anotherpaulg

11 ms·

61.
by anotherpaulg 2y ago
When I was mostly just using Sonnet I was spending ~$100/month on their API. That included some amount of bulk API use for benchmarking, not just my interactive AI coding.
62.
by anotherpaulg 2y ago
Not using shebang, but I've recently been using uv as an "installer". It's hard to package and distribute python CLI tools with complex dependencies. I used uv in a couple of novel ways: 1. I copied their `curl | sh` ins
63.
by anotherpaulg 2y ago
It works like normal git blame -- it literally uses git blame. Whoever changed a line last gets credit. Only the new or newly changed lines in each release are considered. So no, "lines/diffs otherwise untouched" are NOT cons
64.
by anotherpaulg 2y ago
You can use an .aiderignore file to ensure aider doesn't use certain files/dirs/etc. It conforms to the .gitignore spec.
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by anotherpaulg 2y ago
Thanks for your interest in aider. Yes, absolutely you can work with local models. Here are the docs for working with lmstudio and ollama: https://aider.chat/docs/llms/lm-studio.html https://aider.chat&
66.
by anotherpaulg 2y ago
Glad to hear you’re finding aider useful! There’s a faq entry about how these stats are computed [0]. Basically using git blame, since aider is tightly integrated with git. The faq links to the script that computes the stats. It’s not desig
67.
by anotherpaulg 2y ago
> 99% of the code in this PR [for llama.cpp] is written by DeekSeek-R1 It's definitely possible for AI to do a large fraction of your coding, and for it to contribute significantly to "improving itself". As an example, aid
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by anotherpaulg 2y ago
Yes, aider can also work in architect/editor mode [0] which tends to produce the best results [1]. An architect model solves the coding problem and describes the needed changes however comes naturally to it. The editor model then takes
69.
by anotherpaulg 2y ago
This is a harsh foot-gun that seems to harm many ollama users. That 2k default is extremely low, and ollama *silently* discards the leading context. So users have no idea that most of their data hasn’t been provided to the model. I’ve had t
70.
by anotherpaulg 2y ago
I'm guessing you're interesting in R1+Sonnet because of the recent SOTA benchmark result? It does seem to be a powerful architect/editor combo. https://aider.chat/2025/01/24/r1-sonnet.html
71.
by anotherpaulg 2y ago
Yes, that is true. Aider expects to work with the LLM to automatically apply edits to the source files. This requires precision from the LLM, which is what breaks down when you overload them with context.
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by anotherpaulg 2y ago
In my experience with AI coding, very large context windows aren't useful in practice. Every model seems to get confused when you feed them more than ~25-30k tokens. The models stop obeying their system prompts, can't correctly fi
73.
Using Uv as an Installer(aider.chat)
7 points by anotherpaulg 2y ago|1 comments
74.
by anotherpaulg 2y ago
Mostly I just review the chat output. But I also use /diff to see actual unified diffs of the last AI change. Or if I have made a sequence of changes, I'll use my normal git tools to diff the entire branch, etc.
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by anotherpaulg 2y ago
I use aider to work on the aider code base, which is approaching 30k lines of python. Aider writes about 70% of the new code in each release [0]. So it's doing some fairly heavy lifting in a non-trivial code base. Some pragmatic tips:
76.
by anotherpaulg 2y ago
Absolutely! Code has vastly more useful structure than prose. Aider exploits all this structure, using a "repository map" [0]. It use tree-sitter to build a call graph of the code base. Then runs a graph optimization on it with re
77.
by anotherpaulg 2y ago
I ran a few experiments by adding 0, 1 or 2 "write better code" prompts to aider's benchmarking harness. I ran a modified version of aider's polyglot coding benchmark [0] with DeepSeek V3. Here are the results:
78.
by anotherpaulg 2y ago
I have long relied on a print-debug function I wrote for python called dump(). You do dump(foo) and it will print out "foo: value". Where the variable name "foo" is magically pulled out of the source code and "value
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by anotherpaulg 2y ago
Super interesting, thanks for sharing. How large a corpus of domain specific text do you need to obtain a useful knowledge graph? Aider has been doing PageRank on the call graph of code repos since forever. All non trivial code has lots of
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by anotherpaulg 2y ago
I found a related set of experiments that include gpt-3.5-turbo-instruct, gpt-3.5-turbo and gpt-4. Same surprising conclusion: gpt-3.5-turbo-instruct is much better at chess. https://blog.mathieuacher.com/GPTsChessEloRatingL
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by anotherpaulg 2y ago
This is exactly how aider shares code context with LLMs using a “repo map”. It uses tree sitter to collapse relevant files into this sort of folded version, showing function signatures but eliding their bodies. https://aider.chat
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by anotherpaulg 2y ago
The new Sonnet tops aider's code editing leaderboard at 84.2%. Using aider's "architect" mode it sets the SOTA at 85.7% (with DeepSeek as the "editor" model). 84% Claude 3.5 Sonnet 10/22 80% o1-previ
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by anotherpaulg 2y ago
You can do this to quickly work on a small script: aider --no-git oneoffscript.py
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by anotherpaulg 2y ago
The article describes experiments with aider that show the benefits of using a pair of LLMs to make changes in an existing code base: - An Architect model solves the coding task. - An Editor model edits the source code. This approa
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by anotherpaulg 2y ago
The new Gemini models perform basically the same as the previous versions on aider's code editing benchmark. The differences seem within the margin of error. https://aider.chat/docs/leaderboards/
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OpenAI o1-preview is SOTA on the aider leaderboard(aider.chat)
5 points by anotherpaulg 2y ago|1 comments
87.
by anotherpaulg 2y ago
OpenAI o1-preview is SOTA at 79.7% on the aider code editing leaderboard, with "whole" edit format. With the practical "diff" format, it ranks between GPT-4o & Sonnet. 80% o1-preview whole 77% Sonnet diff 75%
88.
by anotherpaulg 2y ago
Yi-Coder scored below GPT-3.5 on aider's code editing benchmark. GitHub user cheahjs recently submitted the results for the 9b model and a q4_0 version. Yi-Coder results, with Sonnet and GPT-3.5 for scale: 77% Sonnet 58% GPT-3.5
89.
by anotherpaulg 2y ago
There’s a little demo video in the Twitter link that might be worth checking out. But ya, aider can: code something for you, run it, capture the terminal output with pexpect and iterate further based on that.
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by anotherpaulg 2y ago
I just used python pexpect [0] for a fun project. I believe pexpect was inspired by the original Unix expect command. I used it to let aider suggest and run shell commands [1]. This is handy, so aider can offer to run the code it just wrote

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