My

My first vibecoded Rust project, how do I share the AI sessions?

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My first vibecoded Rust project, how do I share the AI sessions?

Hey HN! I built this Rust CLI tool to scratch my own itch in about 3 hours using AI coding (Claude Code + Cursor) and wanted to share it as a concrete example to support the "AI makes you more productive" camp. I know that people talk about being more productive, but then catch flak with "so what did you ship". What it does : This tool solves my problem: You have a JSON file that changes regularly, and you want to track its history without storing dozens of full copies. json-archive creates a .json.archive file next to your original JSON file. Each time you run the tool, it calculates only what changed and appends those deltas to the archive. You get complete history with minimal storage overhead. The archive format is human-readable JSONL (not binary), designed to be hackable and easy to inspect, debug, and pipe into other scripts or web visualizations. Repo: https://github.com/PeoplesGrocers/json-archive (AGPL so maybe don't look at it on work computers) Why I'm posting : People keep saying AI coding makes them productive, but there aren't many concrete examples to study. So here's mine: - Bunch of working Rust with documenation - Working tool with proper error handling, testing, and real examples - Yes, some docs have that "AI feel" and the code takes a bit of a brute force approach. - But I'm happy with the file format and the API it came up with - It works and I only had to read/understand code rather than write everything from scratch My question for HN : I have all the Claude Code and Cursor session transcripts that went into building this but I don't know a good way share them so you all can judge whether I'm using these tools effectively or like a noob Is there something out there? I'm looking for a way to publish these transcripts and maybe leave comments? If you're curious about AI-assisted development or want concrete examples before adopting it yourself, I think this could be a useful data point. And if you know how I should share these sessions, I'd love to hear suggestions!

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, computer · Missing: mac, agents, macos
89%89% 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
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
28%28% 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · 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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