OpenClaw Multi-Source Tech News Digest: 100+ Sources in One Daily Brief
Instead of manually checking dozens of feeds, OpenClaw can aggregate, deduplicate, score, and deliver one high-signal daily digest.
0) TL;DR (3-minute launch)
- Tech founders lose time jumping across RSS readers, X lists, GitHub release pages, and search tabs.
- Workflow in short: Inputs: → Process: → Outputs:.
- Start fast: Install digest skill and set daily schedule (e.g., 09:00).
- Guardrail: Beware source bias from over-indexed communities.
1) What problem this solves
Tech founders lose time jumping across RSS readers, X lists, GitHub release pages, and search tabs. This workflow centralizes discovery while reducing noise through scoring and deduplication.
3) Workflow map
- Inputs: RSS feeds, X/Twitter accounts, GitHub repos, web-search topics
- Process: collect → merge → dedupe → score by source/recency/engagement
- Outputs: concise daily digest to Telegram, Discord, or email
4) MVP setup
- Install digest skill and set daily schedule (e.g., 09:00)
- Start with 20-30 high-quality sources first
- Define score thresholds for “must-read” vs “optional”
- Deliver to one channel before adding multi-channel routing
5) Prompt template
Generate my daily tech digest from configured sources. Requirements: - include only high-signal updates from last 24h - group by AI tooling, infra, models, and product launches - include why this matters in one line per item - cap digest at 12 items - end with top 3 actionable takeaways
5) Cost and payoff
Cost
Initial source curation takes effort; then mostly maintenance-light.
Payoff
Higher information quality, lower context-switching overhead.
Tip
Cut low-value sources aggressively every week.
7) Risk boundaries
- Beware source bias from over-indexed communities
- Keep dedupe strict to avoid repeated stories
- Use short summaries to avoid token bloat
8) Implementation checklist
- Define one measurable success KPI before going live
- Run in shadow mode for 3-7 days before full automation
- Add explicit human-override for sensitive operations
- Log every automated action for weekly review
- Document fallback and rollback steps
9) FAQ
How soon can this use case show results?
Most teams see initial value in the first 1-2 weeks if they start with a narrow scope and clear metrics.
What should be automated first?
Start with repetitive, low-risk tasks. Keep high-impact or ambiguous decisions behind human approval.
How do I avoid quality regressions over time?
Review logs weekly, sample outputs, and tune prompts/rules continuously as data and workflows evolve.