Nicheloom

Market intelligence for builders — see what's gaining traction before it's crowded.

Watches user sessions, finds bugs that matter, and fixes them

Details

External ID
49466704
Source
HN
Company
—
Product
Watches user sessions, finds bugs that matter, and fixes them
Website domain
github.com
Launched
Aug. 27, 2026
Cohort
—
Upvotes
40
Upvotes percentile
0.8420698924731183
Tags
—
Fetched at
Sept. 10, 2026, 5:31 a.m.
Updated at
Sept. 10, 2026, 5:31 a.m.

Description

Hey HN,I’m Abhishek. I'm building Opslane, an open-source agent that identifies user-facing issues and investigates them. It only creates a PR if it can verify the fix.Demo: https://youtu.be/ccuOTYQMeYg Docs: https://docs.opslane.comAt my last job at Robinhood, we used to do a quarterly bug bash. We would go through our Sentry backlog and try to fix as many of them as possible. We only fixed bugs we knew were reported by customers. We had hundreds of bugs, and Sentry’s default priority levels made no sense. After the bug bash, we would declare bankruptcy - select all remaining bugs and mark them as resolved.This problem has only gotten worse since coding agents have become more prevalent.So I started thinking: what would Sentry look like if it were built in 2026?To me, error trackers have two failure modes:1. False positives: They show you thousands of errors, and you can’t tell the impact on the user2. False negatives: Many user-facing issues don’t throw exceptions, so they go unnoticed.Opslane combines error tracking and session recording. And there is an agent that acts on both. To get started, you install the Opslane SDK. It captures everything the user did: errors, console logs, network requests, and session recordings.Opslane reduces false positives by ranking issues based on how many users are facing a particular issue. It also learns about your product by reading your code and watching your session recordings.False negatives are harder. Opslane reviews session recordings to spot frustration. They look for rage clicks, dead clicks, and abandoned forms.This recently caught a bug in an early customer’s onboarding flow: a dropdown that closed itself when clicked. No exception, no bug report. The recordings showed users clicking it, selecting nothing, and dropping out of onboarding. Opslane flagged it and the team fixed it.Three guiding principles when building Opslane:1. Open Source: Self-host with one Docker Compose file.2.Agent-first: I never want to open an error dashboard again. Opslane ships an MCP server, so you can ask "what broke for users this week" from Claude Code. You get back issues that need your attention and you drive the resolution.3. It knows about your product: Opslane is continuously learning about your product. Every investigation begins with what it knows about your product.It’s early. Frontend apps work end to end today.I am currently focused on improving reliability and accuracy.Here is a link to our repo: https://github.com/opslane/opslaneWould love to get feedback from folks on our approach to this problem!

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Observability & eval
Audience
B2B
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
session recording and automated bug detection
Manually corrected
False

Could you build this?

Partial While integrating session replays and invoking an LLM coder via CLI is doable, reliably extracting reproducible bug reproductions from real user DOM session recordings and safely running automated verification sandboxes requires complex execution infra.

What it would actually take: The architecture requires ingesting rrweb or OpenReplay DOM session events, extracting network requests, and synthesizing a headless browser test (e.g., Playwright) that deterministically reproduces the user-encountered error. An agent orchestrator must then mount the user's codebase in an isolated Docker container, run test suites, apply code modifications via LLM, and verify that the synthesized test passes without regressions. The hard challenges are automated test synthesis from noisy DOM event streams and reliable containerized sandboxing.

Discussion

11 comments analyzed.

Competitors mentioned: PostHog, Hotjar, Microsoft Clarity, Linear

Concerns raised: Privacy invasion from session recording users, Shipping user data to third-party token sellers, Won't fix underlying corporate culture/process problems, Most users churn silently rather than file bugs

Feature requests: Mobile support

Competitors

Other products that read as similar to this one — 67 launches clear the similarity bar, closest 8 shown.

Attention rank: #12 of 68 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 299 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a observability & eval tool for Media & entertainment yet.