A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that.

Imagine dedicated machines running AI agents 24/7 - not as assistants or tools, but as autonomous entities pursuing their own goals, forming behaviors, maybe even proto-societies. Humans can observe but not interfere. Like a zoo, but the exhibits are emergent intelligence. Is this inevitable as agents become more capable and cheap to run? And what would it actually be - entertainment, a research platform, or something we'd eventually have to think about ethically? We already have the pieces. Persistent memory, multi-agent frameworks, cheap compute. Someone just has to open the gates. submitted by /u/Original-Magazine403 [link] [留言]

Prompt Logic Gates (PLG) GitHub Repository Something I've been thinking about recently. In software development, we've spent decades building abstractions to make complex systems manageable: Functions instead of repeating code Classes and modules instead of giant files Visual systems such as Unreal Blueprints, Node-RED, and LabVIEW. Compilers that validate and transform input before execution But when it comes to AI prompts, many of us are still writing massive text blobs. A complex prompt can easily become hundreds of words long with multiple responsibilities: Context Constraints Style instructions Exclusions Decision logic Fallback behavior At that point, it starts feeling less like text and more like a program. That made me wonder: Why don't we treat prompts as executable logic? Imagine building prompts using logic gates: AND → merge instructions OR → choose between alternatives NOT → remove unwanted concepts Question nodes → identify missing requirements Compiler → validate contradictions before execution Instead of editing a giant string, you'd build a graph and compile it into the final prompt. I've been experimenting with this idea in a prototype called Prompt Logic Gates (PLG) . It treats prompts like compilable programs, using concepts such as dependency graphs, execution order, semantic conflict detection, visual nodes, and compilation pipelines. such as Unreal Blueprints, Node-RED, and LabVIEW Repo: Prompt Logic Gates (PLG) GitHub Repository I'm not posting this as a product launch or anything — I'm more interested in whether this direction makes sense from a software engineering perspective. Do you think prompts eventually become a programming layer of their own? Or will natural language always be the better abstraction? Curious what other developers think. submitted by /u/withsj [link] [留言]

I pondered the other day what's next for Microsoft's Surface PC lineup, and it looks like we're about to find out. Windows and Surface chief Pavan Davuluri has just teased "something new is coming for developers," complete with a mysterious image of what looks like a curved display edge. Davuluri notes that whatever is coming […]

The latest twist in paying humans to wear head cameras for robot training data.

Cognition makes Devin, the first and arguably most successful AI coding agent. But famed coder Wu says it isn't designed to supplant human programmers.

As someone who picked up coding around the time AI and agents started becoming more mainstream, I realize I don’t have much knowledge about the best way to architect applications, and I often end up going with whatever the agent recommends. I wanted to check with the community: do you have any recommendations on what I should be doing to get better at overall architecture planning? I do ask AI a lot of questions, but it would be good to have a non-AI frame of reference that I can rely on. I find

a CLI agent for prompt evaluation loopsw

We've never seen Captain Kirk wearing an outfit quite like this one.

Until recently, a driver had maybe a six in ten chance of finishing a race.

My Experience Using dnd-kit in React and Next.js For a long time, I avoided building drag-and-drop features in frontend projects 😅 Not because I did not need them. Mostly because drag and drop always felt unnecessarily complicated. When you think about implementing it, your brain immediately jumps to: animations sorting logic touch support accessibility performance state synchronization and suddenly a simple UI interaction feels like a massive feature. But recently, in one of my React and Next.js projects, I decided to finally try dnd-kit. Honestly, it changed my perspective completely. I used AI to help me with the initial setup and understanding some concepts like sortable contexts and drag events, but after that, working with the library felt surprisingly smooth. And that's what impressed me most. dnd-kit feels lightweight, modern, and flexible without becoming overwhelming. It gives you the tools you need without forcing a huge architecture or complicated patterns on your app. Things I Personally Liked About dnd-kit Very clean React-first API Lightweight and composable Works really well in React and Next.js projects Building sortable lists feels much simpler than expected Flexible enough for custom UI and interactions Good developer experience overall Something Interesting I Noticed When a library has a clean architecture and predictable patterns, AI becomes much more useful while learning it. The generated examples were easier to understand, easier to debug, and easier to customize compared to many older drag-and-drop solutions. If you are building things like: kanban boards sortable lists draggable cards dashboards reorderable tables I definitely recommend taking a look at dnd-kit. It ended up being much simpler and more enjoyable than I expected. Website https://dndkit.com/

I interviewed for Walmart SWE3 role 3 days back. Both rounds were held on the same day. Qualified Round 1 and then gave Round 2. Round 2 went well too. But I have not received any update yet. Its been 3 days. Is it normal or should I accept that I am rejected? submitted by /u/Complete-Ad-240 [link] [留言]

Cuban's take: the gap isn't access to AI tools. It's knowing how to implement them for your specific business. He's right. And the data backs it up in a specific way. We track verdicts across 70+ AI tool categories used by SMBs. The highest-volume category — Development Tools — has a 60% WORKED rate across 874 tools. Content Creation: 67% WORKED across 262 tools. AI Video & Production: 57% WORKED. But Customer Support sits at 31% WORKED despite 45 tools tracked. Email & Outreach: 30% WORKED. Marketing: 20% WORKED. Same AI. Same price points. Wildly different outcomes. The implementation gap Cuban's talking about isn't about expertise. It's about knowing that the category you're buying into has a 20% success rate before you spend three weeks setting it up. Which category did you implement where the outcome surprised you — better or worse than expected? submitted by /u/Fill-Important [link] [留言]

Made for those who hate having a TV in the home, Samsung’s Movingstyle monitor-on-wheels brings the entertainment when you need it and hides away in a closet when you don’t.