Blog

Shannon laid the foundation in 1948. Seventy-eight years later, his framework still explains why Transformers work, what cross-entropy loss actually means, and why your model can never be smarter than its training data.

A deep dive into the architecture, language choices, and hard-won lessons behind building a production quantitative trading platform with Rust, Go, and Python — from nanosecond risk checks to RL-driven signal generation.

A practical guide to building production-grade AI Agent systems. Covers single-agent design, multi-agent orchestration, MCP protocol, Computer Use, cost control, and enterprise deployment patterns.

A deep dive into every component of the Transformer architecture—from Tokenization to Attention mechanisms, from forward propagation to code implementation. For developers who want to truly understand how GPT and ChatGPT work.

Reflecting on how 2025 normalized agent workflows and reasoning models, and why 2026 feels less like a prediction and more like a state you can already opt into.

Bridging the gap between logic and learning—how tensor equations create AI systems that think both symbolically and intuitively.

An architectural deep-dive into Shannon, a self-hosted multi-agent platform that addresses the three hardest problems in production AI: runaway costs, non-deterministic failures, and security vulnerabilities—through deliberate technology choices in Rust, Go, and Python.

Demystifying quantitative trading for AI practitioners — what quant funds actually do, why reinforcement learning fits markets better than LLMs, and where the real barriers lie.

Reflecting on Andrej Karpathy’s 2015 RNN post and the surprising evolution of LLMs with transformers and fine-tuning.

A step-by-step walkthrough of the Transformer architecture—from input embeddings and positional encoding to self-attention and the decoder-only GPT variant.