Ve

Vertex, your personal archiver and annotator

Hacker News

Vertex, your personal archiver and annotator

Hey Hacker News! I'd like to showcase Vertex. Vertex is a browser extension [1] (currently only Chrome) and a web app [2] for saving bookmarks (vertices) along with the source documents. You can save optional notes along with (almost) everything you can save on Vertex. You can organise your vertices into sets [3] or connect them together as you see fit. Your vertices can hold snapshot(s) of the source. These are self-contained, parseable MHTML documents which stay immutable independently of the source. Additionally, the Vertex extension will highlight your saved links (along with notes) as you browse. I built Vertex because of problems I faced — links I cared about silently changed or disappeared, and once my collection got large enough, I lost track of which identical-looking URL I wanted. Vertex sits between existing archiving systems like ArchiveBox [4] and note-taking applications like Obsidian [5] — snapshots on one side, annotations and a graph on the other. And unlike Wayback Machine's "Save Page Now" [6], the browser extension enables you to capture even login-gated pages. I've used Haskell for the backend and TypeScript for the frontend. I've run production Haskell for 10+ years, so that's what came most naturally to me. Future improvements: - Firefox extension - Better mobile support The free tier [7] retains archives for 30 days. I'd love any feedback, so please give it a spin! [1]: https://chromewebstore.google.com/detail/vertex/dhhilgfkipba... — Chrome Web Store [2]: https://vertexapp.io — Onboarding [3]: https://vertexapp.io/vertex/set/56qE75Nh7RDuL5UUiloZK — Example shared set [4]: https://archivebox.io/ — ArchiveBox [5]: https://obsidian.md/ — Obsidian [6]: https://web.archive.org/ — Wayback Machine [7]: https://vertexapp.io/pricing — Pricing

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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.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google, new · Missing: agents, macos, agent
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, hacker news · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, google, way · Missing: mobile apps, ios, entrepreneurs
52%52% 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
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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