35 ms·

Coding with LLMs in the summer of 2025 – an update

600 points by antirez 1y ago

stillsut 1y ago
Overall strong piece of writing. This part resonated with me as aptly described: > more/better in the same time used in the past — which is what I do), when left alone with nontrivial goals they tend to produce fragile code bases that are larger than needed, complex, full of local minima choices, suboptimal in many ways. And this part felt like a "bitter lesson" anti-pattern: > Avoid any RAG that will show only part of the code / context to the LLM. This destroys LLMs performance. You must be in control of what the LLM can see when providing a reply. Ultimately I think cli agents like claude-code and gemini-cli and aider will be controlling the context dynamically, and the human should not be spending premature optimization time on this activity. If anyone's interested I've got some very exact stats on prompts and accepted solution linked in my LLM proof of concept repo: https://github.com/sutt/agro/blob/master/docs/dev-summary-v1.md https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...
theodorewiles 1y ago
My question on all of the “can’t work with big codebases” is how would a codebase that was designed for an LLM look like? Composed of many many small functions that can be composed together?
- antirez 1y ago
  I believe it’s the same as for humans: different files implementing different parts of the system with good interfaces and sensible boundaries.
  - dkdcio 1y ago
    this is a common pattern I see -- if your codebase is confusing for LLMs, it's probably confusing for people too
    - physicles 1y ago
      This fact is one of the most pleasant surprises I’ve had during this AI wave. Finally, a concrete reason to care about your docs and your code quality.
      - aitchnyu 1y ago
        "What helps the human helps the AI" in https://blog.nilenso.com/blog/2025/05/29/ai-assisted-coding/ https://blog.nilenso.com/blog/2025/05/29/ai-assisted-coding/ In future I'll go "In the name of our new darling bot, let us unit test and refactor this complicated thing".
  - afro88 1y ago
    Well documented helps a lot too. You can use an LLM to help document a codebase, but it's still an arduous task because you do need to review and fix up the generated docs. It will make, sometimes glaring sometimes subtle, mistakes. And you want your documentation to provide accuracy rather than double down on or even introduce misunderstanding.
- Hasnep 1y ago
  And my question to that is how would that be different from a codebase designed for humans?
  - __MatrixMan__ 1y ago
    I think it means finer toplevel granularity re: what's runnable/testable at a given moment. I've been exploring this for my own projects and although it's not a silver bullet, I think there's something to it. ---- Several codebases I've known have provided a three-stage pipeline: unit tests, integration tests, and e2e tests. Each of these batches of tests depend on the creation of one of three environments, and the code being tested is what ends up in those environments. If you're interested in a particular failing test, you can use the associated environment and just iterate on the failing test. For humans with a bit of tribal knowledge about the project, humans who have already solved the get-my-dev-environment-set-up problem in more or less uniform way, this works ok. Humans are better at retaining context over weeks and months, whereas you have to spin up a new session with an LLM every few hours or so. So we've created environments for ourselves that we ignore most of the time, but that are too complex to be bite sized for an agent that comes on the scene as a blank slate every few hours. There are too few steps from blank-slate to production, and each of them is too large. But if successively more complex environments can be built on each other in arbitrarily many steps, then we could achieve finer granularity. As a nix user, my mental model for this is function composition where the inputs and outputs are environments, but an analogous model would be layers in a docker files where you test each layer before building the one on top of it. Instead of maybe three steps, there are eight or ten. The goal would be to have both whatever code builds the environment, and whatever code tests it, paired up into bite-sized chunks so that a failure in the pipeline points you a specific stage which is more specific that "the unit tests are failing". Ideally test coverage and implementation complexity get distributed uniformly across those stages. Keeping the scope of the stages small maximizes the amount of your codebase that the LLM can ignore while it works. I have a flake output and nix devshell corresponding to each stage in the pipeline and I'm using pytest to mark tests based on which stage they should run in. So I run the agent from the devshell that corresponds with whichever stage is relevant at the moment, and I introduce it to onlythe tests and code that are relevant to that stage (the assumption being that all previous stages are known to be in good shape). Most of the time, it doesn't need to know that it's working stage 5 of 9, so it "feels" like a smaller codebase than it actually is. If evidence emerges that I've engaged the LLM at the wrong stage, I abandon the session and start over at the right level (now 6 of 9 or somesuch).
- Keyframe 1y ago
  like a microservice architecture? overall architecture to get the context and then dive into a micro one?
- exitb 1y ago
  And on top of that - can you steer an LLM to create this kind of code? In my experience the models don’t really have a „taste” for detecting complexity creep and reengineering for simplicity, in the same way an experienced human does.
  - lubujackson 1y ago
    I am vibe coding a complex app. You can certainly keep things clean but the trick is to enforce a rigid structure. This does add a veneer of complexity but simplifies " implement this new module" or "add this feature across all relevant files".
- victorbjorklund 1y ago
  I found that it is beneficial to create more libraries. If I for example build a large integration to an API (basically a whole api client) I would in the past have it in the same repo but now I make it a standalone library.
qweiopqweiop 1y ago
This matches my take, but I'm curious if OP has used Claude code.
- antirez 1y ago
  Yep when I use agents I go for Claude Code. For example I needed to buy too many Commodore 64 than appropriate lately, and I let it code a Telegram bot advising me when popular sources would have interesting listings. It worked (after a few iterations) then I looked at the code base and wanted to puke but who cares in this case? It worked and it was much faster and I had zero to learn in the proces of doing it myself. I published a Telegram library for C in the past and know how it works and how to do scraping and so forth.
  - Keyframe 1y ago
    For example I needed to buy too many Commodore 64 than appropriate lately Been there, done that! for those one-off small things, LLMs are rather cool. Especially Cloude Code and Gemini CLI. I was given an archive of some really old movies recently, but files were bearing title names in Croatian instead of original (mostly English ones). So I claude --dangerously-skip-permissions into the directory with movies and in a two-sentence prompt I asked it to rename files into a given format (that I tend to have in my archive) and for each title to find original name and year or release and use it in the file.. but, before commiting rename to give me a list of before and after for approval. It took like what, a minute of writing a prompt. Now, for larger things, I'm still exploring a way, an angle, what and how to do it. I've tried from yolo prompting to structured and uber structured approaches, all the way to mimicking product/prd - architecture - project management / tasks - developer/agents.. so far, unless it's rather simpler projects I don't see it's happening that way. Most luck I had was "some structure" as context and inputs and then guiding prompting during sessions and reviewing stuff. Almost pair-programming.
apwell23 1y ago
> ## Provide large context I thought large contexts are not necessarily better and sometimes have opposite effect ?
- antirez 1y ago
  LLMs performance will suffer from both insufficient context and context flooding. Balancing is an art.
- NitpickLawyer 1y ago
  I found it depends very much on the task. For "architect" sessions you need as much context as you can reasonably gather. The more the merrier. At least gemini2.5 pro will gather the needed context from many files and it really does make a difference when you can give it a lot of it. On coding you need to aggressively prune it, and only give minimum adjacent context, or it'll start going on useless tangents. And if you get stuck just refresh and start from 0, changing what is included. It's often faster than "arguing" with the LLM in multi-step sessions. (the above is for existing codebases. for vibe-coding one-off scripts, just go with the vibes, sometimes it works surprisingly well from a quick 2-3 lines prompt)
apwell23 1y ago
> Coding activities should be performed mostly with: Claude Opus 4 I've been going down to sonnet for coding over opus. maybe i am just writing dumb code
- stpedgwdgfhgdd 1y ago
  That is also what Anthropic recommends. In edge cases use Opus. Opus is also way more expensive. (Don’t forget to switch back to Sonnet in all terminals)
- jtonl 1y ago
  Most of the time Sonnet 4 just works but need to refine context as much as you can.
- northern-lights 1y ago
  In my experience as well, Sonnet 4 is much better than Opus. Opus is great at the start of a project, where you would need to plan things, structure the project, figure out how to execute but it cannot beat Sonnet is actually executing it. It is also a lot cheaper.
- cyral 1y ago
  Opus is too expensive and I find it goes way off the rails often (just writing way way too much. Maybe that could be controlled with a better prompt on my end). Sonnet gives me more realistic code that isn't too overengineered.
- leemoore 1y ago
  Same, if you dont give opus big enough problems it's more likely to go off the rails. Not much more likely but a little more likely
  - jfkfibkririfk 1y ago
    Did your meditation pay off? Did you hit stream entry?
quantumHazer 1y ago
I'm going a little offtopic here, but I disagree with the OPs use of the term "PhD-level knowledge", although I have a huge amount of respect for antirez (beside that we are born in the same island). This phrasing can be misleading and points to a broader misunderstanding about the nature of doctoral studies, which it has been influenced by the marketing and hype discourse surrounding AI labs. The assertion that there is a defined "PhD-level knowledge" is pretty useless. The primary purpose of a PhD is not simply to acquire a vast amount of pre-existing knowledge, but rather to learn how to conduct research.
- antirez 1y ago
  Agree with that. Read it as expert-level knowledge without all the other stuff LLMs can’t do as well as humans. LLMs way to express knowledge is kinda of alien as it is different, so indeed those are all poor simplifications. For instance an LLM can’t code as well as a top human coder but can write a non trivial program from the first to the last character without iterating.
  - spyckie2 1y ago
    Hey antirez, What sticks out to me is Gemini catching bugs before production release, was hoping you’d give a little more insight into that. Reason being is that we expect ai to create bugs and we catch them, but if Gemini is spotting bugs by some way of it being a QA (not just by writing and passing tests) then that perks my interest.
    - jacobr1 1y ago
      Our team has pretty aggressively started using LLMs for automated code review. It will look at our PRs and post comments. We can adding more material for different things for it to consider- from a looking at a summarized version of our API guidelines, general prompts like, "You are an expert software engineer and QA professional, review this PR and point out any bugs or other areas of technical risk. Make concise suggestions for improvement where applicable." - it catches a ton of stuff. Another area we've started doing is having it look at build failures and writing a report on suggested root causes before even a human looks at it - saves time. Or (and we haven't rolled this out automatically yet but are testing a prototype) having it triage alarms from our metrics, with access to the logs and codebase to investigate.
deleted 1y ago
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Keyframe 1y ago
Unlike OP, from my still limited but intense month or so diving into this topic so far, I had better luck with Gemini 2.5 PRO and Opus 4 on more abstract level like architecture etc. and then dealing input to Sonnet for coding. I found 2.5 PRO, and to a lesser degree Opus, were hit or miss; A lot of instances of them circling around the issue and correcting itself when coding (Gemini especially so), whereas Sonnet would cut to the chase, but needed explicit take on it to be efficient.
- khaledh 1y ago
  This is my experience too. I usually use Gemini 2.5 Pro through AI Studio for big design ideas that need to be validated and refined. Then take the refined requirements to Claude Code which does an excellent job most of the time in coding them properly. Recently I tried Gemini CLI, and it's not even close to Claude Code's sharp coding skills. It often makes syntax mistakes, and get stuck trying to get itself out of a rut; its output is so verbose (and fast) that it's hard to follow what it's trying to do. Claude Code has a much better debugging capability. Another contender in the "big idea" reasoning camp: DeepSeek R1. It's much slower, but most of the time it can analyze problems and get to the correct solution in one shot.
- antirez 1y ago
  Totally possible. In general I believe that while more powerful in their best outputs, Sonnet/Opus 4 are in other ways (alignment / consistency) a regression on Sonnet 3.5v2 (often called Sonnet 3.6), as Sonnet 3.7 was. Also models are complex objects, and sometimes in a given domain a given model that on paper is weaker will work better. And, on top of that: interactive use vs agent requires different reinforcement learning training that sometimes may not be towards an aligned target... So also using the model in one way or the other may change how good it is.
- jpdus 1y ago
  This is also confirmed by internal cline statistics where Opus and Gemini 2.5 pro both perform worse than Sonnet 4 in real-world scenarios https://x.com/pashmerepat/status/1946392456456732758/photo/1 https://x.com/pashmerepat/status/1946392456456732758/photo/1
bgwalter 1y ago
Translation: His company will launch "AI" products in order to get funding or better compete with Valkey. I find it very sad that people who have been really productive without "AI" now go out of their way to find small anecdotal evidence for "AI".
- brokencode 1y ago
  I find it even more sad when people come out of the woodwork on every LLM post to tell us that our positive experiences using LLMs are imagined and we just haven’t realized how bad they are yet.
  - on_the_train 1y ago
    If LLMs were actually useful, there would be no need to scream it everywhere. On the contrary: it would be a guarded secret.
    - neuronexmachina 1y ago
      In my experience, devs generally aren't secretive about tools they find useful.
      - fellowniusmonk 1y ago
        People are insane, you can artificially pine for the simpler betters times made up in your mind when you could give oracle all your money. But I would stake my very life on the fact that the movement by developers we call open-source is the single greatest community and ethos humanity has ever created. Of course it inherits from enlightenment and other thinking, it doesn't exist in a vacuum, but it is an extension of the ideologies that came before it. I challenge anyone to come up with any single modern subcultures that has tangibly generated more that touches more lives, moves more weight, travels farther, effects humanity more every single day from the moment they wake up than the open source software community (in the catholic sense obviously). Both in moral goodness and in measurable improvement in standard of living and understanding of the universe. Some people's memories are very short indeed, all who pine pine for who they imagined they were and are consumed by a memetic desire of their imagined selves.
dcre 1y ago
“Always be part of the loop by moving code by hand from your terminal to the LLM web interface: this guarantees that you follow every process. You are still the coder, but augmented.” I agree with this, but this is why I use a CLI. You can pipe files instead of copying and pasting.
- lmeyerov 1y ago
  Yeah it is also a bit of a shibboleth: vibes coding, when I'm productive for the 80% case with Claude code, is about the LLM cranking for 10-20min. I'm instructing & automating the LLM on how to do its own context management, vs artisanally making every little decision. Ex: Implementing a spec, responding to my review comments, adding wider unit tests, running a role play for usability testing, etc. The main time we do what he describes of manually copying into a web ide is occasionally for a better short use of a model, like only at the beginning of some plan generation, or debug from a bunch of context we have done manually. Like we recently solved some nasty GPU code race this way, using a careful mix of logs and distributed code. Most of our job is using Boring Tools to write Boring Code, even if the topic/area is neato: you do not want your codebase to work like an adventure for everything, so we invest in making it look boring. I agree the other commenter said: I manage context as part of the skill, but by making the AI do it. Doing that by hand is like slowly handcoding assembly. Instead, I'm telling Claude Code to do it. Ex: Download and crawl some new dependency I'm using for some tricky topic, or read in my prompt template markdown for some task, or generate and self-maintain some plan.md with high-level rules on context I defined. This is the 80% case. Maybe one of the disconnects is task latency vs throughput as trade-offs in human attention. If I need the LLM to get to the right answer faster, so the task is done faster, I have to lean in more. But my time is valuable and I have a lot to do. If rather spend 50% less of my time per task, even if the task takes 4x longer, by the LLM spinning longer. In that saved human time, I can be working on another task: I typically have 2-3 terminals running Claude, so I only check in every 5-15min.
  - airstrike 1y ago
    Your strategy only works for some domains.
indigodaddy 1y ago
Since I’ve heard Gemini-cli is not yet up to snuff, has anyone tried opencode+gemini? I’ve heard that with opencode you can login with Google account (have NOT confirmed this, but if anyone has any experience, pls advise) so not sure if that would get extra mileage from Gemini’s limits vs using a Gemini api key?
dakiol 1y ago
> Gemini 2.5 PRO | Claude Opus 4 Whether it's vibe coding, agentic coding, or copy pasting from the web interface to your editor, it's still sad to see the normalization of private (i.e., paid) LLM models. I like the progress that LLMs introduce and I see them as a powerful tool, but I cannot understand how programmers (whether complete nobodies or popular figures) dont mind adding a strong dependency on a third party in order to keep programming. Programming used to be (and still is, to a large extent) an activity that can be done with open and free tools. I am afraid that in a few years, that will no longer be possible (as in most programmers will be so tied to a paid LLM, that not using them would be like not using an IDE or vim nowadays), since everyone is using private LLMs. The excuse "but you earn six figures, what' $200/month to you?" doesn't really capture the issue here.
- azan_ 1y ago
  Paid models are just much, much better.
  - dakiol 1y ago
    Of course they are. I wouldn't expect otherwise :) But the price we're paying (and I don't mean money) is very high, imho. We all talk about how good engineers write code that depends on high-level abstractions instead of low-level details, allowing us to replace third party dependencies easily and test our apps more effectively, keeping the core of our domain "pure". Well, isn't it time we started doing the same with LLMs? I'm not talking about MCP, but rather an open source tool that can plug into either free and open source LLMs or private ones. That would at least allow us to switch to a free and opensource version if the companies behind the private LLMs go rogue. I'm afraid tho that wouldn't be enough, but it's a starting point. To put an example: what would you think if you need to pay for every single Linux process in your machine? Or for every Git commit you make? Or for every debugging session you perform?
    - azan_ 1y ago
      > I'm not talking about MCP, but rather an open source tool that can plug into either free and open source LLMs or private ones. That would at least allow us to switch to a free and opensource version if the companies behind the private LLMs go rogue. I'm afraid tho that wouldn't be enough, but it's a starting point. There are open source tools that do exactly that already.
cheschire 1y ago
I find agentic coding to be best when using one branch per conversation. Even if that conversation is only a single bugfix, branch it. Then do 2 or 3 iterations of that same conversation across multiple branches and choose the best result of the 3 and destroy the other two.
nlh 1y ago
Can anyone recommend a workflow / tools that accomplishes a slightly more augmented version of antirez’ workflow & suggestions minus the copy-pasting? I am on board to agree that pure LLM + pure original full code as context is the best path at the moment, but I’d love to be able to use some shortcuts like quickly applying changes, checkpoints, etc. My persistent (and not unfounded?) worry is that all the major tools & plugins (Cursor, Cline/Roo) all play games with their own sub-prompts and context “efficiency”. What’s the purest solution?
- schneehertz 1y ago
  Github Copilot's Edit mode allows you to manually specify the context, and it runs only once each time to write code with diff checking, without entering a agent loop.
- cheeseface 1y ago
  Claude Code has worked well for me. It is easy to point it to the relevant parts of the codebase and see what it decides to read itself so you provide missing piece of code when necessary.
  - afro88 1y ago
    This is almost the opposite of what OP is asking, and what the post from antirez describes.
- afro88 1y ago
  You can actually just put Cursor in manual mode and it's the same thing. You 100% manage the context and there's no agentic loop. If your codebase fits in the context window, you can also just turn on "MAX" mode and it puts it all in the context for you.
- bGl2YW5j 1y ago
  I use Jetbrains AI assistant for its great integration with the editor and the codebase, and have been experimenting with Claude Code too. Jetbrains Assistant still has better editor integration for things like reviewing generated diffs and generating code based on currently selected code. My augmented workflow is to “chat” with GPT because it’s free and powerful, to refine my ideas and surface things I hadn’t thought about. Then I start writing code to get in to the flow of things. I’ve found if I use the LLM straight away, I disengage, become lazy and lose context and understanding over the code. In these situations, I’ve had to redo the code more often than not. Lack of understanding is one part of why, but more importantly, disengaged prompting leads to vague and incorrect outcomes. When I’m very clear in my head about my goal, I create a prompt either directly from my cursor, or if the changes are larger, I ask the LLM to not apply the changes but instead show them to me. I do both these things within the IDE in the chat window. I review the code and sometimes I’m happy applying it as is, other times I copy and paste it and tweak it manually. I’ve got barebones rules set up; I haven’t felt the need to go overboard. Jetbrains Assistant does a good job of passing relevant context to the model. I keep my prompts top-down (https://en.wikipedia.org/wiki/BLUF_(communication) https://en.wikipedia.org/wiki/BLUF_(communication)) and explicit. Sometimes I’m much more detailed than others, and I’ve found that extra detail isn’t always necessary for a good result.
airstrike 1y ago
I think all conversations about coding with LLMs, vibe coding, etc. need to note the domain and choice of programming language. IMHO those two variables are 10x (maybe 100x) more explanatory than any vibe coding setup one can concoct. Anyone who is befuddled by how the other person {loves, hates} using LLMs to code should ask what kind of problem they are working on and then try to tackle the same problem with AI to get a better sense for their perspective. Until then, every one of these threads will have dozens of messages saying variations of "you're just not using it right" and "I tried and it sucks", which at this point are just noise, not signal.
- cratermoon 1y ago
  They should also share their prompts and discuss exactly how much effort went into checking the output and re-prompting to get the desired result. The post hints at how much work it takes for the human, "If you are able to describe problems in a clear way and, if you are able to accept the back and forth needed in order to work with LLMs ... you need to provide extensive information to the LLM: papers, big parts of the target code base ... And a brain dump of all your understanding of what should be done. Such braindump must contain especially the following:" and more. After all the effort getting to the point where the generated code is acceptable, one has to wonder, why not just write it yourself? The time spent typing is trivial to all the cognitive effort involved in describing the problem, and describing the problem in a rigorous way is the essence of programming.
  - tines 1y ago
    I would assume the argument is that you only need to provide the braindump and extensive information one time (or at least, collect it once, if not upload once) and then you can take your bed of ease as the LLM uses that for many tasks.
    - skydhash 1y ago
      The thing is no one writes that much code, at least anyone that cares about code reuse. Mostly the times is spent collecting the information (especially communication with stakeholder), and verifying that the code you wrote didn't break anything.
skippyboxedhero 1y ago
Have used Claude's GitHub action quite a bit now (10-20 issue implementations, a bit more PR reviews), and it is hit and miss so agree with the enhanced coding rather than just letting it run loose. When the change is very small, self-contained feature/refactor it can mostly work alone, if you have tests that cover the feature then it is relatively safe (and you can do other stuff because it is running in an action, which is a big plus...write the issue and you are done, sometimes I have had Claude write the issue too). When it gets to a more medium size, it will often produce something that will appear to work but actually doesn't. Maybe I don't have test coverage and it is my fault but it will do this the majority of the time. I have tried writing the issue myself, adding more info to claude.md, letting claude write the issue so it is a language it understands but nothing works, and it is quite frustrating because you spend time on the review and then see something wrong. And anything bigger, unsurprisingly, it doesn't do well. PR reviews are good for small/medium tasks too. Bar is lower here though, much is useless but it does catch things I have missed. So, imo, still quite a way from being able to do things independently. For small tasks, I just get Claude to write the issue, and wait for the PR...that is great. For medium (which is most tasks), I don't need to do much actual coding, just directing Claude...but that means my productivity is still way up. I did try Gemini but I found that when you let it off the leash and accept all edits, it would go wild. We have Copilot at work reviewing PRs, and it isn't so great. Maybe Gemini better on large codebases where, I assume, Claude will struggle.
- milofeynman 1y ago
  The problem here is the infrastructure required to demo the changes to the user. Like yeah you made a code-change, but now I have to pull it, maybe setup data to get it in the right state, check if it's functioning how I want it to. Looking at the code it produced in a diff can waste a lot of your time if it doesn't even work as expected.
fumeux_fume 1y ago
I currently use LLMs as a glorified Stack Overflow. If I want to start integrating an LLM like Gemini 2.5 PRO into my IDE (I use Visual Studio Code), whats the best way to do this? I don't want to use a platform like Cursor or Claude Code which takes me away from my IDE.
- hedgehog 1y ago
  GitHub Copilot is pretty easy to try within VS Code
  - fumeux_fume 1y ago
    I want to use Gemini 2.5 PRO. I was an early tester of Copilot and it was awful.
    - kgwgk 1y ago
      https://docs.github.com/en/copilot/reference/ai-models/supported-ai-models-in-copilot https://docs.github.com/en/copilot/reference/ai-models/suppo...
      - fumeux_fume 1y ago
        Thank you! When I was testing out Copilot I was stuck with whatever default LLM was being used. Didn't realize you could switch it out for a non-MS/OpenAI model.
        hedgehog 1y ago
        In my testing Sonnet 4 is far better than any of the Google or OpenAI models.
    - haiku2077 1y ago
      Copilot has 2.5 Pro in the settings in github.com, along with claude 4
- 1y ago
brainless 1y ago
Lovely post @antirez. I like the idea that LLMs should be directly accessing my codebase and there should be no agents in between. Basically no software that filters what the LLM sees. That said, are there tools that make going through a codebase easier for LLMs? I guess tools like Claude Code simply grep through the codebase and find out what Claude needs. Is that good enough or are there tools which keep a much more thorough view of the codebase?
DSingularity 1y ago
Sorry if I missed it in the article — what’s your setup? Do you use a CLI tool like aider or are you using an IDE like cursor?
- quantumHazer 1y ago
  He uses vim and copy paste code from web interfaces because he wants to maintain control and understanding of the code. You can find proofs of this setup on his youtube channel [https://www.youtube.com/@antirez https://www.youtube.com/@antirez]
  - antirez 1y ago
    Thanks. Also based on the coding rig you use models may not match the performance of what it is served via web. Or may not be as cheap. For instance the Gemini 2.5 pro 20$ account is very hard to saturate with queries.
- antirez 1y ago
  Terminal with vim in one side, the official web interface of the model in the other side. The pbcopy utility to pass stuff in the clipboard. I believe models should be used in their native interface as when there are other layers sometimes the model served is not exactly the same, other times it misbehaves because of RAG and in general no exact control of the context window.
  - js2 1y ago
    This seems like a lot of work depending upon the use case. e.g. the other day I had a bunch of JSON files with contact info. I needed to update them with more recent contact info on an internal Confluence page. I exported the Confluence page to a PDF, then dropped it into the same directory as the JSON files. I told Claude Code to read the PDF and use it to update the JSON files. It tried a few ways to read the PDF before coming up with installing PyPDF2, using that to parse the PDF, then updated all the JSON files. It took about 5 minutes to do this, but it ended up 100% correct, updating 7 different fields across two dozen JSON files. (The reason for the PDF export was to get past the Confluence page being behind Okta authentication. In retrospect, I probably should've saved the HTML and/or let Claude Code figure out how to grab the page itself.) How would I have done that with Gemini using just the web interface?
speedgoose 1y ago
Thanks for writing this article. I used a similar setup until a few weeks ago, but coding agents became good enough recently. I don’t find context management and copy pasting fun, I will let GitHub Copilot Insiders or Claude Code do it. I’m still very much in the loop while doing vibe coding. Of course it depends on the code base, and Redis may not benefit much from coding agents. But I don’t think one should reject vibe coding at this stage, it can be useful when you know what the LLMs are doing.
cushychicken 1y ago
I’m super curious to see the reactions in the comments. antirez is a big fuggin deal on HN. I’m sort of curious if the AI doubting set will show up in force or not.
appleaday1 1y ago
[flagged]
1024core 1y ago
I have found that if I ask the LLM to first _describe_ to me what it wants to do without writing any code, then the subsequent code generated has much higher quality. I will ask for a detailed description of the things it wants to do, give it some feedback and after a couple of iterations, tell it to go ahead and implement it.
- deleted 1y ago
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- hyperadvanced 1y ago
  Seconded. Winsome strategy, plays to the strengths of the LLM without letting it run wild
lysecret 1y ago
IMO Claude code was a huge step up. We have a large and well structured python code base revolving mostly around large and complicated adapter pattern Claude is almost fully capable to implement a new adapter if given the right prompt/resources.
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krupan 1y ago
What is the overall feedback loop with LLMs writing code? Do they learn as they go like we do? Do they just learn from reading code on GitHub? If the latter, what happens as less and less code gets written by human experts? Do the LLMs then stagnate in their progress and start to degrade? Kind of like making analog copies of analog copies of analog copies?
- Herring 1y ago
  Code and math are similar to chess/go, where verification is (reasonably) easy so you can generate your own high-quality training data. It's not super straightforward, but you should still expect more progress in coming years.
  - cesarb 1y ago
    > Code and math are similar to chess/go, where verification is (reasonably) easy Verification for code would be a formal proof, and these are hard; with a few exceptions like seL4, most code does not have any formal proof. Games like chess and go are much easier to verify. Math is in the middle; it also needs formal proofs, but most of math is doing these formal proofs themselves, and even then there are still unproven conjectures.
    - Herring 1y ago
      Verification for code is just running it. Maybe "verification" was the wrong word. The model just needs a sense of code X leads to outcome Y for a large number of (high-quality) XY pairs, to learn how to navigate the space better, same as with games.
      - krupan 1y ago
        "just running it" is an enormously absurd simplification
        Herring 1y ago
iandanforth 1y ago
The most interesting and divergent part of this post is this bit: "Don’t use agents or things like editor with integrated coding agents." He argues that the copy/paste back and forth with the web UI is essential for maintaining control and providing the correct context.
tomwphillips 1y ago
I'm surprised IDE integration is written off. I've been pleased with Junie's agent mode in IntelliJ. Works well.
mococa 1y ago
I have a good example of how sometimes AI/LLM can write very very inefficient code: https://nullonerror.org/2025/07/12/ai-will-replace-programmers-just-not-yet-because-it-currently-generates-extremely-inefficient-code/ https://nullonerror.org/2025/07/12/ai-will-replace-programme...
- dawnerd 1y ago
  Similarly, AI is really bad at code golf. You’d think it would be great, able to know all the little secrets but nope. It needs verbose code.
  - verbify 1y ago
    I wonder what fine tuning an LLM on code golf examples would produce.
lettergram 1y ago
Contrary to this post, I think the AI agents, particularly the online interface of OpenAI's Codex to be a massive help. One example, I had a PR up that was being reviewed by a colleague. I was driving home from vacation when I saw the 3-4 comments come in. I read them when we stopped for gas, went to OpenAI / codex on my phone, dictated what I needed and made it PR to my branch. Then got back on the road & PR'd it. My colleague saw the PR, agreed and merged it in. I think of it as having a ton of interns, the AI is about the same quality. It can help to have them, but they often get stuck, need guidance, etc. If you treat the AI like an intern and explain what you need it can often produce good results; just be prepared to fallback to coding quickly.
entropyneur 1y ago
Interesting. This is quite contrary to my experience. Using LLMs for things ouside my expertise produces crappy results which I can only identify as such months later when my expertise expands. Meanwhile delegating the boring parts that I know too well to agents proved to be a huge productivity boost.
sitkack 1y ago
I find it serendipitous that Antirez is into LLM based coding, because the attention to detail in Redis means all the LLMs have trained extensively on the Redis codebase. Something that was meant for humans, has now been consumed by AI and he is being repaid for that openness in a way. It comes full circle. Consistency, clarity and openness win again.
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ok123456 1y ago
One way to utilize these CLI coding agents that I like is to have them run static analysis tools in a loop, along with whatever test suite you have set up, systematically improving crusty code beyond the fixes that the static analysis tools offer.
wg0 1y ago
I don't understand. Is author suggesting manually pasting redis C files into Gemini Pro chat window on the web?
- thefourthchime 1y ago
  I was mostly nodding my head until he got to this part. The fundamental requirement for the LLM to be used is: don’t use agents or things like editor with integrated coding agents. So right, is he like actually copying and pasting stuff into a chat window? I did this before Co-Pilot, but with cursor I would never think of doing that. He never mentioned Cursor or Claude Code so I wonder if he's even experienced it.
  - schneehertz 1y ago
    The edit mode of GitHub Copilot requires manually providing context files and does not have RAG or other agent tools. I think this mode is much easier to use than the agent mode.
  - libraryofbabel 1y ago
    Right, this didn’t make much sense to me either. Who’d still recommend copy-and-paste-into-chat coding these days with Claude Code and similar agents available? I wonder if he’s got agents / IDEs like windsurf, copilot, cursor etc where there is more complexity between you and the frontier LLM and various tricks to minimize token use. Claude Code, Gemini CLI etc aren’t like that and will just read in whole files into the context so that the LLM can see everything, which I think achieves what he wants but with all the additional magic of agents like edits, running tests, etc. as well.
    - Implicated 1y ago
      > agents / IDEs like windsurf, copilot, cursor etc where there is more complexity between you and the frontier LLM and various tricks to minimize token use. This is exactly why he's doing it the way he is and why what he describes is still the most effective, albeit labor intensive, way to work on hard/complex/difficult problems with LLMs. Those tricks are for saving money. They don't make the LLM better at its task. They just make it so the LLM will do what you could/should be doing. We're using agents because we're lazy or don't have time or attention to devote, or the problems are trivial enough to solved with these "tricks" and added complexities. But, if you're trying to solve something complex or don't want to have a bunch of back and forth with the LLM or don't want to watch it iterate and do some dumb stuff... curate that context. Actually put thought and time into what you provide the LLM, both in context and in prompt - you may find that what you get is a completely different product. Or, if you're just having it build views and buttons - keep vibing.
jwpapi 1y ago
Thank you very much this is exactly my experience. I sometimes let it vibe code frontend features that area easy to test in an already typed code base (add a field to this form), but most of the time its my sparring partner to review my code and evaluate all options. While it often recommends bullox or has logical flaws it helps me to do the obvious thing and to not miss a solution! Sometimes we have fancy play syndrome and want to code the complicated thing because of a fundamental leak we have. LLMS done a great job of reducing those of my flaws. But just because I’ve not been lazy…
cadamsdotcom 1y ago
Having done a few months of this new “job” of agentic coding I strongly agree with everything in this post. Frontier LLMs are easiest to work with for now. Open models _will_ catch up. We can be excited for that future. You are able to learn things from LLMs, and you can ask them for recommendations for an approach to implement something. Or just tell your LLM the approach to take. Sometimes it overcomplicates things. You’ll develop an instinct for when that’s likely. You can head off the overcomplication ahead of time or ask for refactorings after the initial cut is built for you. After a while you get an instinct for which way will get the work done soonest. Most fascinating of all, it’ll all change again with the next round of frontier models. You don’t need frontier models for every task. For instance I’ve been positively surprised by Github Copilot for straightforward features and fixes. When it’s obvious how to implement, and you won’t need to go back and forth to nail down finer design details, getting an initial PR from Copilot is a great starting place. To everyone starting out, enjoy the ride, know that none of us know what we’re doing, and share what you learn along the way!
benreesman 1y ago
Opus 4 just showed me Claude Code style work evasion heuristics for the first time today. I had been cautiously optimistic that they were just going to run the premium product at the exhorbidant price: you don't always want to pay it, but its there. Untrustworthy is worse than useless.
lunarcave 1y ago
> Despite the large interest in agents that can code alone, right now you can maximize your impact as a software developer by using LLMs in an explicit way, staying in the loop. I think this is key here. Whoever has the best UX for this (right now, it's Cursor IMO) will get the bulk of the market share. But the switching costs are so low for this set of tooling that we'll see a rapid improvement in the products available, and possibly some new entrants.
SamInTheShell 1y ago
I like how this is written in a way that an LLM doing planning can probably infer what to do. Let me know if I hit the nail on the head with what you’re thinking @antirez
vl 1y ago
I use Claude Code with Opus, and article recommends Gemini 2.5 Pro. I want to try it as well, but I don't know a tool which would make experience compatible to Claude Code. Would it make sense to use with Cursor? Do they try to limit context?
- ramraj07 1y ago
  My experience, which seems fairly isolated, is that using Gemini's web chat interface and pasting entire sections of my codebase beats any other agent I've seen. Some come close, and some are good with very large files etc, but if you have a decently organized codebase then using gemini like this beats anything else.
  - vl 1y ago
    I work on established project with medium-size codebase, copying to chat and back is just not practical. This is why Claude Code rocks - it quite often finds relevant parts itself.
    - ramraj07 1y ago
      I use tools like 16x prompt to copy just the relevant files. Sure, the tools like Claude Code finds the relevant files, but what happens after they find files is still suboptimal to what I get if I paste them into gemini myself.
- Karrot_Kream 1y ago
  Gemini has its own Claude Code like tool you can use. https://github.com/google-gemini/gemini-cli https://github.com/google-gemini/gemini-cli
abhi3188 1y ago
A good way to get a model to answer questions about a codebase without overwhelming it or exceeding its token count is to: 1. just give it the directory structure 2. ask it questions based on that 3. after it answers a question ask it if there are any specific code files it needs to better answer the question you asked 4. attach only those files so it can confirm its answer and back it up with code
- alexalx666 1y ago
  you can upload a zip of a feature files as they are in the project and ask to implement it on another platform on chatgpt 4.5, works pretty good.
YetAnotherNick 1y ago
> Coding activities should be performed mostly with: > * Gemini 2.5 PRO > * Claude Opus 4 I think trying out all the LLMs for each task is highly underappreciated. There is no pareto optimal LLM for all skills. I give each prompt to 8 different LLMs using a Mac app. In my experience while Gemini is consistently in top 3 of 8, the difference between best output and Gemini Pro could be huge.
seivan 1y ago
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karel-3d 1y ago
ok when even someone like antirez is doing this stuff, maybe I will eventually try it out ...one day.
ycombadmin1 1y ago
https://news.ycombinator.com/item?id=44633940 https://news.ycombinator.com/item?id=44633940 Error while reading
neves 1y ago
Well, here in the Southern hemisphere it's winter time.
- amelius 1y ago
  Not an AI winter, I hope?
amluto 1y ago
> Always be part of the loop by moving code by hand from your terminal to the LLM web interface Is there any good tooling for making this part easier and less error prone, short of going to a full-fledged agent system?
- faxmeyourcode 1y ago
  I don't aggree 100% with the OP on this suggestion. I use an agent to build a feature, then if it's significantly important enough I will have it open a PR that I can review traditionally after testing. Copying and pasting code may work but when the agent is right there and can go read a file on its own I don't see the point in copying and pasting this way. Really though, like another comment said, I'm probably working on different problems than antirez, so your mileage may vary.
freeone3000 1y ago
This is possibly the first HN AI article that actually matches my experience - where the models are good enough for small pieces other people have done once, or for where you might otherwise write a macro, but for anything beyond the scope of a single file write shitty code; and regardless, always have to be hand-held. It’s a far cry away from “vibe code everything”, “this will eliminate jobs” that the current hype train is pushing, despite clearly using the agentic approach with large context provieee by Opus.
headcanon 1y ago
> Gemini 2.5 PRO | Claude Opus 4 Glad to see my experience is reflected elsewhere. I've found Gemini 2.5 PRO to be the best bang-for-buck model: good reasoning and really cheap to run (counts as 1 request in cursor, where opus can blow my quotas out of the water). Code style works well for me too, its "basic" but thats what I want. If I have only one model to take to my deserted island this is the one I'd use right now. For the heady stuff, I usually use o3 (but only to debug, its coding style is a bit weird for me), saving Opus 4 for when I need the "big guns". I don't have Claude Code (cursor user for now), but if I did I'd probably use Opus more.
kevinprice 1y ago
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600 points by antirez 1y ago

Overall strong piece of writing. This part resonated with me as aptly described: > more/better in the same time used in the past — which is what I do), when left alone with nontrivial goals they tend to produce fragile code bases that are larger than needed, complex, full of local minima choices, suboptimal in many ways. And this part felt like a "bitter lesson" anti-pattern: > Avoid any RAG that will show only part of the code / context to the LLM. This destroys LLMs performance. You must be in control of what the LLM can see when providing a reply. Ultimately I think cli agents like claude-code and gemini-cli and aider will be controlling the context dynamically, and the human should not be spending premature optimization time on this activity. If anyone's interested I've got some very exact stats on prompts and accepted solution linked in my LLM proof of concept repo: https://github.com/sutt/agro/blob/master/docs/dev-summary-v1.md https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...

theodorewiles 1y ago

My question on all of the “can’t work with big codebases” is how would a codebase that was designed for an LLM look like? Composed of many many small functions that can be composed together?

antirez 1y ago
I believe it’s the same as for humans: different files implementing different parts of the system with good interfaces and sensible boundaries.
- dkdcio 1y ago
  this is a common pattern I see -- if your codebase is confusing for LLMs, it's probably confusing for people too
  - physicles 1y ago
    This fact is one of the most pleasant surprises I’ve had during this AI wave. Finally, a concrete reason to care about your docs and your code quality.
    - aitchnyu 1y ago
      "What helps the human helps the AI" in https://blog.nilenso.com/blog/2025/05/29/ai-assisted-coding/ https://blog.nilenso.com/blog/2025/05/29/ai-assisted-coding/ In future I'll go "In the name of our new darling bot, let us unit test and refactor this complicated thing".
- afro88 1y ago
  Well documented helps a lot too. You can use an LLM to help document a codebase, but it's still an arduous task because you do need to review and fix up the generated docs. It will make, sometimes glaring sometimes subtle, mistakes. And you want your documentation to provide accuracy rather than double down on or even introduce misunderstanding.
Hasnep 1y ago
And my question to that is how would that be different from a codebase designed for humans?
- __MatrixMan__ 1y ago
  I think it means finer toplevel granularity re: what's runnable/testable at a given moment. I've been exploring this for my own projects and although it's not a silver bullet, I think there's something to it. ---- Several codebases I've known have provided a three-stage pipeline: unit tests, integration tests, and e2e tests. Each of these batches of tests depend on the creation of one of three environments, and the code being tested is what ends up in those environments. If you're interested in a particular failing test, you can use the associated environment and just iterate on the failing test. For humans with a bit of tribal knowledge about the project, humans who have already solved the get-my-dev-environment-set-up problem in more or less uniform way, this works ok. Humans are better at retaining context over weeks and months, whereas you have to spin up a new session with an LLM every few hours or so. So we've created environments for ourselves that we ignore most of the time, but that are too complex to be bite sized for an agent that comes on the scene as a blank slate every few hours. There are too few steps from blank-slate to production, and each of them is too large. But if successively more complex environments can be built on each other in arbitrarily many steps, then we could achieve finer granularity. As a nix user, my mental model for this is function composition where the inputs and outputs are environments, but an analogous model would be layers in a docker files where you test each layer before building the one on top of it. Instead of maybe three steps, there are eight or ten. The goal would be to have both whatever code builds the environment, and whatever code tests it, paired up into bite-sized chunks so that a failure in the pipeline points you a specific stage which is more specific that "the unit tests are failing". Ideally test coverage and implementation complexity get distributed uniformly across those stages. Keeping the scope of the stages small maximizes the amount of your codebase that the LLM can ignore while it works. I have a flake output and nix devshell corresponding to each stage in the pipeline and I'm using pytest to mark tests based on which stage they should run in. So I run the agent from the devshell that corresponds with whichever stage is relevant at the moment, and I introduce it to onlythe tests and code that are relevant to that stage (the assumption being that all previous stages are known to be in good shape). Most of the time, it doesn't need to know that it's working stage 5 of 9, so it "feels" like a smaller codebase than it actually is. If evidence emerges that I've engaged the LLM at the wrong stage, I abandon the session and start over at the right level (now 6 of 9 or somesuch).
Keyframe 1y ago
like a microservice architecture? overall architecture to get the context and then dive into a micro one?
exitb 1y ago
And on top of that - can you steer an LLM to create this kind of code? In my experience the models don’t really have a „taste” for detecting complexity creep and reengineering for simplicity, in the same way an experienced human does.
- lubujackson 1y ago
  I am vibe coding a complex app. You can certainly keep things clean but the trick is to enforce a rigid structure. This does add a veneer of complexity but simplifies " implement this new module" or "add this feature across all relevant files".
victorbjorklund 1y ago
I found that it is beneficial to create more libraries. If I for example build a large integration to an API (basically a whole api client) I would in the past have it in the same repo but now I make it a standalone library.

qweiopqweiop 1y ago

This matches my take, but I'm curious if OP has used Claude code.

antirez 1y ago
Yep when I use agents I go for Claude Code. For example I needed to buy too many Commodore 64 than appropriate lately, and I let it code a Telegram bot advising me when popular sources would have interesting listings. It worked (after a few iterations) then I looked at the code base and wanted to puke but who cares in this case? It worked and it was much faster and I had zero to learn in the proces of doing it myself. I published a Telegram library for C in the past and know how it works and how to do scraping and so forth.
- Keyframe 1y ago
  For example I needed to buy too many Commodore 64 than appropriate lately Been there, done that! for those one-off small things, LLMs are rather cool. Especially Cloude Code and Gemini CLI. I was given an archive of some really old movies recently, but files were bearing title names in Croatian instead of original (mostly English ones). So I claude --dangerously-skip-permissions into the directory with movies and in a two-sentence prompt I asked it to rename files into a given format (that I tend to have in my archive) and for each title to find original name and year or release and use it in the file.. but, before commiting rename to give me a list of before and after for approval. It took like what, a minute of writing a prompt. Now, for larger things, I'm still exploring a way, an angle, what and how to do it. I've tried from yolo prompting to structured and uber structured approaches, all the way to mimicking product/prd - architecture - project management / tasks - developer/agents.. so far, unless it's rather simpler projects I don't see it's happening that way. Most luck I had was "some structure" as context and inputs and then guiding prompting during sessions and reviewing stuff. Almost pair-programming.

apwell23 1y ago

> ## Provide large context I thought large contexts are not necessarily better and sometimes have opposite effect ?

antirez 1y ago
LLMs performance will suffer from both insufficient context and context flooding. Balancing is an art.
NitpickLawyer 1y ago
I found it depends very much on the task. For "architect" sessions you need as much context as you can reasonably gather. The more the merrier. At least gemini2.5 pro will gather the needed context from many files and it really does make a difference when you can give it a lot of it. On coding you need to aggressively prune it, and only give minimum adjacent context, or it'll start going on useless tangents. And if you get stuck just refresh and start from 0, changing what is included. It's often faster than "arguing" with the LLM in multi-step sessions. (the above is for existing codebases. for vibe-coding one-off scripts, just go with the vibes, sometimes it works surprisingly well from a quick 2-3 lines prompt)

apwell23 1y ago

> Coding activities should be performed mostly with: Claude Opus 4 I've been going down to sonnet for coding over opus. maybe i am just writing dumb code

stpedgwdgfhgdd 1y ago
That is also what Anthropic recommends. In edge cases use Opus. Opus is also way more expensive. (Don’t forget to switch back to Sonnet in all terminals)
jtonl 1y ago
Most of the time Sonnet 4 just works but need to refine context as much as you can.
northern-lights 1y ago
In my experience as well, Sonnet 4 is much better than Opus. Opus is great at the start of a project, where you would need to plan things, structure the project, figure out how to execute but it cannot beat Sonnet is actually executing it. It is also a lot cheaper.
cyral 1y ago
Opus is too expensive and I find it goes way off the rails often (just writing way way too much. Maybe that could be controlled with a better prompt on my end). Sonnet gives me more realistic code that isn't too overengineered.
leemoore 1y ago
Same, if you dont give opus big enough problems it's more likely to go off the rails. Not much more likely but a little more likely
- jfkfibkririfk 1y ago
  Did your meditation pay off? Did you hit stream entry?

quantumHazer 1y ago

I'm going a little offtopic here, but I disagree with the OPs use of the term "PhD-level knowledge", although I have a huge amount of respect for antirez (beside that we are born in the same island). This phrasing can be misleading and points to a broader misunderstanding about the nature of doctoral studies, which it has been influenced by the marketing and hype discourse surrounding AI labs. The assertion that there is a defined "PhD-level knowledge" is pretty useless. The primary purpose of a PhD is not simply to acquire a vast amount of pre-existing knowledge, but rather to learn how to conduct research.

antirez 1y ago
Agree with that. Read it as expert-level knowledge without all the other stuff LLMs can’t do as well as humans. LLMs way to express knowledge is kinda of alien as it is different, so indeed those are all poor simplifications. For instance an LLM can’t code as well as a top human coder but can write a non trivial program from the first to the last character without iterating.
- spyckie2 1y ago
  Hey antirez, What sticks out to me is Gemini catching bugs before production release, was hoping you’d give a little more insight into that. Reason being is that we expect ai to create bugs and we catch them, but if Gemini is spotting bugs by some way of it being a QA (not just by writing and passing tests) then that perks my interest.
  - jacobr1 1y ago
    Our team has pretty aggressively started using LLMs for automated code review. It will look at our PRs and post comments. We can adding more material for different things for it to consider- from a looking at a summarized version of our API guidelines, general prompts like, "You are an expert software engineer and QA professional, review this PR and point out any bugs or other areas of technical risk. Make concise suggestions for improvement where applicable." - it catches a ton of stuff. Another area we've started doing is having it look at build failures and writing a report on suggested root causes before even a human looks at it - saves time. Or (and we haven't rolled this out automatically yet but are testing a prototype) having it triage alarms from our metrics, with access to the logs and codebase to investigate.

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Keyframe 1y ago

Unlike OP, from my still limited but intense month or so diving into this topic so far, I had better luck with Gemini 2.5 PRO and Opus 4 on more abstract level like architecture etc. and then dealing input to Sonnet for coding. I found 2.5 PRO, and to a lesser degree Opus, were hit or miss; A lot of instances of them circling around the issue and correcting itself when coding (Gemini especially so), whereas Sonnet would cut to the chase, but needed explicit take on it to be efficient.

khaledh 1y ago
This is my experience too. I usually use Gemini 2.5 Pro through AI Studio for big design ideas that need to be validated and refined. Then take the refined requirements to Claude Code which does an excellent job most of the time in coding them properly. Recently I tried Gemini CLI, and it's not even close to Claude Code's sharp coding skills. It often makes syntax mistakes, and get stuck trying to get itself out of a rut; its output is so verbose (and fast) that it's hard to follow what it's trying to do. Claude Code has a much better debugging capability. Another contender in the "big idea" reasoning camp: DeepSeek R1. It's much slower, but most of the time it can analyze problems and get to the correct solution in one shot.
antirez 1y ago
Totally possible. In general I believe that while more powerful in their best outputs, Sonnet/Opus 4 are in other ways (alignment / consistency) a regression on Sonnet 3.5v2 (often called Sonnet 3.6), as Sonnet 3.7 was. Also models are complex objects, and sometimes in a given domain a given model that on paper is weaker will work better. And, on top of that: interactive use vs agent requires different reinforcement learning training that sometimes may not be towards an aligned target... So also using the model in one way or the other may change how good it is.
jpdus 1y ago
This is also confirmed by internal cline statistics where Opus and Gemini 2.5 pro both perform worse than Sonnet 4 in real-world scenarios https://x.com/pashmerepat/status/1946392456456732758/photo/1 https://x.com/pashmerepat/status/1946392456456732758/photo/1

bgwalter 1y ago

Translation: His company will launch "AI" products in order to get funding or better compete with Valkey. I find it very sad that people who have been really productive without "AI" now go out of their way to find small anecdotal evidence for "AI".

brokencode 1y ago
I find it even more sad when people come out of the woodwork on every LLM post to tell us that our positive experiences using LLMs are imagined and we just haven’t realized how bad they are yet.
- on_the_train 1y ago
  If LLMs were actually useful, there would be no need to scream it everywhere. On the contrary: it would be a guarded secret.
  - neuronexmachina 1y ago
    In my experience, devs generally aren't secretive about tools they find useful.
    - fellowniusmonk 1y ago
      People are insane, you can artificially pine for the simpler betters times made up in your mind when you could give oracle all your money. But I would stake my very life on the fact that the movement by developers we call open-source is the single greatest community and ethos humanity has ever created. Of course it inherits from enlightenment and other thinking, it doesn't exist in a vacuum, but it is an extension of the ideologies that came before it. I challenge anyone to come up with any single modern subcultures that has tangibly generated more that touches more lives, moves more weight, travels farther, effects humanity more every single day from the moment they wake up than the open source software community (in the catholic sense obviously). Both in moral goodness and in measurable improvement in standard of living and understanding of the universe. Some people's memories are very short indeed, all who pine pine for who they imagined they were and are consumed by a memetic desire of their imagined selves.

dcre 1y ago

“Always be part of the loop by moving code by hand from your terminal to the LLM web interface: this guarantees that you follow every process. You are still the coder, but augmented.” I agree with this, but this is why I use a CLI. You can pipe files instead of copying and pasting.

lmeyerov 1y ago
Yeah it is also a bit of a shibboleth: vibes coding, when I'm productive for the 80% case with Claude code, is about the LLM cranking for 10-20min. I'm instructing & automating the LLM on how to do its own context management, vs artisanally making every little decision. Ex: Implementing a spec, responding to my review comments, adding wider unit tests, running a role play for usability testing, etc. The main time we do what he describes of manually copying into a web ide is occasionally for a better short use of a model, like only at the beginning of some plan generation, or debug from a bunch of context we have done manually. Like we recently solved some nasty GPU code race this way, using a careful mix of logs and distributed code. Most of our job is using Boring Tools to write Boring Code, even if the topic/area is neato: you do not want your codebase to work like an adventure for everything, so we invest in making it look boring. I agree the other commenter said: I manage context as part of the skill, but by making the AI do it. Doing that by hand is like slowly handcoding assembly. Instead, I'm telling Claude Code to do it. Ex: Download and crawl some new dependency I'm using for some tricky topic, or read in my prompt template markdown for some task, or generate and self-maintain some plan.md with high-level rules on context I defined. This is the 80% case. Maybe one of the disconnects is task latency vs throughput as trade-offs in human attention. If I need the LLM to get to the right answer faster, so the task is done faster, I have to lean in more. But my time is valuable and I have a lot to do. If rather spend 50% less of my time per task, even if the task takes 4x longer, by the LLM spinning longer. In that saved human time, I can be working on another task: I typically have 2-3 terminals running Claude, so I only check in every 5-15min.
- airstrike 1y ago
  Your strategy only works for some domains.

indigodaddy 1y ago

Since I’ve heard Gemini-cli is not yet up to snuff, has anyone tried opencode+gemini? I’ve heard that with opencode you can login with Google account (have NOT confirmed this, but if anyone has any experience, pls advise) so not sure if that would get extra mileage from Gemini’s limits vs using a Gemini api key?

dakiol 1y ago

> Gemini 2.5 PRO | Claude Opus 4 Whether it's vibe coding, agentic coding, or copy pasting from the web interface to your editor, it's still sad to see the normalization of private (i.e., paid) LLM models. I like the progress that LLMs introduce and I see them as a powerful tool, but I cannot understand how programmers (whether complete nobodies or popular figures) dont mind adding a strong dependency on a third party in order to keep programming. Programming used to be (and still is, to a large extent) an activity that can be done with open and free tools. I am afraid that in a few years, that will no longer be possible (as in most programmers will be so tied to a paid LLM, that not using them would be like not using an IDE or vim nowadays), since everyone is using private LLMs. The excuse "but you earn six figures, what' $200/month to you?" doesn't really capture the issue here.

azan_ 1y ago
Paid models are just much, much better.
- dakiol 1y ago
  Of course they are. I wouldn't expect otherwise :) But the price we're paying (and I don't mean money) is very high, imho. We all talk about how good engineers write code that depends on high-level abstractions instead of low-level details, allowing us to replace third party dependencies easily and test our apps more effectively, keeping the core of our domain "pure". Well, isn't it time we started doing the same with LLMs? I'm not talking about MCP, but rather an open source tool that can plug into either free and open source LLMs or private ones. That would at least allow us to switch to a free and opensource version if the companies behind the private LLMs go rogue. I'm afraid tho that wouldn't be enough, but it's a starting point. To put an example: what would you think if you need to pay for every single Linux process in your machine? Or for every Git commit you make? Or for every debugging session you perform?
  - azan_ 1y ago
    > I'm not talking about MCP, but rather an open source tool that can plug into either free and open source LLMs or private ones. That would at least allow us to switch to a free and opensource version if the companies behind the private LLMs go rogue. I'm afraid tho that wouldn't be enough, but it's a starting point. There are open source tools that do exactly that already.

cheschire 1y ago

I find agentic coding to be best when using one branch per conversation. Even if that conversation is only a single bugfix, branch it. Then do 2 or 3 iterations of that same conversation across multiple branches and choose the best result of the 3 and destroy the other two.

nlh 1y ago

Can anyone recommend a workflow / tools that accomplishes a slightly more augmented version of antirez’ workflow & suggestions minus the copy-pasting? I am on board to agree that pure LLM + pure original full code as context is the best path at the moment, but I’d love to be able to use some shortcuts like quickly applying changes, checkpoints, etc. My persistent (and not unfounded?) worry is that all the major tools & plugins (Cursor, Cline/Roo) all play games with their own sub-prompts and context “efficiency”. What’s the purest solution?

schneehertz 1y ago
Github Copilot's Edit mode allows you to manually specify the context, and it runs only once each time to write code with diff checking, without entering a agent loop.
cheeseface 1y ago
Claude Code has worked well for me. It is easy to point it to the relevant parts of the codebase and see what it decides to read itself so you provide missing piece of code when necessary.
- afro88 1y ago
  This is almost the opposite of what OP is asking, and what the post from antirez describes.
afro88 1y ago
You can actually just put Cursor in manual mode and it's the same thing. You 100% manage the context and there's no agentic loop. If your codebase fits in the context window, you can also just turn on "MAX" mode and it puts it all in the context for you.
bGl2YW5j 1y ago
I use Jetbrains AI assistant for its great integration with the editor and the codebase, and have been experimenting with Claude Code too. Jetbrains Assistant still has better editor integration for things like reviewing generated diffs and generating code based on currently selected code. My augmented workflow is to “chat” with GPT because it’s free and powerful, to refine my ideas and surface things I hadn’t thought about. Then I start writing code to get in to the flow of things. I’ve found if I use the LLM straight away, I disengage, become lazy and lose context and understanding over the code. In these situations, I’ve had to redo the code more often than not. Lack of understanding is one part of why, but more importantly, disengaged prompting leads to vague and incorrect outcomes. When I’m very clear in my head about my goal, I create a prompt either directly from my cursor, or if the changes are larger, I ask the LLM to not apply the changes but instead show them to me. I do both these things within the IDE in the chat window. I review the code and sometimes I’m happy applying it as is, other times I copy and paste it and tweak it manually. I’ve got barebones rules set up; I haven’t felt the need to go overboard. Jetbrains Assistant does a good job of passing relevant context to the model. I keep my prompts top-down (https://en.wikipedia.org/wiki/BLUF_(communication) https://en.wikipedia.org/wiki/BLUF_(communication)) and explicit. Sometimes I’m much more detailed than others, and I’ve found that extra detail isn’t always necessary for a good result.

airstrike 1y ago

I think all conversations about coding with LLMs, vibe coding, etc. need to note the domain and choice of programming language. IMHO those two variables are 10x (maybe 100x) more explanatory than any vibe coding setup one can concoct. Anyone who is befuddled by how the other person {loves, hates} using LLMs to code should ask what kind of problem they are working on and then try to tackle the same problem with AI to get a better sense for their perspective. Until then, every one of these threads will have dozens of messages saying variations of "you're just not using it right" and "I tried and it sucks", which at this point are just noise, not signal.

cratermoon 1y ago
They should also share their prompts and discuss exactly how much effort went into checking the output and re-prompting to get the desired result. The post hints at how much work it takes for the human, "If you are able to describe problems in a clear way and, if you are able to accept the back and forth needed in order to work with LLMs ... you need to provide extensive information to the LLM: papers, big parts of the target code base ... And a brain dump of all your understanding of what should be done. Such braindump must contain especially the following:" and more. After all the effort getting to the point where the generated code is acceptable, one has to wonder, why not just write it yourself? The time spent typing is trivial to all the cognitive effort involved in describing the problem, and describing the problem in a rigorous way is the essence of programming.
- tines 1y ago
  I would assume the argument is that you only need to provide the braindump and extensive information one time (or at least, collect it once, if not upload once) and then you can take your bed of ease as the LLM uses that for many tasks.
  - skydhash 1y ago
    The thing is no one writes that much code, at least anyone that cares about code reuse. Mostly the times is spent collecting the information (especially communication with stakeholder), and verifying that the code you wrote didn't break anything.

skippyboxedhero 1y ago

Have used Claude's GitHub action quite a bit now (10-20 issue implementations, a bit more PR reviews), and it is hit and miss so agree with the enhanced coding rather than just letting it run loose. When the change is very small, self-contained feature/refactor it can mostly work alone, if you have tests that cover the feature then it is relatively safe (and you can do other stuff because it is running in an action, which is a big plus...write the issue and you are done, sometimes I have had Claude write the issue too). When it gets to a more medium size, it will often produce something that will appear to work but actually doesn't. Maybe I don't have test coverage and it is my fault but it will do this the majority of the time. I have tried writing the issue myself, adding more info to claude.md, letting claude write the issue so it is a language it understands but nothing works, and it is quite frustrating because you spend time on the review and then see something wrong. And anything bigger, unsurprisingly, it doesn't do well. PR reviews are good for small/medium tasks too. Bar is lower here though, much is useless but it does catch things I have missed. So, imo, still quite a way from being able to do things independently. For small tasks, I just get Claude to write the issue, and wait for the PR...that is great. For medium (which is most tasks), I don't need to do much actual coding, just directing Claude...but that means my productivity is still way up. I did try Gemini but I found that when you let it off the leash and accept all edits, it would go wild. We have Copilot at work reviewing PRs, and it isn't so great. Maybe Gemini better on large codebases where, I assume, Claude will struggle.

milofeynman 1y ago
The problem here is the infrastructure required to demo the changes to the user. Like yeah you made a code-change, but now I have to pull it, maybe setup data to get it in the right state, check if it's functioning how I want it to. Looking at the code it produced in a diff can waste a lot of your time if it doesn't even work as expected.

fumeux_fume 1y ago

I currently use LLMs as a glorified Stack Overflow. If I want to start integrating an LLM like Gemini 2.5 PRO into my IDE (I use Visual Studio Code), whats the best way to do this? I don't want to use a platform like Cursor or Claude Code which takes me away from my IDE.

hedgehog 1y ago
GitHub Copilot is pretty easy to try within VS Code
- fumeux_fume 1y ago
  I want to use Gemini 2.5 PRO. I was an early tester of Copilot and it was awful.
  - kgwgk 1y ago
    https://docs.github.com/en/copilot/reference/ai-models/supported-ai-models-in-copilot https://docs.github.com/en/copilot/reference/ai-models/suppo...
    - fumeux_fume 1y ago
      Thank you! When I was testing out Copilot I was stuck with whatever default LLM was being used. Didn't realize you could switch it out for a non-MS/OpenAI model.
      - hedgehog 1y ago
        In my testing Sonnet 4 is far better than any of the Google or OpenAI models.
  - haiku2077 1y ago
    Copilot has 2.5 Pro in the settings in github.com, along with claude 4
1y ago

brainless 1y ago

Lovely post @antirez. I like the idea that LLMs should be directly accessing my codebase and there should be no agents in between. Basically no software that filters what the LLM sees. That said, are there tools that make going through a codebase easier for LLMs? I guess tools like Claude Code simply grep through the codebase and find out what Claude needs. Is that good enough or are there tools which keep a much more thorough view of the codebase?

DSingularity 1y ago

Sorry if I missed it in the article — what’s your setup? Do you use a CLI tool like aider or are you using an IDE like cursor?

quantumHazer 1y ago
He uses vim and copy paste code from web interfaces because he wants to maintain control and understanding of the code. You can find proofs of this setup on his youtube channel [https://www.youtube.com/@antirez https://www.youtube.com/@antirez]
- antirez 1y ago
  Thanks. Also based on the coding rig you use models may not match the performance of what it is served via web. Or may not be as cheap. For instance the Gemini 2.5 pro 20$ account is very hard to saturate with queries.
antirez 1y ago
Terminal with vim in one side, the official web interface of the model in the other side. The pbcopy utility to pass stuff in the clipboard. I believe models should be used in their native interface as when there are other layers sometimes the model served is not exactly the same, other times it misbehaves because of RAG and in general no exact control of the context window.
- js2 1y ago
  This seems like a lot of work depending upon the use case. e.g. the other day I had a bunch of JSON files with contact info. I needed to update them with more recent contact info on an internal Confluence page. I exported the Confluence page to a PDF, then dropped it into the same directory as the JSON files. I told Claude Code to read the PDF and use it to update the JSON files. It tried a few ways to read the PDF before coming up with installing PyPDF2, using that to parse the PDF, then updated all the JSON files. It took about 5 minutes to do this, but it ended up 100% correct, updating 7 different fields across two dozen JSON files. (The reason for the PDF export was to get past the Confluence page being behind Okta authentication. In retrospect, I probably should've saved the HTML and/or let Claude Code figure out how to grab the page itself.) How would I have done that with Gemini using just the web interface?

speedgoose 1y ago

Thanks for writing this article. I used a similar setup until a few weeks ago, but coding agents became good enough recently. I don’t find context management and copy pasting fun, I will let GitHub Copilot Insiders or Claude Code do it. I’m still very much in the loop while doing vibe coding. Of course it depends on the code base, and Redis may not benefit much from coding agents. But I don’t think one should reject vibe coding at this stage, it can be useful when you know what the LLMs are doing.

cushychicken 1y ago

I’m super curious to see the reactions in the comments. antirez is a big fuggin deal on HN. I’m sort of curious if the AI doubting set will show up in force or not.

appleaday1 1y ago

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1024core 1y ago

I have found that if I ask the LLM to first _describe_ to me what it wants to do without writing any code, then the subsequent code generated has much higher quality. I will ask for a detailed description of the things it wants to do, give it some feedback and after a couple of iterations, tell it to go ahead and implement it.

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hyperadvanced 1y ago
Seconded. Winsome strategy, plays to the strengths of the LLM without letting it run wild

lysecret 1y ago

IMO Claude code was a huge step up. We have a large and well structured python code base revolving mostly around large and complicated adapter pattern Claude is almost fully capable to implement a new adapter if given the right prompt/resources.

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krupan 1y ago

What is the overall feedback loop with LLMs writing code? Do they learn as they go like we do? Do they just learn from reading code on GitHub? If the latter, what happens as less and less code gets written by human experts? Do the LLMs then stagnate in their progress and start to degrade? Kind of like making analog copies of analog copies of analog copies?

Herring 1y ago
Code and math are similar to chess/go, where verification is (reasonably) easy so you can generate your own high-quality training data. It's not super straightforward, but you should still expect more progress in coming years.
- cesarb 1y ago
  > Code and math are similar to chess/go, where verification is (reasonably) easy Verification for code would be a formal proof, and these are hard; with a few exceptions like seL4, most code does not have any formal proof. Games like chess and go are much easier to verify. Math is in the middle; it also needs formal proofs, but most of math is doing these formal proofs themselves, and even then there are still unproven conjectures.
  - Herring 1y ago
    Verification for code is just running it. Maybe "verification" was the wrong word. The model just needs a sense of code X leads to outcome Y for a large number of (high-quality) XY pairs, to learn how to navigate the space better, same as with games.
    - krupan 1y ago
      "just running it" is an enormously absurd simplification
      - Herring 1y ago

iandanforth 1y ago

The most interesting and divergent part of this post is this bit: "Don’t use agents or things like editor with integrated coding agents." He argues that the copy/paste back and forth with the web UI is essential for maintaining control and providing the correct context.

tomwphillips 1y ago

I'm surprised IDE integration is written off. I've been pleased with Junie's agent mode in IntelliJ. Works well.

mococa 1y ago

I have a good example of how sometimes AI/LLM can write very very inefficient code: https://nullonerror.org/2025/07/12/ai-will-replace-programmers-just-not-yet-because-it-currently-generates-extremely-inefficient-code/ https://nullonerror.org/2025/07/12/ai-will-replace-programme...

dawnerd 1y ago
Similarly, AI is really bad at code golf. You’d think it would be great, able to know all the little secrets but nope. It needs verbose code.
- verbify 1y ago
  I wonder what fine tuning an LLM on code golf examples would produce.

lettergram 1y ago

Contrary to this post, I think the AI agents, particularly the online interface of OpenAI's Codex to be a massive help. One example, I had a PR up that was being reviewed by a colleague. I was driving home from vacation when I saw the 3-4 comments come in. I read them when we stopped for gas, went to OpenAI / codex on my phone, dictated what I needed and made it PR to my branch. Then got back on the road & PR'd it. My colleague saw the PR, agreed and merged it in. I think of it as having a ton of interns, the AI is about the same quality. It can help to have them, but they often get stuck, need guidance, etc. If you treat the AI like an intern and explain what you need it can often produce good results; just be prepared to fallback to coding quickly.

entropyneur 1y ago

Interesting. This is quite contrary to my experience. Using LLMs for things ouside my expertise produces crappy results which I can only identify as such months later when my expertise expands. Meanwhile delegating the boring parts that I know too well to agents proved to be a huge productivity boost.

sitkack 1y ago

I find it serendipitous that Antirez is into LLM based coding, because the attention to detail in Redis means all the LLMs have trained extensively on the Redis codebase. Something that was meant for humans, has now been consumed by AI and he is being repaid for that openness in a way. It comes full circle. Consistency, clarity and openness win again.

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ok123456 1y ago

One way to utilize these CLI coding agents that I like is to have them run static analysis tools in a loop, along with whatever test suite you have set up, systematically improving crusty code beyond the fixes that the static analysis tools offer.

wg0 1y ago

I don't understand. Is author suggesting manually pasting redis C files into Gemini Pro chat window on the web?

thefourthchime 1y ago
I was mostly nodding my head until he got to this part. The fundamental requirement for the LLM to be used is: don’t use agents or things like editor with integrated coding agents. So right, is he like actually copying and pasting stuff into a chat window? I did this before Co-Pilot, but with cursor I would never think of doing that. He never mentioned Cursor or Claude Code so I wonder if he's even experienced it.
- schneehertz 1y ago
  The edit mode of GitHub Copilot requires manually providing context files and does not have RAG or other agent tools. I think this mode is much easier to use than the agent mode.
- libraryofbabel 1y ago
  Right, this didn’t make much sense to me either. Who’d still recommend copy-and-paste-into-chat coding these days with Claude Code and similar agents available? I wonder if he’s got agents / IDEs like windsurf, copilot, cursor etc where there is more complexity between you and the frontier LLM and various tricks to minimize token use. Claude Code, Gemini CLI etc aren’t like that and will just read in whole files into the context so that the LLM can see everything, which I think achieves what he wants but with all the additional magic of agents like edits, running tests, etc. as well.
  - Implicated 1y ago
    > agents / IDEs like windsurf, copilot, cursor etc where there is more complexity between you and the frontier LLM and various tricks to minimize token use. This is exactly why he's doing it the way he is and why what he describes is still the most effective, albeit labor intensive, way to work on hard/complex/difficult problems with LLMs. Those tricks are for saving money. They don't make the LLM better at its task. They just make it so the LLM will do what you could/should be doing. We're using agents because we're lazy or don't have time or attention to devote, or the problems are trivial enough to solved with these "tricks" and added complexities. But, if you're trying to solve something complex or don't want to have a bunch of back and forth with the LLM or don't want to watch it iterate and do some dumb stuff... curate that context. Actually put thought and time into what you provide the LLM, both in context and in prompt - you may find that what you get is a completely different product. Or, if you're just having it build views and buttons - keep vibing.

jwpapi 1y ago

Thank you very much this is exactly my experience. I sometimes let it vibe code frontend features that area easy to test in an already typed code base (add a field to this form), but most of the time its my sparring partner to review my code and evaluate all options. While it often recommends bullox or has logical flaws it helps me to do the obvious thing and to not miss a solution! Sometimes we have fancy play syndrome and want to code the complicated thing because of a fundamental leak we have. LLMS done a great job of reducing those of my flaws. But just because I’ve not been lazy…

cadamsdotcom 1y ago

Having done a few months of this new “job” of agentic coding I strongly agree with everything in this post. Frontier LLMs are easiest to work with for now. Open models _will_ catch up. We can be excited for that future. You are able to learn things from LLMs, and you can ask them for recommendations for an approach to implement something. Or just tell your LLM the approach to take. Sometimes it overcomplicates things. You’ll develop an instinct for when that’s likely. You can head off the overcomplication ahead of time or ask for refactorings after the initial cut is built for you. After a while you get an instinct for which way will get the work done soonest. Most fascinating of all, it’ll all change again with the next round of frontier models. You don’t need frontier models for every task. For instance I’ve been positively surprised by Github Copilot for straightforward features and fixes. When it’s obvious how to implement, and you won’t need to go back and forth to nail down finer design details, getting an initial PR from Copilot is a great starting place. To everyone starting out, enjoy the ride, know that none of us know what we’re doing, and share what you learn along the way!

benreesman 1y ago

Opus 4 just showed me Claude Code style work evasion heuristics for the first time today. I had been cautiously optimistic that they were just going to run the premium product at the exhorbidant price: you don't always want to pay it, but its there. Untrustworthy is worse than useless.

lunarcave 1y ago

> Despite the large interest in agents that can code alone, right now you can maximize your impact as a software developer by using LLMs in an explicit way, staying in the loop. I think this is key here. Whoever has the best UX for this (right now, it's Cursor IMO) will get the bulk of the market share. But the switching costs are so low for this set of tooling that we'll see a rapid improvement in the products available, and possibly some new entrants.

SamInTheShell 1y ago

I like how this is written in a way that an LLM doing planning can probably infer what to do. Let me know if I hit the nail on the head with what you’re thinking @antirez

vl 1y ago

I use Claude Code with Opus, and article recommends Gemini 2.5 Pro. I want to try it as well, but I don't know a tool which would make experience compatible to Claude Code. Would it make sense to use with Cursor? Do they try to limit context?

ramraj07 1y ago
My experience, which seems fairly isolated, is that using Gemini's web chat interface and pasting entire sections of my codebase beats any other agent I've seen. Some come close, and some are good with very large files etc, but if you have a decently organized codebase then using gemini like this beats anything else.
- vl 1y ago
  I work on established project with medium-size codebase, copying to chat and back is just not practical. This is why Claude Code rocks - it quite often finds relevant parts itself.
  - ramraj07 1y ago
    I use tools like 16x prompt to copy just the relevant files. Sure, the tools like Claude Code finds the relevant files, but what happens after they find files is still suboptimal to what I get if I paste them into gemini myself.
Karrot_Kream 1y ago
Gemini has its own Claude Code like tool you can use. https://github.com/google-gemini/gemini-cli https://github.com/google-gemini/gemini-cli

abhi3188 1y ago

A good way to get a model to answer questions about a codebase without overwhelming it or exceeding its token count is to: 1. just give it the directory structure 2. ask it questions based on that 3. after it answers a question ask it if there are any specific code files it needs to better answer the question you asked 4. attach only those files so it can confirm its answer and back it up with code

alexalx666 1y ago
you can upload a zip of a feature files as they are in the project and ask to implement it on another platform on chatgpt 4.5, works pretty good.

YetAnotherNick 1y ago

> Coding activities should be performed mostly with: > * Gemini 2.5 PRO > * Claude Opus 4 I think trying out all the LLMs for each task is highly underappreciated. There is no pareto optimal LLM for all skills. I give each prompt to 8 different LLMs using a Mac app. In my experience while Gemini is consistently in top 3 of 8, the difference between best output and Gemini Pro could be huge.

seivan 1y ago

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karel-3d 1y ago

ok when even someone like antirez is doing this stuff, maybe I will eventually try it out ...one day.

ycombadmin1 1y ago

https://news.ycombinator.com/item?id=44633940 https://news.ycombinator.com/item?id=44633940 Error while reading

neves 1y ago

Well, here in the Southern hemisphere it's winter time.

amelius 1y ago
Not an AI winter, I hope?

amluto 1y ago

> Always be part of the loop by moving code by hand from your terminal to the LLM web interface Is there any good tooling for making this part easier and less error prone, short of going to a full-fledged agent system?

faxmeyourcode 1y ago
I don't aggree 100% with the OP on this suggestion. I use an agent to build a feature, then if it's significantly important enough I will have it open a PR that I can review traditionally after testing. Copying and pasting code may work but when the agent is right there and can go read a file on its own I don't see the point in copying and pasting this way. Really though, like another comment said, I'm probably working on different problems than antirez, so your mileage may vary.

freeone3000 1y ago

This is possibly the first HN AI article that actually matches my experience - where the models are good enough for small pieces other people have done once, or for where you might otherwise write a macro, but for anything beyond the scope of a single file write shitty code; and regardless, always have to be hand-held. It’s a far cry away from “vibe code everything”, “this will eliminate jobs” that the current hype train is pushing, despite clearly using the agentic approach with large context provieee by Opus.

headcanon 1y ago

> Gemini 2.5 PRO | Claude Opus 4 Glad to see my experience is reflected elsewhere. I've found Gemini 2.5 PRO to be the best bang-for-buck model: good reasoning and really cheap to run (counts as 1 request in cursor, where opus can blow my quotas out of the water). Code style works well for me too, its "basic" but thats what I want. If I have only one model to take to my deserted island this is the one I'd use right now. For the heady stuff, I usually use o3 (but only to debug, its coding style is a bit weird for me), saving Opus 4 for when I need the "big guns". I don't have Claude Code (cursor user for now), but if I did I'd probably use Opus more.

kevinprice 1y ago

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