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Eidos – Offline alternative to Notion

Hacker News

Eidos – Offline alternative to Notion

I'm a big fan of Notion, having used it for 7 years. When I first met Notion, it was just a block document editor, and it didn't excite me until it released the Database. Later, I learned about Airtable, and a bunch of similar products, all of them are SaaS, with such powerful tables but poor performance. Why isn't there a personal offline version table? Most of the time, I don't need to collaborate with others. What I really want is a personalized, offline version of Notion with better performance and more flexibility. So that I can have full control over my data. Notion, like most SaaS products, is not open-source, so I can't customize it to my heart's content. I can only wait and vote for new features, but I can code, and I don't want to wait. I really like the concepts of FOSS, solid, and local-first. SaaS could die. Long may the SQLite. So, I built Eidos based on sqlite for managing my personal data throughout my lifetime in one place. Eidos is a long-term project for me. It looks like Notion, but the core is more like "obsidian.sqlite" with a powerful extension system. Here are a few key ideas: - Eidos is built based on sqlite-wasm and runs entirely in the browser. It can be used immediately, with no installation or configuration required. It's a pure PWA, with full offline support. - A block-styled document editor and an Airtable-like table, built on top of SQLite, where each table is a real SQLite table. - A powerful extension system inspired by Figma plugin and Cloudflare worker. You can write scripts in TypeScript directly in the browser. It is easy to manipulate data in docs, tables, and the file system. It also supports API. - If you're not a developer, you're still in luck. We're living in an AI era. LLM empowers people to craft their own software without writing any code. Eidos deeply integrated with LLM. You can translate, summarize, talk to your data, process table data in batches, and more. It makes your life easier with AI. You can fully customize your prompts and freely choose your LLM provider, without being locked to any vendor. To be honest, so far, there are still some bugs and shortcomings, and it hasn't yet reached my envisioned perfection. There is still some work to be done, but the basic framework has taken shape. I've been working on it for a year and have eaten my own dog food for the past half year. To help Eidos become better (and celebrate the release of the Elden Ring's DLC), I've decided to make it open-source and gather more feedback. Now, I'm going to take a break and play Shadow of the Erdtree. - https://eidos.space

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Actual performance

276points
90comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos
97%97% 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: supports · Missing: reddit linkedin, podcasting, created
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
67%67% 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.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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