AI News Aggregator

This skill aggregates AI news from 7 authoritative sources and produces a comprehensive, deeply-analyzed report. It uses a multi-agent workflow for parallel fetching, verification, sentiment analysis, and expert-informed reporting.

Usage

/ai-news <days>

Arguments:

• days (optional, default: 7) - Number of days to look back from today

Examples:

• /ai-news 3

• Get AI news from the past 3 days

• /ai-news 7

• Get AI news from the past week

• /ai-news

• Same as /ai-news 7

News Sources (7 Total)

Expert & Newsletter Sources

Source Type URL Value

The Batch Expert Newsletter https://www.deeplearning.ai/the-batch/ Andrew Ng's expert analysis

smol.ai Curated Digest https://news.smol.ai/ Daily AI news roundup

Research Sources

Source Type URL Value

HuggingFace Papers Trending Research https://huggingface.co/papers Community-voted papers

Industry News

Source Type URL Value

TechCrunch AI Startup/Funding https://techcrunch.com/category/artificial-intelligence/ VC, launches, M&A

AI News Enterprise https://www.artificialintelligence-news.com/ Business adoption

Community Sources

Source Type URL Value

Reddit ML Community Discussion r/MachineLearning, r/LocalLLaMA Sentiment, hot takes

Hacker News Dev Discussion https://news.ycombinator.com/ Technical discourse

Multi-Agent Workflow

Execute this workflow in order:

Phase 1: Planning (Main Orchestrator)

• Parse the <days> argument (default to 7 if not provided)

• Calculate the date range: [today - days, today]

• Prepare to spawn 7 parallel executor agents

Phase 2: Parallel Execution

Spawn agents in parallel using Bash tool, each running one fetcher script:

Run all 7 fetchers in parallel (from project root)

uv run python .claude/skills/ai-news/scripts/fetch_smol_news.py <days> uv run python .claude/skills/ai-news/scripts/fetch_hf_papers.py <days> uv run python .claude/skills/ai-news/scripts/fetch_hn_ai.py <days> uv run python .claude/skills/ai-news/scripts/fetch_ai_news.py <days> uv run python .claude/skills/ai-news/scripts/fetch_techcrunch.py <days> uv run python .claude/skills/ai-news/scripts/fetch_the_batch.py <days> uv run python .claude/skills/ai-news/scripts/fetch_reddit_ml.py <days> --min-score 20

Key Outputs:

• Each script returns JSON with items, metadata, and source info

• Reddit script includes community_sentiment with hot topics and engagement stats

• The Batch includes expert attribution

Phase 3: Verification & Deduplication

After collecting results from all sources:

• Date Range Validation: Confirm all items fall within [start_date, end_date]

• Deduplication: Remove duplicate stories across sources

• Match by URL or title similarity (>80% match)

• Keep the version with most metadata

• Quality Filter: Remove low-quality or off-topic items

Phase 4: Deep Analysis & Sentiment Extraction

This is the critical phase for producing a valuable report. Perform these analyses:

4.1 Theme Clustering

Group all items into major themes:

• Research & Models: New architectures, benchmarks, capabilities

• Industry & Business: Funding, acquisitions, enterprise adoption

• Tools & Infrastructure: Developer tools, APIs, frameworks

• Policy & Safety: Regulation, alignment, ethics

• Applications: Real-world deployments, use cases

4.2 Trend Identification

For each major theme, analyze:

• What's the narrative arc? (emerging, maturing, declining)

• How many sources cover this topic?

• What's the engagement level (scores, comments)?

4.3 Expert Sentiment Extraction

From The Batch (Andrew Ng) articles:

• Extract key opinions and predictions

• Note any warnings or concerns raised

• Identify recommended actions or takeaways

4.4 Community Sentiment Analysis

From Reddit and Hacker News:

• What are the hot topics people are excited about?

• What criticisms or concerns are being raised?

• What's the overall mood (optimistic, skeptical, concerned)?

• Use the community_sentiment data from Reddit fetch

4.5 Cross-Source Correlation

Identify stories that appear across multiple sources:

• Research paper on HuggingFace + discussed on Reddit

• Industry news on TechCrunch + expert analysis in The Batch

• These cross-source items are often the most significant

Phase 5: Report Generation

Generate a comprehensive, detailed report with these sections:

AI News Report: [Start Date] to [End Date]

Executive Summary

[3-4 paragraphs providing a narrative overview of the most important developments. Start with the single biggest story, then cover 2-3 other major themes. End with a forward-looking statement about what to watch.]

Top Stories This Period

1. [Most Important Story Title]

Sources: [list sources covering this] Why It Matters: [2-3 sentences on significance] Expert Take: [Quote or paraphrase from The Batch if available] Community Reaction: [Sentiment from Reddit/HN if available] [Link to primary source]

2. [Second Most Important Story]

[Same structure...]

3. [Third Most Important Story]

[Same structure...]

Trend Deep Dives

Trend 1: [Trend Name]

What's Happening: [Detailed explanation of the trend] Key Evidence:

• [Paper/Article 1 with link]
• [Paper/Article 2 with link]
• [Paper/Article 3 with link]

Expert Analysis: [What experts are saying - from The Batch, etc.]

Community Sentiment: [What Reddit/HN thinks]

• Hot takes: [Notable comments or discussions]
• Concerns raised: [Any skepticism or criticism]

What This Means: [Implications for practitioners, businesses, researchers]

What to Watch: [Future developments to monitor]

Trend 2: [Trend Name]

[Same detailed structure...]

Trend 3: [Trend Name]

[Same detailed structure...]

Research Highlights

Papers of the Week

[For each top paper from HuggingFace:]

[Paper Title]

• Link: [arxiv/HF link]
• TL;DR: [1-2 sentence summary]
• Why Notable: [What makes this significant]
• Upvotes: [engagement metric]

[Repeat for top 5-10 papers]

Research Themes

[Group papers by theme with brief analysis]

Industry & Business News

Funding & Acquisitions

[List with brief analysis of what it signals]

Product Launches

[Notable AI product launches with impact assessment]

Enterprise Adoption

[Companies adopting AI, partnerships, deployments]

Policy & Regulation

[Any regulatory news or policy developments]

Community Pulse

Hot Topics on Reddit

Top Discussions:

1. [Title] - [score] points, [comments] comments
   • Key debate: [what people are arguing about]
2. [Title] - [score] points, [comments] comments
   • Key insight: [notable comment or consensus]

Community Sentiment:

• Overall mood: [optimistic/skeptical/mixed]
• Hot topics: [list from sentiment analysis]
• Emerging interests: [what's gaining traction]

Hacker News Highlights

[Notable AI discussions with key points]

Expert Corner: The Batch by Andrew Ng

This Week's Key Insights

[Summarize main points from The Batch articles]

Andrew Ng's Take

[Direct quotes or paraphrased expert opinion]

Recommended Actions

[Any actionable advice from expert sources]

What This All Means

For Researchers

[Implications and opportunities]

For Practitioners/Engineers

[What to learn, tools to try, skills to develop]

For Business Leaders

[Strategic implications, investment signals]

For the Broader AI Field

[Where things are heading, big picture trends]

Full Item List

By Date (Most Recent First)

[Complete chronological list with:

• Date
• Title (linked)
• Source
• Brief description if available]

Report Metadata

• Date Range: [Start] to [End]
• Total Items Analyzed: [count]
• Sources Consulted: [list of 7 sources]
• Generated: [timestamp]

Phase 5.1: Persist Report

After generating the report markdown, save it to disk:

cat <<'EOF' | uv run python .claude/skills/ai-news/scripts/write_report.py
--start-date YYYY-MM-DD
--end-date YYYY-MM-DD
--days N
--sources-ok source1,source2
--sources-failed source3
--total-items COUNT <REPORT MARKDOWN HERE> EOF

The script will:

• Write the report to reports/ai-news_START_to_END_TIMESTAMP.md

• Update reports/manifest.jsonl

• Copy to reports/latest.md

• Return JSON with filepath and metadata

Verify the JSON response includes filepath (and other expected fields) after the command runs.

Important: Always run this after displaying the report to the user.

Phase 5.2: Render HTML

After saving the markdown, generate a self-contained HTML version alongside it:

uv run python .claude/skills/ai-news/scripts/render_html.py /path/to/report.md

The script writes /path/to/report.html (same basename) and prints the HTML filepath to stdout. Use the filepath returned from Phase 5.1 as the input path.

Phase 5.3: Upload to Cloudflare Archive (Optional)

If the ADMIN_API_SECRET environment variable is set, upload the HTML report to the Cloudflare archive:

ADMIN_API_SECRET=$ADMIN_API_SECRET uv run python .claude/skills/ai-news/scripts/upload_to_cloudflare.py
/path/to/report.html
--start-date YYYY-MM-DD
--end-date YYYY-MM-DD
--days N
--total-items COUNT

The script uploads the HTML to Cloudflare R2 and updates the KV index. The report will be immediately available at:

• Archive listing: https://julienh15.github.io/AI-News-Reports/archive/

• Direct link: https://ai-news-signup.julienh15.workers.dev/archive/{report_id}

Note: This step is optional and only runs if ADMIN_API_SECRET is available in the environment.

Scripts Reference

All scripts are in .claude/skills/ai-news/scripts/ directory:

Script Source API/Method Special Features

fetch_smol_news.py

smol.ai RSS feed Curated summaries

fetch_hf_papers.py

HuggingFace Date-based URL Upvote counts

fetch_hn_ai.py

Hacker News Algolia API AI keyword filtering

fetch_ai_news.py

AI News HTML scraping Enterprise focus

fetch_techcrunch.py

TechCrunch RSS feed Startup/funding focus

fetch_the_batch.py

The Batch HTML parsing Expert analysis

fetch_reddit_ml.py

Reddit JSON API Sentiment analysis

render_html.py

Markdown python-markdown Self-contained HTML output

upload_to_cloudflare.py

Cloudflare Worker API Upload to R2 + KV archive

Error Handling

• If a source fails, continue with available sources

• Report which sources succeeded/failed in the output

• Minimum viable report requires at least 2 sources

Quality Guidelines

Report Length

• Executive Summary: 300-500 words

• Each Trend Deep Dive: 400-600 words

• Total report: 2000-4000 words depending on activity level

Analysis Depth

• Don't just list items - explain significance

• Connect dots across sources

• Provide actionable insights

• Include both optimistic and critical perspectives

Linking

• Every claim should link to a source

• Use markdown hyperlinks consistently

• Include both discussion links and original sources

Architecture Reference

See references/ARCHITECTURE.md for detailed workflow diagrams and technical specifications.