Co

Collab Word in Web - A collaborative DOCX Editor with MS Word Parity

Hacker News

Collab Word in Web - A collaborative DOCX Editor with MS Word Parity

TLDR: Collab WIW is a DOCX editor with MS Word parity that supports real-time collaborative editing. The collab layer is end to end encrypted and uses ephemeral rooms with stable share URLs. Ever collab room lives in only memory. Collaborator saved copies can bring the room back online at the same URL and reconcile offline edits. The MS Word parity DOCX editor: I made a previous HN post about the editor itself. It is a pure JS DOCX editor that renders and edits the original document directly in the DOM instead of converting it into some other format. I benchmarked it pretty heavily against thousands of pages to push for pixel and feature parity, including equations, tables, 3D objects, headers, footnotes, etc... I am not going to get too deep into that because I already posted a ShowHN earlier. The parity report has all of the fixtures I tested and the actual results. Edit: Link to previous HN https://news.ycombinator.com/item?id=48995304 The server: The collab BE was actually inspired by OpenFront.io :). Users send small edit intents to the server. In an encrypted room the server cannot process those edits because it cannot read them. It just gives each encrypted envelope the next sequence number and sends the same ordered stream to everybody in the room. Collaborative rooms: The rooms are ephemeral and the server keeps their encrypted document state in memory. I chose these constraints because this is a public demo. I wanted people to create and share a document ad-hoc without making an account. I also don't your data plz, no thank you. Also because intents are tracked by document there is some support offline editing support and document reconciliation when it comes back up. Its still a bit ugly so there are limits, if the document can't be cleanly fast forwarded, the editor will try to reconcile up to 50 intents. If there are no conflicts the document fast forward to 2000 tailing intents. After that point you just gotta create a new draft. Just cleaner that way. I am still actively working on improving this system and figuring out how it can be improved. E2EE: 1. Documents, edits, images, cursor positions, etc... are encrypted in your browser and stay encrypted on the server. The document key is generated in the browser and lives in the share link's fragment, and never gets sent to the server. The server just sequences the sealed data. 2. A document also requires require a share code that you send separately from the link. The browser stretches that code and mixes it into key derivation, so the link by itself is not enough to decrypt the room. 4. Resource limits are placed basically everywhere. Blog covers more of it so check it out.

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3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para, including · Missing: reddit linkedin, podcasting, created
87%87% 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: cursor, user, new · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io, including · Missing: https docs, excited, just released
74%74% 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: users, way, para · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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 · Strong signals: active · 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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