Show HN: Auto-Save Claude Code Sessions to GitHub Projects https://ift.tt/GdCZyWv

Show HN: Auto-Save Claude Code Sessions to GitHub Projects I wanted a way to preserve Claude Code sessions. Once a session ends, the conversation is gone — no searchable history, no way to trace back why a decision was made in a specific PR. The idea is simple: one GitHub Issue per session, automatically linked to a GitHub Projects board. Every prompt and response gets logged as issue comments with timestamps. Since the session lives as a GitHub Issue in the same ecosystem, you can cross-reference PRs naturally — same search, same project board. npx claude-session-tracker The installer handles everything: creates a private repo, sets up a Projects board with status fields, and installs Claude Code hooks globally. It requires gh CLI — if missing, the installer detects and walks you through setup. Why GitHub, not Notion/Linear/Plane? I actually built integrations for all three first. Linking sessions back to PRs was never smooth on any of them, but the real dealbreaker was API rate limits. This fires on every single prompt and response — essentially a timeline — so rate limits meant silently dropped entries. I shipped all three, hit the same wall each time, and ended up ripping them all out. GitHub's API rate limits are generous enough that a single user's session traffic won't come close to hitting them. (GitLab would be interesting to support eventually.) *Design decisions* No MCP. I didn't want to consume context window tokens for session tracking. Everything runs through Claude Code's native hook system. Fully async. All hooks fire asynchronously — zero impact on Claude's response latency. Idempotent installer. Re-running just reuses existing config. No duplicates. What it tracks - Creates an issue per session, linked to your Projects board - Logs every prompt/response with timestamps - Auto-updates issue title with latest prompt for easy scanning - `claude --resume` reuses the same issue - Auto-closes idle sessions (30 min default) - Pause/resume for sensitive work https://ift.tt/SuWocmi March 14, 2026 at 10:19PM

Show HN: AI milestone verification for construction using AWS https://ift.tt/cFiIphn

Show HN: AI milestone verification for construction using AWS Hi HN, I built Build4Me to address a trust problem in diaspora-funded construction projects. Many families send money home to build houses but have no reliable way to verify that work is actually being done. Photos can be reused, progress exaggerated, or projects abandoned after funds are sent. Build4Me introduces milestone-based funding where each construction milestone must be verified before funds are released. The system verifies progress using: - geotagged photo capture - GPS location verification - AI image analysis - duplicate image detection It runs on serverless AWS architecture using services like Rekognition, Bedrock, Lambda, DynamoDB, and Amazon Location Service. Would love feedback on the architecture and fraud detection approach. https://builder.aws.com March 13, 2026 at 09:24PM

Show HN: Every Developer in the World, Ranked https://ift.tt/DIYactJ

Show HN: Every Developer in the World, Ranked We've indexed 5M+ GitHub users and built a ranking system that goes beyond follower counts. The idea started from frustration: GitHub is terrible for discovery. You can't answer "who are the best Python developers in Berlin?" or "who identified transformer-based models before they blew up?" without scraping everything yourself. So we did. What we built: CodeRank score - a composite reputation signal across contributions, repository impact, and community influence Tastemaker score - did you star repos at 50 stars that now have 50,000? We track that Comparison Builder - allows users to build comparison graphics to compare devs, repos, orgs, etc. Sharable Profile Graphics - share your scores and flex on your coworkers or the community at large Some things we found interesting: Most-followed ≠ most influential. The correlation between follower count and tastemaker score is surprisingly weak. There's a whole tier of developers who consistently find projects weeks and months before they trend, with almost no public following. Location data on GitHub is a disaster. We spent an embarrassing amount of time on normalization and it's still not anywhere near perfect. Try it: https://coderank.me/ If your profile doesn't have a score, signing in will trigger scoring for your account. Curious what the HN crowd thinks about the ranking methodology, happy to get into the weeds on any of it. https://coderank.me March 13, 2026 at 12:42AM

Show HN: Baltic security monitor from public data sources https://ift.tt/QUptuMa

Show HN: Baltic security monitor from public data sources People around me started repeating stuff from various psyop campaigns on TikTok or other social media they consume. Especially when living in Baltics it's basically 24/7 fearmongering here from anywhere, either it's constant russian disinfo targeted campaigns via their chains of locals or social media campaings or some bloggers chasing hype on clickbait posts, so it was driving me mad, and it is distracting and annoying when someone from your closest ones got hooked on one of these posts and I was wasting time to explain why it was a bs. So I took my slopmachine and some manually tweaking here and there and made this dashboard. Main metric is basically a daily 0-100 threat score, which are just weighted sums and thresholds - no ML yet. https://estwarden.eu/ March 12, 2026 at 09:44PM

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails https://ift.tt/XionT5y

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails I've been building AI agent tooling and kept running into the same problem: agents browse the web, take actions, fill out forms, scrape data -- and there's zero proof of what actually happened. Screenshots can be faked. Logs can be edited. If something goes wrong, you're left pointing fingers at a black box. So I built Conduit. It's a headless browser (Playwright under the hood) that records every action into a SHA-256 hash chain and signs the result with Ed25519. Each action gets hashed with the previous hash, forming a tamper-evident chain. At the end of a session, you get a "proof bundle" -- a JSON file containing the full action log, the hash chain, the signature, and the public key. Anyone can independently verify the bundle without trusting the party that produced it. The main use cases I'm targeting: - *AI agent auditing* -- You hand an agent a browser. Later you need to prove what it did. Conduit gives you cryptographic receipts. - *Compliance automation* -- SOC 2, GDPR data subject access workflows, anything where you need evidence that a process ran correctly. - *Web scraping provenance* -- Prove that the data you collected actually came from where you say it did, at the time you say it did. - *Litigation support* -- Capture web content with a verifiable chain of custody. It also ships as an MCP (Model Context Protocol) server, so Claude, GPT, and other LLM-based agents can use the browser natively through tool calls. The agent gets browse, click, fill, screenshot, and the proof bundle builds itself in the background. Free, MIT-licensed, pure Python. No accounts, no API keys, no telemetry. GitHub: https://ift.tt/zemTEAQ Install: `pip install conduit-browser` Would love feedback on the proof bundle format and the MCP integration. Happy to answer questions about the cryptographic design. March 12, 2026 at 03:15AM

Show HN: Free audiobooks with synchronized text for language learning https://ift.tt/Cbq8faw

Show HN: Free audiobooks with synchronized text for language learning https://ift.tt/U1OjSnL March 12, 2026 at 01:12AM

Show HN: KaraMagic – automatic karaoke video maker https://ift.tt/T09hcbi

Show HN: KaraMagic – automatic karaoke video maker Hi all, this is an early version of a side project of mine. Would love some feedback and comments. I like karaoke and I grew up with the Asian style karaoke with the music video behind and the karaoke lyrics at the bottom. Sometimes I want to do a song and there is no karaoke version video like that. A few years ago I came across ML models that cleanly separate the vocals and the instrumental music of a song. I thought of the idea to chain together ML models that can take an input music video file, extract the audio (ffmpeg), separate the tracks (ML), transcribe the lyrics (ML), burn the lyrics back with timing into the video (ffmpeg), and output a karaoke version of the video. This is an early version of the app, Mac only so far (since I use Mac, despite it being an electron app.. I do eventually want to make a Windows build), I've only let a few friends try it. Let me know what you think! https://karamagic.com/ March 10, 2026 at 11:58PM