Analysis updated 2026-05-18
Set up an automated feed that tracks new discussions about a technical topic across Reddit and GitHub.
Get AI generated summaries of trending developer conversations without checking each platform manually.
Learn a working pattern for combining mismatched API data sources inside n8n.
| engyossefyossry-crypto/technical-radar-engine | 00kaku/gallery-slider-block | 04amanrajj/netwatch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | — | JavaScript | Rust |
| Last pushed | — | 2021-05-19 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | moderate | easy | moderate |
| Complexity | 3/5 | 2/5 | 3/5 |
| Audience | ops devops | general | ops devops |
Figures from each repo's GitHub metadata at analysis time.
Requires a running n8n instance plus paid Groq and OpenRouter API keys.
This project is a pair of automation blueprints for n8n, a visual workflow tool. Together they act as a radar that watches Reddit and GitHub for technical discussions and repositories, then uses an AI model to summarize and analyze what it finds. You do not write code to use it, you import two JSON files into your own n8n dashboard and connect them together. The first blueprint is the main controller. It listens for a trigger, decides what to do next, and hands off deeper research work to the second blueprint, a separate worker piece built to be called on demand. The worker fetches raw data from the Reddit and GitHub APIs, which each return information in very different shapes, and rewrites it into one consistent format with a title, a summary, a link, and a source label, before passing it along. A notable part of the README explains a specific bug the author ran into and fixed: when a workflow step waits for input from two different branches but only one branch is active, the step can freeze forever. The fix was to replace that waiting step with a small piece of custom JavaScript that just collects whatever data shows up, instead of waiting on both paths. The README also describes cleaning up messy text responses from free AI models, since those models sometimes wrap their answers in extra formatting that breaks automated parsing. To run this yourself you need your own n8n instance, either self-hosted with Docker or through n8n's cloud service, plus API keys for Groq and OpenRouter, which are the two AI services the workflows call out to. There is no built in interface beyond n8n itself. The project has no stars yet and is released under the MIT license, so it can be freely reused, modified, or built upon.
Two n8n automation blueprints that watch Reddit and GitHub for tech trends and use AI to summarize findings.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
Verify against the repo before relying on details.