Rift – Open-source AI-native language server for your personal AI SWE

Hacker News

Howdy y'all, I'm Jesse and I'm pleased to share Rift ( https://www.github.com/morph-labs/rift ), an open-source AI-native language server for the IDEs of the future. Software will soon be written mostly by AI SWEs working alongside humans. Codebases and the development environments around them will soon become living artifacts that can anticipate, maintain context on, and execute your every intention. Software development is rapidly evolving and key infrastructure like language servers must evolve with it to support a new generation of developer tools. As part of Rift, we're releasing an open-source VSCode extension which shows what is possible today with a language server that can mediate between LLMs and IDEs, and which takes a first step towards a future where you can build and iterate on entire projects with only natural language gestures. Today, with Rift, you can do all of the following without ever leaving VSCode or copy-pasting from a browser chat window: - Generate an entire workspace, or a module based on other parts of your codebase - Conversationally iterate on code edits over selected regions which are streamed directly into your editor window - Request, review, and iterate on codebase-wide, multi-file edits Everything runs as an agent through Rift's Agents API. You can define your own agents that can interact directly with your IDE and codebase through the language server, and can run arbitrary Python code that can do things like: - provide a conversational UI over the git CLI (e.g. 'suggest fixes for these merge conflicts, then rebase over myBranch') - real-time automatic docstring generation triggered by saving a file or making a git commit - run tools like typecheckers and static analyzers in the background and implement fixes based on their output - automated code review in your IDE with context from GitHub We're proud to integrate with open-source projects like Smol Developer ( https://www.github.com/smol-ai/developer ), GPT Engineer ( https://www.github.com/antonosika/gpt-engineer ), and Aider ( https://www.github.com/paul-gauthier/aider ). Language servers are ubiquitous and see every action you take in an IDE. Rift will soon support recording training data from your interactions so that you can work with a team of truly personalized AI software engineers. At Morph Labs ( https://morph.so ), we believe that the future of software development should be transparent, open-source, and defined collectively by the developer community. The Rift language server and extension will continue to be open-source, extensible, and privacy/security aware with opt-in telemetry and first-class support for custom endpoints and on-device models — no closed source language server binaries, no API calls to mysterious proxy servers. We would love for you to try out Rift and give us feedback! Please let us know what you think in the comments.

Visit product View on Hacker NewsAug 9, 2023

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

98%98% 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 · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

80%80% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

16%16% 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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