Comments (12)
No worries at all, and never let being a junior stop you, the absolute best way to learn is by trying and failing!
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That it VERY interesting indeed. I've been playing about with getting Auto-GPT to improve itself and write it's own code, this might just be the ticket!
Executing unreviewed AI Generated code is a security risk though, so we'd have to think of the safest way to do this.
Submit a pull request!
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I code but only know about web programming, and I'm a junior too... sorry Haha
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I'm planning to implement this over the weekend. Another best paper for self-reflection is this: https://arxiv.org/abs/2304.03442
They also have a way of evaluating the agents with some questions.
@Torantulino @Andythem23
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Executing unreviewed AI Generated code is a security risk though, so we'd have to think of the safest way to do this.
Probably best to have it raise pull requests so that they can be reviewed manually.
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I kinda implemented this. In my implementation I let the agent reflect every N steps which can be quite interesting but requires more testing.
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Also, probably will have to create some kind of benchmarks so that the AI can know the direction it needs to go. "Improve itself" can be a bit vague
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If anyone can get me a GPT4 key, either directly via your org with prepayment or by getting me in touch with someone at OpenAI, I'm willing to implement it. I think it could be a lot of fun, and to add an edge, I'll do it on a fresh Linux w/ firewalled access to only the OpenAI API, and a USB chainsaw.
Jk, not the chainsaw.
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Around weekend I will do a little bit of research around this topic. @algopapi would you mind sharing your implementation? Have you pushed it on your fork?
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@LeonardoLGDS I had no time to check the paper yet but I assumed there are some guidelines on benchmarking provided in paper
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That it VERY interesting indeed. I've been playing about with getting Auto-GPT to improve itself and write it's own code, this might just be the ticket!
I guess, a starting point would be accepting actual constraints/restrictions, aka:
- context window size (restrict to 8k, ideally much less, probably 50% of that)
- restrict changes to a single isolated module (which would mean commands or even better plugins)
- accept that the underlying architecture would then need to work analogous to message passing, and possibly via pipes - anything else won't scale or would change too many places in the source tree at once, basically any agent would consist of a list of other sub-agents, all of which would fork each other as needed (think makefiles/clustering)
- alternatively, come up with a module that can deal with patches/diffs for features spanning multiple files/contexts initially
- to provide sufficient surrounding context, freely use Python docstrings - basically reject PRs that don't have a ton of surrounding context in the form of comments and a ton of unit tests/test coverage
- extend git_operations.py to add support for traversing commit logs (patches + log messages)
- come up with a new plugin to handle github API integration, as per: #15 (comment)
- have unit tests and benchmarks for regression testing purposes
- improve docker integration for CI
- use a custom local LLM to come up with a heatmap (query/idea -> location) that helps identify path/file name and line number based on querying the LLM for an idea/change, so that the agent can determine what set of files/places is likely to be relevant for certain changes, and then narrow down via git logs and commit history
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This issue was closed automatically because it has been stale for 10 days with no activity.
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