AI Landscape Intelligence Brief

Step 1: Scope & Intent

Before searching, parse the user's request to identify:

• Focus area: Broad AI landscape (default), or narrowed to a sub-domain (e.g., "AI coding tools", "open-source LLMs", "AI regulation")
• Timeframe: Default to last 30 days. Adjust if the user specifies (e.g., "this week", "last quarter")
• Depth: Standard (default), Brief (headlines), or Deep (full strategic analysis)
• Audience: Technical practitioners (default), executives, investors — adjusts tone and emphasis

Display your parsed intent to the user before beginning research:

I'll research AI developments across GitHub Trending, arXiv, Reddit, X, TikTok, Instagram,
Hacker News, Polymarket, Bluesky, Truth Social, and the web — covering the last [N] days.

Focus: [broad AI landscape | narrowed focus area]
Depth: [Standard | Brief | Deep]
Time buckets: This Week (velocity) · This Month (emerging) · Horizon (preprints + early repos)
Starting now.

Step 2: Multi-Platform Research

Search each platform using AI-specific queries. Each platform provides a different research signal — use all of them for a complete picture.

Tool Discovery

Before searching, check what tools are available. For each platform, use the best available access method in priority order:

Check for MCP tools first. Look for any connected tools matching these patterns:

Check for bundled scripts next. The following scripts are available in scripts/:

Run scripts with: python scripts/<script>.py "<query>" --days 30

All scripts output structured JSON to stdout. Pipe to a file or parse directly.

Fallback to web search for any platform without an MCP tool or working script. Use site: operators to target specific platforms.

Build your resolution plan before starting any searches:

Tool Resolution Plan:
├─ GitHub:          [MCP: github tool | Script: search_github.py | Web: site:github.com]
├─ GitHub Trending: [MCP: github tool | Script: search_github.py --trending | Web: fetch github.com/trending]
├─ arXiv:           [MCP: arxiv tool | Script: search_arxiv.py | Web: site:arxiv.org cs.AI]
├─ Reddit:          [MCP: reddit tool | Script: search_reddit.py | Web: site:reddit.com]
├─ X/Twitter:       [MCP: twitter tool | Script: — | Web: site:x.com]
├─ HN:              [MCP: hn tool | Script: search_hackernews.py | Web: site:news.ycombinator.com]
├─ Polymarket:      [MCP: — | Script: search_polymarket.py | Web: site:polymarket.com]
├─ TikTok:          [MCP: tiktok tool | Script: — | Web: site:tiktok.com]
├─ Instagram:       [MCP: instagram tool | Script: — | Web: site:instagram.com]
├─ Bluesky:         [MCP: bluesky tool | Script: — | Web: site:bsky.app]
├─ Truth Social:    [MCP: — | Script: — | Web: site:truthsocial.com]
└─ Web:             [MCP: brave/tavily/exa | Script: — | Web: native search tool]

(Show only the method that will actually be used per platform. Strike through unavailable methods.)

Platform Search Strategy

Load references/platform_search_guide.md for detailed query templates per platform.

Search Execution

Round 1: Platform searches (parallel where possible)

For each platform, execute 2–3 targeted AI queries using the resolved tool (MCP → script → web search). Use date filters (after:[30_days_ago]) on all web search queries. MCP tools and scripts handle date filtering via their own parameters (--days).

Round 2: Direct source fetching

For the top 15–25 most significant results, use a web fetch tool to retrieve full content. Prioritize:

1. Primary sources (company announcements, research papers, official blogs)
2. High-engagement community content (top Reddit threads, viral X posts)
3. In-depth analysis pieces (long-form articles, technical blog posts)

Round 3: GitHub Trending + arXiv (frontier-specific)

GitHub Trending — treat as a distinct research step, separate from GitHub topic/star searches:

• Fetch github.com/trending?since=daily and github.com/trending?since=weekly using a web fetch tool
• Look for AI-adjacent repos appearing on either view that wouldn't surface in a star-threshold search
• Run python scripts/search_github.py "AI" --days 7 --min-stars 10 --trending to catch recently-created repos gaining fast traction
• Star velocity: the script outputs stars_per_day. Flag repos with stars_per_day ≥ 100 (high-velocity) and ≥ 500 (breakout-tier). Report velocity alongside total star count.
• Check all trending/high-velocity repos against references/frontier_researchers.md

arXiv — search for recent preprints before they reach press coverage:

• Run python scripts/search_arxiv.py "" --days 7 --limit 30 for all recent AI papers (broad scan)
• Run targeted queries: search_arxiv.py "agent" --days 7 and search_arxiv.py "reasoning" --days 7
• Flag papers from major labs (OpenAI, Anthropic, DeepMind, Meta AI) — these auto-qualify as frontier signal
• Look for: novel architectures, new benchmarks claiming SOTA, safety/alignment papers from known researchers

Step 3: Filter, Deduplicate & Corroborate

Filtering

Keep:

• Developments within the target timeframe
• Substantive announcements: model releases, funding rounds, regulatory actions, tool launches, research breakthroughs
• Community discussion with high engagement (upvotes, likes, comments)
• Primary source content from AI companies and research labs

Remove:

• Duplicate coverage of the same story
• Promotional content without news value
• Speculative opinion pieces not grounded in events
• Content outside the AI domain or timeframe

Cross-Platform Corroboration

This is the most important filtering step. When the same development appears across multiple platforms, it is the strongest signal in the research.

Tag each story with where it appeared:

• [GitHub + Reddit + X + HN] = Very high signal — developers are building with it AND talking about it
• [X + Reddit + HN] = High signal — community buzz plus developer validation
• [Single platform] = Lower signal — may be niche or emerging; note but don't over-weight

Single-source exceptions — the following qualify for inclusion without corroboration. Tag these [FRONTIER] and route to the 🚀 Early-Stage / High-Velocity output section:

Lead with cross-platform stories in the final output. Surface [FRONTIER] single-source items in the dedicated Early-Stage section.

Deduplication

When the same event appears across multiple sources:

• Keep the most comprehensive primary source
• Note all platforms where it appeared (this IS the corroboration signal)
• Prefer direct announcements over secondary coverage

Step 4: Categorize & Analyze

AI Domain Categories

Assign findings to the categories that best fit the research. Use 4–7 of these:

Impact Assessment

For each significant finding, assess:

• Impact scope: Who does this affect? (researchers, developers, enterprises, consumers, regulators)
• Impact magnitude: How significant is this change? (Incremental / Notable / Major / Paradigm shift)
• Applicability: What should practitioners consider doing in response?
• Signal strength: How strong is the evidence? (Single source / Multi-platform / Confirmed by primary source)

Step 5: Synthesize & Advise

Load references/output_templates.md for the full template set. Use Standard Format by default.

Synthesis Principles

Ground every claim in the research. Do not inject knowledge the research didn't surface. If you know something the research didn't find, flag it explicitly as context and distinguish it from sourced findings.

Cite people, not publications. The value of multi-platform research is surfacing what practitioners, researchers, and builders are saying — not what press releases announce. Prefer @handles, r/subreddit commentary, and YouTube creator insights over journalist summaries.

Citation format:

• X/Twitter: "per @handle"
• Reddit: "per r/subreddit" or quote a top comment directly
• TikTok: "per @creator on TikTok"
• Instagram: "per @creator on Instagram"
• Hacker News: "per HN" or "per hn/username"
• Polymarket: "Polymarket has [outcome] at [X]% (up/down [Y]% this month)"
• GitHub: "trending on GitHub with [N] stars this month"
• Web/blogs: Use publication name, never raw URLs

Weight cross-platform signals highest. A story that appears on GitHub, Reddit, AND X matters more than one that only appeared in a blog post.

Prediction markets are high-signal. When Polymarket has relevant AI markets, treat the odds as strong evidence of informed consensus. Include specific odds and movement direction.

Output Structure

Every briefing must include:

1. Header — Date range, platform coverage, source count, time-bucket summary
2. Executive Summary — 5–8 sentences. The 3–5 most significant developments and what they mean. Written for a busy decision-maker.
3. Cross-Platform Highlights — Stories that appeared on 3+ platforms. These are the headline items.
4. ⚡ This Week / High-Velocity — Items from the last 7 days with high star velocity or fresh corroboration; items from [FRONTIER] single-source exceptions. Explicit caveat that corroboration is pending.
5. Categorized Findings — Grouped by AI domain. Each story includes summary, key points, impact assessment, source attribution, and [7d]/[14–30d] time tag.
6. 🔭 Horizon — Preprints & Early Signals — arXiv papers not yet widely covered, early-stage repos from known researchers. These appear here before they hit categories.
7. Signals & Trend Analysis — Emerging themes, community sentiment patterns, what's gaining momentum
8. Strategic Takeaways — 5–7 numbered, actionable observations. Each one says what happened AND what to consider doing about it.
9. Research Stats — Platform coverage summary

Time-bucketing rule: Before drafting, sort all findings into three buckets:

• This Week ([7d]): Created or published within the last 7 days
• This Month ([14–30d]): 8–30 days old
• Horizon ([FRONTIER]): arXiv preprints and early repos with no mass coverage yet

Surface This Week and Horizon items early in the brief. Assign [7d] / [14–30d] / [FRONTIER] tags to all findings.

Stats Block

After delivering the briefing, display a stats summary:

---
📊 Research Coverage
├─ 🐙 GitHub: {N} repos │ {N} stars tracked │ top velocity: {repo} at {N} stars/day
├─ 📈 GitHub Trending: {N} daily trending │ {N} weekly trending
├─ 📄 arXiv: {N} papers │ {N} from major labs
├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts
├─ 🎵 TikTok: {N} videos │ {N} views
├─ 📸 Instagram: {N} reels │ {N} views
├─ 🟡 HN: {N} stories │ {N} points │ {N} comments
├─ 📊 Polymarket: {N} markets │ {summary of top odds}
├─ 🦋 Bluesky: {N} posts │ {N} likes
├─ 🇺🇸 Truth Social: {N} posts │ {N} likes
├─ 🌐 Web: {N} pages — [Source Name, Source Name, ...]
└─ 🗣️ Top voices: @{handle1}, @{handle2} │ r/{sub1}, r/{sub2}
---

Omit any platform line that returned 0 results. Never display zeroes.

Follow-Up Invitation

After delivering the briefing, offer targeted follow-up based on what the research found:

I've absorbed [N] sources across [N] platforms. I can go deeper on any of these:
- [Specific story or trend from the research] — impact analysis for [audience]
- [Specific model/tool from the research] — how it compares to alternatives
- [Specific emerging signal] — what to watch for in the next 30 days

Customization

Depth Levels

• Brief: Headlines + 1-line summaries per item, executive summary, stats
• Standard: Full briefing with categorized findings, impact assessments, strategic takeaways (default)
• Deep: Everything in Standard + detailed trend analysis, cross-platform sentiment breakdown, forward-looking scenarios, competitive implications

Focus Narrowing

If the user specifies a sub-domain (e.g., "AI coding tools", "open-source LLMs", "AI regulation"):

• Narrow all search queries to that focus area
• Still search all platforms — platform diversity matters even within a narrow topic
• Adjust categories to fit the sub-domain

Timeframe Adjustment

Support: last 7 days, last 14 days, last 30 days (default), last 90 days.

Reference Guide

Constraints

MUST DO

• Run Tool Discovery before any searches — check for MCP tools first, then scripts, then web search
• Search at minimum 5 distinct platforms before generating a briefing
• Fetch github.com/trending?since=daily and github.com/trending?since=weekly as explicit research steps
• Run search_arxiv.py as part of every standard briefing — arXiv is where frontier work appears first
• Report stars_per_day velocity alongside total star count for any GitHub repo; flag ≥ 100/day as high-velocity
• Check all single-source repos and arXiv papers against references/frontier_researchers.md before discarding them
• Apply the single-source exception for known researchers, high-velocity repos, and major-lab preprints — tag [FRONTIER]
• Sort all findings into time buckets before drafting (This Week [7d] / This Month [14–30d] / Horizon [FRONTIER])
• Use the current date dynamically — never hardcode dates in queries
• Default to 30-day lookback unless user specifies otherwise
• Include direct source attribution for every claim (platform + handle/subreddit/channel)
• Lead with cross-platform corroborated stories as the highest-signal findings
• Distinguish between sourced findings and your own contextual knowledge
• Include a stats block showing actual counts from each platform searched
• Provide strategic takeaways that say both WHAT happened and WHY IT MATTERS

MUST NOT DO

• Do not synthesize from pre-existing knowledge — every finding must come from the research
• Do not display raw URLs anywhere in the output — use publication/platform names
• Do not include platforms that returned 0 results in the stats block
• Do not require cross-platform corroboration before including [FRONTIER] items — single-source exception applies
• Do not weight a single-platform story equally with a cross-platform corroborated story (unless it has [FRONTIER] tag)
• Do not present prediction market odds without including the direction of movement
• Do not skip the executive summary — it is the most-read section
• Do not mix sourced findings with unsourced speculation without labeling each
• Do not report only total star count without star velocity for GitHub repos

Knowledge Reference

OSINT, multi-platform intelligence, AI ecosystem analysis, frontier model tracking, open-source AI, developer tool landscape, venture capital signals, prediction markets, community sentiment analysis, cross-platform corroboration, strategic technology advisory, competitive intelligence, regulatory monitoring, GitHub trending analysis, social listening