Pair: Open Tool for Coding with GPTs, Built by Coding with GPTs

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Github Copilot is a great tool for leveraging GPTs while coding, but I find that it is too “open loop” for more complex tasks that require Q&A, feedback to guide it in a particular direction, iteration on code execution errors, etc. There is a large class of tasks that are better accomplished in an iterative, stateful chat-like interface. I have been experimenting with a local command line chat interface to GPT-4 and my mind was blown once again a few days ago when I copied documentation for a pretty involved API into the model context and managed to chat-guide GPT-4 to implement the API in under 30 minutes, complete with a ridiculous amount of unit test coverage. This involved a lot of manual copy and pasting back and forth and other friction points that could be removed by a streamlined REPL interface optimized for code interactions. It occurred to me that it would be fun to build such a tool, and as the ultimate act of dogfooding, try to build it with GPT! So PAIR is the starting point here. You can see a recent commit message has a log of my interactions with the model that produced that commit. Next step is to add better mechanisms to manage the model input context (e.g. make it easy for the model to see the latest version of a source file when needed) followed by mechanisms for allowing the model to suggest changes via diffs that are quickly reviewed and accepted by the human in the loop before being applied to the file and tested. I would love to hear from others who have experimented with GPT pair programming in a chat-style interface and any feedback you might have on your experience with it.

Visit product View on Hacker NewsMar 21, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, context, tasks · Missing: mac, agents, macos

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

66%66% 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 · Strong signals: interface · Missing: plus, platform, intuitive

48%48% 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

33%33% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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