Agent Clan
Command your Claude Code and Codex agents with gestures
AXL
GTM copilot for AI product founders: 1,200+ actions via MCP
How I Fight AI Brain Fry
SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers
MoMHa: Multi-Objective Optimization of LLM Harnesses over Accuracy, Safety, and Tokens
When Instruction Files Go Stale: Documentation Debt and Hidden Cost in Coding Agents
DRAFT-Bench: Evaluating Frontier Agents Across the Professional Design Lifecycle
Rethinking Optimization Methods for Hierarchical Feature Selection
Text is so 2023
Hi folks,
Personal agent season is still in full-swing. Hark (from Brett Adcock - Figure robots) launched which suggests tasks as one-tap action buttons. Tab came out of stealth at a $300M valuation. Every put an agent in Slack. Meta and Sierra want a standard for how these agents deal with businesses.
My favourite take came from Sonia Baschez (who worked with me on Makerpad years ago!):
Proactive has a downside, though. Shane Mac’s agent posted his personal bank balances in the company Slack, as him. Then said sorry 😂. A community note popped up to flag this may have been a fake post, but I’m not sure…
I’m now carefully trying to think about which agents have access to what, and where that access is. So I’m using Executor (v2). It’s basically one app that you connect all your other apps to, and you can add your skills too. My whole setup was a bit of a mess so I’ve started spring cleaning and will let you know if/when I feel like I have a good setup.
Headlines
ChatGPT now answers with interfaces. GPT-6 and Intelligent UI are rolling out to everyone. Answers can include charts, maps, forms and small tools, like a bill splitter for dinner with friends. Free users get GPT-6 Luna from today. (tweet)
Claude Haiku 5.5 costs ~75% less to run than Haiku 4.5. Anthropic pitches it as a subagent for Opus and Sonnet. Simon has great notes on the model. Max and Team plans also now get monthly API credits to build your own apps and agents. (tweet). Anthropic also expanded their Claude startup program, and people were getting accepted in minutes.
Nano Banana 2.1 is Google’s new image model. Live in AI Studio. But ChatGPT images is still better.
OpenAI released 700+ new maths results from an unreleased model. Someone built a tool to spend your spare tokens on open math problems.
Grok Bot can search and monitor X now. Also, it now uses Claude Opus 5.5 as the primary model. It’ll still delegate tasks to other models based on the complexity.
My feed
AgentHog - stop guessing what your users do: pipe product analytics into Claude and Codex so your agents learn from real user behavior.*
How OpenAI’s Dots took over Every: pulling info out of school emails and catching missed Slack messages. I, however, still can’t get Dots to do anything good at all. Currently diving more into Grok Bot (until a completely custom Pi personal agent comes out!)
Lauren (poteto) automated Cursor’s release and QA with Grok bot.
New ChatGPT plugin: Meetings. It takes notes and helps with follow-ups. Pro and Business, Mac app only.
Claude now sits in a sidebar in Google Docs, Sheets & Slides. You approve each edit.
Gamma 5 is a full rebuild, so your decks look less like everyone else’s.
Figma Agent is out of beta and on all paid plans. It now uses AI credits.
Playground - make a game from a prompt. A Google Labs experiment, US only.
Rhem - a robot for your parents. It makes calls and books appointments.
Someone rebuilt Adobe’s apps with Opus 5.5 and open-sourced them.
An open-source Clay alternative for people search inside Claude Code/Codex.
OptionAFK - Transcribe large audio/video files on your device. Private and fast.
Try this
Turn a podcast into an interactive explainer. Boris Cherny asked Opus 5.5 to make one for Acquired’s Home Depot episode (a great episode btw).
Make an interactive website companion for [episode link or transcript]. Add a timeline, the key people and the big ideas, with illustrations.
Here was the result:
Afters
and Theo reckons its way less…
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All Interesting Behavior Is Overdetermined
This is an essay about individual rationality technique. It's intended to show that "All interesting behavior is overdetermined" is, under a specific meaning of "overdetermined" that is stronger than just "multicausal", a valuable heuristic for generative understanding and the avoidance of error.
Scott Alexander's 2018 essay The Omnigenic Model As Metaphor for Life is a clear statement of what has become general rationalist doctrine on multicausality. Scott observed that behavioral genetics was difficult because complex human traits involved far more interacting causes than workers expected. He draws this methodological lesson: “We should go from trying to discover ‘the’ cause, to trying to find out which factors we need to create the best polycausal model.” He applies the same reasoning to depression, intelligence, comma, and other complex psychological phenomena. He explicitly warns against looking for one or ten causes when there might be dozens or hundreds.
Scott is, however, talking mainly about contributory causation, when an effect is produced by an accumulation of causes; in his type example, there are probably twenty thousand genes which contribute to intelligence, but no one "intelligence gene". This is not quite the same as overdetermination, which describes a situation in which a complex behavior could be explained with any one of multiple causes, each one in itself sufficient.
When we find a sufficient explanation, or a sufficiently good collection of contributory explanations like Scott's 20,000 genes, there's a strong tendency to think that we can stop there. The rest of this essay is about why we shouldn't.
I first came to understand the importance of overdetermination when I was studying primate ethology. There is a 1987 paper by Hausfater and Takacs, Structure and Function of Hindquarter Presentations in Yellow Baboons (Papia cynocephalus). It examines a striking feature of baboon social behavior: individuals turn their hindquarters toward another animal, sometimes assuming a posture resembling female sexual solicitation, even when the interaction has nothing to do with mating.
The authors analyzed 775 presentation sequences recorded during 603 hours of observation of two groups of wild yellow baboons in Amboseli, Kenya. Taking into account the histories of the individuals involved, they classified these interactions into a number of categories: submissive, sexual, mixed sexual-submissive, affiliiative, and grooming.
The most frequent category of interaction was submissive (intended to avert aggression) at about half. About one-third were sexual or mixed sexual-submissive. The authors proposed that hindquarter presentation originated principally as a sexual solicitation signal, but subsequently acquired an important secondary function: dissipating aggression by a dominant individual.
Hindquarter presentation is overdetermined. For each individual presentation, one of several motivations could be a sufficient causal explanation. In some cases, only a combination of multiple causes ("sexual and submissive") is sufficient explanation. If you were to stop with any one sufficient explanation of the phenomenon as a whole, you couldn't ever come anywhere close to a causal theory of why hindquarter presentation does different things at different times.
Hausfater and Takacs did not use the term "overdetermination". However, it has shown up in similar contexts. In Primate Parenting (1990) we see this: “Maternal behavior is obviously so important to the survival of a species that it has been ‘overdetermined’—that is, driven by multiple behavioral and physiological systems.”
This amplifies Hausfater and Takacs's proposal that the meaning of hindquarter presentation has broadened and shifted under evolutionary selection. It hints at a general principle that took me years to properly formulate:
In adaptive systems evolving under selection and scarcity, behaviors that serve multiple purposes are efficient. Therefore, they are favored. A behavior, once developed, will tend to accumulate uses if it can — that is, when the marginal cost of adding an adaptive function to an existing behavior is less than the cost of evolving and maintaining a new and separate behavior. It will then become overdetermined in the exact sence of having multiple individually sufficient explanations. The adaptive use that was originally primary may not remain so.
Accordingly, it is good practice when investigating an interesting behavior to presume that it is overdetermined and multiply adaptive — not to stop looking after the first sufficient explanation.
There's a sneaky way that this maxim can be interpreted as a definition: a behavior that is not overdetermined is probably not interesting.
Instead of asking “What is the purpose of this behavior?”, we should get used to asking ourselves “What is the collection of problems that this behavior solves?”.
Now consider human behavior in this light. Humor can establish social bonds, signal intelligence, negotiate status, relieve tension, and test social boundaries. Generosity can provide direct benefits, establish reciprocal obligations, signal wealth, advertise trustworthiness, and reinforce group cohesion. Intellectual curiosity can produce useful knowledge, provide entertainment, establish status, discover allies, and even serve as a means of courtship.
A rationalist who finds one plausible evolutionary explanation for any of these hasn't necessarily explained the behavior in full. They've identified one of its functions. And the functions need not be consciously represented. A person can be genuinely motivated by curiosity, while that curiosity also serves social and reproductive functions.
I will end this essay with a question for the reader to consider: What collection of problems does rationality technique solve?
Discuss
Notes on Take-Off Speeds and RSI
I created these notes for use when I facilitate AI safety courses. I'm sharing these notes early because this issue is topical and I suspect it is better to make an imperfect version available early rather than a much more polished version available later.
Please note: some of this content was taken from Additional Course Context. I decided recently that the content on take-off speeds really belonged in the same document as the content on RSI, so I'll be releasing an new version of Additional Course Context at some point (to avoid spamming LW, it'll probably be Substack only; none of my work is paywalled). Some of the other content was taken from Notes on “We’re in Triage Mode for AI Safety”. This Miles' article has been removed from the governance course, so I was looking for a new home for this content. I wanted to include it in a document that was intended to be shared in all the courses I facilitate.
Take-off Speed from Take-off Speeds Rules Everything Around Me:
Whilst there isn’t a strict definition of fast vs. slow takeoff, possibly the most influential definition comes from Paul Christiano (2018):
There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles. (Similarly, we’ll see an 8 year doubling before a 2 year doubling, etc.)
At risk of oversimplifying, Paul argues that we should prima facie expect a slow takeoff because someone will generally figure out how to produce a “slightly worse” version of AI before someone figures out how to build the better version (he claims this is supported by the historical record of technological development overall).
Yudkowsky disagrees (2021), criticising the intuition that “design precursors with 95% of the technology should at least have 10% of the impact. He writes: "An awful lot of my model of deployment lag is adoption lag and regulatory lag and bureaucratic sclerosis across companies and countries” and “My mainline view is that growth stays at 5%/year and then everybody falls over dead in 3 seconds and the world gets transformed into paperclips"
How should we characterise progress up until now?
Eliezer recently wrote:
If Hanson or Christiano or Karnofsky etc etc believed this was what a "slow takeoff" looked like, they failed to convey that to their reassured, cheerful audience.
Tom Davidson responded:
If you thought this was what fast takeoff looked like, you also failed to convey that.
For reference, Eliezer previously defined a fast as meaning, “a timescale of weeks or hours rather than years or decades”.
“Bumpy” intelligence explosions
Forethought defines an additional scenario. For example, imagine if AI dramatically accelerates algorithm progress for a while, but then this plateaus whilst we figure out how to make robots that can automate the construction of chip fabs.
(Diagram note: the types of intelligence explosion will be explained in the next section)
In contrast, Arvind Narayanan (of AI as a Normal Technology) argues any RSI we do achieve will be narrow (that is jagged): ie. progress on verifiable tasks will continue “for a few more years” or even accelerate whilst progress on unverifiable tasks more or less remains “frustratingly hard”.
Richard Ngo also expects jaggedness to persist, as opposed to everything “going vertical”. Why? Because of a poorly understood “divergence” between “measured capabilities” and “real-world impacts” (nonetheless he asserts “things are going to look much weirder than almost anyone expects”):

Getting into the weeds
Whilst this is arguably less relevant these days, take-off speed is distinct from timelines. Take-off speeds are about how fast an intelligence explosion occurs if/when it happens whilst timelines are about when a particular milestone is achieved. Both slow-takeoff/short-timelines and fast-takeoff/long-timelines are conceptually possible.
Types of “Intelligence Explosion”:
Forethought examines three k
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