blog-inspiration

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):

1. Discussion heat — Are people actively debating, not just sharing? High comment counts + strong opinions = good signal.
2. Cross-platform presence — Does it appear on multiple platforms? Topics trending on both HN and Reddit are stronger candidates.
3. Contrarian takes available — Are there expert disagreements or nuanced perspectives? These become the angles that differentiate the blog post.
4. User impact — Does this affect how developers actually work day to day? Prioritize practical over theoretical.
5. 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