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Optimizers beyond SGD: Adam, AdamW, and the learning-rate schedule that matters more
Training a neural network is an optimization problem disguised as an engineering project. We choose an architecture, prepare data, define a loss function, and then repeatedly update millions of parame
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Topics Everyone Is Talking About No415
One Week Using Codex More Than Claude • New Model Context Protocol Roadmap • A Kantian Critique of Justin Biebers Sorry • The Cool Ideas Behind Gleam • PiKVM Adds Webcam Forwarding Support…
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Giving your chatbot memory and personality without prompt bloat
A common first attempt at building a useful chatbot is to place everything inside the system prompt: the assistant’s personality, company rules, user preferences, previous conversations, examples, and
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Deploying vision models to the edge: quantization and latency
Training a computer vision model and deploying that model on a real device are two different engineering disciplines. A network that achieves excellent accuracy in a cloud notebook with a powerful GPU
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Topics Everyone Is Talking About No414
Bun 1.4: Enhanced Compatibility and Leaner JavaScript Tooling • OpenPubkey SSH: Bringing Single Sign-On to SSH Authentication • Rust Supply Chain Attack Compromises Arrayref Releases • From Rust to Zig: A Systems Developers First Impressions • Aaron Swartz, Data Scraping, and Unequal Tech Accountability…
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The Model Context Protocol (MCP): connecting agents to real tools
Large language models are good at reasoning over text, but real-world software systems rarely live inside text alone. A useful AI agent needs to interact with calendars, databases, ticketing systems,
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Quantization in practice: running a capable model on one consumer GPU
Running a capable language model locally is mostly a memory-management problem.
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Topics Everyone Is Talking About No412
Universal health coverage could save 1T and 114k lives a year: study • The Amazon tax • Cursor launches Origin, GitHub alternative • Mojo is now open source
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Rule-based NLP that still wins: regex, spaCy Matcher, and hybrid rule+ML pipelines
Large language models have changed what is possible in natural-language processing, but they have not changed a more basic engineering fact: when a text pattern is narrow, stable, and precisely specif