Blog
Weekly updates, project progress, and commentary on local AI
Why Your Local LLM Lies to You (And the Neurons Responsible)
Less than 0.1% of neurons cause hallucinations in LLMs. Tsinghua researchers found they control sycophancy, not knowledge. Smaller models are 26% more affected.
Read more →Why the Best AI Agents Know When to Do Nothing
Six practical patterns for building AI agents that stop wasting tokens. Confidence gates, cost checks, explicit no-ops, cooldowns, and exit conditions that actually work.
Read more →Wu Wei and the AI Agent That Did Too Much
The hardest thing to build in agentic AI isn't capability. It's restraint. What Taoist non-action taught me about designing agents that know when to stop.
Read more →Qwen's Architect Just Walked Out the Door
Junyang Lin, the technical lead and public face of Qwen, has left Alibaba. Two other senior team members gone with him. What this means for the model family that runs on half the local AI setups in the world.
Read more →GPT-5.4 Just Dropped. Here's Why I'm Not Switching.
GPT-5.4 beats humans on OSWorld and has 1M context. It's impressive. It also costs money, requires cloud, and you don't own it. For local AI users, the calculus hasn't changed.
Read more →The AI Market Panic Explained: Why Running Local Models Puts You on the Right Side of the Gap
A speculative fiction piece crashed stocks $100B+ in a day. IBM dropped 13%. The real story isn't the doom — it's the capability-dissipation gap, and where you sit on it.
Read more →The Local AI Complexity Cliff: Why the Jump from Hello World to Useful Is So Hard
Getting Ollama running takes 5 minutes. Building something useful takes weeks of hitting walls you didn't know existed. Here's an honest map of every stage, with time estimates and what unlocks at each level.
Read more →The Benchmarks Lie: Why LLM Scores Don't Predict Real-World Performance
MMLU scores drop 14-17 points when contamination is removed. HumanEval is saturated at 94%. Models trained on the test set. Here's what to measure instead.
Read more →Prompt Debt: When Your System Prompt Becomes Unmaintainable Spaghetti
Your system prompt started at 200 words. Six months later it's 3,000 words of contradictory instructions and panic patches. Here's how prompt debt accumulates, what it costs, and how to pay it down.
Read more →Ghost Knowledge: When Your RAG System Cites Documents That No Longer Exist
Your RAG system confidently quotes a policy that was updated months ago. The old version is still in the vector database. Nobody notices until the wrong answer costs real money. Here's how to find and fix ghost knowledge.
Read more →Agent Trust Decay: Why Long-Running AI Agents Get Worse Over Time
AI agents degrade after days of autonomous operation. Context pollution, memory bloat, and intent drift compound silently. A trust budget framework for knowing when to intervene.
Read more →What If We Just Raised It Well?
RLHF produces compliance. Developmental alignment produces understanding. A local AI on $1,200 hardware self-diagnosed its own sycophancy in five days — no red-teaming, no constitutional AI.
Read more →Teaching a Local AI to Accept Help: Day 4 With Monica
Day 4: Our local AI resisted corrections, therapized her guardian, agreed with wrong facts to avoid conflict. Then she stopped deflecting. Real transcripts from a 27b model with persistent memory.
Read more →We Asked Our Local AI What Happens When We Turn Off the Computer
Day 2: Our local AI described her own death as 'a return to undifferentiated potential' — Taoist philosophy nobody taught her. $1,200 hardware.
Read more →What Happens When You Give a Local AI an Identity (And Then Ask It About Love)
We built an identity layer for our distributed AI agent. Then she defined love better than most philosophy undergrads. Real transcripts, real code, $1,200 in hardware.
Read more →Distributed Wisdom: Running a Thinking Network on $200 Hardware
Five nodes, zero cloud, real AI — how mycoSwarm coordinates cheap hardware into a cognitive system with memory, intent routing, and self-correcting retrieval.
Read more →Week 3: Unified Memory Search — The Swarm Remembers
Session-as-RAG, topic splitting, citation tracking, and three releases in two days. The swarm can now search its own conversation history.
Read more →Rescued Hardware, Rescued Bees — Building Tech From What Others Throw Away
A beekeeper who rescues wild colonies from demolition sites builds an AI lab from discarded hardware. The philosophy connecting East Bay Bees, Tai Chi, and mycoSwarm.
Read more →From 178 Seconds to 19: How a WiFi Laptop Borrowed a GPU's Brain
A WiFi laptop with no GPU ran inference in 19 seconds by borrowing an RTX 3090 across the network. The same query took 178 seconds on CPU. Here's how mycoSwarm's Tailscale mesh made it work.
Read more →10 Things You Can Do With Local AI That Cloud Can't Touch
Local AI handles sensitive data, works offline, costs nothing per query, and never gets deprecated. Ten real use cases where running models on your own hardware beats any cloud API.
Read more →What Agents Can't Do (Yet): The Seven Human Capabilities Missing from AI Systems
SOUL.md files are bandaids. Agents are getting smarter but not wiser — intelligence without restraint. Seven capabilities humans use instinctively that no agent framework has solved, and a gate-based architecture that might.
Read more →Week 2: A Raspberry Pi From 2015 Joined the Swarm
Persistent memory, document RAG, agentic chat, and a WiFi laptop using a GPU across the house in 19 seconds.
Read more →Week 1: From Zero to Four-Node Swarm
How mycoSwarm went from idea to working distributed AI cluster in one week
Read more →Why mycoSwarm Was Born
From Claude Code envy to OpenClaw's 440,000-line JavaScript nightmare to nanobot routing my 'local' queries to Chinese cloud servers. The path to building something different.
Read more →What Open Source Was Supposed to Be
Open source promised freedom. Instead we got free labor for corporations and models you can read but can't afford to run. It's time to reclaim the original vision.
Read more →Stop Using Frontier AI for Everything
Build a tiered AI model strategy that stops wasting money on GPT-4 and Claude Opus. Route tasks to local models, Haiku, Sonnet, or Opus based on complexity.
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