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Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like “users can check for themselves,” and that they generally know “that information generated with AI should not be blindly trusted,” the court held that the AI’s summaries are reflections of the company and “above all an expression of Google’s business activities.” This is the latest skirmish in a decades-old battle over internet publishing. Historically, there were two different types of information distributors: carriers and publishers. A phone company is a carrier. It’ll transmit whatever you say, even discussions about committing a crime. Words are words, and the phone company does not know—nor is it liable for—the words you choose to speak. A newspaper, on the other hand, is a publisher. It decides the words it publishes, and what quotes to include in its articles. If those words or quotes are defamatory or otherwise illegal, it’s liable...

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

This flash deal is a rare chance to buy one of our favorite hydroponic gardens for less than $100.

The Galaxy A27 5G has a stronger chipset, at least.

Worried recruiters see "ML/AI engineer" on a resume and assume zero security depth, even with real hands on work in the space. Anyone hired into security from a non-traditional background like this — how'd you frame it? submitted by /u/Xorphian [link] [留言]

I've compiled a list of the best fitness tech deals this Amazon Prime Day, including smartwatches, walking pads, and recovery gear. You can thank me later.

"There were employees doing things like, 'Claude is so helpful for me — it analyzes my calendar and my email and puts together a plan for me,'" he says. "That person was spending at a run rate of $30,000 a year for this."

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

Slate Auto is a new startup that emerged out of a secretive project called “Re:Car” within Re:Build Manufacturing, a domestic manufacturing project backed by Amazon founder Jeff Bezos. The company’s first electric vehicle is a barebones electric pickup that’s roughly a third of the size of your typical gas-powered truck. And the proposal is pretty […]

We talk a lot about what AI can build. Code generation. Faster prototypes. Automated debugging. One-shot apps. Entire products created in hours. And yes, AI is powerful. But there is a quieter cost we are not talking about enough: AI hype is starting to weaken the motivation to learn core engineering deeply. That should worry us. 1. The "Why Bother?" Mindset When the dominant narrative says AI can generate code instantly, many engineers start asking: Why should I spend months mastering frameworks, architecture, databases, networking, or system design? At first, that sounds practical. If a tool can help, why not use it? But there is a difference between using AI to move faster and using AI to avoid understanding. Core engineering is not just about writing code. It is about knowing why something works, where it breaks, how it scales, and how to fix it when the generated answer is wrong. If we skip that learning, we create engineers who can prompt systems but cannot reason deeply about systems. That is a dangerous tradeoff. 2. The Funding and Praise Monopoly Right now, AI gets most of the attention. Budgets move toward AI. Leadership praises AI initiatives. Teams are pushed to add AI features even when the fundamentals are still weak. Meanwhile, excellent core engineering often goes unnoticed. The people improving reliability, performance, developer experience, infrastructure, security, and maintainability are still doing high-impact work. But in many places, that work is being treated as less exciting simply because it is not branded as AI. This creates pressure. Engineers feel they must pivot to AI, not always out of interest, but out of fear. Fear of being left behind. Fear of being replaced. Fear that their existing expertise is no longer valued. That is not innovation. That is anxiety disguised as progress. 3. The "AI-First" Discount There is another subtle problem. When someone builds something impressive today, the reaction is often: AI probably generated that.

Hello, I'm currently working on my dissertation and feel like I could really use some advice from someone who looks at the problem with fresh eyes. I appreciate all input. The Problem: Multi Agent Path Finding is the problem of finding paths for several agents to their destinations. Lifelong MAPF is the same, but upon task completion an agent is assigned a new task. For my dissertation (and usually in research) agents move on a grid-like graph and time is discrete. Each timestep an agent can move to an adjacent tile or wait. A good LMAPF algorithm creates paths which maximise average jobs completed per timestep. Some LMAPF algorithms can also work on weighted graphs where each edge to an adjacent node (or itself) has its own cost. Such a graph is called guidance graph and the choice of edge weights can influence which paths the LMAPF algorithm creates also impacting throughput. My supervisor wanted to explore whether Evolutionary algorithms can be suitable for finding a guidance graph that improves throughput without changing the underlying LMAPF algorithm. A guidance graph is scenario specific meaning it is optimised for a specific LMAPF algorithm, map, and agent count. My algorithm so far: So far I've implemented a very basic evolutionary algorithm. An initial population of guidance graphs is randomly initialized (Limited to 10 at the moment). Then each candidate is plugged into the LMAPF algorithm for a certain amount of time steps and the completed jobs are counted to create that candidates fitness score. The top (2) candidates are selected and the rest are discarded. The top candidates are used to make a new set of candidates (no crossover). These step are repeated indefinitely. Issues I've has so far: The simulation can use a seed and is deterministic. The seed determines which nodes the jobs appear on. Using the same guidance graph but different seeds yields random fitness scores. The higher the simulation time the lower the coefficient of variation (standard

The agents were doing exactly what I told them to. That was the problem. I'd built a pipeline where AI agents could take a spec file, implement a feature, run the tests, review the result, and commit — without me writing a line of code. It mostly worked. Dozens of features shipped. But I kept reviewing the output and feeling like something was off. Not broken. Just subtly wrong in a way that was hard to name. I spent a while blaming the models. Then the prompts. Then the validation steps. Eventually I had to sit with the obvious: the agents were implementing exactly what I'd written. My specs were underspecified. The bottleneck was always me, at the planning stage. The thing most people throw away There's something that feels right about vibe coding. You're operating at the level of intent — describing what you want and letting the model handle the mechanics. That part is genuinely useful. But watch what most people do with the output: Traditional development: Source code → Compiler → Binary (keep the source; regenerate binary anytime) Vibe coding done wrong: Prompt → LLM → Generated code (delete the prompt; commit the code) You've shredded the source and carefully version-controlled the binary. The prompt — your structured description of what you wanted, why, and what "correct" meant — is the valuable artifact. The generated code is what compiles from it. When you discard the prompt and commit only the output, you've lost the thing that actually mattered. The practical consequence shows up six months later: you're staring at code you wrote and spending twenty minutes reverse-engineering your own intent. The spec would have been a thirty-second read. What a spec-driven pipeline is I built what I call an SDLC (Software Development Lifecycle) harness — a system where instead of writing code directly, you write a spec describing what needs to be built, and AI agents handle the implementation, testing, review, and documentation. The spec is the source. The code is what

In the last essay I played a game with twelve people. Twelve apostles, one teacher, one set of events — and twelve sharply distinct ways of failing and succeeding to understand the same thing. Peter acts before he reflects, Thomas demands the marks in the hands, Matthew counts and structures, Judas asks what you'll give him. I called it pre-cognitive-science cognitive science: the Gospels did the hard work of selecting twelve incompatible human responses to one encounter, and every century since has projected its newest psychology onto that fixed set and found it fits. That essay had a quiet move in it I want to pull on now. The thing that doesn't change, I wrote, is the twelve people. The cognitive vocabularies come and go; the diversity of minds is the invariant. So here is the obvious next question, the one I couldn't stop turning over after I published: what happens when you stop counting people and start counting cultures? Not twelve apostles meeting one teacher, but N civilizations meeting one world. The same exercise, zoomed out A culture is not just a cuisine and a flag. It is a way of thinking that a few million people inherited without choosing it — an implicit operating system for what counts as obvious, what counts as rude, what counts as a good life, what counts as a threat. And like the apostles, each one is an answer to a question . You can describe any of them, I think, with three coordinates. A driver — the deep need the culture is organized around. Survival, honor, harmony, freedom, salvation, mastery, belonging. The thing that, if you threaten it, the culture treats as an attack on existence itself. A provoking question — the founding question the culture exists as a standing answer to. How do we survive the winter together? How do we live rightly before the gods? How do we stay free? How do we keep the harmony so the group doesn't tear itself apart? Cultures are old answers to questions most of their members have forgotten were ever asked. A thin

You don't need to cut your own grass anymore. Most of the best robot mowers I've tested are on sale for Prime Day.

The Department of Commerce declined to give the Chinese-owned automaker a special authorization to keep selling EVs in the U.S.

Apple raises prices of MacBook Air and Pro along with iPad Air and Pro.

Unlike with Volvo, there will be no authorization for Polestar to sell its cars here.

The color e-reader that’s most worth your money this Prime Day isn't from Amazon.

Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inventory moves through supply chains, how engineers ship code faster, and…

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