LogLens Playground – Query structured logs in browser(WASM,client-side)

Hacker News

Hello HN, I built a CLI tool called LogLens to make querying structured logs (JSON, Logfmt, Nginx) as fast and ergonomic as grep, but with SQL-like capabilities. To make it easier to try without installing anything, I compiled the core Rust engine to WebAssembly and built a playground. The Problem: I often found myself needing to filter complex JSON logs over SSH. grep fails at structure ("find errors where latency > 500"), and jq syntax can be hard to type correctly under pressure. The Solution: LogLens uses a natural query syntax. You can type things like: level is "error" and duration_ms > 500 Key features of the Playground: 100% Client-Side: Your logs are parsed in memory via WASM. No data is ever sent to a server. Structured Parsing: Automatically detects JSON, Logfmt, and common web server formats. Range Queries: Just added support for ranges like status between 200..299 or ts between "10m ago".."now". Unstructured Search: You can treat logs as raw text too: text contains "timeout". The core engine is open source (MIT) and written in Rust. Playground: https://getloglens.com/playground Repo: https://github.com/Caelrith/loglens-core I’d love to hear your feedback on the query syntax and performance!

Visit product View on Hacker NewsNov 21, 2025

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

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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Incorrect prediction on native model

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