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

Un-0 is an image-generation system tool that shows for the first time how the company's technology can replicate conventional AI systems.

Market research company Klue told customers that it believes the hacking group that stole their data is now deleting it. The company, however, warned about a second group of hackers wanting ransom.

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.

Hi HN, Nick here. We’re launching OpenKnowledge ( https://openknowledge.ai/ ), a “what you see is what you get” markdown editor that has direct integrations with Claude, Codex, and Cursor. Available as MacOS app or CLI. Fully free/local and OSS ( https://github.com/inkeep/open-knowledge ). We built this because we wanted a “Google docs” like experience for writing and sharing markdown files across our team. Obsidian is the best alternative we tried, but found it doesn’t have a true “what you see

I have long been on the hunt for the perfect distraction-free writing setup. The latest contender is BYOK, which stands for Bring Your Own Keyboard. It's a simple $199 black plastic rectangle with a low-resolution LCD screen that lets you edit text and does almost nothing else. I've tried dedicated apps. I've even converted an […]

The Oppo Bubble is a smart second screen for your phone, one that can be attached and detached at will, connects wirelessly, and serves as either a selfie screen or a wireless camera remote. It's the best version of this idea I've used yet, but also a frustrating reminder that it could be even better […]

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.

"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."

Users will be able to access their watchlists, real-time market data, live financial news, and Google's AI-powered "Key Moments" feature, which explains why stocks moved.

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