Build Ai

... NIM is part of NVIDIA AI Enterprise, which is designed to... a platform for developers to build and deploy AI models, including large language... with NVIDIA’s goals of promoting AI research and development. Rate Limit... developers to optimize and deploy AI models, including LLMs like Qwen...

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...available. The numbers used to build intuition behind the below distribution ... another likely constraint on AI development. 7 – Converting $ to ... marginal cost of reliable AI power is above ordinary industrial ... give feedback to the AI on its rewriting attempts. I ... text is far from AI generated in my mind - ... and so on. The AI might be more of a ...on how to disclose my AI use. Also happy to share ...

... usage. Tech companies develop sophisticated AI algorithms to forecast supply chain... hedge currency exposures. Furthermore, executives build dynamic teams capable of exploiting...

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...as a way to test AI systems despite generative variability:...Husain: LLM-as-a-Judge Pydantic AI output docs Instructor Guardrails AI Important caveat: ...design Hamel Husain: Your AI Product Needs Evals Promptfoo Step...here. Google says generative AI can be useful for research...content, but using generative AI or similar tools to generate... notes Phase 2: MVP backend Build: FastAPI Postgres pgvector Celery +...

...way you need to build to learn how to build. You can’t become...are having trying to build some of these projects. So ...try and try to build it within a time frame. ...Don’t know CSS? Then build a website without one. Don’t ...little as possible to build one. Keep reducing until you ... clean up after! So build, fail, learn and build again. Rinse and repeat and ...! PS. I wouldn’t use AI unless you want to never ...

... Works​ Google relies heavily on AI and machine learning to scan ... rule of thumb is to build trust gradually: The Warm-up Phase: ...

... capturing a slice of the "AI infrastructure" budget that doesn't depend ...\. \#\#\# 3\. Revenue "Lock\-in": Sovereign AI and Security \(TDX/Security\) This ..." customer base\. While the "cool" AI startups might chase the cheapest ... \(Foundry\):\*\* You get paid to build \*everyone's\* chips\. \* \*\*Layer 2 \(Packaging/... "defensible" investment than the "pure\-play" AI GPU companies that might see ...

... look up reviews, a quick AI search says: The Lenovo IdeaPad... processing power, battery life, and build quality, but requires sacrifices in ... and 512GB to 1TB SSD. Build: Aluminum chassis gives a surprisingly...

... look up reviews, a quick AI search says: The Lenovo IdeaPad... processing power, battery life, and build quality, but requires sacrifices in ... and 512GB to 1TB SSD. Build: Aluminum chassis gives a surprisingly...

According to information, AI Visual Trader will begin commercial launch for large companies in mid-2028. AI that can read visual trading charts. The effect is that prices may become more volatile due to the AI trading war between large institutions Use AI to build your system now Attached Image (click to enlarge)

... used its purchasing power to build a good bike at a... problem solving on YouTube and AI. It was functional right out ...

...revenue generated from smart EVs, AI, and other innovative initiatives reached... worldwide are recognizing the premium build quality, advanced imaging capabilities, ... this software experience is Xiaomi’s AI Assistant (Xiaoai), whose MAU ...Artificial Intelligence. Xiaomi’s specialized large-scale AI model, MiMo-V2.5-Pro, ranked in...Nürburgring-slaying YU7 GT, the skyrocketing AI capabilities of MiMo, or ...

... to: 44-53 🤖 Prompt for AI Agents Verify each finding against ..., user, userIds, usernames) — e.g., build the Joi object schema for ...

... want to see what people build with it. So we’re running...–11. The theme is creative AI tools — things for designers and... card, no catch. Whatever you build is yours — IP stays with...

... off' when running. From Google AI: The term "spotted tabby" refers... has a unique cheetah-like, athletic build. Their hind legs are noticeably...

submitted by /u/NicolasCageFan492 to r/technology [link] [comments]

I’m really interested in creating AI agents at the moment, but I’m finding it hard to know how to get started. It’s a lot of ideas and different ways to go about it, and it’s a little overwhelming when you’re new to it. If you’ve done this before, what’s a good place to begin? Could you suggest easy to use resources, how to learn things in order, or a first project to attempt? I’d also like to hear about things you should definitely do, or typical errors to steer clear of when you are just starting. Thanks for any help!! Edit: Thanks for all your suggestions, I will try Workbeaver and Claude Cowork submitted by /u/MoneyMiserable2545 to r/AI_Agents [link] [comments]

Hey guys, I'm in the process of learning/mastering how to build AI Agents and RAG Systems. As I'm going through some videos/books/forums/chattingwithAI I'm documenting the whole knowledge. I thought of turning the learnings into gamified web experience. But I don't want to build just another platform no one will find helpful. This being said do you think it is a valid idea to pursue? What resources have you used to master building Agents? submitted by /u/Gio_13 to r/AI_Agents [link] [comments]

submitted by /u/Hrmbee to r/technology [link] [comments]

I’m interested in diving into creating AI Agents but I’m not sure where to start. There are so many frameworks, tools, and approaches that it’s a bit overwhelming. Can anyone recommend good starting points, tutorials, or projects for beginners? Any tips on best practices would also be appreciated. Edit: tried ZooClaw.ai after someone mentioned it, gave it a simple goal like research and organizing info, and it handled the steps end to end which made the whole agent concept click way faster. submitted by /u/NecessaryEgg5361 to r/learnmachinelearning [link] [comments]

So this is my first posting on this forum, looking forward to asking questions and answering them. If the category is wrong for this, let me know, so i can change it (If I can) I’ve been getting into the whole AI field over the course of the year and I’ve strictly said to NEVER use cloud based AI (Or under VERY strict and specific circumstances). For example, i was using Opencode’s cloud servers, but only because it was through their own community maintained infrastructure/servers and also it was about as secure as it gets when it comes to cloud AI. But anything else is a hard NO. I’ve been using my main machine (Specs on user) and so far it’s been pretty good. Depending on the model, I can run 30-40B models at about 25-35 tok/s, which for me is completely usable, anything under or close to 10 tok/s is pretty unusable for me. But anyways, that has been great for me, but I’m slowly running into VRAM and GPU limitations, so I think it’s time to get some dedicated hardware. Unlike the mining craze (which i am GLAD i wasn’t a part of), i could buy dedicated hardware for AI, and still be able to use the hardware for other tasks if AI were to ever go flat-line (we wish this was the case, but personally i don’t think it’ll happen), that’s the only reason I’m really fine getting dedicated hardware for it. After looking at what’s around me, and also my budget, because this kind of hardware adds up FAST, I’ve made my own list on what i could get. However, if there are any other suggestions for what i could get, not only would that be appreciated, but encouraged. Radeon Mi25 | This card for me is pretty cheap, about 50usd each, and these cards can get pretty good performance in LLMs, and also some generative AI, (which i am not in any shape or form interested in, but it’s something to point out). Funnily enough, Wendell made a video about this card when it came to Stable Diffusion a couple of years ago, and it was actually pretty good. Nvidia Tesla M-Series Cards | Now hold on, before you pick your pitchforks up and type what I think you are going to say, hear me out. Some of these cards? Yeah they ABSOLUTELY deserve the hate, like the absolute monstrosity that is the M10, and also ANY of the non single gpu cards, (although some of the dual gpu cards are acceptable, but not ALL of them). Some these cards get surprisingly good numbers when it comes to LLMs, which is my whole use case, and they still have some GPU horsepower to keep up with other tasks. Nvidia Tesla P-Series Cards | Same thing with the M-Series, some of these cards are NOT great at ALL, but of them are genuine gems. The P100, is actually a REALLY good card when it comes to LLMs, but they can obviously fall apart on some tasks. What I didn’t know is there is a SXM2 variant of the P100, which gives it higher power and higher clocks, among other thing, which no matter where I look, i cannot find ANYTHING when it comes to AI or ML with these cards, no idea why Radeon Pro Series | Now these cards, I haven’t done much research on them, as much as the others, so I really don’t know about them. Only thing i was interested in was that they were cheap, and had lots of HBM, and about the same VRAM as the others. Nvidia Tesla V100 16GB (Or 32GB if i find a miracle deal) | These cards I recently found out about, and to be honest, these may be what i get. I can get these for about 80-90usd each, and from the videos and forums i have seen on these, i can run some pretty hefty models on here, WAY more than what i would normally be able to, and also comparable GPU perf to like a 6750xt, which is better than my current card. But i am SHOCKED by the adpater prices of these cards, like how TF are the ADAPTERS more than the actual GPU themselves?? I’m still looking for a cheap-ish board to get, but so it isn’t going great In terms of OS, I’ll be using Lubuntu, because I want Ubuntu without all of the bloat and crap that it comes with, and i can still use drivers and etc. In terms of the actual platform, I’ll probably just find some old Xeon platform for cheap or something. doesn’t need to be fancy. I’m fine on ram and storage, I’m pretty plentiful. It’s not gonna be a problem I mainly use LM Studio, and also Opencode (As mentioned in the beginning), but i also use their LMS implementation too, which makes my life a WHOLE lot easier. So far, i haven’t really found any other LM client that i like, whether that be because of complexity or reliability. submitted by /u/FHRacing to r/LocalLLM [link] [comments]

submitted by /u/NeoBahamutX to r/nottheonion [link] [comments]

Hi everyone, I’m interested in diving into creating AI Agents but I’m not sure where to start. There are so many frameworks, tools, and approaches that it’s a bit overwhelming. Can anyone recommend good starting points, tutorials, or projects for beginners? Any tips on best practices would also be appreciated. Thanks in advance! submitted by /u/RiskRaptor to r/AI_Agents [link] [comments]

For a long time I was confused about agents. Every week a new framework appears: LangGraph. AutoGen. CrewAI. OpenAI Agents SDK. Claude Agents SDK. All of them show you how to run agents. But none of them really explain how to think about building one. So I spent a while trying to simplify this for myself after talk to claude for 3 hours. The mental model that finally clicked: Agents are finite state machines where the LLM decides the transitions. Here's what I mean. Start with graph theory. A graph is just: nodes + edges A finite state machine is a graph where: nodes = states edges = transitions (with conditions) An agent is almost the same thing, with one difference. Instead of hardcoding: if output["status"] == "done": go_to_next_state() The LLM decides which transition to take based on its output. So the structure looks like this: Prompt: Orchestrator ↓ (LLM decides) Prompt: Analyze ↓ (always) Prompt: Summarize ↓ (conditional — loop back if not good enough) Prompt: Analyze ← back here Notice I'm calling every node a Prompt, not a Step or a Task. That's intentional. Every state in an agent is fundamentally a prompt. Tools, memory, output format — these are all attachments to the prompt, not peers of it. The prompt is the first-class citizen. Everything else is metadata or tools (human input, mcp, memory etcc). Once I started thinking about agents this way, a lot clicked: - Why LangGraph literally uses graphs - Why agents sometimes loop forever (the transition condition never fires) - Why debugging agents is hard (you can't see which state you're in) - Why prompts matter so much (they ARE the states) But it also revealed something I hadn't noticed before. There are dozens of tools for running agents. Almost nothing for designing them. Before you write any code, you need to answer: - How many prompt states does this agent have? - What are the transition conditions between them? - Which transitions are hardcoded vs LLM-decided? - Where are the loops, and when do they terminate? - Which tools attach to which prompt? Right now you do this in your head, or in a graph with no agent-specific structure. The design layer is a gap nobody has filled yet. Anyway, if you're building agents and feeling like something is missing, this framing might help. Happy to go deeper on any part of this. submitted by /u/Main-Fisherman-2075 to r/ClaudeCode [link] [comments]

Everyone starts somewhere. If you are new to building with AI and you're drowning in "TOP 10 AI AGENT FRAMEWORKS" posts that all contradict each other (it is a mess). That is what I actually use day to day, and believe is not only the most simple for people just starting out, but also the most scalable, generalisable, and production ready. I build AI tools and open-source projects for a living, and I've mass-deleted enough failed experiments to know what works and what doesnt! So here is what I would recommend in 2026 (but give this a month and who knows...): 1. Hear me out... OpenClaw if you just want a working agent right now If you don't want to build from scratch and just want something running today, OpenClaw is the go-to. 60k+ GitHub stars, self-hosted, connects to Telegram/WhatsApp, has memory, scheduling, and a whole tool marketplace. Plug in your API key, connect some services, done, you have an agent that actually does things. The tool ecosystem is the real draw. You can wire up search, email, databases, payments, whatever. For search specifically, Brave killed their free API tier in February which screwed over a LOT of people who'd built on it. I switched to Valyu, free credits on signup, really high quality results, just works as a drop-in replacement and there is an open claw skill for it. (also has deep research which I use for heavy research tasks) Now the honest bit: if you don't know what a CLI is, don't self-host OpenClaw yet. I'm serious. Microsoft Security literally published a blog post about how to run it safely. There have been exposed instances with RCE vulns, sketchy skills on the marketplace, people reporting their agents going into loops and burning through hundreds of dollars of API credits overnight. It's really not bad software, but the problem with an open-source project this viral is that a lot of people don't read the setup instructions properly and end up, to be honest, doing dumb things. 2. Vercel AI SDK + Next.js if you want to build your own thing If you want to build something custom rather than configure something off the shelf, this is the move. The Vercel AI SDK handles 99% of the annoying boilerplate. Their useChat hook gives you a working streaming chat interface in maybe 15 lines of code. The bit that actually matters though: it's provider-agnostic. Write your code once, swap between Claude, OpenAI, Gemini, whatever, without rewriting your app. That's huge when pricing changes every other week. Pair it with Next.js and you've got streaming, server actions, API routes, auth, frontend in one codebase, deploy to Vercel in like 30 seconds. I didn't mean for this to be a Vercel shill post but their ecosystem really is the easiest to get things up and running, especially if you're starting out. And it is also, from my experience, the easiest to scale into serious production applications. 3. OpenAI / Claude for your models Both providers are good. GPT-5-mini for example is super cheap and good enough for most stuff. Claude Opus is incredible at longer context and more careful reasoning. Bit of a hack: Thing most people don't know: OpenAI has a data sharing program where you opt in to let them use your API traffic for training, and in return you get free tokens daily. Like up to 1M tokens/day on the main models. Go to Settings → Organization → Data Controls → Sharing. Obviously don't turn it on if you're handling anything sensitive. But for side projects and experiments? Free tokens are free tokens lol. They've extended the program a few times so check if it's still live. 4. MCPs or Skills for tool use MCPs (Model Context Protocol), Anthropic introduced these, OpenAI and Google have adopted them now. Basically they're connectors that let your agent talk to external services without you writing custom API wrappers for everything. Closest thing to a standard we've got. But more recently, skills (markdown files explaining how to use a service...) became more popular. In most cases, doesn't matter if you use MCP or a skill, but: Ones I'd actually start with: Supabase - agent reads/writes your database directly. Kinda wild to see it work Valyu - allow your agent to search the web, as well as stuff like live financial data Stripe - payments from within the agent PostHog - analytics queries straight from the agent Context7 - this one's slept on. Pulls real-time version-specific docs from actual source repos into your prompt. No more Claude confidently writing code against an API that got deprecated 6 months ago Gmail - read and send email The registry at modelcontextprotocol dot io has hundreds now. Six months ago there were like twelve. And vercel has a skills repository as skills (.) md 5. Cursor or Claude Code to actually write the code You don't have to write everything by hand. Cursor is an AI code editor, Claude Code does similar stuff from the terminal. Tell either one "use the Vercel AI SDK to build me an agent that does X with these MCPs" and you'll have something running in an hour. Not joking. Your ability to articulate what you want to see in the world is the only bottleneck now. The mental model Putting it all together: OpenClaw if you want preconfigured and running today Vercel AI SDK + Next.js if you want to build custom OpenAI or Claude for the brains Valyu for search MCPs for integrations Cursor/Claude Code to build it all Agents aren't magic. They're code that calls an LLM and uses tools. That's it. Overcomplicating it in your head is the thing that actually slows you down. Start messy, ship something, fix it later. Thanks for reading and please ask me anything in the comments or challenge me on anything- happy to go deeper on any of this! submitted by /u/SheepherderOwn2712 to r/AI_Agents [link] [comments]

submitted by /u/ashiqbanana to r/NoStupidQuestions [link] [comments]

submitted by /u/Gari_305 to r/Futurology [link] [comments]

Someone I know posted this photo of her boyfriend graduating from law school this past month. A couple things that make me suspicious are: There is a law school in our state that has similar robes, but black. Shouldn’t the degree have the school crest? The building doesn’t have a name. Surely there would be a rich sponsor. The banner is just gunk words. submitted by /u/rabidanimalsNOM to r/isthisAI [link] [comments]

This was posted on the Ohio Conservatives PAC Facebook page - OP is insisting that is is a real photo, but the uniform nature of the signage, uniforms and the size of the humans relative to the background makes me think it is Al. Link here: https://www.facebook.com/share/1FoPGebGJc/?mibextid=wwXIfr submitted by /u/campaigncrusher to r/isthisAI [link] [comments]

submitted by /u/Ok-Tennis330 to r/Gamingcirclejerk [link] [comments]

I get asked this constantly. "What course should I buy?" or "Which framework is best?" The answer is usually: none of them. If you want to actually build stuff that companies will pay for not just cool Twitter demos, you need to ignore 90% of the noise out there. I've built agents for over 20 companies now, and here is how I'd start if I lost everything and had to relearn it today. Learn Python, not "Prompt Engineering" I see so many people trying to become "AI Developers" without knowing how to write a loop in Python. Don't do that. You don't need to be a Google level engineer, but you need to know how to handle data. Learn Python. Learn how to make an API call. Learn how to parse a JSON response. The "AI" part is just an API call. The hard part is taking the messy garbage the AI gives you and turning it into something your code can actually use. If you can't write a script to move files around or clean up a CSV, you can't build an agent. Don't use a framework at first This is controversial, but I stand by it. Do not start with LangChain or CrewAI or whatever is trending this week. They hide too much. You need to understand what is happening under the hood. Write a raw Python script that hits the OpenAI or Anthropic API. Send a message. Get a reply. That's it. Once you understand exactly how the "messages" array works and how the context window fills up, then you can use a framework to speed things up. But build your first one raw. Master "Tool Calling" (This is the whole game) An LLM that just talks back is a chatbot. An LLM that can run code or search the web is an agent. The moment you understand "Tool Calling" (or Function Calling), everything clicks. It's not magic. You're just telling the model: "Here are three functions I wrote. Which one should I run?" The model gives you the name of the function. You run the code. Then you give the result back to the model. Build a simple script that can check the weather. - Tool 1: get_weather(city) - User asks: "Is it raining in London?" - Agent decides to call get_weather("London"). - You run the fake function, get "Rainy", and feed it back. - Agent says: "Yes, bring an umbrella." Once you build that loop yourself, you're ahead of 80% of the people posting on LinkedIn. Pick a boring problem Stop trying to build "Jarvis" or an agent that trades stocks. You will fail. Build something incredibly boring. - An agent that reads a PDF invoice and extracts the total amount. - An agent that looks at a customer support email and categorizes it as "Angry" or "Happy". - An agent that takes a meeting transcript and finds all the dates mentioned. These are the things businesses actually pay for. They don't pay for sci fi. They pay for "I hate doing this manual data entry, please make it stop." Accept that 80% of the work is cleaning data Here is the reality check. Building the agent takes a weekend. Making it reliable takes a month. The AI will hallucinate. It will get confused if you give it messy text. It will try to call functions that don't exist. Your job isn't just prompting. Your job is cleaning the inputs before they get to the AI, and checking the outputs before they get to the user. The Roadmap If I were you, I'd do this for the next 30 days: Week 1: Learn basic Python (requests, json, pandas). Week 2: Build a script that uses the OpenAI API to summarize a news article. Week 3: Add a tool. Make the script search Google (using SerpApi) before summarizing. Week 4: Build a tiny interface (Streamlit is easy) so a normal person can use it. Don't buy a $500 course. Read the API documentation. It's free and it's better than any guru's video. Just start building boring stuff. That's how you get good. submitted by /u/Warm-Reaction-456 to r/AI_Agents [link] [comments]

submitted by /u/PercentageNonGrata to r/shittymoviedetails [link] [comments]

So last week Meta reported earnings. Beat expectations on basically everything. Revenue up 26%. $20 billion in profit for the quarter but Stock should've gone up right? Instead it tanked. Dropped 12% in two days. Lost over $200 billion in market value. Worst drop since 2022. Why? Because Mark Zuckerberg announced they're spending way more on AI than anyone expected. And when investors asked what they're actually getting for all that money he couldn't give them a straight answer. The spending: Meta raised their 2025 capital expenditure forecast to $70-72 billion. That's just this year. Then Zuckerberg said next year will be "notably larger." Didn't give a number. Just notably larger. Reports came out saying Meta's planning $600 billion in AI infrastructure spending over the next three years. For context that's more than the GDP of most countries. Operating expenses jumped $7 billion year over year. Nearly $20 billion in capital expense. All going to AI talent and infrastructure. During the earnings call investors kept asking the same question. What are you building? When will it make money? Zuckerberg's answer was basically "trust me bro we need the compute for superintelligence." He said "The right thing to do is to try to accelerate this to make sure that we have the compute that we need both for the AI research and new things that we're doing." Investors pressed harder. Give us specifics. What products? What revenue? His response: "We're building truly frontier models with novel capabilities. There will be many new products in different content formats. There are also business versions. This is just a massive latent opportunity." Then he added "there will be more to share in the coming months." That's it. Coming months. Trust the process. The market said no thanks and dumped the stock. Other companies are spending big on AI too. Google raised their capex forecast to $91-93 billion. Microsoft said spending will keep growing. But their stocks didn't crash. Why Because they can explain what they're getting. Microsoft has Azure. Their cloud business is growing because enterprises are paying them to use AI tools. Clear revenue. Clear product. Clear path to profit. Google has search. AI is already integrated into their ads and recommendations. Making them money right now. Nvidia sells the chips everyone's buying. Direct revenue from AI boom. OpenAI is spending crazy amounts but they're also pulling in $20 billion a year in revenue from ChatGPT which has 300 million weekly users. Meta? They don't have any of that. 98% of Meta's revenue still comes from ads on Facebook Instagram and WhatsApp. Same as it's always been. They're spending tens of billions on AI but can't point to a single product that's generating meaningful revenue from it. The Metaverse déjà vu is that This is feeling like 2021-2022 all over again. Back then Zuckerberg bet everything on the Metaverse. Changed the company name from Facebook to Meta. Spent $36 billion on Reality Labs over three years. Stock crashed 77% from peak to bottom. Lost over $600 billion in market value. Why? Because he was spending massive amounts on a vision that wasn't making money and investors couldn't see when it would. Now it's happening again. Except this time it's AI instead of VR. What Meta's actually building? During the call Zuckerberg kept mentioning their "Superintelligence team." Four months ago he restructured Meta's AI division. Created a new group focused on building superintelligence. That's AI smarter than humans. He hired Alexandr Wang from Scale AI to lead it. Paid $14.3 billion to bring him in. They're building two massive data centers. Each one uses as much electricity as a small city. But when analysts asked what products will come out of all this Zuckerberg just said "we'll share more in coming months." He mentioned Meta AI their ChatGPT competitor. Mentioned something called Vibes. Hinted at "business AI" products. But nothing concrete. No launch dates. No revenue projections. Just vague promises. The only thing he could point to was AI making their current ad business slightly better. More engagement on Facebook and Instagram. 14% higher ad prices. That's nice but it doesn't justify spending $70 billion this year and way more next year. Here's the issue - Zuckerberg's betting on superintelligence arriving soon. He said during the call "if superintelligence arrives sooner we will be ideally positioned for a generational paradigm shift." But what if it doesn't? What if it takes longer? His answer: "If it takes longer then we'll use the extra compute to accelerate our core business which continues to be able to profitably use much more compute than we've been able to throw at it." So the backup plan is just make ads better. That's it. You're spending $600 billion over three years and the contingency is maybe your ad targeting gets 20% more efficient. Investors looked at that math and said this doesn't add up. So what's Meta actually buying with all this cash? Nvidia chips. Tons of them. H100s and the new Blackwell chips cost $30-40k each. Meta's buying hundreds of thousands. Data centers. Building out massive facilities to house all those chips. Power. Cooling. Infrastructure. Talent. Paying top AI researchers and engineers. Competing with OpenAI Google and Anthropic for the same people. And here's the kicker. A lot of that money is going to other big tech companies. They rent cloud capacity from AWS Google Cloud and Azure when they need extra compute. So Meta's paying Amazon Google and Microsoft. They buy chips from Nvidia. Software from other vendors. Infrastructure from construction companies. It's the same circular spending problem we talked about before. These companies are passing money back and forth while claiming it's economic growth. The comparison that hurts - Sam Altman can justify OpenAI's massive spending because ChatGPT is growing like crazy. 300 million weekly users. $20 billion annual revenue. Satya Nadella can justify Microsoft's spending because Azure is growing. Enterprise customers paying for AI tools. What can Zuckerberg point to? Facebook and Instagram users engaging slightly more because of AI recommendations. That's it. During the call he said "it's pretty early but I think we're seeing the returns in the core business." Investors heard "pretty early" and bailed. Why this matters : Meta is one of the Magnificent 7 stocks that make up 37% of the S&P 500. When Meta loses $200 billion in market value that drags down the entire index. Your 401k probably felt it.And this isn't just about Meta. It's a warning shot for all the AI spending happening right now.If Wall Street starts questioning whether these massive AI investments will actually pay off we could see a broader sell-off. Microsoft, Amazon, Alphabet all spending similar amounts. If Meta can't justify it what makes their spending different? The answer better be really good or this becomes a pattern. TLDR Meta reported strong Q3 earnings. Revenue up 26% $20 billion profit. Then announced they're spending $70-72 billion on AI in 2025 and "notably larger" in 2026. Reports say $600 billion over three years. Zuckerberg couldn't explain what products they're building or when they'll make money. Said they need compute for "superintelligence" and there will be "more to share in coming months." Stock crashed 12% lost $200 billion in market value. Worst drop since 2022. Investors comparing it to 2021-2022 metaverse disaster when Meta spent $36B and stock lost 77%. 98% of revenue still comes from ads. No enterprise business like Microsoft Azure or Google Cloud. Only AI product is making current ads slightly better. One analyst said it mirrors metaverse spending with unknown revenue opportunity. Meta's betting everything on superintelligence arriving soon. If it doesn't backup plan is just better ad targeting. Wall Street not buying it anymore. Sources: https://techcrunch.com/2025/11/02/meta-has-an-ai-product-problem/ submitted by /u/reddit20305 to r/ArtificialInteligence [link] [comments]

submitted by /u/Wagamaga to r/technology [link] [comments]

Microsoft's cuts are allegedly not concerned with anything beyond the company wanting to get ahead, and stay ahead, in the AI race. Now, this report from MobileGamer.biz shows the inevitable endgame that every bean counter pushing AI and generative AI tools has in mind, being put into practice. That every part of the job they currently pay people to do can be replaced by an AI tool. submitted by /u/ReaddittiddeR to r/gaming [link] [comments]

submitted by /u/Chaotic-Entropy to r/nottheonion [link] [comments]

I've grown tired ofthe increasing amount of AI replies being posted lately on Reddit, so I've started working on a chrome extension to flag & hide it. It's incredible how clean my timeline started to look lately... submitted by /u/Ok-Seaworthiness-293 to r/ChatGPT [link] [comments]