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I read all of Cloudflare's Claude-generated commits
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- SupremumLimit 1y agoIt's an interesting review but I really dislike this type of techno-utopian determinism: "When models inevitably improve..." Says who? How is it inevitable? What if they've actually reached their limits by now?
- Dylan16807 1y agoModels are improving every day. People are figuring out thousands of different optimizations to training and to hardware efficiency. The idea that right now in early June 2025 is when improvement stops beggars belief. We might be approaching a limit, but that's going to be a sigmoid curve, not a sudden halt in advancement.
- deadbabe 1y ago5 years ago a person would be blown away by today’s LLMs. But people today will merely say “cool” at whatever LLMs are in use 5 years from now. Or maybe not even that.
- dingnuts 1y ago5 years ago GPT2 was already outputting largely coherent speech, there's been progress but it's not all that shocking
- tptacek 1y agoMost of the developers I know personally who have been radicalized by coding agents, it happened within the past 9 months. It does not feel like we are in a phase of predictable boring improvement.
- keybored 1y agoRadicalized? Going with the flow and wishes of the people who are driving AI is the opposite of that. To have their minds changed drastically, sure..
- tptacek 1y agoSorry I have no idea what you're trying to say here.
- lcnPylGDnU4H9OF 1y ago> very different from the usual or traditional https://www.merriam-webster.com/dictionary/radical https://www.merriam-webster.com/dictionary/radical Deciding that AI is going nowhere to suddenly deciding that coding agents are how they will work going forward is a radical change. That is what they meant.
- keybored 1y agoDid you miss my second paragraph? https://www.merriam-webster.com/dictionary/paragraph https://www.merriam-webster.com/dictionary/paragraph
- Dylan16807 1y agoCan you explain exactly what you meant by your second paragraph? The ambiguity is why you got that reply. If your second paragraph makes that reply irrelevant, are you saying the meaning was "Your use of 'radicalized' is technically correct but I still think you shouldn't have used it here"?
- dwaltrip 1y agoBold prediction…
- sitkack 1y agoIt is copium that it will suddenly stop and the world they knew before will return. ChatGPT came out in Nov 2022. Attention Was All There Was in 2017, we were already 5 years in the past. Or 5 years of research to catch up to, and then from 2022 to now ... papers and research have been increasing exponentially. Even in if SOTA models were frozen, we still have years of research to apply and optimize in various ways.
- BoorishBears 1y agoI think it's equally copium that people keep assuming we're just going to compound our way into intelligence that generalizes enough to stop us from handholding the AI, as much as I'd genuinely enjoy that future. Lately I spend all day post-training models for my product, and I want to say 99% of the research specific to LLMs doesn't reproduce and/or matter once you actually dig in. We're getting exponentially more papers on the topics and they're getting worse on average. Every day there's a new paper claiming an X% gain by post-training some ancient 8B parameter model and comparing it to a bunch of other ancient models after they've overfitted on the public dataset of a given benchmark and given the model a best of 5. And benchmarks won't ever show it, but even ChatGPT 3.5-Turbo has better general world knowledge than a lot models people consider "frontier" models today because post-training makes it easy to cover up those gaps with very impressive one-prompt outputs and strong benchmark scores. - It feels like things are getting stuck in a local maxima: we are making forward progress, the models are useful and getting more useful, but the future people are envisioning takes reaching a completely different goal post that I'm not at all convinced we're making exponential progress towards. There maybe exponential number of techniques claiming to be ground breaking, but what has actually unlocked new capabilities that can't just as easily be attributed to how much more focused post-training has become on coding and math? Test time compute feels like the only one and we're already seeing the cracks form in terms of its effect on hallucinations, and there's a clear ceiling for the performance the current iteration unlocks as all these models are converging on pretty similar performance after just a few model releases.
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- a2128 1y agoI think at this point we're reaching more incremental updates, which can score higher on some benchmarks but then simultaneously behave worse with real-world prompts, most especially if they were prompt engineered for a specific model. I recall Google updating their Flash model on their API with no way to revert to the old one and it caused a lot of people to complain that everything they've built is no longer working because the model is just behaving differently than when they wrote all the prompts.
- whbrown 1y agoIsn't it quite possible they replaced that Flash model with a distilled version, saving money rather than increasing quality? This just speaks to the value of open-weights more than anything.
- Sevii 1y agoModels have improved significantly over the last 3 months. Yet people have been saying 'What if they've actually reached their limits by now?' for pushing 3 years.
- greyadept 1y agoFor me, improvement means no hallucination, but that only seems to have gotten worse and I'm interested to find out whether it's actually solvable at all.
- dymk 1y agoAll the benchmarks would disagree with you
- thuuuomas 1y agoToday’s public benchmarks are yesterday’s training data.
- BoorishBears 1y agoThe benchmarks also claim random 32B parameter models beat Claude 4 at coding, so we know just how much they matter. It should be obvious to anyone who with a cursory interest in model training, you can't trust benchmarks unless they're fully private black-boxes. If you can get even a hint of the shape of the questions on a benchmark, it's trivial to synthesize massive amounts of data that help you beat the benchmark. And given the nature of funding right now, you're almost silly not to do it: it's not cheating, it's "demonstrably improving your performance at the downstream task"
- tptacek 1y agoWhy do you care about hallucination for coding problems? You're in an agent loop; the compiler is ground truth. If the LLM hallucinates, the agent just iterates. You don't even see it unless you make the mistake of looking closely.
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- groby_b 1y agoIt is "inevitable" in the sense that in 99% of the cases, tomorrow is just like yesterday. LLMs have been continually improving for years now. The surprising thing would be them not improving further. And if you follow the research even remotely, you know they'll improve for a while, because not all of the breakthroughs have landed in commercial models yet. It's not "techno-utopian determinism". It's a clearly visible trajectory. Meanwhile, if they didn't improve, it wouldn't make a significant change to the overall observations. It's picking a minor nit. The observation that strict prompt adherence plus prompt archival could shift how we program is both true, and it's a phenomenon we observed several times in the past. Nobody keeps the assembly output from the compiler around anymore, either. There's definitely valid criticism to the passage, and it's overly optimistic - in that most non-trivial prompts are still underspecified and have multiple possible implementations, not all correct. That's both a more useful criticism, and not tied to LLM improvements at all.
- double0jimb0 1y agoAre there places that follow the research that speak to the layperson?
- sumedh 1y agoMore compute mean more faster processing, more context.
- QuantumNoodle 1y agoWhat is ironic, if we buy in to the theory that AI will write majority of the code in the next 5-10 years, what is it going to train on after? ITSELF? Seems this theoretic trajectory of "will inevitably get better" is is only true if humans are producing quality training data. The quality of code LLMs create is very well proportionate on how mature and ubiquitous the langues/projects are.
- solarwindy 1y agoI think you neatly summarise why the current pre-trained LLM paradigm is a dead end. If these models were really capable of artificial reasoning and learning, they wouldn’t need more training data at all. If they could learn like a human junior does, and actually progress to being a senior, then I really could believe that we’ll all be out of a job—but they just do not.
- SrslyJosh 1y ago> Reading through these commits sparked an idea: what if we treated prompts as the actual source code? Imagine version control systems where you commit the prompts used to generate features rather than the resulting implementation. Please god, no, never do this. For one thing, why would you not commit the generated source code when storage is essentially free? That seems insane for multiple reasons. > When models inevitably improve, you could connect the latest version and regenerate the entire codebase with enhanced capability. How would you know if the code was better or worse if it was never committed? How do you audit for security vulnerabilities or debug with no source code?
- Sevii 1y agoThere are lots of reasons not to do it. But if LLMs get good enough that it works consistently people will do it anyway.
- minimaxir 1y agoWhat will people call it when coders rely on vibes even more than vibe coding?
- roywiggins 1y agoHaruspicy?
- brookst 1y agoWriting specs
- auggierose 1y agoExactly my thought. This is just natural language as a specification language.
- kiitos 1y ago...as an ambiguous and inadequately-specified specification language.
- js2 1y agoDiscussion from 4 days ago when the code was announced (846 points, 519 comments): https://news.ycombinator.com/item?id=44159166 https://news.ycombinator.com/item?id=44159166
- viraptor 1y agoThe documentation angle is really good. I've noticed it with the mdc files and llm.txt semi-standard. Documentation is often treated as just extra cost and a chore. Now, good description of the project structure and good examples suddenly becomes something devs want ahead of time. Even if the reason is not perfect, I appreciate this shift we'll all benefit from.
- IncreasePosts 1y agoI asked this in the other thread (no response, but I was a bit late) How does anyone using AI like this have confidence that they aren't unintentionally plagiarizing code and violating the terms of whatever license it was released under? For random personal projects I don't see it mattering that much. But if a large corp is releasing code like this, one would hope they've done some due diligence that they have to just stolen the code from some similar repo on GitHub, laundered through a LLM. The only section in the readme doesn't mention checking similar projects or libraries for common code: > Every line was thoroughly reviewed and cross-referenced with relevant RFCs, by security experts with previous experience with those RFCs.
- saghm 1y agoSafety in the shadow of giant tech companies. People were upset when Microsoft released Copilot trained on GitHub data, but nobody who cared doing do anything about it, and nobody who could have done something about it cared, so it just became the new norm.
- throwawaysleep 1y agoAs an individual dev, I simply don’t care. Not my problem. Companies are satisfied with the idemnity provided by Microsoft.
- akdev1l 1y ago> How does anyone using AI like this have confidence that they aren't unintentionally plagiarizing code and violating the terms of whatever license it was released under? They don’t and no one cares
- ryandrake 1y agoThis is an excellent question that the AI-boosters always seem to dance around. Three replies already are saying “Nobody cares.” Until they do. I’d be willing to bet that some time in the near future, some big company is going to care a lot and that there will be a landmark lawsuit that significantly changes the LLM landscape. Regulation or a judge is going to eventually decide the extent to which someone can use AI to copy someone else’s IP, and it’s not going to be pretty.
- drodgers 1y ago> Prompts as Source Code Another way to phrase this is LLM-as-compiler and Python (or whatever) as an intermediate compiler artefact. Finally, a true 6th generation programming language! I've considered building a toy of this with really aggressive modularisation of the output code (eg. python) and a query-based caching system so that each module of code output only changes when the relevant part of the prompt or upsteam modules change (the generated code would be committed to source control like a lockfile). I think that (+ some sort of WASM encapsulated execution environment) would one of the best ways to write one off things like scripts which don't need to incrementally get better and more robust over time in the way that ordinary code does.
- sumedh 1y ago> Finally, a true 6th generation programming language! Karpathy already said English is the new programming language.
- declan_roberts 1y agoThese posts are funny to me because prompt engineers point at them as evidence of the fast-approaching software engineer obsolescence but the years of experience in software engineering necessary to even guide an AI in this way is very high. The reason he keeps adjusting the prompts is because he knows how to program. He knows what it should look like. It just blurs the line between engineer and tool.
- tptacek 1y agoI don't know why that's funny. This is not a post about a vibe coding session. It's Kenton Varda['s coding session]. later updated to clarify kentonv didn't write this article
- kevingadd 1y agoI think it makes sense that GP is skeptical of this article considering it contains things like: > this tool is improving itself, learning from every interaction which seem to indicate a fundamental misunderstanding of how modern LLMs work: the 'improving' happens by humans training/refining existing models offline to create new models, and the 'learning' is just filling the context window with more stuff, not enhancement of the actual model or the model 'learning' - it will forget everything if you drop the context and as the context grows it can 'forget' things it previously 'learned'.
- BurritoKing 1y agoWhen you consider the "tool" as more than just the LLM model, but the stuff wrapped around calling that model then I feel like you can make a good argument it's improving when it keeps context in a file on disk and constantly updates and edits that file as you work throguh the project. I do this routinely for large initiatives I'm kicking off through Claude Code - it writes a long detailed plan into a file and as we work through the project I have it constantly updating and rewriting that document to add information we have jointly discovered from each bit of the work. That means every time I come back and fire it back up, it's got more information than when it started, which looks a lot more improvement from my perspective.
- thorum 1y agoHumorous that this article has a strong AI writing smell - the author should publish the prompts they used!
- dcre 1y agoI don’t like to accuse, and the article is fine overall, but this stinks: “This transparency transforms git history from a record of changes into a record of intent, creating a new form of documentation that bridges human reasoning and machine implementation.”
- keybored 1y ago> I don’t like to accuse, and the article is fine overall, but this stinks: Now consider your reasonable instinct to not accuse other people coupled with the possibility setting AI lose with “write a positive article about AI where you have some paragraphs about the current limitations based on this link. write like you are just following the evidence.” Meanwhile we are supposed to sit here and weigh every word. This reminds to write a prompt for a blogpost. How AI could be used for making personal-looking tech-guy who meditates and runs websites. (Do we have the technology? Yes we do)
- ZephyrBlu 1y agoAlso: "This OAuth library represents something larger than a technical milestone—it's evidence of a new creative dynamic emerging" Em-dash baby.
- latexr 1y agoCan we please stop using the em-dash as a metric to “detect” LLM writing? It’s lazy and wrong. Plenty of people use em-dashes, it’s a useful punctuation mark. If humans didn’t use them, they wouldn’t be in the LLM training data. There are better clues, like the kind of vague pretentious babble bad marketers use to make their products and ideas seem more profound than they are. It’s a type of bad writing which looks grandiose but is ultimately meaningless and that LLMs heavily pick up on.
- fpgaminer 1y agoI used almost 100% AI to build a SCUMM-like parser, interpreter, and engine (https://github.com/fpgaminer/scumm-rust https://github.com/fpgaminer/scumm-rust). It was a fun workflow; I could generally focus on my usual work and just pop in occasionally to check on and direct the AI. I used a combination of OpenAI's online Codex, and Claude Sonnet 4 in VSCode agent mode. It was nice that Codex was more automated and had an environment it could work in, but its thought-logs are terrible. Iteration was also slow because it takes awhile for it to spin the environment up. And while you _can_ have multiple requests running at once, it usually doesn't make sense for a single, somewhat small project. Sonnet 4's thoughts were much more coherent, and it was fun to watch it work and figure out problems. But there's something broken in VSCode right now that makes its ability to read console output inconsistent, which made things difficult. The biggest issue I ran into is that both are set up to seek out and read only small parts of the code. While they're generally good at getting enough context, it does cause some degradation in quality. A frequent issue was replication of CSS styling between the Rust side of things (which creates all of the HTML elements) and the style.css side of things. Like it would be working on the Rust code and forget to check style.css, so it would just manually insert styles on the Rust side even though those elements were already styled on the style.css side. Codex is also _terrible_ at formatting and will frequently muck things up, so it's mandatory to use it with an autoformatter and instructions to use it. Even with that, Codex will often say that it ran it, but didn't actually run it (or ran it somewhere in the middle instead of at the end) so its pull requests fail CI. Sonnet never seemed to have this issue and just used the prevailing style it saw in the files. Now, when I say "almost 100% AI", it's maybe 99% because I did have to step in and do some edits myself for things that both failed at. In particular neither can see the actual game running, so they'd make weird mistakes with the design. (Yes, Sonnet in VS Code can see attached images, and potentially can see the DOM of vscode's built in browser, but the vision of all SOTA models is ass so it's effectively useless). I also stepped in once to do one major refactor. The AIs had decided on a very strange, messy, and buggy interpreter implementation at first.
- eviks 1y ago> Imagine version control systems where you commit the prompts used to generate features rather than the resulting implementation. So every single run will result in different non-reproducible implementation with unique bugs requiring manual expert interventions. How is this better?
- cosmok 1y agoI have documented my experience using an Agent for a slightly different task -- upgrading framework version -- I had to abandon the work, but, my learning has been similar what is in the post. https://www.trk7.com/blog/ai-agents-for-coding-promise-vs-reality/ https://www.trk7.com/blog/ai-agents-for-coding-promise-vs-re...
- never_inline 1y ago> Don't be afraid to get your hands dirty. Some bugs and styling issues are faster to fix manually than to prompt through. Knowing when to intervene is part of the craft. This has been my experience as well. to always run the cli tool in the bottom pane of an IDE and not in a standalone terminal.
- brador 1y agoMany of you are failing to conprehend the potential scale of AI generated codebases. Take note - there is no limit. Every feature you or the AI can prompt can be generated. Imagine if you were immortal and given unlimited storage. Imagine what you could create. That’s a prompt away. Even now you’re still restricting your thinking to the old ways.
- latexr 1y agoYou’re sounding like a religious zealot recruiting for a cult. No, it is not possible to prompt every feature, and I suspect people who believe LLMs can accurately program anything in any language are frankly not solving any truly novel or interesting problems, because if they were they’d see the obvious cracks.
- nojito 1y ago> I suspect people who believe LLMs can accurately program anything in any language are frankly not solving any truly novel or interesting problems, because if they were they’d see the obvious cracks. The vast majority of problems in programming aren't novel or interesting.
- latexr 1y agoWhich in no way contradicts my point. There is still a chasm of difference between a tool which can aid with a majority of issues and one which can solve everything, which is what the commenter I replied to is preaching.
- politelemon 1y ago> That’s a prompt away. Currently, it's 6 prompts away in which 5 of those are me guiding the LLM to output the answer that I already have in mind.
- _lex 1y agoYou're talking ahead of the others in this thread, who do not understand how you got to what you're saying. I've been doing research in this area. You are not only correct, but the implications are staggering, and go further than what you have mentioned above. This is no cult, it is the reorganization of the economics of work.
- UltraSane 1y agoI was thinking that if you had a good enough verified mathematical model of your code using TLA+ or similar you could then use an LLM to generate your code in any language and be confident it is correct. This would be Declarative Programming. Instead of putting in a lot of work writing code that MIGHT do what you intend you put more work into creating the verified model and then the LLM generates code that will do what the model intends.
- kookamamie 1y ago> Treat prompts as version-controlled assets This only works if the model and its context are immutable. None of us really control the models we use, so I'd be sceptical about reproducing the artifacts later.
- lmeyerov 1y agoIf/when to commit prompts has been fascinating as we have been doing similarly to build Louie.ai. I now have several categories with different handling: - Human reviewed: Code guidelines and prompt templates are essentially dev tool infra-as-code and need review - Discarded: Individual prompt commands I write, and implementation plan progress files the AI write, both get trashed, and are even part of my .gitignore . They were kept by Cloudflare, but we don't keep these. - Unreviewed: Claude Code does not do RAG in the usual sense, so it is on us to create guides for how we do things like use big frameworks. They are basically indexes for speeding up AI with less grepping + hallucinating across memory compactions. The AI reads and writes these, and we largely stay out of it. There are weird cases I am still trying to figure out. Ex: - feature impl might start with an AI coming up with the product spec, so having that maintained as the AI progresses and committed in is a potentially useful artifact - how prompt templates get used is helpful for their automated maintenance.
- Fischgericht 1y agoSo, it means that you and the LLM together have managed to write SEVEN lines of trivial code per hour. On a protocol that is perfectly documented, where you can look at about one million other implementations when in doubt. It is not my intention to hurt your feelings, but it sounds like you and/or the LLM are not really good at their job. Looking at programmer salaries and LLM energy costs, this appears to be a very very VERY expensive OAuth library. Again: Not my intention to hurt any feelings, but the numbers really are shockingly bad.
- Fischgericht 1y agoYes, my brain got confused on who wrote the code and who just reported about it. I am truly sorry. I will go see my LLM doctor to get my brain repaired.
- kentonv 1y agoI spent about 5 days semi-focused on this codebase (though I always have lots of people interrupting me all the time). It's about 5000 lines (if you count comments, tests, and documentation, which you should). Where do you get 7 lines per hour?
- nojito 1y ago>So, it means that you and the LLM together have managed to write SEVEN lines of trivial code per hour. Here's their response >It took me a few days to build the library with AI. >I estimate it would have taken a few weeks, maybe months to write by hand. >That said, this is a pretty ideal use case: implementing a well-known standard on a well-known platform with a clear API spec. https://news.ycombinator.com/item?id=44160208 https://news.ycombinator.com/item?id=44160208 Lines of code per hour is a terrible metric to use. Additionally, it's far easier to critique code that's already written!
- moron4hire 1y agoI'm sorry, this all sounds like a fucking miserable experience. Like, if this is what my job becomes, I'll probably quit tech completely.
- kentonv 1y agoThat's exactly what I thought, too, before I tried it! Turns out it feels very different than I expected. I really recommend trying it rather than assuming. There's no learning curve, you just install Claude Code and run it in your repo and ask it for things. (I am the author of the code being discussed. Or, uh, the author of the prompts at least.)
- Arainach 1y ago>Around the 40-commit mark, manual commits became frequent This matches my experience: some shiny (even sometimes impressive) greenspace demos but dramatically less useful maintaining a codebase - which for any successful product is 90% of the work.
- Lerc 1y ago>Treat prompts as version-controlled assets. Including prompts in commit messages creates valuable context for future maintenance and debugging. I think this is valuable data, but it is also out of distribution data. Prior to AI models writing code, this won't be present in the training set. Additional training will probably be needed to correlate better results with the new input stream, and also to learn that some of the records would be of its own unreliability and to develop a healthy scepticism of what it has said in the past. There's a lot of talk about model collapse with models training purely on their own output, or AI slop infecting training data sets, but ultimately it is all data. Combined with a signal to say which bits were ultimately beneficial, it can all be put to use. Even the failures can provide a good counterfactual signal for constrastive learning.
- _pdp_ 1y agoI commented on the original discussion a few days ago but I will do it again. Why is this such a big deal? This library is not even that interesting. It is very straightforward task I expect most programers will be able to pull off easily. 2/3 of the code is type interfaces and comments. The rest is by book implementation of a protocol that is not even that complex. Please, there are some React JSX files in your code base with a lot more complexities and intricacies than this. Has anyone even read the code at all?
- JackSlateur 1y agoOf course, this is a pathetic commercial, nothing serious As you say, the code is not interesting, it deals with a well known topic And it required lots of man power to get done tldr: this is a non-event disguised as incredible success. No doubt cloudflare is making money with that AI crap, somehow.
- axi0m 1y ago>> what if we treated prompts as the actual source code? And they probably will be. Looks like prompts have become the new higher-level coding language, the same way JavaScript is a human-friendly abstraction of an existing programming language (like C), which is already a more accessible way to write assembly itself, and the same goes for the underlying binary code... I guess we eventually reached the final step in the development chain, bridging the gap between hardware instructions and human language.
- dgb23 1y agoC, JS etc. Are abstractions in the Dijkstra sense. Coding agents aren’t.
- starkparker 1y ago> Almost every feature required multiple iterations and refinements. This isn't a limitation—it's how the collaboration works. I guess that's where a big miss in understanding so much of the messaging about generative AI in coding happens for me, and why the Fly.io skepticism blog post irritated me so much as well. It _is_ how collaboration with a person works, but the when you have to fix the issues that the tool created, you aren't collaborating with a person, you're making up for a broken tool. I can't think of any field where I'd be expected to not only put up with, but also celebrate, a tool that screwed up and required manual intervention so often. The level of anthropomorphism that occurs in order to advocate on behalf of generative AI use leads to saying things like "it's how collaboration works" here, when I'd never say the same thing about the table saw in my woodshop, or even the relatively smart cruise control on my car. Generative AI is still just a tool built by people following a design, and which purportedly makes work easier. But when my saw tears out cuts that I have to then sand or recut, or when my car slams on the brakes because it can't understand a bend in the road around a parking lane, I don't shrug and ascribe them human traits and blame myself for being frustrated over how they collaborate with me.
- isaacremuant 1y agoLikewise when they use all these benchmarks for "intelligence" and the tool will do the silliest things that you'd consider unacceptable from a person once you've told them a few times not to do a certain thing. I love the paradigm shift but hate when the hype is uninformed or dishonest or not treating it with an eye for quality.
- hooverd 1y agoYour table saw hungers for fingers.
- pontifier 1y agoGarbage in, Garbage out... My experiment with vibe coding was quite nice, but it did require a collaborative back and forth, mostly because I didn't know exactly what I wanted. It was easiest to ask for something, then describe how what it gave me needed to be changed. The cost of this type of interaction was much easier than trying to craft the perfect prompt on the first go. My first prompts were garbage, but the output gradually converged to something quite good.
- iandanforth 1y agoSeveral discussions here should be making references to literate programming but as far as I can see, haven't. For the curious and/or Knuth fans: https://en.wikipedia.org/wiki/Literate_programming https://en.wikipedia.org/wiki/Literate_programming
- mastazi 1y ago> Around the 40-commit mark, manual commits became frequent—styling, removing unused methods, the kind of housekeeping that coding models still struggle with. It's clear that AI generated >95% of the code, but human oversight was essential throughout. But things like styling and unused code removal have been automated for a long time already, thanks to non-AI tools; assuming that the AI agent has access to those tools (e.g. assuming the agent can trigger a linter), then the engineer could have just included these steps in the prompts instead of running them manually. EDIT - I still think there are aspects where AI is obviously lacking, I just think those specific examples are not among them
- buu700 1y agoSpeaking of which, something funny I've noticed when using agents with prettier in a pre-commit hook is that the logs occasionally include the model thanking "me" for cleaning up its code formatting.
- ianks 1y ago<thinking>I’m trying to remember if oauth has a specification or not, but I’m getting conflicting thoughts</thinking>
- GPerson 1y agoWhat are some ethical ways to oppose this? I’ll continue to make clear that at least one voice out here opposes AI in all forms.