标签：#AR

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.

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

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.

"This wasn't a success story. It started as survival." Intro Hey, I'm Supreeth C , a third-year engineering student, open source developer, and professional overthinker from Bengaluru. This is my first blog, and fair warning: it's long. Not "LinkedIn post with 5 bullet points" long. Actually long. This is the story of how I got selected for Google Summer of Code 2026 with CircuitVerse but more honestly, it's the story of how I almost didn't submit a proposal, almost quit twice, and spent a lot amount of time reading codebases on the Bengaluru Metro while missing my stop. Connect with me on GitHub and LinkedIn PS: I'm writing this at 4.05am, because sleep is a myth XD. Act I: The Prequel Second semester. Fresh-faced. Absolutely clueless. I joined Pointblank , the one genuinely breathable space in my Tier-3 college. I can say its the best student-run club overall and the main reason being : everyone around me was terrifyingly good . Codeforces experts and specialists, GSoC mentees, LFX mentees, Smart India Hackathon winners. People whose LinkedIn bios are of several lines. And me? I knew C++. That was it. That was my entire personality. Cue the imposter syndrome : that lovely feeling where you're convinced you snuck into a room you have no business being in, and everyone else is one conversation away from figuring it out. My solution? Chaos. I started learning everything simultaneously: web dev, Android, ML, DevOps, a bit of systems engineering. Jack of all trades, master of none, spiraling fast. I wasn't learning; I was collecting domains like Pokémon and actually using none of them. Then a senior said something that cut clean through the noise: "Find your own path." So I slowed down. Started from the basics of web dev. Attended many hackathons but always ended up in third or fourth and winning: zero . But something clicked anyway. Those hackathons introduced me to open source, and somewhere in that chaos, I gave myself a simple challenge: 4 pull requests for Hacktob

Or any other type of activity other than sitting in front of a computer like sitting in a park, running on a treadmill, etc? I’m curious how much more freedom from deskmaxxing people are getting today from using what’s available with build automation tools and harnesses on Claude Code, Code , Antimatter, etc. like GSD, Superpowers, Smith, Cowork, etc. submitted by /u/dennisplucinik [link] [留言]

Ex‑DeepMind researchers unveiled AI lab Inherent, emerging from stealth with a significant funding round reported at about $50 million (also reported as £40m). The startup plans to pursue AI science research and build lab capabilities to accelerate foundational work and commercialization. submitted by /u/Objective_Farm_1886 [link] [留言]

Amazon bought a 960 megawatt nuclear reactor for AI servers. Microsoft restarted Three Mile Island. Stargate is spending 500 billion dollars on data centres. All of this to do, badly, what your brain does for free on the power of a dim light bulb. The reason is that silicon processes information nothing like the brain does. Rigid chips with identical transistors trying to mimic something soft, three dimensional, constantly rewiring itself, with billions of different neurons each doing something slightly different. Northwestern University just published research showing they printed artificial neurons from MoS2 and graphene ink that produced biologically realistic electrical spikes. They tested on living mouse brain cells. The brain responded as if the signal came from one of its own cells. The breakthrough was accidental. Every other lab had been burning away the polymer residue left in the ink after printing. This team kept it. That residue created the switching behaviour that made the spikes biologically realistic. The neuromorphic computing implications here seem significant. If you can print devices that process information the way neurons do at scale, the energy math changes completely. submitted by /u/filmguy_1987 [link] [留言]

I'm creating a story for my cousin. I think it will be very interesting if this story’s main character can be a 3D character.My project is still in planning stage. I’m writing character descriptions, collecting references from Pinterest and testing some complex shapes using Tripo AI. I plan to continuously improve all the content over time. After I get a version that I like I will put it into Blender for editing and final touches.There is no final version yet but I just want to share this process with the community! I find it is so interesting to watch a story’s concept gradually become concrete lol!! submitted by /u/Final_Floor_789 [link] [留言]

submitted by /u/Hot-Upstairs9603 [link] [留言]

This is a genuine technical question here. I've been looking at systems that use an ensemble of ai models to generate probability estimates for open ended real world events. The claim is that consensus across multiple models produces more calibrated estimates than any single model. this makes sense intuitively and has parallels to ensemble methods in traditional ml. But I'm wondering about the theoretical underpinnings more carefully. The standard ensemble argument relies on errors being somewhat uncorrelated across models. but if all the models are trained on similar data distributions and share architectural similarities, how independent are their errors really? are we just getting false confidence from models that all have the same blind spots? also curious about how these systems handle events that are outside the distribution of their training data. novel events are exactly where you'd want good probability estimates and also exactly where you'd expect the most unreliable performance. submitted by /u/onlyJayal [link] [留言]

Read this release today. Some crazy numbers. The tau2-bench number is 98% across all difficulty levels. That is the one that got me because usually these releases post a strong easy score and then quietly die at hard difficulty. This one... claims it holds. For multi-step agent work that actually matters more than most benchmarks. A model that drifts on step 4 of a 6 step chain is a debugging nightmare regardless of what its SWE score looks like. Raw capability is mid, Toolathlon at 49.5, GDPval at 45.8. So this is clearly a reliability play, not a frontier capability play. Depending on your use case that is either fine or a dealbreaker. 198B sparse MoE 11B activ 400 TPS 256K context Apache 2.0 runs locally on M4 Max and DGX Spark. Has anyone actually put this through agent evals or am I just reading the release card. submitted by /u/Skid_gates_99 [link] [留言]

Submit your session topic before today ends for a chance to speak at TechCrunch Disrupt 2026. Apply now to share your insight and help shape the conversations defining the tech industry.

IDK I might be wrong but.....I don't think it's happening anytime soon. ChatGPT, Claude, Gemini.....they are good....but they are too lazy. Gave them a task to create a Masterdata for all smartphone models being sold by a particular brand. Gave explicit instructions for all models. Explicitly asked for a list 1st and then asked it to create MasterData. Lazy ahh model just put in like 21 popular ones out of the hundreds of the available models and variants. Is this how it will overtake us and replace all the labor intensive work? submitted by /u/naamnhiptahai [link] [留言]

You now have until tonight at 11:59 p.m. PT to lock in Early Bird savings of up to $410 for TechCrunch Disrupt 2026 before prices increase. Join 10,000+ tech leaders in October for one of the most anticipated tech events of the year. Register now.

For nearly 80 years, Ferrari occupied a unique cultural space where its cars were aspirational, even for people who resented those who could afford them. The price, the exclusivity, and the opacity of the buying process allowed Ferrari to sail above ordinary criticism. You might not be able to afford one, but you still wanted […]

Hey all! This is my first time writing a blog. It is about the migration from mongoDB database to a postgreSQL one. How we changed the architecture completely using AI. Any criticism is welcome submitted by /u/SID_069 [link] [留言]

As GenAI starts flooding every platform, I’m beginning to wonder if live sports are one of the last truly AI-resistant industries. You still can’t prompt a model to recreate the real tension of a 14–14 tie-break in a volleyball final and maybe you never will. I read an interesting piece from NJF Holdings about this. Frankly speaking, I barely know who Nicole Junkermann is but she seems to be focused on AI infrastructure and sports rights in AI era. I agree with her, that the more polished and “perfect” AI-generated content becomes, the more valuable becomes true human unpredictability and even mistakes. The basic idea is that sports become more valuable precisely because they can’t be generated. Does that idea hold up, or do you think AI entertainment eventually becomes “good enough” to compete with the real thing? submitted by /u/AssistantStraight983 [link] [留言]

The old (testing in Safari when you don’t have Safari), the new (::checkmark), the in-between (anchor positioning but with HTML), and more. What’s !important #12: Safari Testing, ::checkmark, HTML Anchor Positioning, and More originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

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