Agent context compaction drops every block before the summary at 150K tokens. What survives, what instructions silently replaces, and the usage field that lies.
Here's how to test an LLM's knowledge cutoff with three reproducible probes — the method showing Opus 5 claims May 2026 but answers like January 2026.
vLLM throughput tuning starts with KV cache blocks, not a bigger GPU. The four flags that decide your tokens/sec, and the one that quietly backfires.
Run agent tool calls in parallel by swapping the loop for a DAG planner: ten round trips become two levels, plus the cap, budget and critic on top.
Reinforcement fine-tuning let a 4B open model match GPT-5.6 Sol on retrieval at 100x lower cost. How RFT works, and when it beats prompting a frontier LLM.
Agent harness design decided a benchmark: OpenAI's ARC-AGI-3 score went 13.3% → 38.3% with zero model changes. What that means for your agent loop.
LLM abuse detection failed at the message level and worked at the account level. OpenAI's Cambodia scam ban shows which signal actually catches misuse.
An LLM eval framework turns vibes into scores. How smevals structures tasks, configs, runners and graders — and how to ship your first eval today.
Run 70B LLM on 4GB GPU hardware with AirLLM's layer-by-layer inference. The VRAM math is real — you just pay for it in disk bandwidth. The honest tradeoff.
Build a RAG chatbot in Next.js with the AI SDK: embed the query, search pgvector, stream a grounded answer with citations, and stop hallucinations.
Vercel AI SDK in production: streaming, tool-calling, aborting generations, error retry UX, rate limiting, and cost control — the layer every tutorial skips.
Only 23% of autonomous AI agents reach production in 2026. The demo-to-production gap, why agents fail, and the playbook the winners actually use.
Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns that decide whether it works.
How to build an MCP server, step by step: JSON-RPC 2.0, the Streamable HTTP transport, typed tools, and agent discovery — from a real one I shipped.
RAG vs fine-tuning for LLMs in 2026: a practical decision framework covering architecture tradeoffs, cost, latency, and when to use each in production.