📚 ShroomDog Picks

Karpathy says coding agents started working in December 2025 — not gradually, but as a hard discontinuity. He built a full DGX Spark video analysis dashboard in 30 minutes with a single English sentence. Programming is becoming unrecognizable: you're not typing code anymore, you're directing AI agents in English. Peak leverage = agentic engineering.

Indie hacker Elvis Sun shared his complete workflow using an OpenClaw agent (Zoe) as an orchestrator to automatically spawn Codex and Claude Code agents. 50 commits per day on average, 7 PRs in 30 minutes, three layers of AI code review, and Zoe proactively scans Sentry to fix bugs. Cost: $190/month.

Anthropic analyzed 9,830 Claude.ai conversations and defined 11 observable AI fluency behaviors. Key finding: people who iterate show 2x the fluency. But when AI produces beautiful artifacts, users question its reasoning less. The prettier the output, the more dangerous it gets.

Indie hacker Ramya's OpenClaw agent kept losing its memory. She spent 5 days debugging — from compaction amnesia, garbage search results, retrieval not triggering, long session context loss, to a system prompt that bloated by 28%. Here are her 10 hard-won lessons.

Investment research firm Citrini Research spent 100 hours writing a fictional '2028 Macro Memo': AI gets too good → white-collar layoffs → consumer spending collapses → mortgage crisis → S&P drops 38%. Not a prediction — a scenario. But each step is logical enough to make you uncomfortable. 9,400+ likes, viral across the internet.

Simon Willison launched a new series called Agentic Engineering Patterns — a playbook for working with coding agents like Claude Code and Codex. Lesson one: writing code got cheap, but writing good code is still expensive. Lesson two: 'red/green TDD' is the most powerful six-word spell for agent collaboration.

A Context Engineer at Sully.ai built his entire digital brain inside a Git repo: 80+ markdown/YAML/JSONL files, no database, no vector store. Three-layer Progressive Disclosure, Episodic Memory, and auto-loading Skills — so the AI already knows who he is, how he writes, and what he's working on the moment it boots up.

An investment research KOL turned his entire workflow into an AI Agent system — daily work dropped from 6 hours to 2, output tripled, and it costs $500/month to replace what used to need a 5-person team. Here's exactly how he built it.

Upgrading OpenClaw keeps breaking your agent fleet? This developer's solution: spin up a separate Gateway as a 'family doctor' that does nothing but fix the main Gateway's agents. Been running it through multiple upgrades — rock solid.

Karpathy's post is a reality check for the Claw era. He frames Claws as the next layer above LLM agents, but warns that exposed instances, RCE, supply-chain poisoning, and malicious skills can turn productivity systems into liabilities. His direction: small core, container-by-default, auditable skills.

Mike Chong explains why senior engineers often underestimate good products — once you understand how something works, you can't unsee it, and you lose the ability to appreciate what it feels like. Three examples (OpenClaw heartbeat, Claude in PowerPoint, Klarna AI support) all point to the same lesson: implementation is the method, user feeling is the product.

Simon Willison added a 'Beats' feature to his blog, pulling TILs, GitHub releases, museum posts, tools, and research back into one unified timeline. This isn't a UI tweak — it's a systematic approach to making all your small outputs visible and compounding.

Anthropic engineer Thariq shared hard-won lessons about prompt caching in Claude Code: system prompt ordering is everything, you can't add or remove tools mid-conversation, switching models costs more than staying, and compaction must share the parent's prefix. They even set SEV alerts on cache hit rate. If you're building agentic products, this is a masterclass in real-world caching.

Simon Willison doubles down on his stance: CLI tools beat MCP in almost every scenario for coding agents. Lower token cost, zero extra dependencies, and LLMs natively know how to call --help. Anthropic themselves proposed a 'third way' with code-execution-with-MCP, acknowledging MCP's token waste problem. This article breaks down the full MCP vs CLI trade-off, including a real-world case study from the ShroomDog team.

Nicolas Bustamante — founder of Doctrine (Europe's largest legal information platform) and Fintool (AI equity research competing with Bloomberg/FactSet) — dissects 10 classic moats of vertical software from both the disrupted and disrupting sides. 5 moats destroyed by LLMs, 5 still standing. Includes a three-question risk assessment framework for evaluating your SaaS holdings.

Anthropic releases Claude Sonnet 4.6 — a major upgrade at the same price: Adaptive Thinking, knowledge through August 2025, and training data extending to January 2026 (newer than Opus 4.6). This article compares Sonnet 4.6, Sonnet 4.5, and Opus 4.6 across five dimensions: price, speed, context, knowledge freshness, and use cases — so you can figure out which one to actually use.

Karry shares a complete hands-on guide to setting up Discord with OpenClaw. Core philosophy: 'Configuration as Conversation' — the only manual step in the entire process is grabbing a Token from the Developer Portal. Everything else — Bot connection, Agent personality shaping, Cron Jobs, debugging — happens through conversation. Six markdown files that define an agent's personality weren't written; they grew from living together and stumbling through mistakes.

Bloomberg Beta investor Amy Tam dissects career tradeoffs in the AI era from a VC perch. Her core thesis: the shift from execution to judgment is already happening, and the K-curve is widening — early movers are compounding, while fence-sitters are compounding in the opposite direction. She maps the tradeoffs across FAANG, Quant, Academia, AI Startups, Research Startups, and Big Model Labs.

Will your AI agent's work survive until tomorrow? Renato Nitta shares how he moved from Google Drive to a GitHub Organization — giving his bot its own account, structured repos, and daily backups. Git isn't just version control. It's your agent's long-term memory.

Cognition ships Devin Autofix: review bot comments auto-trigger fixes → CI reruns → loop until clean. Humans only step in for architecture calls. Key insight: a single agent is a tool, but agent + reviewer loop is a system — and systems compound.