DB

DBOS – Transactional Serverless for TypeScript Apps

Hacker News

DBOS – Transactional Serverless for TypeScript Apps

Hello HN! We’re excited to show you DBOS, a transactional serverless SDK and platform that makes it easy to write fault-tolerant, observable, and scalable TypeScript applications. We’re a startup commercializing research we did at Stanford and MIT. ( https://dbos-project.github.io/ ). The main idea behind DBOS is to store EVERYTHING about your application in the database. This gives you some unique features: * Reliable execution – Your program’s execution state is stored in the database, so if it’s ever interrupted, it automatically resumes from where it left off without repeating any work already performed. * Time travel – Every change to your program’s state is recorded in the database, so you can “rewind” time and restore the state of your application to what it was at any point in the past. For example, time travel debugging–take a tricky bug in production, rewind time, and reproduce it locally on your laptop. Today, we released our open-source SDK ( https://github.com/dbos-inc/dbos-ts ) and our free-to-try serverless platform ( https://www.dbos.dev/dbos-cloud ). We’d love for you to try them out and let us know what you think! Here’s a quick guide to get you started: https://docs.dbos.dev/getting-started/ We’re here to answer any questions!

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, open · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
79%79% 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 · Strong signals: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
22%22% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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