Quotations
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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
The most frustrating failure mode right now is that people submit issues that are not in their own voice. They contain an observed problem somewhere, but it has been thrown into a clanker and the clanker reworded it and made a huge mess of it. Typically, it was prompted so badly that the conclusions produced are more often than not inaccurate but always full of confidence. The result is complete guesswork on root causes, fake-minimal repros, suggested implementation strategies, analogies to adjacent but often the wrong code, and long lists of error classes that might or might not matter. [...]
So at least personally, I increasingly want issue reports to be condensed to what the human actually observed:
- I ran this command.
- I expected this to happen.
- This happened instead.
- Here is the exact error or log.
— Armin Ronacher, on slop issues filed against Pi
We have the ability to use compute resources to support our proprietary AI applications (such as Grok 5, which is currently being trained at COLOSSUS II), while also providing access to select compute capacity to third-party customers. For example, in May 2026, we entered into Cloud Services Agreements with Anthropic PBC (“Anthropic”), an AI research and development public benefit corporation, with respect to access to compute capacity across COLOSSUS and COLOSSUS II. Pursuant to these agreements, the customer has agreed to pay us $1.25 billion per month through May 2029, with capacity ramping in May and June 2026 at a reduced fee. The agreements may be terminated by either party upon 90 days’ notice.
— SpaceX S-1, highlights mine
[...] in the last 10 years I’ve learned to really love and respect CSS as a technology.
So I decided years ago that I wanted to react to “CSS is hard” by getting better at CSS and taking it seriously as a technology, instead of devaluing it. Doing that changed everything for me: I learned that so many of my frustrations (“centering is impossible”) had been addressed in CSS a long time ago, and that also what “centering” means is not always straightforward and it makes sense that there are many ways to do it. CSS is hard because it’s solving a hard problem!
— Julia Evans, Moving away from Tailwind, and learning to structure my CSS
[...] On the interesting side is how fungible programming languages are nowadays. Programming languages used to be LOCK IN, and they're increasingly not so. You think the Bun rewrite in Rust is good for Rust? Bun has shown they can be in probably any language they want in roughly a week or two. Rust is expendable. Its useful until its not then it can be thrown out. That's interesting!
— Mitchell Hashimoto, on Bun porting from Zig to Rust
“11 AI agents” is meaningless as a phrase.
If I said “I have 11 spreadsheets” or “I have 11 browser tabs” to do my work, it means about the same thing.
Now, if your CEO has never heard the phrase Ralph Loop, oh man, you are less than 30 days away from your next promotion. I'm not even exaggerating. Walk into his office, close the door, and say, hey chief, been experimenting with something. It's called Ralph Loops. And I think it could change literally everything. And he's gonna say, what's a Ralph loop? And you will say, give me $18,000 worth of API credits and I'll show you. Now you won't actually do anything, because you can't do anything. Because nobody can, because nobody knows what they're doing. But by the time he figures that out, you'll have a new title, and equity bump. [...]
Talk about automation constantly. Nothing arouses the slumbering capitalists than the mention of automation. Drop names too, bro. Like talk about specific team members you can automate out of existence. Be like, yo, I automated Gary, bro. Tag Gary in the message. Tag him in Slack in a very public channel. Be like, yo, I just automated @Gary. His function has been Ralph Looped. And tag your CEO in the same message. You think you're getting laid off after that?
— Mo Bitar, The Unethical Guide to Surviving AI Layoffs, TikTok
The thing about 90% of TDMs [Technical Decision Makers] is that they're motivated primarily by NOT GETTING FIRED. These aren't people who browser Lobsters or push to GH on the weekend. These are people that work 9 to 5, get paid, go home, and NEVER THINK ABOUT WORK AGAIN. So to achieve all that, they follow secular trends supported by analysts and broad public sentiment. Oh, Gartner said that "AI strategy" is most important? McKinsey said "context" needs to be managed? Well, "Context Engine for AI Apps" is going to be defensible. Buy it.
— Mitchell Hashimoto, in a conversation about the design of the Redis homepage
Your AI coding agent, the one you use to write code, needs to reduce your maintenance costs. Not by a little bit, either. You write code twice as quick now? Better hope you’ve halved your maintenance costs. Three times as productive? One third the maintenance costs. Otherwise, you’re screwed. You’re trading a temporary speed boost for permanent indenture. [...]
The math only works if the LLM decreases your maintenance costs, and by exactly the inverse of the rate it adds code. If you double your output and your cost of maintaining that output, two times two means you’ve quadrupled your maintenance costs. If you double your output and hold your maintenance costs steady, two times one means you’ve still doubled your maintenance costs.
— James Shore, You Need AI That Reduces Maintenance Costs
This article was updated after The Times learned that a remark attributed to Pierre Poilievre, the Conservative leader, was in fact an A.I.-generated summary of his views about Canadian politics that A.I. rendered as a quotation. The reporter should have checked the accuracy of what the A.I. tool returned. The article now accurately quotes from a speech delivered by Mr. Poilievre in April. [...] He did not refer to politicians who changed allegiances as turncoats in that speech.
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