dead simple, but the implications are huge. Skills create a portable, reusable, composable way to give agents domain expertise, and they now work across most major harnesses. That means you can build a skill once and use it in Claude, Cursor, AMP, or any other agent tool that supports the standard.

Eleanor made the point that skills are an admission that we now have general‑purpose agents. The model can do the work, but it doesn’t know your preferences, your domain, your workflows. Skills are how you teach it those things. We also talked about how scripts inside skills reduce variance because you’re not asking the model to invent code every time; you’re just invoking trusted tools.

What really clicked for me this week is how easy it is to create skills using an agent. You don’t need to hand‑craft directories. You can describe your workflow, or even just do the task onc...