#gpt

A practical, fully tested GPT implementation in PyTorch with causal attention, training, sampling, baselines, checkpoints, and 25 tests. Trained on Tiny Shakespeare with 813K parameters and a final validation loss near 1.78.

Explore GPT-6.1 Sol, OpenAI’s cost-efficient model for agentic coding, professional workflows, and scalable AI solutions.

Learn how rogue AI agents can drain cloud credits and what it means for production automation teams.

If you are building a writing tool for developers, the model choice is the largest line item on your...

The idea of shipping a server image small and letting it grow in place is not new; my own server...

Self-attention treats every token in the sequence the same way: it computes attention scores based...

A 1M context window sounds like a free upgrade. It is not. Cost and latency climb with every dumped...

GPT-6 Astra: The AI That Can Actually Use Your Computer OpenAI's latest model goes beyond...

A deep dive into GPT‑Astra’s architecture, capabilities, and impact on AI development.

How to combine OpenAI’s GPT with DataStax Astra to build scalable AI applications with vector search and low‑latency storage.

The tokenizer (Phase 2) solves half the problem: text becomes a sequence of integer IDs. But an ID,...

As a Lead SWE, I’ve seen a lot of tech, but GPT-6 Astra feels like a real workflow shift. Here’s my deep dive.