A friend’s AI resume draft claimed an Azure certification he never had. Three hours of prompt tweaking did not stop it, but indexing his verified work history did, and a grounded mini model beat an ungrounded larger one.
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.
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.
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.
A photography portfolio was sitting at a 6 second LCP on mobile and a 25KB masonry script was doing it. Native CSS masonry does the same job, with an @supports fallback to columns for the browsers that are not there yet.
A weather dashboard that tells people what to wear, built on Databricks and GPT-4o-mini instead of a pile of if-statements. Covers the extraction class, the LLM transformation step, and the hourly job that keeps the Delta Table fresh.
An AI engineer wires up models that already exist. A machine learning engineer builds the model. Which one you hire decides whether you ship in two weeks or spend six months in a sandbox, and here is how I tell the two jobs apart.
The RTX 30-series has no hardware FP8, so I packed four FP8 values into one FP32 container and unpacked them inside a Triton kernel. On a client’s 3080s that cut the memory bottleneck and ran about 3.3x faster.