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I wrote a book for engineers building production AI systems

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I wrote a book for engineers building production AI systems

I wrote this because I kept watching the same mistakes: Teams chain prompts, call it "agentic," then wonder why everything breaks under real-world pressure. The book covers what actually works: memory systems, orchestration patterns, multi-agent coordination, observability, and real examples from systems that actually ship. Download the first 3 chapters (no email): https://productionaibook.com/hn I'd love to hear your production AI war stories. What's actually breaking? What patterns have you discovered? Giving away 15 copies to the most insightful comments.

Visit productView on Hacker NewsNov 25, 2025
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, email · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% 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
42%42% 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
21%21% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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