标签：#artificial

🚀 SpaceX signed a massive $920 million monthly deal with Google for 110,000 Nvidia chips — this is a huge infrastructure play ahead of their monster $1.7 trillion IPO. 🏛️ The Trump administration is discussing taking equity stakes in top AI firms — this would make the public official partners in the upside of AI-driven economic growth. 🔓 Meta's automated AI support was hacked to take over high-profile accounts — it proves that offloading critical security tasks to AI can create dangerous, easily exploited vulnerabilities. 🧠 Tech workers are trading hours of manual labor for high-level strategy thanks to AI — while tasks now take minutes, humans are still needed for crucial, complex decision-making. submitted by /u/Ok_Muffin_7347 [link] [留言]

Just shipped this project and wanted to share the full technical breakdown because hotel/hospitality AI doesn't get much attention compared to the usual chatbot and SaaS use cases. The client manages 500 hotel properties. Their support team was manually handling around 15,000 guest emails per day. Same questions over and over across hundreds of hotels but each one still needed a human to read it, understand it, find the answer, and reply. Here's how the system works end to end: Layer 1: Email ingestion and question extraction This was the hardest part. Guest emails are messy. A typical one looks like: "Hi there, we're coming for our anniversary on the 20th and I was wondering if you have any room upgrades available. Also is the spa open to guests or do we need to book separately? We're driving so need to know about parking too. Last time we stayed the wifi was a bit slow in our room, has that been fixed? Thanks!" That's four separate questions plus a complaint wrapped in one email. If you just embed the whole thing and search the FAQ database you get a blended result that partially answers one or two questions and misses the rest. So I built an extraction layer that reads the full email and breaks it into individual questions. It handles directly stated questions ("is the spa open?"), implied questions ("we're driving" implies they need parking info), complaints that need acknowledgment but aren't FAQ-searchable ("wifi was slow"), and informational context that shouldn't be treated as a question at all ("coming on the 20th"). Getting this extraction reliable was probably 40% of the total development time. Layer 2: FAQ knowledge base with vector search All hotel FAQs get embedded and stored in a vector database. Different properties have different amenities, policies, and details so the search is scoped per hotel. When a guest emails the Berlin property asking about breakfast, it searches the Berlin FAQ, not the Munich one. Each extracted question from Layer 1 gets s

submitted by /u/BhaswatiGuha19 [link] [留言]

Most recommendations I’m seeing are for generating presentations from a topic, but I already HAVE the content. Problem is it’s usually: messy notes meeting transcripts random docs giant walls of text Main thing I want is help turning all of that into slides that are actually readable. Does anything handle that well right now? submitted by /u/ragsyme [link] [留言]

the interesting shift is when AI stops sitting in a chat box and starts sharing the browser with you. submitted by /u/Old_Command_7050 [link] [留言]

idk maybe this is obvious to people already working in bigger teams, but the AI coding tool cost thing feels like early cloud all over again. Everyone keeps saying tokens are getting cheaper, which is true, but then somehow companies are still freaking out about AI bills. And I think the reason is pretty simple: people are treating these tools like normal SaaS seats when they are really more like metered infra. Like with a normal dev tool you kind of know the cost. X users, Y dollars per month, done. But with agentic coding tools one small request can quietly turn into a bunch of model calls, context loading, tool calls, retries, verification, more retries, etc. From the user side it looks like “fix this bug” or “write this function” but underneath it may have done a whole mini workflow. And then there is the other cost which I feel people don’t talk about enough: reviewing the generated code. Sometimes the code works but it adds weird duplication, misses existing abstractions, or creates stuff that someone has to clean up later. So the bill is not just tokens. It is also review time + maintenance + future tech debt. Not saying these tools are bad btw. I use them too and they are obviously useful. But it feels like the industry is moving from the fun phase of “look what this can do” to the boring phase of “who is paying for all these calls and did this actually ship anything useful?” Curious if teams are actually tracking this properly yet. Like cost per PR, cost per resolved ticket, cost per workflow etc. Or is it still mostly hidden under “AI productivity” and vibes. submitted by /u/Old_Cap4710 [link] [留言]

I work in IT admin for a major company. Started teaching myself AI automation tools in my own time. Applied them to my workload, my output doubled, got noticed and promoted. Became the go to person for AI integration across departments. But here’s the part that sits heavy with me. Once leadership saw what AI could do, they started looking at headcount differently. People who had been there 10, 15 years. Gone. Not because they did anything wrong. Just because a system could now do their job cheaper. I benefited from knowing AI early. Others paid the price for not knowing it yet. Is that their fault? The company’s fault? Just the way progress works? Genuinely asking because I don’t have a clean answer. submitted by /u/PickYourJawnUp [link] [留言]

I’ve been a movie reviewer for a couple of years, and occasionally people assume my reviews are AI-generated. The thing is, I’ve spent years developing my writing through extensive reading, English classes, and a lot of practice. Because of that, my writing tends to be polished and structured, which I think may be why some AI-detection tools flag it. What I’m curious about is how accurate these AI detectors actually are. Some people have compared my work to AI-generated writing, and when I’ve run my reviews through different AI checkers, I get completely different results. One detector might say a review is 100% AI-generated, another might say 70% or 80%, and another might classify the same review as entirely human-written. Some call it AI, some call it human, and the results seem to be all over the place. None of my reviews are AI-generated. Every review I’ve published has been written entirely by me, without using AI to generate any part of the writing. I just don’t understand how the same piece of writing can receive such wildly different results depending on which detector is being used. Are these tools accurate in any way, shape, or form? submitted by /u/CheesecakePlayful240 [link] [留言]

I’ve been a movie reviewer for a couple of years, and occasionally people assume my reviews are AI-generated. The thing is, I’ve spent years developing my writing through extensive reading, English classes, and a lot of practice. Because of that, my writing tends to be polished and structured, which I think may be why some AI-detection tools flag it. What I’m curious about is how accurate these AI detectors actually are. Some people have compared my work to AI-generated writing, and when I’ve run my reviews through different AI checkers, I get completely different results. One detector might say a review is 100% AI-generated, another might say 70% or 80%, and another might classify the same review as entirely human-written. Some call it AI, some call it human, and the results seem to be all over the place. None of my reviews are AI-generated. Every review I’ve published has been written entirely by me, without using AI to generate any part of the writing. I just don’t understand how the same piece of writing can receive such wildly different results depending on which detector is being used. Are these tools accurate in any way, shape, or form? submitted by /u/CheesecakePlayful240 [link] [留言]

I am really interested in learning intimately AI I don't really have good math skills but I am very good at computers in technology. I really would love to get into the intricacies and understand ai on a very deep level. But I'm better with verbal learning and being able to interact and ask questions then just with texts and reading. I've tried some in the past and gotten a little bit of an education from AI itself but I want to go deeper with somebody who really understands the tech what is the best way for me to do that. So what are the best schools for that submitted by /u/crazyhomlesswerido [link] [留言]

I've been chasing a reliable IPTV service for almost two years. Tried six different providers. Three of them died within a month. One had channels that buffered so bad I thought my internet was broken (it wasn't). One had zero customer support — my ticket sat unanswered for 11 days before I gave up. 👉 Visit official website - VIKINGITV so when people ask me "what's the best IPTV service provider in 2026?" — I don't give a quick answer anymore. I give them this post. What Makes an IPTV Service Actually Worth Paying For? Before I name names, let me break down what I actually tested for — because most comparison posts skip this entirely. Uptime during live events . Any IPTV can stream a Tuesday night rerun. The real test is the Super Bowl, UFC 300, Premier League matchday. Does it hold? Does it buffer? Does it die at halftime? Channel count vs. channel quality. I've seen providers brag about "50,000 channels" and half of them are dead links or SD streams that look like they're coming through a 2009 satellite dish. Numbers mean nothing without stability. Device support. I use Firestick at home and sometimes watch on my phone when I'm traveling. I need something that actually works across both without needing a computer science degree to set up. Customer support response time. This is the one most people ignore until something breaks at 9 PM on a Saturday. The IPTV Services I Tested in 2026 I won't drag this out with a fake "I tested 20 services" list. I'm talking about what I actually used long enough to form a real opinion. After everything I went through, VIKINGITV is the one I stayed with. Here's why. VIKINGITV — The One That Finally Stuck When I first heard about VIKINGITV I was skeptical. I'd been burned before. But a few things stood out after I actually got the subscription: Channel library- 65,000+ live TV channels. Not padded numbers — actual working channels. Sports, news, entertainment, regional content across USA, Canada, UK, Latin America, and Europe. I che

Creating an intelligence network where signals are turned into intelligence. Goal is to create network/digital ecosystems of intelligence. Any feedback is appreciated. Still early in the works check it out https://echonaxnetwork.com/ submitted by /u/stock-market [link] [留言]

https://preview.redd.it/efov9ttgdr5h1.png?width=1774&format=png&auto=webp&s=a24d224ca99a389793d08b1ea67d90817740d7f0 IntiDev AgentLoops Feedback Loops for Agentic Workflows submitted by /u/StevenVincentOne [link] [留言]

Lately I've been experimenting on an open-source project called Canary. https://preview.redd.it/c4dgxw22lq5h1.png?width=1920&format=png&auto=webp&s=304f37871aa9b7ee0a084d8b59207fae51d8b7bc It takes a code diff, identifies the UI flows that are likely affected, and then uses Claude Code to test those paths in a real browser. Every run captures video, screenshots, network traffic, HAR files, console logs, and Playwright traces. The result is both a validation run and a replayable Playwright script. submitted by /u/wixenheimer [link] [留言]

A squat can look simple until it starts going wrong. Knees drift, backs round, shoulders tighten, and without someone watching closely, small mistakes can pile up into pain or injury. That problem became harder to ignore during the pandemic, when many people moved their workouts into living rooms and garages. submitted by /u/Brighter-Side-News [link] [留言]

Designing materials that steer light is a slow kind of trial and error. Each candidate structure must be tested in computer simulations, and every new data point can take anywhere from ten minutes to an hour to produce. That bottleneck has made one thing clear. Smarter machine learning is useful only if it can learn faster, too. submitted by /u/Brighter-Side-News [link] [留言]

The world knows that Taiwan is the only geopoliticial chockpoint of ai. Realistically speaking, which country / countries can replace it in mid term and long term? and why it hasn't happened yet? submitted by /u/houmanasefiau [link] [留言]

Tired of Buffering and Scam IPTV Providers? Here’s What I Found After Testing 7 Services If you’ve spent any time looking for a reliable IPTV service, you already know how frustrating it can be. Most providers overpromise and underdeliver. Fake channel counts, endless buffering, poor support, and in some cases, services that disappear right after payment. After testing seven different streaming services over the last three months, one stood out as genuinely reliable: 𝐗𝐭𝐫𝐞𝐚𝐦𝐨.𝐜𝐨𝐦 ⸻ ⚠️ The IPTV Scam Problem in 2026 The streaming market is full of questionable providers. Common red flags include: Services disappearing after payment No working customer support Channels that never load Fake “4K” labels on low-quality streams No free trial offered Zero transparency about who runs the service 𝐗𝐭𝐫𝐞𝐚𝐦𝐨.𝐜𝐨𝐦 has been the opposite of that in my experience. It offers a free trial, transparent pricing, responsive support, and has been consistently stable. ⸻ ✅ Why 𝐗𝐭𝐫𝐞𝐚𝐦𝐨.𝐜𝐨𝐦 Stands Out 🔴 Live TV & Sports Coverage NFL, NBA, MLB, UFC, WWE Premier League, Champions League, FA Cup, La Liga, Serie A Sky Sports, TNT Sports, ESPN, FOX Sports and more PPV events included 📺 Entertainment Channels BBC, ITV, Channel 4, Channel 5 (UK) NBC, ABC, CBS, FOX (USA) Large VOD library with movies and TV series International channels including French, German, Arabic, Spanish, and Italian ⚡ Stream Quality HD and 4K streams Fast channel switching Anti-buffering infrastructure Stable performance during peak hours and major sporting events ⸻ 📱 App Compatibility One thing I liked was how easy it was to use with different IPTV apps. Supported Apps ✅ TiviMate ✅ Chillio ✅ IBO Player ✅ BOB Player ✅ IPTV Smarters Pro ✅ GSE Smart IPTV ✅ Lazy IPTV ✅ Perfect Player ✅ OTT Navigator ✅ Sparkle TV ✅ VLC Media Player ✅ Kodi (PVR IPTV) ✅ XCIPTV Player ✅ Net IPTV ⸻ 🖥️ Supported Devices ✅ Amazon Firestick & Fire TV ✅ Android TV & Android Phones ✅ Apple TV & iPhone/iPad ✅ Samsung Smart TVs ✅ LG Smart TVs ✅ MAG Boxes ✅ Win

I keep seeing "AI took these jobs" every time a company does layoffs, and I'm not convinced it's the main driver. A few things I keep coming back to. The industry cut around 122,500 jobs in 2025, down from about 153,000 in 2024. AI was named as a direct reason in fewer than 8% of those announcements. So for the other 90 percent plus, something else was going on. Actual AI adoption inside companies is also lower than the marketing suggests. Full org-wide rollout is still in the single digits in the surveys I've seen. Plenty of teams have a ChatGPT subscription and call themselves "AI-driven", but that is not the same as AI doing real work in the pipeline. My read: AI usually isn't replacing people directly. Managers see devs shipping more code and assume they can cut headcount, and companies are moving tight budgets toward expensive AI infra and tooling. But coding is a small part of the job, so "more code per dev = fewer devs" rarely holds up. I don't think AI is taking most jobs. I think it's adding pressure to a market that was already rough for other reasons (economy, over-hiring in 2021-2022, investor expectations). For people who work in eng or hiring: when you've seen layoffs up close, how often was AI genuinely the reason versus the convenient public explanation? submitted by /u/Empiree361 [link] [留言]

The company behind Claude has two openings on its creative team. The enterprise copy lead pays up to $320,000. The head of copy and content goes up to $400,000. Both roles come down to the same task: take dense, technical product features and write about them so people actually want to read. So the company building a tool that writes is paying engineer money for humans who write. Andrej Karpathy joined Anthropic this month and recently rated copywriting an 8 or 9 out of 10 for AI exposure, a job the machines are coming for fast. Anthropic posted the roles anyway. Their president, Daniela Amodei, studied literature in college and keeps arguing that the humanities get more valuable as the models get smarter, not less. I think she is right, and these salary numbers back her up. Generating text was never the bottleneck. The hard part is taste. Knowing your audience. Cutting the line that does not earn its place. Deciding what to leave out, which almost nobody gets credit for and everybody notices when it is missing. Writing more is easy. Writing the right thing, for the right people, at the right moment is what companies are paying for. submitted by /u/evankirstel [link] [留言]