https://xitter.cf/t.co/1gBr9ZEOcH

Auckland, New Zealand
Joined August 2008
Introducing my new side project - Fai. github.com/Krzysztof-Cieslak… It’s a functional programming language with which I try to explore how we can design better “classical” software engineering tools specifically for AI agents. A couple of design choices I've made are described ⬇️
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Josh Wulf retweeted
anthropic’s lead engineer btw.
Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…
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What if Spotlight actually understood the meaning inside your files? DigUp is an open-source macOS menu bar app powered by EmbeddingGemma 2 that brings 100% local, multimodal semantic search to your Mac. Instead of searching for file names, you can use natural language to: 📸 Find specific scenes in your photos ⏳ Jump to the exact timestamp in audio or video 💻 Search through your codebase by intent Everything runs locally in a single vector space.
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Omarchy is getting a per-workspace or global option to run as a traditional desktop with a dock. Great set of training wheels for anyone new to tiling window managers who need a safe space until it clicks! Comes with Alt+Tab, minimization, left/right/top/bottom positioning.
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Agentic Engineering is more like gardening than factory work.
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Agentic Engineering is more like gardening than factory work:
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It’s like a four-volume Geoffrey Huntley’s Greatest Hits Mega-mix.
🗞️ nobody should type code by hand anymore. here's what's left. ghuntley.com/agency/
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Part 4 of my conversation with @GeoffreyHuntley: "Open Source Is Now a Security Vulnerability" When everyone runs the same stack, one zero-day hits everyone. AI makes bespoke software cheap. youtube.com/watch?v=CXNqVQTz…
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"Skill packs are horoscopes for software engineers. Just because it's there doesn't mean it works." Part 3 with @GeoffreyHuntley: You're Making Your AI Dumber (Here's How to Stop) youtube.com/watch?v=8FwApXjk…
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Part 2 of my conversation with @GeoffreyHuntley: "Nobody Can Review This Much Code. Now What?" Geoff rebuilt three of his former employers' products in under 10 days, for a conference talk. So who checks all that code? Not PR reviews. youtube.com/watch?v=I8F5wagY…
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Had a great conversation today with @GeoffreyHuntley, the creator of loop engineering and LLM industry visionary. Released in four parts - Part 1: "Is Coding Over? Geoffrey Huntley on the Future of Developers." youtube.com/watch?v=ziXOV0b1…
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Josh Wulf retweeted
OH: “we are control systems engineers now. inline any signal that helps with the control signal”
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Deno is Done. RIP. @rough__sea and crew have joined Cloudflare to build Durable Objects as a substrate for distributed workflows. New primitives unlock new abstractions. Let's go! youtu.be/gyAQ7-Buha0
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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.
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"your edge right now is understanding the possibilities and being curious" Using AI for software engineering is now the default. The engineers I've seen who have the edge are ambitious and have been grinding with agents for years on the job at this point. It's a compounding effect. For teams still unsure or catching up, the smartest thing I can advise is to be even more ambitious and aggressively share what you learn with your peers. If you do all this work privately, only you benefit. If your team only gets to experiment with AI after hours, you've quietly told them using it doesn't count. Senior engineers should be the first to make this shift. Their decades of taste in system design and failure modes is the secret sauce that makes agent output good. They should direct their experience to building with agents better safety. Then show whats working to others to replicate.
🗞️ if your team is too busy doing their 'normal job' to experiment with AI, you're preparing them to be replaced ghuntley.com/replaced/ In this post, I guess this's the last time I'll say it. AI use is no longer optional if you wish to be employed. What follows is advice for current employees and hiring managers on how to hire in 2026 to source AI-first candidates.
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Rethinking PR Reviews — a thread. PR review pre-LLM was about code quality, but — crucially — it also functioned to educate new developers about the code base, conventions, and engineering culture. With LLMs writing the code, both aspects change....
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Defects detected either in human review or via the regression net need to feed back to the terrain of implementation by asking the question: "How do we make this defect class easy to get right and impossible for an agent to get wrong?"
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LLMs don't "learn", but the *system* that they operate in can be continuously improving.
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