I ported my background task queue to TypeScript

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Hello there, One year ago I posted[0] on HN[1] a replacement for Celery, a background task queue for Python. WakaQ has been running in production[2] for over a year, and it's performed flawlessly. I've even been able to reduce the amount of worker machines needed, saving compute costs, even though the number of tasks executed has increased over time[3]. Now I'm starting a new Next.js project using TypeScript, tRPC, Prisma, hosted on DigitalOcean App Platform and I needed to execute code in the background without Python. So, I've ported WakaQ into TypeScript. I just got it working today so it probably has room for improvement, but it's working in production! Let me know what you think after checking out the repo[4]. [0] https://wakatime.com/blog/56-building-a-background-task-queu... [1] https://news.ycombinator.com/item?id=32730038 [2] https://wakatime.com [3] https://twitter.com/alanhamlett/status/1694460497276305427 [4] https://github.com/wakatime/wakaq-ts

Visit product View on Hacker NewsOct 17, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

78%78% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, tasks · Missing: agents, macos, agent

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

35%35% 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

14%14% 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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