161–170 of 181 posts
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Gemini 2.5 Pro Experimental (a frontier model) has 5 RPM and 25 RPD. Gemini 2.5 Flash Preview 04-17 another powerful model has 10 and 500. OpenAI also allows you to use their API for free if you agree to share the tokens.
What are "RPM" and "RPD"? I assume not Revolutions Per Minute?
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> Answers like this are sort of what makes me wonder what most engineers are smoking when they think AI isn’t valuable. Honestly, I wonder if I'm living in some parallel universe, because my experience is that "most engineers" are far from that position. The reactions I'm seeing are either "AI is the future" or "I have serious objections to and/or problems with AI". If you're calling the latter group "the outright di…
The value is that it got the details correct as you admit. That alone is worth the price of admission. Even if I need to rewrite or edit parts it has saved me time, and has raised the quality of PRs being submitted across the board. The key point with these tools is *Accountability*. As an engineer you are still accountable for your work. Using any tool doesn't take that away. If the PR tool gets it wrong, and you st…
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You don't even need such fancy examples. There are plenty of codebases where people are working with code that is over a decade old and has several paradigms all intermixed with a lot of tribal knowledge that isn't documented in code or wiki. That is where AI sucks. It will not be able to make meaningfully change in that environment. There is also the frontend and tnpse code bases don't need to be very old at all bef…
When is the last time you tried using LLMs against a large, old, crufty undocumented codebase? Things have changed a lot in the past six weeks. Gemini 2.5 Pro accepts a million tokens and can "reason" with them, which means you can feed it hundreds of thousands of lines of code and it has a surprisingly good chance of figuring things out. OpenAI released their first million token models with the GPT 4.1 series. OpenA…
Maybe in a generation or two codebases will become more uniform and predictible if fewer humans do it by hand. Same with self driving cars, if there were no human drivers out there the problem would become trivial to conquer.
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When is the last time you tried using LLMs against a large, old, crufty undocumented codebase? Things have changed a lot in the past six weeks. Gemini 2.5 Pro accepts a million tokens and can "reason" with them, which means you can feed it hundreds of thousands of lines of code and it has a surprisingly good chance of figuring things out. OpenAI released their first million token models with the GPT 4.1 series. OpenA…
Ultimately LLMs don’t really understand what the code does at runtime. Sure, just parsing out the codebase can help make a good guess but in some cases it’s hard to trust LLMs with changes because the consequences are unknown in complex codebases that have weird warts nobody documented. Maybe in a generation or two codebases will become more uniform and predictible if fewer humans do it by hand. Same with self drivin…
They still make mistakes, and yeah they're still (mostly) next token predicting machines under the hood, but if your mental model is "they can't actually predict through how some code will execute" you may need to update that.
Very cool, thanks for sharing. I imagine that this will make a lot of my fellow technical writers (even more) nervous about the future of our industry. I think the reality is more along the lines of: * Previously, it was simply infeasible for most codebases to get a decent tutorial for one reason or another. E.g. the codebase is someone's side project and they don't have the time or energy to maintain docs, let alone…
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You don't even need such fancy examples. There are plenty of codebases where people are working with code that is over a decade old and has several paradigms all intermixed with a lot of tribal knowledge that isn't documented in code or wiki. That is where AI sucks. It will not be able to make meaningfully change in that environment. There is also the frontend and tnpse code bases don't need to be very old at all bef…
When is the last time you tried using LLMs against a large, old, crufty undocumented codebase? Things have changed a lot in the past six weeks. Gemini 2.5 Pro accepts a million tokens and can "reason" with them, which means you can feed it hundreds of thousands of lines of code and it has a surprisingly good chance of figuring things out. OpenAI released their first million token models with the GPT 4.1 series. OpenA…
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You don't point this tool at the documentation though. You point it at a repo. Granted, this example (and others) have plenty of inline documentation. And, public documentation is likely in the training data for LLMs. But, this is more than just a prompt. The tool generates really nicely structured and readable tutorials that let you understand codebases at a conceptual level easier than reading docstrings and code.…
I’m not saying that the tool is useless, I was confuting your argument about being a project WITHOUT docs. LLM can write passable docs, but obviously can write better docs of project well documented in training data. And this example is probably in training data as of April 2025
I think Gemini 2.5 Pro is doing a lot of the heavy lifting here. I have tried this sort of thing before (documentation, not tutorials, granted) and it wasn't anywhere near this good.