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Bloop – Answer questions about your code with an LLM agent

Hacker News

Bloop – Answer questions about your code with an LLM agent

Hi HN! We launched bloop 10 weeks ago ( https://news.ycombinator.com/item?id=35236275 ) and received a huge amount of feedback (both positive + constructive). We've undertaken a rewrite of the core search framework, which now acts as an LLM agent, significantly improving the number of queries that can be successfully answered. There's a bunch of hype surrounding LLM agents, but we're positive this is one of the first implementations of an agent that can deliver immediate value for engineers working on existing projects, especially larger ones. We'll do a full write up of how the agent works and the tools it can use soon, but we wanted to share our progress, now that we've got a stable release. bloop is a developer assistant that uses GPT-4 to answer questions about your codebase. The agent searches both your local and remote repositories with natural language, regex and filtered queries. Some of the ways engineers use bloop to improve their efficiency when working on large codebases: - Summarise how large files work and how multiple files work together - Understand how to use open source libraries when documentation is lacking - Identify the origin of errors - Ask questions about English-language codebases in other languages - Reduce code duplication by checking for existing functionality - Write new code, taking into account existing codebase context (eg: "write a dockerfile for this project") bloop runs as a free desktop app on Mac, Windows and Linux: https://github.com/bloopAI/bloop/releases/latest . On desktop, your code is indexed with a MiniLM embedding model and stored locally, meaning at index time your codebase stays private. 'Private' here means that no code is shared with us or OpenAI at index time, and when a search is made only relevant code snippets are shared to generate the response. (This is more or less the same data usage as Copilot). We also have a paid cloud offering for teams ($45 per user per month). Members of the same organisation can search a shared index hosted by us and will get access to enterprise only features down the line (currently there's no feature gap between desktop and cloud).

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, 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: paid · 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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