#TowardsdatascienceCOM

Neural networks won’t settle the measurement problem — but they are quietly changing how we see, predict and protect quantum superpositions.

When a tool breaks, an agent can keep going and write a confident answer from nothing while the dashboard stays green.

Teaching foundation models new skills without breaking them is hard. A new reinforcement learning-based approach might have the power to change that.

The same three workloads show a much stronger JIT, but it still doesn't help every program

The tool found the right passage but missed the mismatch, and the audit shows where rule-based checks break down.

I rated my confidence 4 out of 5, then couldn’t find a planted bug in 8 minutes. Here is the tool, the run, and its limits.

How participant-aware evaluation changed performance across three machine-learning models

"Always switch" is true for one particular host. Watch a few episodes and you can work out which host you've got.

Testing sensor reduction, feature ranking, and generalization to unseen participants

A million-token output window changes what you can hand a model in one go, and the benchmarks and the bill show who it actually suits.

I tested LLM routing to understand why switching models mid-session can erase the savings promised by cheaper inference

Training models on procedurally generated datasets for card detection and segmentation