# Quick Brown Foxxx Myai AI Edge Research > AI Edge Research - Skill: `tomevault-io/quick-brown-foxxx-myai-ai-edge-research` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/quick-brown-foxxx-myai-ai-edge-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/quick-brown-foxxx-myai-ai-edge-research/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/quick-brown-foxxx-myai-ai-edge-research --- # 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/` showing ~20 recent posts. Use `web_fetch` on that URL to scan recent content. Also: `web_search` with `site:t.me/s/ [keywords]` to find indexed older posts. Alternative access methods: RSS via RSSHub (`rsshub.app/telegram/channel/`) or RSS-Bridge (`rss-bridge.org/bridge01/?action=display&bridge=Telegram&format=Mrss&username=`). 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. --- > Source: [quick-brown-foxxx/myai](https://github.com/quick-brown-foxxx/myai) — distributed by [TomeVault](https://tomevault.io).