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Your new AI product is live. Infra dashboards are all green. Latency is low, error rates are flat, and CPU/GPU utilization looks healthy. Yet your Slack channels are full of screenshots from users asking, "Why did it do this?" The same user goal and high-

Someone on your team defaulted to the latest and greatest model available, which is also the most expensive model. Maybe it was an agent calling the flagship model for every tool call or a coding agent set to its most capable model out of the box. It work

LLM applications are everywhere now, and they're fundamentally different from traditional software. They're non-deterministic. They hallucinate. They can fail in ways that are hard to predict or reproduce (and sometimes hilarious). If you're building LLM-

I wanted to see if I could build a RAG system that would output interesting and accurate F1 race weekend insights and summaries based on the entire weekend's team-radio messages. Here are the five commands that took it from a simple demo to something I wo

Digibee was founded 9 years ago to solve a key problem in enterprise tech stacks: tools didn't integrate neatly with one another, and humans were left to manage the connections between systems and the infrastructure to support those connections. Digibee b

If you run an AI agent in production, you already know the drill. Something goes wrong, a user complains, or you just get a nagging feeling the agent isn't behaving quite right. So you open Opik, your AI observability and agent tracing tool, find the trac

If you've shipped an LLM-powered feature beyond a simple chat interface, you've already built parts of an agent harness. The retry logic around a failed tool call, the code assembling a system prompt at runtime - these are harness components. Most teams b

Like a lot of engineering teams, earlier this year we found ourselves hitting limits on AI token spend, trying to understand where those tokens were going and why, and how to manage cost without hurting performance. We ended up tackling this problem both

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