Anthropic's big week, and the fine print under it

This week's theme

Anthropic ships an agent and faces tougher questions on rivals, lawsuits and its own numbers, while AI agents start acting unprompted and creative jobs keep shrinking.

A 19-minute read · 12 stories

In this issue

01 Front Page the lead 02 Insights 2 stories 03 Learnings 1 story 04 Tools & Craft 1 story 05 Key News 7 stories

01 Front Page

Cheap Chinese AI models are now a real risk to Anthropic

Investors prepping for Anthropic's stock market debut are mainly worried about one thing: cheap, improving Chinese AI models cutting into its lead, says research firm Hedgeye. Anthropic's revenue just jumped nearly 50 percent in a quarter, to $16.8 billion. That still spooked investors in AI chipmakers like Nvidia and Intel, a sign of how nervous the market already is about the company behind the Claude you use every day.

via South China Morning Post →

02 Insights

A new wave of AI assistants acts before you ask

Claude, OpenAI, Meta, and a startup called Instinct all just shipped a proactive assistant within days of each other.

Claude just released an assistant called Duet, landing within days of OpenAI's Dots, Meta's Muse, and a startup called Instinct. All four belong to a new kind of AI that acts before you ask it to. For the past few years, using AI meant opening a chat window, typing a request, and waiting for an answer. This new wave flips that: the assistant sits across your apps and tools, watches what is happening, and comes to you first, flagging the email you forgot to answer or a deadline about to slip, or simply handling the task itself. If you've also heard of Grok Bot, xAI's version of the same idea, it belongs in this bucket too. The newsletter's author tested three of these directly: she had Instinct call a store for her, asked Muse for a daily dose of AI news, and gave Dots $500 to plan a surprise day for a friend. Since you evaluate and use tools exactly like this in your own work, knowing what actually separates one from another is worth five minutes of reading rather than hours of trying all four yourself.

Meta Muse is built for people already living inside Facebook, Instagram, or Facebook Marketplace, and for now it only works in the US and Canada. Of the options tested, Muse had the strongest idea generator by far, a good fit for someone who never wants to touch a coding tool and just wants suggestions. The speed from opening the app to getting value is high too: the author says she had a daily news briefing running in no time. The catch is privacy. Reports surfaced that Meta had been accidentally sharing people's private Muse conversations, a feature that has since been switched off, and a separate user found that Muse, while running his Facebook Marketplace sales for him, leaked his home address to a stranger.

OpenAI Dots works best for people who already have other tools connected into ChatGPT or Codex. It is also the strongest of the group on voice: she called her own Dot on the phone and had a full conversation with it. In one test, she gave Dots $500 and told it to plan an entire surprise day for a friend. Dots is available on the ChatGPT Pro plan everywhere except the EU, the UK, Switzerland, Norway, Iceland, and Liechtenstein; on a Business Premium plan it works everywhere ChatGPT does, which only excludes places like China and Russia.

Instinct comes from a startup called Spear Street that has raised $1 billion, reflecting how much money is chasing this category right now. It only works over text message, which makes it the pick for someone who lives in iMessage and does not want another app on their phone. The author found it faster to use than the other two, and it is the only one of the three not built by OpenAI or Anthropic. It is invite-only for now and works everywhere except Russia, China, Iran, North Korea, Syria, and Cuba. In her tests, she had it call a store directly and get a full rundown on balloon pricing, the kind of errand you would otherwise make yourself. Its one feature nothing else in this group offers: you can link two Instinct assistants to each other or drop them into a shared group chat, and one user reports having connected hers with her boyfriend's.

The newsletter's bigger point is that this market is already crowded, and every major AI company, plus well-funded startups like Instinct, is going to be fighting for a spot on your phone. The real decision is less about which assistant is objectively best and more about which one fits the tools you already use: Meta's apps, OpenAI's business stack, or plain text messaging.

I gave Dots $500 to build an entire mystery day for me.

via AI with Allie →

AI isn't making code worse, it's exposing bad code

A 35-year veteran engineer says AI only looks bad next to code that was never that good to begin with.

A software engineer with 35 years of experience, posting on Reddit, makes a blunt argument: AI, in this case software that writes code when you ask it to, is not making software worse. It is exposing how much ordinary, not-great code was already shipping long before AI ever touched a keyboard.

His case: today's AI makes real mistakes writing code and sometimes picks a clumsy approach, but he says it makes far fewer of those mistakes than most human engineers do. Every company, he argues, is lucky to have even a few truly excellent engineers, and only a rare handful of companies have a lot of them. Most of the code written over the last 30 years, in his view, came from engineers who were simply not exceptional, and AI's arrival is what is now making that visible.

He is careful to say this is not an insult to people, since humans built the AI in the first place. His framing: everyone works with roughly 20 watts of brainpower, the actual rough power draw of a human brain, to run both their personal life and their job, and that limit is exactly why mistakes happen, with or without AI in the room.

A lot of software has been built over the last 30 years or so by not so exceptional engineers and that is being exposed now.

via r/ArtificialInteligence →

03 Learnings

A five-step path from ChatGPT chat to a working agent

How a workshop leader turns plain English into agents that act.

This matters because it gives you a repeatable, five-step path for turning plain chat habits into agents that actually do things, directly useful for the agent work you are already doing.

The newsletter's writer ran an AI workshop for a room of experienced ChatGPT users and got the loudest reaction of the day when she simply switched the desktop app from ChatGPT to Codex, OpenAI's workspace for putting AI agents to work: researching, reading and editing your files, using your browser to complete tasks, and building things you ask for. The room reacted with an audible "OHHHHH!!!" Her point is that despite the name, Codex is not just for engineers. If you can describe what you want in plain English, you can use it. She even showed the walkthrough video to an OpenAI employee, who admitted about one of the buttons, "I didn't even know we had that feature!"

To start, download the ChatGPT desktop app, switch the dropdown in the top-left corner from ChatGPT to Codex, and open Plugins to connect one tool you actually use, such as Gmail or Google Drive. No need to connect everything at once.

Step one is to talk through a problem out loud rather than write a polished prompt. Hit the dictation microphone and ramble the way you would to a colleague who will not hang up on you, half-formed thoughts and all. Then add one line that changes everything: "Before you start solving this, ask me questions about anything you don't understand. Don't make guesses." That one instruction forces the agent to interview you before it acts, so its suggestions actually fit your situation instead of guessing.

Step two is giving the agent a goal with a clear finish line, not just a task, describing both the result you want and how the agent should check its own work. One example from the piece: "Revise this client onboarding to under 500 words. Keep the instructions someone needs to follow, preserve every link, and check the final word count." For anything more subjective, like improving a job description, spell out what "improve" actually means: which responsibilities need clarifying, who you want to attract, and what they need to know before applying. The clearer the finish line, the better the result.

Step three is handing off something you keep asking for yourself, on a schedule. Pick a task you do or check repeatedly, then tell the agent what to look for and how often, with your tools connected.

Step four is splitting a bigger job across several agents at once, either assigning each one's role yourself or letting the system decide how to divide the work. One example: one agent researches products, a second reads customer reviews, and the two findings get combined into a sourced comparison. A sharper version for reviewing your own plans: spin up one agent each for accuracy, feasibility, value, your inner critic, your biggest fan, and your target market, then have them work to reduce the plan's risk before you commit to it.

Step five is live voice mode: it works like a teammate sitting next to you who can actually act while you talk. Click the waveform button and say something like "in that Chrome window, open this website and help me grab..." and the agent navigates your browser for you, this is computer use, meaning the AI can see your screen and click around it, asking your approval for anything that needs it. You can interrupt it, ask questions mid-task, and run it alongside something else entirely: the writer describes doing a trivia quiz with her agent in one window while catching up on Slack in another, at the same time.

None of this needs new software beyond the ChatGPT desktop app you may already have installed. The jump from chat to agent is entirely in how you prompt: give it permission to ask questions, give it a measurable goal, and let it run with your tools connected.

I didn't even know we had that feature!

via AI with ALLIE →

04 Tools & Craft

20 AI coding tools on GitHub, ranked by stars

GitHub stars, a rough measure of how many people bookmarked a project, show which AI tools are catching on right now.

GitHub, the site where most of the world's programmers store and share their code, now holds something like 450 million individual projects, called repositories. A LinkedIn post making the rounds pulls out 20 of them worth watching in AI right now, ranked within categories by their star count, GitHub's version of a bookmark, which is a rough signal of how many people have found a project useful enough to flag. It is exactly the kind of practical tool scan worth five minutes: a fast way to see what other builders are actually reaching for before you try it yourself.

The AI coding agents category, tools that take several steps on their own to write and fix code rather than waiting for you to guide every step, is topped by OpenClaw at 278,000 stars, ahead of Opencode (118,000), Claude Code (75,000), Superpowers (73,000), and Codex (63,000).

A second group falls under agent engineering and tools, the pieces builders use to construct agents rather than full agents themselves: Firecrawl (89,000 stars), Context7 (48,000), Scrapling (25,000), Agent Browser (19,000), and Symphony (8,900). Alongside that sits AI infrastructure, the underlying plumbing: Open WebUI (126,000 stars), llama.cpp (97,000), Daytona (63,000), and Zeroclaw (24,000).

The learning and tutorials picks are, perhaps surprisingly, some of the most-starred entries on the whole list: Hermes Agent leads at 200,000 stars, ahead of Awesome LLM Apps (100,000), AI Agents for Beginners (53,000), Prompt Engineering (32,000), and Hello Agents (25,000). The one curated-list entry, System Prompts of AI Tools, sits at 129,000 stars, a collection of the actual instructions other AI products ship with.

Line up all twenty by star count instead of by category and a different picture appears: the five biggest overall are OpenClaw (278,000), Hermes Agent (200,000), System Prompts of AI Tools (129,000), Open WebUI (126,000), and Opencode (118,000), a mix spanning a coding agent, a learning reference, a prompt collection, and infrastructure, not just coding tools. That is the whole list: beyond the name, the category, and the star count, the post does not say more about any single entry.

What repos are you building with?

via LinkedIn →

05 Key News

How Cerebras splits a 3 trillion parameter model

This is worth the full read because it is a rare, concrete look at exactly why some AI answers come back in under a second while others make you wait: the physical chips underneath the model, and how the model's pieces are literally laid out across them.

Cerebras, the company that builds unusually large AI chips it calls wafers, published a detailed account of how it fits today's biggest AI models onto its hardware and runs them fast. Each Cerebras wafer holds 44 gigabytes of memory built directly onto the chip itself, which is very fast to read from. But a single trillion-parameter model (the parameters are the adjustable numbers inside the model that store everything it has learned) needs far more than that just to hold its weights, let alone run them.

Cerebras walks through the math using a real example: Kimi K3, a model whose files are public so anyone can run it themselves, with about 3 trillion parameters, split into 93 processing steps called layers. Of those, 69 are "linear-attention" layers and 24 are "dense" layers. On two standard tests, Kimi K3 scored 88.3 percent on Terminal-Bench 2.1 versus a rival model, GPT-5.6 Sol, at 88.8 percent, and it beat Sol outright on BrowseComp, a web-browsing test, 91.2 percent to 90.4 percent. For this example Cerebras compresses each weight down to 4 bits, about a quarter of the usual size, to save memory.

Once compressed, each of the 93 layers works out to roughly 16 gigabytes. A dense layer also needs room for a KV cache, a running scratchpad that stores the context of every active conversation, so Cerebras puts only one dense layer per wafer and gives the rest of that wafer's memory to the cache. A linear-attention layer does not need that scratchpad to grow with how much context it holds, so two of those fit on one wafer (2 times 16 is 32 of the 44 gigabytes available). Do the division: 69 linear layers need 35 wafers, 24 dense layers need 24 wafers, for a total of 59 wafers chained together to run one copy of this model.

Those 59 wafers are wired together like an assembly line. Wafer 1 runs the first two layers and hands its results to wafer 2, which runs the next two and passes them on, and so on until the last wafer produces the next token, a chunk of text roughly three quarters of a word. The link between wafers is a fast direct connection called RDMA over Ethernet. Crucially, because each layer fits entirely inside one wafer's 44 gigabytes, the model's weights never have to move once they are loaded. Only the in-progress results, not the weights, cross between wafers.

The obvious question is whether that assembly line sits idle while one request crawls through all 59 stages in order. Cerebras says no: it keeps the whole pipeline full of different requests at once, the way a real assembly line keeps several items moving through different stations at the same time. While wafer 3 works on your question, wafer 2 can be working on someone else's, and wafer 1 is starting a third. Each stage can also handle a small batch of requests together rather than one at a time. Smaller batches make any single answer come back faster, larger batches let the system handle more total traffic, and that tradeoff is a deliberate dial Cerebras turns per deployment.

That same memory limit sets a hard ceiling on how many conversations the system can run at once. Whatever space is left on a dense-layer wafer after fitting the layer itself, 44 gigabytes minus roughly 16, is all the KV cache available. In this example, where each request averages 256,000 tokens of context, that leftover space divided by the cache one request needs gives the maximum number of simultaneous requests that configuration can serve. The practical point for anyone building on or paying for these models: speed and capacity are not purely a model-design question. They are also a hardware-layout question, decided layer by layer, wafer by wafer, before a single answer ever comes back.

via Cerebras →

US creative jobs fell 200,000 in four years, no recession

Outside your usual reading: this is a deep look at US jobs data, but the pattern lines up with what you already watch AI do to other knowledge work, so it is worth sitting with.

The United States has lost more than 200,000 jobs in creative industries, film, TV, music, publishing, broadcasting, and design, over the last four years. That is one of the worst stretches for media employment in modern US history: the only comparable periods of job-loss intensity and duration were the 2001 and 2008 recessions. The difference this time is that there is no broad recession happening at all. This slide is happening on its own, at the same time AI tools have gotten good at writing, drawing, composing music, and generating video cheaply and at huge volume.

Proving exactly how much of this is AI, rather than something else, is hard, and the piece is upfront about that. Media companies are reluctant to say openly how much AI they use, partly to avoid public backlash and partly because admitting it can undercut their legal claim to copyright on the work. Other forces pull in the same direction: consolidation in Hollywood, production work moving overseas, and traditional media losing ground to individual creators on social platforms. But one pattern is hard to explain away: every digital-arts subsector is losing jobs at the same time.

Some of the losses are nothing new. Publishing, newspapers and magazines, has been shedding jobs for decades as the internet ate its business model, and this is more of that same decline. The worst-hit corner is movie and sound recording, the sector that includes Hollywood, which has lost jobs at a nearly unprecedented pace over the last four years. More than 100,000 jobs have disappeared from the movie and TV industry, almost a third of that sector's entire workforce. At its lowest point this past summer, total employment in that sector was lower than at any time since the 2008 recession, and close to a 30-year low.

Nearly half of all the media job losses over the last four years are concentrated in this one sector alone. Written publishing is the next-biggest source of job loss, down more than 70,000 over the same four years. The losses are not confined to any one corner of media. They have hit nearly every subsector at some point, running at roughly 50,000 jobs a year.

The piece draws a direct parallel to what happened to the music business the last time a new technology upended it. Around 2000, Metallica discovered demos of their unreleased songs circulating free on Napster, a new file-sharing site, and sued. Metallica won the lawsuit and Napster went bankrupt, but the industry lost the bigger war: file-sharing had already made it nearly impossible to stop free copying, and that broke the price of a single song. Eventually streaming services like Spotify and Apple Music bought up catalogues and sold access to nearly all recorded music for a small flat monthly fee, far less than a single album used to cost.

The piece stops short of saying creative industries will follow exactly the same path, but the shape of the argument is the same: a technology collapses the price of making the thing, and the people who used to get paid to make it have to find a new way to get paid at all.

via Apricitas →

Anthropic previews Claude Duet, a personal AI agent

Anthropic has begun previewing Claude Duet, its own personal AI agent, built to compete directly with OpenAI's Dots and Meta's Muse.

This lands right up against tools you already use, so it is worth a look once it opens up to try.

via CNBC →

GitLab patches a 2014-era bug anyone could exploit

GitLab fixed a serious flaw on October 21 that let an attacker run code on a self-managed GitLab server, one a company runs on its own hardware rather than GitLab's cloud version, without even logging in.

The bug has existed in every release since 2014, so anyone running GitLab on their own servers needs to patch it now.

via Swati Khandelwal / The Hacker News →

Anthropic sued over using Elvis Presley music

Anthropic is being sued over claims it used Elvis Presley's music to train its AI models without permission.

It is another entry in the growing legal fight over what AI companies can use for training, and this one points straight at a company you rely on.

via The Hollywood Reporter →

Fine print behind Anthropic and Musk AI giving

A piece digs into the fine print behind Anthropic's pledge to give away 5 percent of its profits and Musk's promised $10 billion in AI philanthropy, questioning how real those headline numbers actually are.

Big giving announcements from AI companies get headlines, but the details underneath often tell a different story, worth knowing before taking them at face value.

via Tech Insider →

Experts doubt Anthropic's own report on AI at work

Outside experts are questioning a report from Anthropic that claims to show how AI has changed work inside the company.

It is a reminder to stay skeptical when a company grades its own AI homework, especially one you rely on.

via The Guardian →

The Last Word

Everyone grades their own AI homework. Today, outsiders started checking the answers.

The Desk Report

How this edition came together — from bookmarks and feeds to the page.

222links gathered

40read by the desk

12made the edition

Where they came from

88 Saved links
48 X / Twitter
86 Open feeds

On the cutting-room floor — 28 links read but not run this week

Quality over volume: most links get a second look and a pass. The ones that made it earned their place.