EnvHarness is a programmable framework designed to convert static digital environments into dynamic learning grounds for AI agents. By wrapping existing environments in a modular layer, it allows for the customization of initial states (Stage), interaction rules (Contract), and task length (Chain) without altering the underlying code or human-built verifiers. To automate this, the authors developed EnvRigger, an autonomous loop that identifies agent vulnerabilities through behavioral diagnosi...

NVIDIA's Groq three LPX inference chip enters full production at 3,400 tokens per second on long-context workloads. Uber's agents now write more than 70% of pull requests after 18 months of platform work. Same week, the inference engine running those agents has a CVE on it. We go through what the chip changes for agentic latency, how Uber built an MCP gateway and 40-million-entry context graph to make it work, the parser vulnerability that lets a model execute arbitrary code on its host machine, and

David and Lily reflect on how the Explore, Describe, Present framing emerged through years of research methods support with the West African Community of Practice. They discuss how long-term collaboration helped develop a simple language for thinking about the different roles of data visualisation, including the importance of understanding your audience when presenting results.

Status reports are one of the most time-consuming recurring deliverables in project management — and one of the most reliably ignored. This episode of Development digs into why that happens and, more usefully, what project managers can do about it before the next report goes out. The root cause isn't effort or data quality; it's a structural mismatch between how PMs narrate progress and how executives actually consume information.The episode walks through a concrete,

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, while exploring why accuracy alone can't capture what makes a recommendation useful, surprising, or meaningful.

In 1832, 20-year-old math prodigy Évariste Galois frantically scribbled his final mathematical breakthroughs just hours before dying in a tragic pistol duel. Those chaotic notes didn't just solve a 300-year-old algebra mystery—they birthed an entirely new branch of mathematics.Welcome to the Math Deep Dive podcast. In this episode, we explore the profound world of Galois Theory, the ultimate mathematical "dictionary" that translates impossible number puzzles into the elegant language

Paul Tannery didn't begin his career intending to become one of the most influential historians of mathematics. He trained as an engineer at the École Polytechnique and spent much of his professional life working in France's state tobacco administration. But after work, Tannery pursued a very different passion: ancient mathematics, astronomy, philosophy, and the history of science. What began as an intellectual interest became a lifetime of scholarship. 3 TAKEAWAYS: 1. How Paul Tannery transformed

In this episode, we meet the CEO of one of the most important institutions in mathematics - arXiV. Around since 1991, the website is a publicly accessible home for mathematical research before it is officially published in journals. Around 1,000 articles are published on arXiv every day — it has hosted everything from the proof of the Poincaré Conjecture to some of the newest research from AI companies like DeepSeek and Gemini. We spoke to Ramin Zabih, a Professor at Cornell Tech and Interim Chief

Organizations have more data, more dashboards, and more sophisticated analytics tools than ever before. And yet, many still struggle to make confident decisions quickly. Sometimes the problem isn't the data itself. It's what happens between the insight and the action: unclear ownership, competing versions of the truth, slow review cycles, and analytics that sit beside the business instead of helping shape it. In today's episode of Resoundingly Human: The INFORMS Podcast, I'm joined by Ritish

Lotte Harper is online and recording. Be a part of the Lotte Harper family. Learn daily about software operations, database, security, frontend JavaScript, HTML and CSS along with JS APIs, WebSocket consumers, WebRTC, machine learning (ML), artificial intelligence (AI) and rendering engines, as well as Python server backend logic with requests, PyPI packages, OpenCV, **** recognition, barcode scanning, video and audio editing and sync, podcasting, universal translation, blogging,

Lotte Harper is online and recording. Be a part of the Lotte Harper family. Learn daily about software operations, database, security, frontend JavaScript, HTML and CSS along with JS APIs, WebSocket consumers, WebRTC, machine learning (ML), artificial intelligence (AI) and rendering engines, as well as Python server backend logic with requests, PyPI packages, OpenCV, **** recognition, barcode scanning, video and audio editing and sync, podcasting, universal translation, blogging,

Lotte Harper is online and recording. Be a part of the Lotte Harper family. Learn daily about software operations, database, security, frontend JavaScript, HTML and CSS along with JS APIs, WebSocket consumers, WebRTC, machine learning (ML), artificial intelligence (AI) and rendering engines, as well as Python server backend logic with requests, PyPI packages, OpenCV, **** recognition, barcode scanning, video and audio editing and sync, podcasting, universal translation, blogging,

An in-depth look at Lev Vygotsky’s Zone of Proximal Development and Bruner’s instructional scaffolding. Through a driving-lesson metaphor, we explore how guided support turns hard problems into productive struggles, the role of cognitive load and worked examples, and the rise of adaptive AI tutors that personalize learning at scale—without sacrificing rigor or autonomy. Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical infor...

System prompts are the invisible policy layer inside every AI tool your business runs — and most of them are written like rough drafts rather than operational documents. This episode of Development tackles a failure pattern that founders and ops teams hit repeatedly: an internal tool that performs well in testing collapses into inconsistency the moment real users, real inputs, and real edge cases arrive. The fix isn't technical. It's in how the prompt is written. The episode walks through

What happens when actuaries move beyond the model and into the heart of business strategy? In this episode of The Strategic Pivot, Jundie Wei, FSA, MAAA, and FP&A leader John Visicaro explore how actuaries are becoming essential partners in forecasting, capital management, and executive decision-making. From translating complex assumptions into business impact to answering the critical "so what?" behind the numbers, this conversation highlights the growing role of actuaries in shaping the future of

Dr. Evan Miyazono joins Jacob to talk about his path from quantum networking and Protocol Labs to founding Atlas Computing. They discuss why Atlas moved away from formal methods research and toward what Evan calls “field strategy”: identifying important problems, figuring out what interventions could actually address them, and finding the people best positioned to make them happen. The conversation also covers the resilience gap map, coordination failures, AI safety, "value alignment," and

In this episode of "In the Interim…", Dr. Don Berry provides a detailed account of co-chairing the 1997 NIH consensus development conference on mammography for women in their 40s. His conversation with Dr. Scott Berry covers the statistical and clinical complexities of breast cancer screening, addressing lead time and length bias, trial design limitations. Don discusses the panel’s finding, based on randomized trials and meta-analysis, that the average benefit of screening women in their

SPADE is a reinforcement learning framework that enables a single large language model to achieve open-ended self-improvement by playing two distinct roles. One part of the model acts as an Environment Designer, writing executable Python code to create complex, multi-turn training tasks, while the other part acts as a Reasoning Agent that learns to solve them. To ensure these tasks are challenging yet possible, the system uses a hint-based regret reward, which encourages the designer to creat...

Evaluation criteria are the closest thing a solicitation offers to an answer key — yet most proposal teams treat them as an afterthought. This episode of Development makes the case for flipping that habit entirely: reading the evaluation criteria section first, reading it slowly, and letting it drive every structural decision before the writing begins. The difference between a technically compliant proposal and a winning one often comes down to whether the team understood what was actually

Podcast Show NotesWhat makes a route the best route? The shortest distance? The quickest journey? The cheapest option? Or simply whichever one Google tells you to take?In this AfterMaths Summer Special, Jon and Becky explore the maths of getting away — from Google Maps and traffic predictions to queues, theme parks and an 18th-century puzzle that helped create an entirely new area of mathematics.Along the way, they discuss the Seven Bridges of Königsberg, Euler's ingenious solution and what it can