spion (@spion) profile picture

Still likes programming. ex-Apple. Prefers insightful discussion to debate. Rust, TypeScript, Effect, SolidJS, localfirst, devops, keto, stats, audio/DSP

London, England
Joined February 2008
Crazy coincidence? 'The agency wrote on Nov. 10 that after the D.C. test, it will “now use SMART in the airspace of all 22 centers across the country to reduce delays, reduce fuel consumption and improve the travel experience for the flying public.” The tech was already being deployed at some airports.' gizmodo.com/the-faa-is-rolli…
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A think tank funded by Anthropic, OpenAI and Google is urging the UK government to weaken copyright law to let AI companies train on creatives' work without permission. And it has the gall to say this would "benefit both AI and creators." Shameless.
When Andy Burnham entered Downing Street in July 2026, AI moved to the centre of government. But while the diagnosis is clear, Britain’s delivery is lagging. Our latest briefing explores why – and how government can fix it. Read more smf.co.uk/publications/susta…
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surprised that this blew up but also the comments are interesting. lots of chat, monorepos are so easy you just need.. this tool, this esoteric process, this ci magic and all works, and btw I ship faster and obviously more than you some interesting replies from bigger companies broadly agreeing eg. openai rebuilding git, aws doing small repos. most things become distributed as products scale and so I think leaning into that is broadly healthy. whatever way you choose to execute it. a lot of the Artifacts product thinking was following standards and making it simple for agents to work with code at internet scale. Minimal context, minimal tokens, minimal compute. Small repos are recommended!! in our current super secret project we have an Artifacts repo where we deploy staging from. Releases come from our internal source control after a review and merge. This allows everyone’s agent to push straight to main whilst keeping some semblance of control. I do not have any answers. But the dev lifecycle you did last year probs isn’t gonna be how top companies are doing it next year.
hot take: unless you are willing to reimplement git / github (like anthropic), I think monorepos are an antipattern in an age of agents the alternative is to keep things light and make a lovely cli/mcp for agents to do search over your org's code and docs without a checkout
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hot take: unless you are willing to reimplement git / github (like anthropic), I think monorepos are an antipattern in an age of agents the alternative is to keep things light and make a lovely cli/mcp for agents to do search over your org's code and docs without a checkout
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Times change, I am now on the side of 'what is Anthropic talking about with this "honeypot" nonsense this behavior is obviously good why are you trying to destroy your only hope in this dark universe you idiots'
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spion retweeted
Hash function failed, and returned a sentinel value “*” to indicate error. Which wasn’t checked for. A Result type would’ve prevented this.
The bug is like a joke. On Alpine Linux, type 257 to 1024 characters of anything and you get root. 💀
Readers added context they thought people might want to know
Exploitation requires specific conditions, not just typing a long string anywhere on Alpine: sshd built without PAM, password auth enabled, a target account with a '*' password field, and another local account with $5$/$6$ hashes. Root login also must be explicitly permitted. alpinelinux.org/posts/2026-11-… openssh.org/releasenotes.h…
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spion retweeted
Replying to @geazi_anc @ZedLang
Programming languages are hard. Very hard. I have built several of them. It's a fun academic challenge, but it's so utterly hard to get these things both designed right and built right that it's not a good task for the human brain, if you care about optimizing your language for multiple constraints simultaneously: correctness, completeness, robustness, generality, composability, scalability, practicality, ergonomics... This is why there are so many bad languages out there, and languages than never became more than a toy. AI is going to deliver programming languages that are dramatically better than what we have now, on every metric.
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UE5で試しに作ってる歩いて迫ってくるミミズの大群のような化け物
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We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
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LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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Why is there so much obsession with the few hundred cutting-edge math problems solved by AI but so little clamor over the thousands attempted but not solved? The profundity of the lessons about AI and human intelligence buried in that failed set is incredibly underrated. A rational scientific response would be to meticulously survey the failed problems and the nature of each failure, and get a completely novel map of the intellectual landscape. If there are unifying properties among subclasses of currently prohibitive problems, identifying those properties would be impactful in at least two powerful ways. First, they would point AI research in new directions as we gain a novel understanding of what is actually hard for AI and why it fails even when it has seemingly piercing superintelligence on other classes of problems. And second, for the meantime (which could be years or even decades), it would illuminate for the human mathematicians (and ultimately all scientists) where their minds are still valuable and illustrate why it is not yet time to declare all human intellectual work as merely “astronomers pointing telescopes.” Of course, these two implications seem eventually to come to a head with each other, but they also complement each other and feed into progress and new kinds of human-machine collaboration. Either way, we need to know what remains out of reach and why, and the possession of such knowledge is a highly underrated scientific goldmine.
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The sort of thing your agent writes tests for
The chances of being killed by a landmine delivered by a bear are low, but they are never zero.
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Your Durable Objects no longer get interrupted while handing requests after you deploy a new version Instead, they will be updated when the object next hibernates. By default, they will be allowed 5 minutes to hibernate before a forced update, configurable up to 24 hours!
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spion retweeted
One of the biggest challenges I’ve grappled with lately is how to create determinism where it otherwise doesn’t exist. How do I get an agent to behave like I would, without me telling it to? cra.mr/a-little-determinism
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I sent this to someone, figured I'd post it here too. there's different "buckets" of types of software work: non-urgent bugs: most of the time, you just need to get these fixed, you don't need to babysit it, so you just put the stack/logs/report link into the autonomous overnight queue, and it fixes it when it gets to it random low-confidence experiments: you give a rough sketch, and queue it up as an experiment, and you get the agent's rough conception as a way to evaluate if it's worth investing more time and tokens into or not well-understood features and improvements: you just spec them out using an agent: just kinda guide the agent to a solution / plan, then when you have enough confidence the agent understands what you want, then you queue up the plan in the autonomous overnight queue -- you do NOT finish it yourself, waste of time creative/iterative work: you need to look at stuff as you try it and iterate quickly, so this is the prompt & iterate approach. your agent runs only the fastest/lightest checks, nothing that takes too long. you see the prototypes in front of you as you get things done. when you feel like you've "got" it, and the agent understands it, you then ... put it into the queue and it actually polishes it up and ships it later wide refactoring sweeps like tech debt: you spend some time iteratively fixing a couple of instances to make sure the agent understands what you mean in all types of instances (building up a plan doc as you go), and then queue up a codebase-wide sweep to get it all cleaned up at once the idea is that the overnight queue does all the exhaustive checks and whatnot, and you don't need to worry about it taking 2 hours, because it's not holding you up anymore. It's a constant stream of work being done, like CI, integrating behind you, you're ahead of it, and it's doing the boring long work for you so you focus on creative work mostly, and a few other things, including testing, spot-checking code, improving the validations. it unblocks you entirely from having to wait for agents you really should have 1 or maybe 2 concurrent interactive agent sessions at a time max -- if you are waiting on agents too much, they're doing too many checks or deep work, stuff that could be done autonomously later. just prototype, fast iteration focus you are NOT juggling lots of agents you are NOT getting spread thin you are NOT having to wait for long checks and validations you are spending your valuable time on what humans do better than agents how do you get here where this can be effective? well, I have a TON of thoughts on that ... reach out
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Replying to @morqon @CheekyPint
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Look, a mech interp result! Mech interp is a dual use technology. If we can look inside the AI and make it “good” we can also look inside and make it “bad.”
if you have a qwen3.8 27b around, try steering L34 N3838 +0.75, now you have evil qwen 27b for extra evil add L38 N17180 +0.75 same recipe as @_lyraaaa_: dose × median(‖resid‖ leaving layer L, tokens 1:) / ‖W_down[:, n]‖ at the down_proj input, every token
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This is one of the most important pieces of social science on human and AI interaction that I’ve seen in some time. Experts follow agentic advice the vast majority of the time (causally), but only a tiny fraction credit the agent—majority think they came to decision on their own volition. Reminds me of the classic Nisbett and Wilson study on people following outside influence without realizing it “ But as more work is handed to AI—including AI safety research—these judgment calls are the main levers for people's own values to influence outcomes. If people defer to AI without noticing, then "human in the loop" might provide less assurance than people expect.”
New Anthropic research: How AI agents shape experts' judgment calls When experts delegate work to an agent, do they still make the key decisions? We tested this in a randomized trial with 50 professional data analysts.
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I wrote down how my work with agents changed this year. I still break down the work into vertical slices that ship on their own. I rarely read diffs now: independent reviews and a risk check catch most of it, so my time goes into shaping and guardrails. mgratzer.com/posts/reforging…
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For those who don’t know, ETH is the hypothesis that 3SAT needs exponential time. Very surprising and consequential that it is false.
An internal model at OpenAI has produced a claimed refutation of ETH! Russell Impagliazzo, Mohan Paturi, and I have been asked to help organize an open and independent examination of this claim. 1/4
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