Fact-checked guides for AI, dev, and systems
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The Best Local LLMs You Can Actually Run on 16GB and 32GB RAM (2026)
Local LLMs are free to download, so the real cost is RAM. Here's the arithmetic that decides what fits — about 0.6–0.65 GB per billion parameters, plus overhead, plus the KV cache — and a spec-checked shortlist of models that clear the 16GB and 32GB bars.
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LLM API Pricing Compared: GPT vs Claude vs Gemini vs Open Models (2026)
A vendor-neutral 2026 guide to what actually decides your LLM bill: output rates, not headline input prices, and the five levers — caching, batch, length tiers, service tiers, and prompt design — that move the invoice more than switching vendors. Ends with a dated price snapshot, a worked cost example, and a pick-by-use-case verdict.
VPRM: A Critical Read of Verifiable Process Reward Models
Seven places where the VPRM paper's abstract, prose, and citations do not line up with its own tables — including two coherence numbers that cannot both be right.
Running TRELLIS.2 on a Mac (3/3): The Optimizations That Actually Won
The optimizations that survived measurement: load time cut 5x, mesh extraction 67x, and a sixth twist that overturned my own conclusion about MPS attention.
QLoRA Explained: Fine-Tuning Big LLMs on a Single GPU
QLoRA finetunes a frozen 4-bit base model into 16-bit LoRA adapters, dropping the memory to adapt a 65B model from over 780 GB to under 48 GB — one GPU instead of a cluster, with no reported quality loss. Here's how NF4, double quantization, and paged optimizers make 4-bit training work, and what the paper proves versus infers.
OpenClaw: Set Up a Self-Hosted AI Assistant (Beginner Guide)
OpenClaw is an open-source, self-hosted AI assistant you run on your own machine and talk to through chat apps like Telegram and WhatsApp. This beginner guide walks through prerequisites, install, onboarding, connecting a WhatsApp channel, picking a model, and the safety basics for a tool that runs real shell commands.
Mixtral and Mixture-of-Experts, Explained
Mixtral 8x7B holds ~47B parameters but activates only ~13B per token, matching or beating Llama 2 70B and GPT-3.5 on most metrics at a fraction of the active compute. A walkthrough of how sparse mixture-of-experts works, what the benchmarks show, and the surprising finding that expert assignment appears to align more with syntax than with domain.
RLVR Explained: Verifiable Rewards and Where They Break Down
Most RLVR explainers stop at "it works." This one adds the three published results that complicate it — pass@k, spurious rewards, and what DeepSeek-R1 actually shipped.
FlashAttention Explained: Why Modern LLMs Run Faster
FlashAttention computes exact attention without ever materializing the N×N matrix in GPU memory, treating attention as a memory-movement problem instead of a FLOP-counting one. The payoff: up to 3× faster GPT-2 training, 15% off the BERT-large MLPerf record, and the long context windows modern LLMs rely on.
Running TRELLIS.2 on a Mac (2/3): Five Bottlenecks I Guessed Wrong
Five confident guesses about where the Apple Silicon port was slow — and how honest measurement killed every one, plus the profiling methodology that made the numbers trustworthy.