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From the blog · Case study The Bake-Off: My Real Voice vs My Voice Clone Daniel recorded his half of a conversation on his phone. The other half is Meorav, a text-to-speech model trained on 88 minutes of his voice. How the clone is made, how the episode was stitched together, and where the clone still slips. 
#6243: Inside the Mixture of Experts Router
What actually decides which expert handles your token? A look inside the router, from sigmoid affinities to load balancing.
#6242: Training a Model on Your Own Taste
What does it actually take to train a small model on your editorial taste using pairwise preferences and DPO?
#6241: Why Your Synthetic Data Isn't as Diverse as You Think
Fifty-nine percent of one GPT-4 dataset started with "Once upon a time." Correct examples can still make a terrible training set.
#6240: Gold vs Silver Training Data: What Makes Labels Trustworthy
Human-checked "gold" labels and machine-made "silver" ones aren't as different as the names suggest — and the gap matters.
#6239: Testing Synthetic Data Generators Before You Scale
A benchmark trains student models on each generator's output to find out which model actually writes useful training data.
#6238: Which AI Models Actually Generate Good Training Data?
Open weights vs closed APIs for synthetic data, and why IFEval scores don't tell you which model will hold up.
#6237: Small Models, Big Questions: When Cheap Fables Get Expensive
Three million fables for pennies — but can a small model tell a good example from a plausible one?
#6236: Synthetic Logs Without Leaking Your Clients
You can rewrite your logs with a cheap model — but can you keep the task boundaries while losing the client names?
#6235: When Your AI Agent Has Too Many Skills
Your agent has 55,000 skills installed — and it's spending the whole task just reading the menu.
#6234: Phone Mic Beats a Dictation Mic: How?
Your phone's internal mic may beat a dedicated dictation microphone. Here's how to route it to your desktop without losing what makes it work.