ai-news-digest

Read interests/ai-news/context.md to get:

• Last AI Coverage date (updated by both this skill and /news — whichever ran last)
• User preferences

2. Determine Coverage Period

• First run (no previous coverage): Last 24 hours
• Previous coverage exists: From the Last AI Coverage date to now — /news counts; don't re-cover AI items a recent /news digest already reported, go deeper instead
• --full flag: Last 7 days regardless of history

3. Search for News

Find recent AI news across models, coding tools, and productivity. Fan this out across parallel subagents rather than running every search sequentially — it's faster and keeps the main context clean (only distilled results return, not raw search dumps).

Parallel fan-out (preferred)

Spawn three research agents — one per category (Models, Coding Tools, Productivity) — all in a single message (multiple Agent calls in one turn, Explore or general-purpose type). Give each agent:

• Its category's search queries (from the groups below)
• The coverage period (from step 2)
• The relevant source tiers and the filter criteria below
• This instruction: "Return ONLY a compact markdown list, each item - **Mon D** — one-liner ([Source](url)). No preamble. Drop hype, funding-only news, and anything outside the coverage window."

When all three return, the main thread owns synthesis — agents only gather. Specifically:

• Dedupe across them (a model release may surface in more than one category — keep it in the most relevant one).
• Apply any filter rules an individual agent couldn't judge across categories, then categorise into step 4's Models / Tools / Productivity and format per step 5.

Sequential fallback

Fall back to running the WebSearch queries directly across different source types in parallel batches (using the queries below) if either: subagents aren't available, or the run is scoped to a single --topic (just one category, so fan-out adds nothing). The synthesis responsibilities above (dedup, filter, categorise, format) apply identically in the sequential path.

Search Queries

Models & Announcements:

"AI model release" OR "new AI model" [time period]
"Claude" OR "GPT" OR "Gemini" OR "Llama" announcement [time period]
Anthropic OR OpenAI OR Google AI blog [time period]

Coding Tools:

"Cursor" OR "Copilot" OR "Claude Code" OR "Windsurf" update [time period]
"Aider" OR "Cline" OR "Continue" AI coding [time period]
AI IDE coding assistant news [time period]

Technical & Deep Dives:

site:reddit.com/r/LocalLLaMA AI news [time period]
site:reddit.com/r/MachineLearning new model [time period]
site:reddit.com/r/ClaudeAI OR site:reddit.com/r/ChatGPT update [time period]
site:news.ycombinator.com AI model OR AI coding [time period]

Productivity & Workflows:

AI prompting technique OR AI workflow [time period]
AI agent automation [time period]
"vibe coding" OR AI-assisted development [time period]

Source Tiers (search all, prioritize accordingly)

Tier 1 — Official Sources:

• Anthropic blog, OpenAI blog, Google AI blog, Meta AI blog
• GitHub blog, Cursor changelog, official tool announcements

Tier 2 — Quality Tech News:

• The Verge, Ars Technica, TechCrunch, Wired
• VentureBeat AI, The Information, MIT Technology Review

Tier 3 — AI-Focused Publications:

• The Rundown AI, TLDR AI, AI News, Import AI newsletter
• Latent Space podcast/blog, Simon Willison's blog

Tier 4 — Community & Technical:

• Reddit: r/LocalLLaMA, r/MachineLearning, r/ClaudeAI, r/ChatGPT, r/cursor
• Hacker News (news.ycombinator.com)
• Twitter/X from known AI researchers and practitioners
• ArXiv (for significant papers with practical implications)

Tier 5 — Developer Communities:

• Dev.to, Hashnode, Medium (verified authors)
• GitHub trending repos and discussions
• Discord announcements (Cursor, Anthropic, etc.)

Filter Criteria

Include if:

• New model release or major update
• New AI coding tool or significant feature
• Practical technique for AI productivity
• Impacts how practitioners work with AI

Exclude if:

• Pure hype without substance
• Funding/valuation news (unless product impact)
• Academic paper without practical implications
• Not relevant to coding/productivity focus

4. Categorize Results

Group items into three categories:

Models

New model releases, benchmarks, capability updates from major labs.

Coding Tools

Updates to Cursor, Copilot, Claude Code, Windsurf, Aider, and similar.

Productivity

Prompting techniques, workflows, automation, agents, practical tips.

5. Format Output

Group items by category, with bold date prefix:

## Models

- **Feb 3** — Claude 3.5 Opus released with improved reasoning ([Anthropic](url))
- **Feb 2** — GPT-4.5 announced with 1M context ([OpenAI](url))

## Tools

- **Feb 3** — Cursor adds multi-file editing ([Cursor Blog](url))

## Productivity

- **Feb 2** — New prompting technique for complex reasoning ([Source](url))

Format rules:

• Category headers as ## Models, ## Tools, ## Productivity
• Each item: - **Mon D** — One-liner ([Source](url))
• Most recent items first within each category
• Omit empty categories

End with:

Want more detail on any of these? Just ask.

6. Update Context

Update interests/ai-news/context.md:

1. Add row to the Deep Dive History table:

   | YYYY-MM-DD | [coverage period] | [item count] |
   

2. Update the Last AI Coverage section:

   **Date**: YYYY-MM-DD
   **Source**: `/ai-news` deep dive (covering [start] to [end])
   **Last deep dive (`/ai-news`)**: YYYY-MM-DD
   

7. Handle "Tell Me More"

If user asks for more detail on an item:

1. Use WebFetch on the source URL
2. Summarize key details (2-3 paragraphs)
3. Note practical implications
4. Offer to search for related coverage