A jewelry retailer’s smart crop kept locking onto diamond reflections, because edge detection flagged every one of them. Harris corner detection found the structural points instead. Here is the R score logic and the OpenCV implementation.
A client’s weekend-built AI agent fell apart the moment real users touched it. Here is what the NeMo Agent Toolkit handled for us that my own custom wrapper could not: YAML config, grounded tools, and a server you get for free.
An AI coding agent invented table names on a 50k SKU WooCommerce sync until I wrote the schema down for it. Here are the context files I now keep in every project, and why decisions made in meetings need to end up in the repo too.
A 60-page research PDF nobody ever opened became a set of virtual personas the team could actually question mid-sprint. Here is the setup, the kind of data you need to feed it, and the mistake that makes the whole thing useless.
REINFORCE was too slow for a warehouse path-finding bot, so I switched to the actor-critic method and hit three problems on the way: an undetached TD target, snapshot rewards the agent gamed, and a gamma that erased the payoff.
A store selling pet supplies and gaming gear had a search bar that put cat nip next to Logitech mice. Notes on how self-attention and the Query-Key-Value setup finally gave that search enough context to tell an animal from a USB device.
A client’s landing page felt dead and my first instinct was to import GSAP. Here is what I did instead: staggered CSS timings on a few SVG groups, transforms only, and a reduced-motion check.
Twenty near-identical buttons in one design file cost me three hours per border-radius tweak. Component variants in Penpot fixed that, and DTCG tokens made the handoff to CSS boring, which is exactly what you want.
I built a client the dashboard they asked for: 24 SQL queries, three tabs, four seconds slower. They stopped opening it after a week. What worked was cutting every metric that would not change a decision, and burying the data dump in an export tab.
A health-tech client had 99.8% accuracy on a disease that shows up in 0.2% of patients. Their agent said no to everyone. Here is how I pulled a continuous risk score out of a binary agent so the AUC comparison against their XGBoost model meant something.