Microsoft is heading to San Francisco this week in a bid to win back developers at its Build conference. I've been attending Build since the days when Microsoft called it the Professional Developers Conference, and I can't remember a more pivotal moment. As Microsoft continues to reshuffle its entire business around AI, it's moving Build […]

MLE-Bench scores have jumped from 30% to 80% over the last two years. But how much of that is real algorithmic progress vs. better base models + problem definition shifts + overfitting? Turns out: not much. Once you control for the same step budget and models, and then test on a different set of tasks, the two-year-old AIDE algorithm matches modern agent/evolutionary search systems. Figure from FML-Bench, a new automated ML research benchmark, which unifies the code editing agent, step definition, and val/test split, and tries to benchmark the algorithmic efficiency (search/memory) of the agents. paper link: https://arxiv.org/pdf/2605.17373 test improvement and pairwise win-rate submitted by /u/Educational_Strain_3 [link] [留言]

​ Right now, enterprises worldwide are caught in an "AI Mania." Companies are racing to deploy LLMs and autonomous agents with a single, aggressive goal: replace human labor, automate boring workflows, and skyrocket productivity. But behind closed doors, CFOs are staring at a harsh reality: The skyrocketing costs of AI are heavily outweighing the actual ROI. Why is this happening? Because most organizations fall into the superficial AI trap. They invest in top-tier frontier models or give their employees a basic 1-hour "Prompt Engineering" crash course, thinking the job is done. It isn't. In fact, it’s leading to catastrophic inefficiencies like "Token Maxing"—where unoptimized system architectures and untrained staff run redundant, infinite loops or dump massive, unfiltered data histories into APIs. The result? Astronomical bills with near-zero added business value. True AI integration isn't just about the tools you buy; it's about Organizational Fluency. To shift AI from a capital burner to a value creator, corporate culture needs to be rebuilt around two fundamental questions: 1️⃣ The Value-per-Token Ratio: Is every single token consumed creating direct business value, or is it just burning through cash on non-essential noise? 2️⃣ Task Automation vs. Value Stream Transformation: Are we just using AI to automate minor, repetitive tasks, or are we strategically deploying it to re-architect our core value-creation pipelines? The Solution? Look at the Architecture. Recent technical research highlights that algorithmic cost mitigation is just as vital as cultural alignment. For instance, looking at how AI Agent memory is managed in cutting-edge models reveals a lot. Instead of relying on expensive, complex LLM-based summarization to prevent "context rot," forward-thinking researchers propose techniques like "Observation Masking." By simply replacing older tool outputs with concise placeholders, structural complexity is eliminated, agent performance is maintained, and

Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]

Today I’m talking with Harvey Mason Jr., who is CEO of the Recording Academy — that’s the outfit that puts on the Grammy Awards. I last talked to Harvey in 2024, when it was obvious that generative AI would upend the music industry, but still not exactly clear how that would happen. Well, it’s been […]

Shopify introduced GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution. The redesign improves large-scale GraphQL performance with up to 15x faster field execution, 6x lower GC overhead, and +4s P50 latency gains. It focuses on execution-layer efficiency and batched resolver processing for high-cardinality commerce queries. By Leela Kumili

I'm interested in building a new website for my business and am debating on whether or not I should hire a professional or design one by myself using AI. I've seen a lot of pretty nice sites built with AI tools like Claude, but I'm skeptical as to whether or not they are built appropriately. If anyone has opinions about the pros/cons of using an AI tool vs hiring someone I would appreciate hearing them. Thanks in advance! submitted by /u/HawgBandit [link] [留言]

One thing I find interesting about reasoning models is that the hard question is often budget placement, not headline capability. Ring-2.6-1T is a trillion-parameter reasoning model for agent workflows with high and xhigh reasoning-effort modes. If an AI agent only gets a heavier reasoning pass in one place, I would put it before it takes an external action, after it updates state, or before it gives the final explanation to a user. Where would you spend that budget first? submitted by /u/babyb01 [link] [留言]

Hi everyone, If you have purchased (at least one) GPU(s) for ML/DL studies and research: How is your experience and is it worth it? What do you use it for and how is the ROI? I have a MacBook Pro with M4 from some years ago, while MPS is useful in many occasions, it's no substitute for a NVDA GPU with CUDA support. So recently I am considering getting a 5060 Ti 16GB , but a GPU cannot run itself, so I then also need to buy other parts (e.g., CPU, RAM, SSD, motherboard, and so on...), which has been getting more expensive lately, especially the RAM. Since I'm still in job-seeking mode, I will mostly use it for learning DL, RL, and LLM-related things and local experiments (e.g., Stanford CS336), or low-level ones like GPU kernel programming and so on. Do you think a local physical GPU would help, or in my case a cloud service like Modal would suffice? Many thanks! submitted by /u/hedgehog0 [link] [留言]

The popular fitness-tracking platform, Strava, is restricting access to its API as part of efforts to clamp down on AI scraping, as reported earlier by TechCrunch. Developers who want to build an app using Strava's data now need to pay for a flat $11.99 / month subscription. In an update on its developer hub, Strava […]

submitted by /u/loosedolphin [link] [留言]

patch-client@4.0.4 went out through the project's own github action OIDC trusted publisher today and not any stolen token or a typosquat anything, we saw that the actual release pipeline produced it. this runs on npm install, steals cloud creds and self propagates by injecting fake CodeQL workflows into repository the stolen tokens can reach. 32 packages is currently sharing the same publisher so the window of exposure isn not only just a single package. if you have anything from related to / redhat-cloud-services in your tree, 4.0.3 is the last clean version. submitted by /u/BattleRemote3157 [link] [留言]

Things are bad out there. Despite 2026 shaping up to be a great year when it comes to actual games, it couldn't really be worse for the people that make them or the industry as a whole. Hardware prices keep going up, layoffs have shown no signs of stopping, and even big-budget titles backed by […]

BadHost is a high-severity authentication bypass vulnerability in the widely used Python web framework Starlette, with 325 million weekly downloads. The flaw allows attackers to use malformed HTTP Host headers to bypass path-based access controls and access sensitive AI agent infrastructure, among other systems. By Sergio De Simone