FundamentalsModel Parallelism: How Huge Models Are Split to Run
Tensor, pipeline and expert parallelism split a model across devices in different ways. Each moves a different cost onto the network, and that decides latency.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Fundamentals
ModelsModel Release Cadence in 2026: Building for a Moving Target
Four major open-weight releases in four months. What that pace does to your evaluation harness, your version pins and your migration budget.
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ModelsModel Selection for Cost Ceilings: Working Backwards From a Budget
When the budget is fixed before the model is chosen, selection inverts. How to derive a per-request token allowance and pick what fits inside it.
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FundamentalsModel Size vs Capability: Why Bigger Stopped Meaning Better
Parameter counts once tracked capability closely and no longer do. What size still predicts, what it never predicted, and what to check instead.
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ModelsModels That Fit on One GPU: What You Can Actually Run
The single-accelerator threshold decides your whole deployment. Which 2026 models clear it, how to do the memory arithmetic, and what you give up.
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FundamentalsMulti-Head Latent Attention Explained
MLA compresses the KV cache into a small latent vector instead of sharing heads. Why it trades compute for memory, and which models bet on it.
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ModelsMulti-Model Strategies: Splitting Work Across Two or Three Models
One model for everything is simple and usually wasteful. How to split a workload by difficulty, and when the routing complexity is not worth it.
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FundamentalsNeedle-in-a-Haystack Tests: What They Prove and What They Miss
A perfect needle-in-a-haystack chart says a model can find one planted sentence. It says very little about whether it can reason over your documents.
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Cost & PricingNegotiating AI Contracts: What Is Actually on the Table
Rate cards are the least negotiable part of an AI vendor contract. What you can actually move on retention, rate limits, SLAs and exit terms, and what leverage you need.
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GuidesNeovim LLM Setup: Adapters, Keys and Custom Endpoints
Wire a Neovim LLM plugin to your own OpenAI-compatible endpoint — how adapters are shaped, where the key should come from, and why streaming often looks broken.
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ModelsOpen Model Pricing Compared: What the Per-Token Rate Hides
Input and output rates across the open-weight field, and why the cheapest model per token is frequently not the cheapest per completed task.
ReadModelsOpen-Weight Model Licences Compared: What You Can Ship
MIT, custom terms with revenue thresholds, and everything between. Which 2026 open-weight models you can build a product on without conditions.
ReadShowing 181–192 of 404 articles