BuzzScope – A Tech Radar for Engineers [video]

Hacker News

How I Built My Own Tech Radar (BuzzScope): From Data Streams to Daily Insight As a technology evangelist, I’m always looking for better ways to keep up with what’s happening in tech. So I built a small system — BuzzScope, my personal tech radar. It currently has 3 key functions: 1⃣ Historical Insight: Search any keyword, and BuzzScope finds the most popular posts and active contributors from multiple platforms (Hacker News, Reddit, YouTube, and even selected Discord servers). Behind the scenes, historical data from HN and Discord are archived in Parquet format, which allows columnar access and faster queries across millions of records. Reddit and YouTube, on the other hand, are fetched on demand for real-time trending results. 2⃣ Daily Pulse: It automatically gathers trending posts from the past 24 hours (based on thresholds) and sends them to my inbox — quick and actionable. 3⃣ AI Analysis & Re-distribution: Then, multiple LLM agents collaboratively analyze the results to detect short-term trends and even generate tailored repost content for different platforms. Each agent works in parallel rounds — seeing global context but only refining its own results — until the network “converges.” The outcome? I now get a living snapshot of the tech landscape every morning

Visit product View on Hacker NewsOct 31, 2025

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

89%89% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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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