Research from the things we build.
AI systems, persistent context, agent skills, website design, 3D production, games, automation, and the infrastructure behind our products.
AI Usage Limits Are Exposing the Real Problem With Autonomous Agents
Usage caps interrupt AI agents, but lost context and repeated work create the deeper problem. Here's why shared memory and execution continuity matter.
Read articleAI 3D Modeling Quality Control: Automating Craftsmanship With Construction Rules
Learn how symmetry, construction constraints, clearance checks, and pre-render validation improve the reliability of AI-generated 3D game assets.
Read articleAI Agent Memory Is Not Enough: Compile Competence Into the Execution Layer
AI agent memory preserves what happened. Reliable systems also preserve how verified work gets done by compiling proven procedures into deterministic execution.
Read articleAI Agent Orchestration: Stop Making Models Choose Deterministic Tool Workflows
AI agents waste latency when models repeatedly choose tool sequences that code can compile. Keep a small primitive set, compile known workflows, and reserve model reasoning for genuine uncertainty.
Read articleAI Memory and Context Engineering: How a Frozen Model Gets Better Without Retraining
Why persistent external state can make the same frozen AI model materially more capable over time without changing a single parameter.
Read articleThe Image Generator Is Becoming a 3D Runtime
Why visual AI is moving from isolated pixels toward persistent spatial state, controllable cameras, automated visual QA, and scene-first generation.
Read articleAI Blender Workflow: Stop Prompting 3D Models and Start Compiling Geometry
Why AI gets more reliable in Blender when 3D creation is converted from visual guessing into bounded, executable geometry.
Read articleProcedural Content Generation: Build the Vocabulary Before You Automate the World
Why reliable procedural worlds start with approved modules, then connection rules, and only then automated composition.
Read articleAI Agent Skills: How Procedural Memory Lets AI Systems Improve Without Retraining
Reusable, testable skills can preserve how work gets done outside model weights and survive the model that discovered them.
Read articleTechnical Debt in AI Software: The Open Door Problem
Why wrapper accumulation, soft deprecation, and legacy paths become more dangerous when agents can rediscover obsolete choices.
Read articleAI Gaming Companions: Your Personal AI Will Play With You, Against You, and Coach You
How persistent personal AI could move across games, compete with players, and use specialist engines for coaching.
Read articleEnterprise AI Orchestration: How Humans and AI Agents Can Share Institutional Memory
A shared cognitive layer where humans and multiple AI agents inherit durable knowledge, permissions, operating state, and execution pathways.
Read articleThe Model Should Not Be the Runtime
AI becomes more durable when models can change while tools, execution, verification, and learned constraints remain.
Read articleModels Are Disposable. State Is Not.
The durable advantage is preserving verified state, objectives, permissions, and execution history so intelligence can change without restarting work.
Read articleYour AI Should Already Know Your Website
A controlled API or MCP surface plus durable context turns a website into something an authorized AI can understand continuously and operate.
Read article