
LLM Prompt Caching: Cut API Costs 90%
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.
33 articles

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.

Step-by-step tutorial to build an MCP server in TypeScript with @modelcontextprotocol/sdk and Zod. Three tools, stdio transport, Inspector debugging, Claude Desktop/Cursor integration, and npm publish.

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.

Go from 800 to 15,000+ requests/second with clustering, Fastify, connection pooling, Redis caching, and event loop optimization. Real benchmarks included.

April 2026's self-propagating npm worm used postinstall hooks to scrape developer tokens (npm, GitHub, AWS), then auto-publish backdoored package versions. Detection steps, rotation playbook, and the structural defenses (ignore-scripts, pnpm, sandboxed CI, signed publishes).

A practical playbook for scaling Next.js apps to 100k users using ISR, multi-layer caching, CDN optimization, and backend tuning -- all for under $500/month.

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.

Vercel's April 6-9 edge function incident exposed env-vars and secrets in a subset of deployments. Timeline, scope, what was actually exposed, the rotation playbook for affected teams, and the structural defenses that prevent this next time.

Compare serverless (Lambda, Cloud Run) and containers (ECS, EKS, Fargate) on cold starts, pricing, scaling, vendor lock-in, and local development. Learn when to use each compute model.
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