Fine-Tuning Agentic AI: A Practical Guide
In this article, you will learn how to fine-tune an agentic AI system holistically, covering all four critical dials: training data, parameter-efficient fine-tuning, runtime hyperparameters,…
A Gentle Introduction to Model Distillation
In this article, you will learn what model distillation is, how it has evolved for large language models, and why it has become one of…
REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff
A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend on underlying environmental reversibility, a property absent in real world manipulation, where events such as pushing objects off tables or spilling granular substances cannot be undone. We introduce REVERSAL-BENCH, a benchmark that controls reversibility via a …
Reduce time-to-hire for quality candidates with AI-powered Amazon Connect Talent
Hiring at scale in industries such as retail, logistics, hospitality, and others has its fair share of challenges. Recruiting teams are expected to fill hundreds of roles within tight timelines, often with limited capacity and with tools that weren’t designed to seamlessly work together. As a result of this, applications pile up, phone screens get …
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How Orange built FinOps accountability, and why agents are next
At Orange, the leading France-based multinational telecom provider, there are days when engineering teams set aside their delivery backlogs and spend the day cleaning up cloud spend together. There’s a leaderboard. There are goodies on the line. Experienced practitioners guide the newcomers, so people learn the work while doing it. By the end of the …
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Here’s What the AI Apocalypse Could Look Like
This week on “Uncanny Valley,” we discuss three possible AI doomsday scenarios, AI safety, and the unexpected bipartisan alliance forming against AI.
AI still isn’t as good at recognizing objects as people are, new test shows
Artificial intelligence has gradually made its way into many areas of life with abilities that often match or surpass those of humans. But a study published in the journal iScience finds that AI-powered machines have trouble recognizing objects from their overall shapes when aspects of an image are distorted. The findings offer a new way …
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For anyone wondering how I manage to do this, here’s a quick explanation with a small tutorial
First, in Minimax, I use a prompt like this: “The character remains completely frozen in place, perfectly still like a statue. The camera smoothly orbits 360 degrees around the character in one continuous shot. No character movement, no pose change, no cuts.” Then I use COLMAP: Create a new database and import the frames extracted …
Shared Selective Persistent Memory for Agentic LLM Systems
Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive—irrelevant context degrades generation quality. We introduce shared selective persistent …
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Improving HCLS AI reasoning with open-source agent skills
AI agents built on foundation models (FMs) often misapply healthcare and life sciences (HCLS) decision frameworks, even when they’ve seen the guidelines in training and in the system prompt. Ask an agent to classify a TP53 missense variant using ACMG/AMP criteria. It will cite the correct framework but misapply evidence categories, skip population frequency thresholds, …
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