Tag archive
The Kuleshov Group's guide to building a diffusion language model quietly changes the economics of running AI. Here's what parallel decoding means for a small team paying in USD.
Why the LiteLLM MCP authentication bypass (CVE-2026-59822) matters beyond the gateway itself, and how to reduce the exposure it creates.
Claude's cache needs a 512–4,096 token prefix depending on model — miss it and nothing errors, nothing caches. Here's how to verify and fix
What Is vLLM: The Fast Inference Engine for Large Language Models TL;DR: vLLM is an...
Finance will hand you a per-token comparison. Local inference at some fraction of a cent per thousand...
Local models can be a strong fit for agentic systems, but mostly in narrower workflows where privacy, latency, and cost matter more than peak reasoning. This piece looks at where they help, where they fail first, and why a hybrid setup usually wins.
Exact-match LLM caching only works if two equivalent requests fingerprint to the same key. The seven normalisation pitfalls that break naive
The architectural decisions that separate controlled spend from compounding surprises AI and...
Real first-party data: Anthropic prompt caching cut Citare's AI bill 25-35% on parsing-heavy workloads. What works, what doesn't, what burned me $20.
MemMachine stores raw conversation episodes instead of LLM-extracted summaries, reaching 93% on LongMemEvalS with 80% fewer tokens than Mem0.
--- title: "Client-Side LLM Optimization Is Misunderstood" description: "Client-side LLM inference...
The current standard for LLM hallucination detection is a structural liability. In production...