Glean 拾遗
中 EN
Submit a link
Daily · timeline

A few picks a day.

Thu, Jun 11, 2026 3picks
← 06-10
Calendar ▾
2026 · 06
MoTuWeThFrSaSu 123456789101112131415161718192021222324252627282930
has picks today
06-12 →
06:00

AI Agent Skill: Cross-Platform Social Search and 30-Day Synthesis

AI 代理技能：跨平台社交搜索与 30 天舆情简报

/last30days is an AI agent skill that aggregates the latest content from Reddit, X, YouTube, TikTok, Hacker News, and more into a 30-day briefing. It uses entity pre-research to identify key people, communities, and topics, then searches in parallel and scores by real engagement (upvotes, likes, money) rather than SEO. An AI synthesizes a cited, in-depth summary. Open-source (MIT), it supports Claude Code and 50+ agent frameworks. Ideal for engineers, PMs, and researchers needing a quick, grounded update before meetings or decisions.

github.com · 27 min · AI Agents · Open Source · Social Media · Web Search
06:00

AI Skills Marketplace for Product Managers: 100+ Structured Workflows from Discovery to Growth

产品经理的 AI 技能市场：100+ 结构化工作流，从发现到增长

pm-skills is an AI skills marketplace for product managers, packing 100+ codified PM skills and 42 chained workflows into 9 installable plugins. It transforms established product methodologies by Teresa Torres, Marty Cagan, and others into structured, step-by-step AI-guided processes, going beyond generic text generation to enforce product-thinking rigor. Covering the full lifecycle from discovery to growth, it works as a Claude Code/Cowork plugin and offers cross-platform skill support. Ideal for PMs and founders who want to embed AI into their decision-making workflow, not just accelerate document output.

github.com · 29 min · Agent Skill Repository · Ai Tooling · Claude Code Marketplace · Product Management
06:00

Designing loops with Fable 5: self-correction and memory in agentic workflows

Claude Fable 5 实战：用自校正循环和跨会话记忆打磨代理任务

The author shares two practical directions for improving agentic workflows with Anthropic's Claude Fable 5 model: self-correction loops and cross-session memory. In a Parameter Golf challenge—train the best model within a 16MB artifact in under 10 minutes on 8×H100 GPUs—Fable 5 improved the training pipeline roughly 6× more than Opus 4.7 when using Claude Managed Agents with Outcomes judged by an independent verifier sub-agent against nine checkable criteria. Fable 5 bet on larger structural changes and pushed through a quantization regression, while Opus 4.7 stuck to tuning scalar hyperparameters. For memory, the author used a SQL-based task from Continual Learning Bench 1.0 with filesystem-backed memory across agent sessions. Sonnet 4.6 only logged failures and guesses; Opus 4.7 built flagged schema references but verified only 17% of questions; Fable 5 reached 73% verification coverage in the best run and distilled learnings into general rules. Engineers interested in agent architecture and model capability boundaries will find the experiments relevant.

x.com · 5 min · Agent Architecture · AI Agents · AI Engineering · Anthropic · Context Engineering