| name | blog-inspiration |
| description | Search Reddit, X/Twitter, and Hacker News for trending AI & Programming topics to find blog post inspiration |
| disable-model-invocation | true |
| allowed-tools | WebSearch, WebFetch, Read, Write |
| argument-hint | ["optional topic filter","e.g. \"AI agents\" or \"local LLMs\""] |
Blog Inspiration Finder
Search Reddit, X/Twitter, and Hacker News for the hottest AI & Programming discussions, then evaluate them as potential blog post topics using the journalism workflow from docs/research.md and the memory file tech-journalism-workflow.md.
Step 1: Source Discovery
Run these searches in parallel to find trending discussions from the last 7 days. If the user provided $ARGUMENTS, use that as an additional filter on all searches.
Hacker News
Search for: site:news.ycombinator.com AI OR programming OR LLM OR "machine learning" OR "developer tools" (last 7 days)
Also fetch https://hn.algolia.com/api/v1/search?tags=front_page&query=AI+programming&hitsPerPage=15 to get current front page items with point counts.
Reddit
Search for: site:reddit.com (r/programming OR r/MachineLearning OR r/LocalLLaMA OR r/artificial OR r/ExperiencedDevs) AI OR LLM OR agents (last 7 days)
X / Twitter
Search for: site:x.com AI OR LLM OR "developer tools" OR programming (last 7 days)
Also search for: site:nitter.net AI programming as fallback.
Step 2: Signal Scoring
For each discovered topic, evaluate it on these criteria (from Phase 1 of the journalism workflow):
- Discussion heat — Are people actively debating, not just sharing? High comment counts + strong opinions = good signal.
- Cross-platform presence — Does it appear on multiple platforms? Topics trending on both HN and Reddit are stronger candidates.
- Contrarian takes available — Are there expert disagreements or nuanced perspectives? These become the angles that differentiate the blog post.
- User impact — Does this affect how developers actually work day to day? Prioritize practical over theoretical.
- Freshness — Is there a specific trigger (launch, paper, incident) or is it a slow-burn trend?
Step 3: Check Against Existing Posts
Read the list of existing posts from posts/ directory. Discard any topics already covered. Flag topics that could be interesting follow-ups to existing posts.
Step 4: Present Results
Output a ranked list of the top 5 topics in this format:
## 1. [Topic Title]
**Signal strength:** 🔥🔥🔥🔥 (4/5)
**Sources:** [HN](link) | [Reddit](link) | [X](link)
**Discussion heat:** [one line on the debate]
**Blog angle:** [one line pitch using Phase 4 framework — lead with the user problem]
**Key community insight:** [the most interesting take from comments that wouldn't be in the primary source]
**Cross-platform:** Yes/No
After the list, suggest which topic you'd pick and why, referencing the Phase 4 angle questions:
- What problem does this solve?
- Why is this different from what exists?
- What does this mean for users?
Step 5: Optional Deep Dive
Ask the user if they want to go deeper on any topic. If yes, run Phase 2 (Primary Source Research) and Phase 3 (Community Insight Gathering) from the journalism workflow:
- Fetch and read the original source
- Read the top comments from the HN/Reddit threads
- Extract expert perspectives, contrarian takes, and recurring themes
- Save findings to
docs/research.md in the same format as previous research notes
