GistHive – Code snippet manager with team workspaces

Hacker News

Hi HN, I'm a dev, and I built GistHive over the past 9 months to solve various issues I was facing: - GitHub gists is terrible for organising snippets. Older code took an age to find. - Lack of team gists (they've been promising it for 8+ years and still nothing) - Private gists on GitHub aren't actually all that private - When a team member leaves, they take their gists with them Having joined a new team last year, I needed a way to share code patterns across the team quickly and easily and more importantly, privately. Tech stack: Laravel backend, React frontend. Subscription management with Stripe, and used PostHog for product analytics. Free for individual developers, 3 free seats for teams and $4/user/month for any additional seats. I also have a VS Code extension I'm currently working on that will allow inserting code in-place and saving code snippets from within VSC without ever leaving, so this app almost directly integrates with the existing dev environment. I'd love feedback from the community. What am I missing? What would make this more useful for your workflow? What are your thoughts? While it's useful to me, I'd love it if it were useful to others too. Appreciate it! Live at: https://gisthive.app/

Visit product View on Hacker NewsNov 27, 2025

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

96%96% 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

77%77% 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: exist, existing, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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.

Correct prediction on native model

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