Agentic Task Queue – batch processing of agentic tasks

Hacker News

https://github.com/whydna/atq I had a huge number of database rows that each needed LLM reasoning and tool use. Existing harnesses like Claude Code don't handle this well - give it a batch and it'll try to write you a script instead of actually reasoning over each item. Even when it works, context bloats after each turn - gets expensive, slow, and starts hallucinating. And there's no built-in retry handling or parallelization. So I built this tool. Define the task as a prompt, pipe in your task list, set concurrency, model, tools, etc. Open to feedback!

Visit product View on Hacker NewsMar 24, 2026

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Launch Intel predictions

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

94%94% 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: para · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, pipe · Missing: https docs, excited, just released

47%47% 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: para · Missing: mobile apps, ios, personal

46%46% 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.

Correct prediction on native model

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