Every weekday issue of Inference, from the very beginning. Free to read, forever.
AI safety research shapes what models can do and what's legal to ship. Most builders skip the papers. Here's a fifteen-minute reading framework that gets you the signal without the academic overhead.
Single-agent systems fail in predictable ways. Multi-agent systems fail in the handoffs — handoff drift, conflicting world models, cascading overconfidence. Here's the failure map and the cheapest fix for each mode.
Your agent runs great in a demo. Then a real user hits it across three sessions, references something from last week, and it has no idea what they're talking about. Here's the memory architecture that actually fixes it.
Fine-tuning got cheap enough that it's on every roadmap. Most of those projects are aimed at a problem fine-tuning can't fix. Here's how to tell which kind of problem you actually have.
Open-weight models from Meta, Mistral, DeepSeek, and Qwen have closed most of the gap with frontier closed models — and that's quietly rewritten the build-vs-buy calculus for almost every AI startup. Here's the new math.
Prompt engineering didn't disappear — it got absorbed into a bigger discipline. Here's what actually matters now, and the eval habit that replaces "finding the right wording."
Long-running agents are finally viable in production. They also fail in five predictable ways — context poisoning, tool hallucination, stuck loops, scope creep, and silent failure. Here's the map and the cheapest guardrail for each.
Long context windows didn't kill retrieval — but they killed most of the reasons teams reach for it first. Here's how to tell which camp you're in, plus a template for fighting the "lost in the middle" effect.
Context windows hit 1M+ tokens and most teams are still building like it's 2022. We break down what changes in your stack, what to throw out, and the one architecture decision that actually scales.
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