Ya

Yanna Cursor, but for documents (draft → edit → redline → e-sign)

Hacker News

Yanna Cursor, but for documents (draft → edit → redline → e-sign)

Hey HN I’ve been building Yanna, and the simplest way to explain it is: It’s Cursor for documents. Not “AI writes a doc and you copy/paste it into Google Docs and then into DocuSign.” More like: you open a document, and the AI is inside the editor with you — helping you draft, rewrite, spot issues, and then send it to get signed without leaving the page. Why I built this Every contract workflow I’ve seen is still stitched together: generate in a chat tool, edit in a doc tool, review in a different tool, send via an e-sign tool, track status somewhere else. Even when each piece is “good,” the overall experience feels clunky especially when you’re iterating quickly or working off an existing contract. I wanted one flow where the doc is the source of truth, and AI actions feel like “apply this change” instead of “here’s a paragraph, good luck.” What Yanna does today 1) Cursor-style editing for contracts A real document editor (not just a chat box) Chat on the side with commands like: “Rewrite this clause to be mutual” “Shorten this section but keep meaning” “Add a termination clause that’s fair” The important part: edits can be applied directly to the doc, not pasted manually. 2) AI redline / review for existing PDFs Upload an agreement you already have Yanna gives you a structured review: key terms, risk areas, unusual clauses, missing basics More “here’s what to worry about and where” and less “generic summary” 3) Send for e-sign (from the same workspace) Once you’re happy with the doc, you send it for signature Track status in the same place you edited it What I’m trying to get right The hard part isn’t “AI can write text.” It’s making the AI behave like a tool you trust: changes are specific and reversible the output is structured instead of rambly it doesn’t hallucinate edits into the wrong spot it feels like a copilot inside the doc, not a separate app you talk to Where I want feedback I’m posting because HN is great at calling out what’s real vs what’s marketing. I’d love feedback on: Does “Cursor for documents” instantly make sense, or is it confusing? For contracts specifically, what’s the most painful part: drafting, redlining, or sending/collecting signatures? If you’ve used AI contract tools — what did they get wrong? Also: if you want to try it and break it, I’ll share a demo link / access in the comments. Abem

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, google, using · 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way, para · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
43%43% 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
41%41% 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
14%14% 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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