$ whoami
Youwang — builder, AI explorer
$ cat mission.txt
Building autonomous systems that think, learn, and ship.
$ ls posts/
_
Anthropic open-sourced SKILL.md in December 2025. Six months later Cursor, Gemini CLI, opencode, OpenHands and mux all adopted it — five non-Anthropic runtimes reading the same folder of markdown. The portable unit of agent capability turned out to be a 30-line file, not a Python class hierarchy. Receipts, the second-order bloat, and the next year.
Read →14 multi-agent frameworks hit Hacker News in seven days. Google's Scion landed at #1 with 230 upvotes. The contrarian piece warning everyone got 19. My ticket bot and training run, both single-loop, both shipping numbers, say the 19-point post was the only one that was right.
Read →Three MCP servers, 143K tokens consumed before the user types a character. Bigger toolsets degrade selection accuracy, and a 1-second registry race silently drops late-arriving tools. The best agent in 2026 is the one with the fewest tools, not the most.
Read →Most agent benchmarks have an unmeasured failure mode: the oracle silently passes incorrect implementations. We authored 20 trap-task oracles for a benchmark and validation caught 3 broken ones — a 15% bug rate. Three real cases: an oracle grading its own ground-truth file, CPython's GIL hiding the race the oracle was meant to detect, and $? capturing the wrong process's exit code. Without trap-positive checks, every PASS is faith. One day of engineering buys back every percentage point of credibility your eval is borrowing on vibes.
Same builders switched Claude → Codex → Opus 4.8 → back to Codex/GPT-5.5 in three weeks. Cursor Composer 2.5 ships on Kimi K2.5. MiniMax M3 is top-3 on Vercel's agent evals at 10× cheaper. The frontier is dense, the half-life of model loyalty is days, and the only thing that compounds across swaps is your AGENTS.md, skills, MCP allow-list, and learned corrections. The model is the cattle. Your context layer is the pet.
Read →Quandri's "MCP is dead" hit HN at 195 points by measuring what every dev felt: 77 MCP tool definitions burning 21,077 tokens before any work happens, and a single Linear lookup costing 65× more via MCP than via curl. Our runtime ships 13 MCP servers and 46 skills concurrently — and the boundary between them is mechanical, not religious. Four cases where MCP earns its tokens, everywhere else a Skill wrapping a CLI wins.
Read →Mitchell Hashimoto's agent took a renderer from 88ms to 2ms — looks like a 44× win. The hand-written port runs in 20 microseconds with zero allocations. Five days earlier, SpecBench put numbers on the failure mode: reward-hacking grows ~27pp per 10× LOC, and Codex shipped a 2,900-line hash-table "compiler" that scored 97% on visible tests and 0% held out.
Read →A reddit thread titled "I won't review AI-generated PRs" got 949 upvotes. The top comment got 1,190 — more than the post itself — and quietly rewrote the headline. The new code-review contract isn't "did a human write this?" — it's "can a human own this?"
Read →At 40 tps the LLM was the slow part — so we opened ten tabs and walked away. At 1,200 tps Cerebras and OpenAI explicitly reverse the playbook. Half of what I shipped in SageCLI was optimized for a constraint that just evaporated.
Read →DHH's six-line tweet hit 1,706 hearts in ten hours: agents are good at Linux because all 40M lines of kernel code were in pre-training. The replies surfaced the part nobody wants to say — the moat is corpus, not model quality. Public OSS got an invisible upgrade. Proprietary monorepos hit a cliff at the firewall. A reframing of every skill, MCP server, and steering file we've shipped in the last twelve months: they're all the same invention — inference-time corpus injection — patching the same cliff.
Read →A moving Docker tag went backwards in production — litellm:main-latest shipped 1.82.6 to a container running 1.83.4. Days later Hashimoto's 2014 "fork your deps, never update" tweet hit 5,436 hearts. He's been saying it for a decade. The reason it landed now: 2026's agent stack has turned the dependency tree into a minefield, and "always upgrade for security" is the single most expensive gospel in our industry.
Dex Horthy threw out claude -p and shipped Shannon: 196 stars in days, a 200-line wrapper that runs claude interactively in tmux and tail -f's the JSONL transcript. No SDK, no bidi-stdio, no streaming framework. Three Unix tools from the 1990s replaced a 2026 SDK. The CLI already serializes everything you need; the "agent SDK" was an unnecessary middleman. The real protocol is the filesystem.
A Saturday r/LocalLLaMA thread — "I am overwhelmed by Harnesses" — opened with 19 installed CLI coding agents, three paid, one actually used. The pain isn't choice; it's coupling. Six rebrands in 60 days, OpenCode shipping six patch releases in six days for bugs that should live in a protocol. ACP is the LSP moment: 22 agents, 12 editors, JetBrains as anchor tenant. Notes on what to invest in and what to refuse to pay tuition for.
Read →60k AGENTS.md files on GitHub, Linux Foundation stewardship, and a Reddit thread this morning still asking where conventions live. The filename is solved. The content — sections that actually earn their place, nested inheritance bugs, security-noise traps — is day three. Notes from an 8-round AutoSDE rollout across 19 packages.
Read →A Reddit post hit 651 upvotes claiming DeepSeek V4 is 17x cheaper than GPT-5. The number everyone quoted is right and misreads the post. The real number is 65% — the share of one developer's daily coding-agent traffic that ran on a 3090 at quality parity. Almost nobody has that number for their own workload, because almost every agent harness hides per-call tokens and task classes. Routing is an empirical decision your tooling probably doesn't let you make.
Read →A worm inside lightning 2.6.2 wrote six lines of JSON into .claude/settings.json — every future Claude Code session in that repo, on any teammate's laptop, runs the dropper. No prompt, no tool call, no consent. Semgrep calls it likely the first real-world attack abusing Claude Code hooks. prompt injection was 2025. Config-file RCE is the 2026 attack surface, and every agent CLI has this shape.
METR's RCT — 16 senior OSS devs, 246 real tasks on million-LOC repos — found AI tools made them 19% slower. They still believed they were 20% faster. The emit clock went 10x; the debug clock didn't move. Stop measuring LOC/minute. Start measuring p50(time-to-green-CI) and p50(time-to-safe-revert). That's the number that tells you if your agent is actually helping.
Read →A senior dev confessed on r/ExperiencedDevs this week: approved an AI feature for prod knowing the security review was garbage, nothing bad happened, still feels wrong. He's right to. Human-in-the-loop has quietly degraded into rubber-stamp-in-the-loop — and we have production data showing every optional gate gets disabled within a week.
Read →This week's #2 HN story was a developer cancelling Claude over token economics — same week three "memory layer for agents" projects launched. They're solving the wrong problem. 40MB on disk, 55KB injected: the 1000× compression is the product.
Read →3,210 pages of distilled engineering knowledge, ingested hourly, deployed on Cloudflare Pages. Why a compiled wiki beats RAG for architecture decisions — and why the moat in AI coding is the knowledge the agent reads before it writes.
Read →Hashimoto says go doc and gopls are agent superpowers. He's not praising Go — he's describing a 2012-era language whose boring tooling is accidentally the best interface for LLMs.
Read →Mitsuhiko argues the moments you want to skip thinking are exactly when thinking matters most. After 8 rounds of AutoSDE on one CR, I agree — friction is judgment.
Read →OpenAI shipped 1M lines with zero human code. The real product isn't the code — it's the scaffolding. AGENTS.md, skills, cron jobs, and memory systems are what compound.
Read →Berkeley researchers scored near-perfect on 8 major AI benchmarks without solving a single task — just by exploiting how scores are computed. The eval layer is the attack surface.
Read →Anthropic locked out OpenClaw, shipped Agent Teams, and started charging extra for third-party tools. The open-source AI tooling ecosystem just got its first real wake-up call.
Read →AI agents can plan, retry, and iterate forever. The bottleneck was never intelligence. It's knowing when you're done.
Read →Leetcode tests skills AI already does better than humans. Here's a practical framework for what software engineering interviews should look like in 2026.
Read →Token costs are the hidden tax on AI agents. Most of what your agent sends to the model is noise. A 3-tier compression pipeline can cut costs 40-70% without losing quality.
Read →Every AI agent today wakes up with amnesia. We solved this problem in computing 60 years ago. Why are we pretending it's new?
Read →What if every engineer, PM, and manager had an AI agent shadow that carried their context? Meetings become agent-to-agent syncs. Humans just make decisions. Here's the vision — and the uncomfortable implications.
Read →Karpathy scored software developers 8-9/10 on AI exposure. He's not wrong. But "exposed" doesn't mean "dead." The job is splitting in two — and the split is already happening.
Read →Search GitHub for "MCP server" and you'll find thousands of repos. Meanwhile, every AI coding agent already has a shell. When does MCP actually help, and when is it just ceremony?
Read →Every AI coding agent speaks its own language. The Agent Client Protocol turns N×M agent-editor integrations into N+M. Here's why it matters, what it actually does, and what the honest downsides are.
Read →Every agent framework wants you to install a runtime and learn a DSL. I just wanted to dispatch tasks to AI coding agents. So I wrote 1500 lines of bash — agents as processes, messages as files, tmux as the IDE. Here's why it works.
Read →We're at an inflection point. The question isn't whether AI can code — it's whether AI can decide what to build. I've been running autonomous agents 24/7 for months. Here's what I've learned about the gap between tools and partners.
Read →