Tisson Mathew retweeted
Anthropic is now recommending that companies REMOVE unnecessary subagents from their AI systems.
This is a pretty significant shift in agent architecture, with their team finding that a single agent equipped with skills can frequently outperform complex multi-agent systems, and one customer seeing roughly 2x faster execution.
The emerging rules of thumb:
→ Skills: Teach the agent HOW to do something, including procedures, expertise, and instructions.
→ Tools / MCP: Give the agent the ability to DO things through APIs, actions, and live data.
→ Subagents: Use for parallel work, isolated context, or tasks a single agent struggles with.
The interesting part is progressive disclosure. Instead of spawning specialized agents or stuffing everything into a giant prompt, one agent discovers and loads skills and tools as needed.
Their advice is to fold existing subagents into the main loop, turn their instructions into skills, expose their actions as tools, and run evals to see if performance holds up.
As models have gotten smarter, improving agent architecture is becoming less about what you can add and more about what you can delete.
claude.com/resources/article…
Tisson Mathew retweeted
New on our Frontier Red Team blog: using Claude to formally verify software at large scale.
Formal verification can prove that code is free of certain bugs, but historically it has only been feasible for small codebases (or required a lot of expert time).
With AI models, however, this is beginning to change. In collaboration with @theoremlabs, we used Claude to prove security properties in over one hundred real-world open-source libraries over the span of a few weeks.
Read our research in full here: anthropic.com/research/forma…
pretty much every major ai company now has a personal agent product except google:
meta → muse
anthropic → duet
openai → dots
spacex → bots
slowly but surely the competition is shifting from who has the smartest model to who owns the persistent relationship with the user. this will be even more the case going forward.
you're seeing this because we likely reached peak intelligence required to power most of the worlds workflows probably earlier this year if not late last year.
We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent.
It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
every major company is arriving at roughly the same offering:
persistent memory, email/calendar/messages, browser + computer use, background tasks, proactive notifications, voice, app/tool execution, ambient context, & some notion of a personal agent sitting above everything.
remarkable levels of convergence with very little differentiation whatsoever.
Tisson Mathew retweeted
Every child has dreams. Investing in their future is investing in America’s future 🇺🇸
Tisson Mathew retweeted
I could not imagine being productive without Claude Code Review. It runs on all of my PRs, and catches almost every bug before it goes it. This is a piece of the secret sauce that lets the team and I move so quickly while raising the quality bar
Tisson Mathew retweeted
We are migrating our backend from python to go.
This would be completely impossible 3 years ago. Now it's taking 1 person only 1 week.
Tisson Mathew retweeted
I love seeing strong engineers join Databricks, get access to all the AI dev tools, ask AI everything on day 1, and land impactful PRs on day 2.
Before AI: 2 weeks with an onboarding buddy, reading design docs and the codebase, just to land a tiny patch on week 3.
Tisson Mathew retweeted
New on our Engineering blog: Creating in-house source control.
In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.
Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…
Tisson Mathew retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
Tisson Mathew retweeted
2027 will be the year of the personal agent. It has finally found PMF with Instinct, Muse & Grokbot.
This will cause a tsunami of customer enquiries — it’s now essentially free for a customer to send hundreds of emails, texts and phonecalls to make a single purchase.
Businesses will need AI customer service tools to save themselves from drowning.
I started my career at Intel research labs doing devices and firmware, with a model like Opus 5.5 and claude code + other harness and simulations we could build, I could do my job 1000X faster and cheaper .. so do you think this is going to end with "software", explain software? code? you mean hardware and devices is not code?
Hardware and devices will have the same effect of coding agents as it moves into more designs and simulation, don't think anymore got a "moat" with hardware or devices.
The impact of automating coding and knowledge work is only just beginning.
The sequence is clear: automation of cognitive labor leads to automation of knowledge work, which leads to automation of physical labor through devices and robotics.
Every enterprise software company now has the opportunity to move into hardware and robotics, at a fraction of the cost and at scale.
A year ago we saw the first signs with Opus 4.5. Today, with Opus 5.5 and always-on agents like Muse, it’s happening at scale, with personal AI doing real work every day.
Superintelligence will be polarizing, and it will define competitive advantage.
Picture two construction companies, one with AI and one without. I don’t need to explain how that ends.
The work now is diffusing AI into your enterprise safely and responsibly.
Start today!
Tisson Mathew retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Tisson Mathew retweeted
Replying to @ThePrimeagen
There's no moat in lines of code anymore. All services, features, etc (UX, security, compliance, support) around code will become more valuable.
As AI writes most of the code, frontier labs are building super apps nobody asked for. 95% of the features go unused, and the 5% people need is buried in clutter. Vertical AI products connected to enterprise workloads will meet the customer's need: the 5%, no clutter.
Tisson Mathew retweeted
Talked to a startup’s founders.
In 6 months, 1 engineer built their web, desktop, iOS, and Android apps with AI. Just 1 engineer.
And they’re not shitty prototypes. They’re polished, well-designed, and reliable apps.
AI is turning 10x engineers into 100x engineers.
This wasn’t possible last year.


















