Could AI Simulate Billions of Possible Human Lives?
An exploration of large language models as compressed maps of human experience, and what a branching world model built on them might reveal.
Tag: llm
Testing Small Local LLMs on Modest Hardware: A Portable Workflow
A repeatable way to download, serve, benchmark, and choose small GGUF models for Hermes and coding-agent work across different CPU and memory budgets.
Golden Rules for Cheaper, Safer LLM Agents
Notes on turning repeated AI workflows into scripts, skills, cron jobs, and guardrails so agents become cheaper, safer, and more useful over time.
Loop Engineering and Graph Engineering: Working With LLMs
A practical reflection on prompt, context, memory, loop, and graph engineering, and what each layer reveals about working with LLMs day to day.
Life Is Not Git
A personal reflection on Life OS, LLM memory, and digital twins: why being perfectly archived and searchable is not the same as being alive.
Building an AI English Conversation Practice App
Notes on building an AI speaking practice app: STT, TTS, Gemini, ElevenLabs, latency, topic control, and useful feedback for oral English learners.
Cloud Run as a Gemini proxy for an unsupported region
How I used Google Cloud Run as a small AI gateway so clients in an unsupported region can still call Gemini through a supported GCP region.
Tiny Puzzles for Testing and Debugging AI Agents
Small probe tasks for testing local AI agents: function calling, skill awareness, prompt-length pressure, cache behavior, and layer-by-layer debugging.
LM Studio Local Agent Runbook: Pi and OpenCode Step by Step
A step-by-step runbook for wiring LM Studio as a local OpenAI-compatible backend for Pi agents and OpenCode, with config examples and troubleshooting.
How an LLM Coding Agent Actually Builds Software
How LLM coding agents actually build software: the model, the tool loop, context management, precise patching, and verification against real tests.
Using LM Studio and Gemma as a Local Engine for Coding Agents
Using LM Studio and Gemma as a local OpenAI-compatible backend for coding agents: what broke, which settings mattered, and where it still falls short.
Building a Local Coding Agent (Codex/Claude Code Style) in Gemma
Building a local coding agent around Gemma 4 26B taught me the model wasn't the problem: my agent loop was, and fixing the loop is what made it work.
A Local, Multi-Tenant Graph RAG System with LightRAG
How a local Ollama and LightRAG stack turns Graph RAG into a cheap, multi-tenant retrieval layer that only sends precise context to cloud models.
Token Efficiency for AI Coding Agents in 2026
Six practical techniques for cutting token spend in AI coding agents, from skeleton indexes to symbol-level retrieval and semantic caching.
MCP, Skills, Agents, Rulesets, and Hooks: A Practical Map
A practical introduction to the main building blocks of modern AI development, with diagrams that show how MCP, skills, agents, rulesets, and hooks work together.
Intelligent Applications and the Personal OS
The closing piece of a 12-part series on how software is shifting from apps you click through to intelligent systems and a personal OS that act for you.
AI Employees: Building Your Digital Workforce
What changes when AI moves from a tool you operate to an autonomous agent you manage like an employee, and how to do that responsibly.
The One-Person Company: AI as Force Multiplier
How AI lets a single founder cover work that used to require a small team, and the real trade-offs of choosing to run a company alone.
The AI-Era Developer: From Coder to AI System Architect
What a developer's job actually becomes when AI can write the code: which skills stop mattering and which ones become the real differentiator.
AI's Impact on the Software Development Lifecycle
How AI is changing requirements, design, coding, testing, deployment, and maintenance, and which parts of the software lifecycle stay stubbornly human.
From Logic-Driven to Context-Driven Software
How software built by encoding explicit rules is giving way to software built by shaping context, and what that changes about the engineering job.
Context Engineering: The New Software Engineering
Why reliable AI behavior comes from designing the whole context, not tuning a single clever prompt, and the five layers that make up good context.
Why AI Applications Are Ecosystems, Not Software
Why AI applications behave like ecosystems shaped by feedback loops and user adaptation, and what that means for how you design and monitor them.
AI Application Architecture: LLM + Memory + Tools
The five components a production AI application actually needs beyond the model itself: memory, tools, retrieved knowledge, and structured context.
LLM Strengths and Limitations: A Practical Framework
A practical framework for deciding when an LLM is the right tool for a task, when it isn't, and how to architect around its limitations.
Generalization: Why AI Looks Smart
How LLMs generalize from patterns instead of understanding, where that generalization holds up in practice, and where it quietly breaks down.
What is LLM: The Nature of Language Models
What large language models actually are: probabilistic token predictors, not databases or reasoning engines, and why the distinction matters.