标签：#AI

Though Gen Z has developed a reputation for being so disinterested in sex that they don't even want to see it on TV, the popularity of series like Heated Rivalry and The Summer I Turned Pretty has made it very clear that more than a few young people do, in fact, like their entertainment a […]

It's early June, which means it's video game event season once again. Now that E3 has been gone for a few years, a bunch of showcases and presentations have started to fill the void, including big productions like Summer Game Fest Live and smaller affairs like Wholesome Games Direct. If you love following gaming news, […]

Alternative search engine DuckDuckGo launches 'no AI' web extensions for Chrome and Firefox users.

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 […]

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 […]

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 […]

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

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 […]

Crescent Island is an air-cooled chip that uses LPDDR5 memory.

An LLM-written kernel benchmarked 38% faster on a microbench. Here is what kernel-level validation showed it actually did at runtime. TL;DR Multi-agent LLMs are now writing CUDA kernels (RightNow AI’s AutoKernel, Meta’s KernelEvolve, a multi-agent system claiming 38% speedup on Blackwell). Source-level benchmarks measure clean throughput on a single isolated kernel. They do not measure SM occupancy under co-scheduling, DRAM bandwidth saturation, dispatcher off-CPU during a real serving workload, or NCCL wait correlation with sibling kernels. Kernel-level validation closes that gap: an eBPF trace of the same kernel running under the same workload as production answers all four questions in one capture. The kernel-writing wave Three pieces of work in April surfaced the same pattern: agents generate CUDA kernels, then quote a single throughput number against a baseline. RightNow AI’s AutoKernel (announced Apr 6) – LLM agents iteratively rewrite CUDA kernels for a target metric, claiming substantial speedups on selected microbenchmarks. Meta’s KernelEvolve – similar shape: agents propose kernel variants, rank by throughput, keep the best. Multi-agent system on Blackwell (Apr 29 reports) – claims a 38% speedup on a public kernel benchmark using a coordinated agent setup. All three are real research, all three produce real kernels, and all three report numbers that come from microbenchmarks. The microbench setup is exactly what you want for the optimization loop. It is not what you get in production. What microbenchmarks do not see Run an LLM-generated kernel under nvprof or nsight-compute on an otherwise-idle GPU and the throughput number is real. Put the same kernel in front of a vLLM serving workload and four properties change immediately: SM occupancy under co-scheduling. The kernel that achieves 95% SM occupancy in isolation will achieve 40-50% with three other kernels sharing the same SMs. The optimizer never sees this regime. DRAM bandwidth saturation. A kernel tha

The loop I didn't notice closing Seven weeks ago I started using AI for work. Two weeks after that, I published an article. Seven weeks after that — today — the article is one of sixteen, and they are all in a memory file that the AI reads at the start of every new conversation. I didn't notice the loop until I named it. This is a note about that loop, what it is, what it isn't, and why I keep publishing even though the loop doesn't strictly need me to. The shape It runs like this: I decide what to do. I work it out with the AI — usually in dialogue, sometimes by pasting raw code or data. The dialogue becomes a record. Sometimes a memory entry. Sometimes a published article. The record becomes context for the next conversation, which informs the next decision. It didn't look this clean while it was happening. The numbering is hindsight. From inside, the steps overlap. The first step is the one I keep. Direction is mine: what to build, what to write, what to negotiate. The history that shapes those decisions — twenty-four years of solo work, my company, my family, my health — is also mine. The AI is not setting direction. The second step is where most of the leverage is. I describe what I want to do as completely as I can, sometimes by handing over source code. Then I ask: does this look right? Is there a path I'm missing? Where would this break? I'm opening drawers — possibilities I half-saw in my own head — and checking which ones open cleanly. When one opens cleanly, that is the GO signal. Not "will this succeed" but "this is doable, so do it." The third step happens almost without effort. The conversation already exists as text. Some of it becomes a memory entry I add deliberately. Some of it becomes raw material for an article. The article writes itself partly because I have already explained the thing to the AI. The fourth step is the one that took longest to arrive — and the one I want to be most careful about describing. Three phases, not one The loop didn't

AI agents are getting smarter fast. They can reason through tasks, manage workflows, call tools, and automate decisions across applications. But as these systems become more capable, one challenge becomes impossible to ignore: reliability. How do you know your AI agent is making the right decisions consistently? How do you test workflows that involve memory, reasoning, and multiple execution steps? And how do you debug failures when outputs become unpredictable? That’s exactly what this free live webinar, “ Testing AI Agents in Python: Building Reliable Evals with LangGraph & LangSmith ,” is focused on. The session includes a “ Live demo of the AI agent evaluation pipeline ,” where you’ll see how developers are building structured evaluation workflows using LangGraph and LangSmith to test, trace, and improve AI agent performance in real-world scenarios. Here is the link to register .. Who Should Join This Session? This webinar is designed for developers and technical teams working with AI systems, especially: Python developers building AI agents or LLM workflows AI engineers exploring evaluation and observability Architects designing production-ready AI systems Product teams experimenting with AI automation Founders building intelligent applications faster Whether you’re actively deploying AI agents or still evaluating the ecosystem, this session will give you a clearer understanding of how reliable AI systems are actually built. What You’ll Learn During the Webinar This isn’t a high-level AI trends session. The focus is practical implementation, testing workflows, and evaluation strategies developers can actually use. In this webinar, you’ll learn: Why evaluation matters for modern AI agents How LangGraph helps manage complex agent workflows How LangSmith can trace and monitor agent execution Ways to create repeatable and scalable evaluation pipelines Practical approaches for debugging and improving AI agent behavior See the AI Evaluation Pipeline Live One of the b

📚 This is Part 1 (Opening) of the UtlKit Tech Series — Next: [Architecture & Trade-offs →] As a frontend developer, I've used countless online tools. And almost all of them suck: Sign-up required — just to format a JSON string? Ad overload — the actual tool gets squeezed into a corner Privacy concerns — your JSON might contain API keys, and the tool sends it to a server Fragmented — formatters on one site, Base64 on another, hashing on a third So I decided to build one that doesn't: no sign-up, no ads, pure client-side computation, data never leaves the browser. The goal was simple — if I need this tool, someone else does too. The result is utlkit.com : 150+ tools, 8 categories, zero server costs. Requirements Requirement Meaning Pure client-side All logic runs in the browser Zero server cost Static hosting, no Node.js backend 150+ pages One page per tool, SEO-friendly Bilingual (EN/ZH) i18n support Dark/Light mode User preference Mobile responsive Works on all devices Why Not Other Frameworks? Option Pros Cons Verdict Vanilla HTML/JS Simple Managing 150+ pages is painful Too slow VuePress / VitePress Fast Docs-oriented, not for interactive tools Not flexible enough Nuxt SSR Powerful Needs a server Violates zero-cost principle Next.js 15 + output: 'export' SSR SEO + client interactivity + static hosting Has pitfalls (covered later) ✅ Best balance The Key Decision: output: 'export' // next.config.js const nextConfig = { output : ' export ' , // Static export trailingSlash : true , // Required for static files images : { unoptimized : true }, // No image optimization server } This means: ✅ Build output is plain HTML/CSS/JS files ✅ Deployable to any static host (Cloudflare Pages, Vercel, GitHub Pages) ✅ Zero server cost ❌ No API Routes, no Server Components, limited dynamic routing Deployment: Zero Cost on Cloudflare Pages Build output : out/ directory, ~14 MB Hosting : Cloudflare Pages Domain : utlkit.com Monthly cost : $0 Build Pipeline npm run build → next build ( o

Over the past few months, I’ve increasingly noticed something through my network: more people from non-technical backgrounds are building software as AI tooling improves. Designers are prototyping product ideas. Product managers are testing workflows. Founders are building MVPs. Operators are creating internal tools. People who would not have called themselves “technical” a year ago are now using AI to make ideas tangible. I think this is genuinely exciting. It has never been easier to create. I even attended a hackathon where participants only had 20 minutes to build a demoable product! This raises the question: When AI makes building easier, how do we make sure understanding does not disappear? I recently published Thinking in the Age of AI , a guide for software engineers (you can check out my previous post here ). That guide focused on individual reflection for engineers: how to keep developing technical intuition, reasoning, and judgment while using AI tools. But the landscape has changed quickly. AI-assisted building is no longer only an engineering workflow. It is becoming a builder workflow accessible to all. And by builders, I mean anyone using AI to turn ideas into software-like artifacts: vibe coders designers product managers founders operators marketers students non-engineering team members So I wanted to create a new version of the system for this wider builder audience. Thinking in the Age of AI: Builder Edition The opportunity is real I do not think we should dismiss this shift. I have spoken with people from all kinds of backgrounds who are actively building now. People who previously had to wait for engineering time can now create something concrete. That changes the conversation. Instead of describing an abstract idea, you can show a flow. Instead of writing a long product spec, you can prototype the interaction. Instead of asking “would this work?”, you can test a rough version. That is powerful. But there is a trap. A prototype can look much mor

I recently launched SpotClause, a small AI contract review and reader tool. The idea came from a simple problem: contracts are often difficult to read, especially for freelancers, consultants, and small teams who receive agreements but do not work with contract language every day. SpotClause helps users: summarize contracts in plain language identify key clauses understand payment terms, renewal terms, cancellation language, obligations, and deadlines compare two contract versions and see added, removed, changed, and unchanged wording I also added a Contract Clause Library with plain-language explanations of common clauses like cancellation clauses, renewal clauses, payment terms, confidentiality clauses, and notice periods. You can try the AI Contract Review Tool here: AI Contract Review Tool You can explore the Contract Clause Library here: Contract Clause Library SpotClause is not a law firm and does not provide legal advice. The goal is to help people understand contract language more clearly. I would appreciate feedback on: whether the homepage explains the product clearly whether the AI contract review page feels understandable what clause explanations would be useful to add next

OpenAI breaks ground on a 1GW data center project in Michigan as part of Stargate, building AI infrastructure to expand access, create jobs, and support communities.

Shopify Staff Engineer Guilherme Carreiro discusses building and scaling highly customizable platforms. Using Shopify’s Liquid theme system as a case study, he explains how to balance extreme design flexibility with low-latency performance under massive traffic. He shares insights on implementing secure domain-specific languages, native code extensions, and resilient developer tooling. By Guilherme Carreiro

I tried to explain OpenAI’s solution more clearly than OpenAI did.

Dozens of people have complained to the Federal Trade Commission about Norse Atlantic Airways’ tech-first customer service operation. Some said they lost thousands of dollars.

Michael Stiefel spoke to Sonya Natanzon, about the intersection of technical and social aspects of software architecture. Understanding the business and how a company operates is more important than the specific technologies used. Effective requirements analysis requires focusing on problems to be solved that describe good and bad outcomes, rather than statements of need or solution statements. By Sonya Natanzon