Topic
1908 articles found
Software engineering has experienced two seismic shifts this century. First was the rise of the open source movement, which gradually made code accessible to developers and engineers everywhere. Second, the adoption of development operations (DevOps) and agile methodologies took software from siloed
One investor who has backed both companies told the FT that justifying OpenAI's recent round required assuming an IPO valuation of $1.2 trillion or more — making Anthropic's current $380 billion valuation look like the relative bargain.
By compiling a simple program directly into transformer weights. The post I Built a Tiny Computer Inside a Transformer appeared first on Towards Data Science.
What has changed in the past five years in the role and importance of generalists in data teams The post Range Over Depth: A Reflection on the Role of the Data Generalist appeared first on Towards Data Science.
How production models fail over time, and how to catch and fix it before it breaks trust. The post Your Model Isn’t Done: Understanding and Fixing Model Drift appeared first on Towards Data Science.
Learn how to apply coding agents to all tasks on your computer The post How to Apply Claude Code to Non-technical Tasks appeared first on Towards Data Science.
Many companies are taking a slower, more controlled approach to autonomous systems as AI adoption grows. Rather than deploying systems that act on their own, they are focusing on tools that assist human decision-making and keep control over outputs. This approach is especially clear in sectors where
Models like Google Gemma 4 are increasing enterprise AI governance challenges for CISOs as they scramble to secure edge workloads. Security chiefs have built massive digital walls around the cloud; deploying advanced cloud access security brokers and routing every piece of traffic heading to externa
Uri Maoz loved doing his human research, back when he was getting his PhD. He was studying a very specific topic in computational neuroscience: how the brain instructs our arms to move and how our gray matter in turn perceives that motion. Then his professor asked him to deliver an undergrad lecture
Grizzly bears have made such a comeback across eastern Montana that in 2017, the state hired its first-ever prairie-based grizzly manager: wildlife biologist Wesley Sarmento. For some seven years, Sarmento worked to keep both the bears, which are still listed as threatened under the Endangered Speci
Kepler Communications is flying 40 GPUs in Earth orbit. And its latest customer is Sophia Space.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. You have no choice in reading this article—maybe How do humans make decisions? The question has been on Uri Maoz’s mind since he read an article in his early twen
While many startups founded prior to the emergence of ChatGPT are struggling to position themselves for the AI era, Vercel, a 10-year-old dev tool and website hosting platform, is benefiting from the explosion of AI-generated apps and agents.
If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts th
Stanford’s latest AI Index shows a widening gap between experts and the public, with rising anxiety over jobs, healthcare, and the economy.
The new features would be geared toward enterprise customers, with better security controls than the famously risky open source OpenClaw agent.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a s
The acquisition indicates a capability that OpenAI is building into ChatGPT: financial planning.
Most ReAct-style agents are silently wasting their retry budget on errors that can never succeed. In a 200-task benchmark, 90.8% of retries were spent on hallucinated tool calls — not model mistakes, but architectural flaws. This article shows why prompt tuning won’t fix it, and the three structural
Master method chaining, assign(), and pipe() to write cleaner, testable, production-ready Pandas code The post Write Pandas Like a Pro With Method Chaining Pipelines appeared first on Towards Data Science.