标签：#ci

Now that almost everyone can use AI tools, I’m curious what skill will actually separate people moving forward. Is it prompting? Taste and judgment? Knowing how to verify outputs? Domain expertise? Workflow design? Or something else? My current take is that AI makes execution faster, but it does not replace knowing what good work should look like. The people who can guide, check, and apply AI well may become more valuable than people who only know how to generate outputs. What skill do you think will matter most in the next few years? submitted by /u/GlobalOpsNotes [link] [留言]

Yeah, so "AI will eat the world" or "AI changes everything" - well, its certainly changed traffic patterns on the web. submitted by /u/Objective_Farm_1886 [link] [留言]

The USDA this week confirmed the first known infection of the carnivorous fly larva, which feast on the flesh of living mammals, after the United States eradicated the nightmare bugs in the 1960s.

Can an AI decide that it's going to nefariously drive the human it's interacting with slowly insane? submitted by /u/Numerous-Cup1863 [link] [留言]

submitted by /u/esporx [link] [留言]

These to seem to be confused and mixed up often. How do you pick those apart? submitted by /u/phamsung [link] [留言]

submitted by /u/WrongdoerRough4712 [link] [留言]

DIYer and TikTok user Annike Tan, who goes by @ubeboobey, can carry her cyberdeck around without anyone noticing because it doesn't look like a computer at all. Tan, who has been featured in The Cut and Wired, went viral earlier this year with a mermaid-themed cyberdeck she made inside an old purse. She has since […]

Hi, everyone I am working as a Software engineer. The past few years I oversleep a little bit in scope of AI mostly because I am sceptical about it. I decided that I would like to move on and be more up to date with it and potential use of it. How do you use it in day to day habits or work? How to monetize it? submitted by /u/Blvckhype [link] [留言]

Musk can't be trusted to protect X user privacy, public commenters warn FTC.

Meta may have found one way to slash its massive data center bill: tents.

For those doing heavy AI programming or running local models on mobile hardware: Is the current generation of iPhone Pro or Samsung Galaxy Ultra actually making a difference in your workflow, or is it mostly a gimmick right now? submitted by /u/Spirited_Good9789 [link] [留言]

On Uncanny Valley, we dive into the IPO bonanza that the top AI companies are embarking on to the point where some real estate listings are looking for not just regular old cash, but Anthropic stock.

We've poured hundreds of billions into bigger models, bigger clusters, bigger training runs, all pointed at AGI. And yet: the model still rebuilds context every few turns, still forgets what you told it ten messages ago, still degrades over long horizons. The capability is staggering and the continuity is brittle. We keep making the pattern-matcher bigger and acting surprised when a bigger pattern-matcher is still a pattern-matcher. Start with the measurement problem, because it sets up everything else. Faster output and better output are not the same thing. The industry measures speed. Tokens per second, FLOPs, parameters, because speed is easy to measure. But *effective* output, the useful work you actually get before the model starts reconstructing or fabricating what it already knew, is a different axis entirely. And on that axis, raw hardware speed tells you almost nothing. A system that generates twice as fast but burns half its output re-establishing context it should have retained isn't ahead. We've been optimizing the number that's easy to read instead of the one that matters. Here's the part I think gets skipped entirely. Current systems have no intrinsic drive. They don't want anything. They sit idle until prompted and optimize the next token. A bacterium has more impetus than a frontier model, it has a goal (find food, avoid toxin) and acts on it unprompted. That's not intelligence, it's drive, and drive is the thing evolution built *first*, hundreds of millions of years before cognition. We built the cortex and skipped the brainstem. So the bet that "scale the transformer until AGI falls out" may be optimizing the wrong layer entirely. You can't scale your way into goal-generation if goal-generation isn't a function of scale. If genuine intelligence needs a motivational substrate, something that forms its own goals and acts on them, then no cluster on earth produces it by getting larger, because it's an architecture problem, not a compute problem. That

Hey all. Been vibe coding for almost 2 years now (I think?). Previously was more focused on traditional micro-saas but recently decided to go in a different direction and see how far I could push lovable and try and make a commercial grade browser based game. Built it with Lovable + Supabase + Stripe -- full commercial browser game, gyroscope controls on mobile, no app store needed. Generated all my assets (I know, I know, there aren't a ton) with a combination of Gemini to prototype and the GPT 2 to finalize. I've made a few small games here and there that generally only get used by my kiddos, but with this one I wanted to try and create a full gaming experience (login rewards, leaderboard, store, powerup mechanics, simulated ads, etc.) Put a $100 bounty on it for the first player to reach level 100 on mobile. Nobody has claimed it since launch. So genuinely asking -- is it too hard, or is that the point? tiltra.io P.S. It is currently playable on both desktop and mobile but with the gyro mechanic it is definitely more fun and challenging on mobile. submitted by /u/BeltwayBro [link] [留言]

Scientists in Finland found bees could solve an insect version of the classic "box-and-banana" problem.

Opus 4.6 or sonnet 4.6 for rping Currently running on pro right now Im unsure what to choose between the two in terms of rping cause i prefer creative writing, stay in character, deep emotional prose, good character development, good memory, good character emotionals and stuff like that So far im using opus 4.6 but it drains the limits relatively quick For the sonnet i can use for hours and still be fine So like im wondering which is better for rping? I havent tested both deeply Also if they're an even better option, pls tell me. submitted by /u/Turbulent_Arrival_55 [link] [留言]

Gartner updated their 2026 forecast to $2.5 trillion in global AI spending. Same week, MIT's NANDA Initiative dropped a follow-up: 95% of enterprise gen AI projects deliver zero measurable return. Not low return. Zero. I've been on the delivery side of 14 of these projects since January. The MIT number doesn't surprise me. If anything it's generous. 1. 73% of the engineering work that gets AI into production has nothing to do with the model. Data pipelines, integration layers, legacy system remediation, human-in-the-loop tooling. That's where the hours go. The model is 27% of the work but gets 70%+ of the budget. Every time. 2. The budget ratio between projects that ship and projects that stall is almost exactly inverted. We tracked this through ticket history and commit logs across 14 engagements. Projects that made it to production: roughly 30% model, 70% infrastructure. Projects that stalled: 70% model, 30% infrastructure. Most companies think they're at 50/50. They're not even close. 3. One client went from 71% Copilot adoption to 34% in six months. Two other AI platform licenses dropped under 12%. Combined licensing: $340K/year. The tools worked fine. Nobody redesigned workflows to actually use them. 4. The median data error rate across our engagements is 14%. Teams always guess 5-10%. One client found 23% in month four of a $310K build. That's two months of an ML engineer building training pipelines against garbage data. $36K in salary discovering a problem a data audit would have caught in a week. 5. Medtech company. Four concurrent AI pilots. No kill criteria. $920K in engineer salary. Eleven months. Shipped: nothing. I've now seen this at six companies now. Nobody defines when to stop spending. So nobody stops. 6. Individual gains are real. Company-level ROI stays flat. HCLTech and Writer both found this from different angles. Only 29% of companies see significant ROI from gen AI, despite people at their desks reporting productivity jumps as high as 5x. I m

I have been researching how startups and developers manage AI spending across OpenAI, Claude, Gemini and other models. Many people seem to rely on spreadsheets, rough estimates or provider dashboards. I'm curious: How are you tracking AI costs today? What is the biggest frustration in your workflow? Trying to understand the problem space better before building additional features. submitted by /u/OneDisastrous7969 [link] [留言]

Code reviewed by WIRED uncovered an unreleased face-recognition system embedded in Meta’s smart glasses platform. It’s designed to identify people via biometric data stored on users’ phones.