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ai-edge-research

Writing & research

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Updated 10-16 04:55

About this skill

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 stories
  • search_by_date?query=X&tags=story&numericFilters=created_at_i>TIMESTAMP — recent stories by date
  • tags=ask_hn — people asking what to use (good landscape snapshot)
  • Use hitsPerPage=20, numericFilters with 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=weekly or monthly, then fetch the page
  • web_search for github.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:

  1. Source diversity: How many independent source types mentioned it? (HN + Reddit + GitHub = strong. Single source = flag it.)
  2. 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.
  3. Adoption trajectory: Is it being compared TO something established (rising challenger)? Is it being compared FROM something established (people migrating away)?
  4. 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.