ModelsMIT-Licensed Models: What You Can Actually Build On
GLM-5.2 and both DeepSeek V4 variants ship under MIT. Kimi does not. Why the licence column decides more architecture than any benchmark score.
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.
ModelsModelsModel Deprecation and Migration: Planning for the Sunset
Every model you depend on will be retired. How to track deprecation notices, size the migration and move without a quality cliff on the cutover day.
ReadModelsModel Drift in Production: Detecting Quality Change With No Error
Model output quality can degrade without a single failed request. The signals that move first, how to instrument them, and what to do when one shifts.
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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.
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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.
ReadModelsMulti-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.
ReadShowing 505–516 of 731 articles