Week of Jun 15
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ReadLoop Library
Jun 21
Matthew Berman launched Loop Library (Jun 2026) — a curated open repo of ~26 runnable agent loops. Every entry specifies: trigger, action, proof, memory, and stopping condition. Proof and stopping condition are first-class — not afterthoughts.
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WTF Is a Loop? Part 2: The 15 Loops People Are Actually Using
Matt Van Horn · Jun 20
Matt Van Horn cataloged 15 agent loops people are actually using in practice — a practical counterpart to the theoretical loop-engineering canon. Builds on his Part 1 debate map from Jun 8.
Week of May 25
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Trust Layer for AI-Generated Office Files (Second AI Attack)
Nate B Jones · May 31
Nate B Jones's 4-stage trust-layer workflow for AI-generated office files: a hostile-reviewer prompt plus two-model QC (Codex ⇄ Opus 4.7) producing one verified output — the 'second AI attack' that catches what a single generation pass misses.
Week of May 11
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Skills: The Application Layer
Barry Zhang & Mahesh Murag (Anthropic) · May 17
"We stopped building agents and started building Skills." The clearest framing of Skills as the software layer of the agent stack.
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Software 3.0
Andrej Karpathy (interviewed by Stephanie Zhan) · May 17
The Software 3.0 framing: 1.0 = explicit code, 2.0 = learned weights, 3.0 = prompted models. Karpathy on why teams should change behavior the day they believe it.
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How We Build Effective Agents
Barry Zhang (Anthropic) · May 17
Direct read-across to ADR-001 and the Cortex agent layer. The talk to send anyone "thinking about agents."
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Practical Claude Code Tips
Boris Cherny (Anthropic) · May 17
Practical, no-history-no-theory walkthrough: terminal setup, codebase Q&A as the onboarding wedge, memory files, the Claude Code SDK in CI, "don't index, ask Git."
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Claude on Vertex AI with the ADK
Ivan Nardini (Google Cloud) · May 17
Walkthrough of Google Cloud's agent stack as competitor/complement to Anthropic-native + Vercel AI SDK setups.
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May 17
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Pinecone Just Demoted Vector Search. Here's the Knowledge Layer.
Nate B Jones · May 13
Nate B Jones argues the AI-agent-memory war has moved past embed-and-retrieve. Even Pinecone is repositioning vectors as one component of a broader knowledge layer that includes graph relationships, structured data, and contextual retrieval. The thesis: production agents need a layered knowledge stack, not just RAG.
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Full Walkthrough: Workflow for AI Coding
Matt Pocock · May 13
Pocock's consolidated AI-coding workflow built around skills (structured prompts with categories, validation checkpoints, bundled resources).
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Software Fundamentals Matter More Than Ever
Matt Pocock · May 13
In the AI age, fundamentals (DDD, encapsulation, type safety) compound the value of AI tooling. Short, sharp, quotable.
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ReadEngineers, DELETE the BASH Tool: Agentic Security
IndyDevDan · May 11
Argues that the Bash tool inside Claude Code (and most agent harnesses) is a ticking time bomb: prompt injection or a single bad prompt can escalate to destroying production. Walks through concrete sandboxing patterns to remove or constrain Bash while preserving capability.
Week of Feb 16
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AI Phase Transition: Capability Overhang and the December 2025 Convergence
Feb 21
Argues that December 2025/January 2026 is a phase transition in AI — not one breakthrough but the convergence of model releases, orchestration patterns, and proof points crossing thresholds simultaneously. The result: a massive capability overhang where what's possible has leaped ahead of what's being adopted.
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200-Line Prompt Killed $285B: The SaaS Apocalypse and What Survives
Feb 21
Anthropic's co-work legal plugin — ~200 lines of markdown — triggered $285B in market-cap destruction across SaaS, legal tech, and data analytics. The argument: per-seat SaaS pricing is structurally broken, but the data and accountability underneath it are not. A pricing-model crisis, not a technology crisis.
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Code Costs Nothing, Knowing What to Build Costs Everything
Feb 21
The bottleneck in software development is shifting from implementation to specification. AI agents write code fine, but their errors are conceptual, not syntactic — they build exactly what was asked, and what was asked is wrong. The most valuable skill is now describing systems precisely enough for AI to build them.
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AI Scare Trade: Market Reflexivity and Sector Repricing
Feb 21
Analyzes how AI announcements triggered cascading stock crashes across 8 sectors in 10 days — software, private credit, insurance, wealth management, real estate, logistics, drug distribution, commercial office. Hundreds of billions in market cap. The pattern: dump first, analyze later; the drops then create the reality they feared.
Week of Feb 2
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Claude Code Agent Teams
AICodeKing · Feb 7
Agent Teams is Claude Code's multi-agent orchestration feature (experimental). A team lead spawns multiple independent Claude Code instances that work as a coordinated team with peer-to-peer messaging, shared task lists, and dependency tracking — full sessions, not hub-and-spoke sub-agents.
Week of Jan 26
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AI Learning Curriculum for Beginners
Cory 🦢 Real Bitcoin @ Swan.com · Jan 31
A curated list of five essential AI learning resources including foundational essays, podcasts, YouTube channels, and blogs. The recommendations progress from big-picture understanding to technical details and current developments.
Week of Jan 19
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Complete Claude Code Configuration Repository
NirD · Jan 25
A public GitHub repository (everything-claude-code by affaan-m) provides a comprehensive, battle-tested configuration system for Claude Code including agents, skills, hooks, commands, rules, and MCP configs. The repository serves as a complete 'operating system' for Claude Code power users who want to avoid building configurations from scratch.
Week of Jan 12
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Saved Link: @iruletheworldmo
@iruletheworldmo · Jan 13
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Saved Link: @davidondrej1
@davidondrej1 · Jan 13
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Saved Link: @AntoineRSX
@AntoineRSX · Jan 13
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Saved Link: @thegarrettscott
@thegarrettscott · Jan 13
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10 Free AI Courses for 2026
Abhishek · Jan 13
A curated list of 10 free AI courses covering generative AI, coding assistants, prompt engineering, and computer science fundamentals. The courses are from reputable sources like NVIDIA, DeepLearning.AI, Anthropic, Microsoft, Harvard, and OpenAI.