标签：#AR

AI systems are moving from answering questions to taking actions. That changes the risk. A wrong chatbot answer is annoying. A wrong action inside email, CRM, payments, customer support, or internal data can create real damage. So maybe the next big AI challenge is not just better reasoning. It is knowing: what the AI can access what it can do alone what needs approval who is accountable when it fails As AI agents become more common, who do you think should be responsible when they make a bad decision? submitted by /u/Alpertayfur [link] [留言]

https://preview.redd.it/pv22tsg2ib4h1.png?width=1918&format=png&auto=webp&s=dfeda1000090dc99c57c8150e4de46cfe2ba2e29 I just wanted him to give me a prompt, which then i can give to Nano Banana pro and generate me a completely random thumbnail, i wanted to test its capabilities, but instead of a prompt, he gave me this... 😭😭😭😭😭 submitted by /u/ObjectiveOrchid5344 [link] [留言]

c_std: A Leak-Free, Cross-Platform Standard Library for Modern C Bringing the comfort of the C++ STL and Python's standard library to C17 — without leaving C A technical white paper. Executive summary C is still the substrate of the computing world — kernels, databases, language runtimes, embedded firmware, and the inner loops of nearly everything else. Yet the moment you step away from the kernel and try to write ordinary application code in C, you feel the gap: no growable vector, no hash map, no JSON parser, no string type that doesn't invite a buffer overflow. You either pull in a grab-bag of mismatched third-party libraries, each with its own conventions and failure modes, or you re-implement the same dynamic array for the hundredth time. c_std is an attempt to close that gap deliberately and coherently. It is a single, consistent library — written in pure C17 — that reimplements a large slice of the C++ Standard Library (containers, algorithms, smart pointers) alongside many Python-style conveniences ( json , regex , random , statistics , csv , config , even turtle graphics). It targets Windows and Linux from one source tree, compiles cleanly under -Wall -Wextra , and — this is the part I care about most — is verified leak-free under Valgrind , module by module, example by example. This paper explains the design philosophy, the architecture, and the engineering discipline that makes a library like this trustworthy enough to build on. 1. The problem: C's missing middle Every C programmer knows the two extremes. At the bottom, the language itself: pointers, malloc , memcpy , raw arrays. At the top, whatever the platform hands you — <windows.h> or POSIX, OpenSSL, a JSON library someone wrapped a decade ago. The middle — the layer the C++ STL and Python's batteries-included standard library occupy — is missing. That missing middle has a real cost. It shows up as: Re-invention. Teams write their own vector, their own string builder, their own linked list, each subt

GitHub: Prompt-Logic-Gates-PLG Over the past few days, I've shared my research project Prompt Logic Gates (PLG) and received a lot of interesting feedback. Some people loved the idea, some were skeptical, and many raised valid questions. The most common reaction was: > "Natural language is already the abstraction layer. Why add logic gates?" That's a fair question. My goal isn't to replace natural language prompting. In fact, natural language remains at the center of PLG. The idea is to explore what happens when prompts stop being a single request and start becoming systems. The Problem When we write prompts, we're converting our ideas, requirements, constraints, and expectations into text. For simple tasks, this works perfectly. But as prompts grow, they often include: Multiple objectives Business rules Style constraints Context dependencies Exclusions Fallback instructions Tool orchestration At that point, prompts become harder to maintain. Contradictions appear. Priorities become unclear. Context gets mixed together. The prompt is still text, but the complexity starts to resemble a system. What is PLG? Prompt Logic Gates (PLG) is a visual prompt engineering experiment that explores whether prompts can be organized before being sent to an AI model. Instead of writing one giant prompt, users create prompt components and connect them using semantic logic gates. The AI then analyzes the graph and compiles a final structured prompt. How It Works AND Gate When multiple instructions exist, the system evaluates them against the current context and determines which instruction is more foundational. The higher-priority instruction is applied first. OR Gate When multiple options are available, the system selects the most contextually relevant option instead of blindly including everything. NOT Gate Defines exclusions and negative constraints. It explicitly tells the system what should not be done, reducing contradictions and ambiguity. Ask Questions Gate If the system detec

Do you use the "Act as..." segment in your prompts? Do you think it's effective and why? I know it depends on the rest of the prompt, as well as the main goal, but i'm asking if it's working overall. submitted by /u/ObjectiveOrchid5344 [link] [留言]

With AI becoming part of almost everything now—work, business, investing, coding, spreadsheets, content creation, and more—I'm curious about real-world use cases. What's the one thing you use AI for regularly that has genuinely saved you time, made you money, improved your productivity, or solved a problem? Looking for practical examples rather than just "I use ChatGPT." What specific tasks have you automated or improved with AI? submitted by /u/Acrobatic-Shop4602 [link] [留言]

Standard AI 3D generators (like Meshy or Tripo) are limited. They produce solid, monolithic 3D objects that look good but are practically useless, because: - Want to rig or animate it for a game? Can't easily do that, because it’s a dead, monolithic blob instead of a functional, modular asset. - Want to change the arm of a robot you generated? Regenerate the entire asset. - Want to edit something manually? The whole thing collapses because it's not actually structured. Free github project here: https://github.com/RareSense/Nova3D But you'll need to bring your own API Key (BYOK) Under the hood (if you're interested): It uses an LLM as a structured code compiler, instead of an image generator. It writes native Blender Python (bpy) code blocks that target specific nodes in the scene graph. The trick is that everything compiles through Blender's actual scene graph structures instead of pixel or point-cloud diffusion. Final export is a clean multi-part GLB with transform nodes and working pivot axes preserved. submitted by /u/mhb-11 [link] [留言]

A group of 20 Snap alumni has come together to launch a fund called Ghost Angels to back the next generation of social media.

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible.

The legacy book market and use of AI for this are going to be insane. Less than 1% of the US population writes a book. This is what AI is used for: to stop doing tedious stuff and actually do stuff that matters. https://preview.redd.it/fcn6d2t7ta4h1.png?width=2752&format=png&auto=webp&s=5ab6effcafc1e2156903d274f6a4411e53bd9d37 submitted by /u/jdawgindahouse1974 [link] [留言]

Meta seems to be making big bets on AI-powered hardware.

Event sourcing gets sold on immutability. You don't update, you don't delete, you only append, so the history is permanent. It mostly isn't. The events are immutable because your code agrees not to touch them, not because anything actually stops it. Underneath they're still rows in Postgres, and rows have a DBA with write access. A migration that "cleans up" old data. A 2 a.m. query run against the wrong connection. A backup restored with slightly different bytes in it. Change one of those rows and a replay won't blink. The aggregate rebuilds, the projections rebuild, everything looks fine. Usually the first person to notice is a customer whose balance is off, and by then the trail is cold. Chain each event into the next The trick is small. Give every row two extra columns: a hash of its contents, and the hash of the row before it. #1 AccountOpened prev=00000… hash=70be4f… │ ▼ #2 AmountDeposited prev=70be4f… hash=796018… │ ▼ #3 AmountWithdrawn prev=796018… hash=6a0260… The hash is SHA-256(previousHash || json(payload)) . Nothing exotic. The point is that each hash depends on the one before it. Edit a payload and its hash stops matching. Rewrite that hash to cover for the edit, and now the next row's pointer is wrong. You can't fix one without breaking the next. About forty lines of it Appending an event hashes it together with the previous one: public HashChainedEntry Append ( object payload ) { var previousHash = _entries . Count == 0 ? GenesisHash : _entries [^ 1 ]. Hash ; var hash = ComputeHash ( previousHash , payload ); var entry = new HashChainedEntry ( _entries . Count + 1 , payload , previousHash , hash ); _entries . Add ( entry ); return entry ; } internal static byte [] ComputeHash ( byte [] previousHash , object payload ) { var payloadJson = JsonSerializer . SerializeToUtf8Bytes ( payload , payload . GetType ()); var combined = new byte [ previousHash . Length + payloadJson . Length ]; Buffer . BlockCopy ( previousHash , 0 , combined , 0 , previousHash .

It’s hard to believe how quickly the tech landscape is evolving, especially with AI/ML at the forefront. Just the other day, I had the chance to attend the Mistral AI Now Summit, and wow, what an experience! I walked in with a notebook full of questions and a mind buzzing with curiosity. I left with a treasure trove of insights, a few new friends, and even more questions. Ever wondered what it’s like to dive deep into the world of AI with some of the brightest minds? Let me take you on a journey through my day at the summit and the lessons I picked up along the way. A New Era of AI Walking into the venue felt electric. The air was thick with excitement, and the buzz was palpable. I’ve been exploring AI for a few years now, and it feels like we’re at the cusp of something monumental. Mistral’s focus on open-weight models really got me thinking. What if we could democratize AI further? Imagine a world where innovation isn't locked behind corporate walls but accessible to everyone. I remember a speaker discussing how open models can help small startups compete against big players. It hit home because I’ve been that small developer trying to fight the good fight. Real-World Applications One of the discussions that resonated with me was about real-world applications of large language models (LLMs). I’ve dabbled in a few projects where I used Hugging Face's Transformers library to create chatbots, but hearing actual use cases from businesses was enlightening. For instance, a startup shared how they used a fine-tuned model to improve customer service response times by 50%. Can you imagine the time and money saved? It got me thinking about how I could implement something similar in my own projects. Here’s a quick code snippet that I’ve found useful when fine-tuning a model for a chatbot: from transformers import Trainer , TrainingArguments training_args = TrainingArguments ( output_dir = " ./results " , evaluation_strategy = " epoch " , learning_rate = 2e-5 , per_device_tra

Last week a company nobody can name spent $500 million in a single month on Anthropic's Claude API. Not $500K. Not $5M. Half a billion dollars. In one month. Because nobody set a spending limit. Uber burned through its entire 2026 AI coding budget by April . Four months into the year, done. Microsoft quietly cancelled its internal Claude Code licenses and told engineers to go back to GitHub Copilot. All three stories broke within days of each other, and they all point to the same thing. Token-based billing, when given to an ungoverned team, is a financial weapon pointed at your own company. Every prompt, every context window, every agentic loop gets billed. An engineer running Claude Code seriously can rack up $500 to $2,000 a month just by doing their job well. The answer is not stricter policies. The answer is owning the infrastructure and making tokens free. This article breaks down exactly how to do that for a 100-person engineering team for under $1 million, with real 2026 hardware prices and honest tradeoffs. The Root Problem: You Are Renting the Meter When your team uses Claude Code or any external AI API, you do not own anything. You rent compute by the token. The model is not yours. The data leaves your building on every single request. The bill scales with how well your engineers actually use the tool. That last part is the trap. The better your engineers get at using AI, the more it costs you. Uber's Claude Code adoption jumped from 32% to 84% of their 5,000-person engineering org. That is a success story that turned into a budget crisis. Owning the infrastructure flips this completely. The better your engineers get at using AI, the more value you extract from hardware you already paid for. The Solution: Private On-Premise AI The setup is straightforward: Buy GPU server hardware once Download a state-of-the-art open-source model (free) Run an inference server that speaks the OpenAI API format Point Claude Code, Cursor, or any agent at your local endpoint

I'm a builder. I taught myself to run servers because freelancers kept burning my money. West London, 2021. I was standing on a site holding a cup of tea that had gone cold an hour earlier, watching a crew argue about where a wall should go. That's my actual job. Schedules, suppliers, the kind of problems that only exist at 7am when half the crew hasn't shown up and the client is already phoning. But my head was somewhere else. I'd been chewing on an idea for a classifieds platform for months. Not a grand vision, nothing with a business plan and projections. Just a gap I could see — a way to connect buyers and sellers that felt easier and more global than what was out there. The problem was that I knew nothing about programming. And I mean nothing. I didn't know what a database was. I'd never written a line of code. My entire technical CV was "reasonably good at not breaking my own phone." So I did what most people in my position do. I tried to buy my way in. The expensive year I found a ready-made classifieds script online. Looked professional, had features, didn't cost the earth. The smart shortcut, I told myself. Then I hired a freelancer to customise it. Then another one, when the first disappeared mid-project. Then another, when the second delivered something that worked on a good day and fell over on a bad one. Here's the thing nobody warns you about hiring freelancers when you can't read code: you can't judge the work. You can't tell the difference between someone who wrote something clean and someone who duct-taped it together to last until the invoice clears. Both show you the same thing — a screen where the button does what the button's meant to do. So you pay, you say thanks, you move on. And three months later the button stops working and the freelancer's gone. Meanwhile the bots had found me. Within weeks of going live, automated scripts were hammering the contact form, then the registration page, then the login. "It's normal," a freelancer told me. "Ha

I have been doing URL redirect work for client sites for some time now. It’s one of those jobs that’s never quite urgent enough to automate, but tedious enough to dread, especially after a migration when you have hundreds of them. Recently tried it. Connected my AI agent with MCP to handle it. I told it to build a set of redirects and it did. No dashboard, no wrestling with CSVs, no clicking through settings. Teaching in plain language. In seconds. And what I was surprised by was not the speed, but the amount of mental overhead such a task involves. You’re not just doing the task you’re context switching into a tool, remembering where things are, making sure nothing breaks. Giving it to an agent removes all of it. What really made me trust it for real client work was the dry-run feature. See exactly what is changing, before it changes. No surprises here. Curious if anyone else has been using MCP for infrastructure tasks, redirects, DNS, workspace management. I think we are at the start of something that is going to quietly gobble up a lot of tedious technical work. submitted by /u/Scary_Bag1157 [link] [留言]

I have an ECCV paper main conf. Can I submit the same to a workshop at some other place happening before ECCV? The other workshop (non archival) will be after the final decisions of eccv come. Under any result in eccv- acceptance or rejection, how will this affect it? Im not the main author at eccv. Workshop is women event and I submitted the abstract for the workshop. How do things work here, educate me pls. submitted by /u/Active-Tip3130 [link] [留言]

Ive subscribed and operated with the notion of build, fail, grow, and it has always been a humbling process, but recently I have been hearing about a “new” feeling of failure. "I tried my best and it didn't work." -> Move on "I had this super intelligent tool and STILL failed."-> Rinse and repeat Its like AI accelerates idea failure and because it is embedded in a hyper rinse & repeat, the feeling of failure is amplified. Is anyone else feeling or seeing this? submitted by /u/Outrageous-Pop-2853 [link] [留言]

I want to develop an LLM that can solve open-ended math problems (such as proof-only problems). This means that RLVR where we use the final answer alone as reward signal is not enough. Since SFT is useless here and GRPO/PPO methods will not have an appropriate reward function, what kind of fine-tuning can I do? For data, I will use the MathNet dataset. submitted by /u/TechNerd10191 [link] [留言]

has many of you know Their are a growing number of CEOs who are looking too replace human workers. We need too start Boycotting companies who replace Human workers with ai. People start calling your elected officials and demand they support legislation restricting Ai and how companies can use it. submitted by /u/thegreatdouchebag69 [link] [留言]