Delegation chains, the confused deputy, and the protocols you actually deploy Agents rarely act alone. A user asks an agent to do something. The agent calls a tool. The tool calls another agent. By the time work gets done, three or four actors have touched the request, each acting on behalf of the one before...
The post Delegation chains, the confused deputy, and the protocols you...
Governing 5 agents is a review process. Governing 500 agents is an infrastructure problem. Manual reviews and team-level approvals work when a handful of agents are visible and closely watched. Once agents spread across business units, tools, and environments, that oversight breaks down. Enterprises need an AI agent governance model that includes centralized identity, reusable...
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GPT-5.6 Sol and Claude Fable 5 are currently fighting for the frontier-model crown. Fable 5 holds a slight edge in general intelligence, while Sol hits back with stronger coding performance, faster execution and much lower pricing. In fact, GPT-5.6 Sol is priced closer to Claude Opus 4.8 than to Fable 5, which makes this comparison […]
The post GPT-5.6 Sol vs Claude Fable 5: Benchmarks,...
From BARC’s 2026 Data Sovereignty survey of 320 organizations worldwide: how sovereignty has moved from a compliance requirement to a strategic condition for running data- and AI-driven processes at acceptable risk.
In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell, from the hundreds of olfactory receptors in the human nose to the challenge of mapping the relationship between molecular structure and odor, ensuring safety regulations are met,...
THE GIST ▸ What it is: A 3,826-line system prompt steering Claude Fable 5 inside the Claude app, pulled from a public GitHub archive. ▸ What’s in it: Rules about safety, tone and restraint. ▸ Why it matters: it shows a frontier “AI” is far more an engineered rulebook than a mysterious mind. Before your […]
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Prompts shape every interaction with a large language model. Clear instructions produce focused, useful responses, while vague ones often lead to inconsistent results. This becomes harder when teams need the same task completed repeatedly in a fixed format, tone, or structure. Meta-prompting asks the model to design a reusable prompt, template, checklist, or workflow before […]
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Two short clips. One question: how alike do they look? Sounds trivial, it isn’t, and I learned that the slow way. My setup: one reference clip, eight others to rank against it, all waterfalls (more on why in a second). I figured this was an afternoon job, grab a model, compute a number, move on. […]
The post How to Measure Video Similarity: 6 Techniques I Tested (and the One I...
Most real-world classification problems are imbalanced. Fraud, disease, churn, and defects are rare by nature. Standard classifiers chase accuracy, so they quietly ignore the very class you care about. For years, SMOTE was the reflex fix that everyone reached for first. But SMOTE often fails on the messy, high-dimensional data that production systems actually see. […]
The post Handling...
LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are […]
The post RAG Evaluation...