Open-source coding assistant with terminal access

Hacker News

Sweep is an AI coding assistant that reads your files and runs code in your terminal. It takes a list of files and instructions, and performs edits using GPT4. For example, you can tell Sweep to reference a.tsx to localize b.tsx. We previously worked on Sweep as a bot that generated pull requests from GitHub issues. We learned that AI needs validation (like formatters and unit tests) to code properly. We tried setting up a code sandbox for each user but this didn't work well. We’re now relying on the prebuilt dev environment on their machine to avoid having to install dependencies. LLM based pipelines are also error-prone. Our previous project was a pipeline that turns the issue into relevant files as context, relevant files to a generated plan of files to change, and then the generated plan to a series of code changes. It’s frustrating when one/all of these steps fail and you’re forced to start over. You can now intervene at each step, making the entire process more reliable. Sweep is open source (licensed under AGPL) and runs entirely on your laptop. The only remote calls will be to OpenAI. We'd love to help you perform software chores like version upgrades, where you describe a set of changes to have Sweep apply over multiple files. You can install Sweep here with a one-line command: https://docs.sweep.dev/assistant . We'd love to hear any feedback you have!

Visit product View on Hacker NewsFeb 8, 2024

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

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.

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

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

38%38% 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

34%34% 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

13%13% 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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