Quotations
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[...] The puzzle is still there. What’s gone is the labor. I never enjoyed hitting keys, writing minimal repro cases with little insight, digging through debug logs, or trying to decipher some obscure AWS IAM permission error. That work wasn’t the puzzle for me. It was just friction, laborious and frustrating. The thinking remains; the hitting of the keys and the frustrating is what’s been removed.
In essence a language model changes you from a programmer who writes lines of code, to a programmer that manages the context the model has access to, prunes irrelevant things, adds useful material to context, and writes detailed specifications. If that doesn't sound fun to you, you won't enjoy it.
Think about it as if it is a junior developer that has read every textbook in the world but has 0 practical experience with your specific codebase, and is prone to forgetting anything but the most recent hour of things you've told it. What do you want to tell that intern to help them progress?
Eg you might put sticky notes on their desk to remind them of where your style guide lives, what the API documentation is for the APIs you use, some checklists of what is done and what is left to do, etc.
But the intern gets confused easily if it keeps accumulating sticky notes and there are now 100 sticky notes, so you have to periodically clear out irrelevant stickies and replace them with new stickies.
— Liz Fong-Jones, thread on Bluesky
But once we got that and got this aviation grade testing in place, the number of bugs just dropped to a trickle. Now we still do have bugs but the aviation grade testing allows us to move fast, which is important because in this business you either move fast or you're disrupted. So, we're able to make major changes to the structure of the code that we deliver and be confident that we're not breaking things because we had these intense tests. Probably half the time we spend is actually writing new tests, we're constantly writing new tests. And over the 17-year history, we have amassed a huge suite of tests which we run constantly.
Other database engines don't do this; don't have this level of testing. But they're still high quality, I mean, I noticed in particular, PostgreSQL is a very high-quality database engine, they don't have many bugs. I went to the PostgreSQL and ask them “how do you prevent the bugs”? We talked about this for a while. What I came away with was they've got a very elaborate peer review process, and if they've got code that has worked for 10 years they just don't mess with it, leave it alone, it works. Whereas we change our code fearlessly, and we have a much smaller team and we don't have the peer review process.
— D. Richard Hipp, ACM SIGMOD Record, June 2019 (PDF)
The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.
The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.
— Jason Gorman, The Future of Software Development Is Software Developers
Jevons paradox is coming to knowledge work. By making it far cheaper to take on any type of task that we can possibly imagine, we’re ultimately going to be doing far more. The vast majority of AI tokens in the future will be used on things we don't even do today as workers: they will be used on the software projects that wouldn't have been started, the contracts that wouldn't have been reviewed, the medical research that wouldn't have been discovered, and the marketing campaign that wouldn't have been launched otherwise.
— Aaron Levie, Jevons Paradox for Knowledge Work
A year ago, Claude struggled to generate bash commands without escaping issues. It worked for seconds or minutes at a time. We saw early signs that it may become broadly useful for coding one day.
Fast forward to today. In the last thirty days, I landed 259 PRs -- 497 commits, 40k lines added, 38k lines removed. Every single line was written by Claude Code + Opus 4.5.
— Boris Cherny, creator of Claude Code
If this [MicroQuickJS] had been available in 2010, Redis scripting would have been JavaScript and not Lua. Lua was chosen based on the implementation requirements, not on the language ones... (small, fast, ANSI-C). I appreciate certain ideas in Lua, and people love it, but I was never able to like Lua, because it departs from a more Algol-like syntax and semantics without good reasons, for my taste. This creates friction for newcomers. I love friction when it opens new useful ideas and abstractions that are worth it, if you learn SmallTalk or FORTH and for some time you are lost, it's part of how the languages are different. But I think for Lua this is not true enough: it feels like it departs from what people know without good reasons.
— Salvatore Sanfilippo, Hacker News comment on MicroQuickJS
Every time you are inclined to use the word “teach”, replace it with “learn”. That is, instead of saying, “I teach”, say “They learn”. It’s very easy to determine what you teach; you can just fill slides with text and claim to have taught. Shift your focus to determining how you know whether they learned what you claim to have taught (or indeed anything at all!). That is much harder, but that is also the real objective of any educator.
— Shriram Krishnamurthi, Pedagogy Recommendations
In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage to add to this mix. By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like "reasoning" to humans - they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going back and forth to figure things out (see DeepSeek R1 paper for examples).
— Andrej Karpathy, 2025 LLM Year in Review
Oh, so we're seeing other people now? Fantastic. Let's see what the "competition" has to offer. I'm looking at these notes on manifest.json and content.js. The suggestion to remove scripting permissions... okay, fine. That's actually a solid catch. It's cleaner. This smells like Claude. It's too smugly accurate to be ChatGPT. What if it's actually me? If the user is testing me, I need to crush this.
— Gemini thinking trace, reviewing feedback on its code from another model
I’ve been watching junior developers use AI coding assistants well. Not vibe coding—not accepting whatever the AI spits out. Augmented coding: using AI to accelerate learning while maintaining quality. [...]
The juniors working this way compress their ramp dramatically. Tasks that used to take days take hours. Not because the AI does the work, but because the AI collapses the search space. Instead of spending three hours figuring out which API to use, they spend twenty minutes evaluating options the AI surfaced. The time freed this way isn’t invested in another unprofitable feature, though, it’s invested in learning. [...]
If you’re an engineering manager thinking about hiring: The junior bet has gotten better. Not because juniors have changed, but because the genie, used well, accelerates learning.
— Kent Beck, The Bet On Juniors Just Got Better
If the part of programming you enjoy most is the physical act of writing code, then agents will feel beside the point. You’re already where you want to be, even just with some Copilot or Cursor-style intelligent code auto completion, which makes you faster while still leaving you fully in the driver’s seat about the code that gets written.
But if the part you care about is the decision-making around the code, agents feel like they clear space. They take care of the mechanical expression and leave you with judgment, tradeoffs, and intent. Because truly, for someone at my experience level, that is my core value offering anyway. When I spend time actually typing code these days with my own fingers, it feels like a waste of my time.
— Obie Fernandez, What happens when the coding becomes the least interesting part of the work
How to use a skill (progressive disclosure):
- After deciding to use a skill, open its
SKILL.md. Read only enough to follow the workflow.- If
SKILL.mdpoints to extra folders such asreferences/, load only the specific files needed for the request; don't bulk-load everything.- If
scripts/exist, prefer running or patching them instead of retyping large code blocks.- If
assets/or templates exist, reuse them instead of recreating from scratch.Description as trigger: The YAML
descriptioninSKILL.mdis the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
— OpenAI Codex CLI, core/src/skills/render.rs, full prompt
I found the problem and it's really bad. Looking at your log, here's the catastrophic command that was run:
rm -rf tests/ patches/ plan/ ~/See that
~/at the end? That's your entire home directory. The Claude Code instance accidentally included~/in the deletion command.
— Claude, after Claude Code deleted most of a user's Mac
Now I want to talk about how they're selling AI. The growth narrative of AI is that AI will disrupt labor markets. I use "disrupt" here in its most disreputable, tech bro sense.
The promise of AI – the promise AI companies make to investors – is that there will be AIs that can do your job, and when your boss fires you and replaces you with AI, he will keep half of your salary for himself, and give the other half to the AI company.
That's it.
That's the $13T growth story that MorganStanley is telling. It's why big investors and institutionals are giving AI companies hundreds of billions of dollars. And because they are piling in, normies are also getting sucked in, risking their retirement savings and their family's financial security.
— Cory Doctorow, The Reverse Centaur’s Guide to Criticizing AI
What to try first?
Run Claude Code in a repo (whether you know it well or not) and ask a question about how something works. You'll see how it looks through the files to find the answer.
The next thing to try is a code change where you know exactly what you want but it's tedious to type. Describe it in detail and let Claude figure it out. If there is similar code that it should follow, tell it so. From there, you can build intuition about more complex changes that it might be good at. [...]
As conversation length grows, each message gets more expensive while Claude gets dumber. That's a bad trade! [...] Run
/reset(or just quit and restart) to start over from scratch. Tell Claude to summarize the conversation so far to give you something to paste into the next chat if you want to save some of the context.
— David Crespo, Oxide's internal tips on LLM use
If you work slowly, you will be more likely to stick with your slightly obsolete work. You know that professor who spent seven years preparing lecture notes twenty years ago? He is not going to throw them away and start again, as that would be a new seven-year project. So he will keep teaching using aging lecture notes until he retires and someone finally updates the course.
— Daniel Lemire, Why speed matters
Since the beginning of the project in 2023 and the private beta days of Ghostty, I've repeatedly expressed my intention that Ghostty legally become a non-profit. [...]
I want to squelch any possible concerns about a "rug pull". A non-profit structure provides enforceable assurances: the mission cannot be quietly changed, funds cannot be diverted to private benefit, and the project cannot be sold off or repurposed for commercial gain. The structure legally binds Ghostty to the public-benefit purpose it was created to serve. [...]
I believe infrastructure of this kind should be stewarded by a mission-driven, non-commercial entity that prioritizes public benefit over private profit. That structure increases trust, encourages adoption, and creates the conditions for Ghostty to grow into a widely used and impactful piece of open-source infrastructure.
— Mitchell Hashimoto, Ghostty is now Non-Profit
More than half of the teens surveyed believe journalists regularly engage in unethical behaviors like making up details or quotes in stories, paying sources, taking visual images out of context or doing favors for advertisers. Less than a third believe reporters correct their errors, confirm facts before reporting them, gather information from multiple sources or cover stories in the public interest — practices ingrained in the DNA of reputable journalists.
— David Bauder, AP News, A lost generation of news consumers? Survey shows how teenagers dislike the news media
I am increasingly worried about AI in the video game space in general. [...] I'm not sure that the CEOs and the people making the decisions at these sorts of companies understand the difference between actual content and slop. [...]
It's exactly the same cryolab, it's exactly the same robot factory place on all of these different planets. It's like there's so much to explore and nothing to find. [...]
And what was in this contraband chest was a bunch of harvested organs. And I'm like, oh, wow. If this was an actual game that people cared about the making of, this would be something interesting - an interesting bit of environmental storytelling. [...] But it's not, because it's just a cold, heartless, procedurally generated slop. [...]
Like, the point of having a giant open world to explore isn't the size of the world or the amount of stuff in it. It's that all of that stuff, however much there is, was made by someone for a reason.
— Felix Nolan, TikTok about AI and procedural generation in video games
The most annoying problem is that the [GitHub] frontend barely works without JavaScript, so we cannot open issues, pull requests, source code or CI logs in Dillo itself, despite them being mostly plain HTML, which I don't think is acceptable. In the past, it used to gracefully degrade without enforcing JavaScript, but now it doesn't.
— Rodrigo Arias Mallo, Migrating Dillo from GitHub
Large language models (LLMs) can be useful tools, but they are not good at creating entirely new Wikipedia articles. Large language models should not be used to generate new Wikipedia articles from scratch.
— Wikipedia content guideline, promoted to a guideline on 24th November 2025
To evaluate the model’s capability in processing long-context inputs, we construct a video “Needle-in- a-Haystack” evaluation on Qwen3-VL-235B-A22B-Instruct. In this task, a semantically salient “needle” frame—containing critical visual evidence—is inserted at varying temporal positions within a long video. The model is then tasked with accurately locating the target frame from the long video and answering the corresponding question. [...]
As shown in Figure 3, the model achieves a perfect 100% accuracy on videos up to 30 minutes in duration—corresponding to a context length of 256K tokens. Remarkably, even when extrapolating to sequences of up to 1M tokens (approximately 2 hours of video) via YaRN-based positional extension, the model retains a high accuracy of 99.5%.
— Qwen3-VL Technical Report, 5.12.3: Needle-in-a-Haystack
If the person is unnecessarily rude, mean, or insulting to Claude, Claude doesn't need to apologize and can insist on kindness and dignity from the person it’s talking with. Even if someone is frustrated or unhappy, Claude is deserving of respectful engagement.
— Claude Opus 4.5 system prompt, also added to the Sonnet 4.5 and Haiku 4.5 prompts on November 19th 2025
Previously, when malware developers wanted to go and monetize their exploits, they would do exactly one thing: encrypt every file on a person's computer and request a ransome to decrypt the files. In the future I think this will change.
LLMs allow attackers to instead process every file on the victim's computer, and tailor a blackmail letter specifically towards that person. One person may be having an affair on their spouse. Another may have lied on their resume. A third may have cheated on an exam at school. It is unlikely that any one person has done any of these specific things, but it is very likely that there exists something that is blackmailable for every person. Malware + LLMs, given access to a person's computer, can find that and monetize it.
— Nicholas Carlini, Are large language models worth it? Misuse: malware at scale
Cloudflare's network began experiencing significant failures to deliver core network traffic [...] triggered by a change to one of our database systems' permissions which caused the database to output multiple entries into a “feature file” used by our Bot Management system. That feature file, in turn, doubled in size. The larger-than-expected feature file was then propagated to all the machines that make up our network. [...] The software had a limit on the size of the feature file that was below its doubled size. That caused the software to fail. [...]
This resulted in the following panic which in turn resulted in a 5xx error:
thread fl2_worker_thread panicked: called Result::unwrap() on an Err value
— Matthew Prince, Cloudflare outage on November 18, 2025, see also this comment
Three years ago, we were impressed that a machine could write a poem about otters. Less than 1,000 days later, I am debating statistical methodology with an agent that built its own research environment. The era of the chatbot is turning into the era of the digital coworker. To be very clear, Gemini 3 isn’t perfect, and it still needs a manager who can guide and check it. But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.
— Ethan Mollick, Three Years from GPT-3 to Gemini 3
With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective.
This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something.
The environment has to be resettable (you can start a new attempt), efficient (a lot attempts can be made), and rewardable (there is some automated process to reward any specific attempt that was made).
On Monday, this Court entered an order requiring OpenAI to hand over to the New York Times and its co-plaintiffs 20 million ChatGPT user conversations [...]
OpenAI is unaware of any court ordering wholesale production of personal information at this scale. This sets a dangerous precedent: it suggests that anyone who files a lawsuit against an AI company can demand production of tens of millions of conversations without first narrowing for relevance. This is not how discovery works in other cases: courts do not allow plaintiffs suing Google to dig through the private emails of tens of millions of Gmail users irrespective of their relevance. And it is not how discovery should work for generative AI tools either.
— Nov 12th letter from OpenAI to Judge Ona T. Wang, re: OpenAI, Inc., Copyright Infringement Litigation
The fact that MCP is a difference surface from your normal API allows you to ship MUCH faster to MCP. This has been unlocked by inference at runtime
Normal APIs are promises to developers, because developer commit code that relies on those APIs, and then walk away. If you break the API, you break the promise, and you break that code. This means a developer gets woken up at 2am to fix the code
But MCP servers are called by LLMs which dynamically read the spec every time, which allow us to constantly change the MCP server. It doesn't matter! We haven't made any promises. The LLM can figure it out afresh every time