We built our own technology radar

Hacker News

A technology radar helps you monitor and assess emerging trends systematically. Depending on the context a “trend” can be technical, business-related or social. While such as a technology radar is traditionally reserved for big companies, we’ve found it’s just as valuable for us as a small startup, especially since AI is evolving rapidly (AI is one of our main technologies). The radar helps us track fringe-innovation (like the latest agentic framework form Github) and assess tech beyond the hype. We built it with our platform (Researchly), but in the post I have also included an N8N workflow and data schema for a do-it-yourself version. The front-end was inspired by: https://github.com/zalando/tech-radar

Visit product View on Hacker NewsSep 22, 2025

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

59%59% 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 · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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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