Writing
Notes from the production layer.
Technical notes, experiments and post-mortems from applied AI, automation and digital product systems. Each one starts with the same question: what actually happened when the system had to produce an outcome?
position: fixed Stopped Working Because a Parent Had an Entry Animation
A decorative slide-up animation left an identity transform on an ancestor, which made it the containing block for everything fixed inside it.
Approval Is Not Publication
Thirty seven items in the queue, one approved, nothing published. The last arrow in the diagram is the only one that pays.
Neural Rendering Is Crossing From Reconstruction Into Synthesis
Reconstruction filled in what sparse sampling missed. DLSS 5 generates appearance the renderer never computed.
The Renderer Is Becoming a Training Data Engine
The renderer used to sit at the end of the pipeline. Physical AI gives the same scene a second job: teaching a model.
One add_header in a location Block Deleted Every Security Header on That Page
Inheritance applies only if the current level has no add_header at all, so a single cache directive removed four security headers.
Local AI Is Becoming a Compute Fabric
Local AI meant one model on one machine. Routing inference across the devices already on a network changes the unit.
Agent Infrastructure Is Becoming a Product Category
Every team used to build the loop, the store, the sandbox. That layer is being sold rather than written.
Choosing a local model with a stopwatch, not a benchmark
Three models, five hard tasks, code actually executed. The official build was 2.4 times faster and more accurate than a community repack of the same model.
We measured real time lighting against baked light, and baked won
A 3D product simulation that had to look like an offline render. The real time version ran at 60 frames per second and looked like clay. Here is the measurement and the architecture that replaced it.
What actually broke in an agency run by agents
Three failures from a delivery stack that runs on AI. None of them were the model's fault, and all three reported success while producing nothing.
A Task Without A Check Command Is Not Automated
If a task has no command that can fail, the pipeline advances on the appearance of work.
A Gate The Model Writes Is A Gate The Model Loosens
Three quality gates returned green while the work behind them was wrong, each for a different reason.
Two Python Servers Listened on the Same Port on Windows and the Requests Split Between Them
SO_REUSEADDR is on by default in http.server, and on Windows that lets a second process bind a port that is already being listened on.
The Scoring Model Was Wrong And It Put The Worst Lead First
A weighted sum let one axis substitute for the other, so a company with money and no problem ranked in the top twenty.
Library cublas64_12.dll Is Not Found: faster-whisper Loads, Then Dies on the First Transcribe
The model loads cleanly and fails at the first encode, because CTranslate2 wants CUDA 12 while the PyTorch build is on 13.