41 articles
Anthropic 90% off with explicit breakpoints, OpenAI 50% auto, Bedrock per-region. Real cost math, when caching pays off, where to put cache markers, and the system-prompt design rules that make it work.
MLflow wins OSS + model registry, W&B wins research UX + Sweeps ($50/user/mo), DVC wins data lineage + git-native pipelines ($20/user/mo). Feature matrix, migration paths, and a clear decision matrix.
Prompt caching cuts LLM API bills 50-90% by reusing the KV cache for stable prefixes. Anthropic, OpenAI, Gemini, and vLLM compared with real pricing, implementation patterns, and four workload simulations.
TDD doesn't work on non-deterministic LLM outputs. Eval-driven development is the analog: paired prompt-expectation sets, rule-based or LLM-judge scoring, run as a regression suite on every change. Tools (promptfoo, Braintrust, OpenAI Evals), real customer-support example, and what evals catch that code review misses.
A deep dive into running large language models on CPUs. Includes performance benchmarks, limitations, and optimization strategies.
A practitioner comparison of LangGraph, CrewAI, and AutoGen -- benchmarks on research, code gen, and data analysis agents with code examples, token efficiency, and production guidance.
A practical guide to the Model Context Protocol (MCP) -- Anthropic's open standard for connecting AI models to external tools and data. Architecture, TypeScript server examples, and security best practices.
VRAM requirements, tokens/sec benchmarks, and cost-per-token analysis for every GPU worth considering in 2026. Covers RTX 4060 through H100, multi-GPU setups, Apple Silicon, and budget tiers from $260 to $22,000.
The marketing says self-evolving; the engineering says adapter tuning + adaptive tool selection + memory consolidation on a static base. Honest breakdown of what M2.7 ships, when it wins, and when it doesn't.
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