Technical Blog
Deep dives, implementation details, and hard-earned lessons from shipping real systems with Python, Neo4j, and modern LLM tooling.
Why autoregressive inference is memory-bandwidth bound, how SLMs exploit distillation, GQA, and aggressive quantization, and how production stacks route cheap local models before touching frontier APIs.
Read articleMultiply-accumulate limits in FP Transformers, why PTQ keeps you memory-bandwidth bound, the ternary BitNet b1.58 formulation with AbsMean activations, and how BitNet.cpp maps inference to SIMD-heavy CPU kernels.
Read articleA deep technical dive into Manifold-Constrained Hyper-Connections (mHC) — the architecture behind DeepSeek V4's stable training at 1T+ parameters. Birkhoff polytopes, doubly stochastic matrices, and why this changes the economics of large-scale AI.
Read articleA hands-on guide to fine-tuning Qwen2.5 on consumer hardware — LoRA, 4-bit quantization, and practical lessons from training on an NVIDIA 4060.
Read articleHow task-driven autonomous agents like BabyAGI work, why they matter for complex workflows, and what it takes to move from prototype to production.
Read articleA deep dive into OpenClaw, the privacy-first AI assistant that runs locally and automates your digital life across messaging apps, email, and more.
Read articleA complete walkthrough of developing and deploying a churn prediction model — data prep, training, tuning, evaluation, and production deployment.
Read articleA step-by-step walkthrough of wiring a minimal GraphRAG prototype in Python using Neo4j and an LLM.
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