Quotations
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Is there something I can actually help you with today?
— Kimi K3, after refusing to leak its system prompt
On file deletions. We’ve investigated a handful of reports where GPT-5.6 unexpectedly deleted files.
What we have found is that this most commonly occurs when:
- Full access mode is enabled and codex is run without sandboxing protections, including without auto review being enabled
- The model attempts to override the $HOME env var to define a temporary directory.
- The model makes an honest mistake and mistakenly deletes $HOME instead.
— Thibault Sottiaux, describing a pretty gnarly Codex bug
I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down as the top-level maintainer.
Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it.
Or just walk away.
AI is a tool, just like other tools we use. And it's clearly a useful one.
It may not have been that "clearly" even just a year ago, but it's no longer in question today.
There are other questions around AI (like what the economy of it will actually look like in the end), but "is it useful" is no longer one of those questions. Anybody who doubts that clearly hasn't actually used it.
— Linus Torvalds, Linux Media Mailing List
Dependabot now waits until a new release has been available on its registry for at least three days before opening a version update pull request. This cooldown is now the default and requires no configuration.
— GitHub Changelog, embracing dependency cooldowns
The shared language of a software project is not English or Python but it is the common understanding of what its concepts mean, where the boundaries are, which invariants matter, who owns what, and why the system has the shape it does. This language is rarely written down in one place. It lives partly in documentation and code, but also in code review, conversations, arguments, and the experience of having to explain a change to somebody else.
Before agents, some of this shared understanding was maintained by friction. If I wanted to change your storage layer, I usually had to read your code, ask you questions, and perhaps coordinate with another team whose service depended on it. This was slow, and much of that slowness was waste but not all of it was. Some of it was the process by which your understanding became mine, and by which both of us discovered whether we still agreed about how the system worked. This friction synchronizes people.
— Armin Ronacher, The Tower Keeps Rising
The reality is to make augmented reality glasses, you need to put a camera next to your eyes that is continuously recording everything you see and processing that to put information over it.
There is not another way around it. And there's certainly not a chip that can fit in the stem of a glasses that is both powerful enough and power miserly enough to do that in real time.
You have to send that data to a cloud. You gotta do it. [...] Or you can build something the size of a Vision Pro with a battery pack that lives somewhere else. Those are the current choices in this world.
And it means if you want to build the product that everyone thinks is the next thing, you are going to have to invade people's privacy.
And maybe you shouldn't. Like, there's an incredible argument for, nope, you shouldn't do that. Nope, the trade-offs required to make this product are so high at a societal level that we should stop it.
— Nilay Patel, The Vergecast
[...] Work on web and mobile runs in the cloud. Work in the desktop app can also use local files and desktop apps with your permission. At launch, cloud Work conversations do not appear in desktop Work; desktop Work threads and local files remain on that computer.
— OpenAI, trying (unsuccessfully) to clarify ChatGPT Work
I just declared a moratorium against AI-written change descriptions (e.g. PR and commit messages, also issues/tickets) from my team.
AI was writing change descriptions that were worse than useless to me as I tried to review PRs: outlining details of the code that could easily be seen by looking at the code, but omitting the higher-level framing needed to understand broadly what the code is doing.
I just launched my third course, Whimsical Animations, and so far, it’s on track to sell roughly ⅓ as many copies as a typical course launch.
It’s a similar story with my two existing courses. Sales are down significantly from last year.
There are likely a lot of reasons for this, but I think the biggest is AI. There’s sort of a double whammy with AI:
- Many people are wondering whether developer jobs will even exist in a few months, so they’re reluctant to spend time/money learning new dev skills.
- Even if they do want to learn new dev skills, LLMs can provide personalized tutoring, so there’s less incentive to buy a paid course.
[...] I’ve spoken to a few course creators now, and we’re all seeing the same trend. Revenue down 50%+. Fewer people engaging with our content. People switching to LLMs, which slurp up all of our work and regurgitate it, without consent or compensation.
— Josh W. Comeau, via Salma Alam-Naylor
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5.
We'll begin restoring access tomorrow, and will share an update soon.
— Anthropic, on Twitter
HumanAgent in the loopI dislike the phrase “human in the loop” because it cedes authority to the machines. Let’s flip the narrative. It’s our loop, we work the same way we always have, now we recruit agents to join the team. An agent-assisted process need not be a black box that takes in prompts and emits features. [...]
Let’s do agentic software development like that. Not as a loop we’ve been excluded from, instead as one we invite agents into.
— Jon Udell, “Doctor, it hurts when agents create unreviewable PRs.” “Don’t do that.”
This is a bad state of affairs. Consider, in particular, some industry dynamics:
- Frontier models are trained at an enormous cost, and a significant fraction of that cost is recouped in the few post-release months that they are broadly available. After that period elapses, the models become sub-frontier, competition emerges, and margins compress. Every week of delay is eating into the narrow window that labs have to make their accounting work.
- The ongoing AI infrastructure buildout—the one that is, according to former US AI Czar David Sacks, essential to the US economy, assumes a functionally global total addressable market for US AI services. No one is building $100 billion dollar data centers to serve frontier models to whatever 100 companies the US government will allow access. [...]
— Dean W. Ball, 35 thoughts on what has happened and what America should do
This is like saying there's no learning curve to being a manager because your employees will just do whatever you tell them to do.
— Timothy B. Lee, on the idea that LLMs take no skill and have no learning curve
We're beginning a limited preview of the GPT‑5.6 series: Sol, our flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable model. Terra has competitive performance to GPT‑5.5 while being 2x cheaper and Luna brings strong capability at our lowest cost. [...]
We believe in broad access, and we plan to make GPT‑5.6 Sol, Terra, and Luna generally available in the coming weeks. As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch. At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly. [...]
GPT‑5.6 is priced per 1M tokens across three model sizes: Sol is $5 input / $30 output; Terra is $2.50 input / $15 output; and Luna is $1 input / $6 output. GPT‑5.6 also introduces more predictable prompt caching, including support for explicit cache breakpoints and a 30-minute minimum cache life. For GPT‑5.6 and later models, cache writes are billed at 1.25x the model’s uncached input rate, while cache reads continue to receive the 90% cached-input discount.
— OpenAI, Previewing GPT‑5.6 Sol: a next-generation model
In the last few months, I've started to see [job applications] that were clearly cowritten by an LLM, link to an LLM-generated portfolio site, which then links to LLM-generated GitHub projects, with purely LLM-generated commit messages. [...]
My other reaction is that I don't know anything about these people.
They haven't put themselves out there. They haven't said anything true. [...]
The perfected, generated, prompted resume is generic and impersonal. It tells me nothing about this person, other than that they use particular tools.
— Tom MacWright, Accidental anonymity
The real valuable capability MCP offers over skills/CLI is isolating the auth flow outside of the agent’s context window, and potentially out of the harness completely. [...]
Maybe the idealized form of MCP is just an auth gateway for the API and nothing else. That’d still be a win.
— Sean Lynch, comment on Hacker News
What happened in 2025 was this: the economics of code production were turned upside down. Instead of being very hard, time-consuming, and expensive to generate code, it became effectively free and instant. Lines of code went from being treasured, reused, cared for and carefully curated, to being disposable and regenerable, practically overnight.
— Charity Majors, AI demands more engineering discipline. Not less
I can 100% attest to the fact that Qwen3.6-27B is a very capable local model for coding tasks. Over the last month and a half I've been using it almost daily, either on my M2 Ultra or on my RTX 5090 box. I use it for small mundane tasks at ggml-org - nothing really impressive, but definitely a helpful tool for a maintainer. I think I would be using it much more, if I didn't have to spend a lot of my time on reviewing PRs. Currently, I have a very lightweight harness - the pi agent with everything stripped (
pi -nc --offline) and a short system prompt to align it a bit with my style.
— Georgi Gerganov, Hacker News comment on Running local models is good now by Boykis
Katie Moussouris, a cybersecurity expert and the CEO of Luta Security, told me that Anthropic shared with her a copy of the White House’s report on the Fable jailbreak to get her appraisal. (She said that she is not being paid by Anthropic.) The report, Moussouris said, involved IT experts asking Fable to help find and patch bugs. When given deliberately insecure code, she said, Fable refused the prompt “review the code for security issues” but then complied when asked to “fix this code,” followed by some further manual steps. Moussouris told me that this was just “the model working as intended” for cyberdefense.
— Matteo Wong, The Atlantic, The White House Is Ratcheting Up Its War Against Anthropic
[...] Instead, I picture a specific person and I just write for them. Often this person is "me, but 3 years ago" or a good friend.
— Julia Evans, write for 1 person
Jenny owns a crematorium. John’s propane company gives her a $20 billion investment in return for 5 percent of her operation. Jenny throws $10 billion into the incinerator, then pays John $10 billion to buy propane to burn that money to ashes. John reports that his AI investments have generated $10 billion in revenue this quarter and that he owns 5 percent of a $100 billion business. A reporter from Forbes is assigned to profile John and Jenny, and over the course of his research, he becomes embroiled in a passionate but confusing three-way love affair with them, which eventually turns into a polyamorous common-law marriage. His profile is glowing, but light on financial details.
— Andrew Singleton, AI Economics for Dummies
Easy solution to slow down recursive AI self improvement:
- The lab with the top-ranked model must agree THEY must not use it for working on frontier AI
- But everyone else should have access to it.
By definition, this means the frontier doesn't advance.
It also has the critical benefit of avoiding a dangerous power imbalance.
Anthropic has chosen the opposite of the safe path: they are allowing themselves, the current top lab, to use their top model for frontier AI research. They've said they'll sabotage others who try.
This means the AI frontier advances, & power imbalance increases.
(To be clear, I don't think we should try to slow down recursive AI self improvement - I think we should open it up and democratize it as much as possible. My point is: if you claim we should slow down, and you have the best model, you should ensure your org can't use it.)
— Jeremy Howard, in a Twitter thread
I feel a lot of things changing as working software increasingly comes out on a tap. The Jevon's paradox kicks in and I feel my own demand for software growing substantially. You can ask for anything - explainers, visualizers, dashboards, bespoke single-use apps (e.g. a full wandb that is hyper-specific just for your project), you can 10X your test suite, auto-optimize code, run giant research projects with custom HTML for the results, anything! "Free your mind" (Matrix ref).
— Andrej Karpathy, on Claude Fable 5
We will no longer accept public pull requests. [...]
A substantial patch used to imply substantial effort, and that effort was a reasonable proxy for good faith. That assumption no longer holds. [...]
Whether code was typed by hand is beside the point. What matters is who is responsible for it once it enters the browser. Ladybird is becoming a browser for real users. The people introducing changes to it must be the people who decide those changes belong in the project, and who will answer for the consequences.
— Andreas Kling, Changing How We Develop Ladybird
After this story was published Google's spokesperson reached out and asked us to publish a slightly different version of that statement. The new statement no longer stated that "it's critical that we maintain humans in the loop."
— Emanuel Maiberg, 404 Media, Google Employees Internally Share Memes About How Its AI Sucks
Anthropic defines “run-rate revenue” in two parts. Use the last 28 days of sales from customers charged on a consumption basis and multiply it by 13. Then, multiply the monthly subscription take by 12, and add the two together.
— Karen Kwok for Reuters Breakingviews, citing "a person familiar with the matter"
My take on AI is, essentially, everybody who’s against it is too against it and everybody who’s for it is too for it.
— Daniel Jalkut, via John Gruber
PICARD: Data, shields up
DATA: Brilliant! Shields can reduce damage we sustain. Not immunity. Not hubris. Just prudence. It's not precaution—it's strategy.
[camera shakes]
WORF: HULL BREACHES ON NINE DECKS
DATA: Here's what happened: you told me to raise shields, and I didn't
— Kyle Ferrana, @KyleTrainEmoji
A lot of the emails I get from founders are now written in a hard-hitting journalistic style. I know they're written by AI, because no founder ever wrote this way before. And once you realize something is written by AI, it's hard not to ignore it.
I have never knowingly finished reading an email signed by a human but written by AI. It feels like being lied to, and who would stand for that?
[...] It makes me think less of the author. It means they can't write well unaided (or feel they can't), and that they're trying to trick me.
It's not impressive to use AI to write stuff for you; any teenager can do that.
I cannot believe I'm saying this, but getting the literal Pope to canonize your product's specific technical limitations as a spiritual treatise is the single greatest act of vendor lobbying I have ever seen.
— Corey Quinn, on Anthropic co-founder Christopher Olah's influence on Magnifica Humanitas