I built TreeSnap – turn your code into a prompt ready bundle

Hacker News

I built over 3-4 weeks to solve my own pain point, picking up rust along the way. It has a browser version that works online/offline and cross platform desktop as well. It was a pretty intense sprint. About TreeSnap, well it simplifies this preparation of sharing code with Chat LLMs. In my use case, making o3 via ChatGPT a lot more cost-effective to use for these tasks compared to Cursor: How it works: Select files/folders → generates a single text bundle + ASCII tree Instantly shows per-file and total token counts (list & treemap views) Quickly trim unnecessary files or folders to match context limits Fully offline and private (Tauri desktop app) Also available as a free browser app—offline and private Built in ~3 weeks, I use it daily to aid my workflow and reduce my expenses by minimizing the need to rely on Cursor's pricier model usage. I still rely on Cursor with grok 3 or gemini pro 2.5 pro mostly without getting crazy with usage fees. Feedback welcome—especially UX, performance, and pricing.

Visit product View on Hacker NewsApr 28, 2025

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Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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 · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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