I wrote a database engine in TypeScript

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Hi! I wanted to understand more about databases' internals so I wrote one from scratch. It has a T-SQL inspired syntax with support for functions and procedures. It can be used stand-alone as a SQL engine or with a server allowing for persistence and replication to other connected clients. Performance are nothing near sqlite of course but that's beside the point. It’s a small database engine that can run in a web app as a way to store session data, do small calculations on a web worker, store the document/data the user is editing or facilitate “multiplayer” feature by broadcasting the queries the web app is running. The server runs in a container for that specific document and shutdowns automatically after a set amount of minutes of inactivity. Why did I spend time on this? I am self taught and have been a software developer for about 20 years; moved to the UK for about 10 years, now in Spain and I'm still looking for the place I want to call home. My lack of diploma has never been an issue in Europe but if I want to discover other horizons, having one makes things easier. There's a process in France call "Validation des acquis" (Validation of knowledge) which allows anybody at whatever level to present to a panel of professionals and academics a request to validate one experience and deliver a diploma. That diploma is completely equivalent to a diploma delivered from a university. The experience must validate all of the targeted diploma curriculum so I thought digging into databases will help with that... alex at ixai.net. I'm also looking for a remote position as a Senior software developer

Visit product View on Hacker NewsSep 28, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

88%88% 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: user, activity · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, 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

60%60% 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

31%31% 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

26%26% 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

23%23% 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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