-
Tokenization pitfalls: why your LLM miscounts characters and money
If you’ve ever asked an LLM to count letters in a word, estimate API cost, or enforce a strict character limit, you’ve probably seen surprising results.
-
How transformer self-attention actually works, with a runnable toy
Transformers power modern large language models, image generators, coding assistants, and multimodal AI systems. The core idea behind them is surprisingly compact: self-attention lets every token deci
-
Object detection today: from YOLO to open-vocabulary models
Object detection sounds simple:
-
The bias-variance tradeoff, finally explained with runnable plots
Machine learning models rarely fail because they are too simple or too complex by accident. More often, they fail because they learn the wrong amount of information from data.
-
Topics Everyone Is Talking About No365
A Software Engineering Interview Question Worth Asking: Computing the Median • Grok 4.5 Raises the Bar for Coding and AI Agents • FTC Settlement Grants John Deere Owners the Right to Repair
-
Topics Everyone Is Talking About No364
Decoding the Obfuscated Bash Script on a Uniqlo T-Shirt • Apple Expands Broadcom Partnership to Boost U.S. Chip Production • Building a Healthier Future for Rust Clippy