quick-brown-foxxx AI Edge Research
Research what's actually happening in AI — optimized for practitioner signal, not marketing noise.
Standard web search for AI topics is heavily polluted with SEO listicles, AI-generated content about AI, and marketing from tools that invested in content 6-12 months ago. Real practitioner signal lives in community platforms where builders share what they're actually using.
Before starting, read references/source-seeds.md for Telegram channel and Twitter practitioner
seed lists to use alongside the methods below.
Core Principle
This is a guide, not a script. Adapt your approach to the topic, scope, and available tool budget. A narrow question ("what's the best local embedding model right now") needs 3-5 targeted searches. A broad sweep ("what's new in AI agents") needs 10-20+ across multiple sources. Use judgment.
Sources & Methods
Hacker News (Algolia API) — highest signal for tools people are shipping
Query http://hn.algolia.com/api/v1/ directly via web_fetch. No auth needed, returns JSON.
Key endpoints and parameters:
search?query=X&tags=show_hn&numericFilters=created_at_i>TIMESTAMP— Show HN posts (people shipping things, highest signal)search?query=X&tags=story&numericFilters=points>30,created_at_i>TIMESTAMP— high-engagement storiessearch_by_date?query=X&tags=story&numericFilters=created_at_i>TIMESTAMP— recent stories by datetags=ask_hn— people asking what to use (good landscape snapshot)- Use
hitsPerPage=20,numericFilterswith Unix timestamps (1 week = 604800s, 1 month = 2592000s, 3 months = 7776000s)
From results, extract: title, url, points, num_comments, created_at, objectID (→ news.ycombinator.com/item?id={objectID}).
For interesting stories, fetch the HN discussion page — practitioner opinions in comments are often more valuable than the linked article.
Signal: Show HN >100 points = strong. Ask HN >50 comments = tool landscape goldmine. High comments + low points = controversial but interesting.
GitHub — star velocity matters more than total stars
- Search for
github.com/trending?since=weeklyormonthly, then fetch the page web_searchforgithub.com [topic] stars:>500 created:>YYYY-MM-DD- A repo gaining 2K stars this week > a repo with 50K total but flat growth
When you find a promising repo, quickly check: created date, external contributors (not just author), last commit, license (permissive = more adoption).
Reddit Practitioner Subreddits
Search these via web_search with reddit r/[subreddit] [topic] [current year] or site:reddit.com/r/[subreddit] [topic]:
- r/LocalLLaMA — local models, deployment, quantization, hardware
- r/MachineLearning — research trends, papers, techniques
- r/vibecoding — AI coding tools, agents, IDE integrations
- r/ClaudeAI — Claude ecosystem, Claude Code, MCP usage
- r/ChatGPTCoding — AI coding workflows, tool comparisons
- r/mcp — Model Context Protocol, MCP servers, integrations
Prioritize subreddits relevant to the specific topic rather than always hitting all of them. Best posts: "I built X with Y", "I switched from X to Y because...", "after 3 months of using X...", benchmark comparisons.
Twitter/X — degraded but still has signal
Twitter's API is locked down, so the primary method is web_search with site:x.com:
site:x.com [topic] [current year]site:x.com [practitioner handle] [topic][topic] "I've been using" OR "I switched to" site:x.com
See references/source-seeds.md for a seed list of AI practitioners worth checking directly.
Telegram Public Channels — one more practitioner community
Public channels have a web preview at https://t.me/s/<channel> showing ~20 recent posts.
Use web_fetch on that URL to scan recent content.
Also: web_search with site:t.me/s/<channel> [keywords] to find indexed older posts.
Alternative access methods: RSS via RSSHub (rsshub.app/telegram/channel/<name>)
or RSS-Bridge (rss-bridge.org/bridge01/?action=display&bridge=Telegram&format=Mrss&username=<name>).
Sometimes they might work better or worse than raw web access approach.
See references/source-seeds.md for the seed channel list. Do a quick health check before relying
on any channel: is the last post within 2 weeks? If not, skip it.
Complementary Sources
- hype.replicate.dev — aggregated trending AI/ML content with engagement scores
- Papers With Code / Arxiv — for technique-level trends (not product-level)
- General web search with practitioner signal terms:
[topic] "I've been using" OR "I switched to" OR "in production" site:news.ycombinator.com OR site:reddit.com
What to Ignore
- "Top N AI Tools in 202X" listicles
- Product landing pages and marketing blogs
- SEO-optimized aggregator sites
- AI-generated roundup articles
- YouTube "review" videos that are sponsored content
If a tool already appears in multiple listicles, it's 3-6 months past its "edge" moment. Still worth mentioning as established, but it's not a new finding.
Cross-Referencing & Triangulation
For each tool/framework/technique found, consider:
- Source diversity: How many independent source types mentioned it? (HN + Reddit + GitHub = strong. Single source = flag it.)
- Practitioner vs marketing: Is the signal from builders or marketers? Red flags: polished landing page, no public repo, only found in listicles. Green flags: Show HN by the author, real GitHub issues from external users, production experience reports.
- Adoption trajectory: Is it being compared TO something established (rising challenger)? Is it being compared FROM something established (people migrating away)?
- Marketing lag: If already in listicles → stale signal. If only in practitioner channels → fresher signal but less proven. Sweet spot: discussed in HN/Reddit but NOT yet in listicles.
Categorization
Use these tiers as a rough guide — not every finding needs a rigid score, and items can sit between tiers:
| Tier | Typical Profile |
|---|---|
| 🔴 Bleeding Edge | 0-3 weeks in discourse. 1-2 practitioner mentions, <500 GH stars, no tutorials exist yet. Worth watching, not adopting. |
| 🟠 Rising | 3-8 weeks. Multi-community discussion, 500-5K GH stars with high velocity. First "how to" posts appearing. Worth trying as early adopter. |
| 🟡 Establishing | 2-3 months. Production use cases reported, "X vs Y" comparisons appearing, 5K+ GH stars with sustained growth. Safe to evaluate for production. |
| 🟢 Battle-Tested | 3+ months. Documented production use, stable releases, considered the default for its niche. In this report because it's still recent or had a significant update. A 🟢 with strong upward momentum = established tool experiencing renewed hype. |
Also note ⚠️ Declining / Overhyped when you see: migration-away stories, abandoned repos, negative sentiment shift, still in listicles but practitioners moved on.
When in doubt, err toward the less-hyped tier. Never label something 🟢 without clear production evidence.
Output
Adapt the report format to the query. A narrow topic might just need a focused list. A broad sweep warrants sections by tier. Always include:
- What was found, where it was found, and a candid assessment
- The tier classification
- Anything found in one community but absent from others (unique signal)
- Things that are declining or overhyped (saves the user time)
- Which sources were actually consulted and any limitations hit
Don't force a rigid template — the goal is to give the user a clear, honest picture of the landscape that they couldn't have gotten from a Google search.