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I put aider in a ReAct loop and it works well

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I put aider in a ReAct loop and it works well

Hello HN, I've recently been trying out and using the latest and greatest software development agents, including Windsurf and Cursor. Before using those, I had been using aider for everyday practical SWE tasks, but aider is not very agentic --it's best at one-off, scoped programming tasks. I did a quick test by putting aider in a ReAct loop using create_react_agent from langchain. To my surprise, it ended up working very well --it's able to solve programming tasks that Windsurf has failed at. The agent has access to many tools such as ripgrep, fuzzy find, filesystem navigation, etc. One tool, in particular, is an expert tool where it can use a dedicated reasoning model, such as OpenAI's o1, to reason about logic, bugs, or complex planning tasks. It's been really interesting to see when and how it decides to use these tools. The project does support plugging in any model you want to use, but so far I've found it to work best with the latest claude as the agent, and o1-preview (or o1 if you have access to it) for the expert reasoning model. This is something I put together out of practical need, so if you have ideas, feedback, or especially PRs, those are welcome. The project is licensed as Apache 2.0 (same as aider) and is intended to be a public good --our open tool so we aren't all dependent on proprietary tools and services to get our work done. The dream is that it will eventually work well on fully open/local models. Cheers

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, apple
97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: filesystem, ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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