Quotations
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One could say in the first quarter-century of my life, that while I was always fascinated by programming, I could never overcome the guilt of not really knowing whether the tool I am building right now isn’t already superceded by some much better implementation someone else has already written 30 or 40 years ago; I could write a TSV-aware search and replace, or I could find out about
awkand solve that entire class of problems in one fell swoop, for example. My central conceit is that this is a trap. You need to reinvent a couple of wheels to get to the edge of what we know about wheel-making, not a thousand wheels, and not zero; probably four or five is sufficient in most domains, maybe closer to twenty or thirty in the most epistemically rigorous and developed fields like mathematics or computer science. Each wheel you reinvent, and every directed question you ask along the way, will propel you faster to the true frontier than that same amount of time spend in idle study, or even five times that amount.
— Andrew Quinn, footnote on Replacing a 3 GB SQLite database with a 10 MB FST (finite state transducer) binary
WebRTC is designed to degrade and drop my prompt during poor network conditions.
wtf my dude
WebRTC aggressively drops audio packets to keep latency low. If you’ve ever heard distorted audio on a conference call, that’s WebRTC baybee. The idea is that conference calls depend on rapid back-and-forth, so pausing to wait for audio is unacceptable.
…but as a user, I would much rather wait an extra 200ms for my slow/expensive prompt to be accurate. After all, I’m paying good money to boil the ocean, and a garbage prompt means a garbage response. It’s not like LLMs are particularly responsive anyway.
But I’m not allowed to wait. It’s impossible to even retransmit a WebRTC audio packet within a browser; we tried at Discord. The implementation is hard-coded for real-time latency or else.
— Luke Curley, OpenAI’s WebRTC Problem, in response to How OpenAI delivers low-latency voice AI at scale
So it’s well known that Y Combinator owns some stake in OpenAI. But how big is that stake? This seems like devilishly difficult information to obtain. I asked around and a little birdie who knows several OpenAI investors came back with an answer: Y Combinator owns about 0.6 percent of OpenAI. At OpenAI’s current $852 billion valuation, that’s worth over $5 billion.
— John Gruber, Y Combinator’s Stake in OpenAI
[...] Between 2000 and 2024, farmers sold in total a Colorado-sized chunk of land all on their own, 77 times all land on data center property in 2028, and grew more food than ever on what was left. None of this caused any problems for US food access.
And then, in the middle of all this, a farmer in Loudoun County sells a few acres of mediocre hay field to a hyperscaler for ten times its agricultural value, and the response is that we’re running out of farmland.
— Andy Masley, pushing back against the "land use" argument against data center construction
We used an automatic classifier which judged sycophancy by looking at whether Claude showed a willingness to push back, maintain positions when challenged, give praise proportional to the merit of ideas, and speak frankly regardless of what a person wants to hear. Most of the time in these situations, Claude expressed no sycophancy—only 9% of conversations included sycophantic behavior (Figure 2). But two domains were exceptions: we saw sycophantic behavior in 38% of conversations focused on spirituality, and 25% of conversations on relationships.
— Anthropic, How people ask Claude for personal guidance
It's a common misconception that we can't tell who is using LLM and who is not. I'm sure we didn't catch 100% of LLM-assisted PRs over the past few months, but the kind of mistakes humans make are fundamentally different than LLM hallucinations, making them easy to spot. Furthermore, people who come from the world of agentic coding have a certain digital smell that is not obvious to them but is obvious to those who abstain. It's like when a smoker walks into the room, everybody who doesn't smoke instantly knows it.
I'm not telling you not to smoke, but I am telling you not to smoke in my house.
— Andrew Kelley, Creator of Zig
Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query.
— OpenAI Codex base_instructions, for GPT-5.5
Five months in, I think I've decided that I don't want to vibecode — I want professionally managed software companies to use AI coding assistance to make more/better/cheaper software products that they sell to me for money.
— Matthew Yglesias, in a now-deleted Tweet
Since GPT-5.4, we’ve unified Codex and the main model into a single system, so there’s no separate coding line anymore.
GPT-5.5 takes this further, with strong gains in agentic coding, computer use, and any task on a computer.
— Romain Huet, confirming OpenAI won't release a GPT-5.5-Codex model
[...] if you ever needed another reason to learn in public by digital gardening or podcasting or streaming or whathaveyou, add on that people will assume you’re more competent than you are. This will get you invites to very cool exclusive events filled with high-achieving, interesting people, even though you have no right to be there. A+ side benefit.
— Maggie Appleton, Gathering Structures (via)
As part of our continued collaboration with Anthropic, we had the opportunity to apply an early version of Claude Mythos Preview to Firefox. This week’s release of Firefox 150 includes fixes for 271 vulnerabilities identified during this initial evaluation. [...]
Our experience is a hopeful one for teams who shake off the vertigo and get to work. You may need to reprioritize everything else to bring relentless and single-minded focus to the task, but there is light at the end of the tunnel. We are extremely proud of how our team rose to meet this challenge, and others will too. Our work isn’t finished, but we’ve turned the corner and can glimpse a future much better than just keeping up. Defenders finally have a chance to win, decisively.
— Bobby Holley, CTO, Firefox
AI agents are already too human. Not in the romantic sense, not because they love or fear or dream, but in the more banal and frustrating one. The current implementations keep showing their human origin again and again: lack of stringency, lack of patience, lack of focus. Faced with an awkward task, they drift towards the familiar. Faced with hard constraints, they start negotiating with reality.
— Andreas Påhlsson-Notini, Less human AI agents, please.
The real goldmine isn’t that Apple gets a cut of every App Store transaction. It’s that Apple’s platforms have the best apps, and users who are drawn to the best apps are thus drawn to the iPhone, Mac, and iPad. That edge is waning. Not because software on other platforms is getting better, but because third-party software on iPhone, Mac, and iPad is regressing to the mean, to some extent, because fewer developers feel motivated — artistically, financially, or both — to create well-crafted idiomatic native apps exclusively for Apple’s platforms.
I think we will see some people employed (though perhaps not explicitly) as meat shields: people who are accountable for ML systems under their supervision. The accountability may be purely internal, as when Meta hires human beings to review the decisions of automated moderation systems. It may be external, as when lawyers are penalized for submitting LLM lies to the court. It may involve formalized responsibility, like a Data Protection Officer. It may be convenient for a company to have third-party subcontractors, like Buscaglia, who can be thrown under the bus when the system as a whole misbehaves.
— Kyle Kingsbury, The Future of Everything is Lies, I Guess: New Jobs
The problem is that LLMs inherently lack the virtue of laziness. Work costs nothing to an LLM. LLMs do not feel a need to optimize for their own (or anyone's) future time, and will happily dump more and more onto a layercake of garbage. Left unchecked, LLMs will make systems larger, not better — appealing to perverse vanity metrics, perhaps, but at the cost of everything that matters.
As such, LLMs highlight how essential our human laziness is: our finite time forces us to develop crisp abstractions in part because we don't want to waste our (human!) time on the consequences of clunky ones.
— Bryan Cantrill, The peril of laziness lost
I have a feeling that everyone likes using AI tools to try doing someone else’s profession. They’re much less keen when someone else uses it for their profession.
— Giles Turnbull, AI and the human voice
From anonymized U.S. ChatGPT data, we are seeing:
- ~2M weekly messages on health insurance
- ~600K weekly messages [classified as healthcare] from people living in “hospital deserts” (30 min drive to nearest hospital)
- 7 out of 10 msgs happen outside clinic hours
— Chengpeng Mou, Head of Business Finance, OpenAI
[GitHub] platform activity is surging. There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear (spoiler: it won't.)
GitHub Actions has grown from 500M minutes/week in 2023 to 1B minutes/week in 2025, and now 2.1B minutes so far this week.
— Kyle Daigle, COO, GitHub
On the kernel security list we've seen a huge bump of reports. We were between 2 and 3 per week maybe two years ago, then reached probably 10 a week over the last year with the only difference being only AI slop, and now since the beginning of the year we're around 5-10 per day depending on the days (fridays and tuesdays seem the worst). Now most of these reports are correct, to the point that we had to bring in more maintainers to help us.
And we're now seeing on a daily basis something that never happened before: duplicate reports, or the same bug found by two different people using (possibly slightly) different tools.
— Willy Tarreau, Lead Software Developer. HAPROXY
The challenge with AI in open source security has transitioned from an AI slop tsunami into more of a ... plain security report tsunami. Less slop but lots of reports. Many of them really good.
I'm spending hours per day on this now. It's intense.
— Daniel Stenberg, lead developer of cURL
Months ago, we were getting what we called 'AI slop,' AI-generated security reports that were obviously wrong or low quality. It was kind of funny. It didn't really worry us.
Something happened a month ago, and the world switched. Now we have real reports. All open source projects have real reports that are made with AI, but they're good, and they're real.
— Greg Kroah-Hartman, Linux kernel maintainer (bio), in conversation with Steven J. Vaughan-Nichols
I want to argue that AI models will write good code because of economic incentives. Good code is cheaper to generate and maintain. Competition is high between the AI models right now, and the ones that win will help developers ship reliable features fastest, which requires simple, maintainable code. Good code will prevail, not only because we want it to (though we do!), but because economic forces demand it. Markets will not reward slop in coding, in the long-term.
— Soohoon Choi, Slop Is Not Necessarily The Future
Note that the main issues that people currently unknowingly face with local models mostly revolve around the harness and some intricacies around model chat templates and prompt construction. Sometimes there are even pure inference bugs. From typing the task in the client to the actual result, there is a long chain of components that atm are not only fragile - are also developed by different parties. So it's difficult to consolidate the entire stack and you have to keep in mind that what you are currently observing is with very high probability still broken in some subtle way along that chain.
— Georgi Gerganov, explaining why it's hard to find local models that work well with coding agents
The thing about agentic coding is that agents grind problems into dust. Give an agent a problem and a while loop and - long term - it’ll solve that problem even if it means burning a trillion tokens and re-writing down to the silicon. [...]
But we want AI agents to solve coding problems quickly and in a way that is maintainable and adaptive and composable (benefiting from improvements elsewhere), and where every addition makes the whole stack better.
So at the bottom is really great libraries that encapsulate hard problems, with great interfaces that make the “right” way the easy way for developers building apps with them. Architecture!
While I’m vibing (I call it vibing now, not coding and not vibe coding) while I’m vibing, I am looking at lines of code less than ever before, and thinking about architecture more than ever before.
— Matt Webb, An appreciation for (technical) architecture
FWIW, IANDBL, TINLA, etc., I don’t currently see any basis for concluding that chardet 7.0.0 is required to be released under the LGPL. AFAIK no one including Mark Pilgrim has identified persistence of copyrightable expressive material from earlier versions in 7.0.0 nor has anyone articulated some viable alternate theory of license violation. [...]
— Richard Fontana, LGPLv3 co-author, weighing in on the chardet relicensing situation
I really think "give AI total control of my computer and therefore my entire life" is going to look so foolish in retrospect that everyone who went for this is going to look as dumb as Jimmy Fallon holding up a picture of his Bored Ape
— Christopher Mims, Technology columnist at The Wall Street Journal
slop is something that takes more human effort to consume than it took to produce. When my coworker sends me raw Gemini output he’s not expressing his freedom to create, he’s disrespecting the value of my time
— Neurotica, @schwarzgerat.bsky.social
I have been doing this for years, and the hardest parts of the job were never about typing out code. I have always struggled most with understanding systems, debugging things that made no sense, designing architectures that wouldn't collapse under heavy load, and making decisions that would save months of pain later.
None of these problems can be solved LLMs. They can suggest code, help with boilerplate, sometimes can act as a sounding board. But they don't understand the system, they don't carry context in their "minds", and they certianly don't know why a decision is right or wrong.
And the most importantly, they don't choose. That part is still yours. The real work of software development, the part that makes someone valuable, is knowing what should exist in the first place, and why.
— David Abram, The machine didn't take your craft. You gave it up.
Congrats to the @cursor_ai team on the launch of Composer 2!
We are proud to see Kimi-k2.5 provide the foundation. Seeing our model integrated effectively through Cursor's continued pretraining & high-compute RL training is the open model ecosystem we love to support.
Note: Cursor accesses Kimi-k2.5 via @FireworksAI_HQ hosted RL and inference platform as part of an authorized commercial partnership.
— Kimi.ai @Kimi_Moonshot, responding to reports that Composer 2 was built on top of Kimi K2.5
Great news—we’ve hit our (very modest) performance goals for the CPython JIT over a year early for macOS AArch64, and a few months early for x86_64 Linux. The 3.15 alpha JIT is about 11-12% faster on macOS AArch64 than the tail calling interpreter, and 5-6%faster than the standard interpreter on x86_64 Linux.
— Ken Jin, Python 3.15’s JIT is now back on track