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An open-source, local, read-only MCP server that checks your request before Google quietly hands back nothing Ask the Google Search Console API for clicks filtered by country us and you get zero rows. No error, no warning, just nothing. It's not the only one.
Count tokens before every LLM API call, set max_tokens per Express route, and recover cleanly from streaming truncation without surfacing errors to users.
Five vision-capable LLMs compared for product photo captions, receipt parsing, and chart understanding in 2026, with per-1,000-image cost math.
Build a multi-turn Node.js chat API with server-side message history, context compaction, and hard per-session token budget enforcement in under 90 lines.
Client backoff with jitter, proxy fallback, and dead-letter queue: the three Node.js layers that stop cascading LLM failures in production.
Prompt injection hijacks your LLM via user input, retrieved docs, and tool results. Three attack surfaces, and the defenses that actually work in production.
Per-user LLM cost at 1,000 daily users in 2026: token math across chat, extraction, and RAG at two model tiers, with break-even math for solo devs.
An in-process LLM request queue for Node.js: concurrency cap, per-user fairness, and backpressure under 60 lines. No Redis or BullMQ needed in 2026.
Redis exact-match caching cuts LLM API spend at scale. Working Node.js middleware, 30-minute TTL trade-off, and when semantic search is worth the overhead.
An AI agent is an LLM in a tool-calling loop. How loops work, why they stall or run away, and the 3 architecture decisions that matter in production.
Add per-user LLM budget caps with Redis: soft warnings at 80%, hard stops at 100%, and automatic model downgrade. Full Node.js middleware under 60 lines.
Fine-tuning LLMs in 2026 costs $3-$30 in GPU time for open-weight models, or a 50% inference premium on GPT-4.1. Break-even math across three real scenarios.
Build a production RAG API in Node.js using pgvector: text ingestion, chunked embeddings, vector search, and source-cited answers in under 80 lines.
Why LLMs hallucinate, the three error types, prompting patterns that reduce them, and the one production eval that catches failures before your users see them.
JSON mode vs structured output: how constrained decoding works, when 3% failure rates become a real cost, and which enforcement approach your pipeline needs.
Add token counting, sliding window, and summarization compaction to a Node.js chat route to keep conversations coherent without blowing the context budget.
Per-session cost math for 1,000 daily LLM chat sessions at three model tiers. Why stateless APIs compound costs and when sliding-window compaction pays off.
Five LLMs ranked on unit test quality, edge case coverage, and cost per 1,000 tests in 2026. Claude Haiku 4.5 is the value pick for solo devs.
Best LLMs for business email writing in 2026: ranked by tone control, length discipline, and per-email API cost for solopreneurs.
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