llama.cpp

Sixteen bytes per parameter in, two out — a walk through the forward pass, backward pass, and optimizer that turn a dataset into new weights.

Hosted batch APIs sell you half-price tokens. Here's how to get the same file-in, file-out workflow on your own Linux box.

Ubuntu 26.04 LTS shipped in April. Point releases, HWE kernels, and SRUs have quietly closed most of the freshness gap — but not everywhere.

Tensor, pipeline, or offload? What actually happens when you run one model across several GPUs — including mismatched ones.

Why one tokens-per-second number can't describe an LLM server — and which metrics actually tell you if it's working.

Five inference engines, one frontier MoE, and a shortlist that collapses depending on which GPU you already bought.

Three ROCm releases in six months — build system, agentic tooling, data movement. What actually changed for AMD GPU owners.

2.96 million model repos, and 85.6% have under 200 downloads. What ModelScope, Kaggle, and Ollama actually do differently.

Lowering temperature should make output safer, not incoherent. Daniel's DeepSeek scripts say otherwise — and the reason may be stranger than he thi...

Why "don't say X" prompts backfire, and how a tiny grammar-constrained model can scrub a script without breaking its grammar.

You don't need 10,000 examples to make a model sound like you. The real number is closer to 100 — if the edits are opinionated.

Why does your phone have no VRAM number? A tour of Android's runtime layer and what it takes to run ASR locally.

Why your "AI server" should look more like a database than a web app, and how to actually build one.

Why running AI on CPU isn't a toy — and when the GPU advantage vanishes.

Why conversational AI tools are making batch document processing slower, costlier, and less reliable — and what to do about it.

How Omi's $99 dev kit lets you build a local-first voice productivity system that watches your screen.

Why your AI ignores formatting instructions and how to fix it with pipeline architecture, not model swaps.

A deep code review of Snowglobe, IQT Labs' open-source LLM wargaming system that ran real national security simulations before being archived. What...

Why are we squeezing massive cloud models onto desktops? Meet the "native" AI revolution.

Why run LLMs locally? We break down Ollama, llama.cpp, vLLM, and llamafile—and when to use each.

Your desktop is becoming a life support system for AI agents. We explore the sharp trade-offs between local-first and cloud-native architectures.

Those cryptic letters on Hugging Face actually map how much brain power you trade for speed.

Explore how the Language Server Protocol is being repurposed to integrate AI directly into code editors, unifying development workflows.

Discover why the AI runtime is the unsung hero of the tech stack, determining whether your AI feels like a snappy conversation or a slow crawl.

Why force AI to talk like humans? Explore how agents are ditching English for high-speed "mind-melding" and latent space communication.

Is the GPU's reign over? Discover how modern CPUs and clever optimization are bringing powerful AI models to the hardware you already own.

Can local AI fix your messy typing in real-time? Explore the tech behind "transparent buffers" that turn sloppy drafts into polished prose.

Ever wondered if you could build your own speech recognition tool? We dive deep into crafting custom ASR.