Or

Organize and Compute in Writing via Sowhat Text Editor

Hacker News

Organize and Compute in Writing via Sowhat Text Editor

Hello HN, The demo here shows the companion editor [0] for the sowhat language [1]. The purpose of the editor is two things: - Makes building and organizing a collection of writing easier. It does this by extracting different elements such as tags and folders from a collection of writing then presenting those elements via in-context autocomplete. - Makes the collection of writing more useful by including notation elements such as events, beans [2] and formulas to support computing within the writing itself. A while back I released sowhat, the language supporting Tap [3]. Sowhat and the companion editor are, together, a cornerstone of Tap. Tap is a system for collecting writing and other digital matter. The editor here can be used in conjunction with sowhat to build your own tools. Or, it can provide a foundation to augment and enhance Tap. My goal is to open source as much of Tap as possible because: - It's fun to build with other people - It should be as easy as possible for someone to customize their Tap experience. There's no way to accommodate all the UXs one might want within Tap. These tools are intended to enable a developer to create their own experiences. - For those that use Tap but the then leave, a person's exported notes can maintain their original organization and meaning. - Opens a path for others to provide feedback and enhancements to the open source tools and Tap itself. 0. https://github.com/tatatap-com/sowhat-editor , demo: https://tatatap-com.github.io/sowhat-editor/ 1. https://github.com/tatatap-com/sowhat 2. https://github.com/tatatap-com/sowhat#beans 3. https://www.tatatap.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, notes, open · Missing: mac, agents, macos
70%70% 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: including · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
54%54% 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: way · Missing: mobile apps, ios, personal
49%49% 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
12%12% 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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