标签：#AR

[While preparing for interviews, I realized I had a strange habit. I would solve a problem, get stuck, open the solution, understand it, and move on feeling productive. A few days later, I couldn’t solve a similar problem on my own. The issue wasn’t lack of practice. The issue was that I was consuming solutions faster than I was developing problem-solving skills. So I changed my approach. Instead of looking for answers, I started forcing myself to think longer, write down my ideas, identify where I was stuck, and only then seek guidance. That worked much better. But I couldn’t find a tool that supported this style of learning. Most platforms either: Give you the answer. Give you the editorial. Give you AI that writes the code for you. So I started building my own. The goal was simple: An AI coach that guides the thought process instead of generating the solution. Over time I added: DSA practice System Design preparation Low-Level Design preparation Company-wise interview questions Topic-wise strength and weakness analysis Personalized revision lists The interesting part wasn’t building it. The interesting part was realizing that interview preparation is less about collecting solutions and more about training how you think. What has helped you improve more during interview prep? Reading solutions? Or struggling with the problem first? Sde vault - https://sdevaultweb.onrender.com/

AI Does Not Cancel Reality I watched the conversation between Mo Gawdat and Marina Mogilko about the future of AI. The conversation is strong. It contains important ideas, but it also contains many claims that sound large in scale, although on closer inspection they rely on very broad generalizations. AI is indeed changing the labor market, education, startups, content, hiring, and ways of thinking. But it does not cancel money, connections, trust, the human vector, creativity, necessity, morality, or people’s ability to adapt. Video on YouTube AI in hiring: automation amplifies chaos Many people have entered the job market. Companies receive huge volumes of resumes. HR departments cannot handle the volume. It is natural that part of the selection process is moving to AI. But there is a serious problem here. Candidates are also starting to play against AI. Resumes are adjusted to vacancies. Cover letters are assembled around keywords. Profiles become optimized for the filter, not for real work. In such a system, the best specialist does not necessarily pass. Often, the person who understood the selection mechanism better passes. The result: the picture becomes cleaner, while the quality of the decision becomes lower. The company gets not the strongest candidate, but the candidate who matched the algorithm best. This leads to lower hiring quality, lower productivity, and slower development. “I built a startup in six weeks”: a product is not a startup The conversation includes the idea that an AI startup would once have taken years and hundreds of engineers, and now it can be built in weeks. Technically, this is true. Prototypes are now built faster. Small teams have powerful tools. One person can now do more than a group could do before. But two different things are mixed here. Building a product faster has become real. Building a startup faster has become real only when resources are present. A startup is not only code. A startup is money, connections, trust, reputa

Unpopular opinion: most AI tools don’t actually save time. They just move the work around. You still have to prompt it, check it, edit it, and sometimes redo it. That’s not automation — that’s just a different kind of work. The only ones I’ve seen genuinely cut time are search tools like Perplexity and coding tools like Cursor. Everything else feels like it’s optimized for the demo, not real use. Change my mind submitted by /u/aiprotivity_ [link] [留言]

Hi! I’ve been working in IT for over seven years now, and my office is next to some healthcare professionals. During a lunch break sitting on a bench in the sun, one of them asked me: If I enter my patients’ personal information into ChatGPT, is that a problem? I wasn’t sure how to answer him, in my opinion, yes, but what do you think? I’d be curious to hear your thoughts, and if there are any studies on the subject, I’d love to see them too! Thanks in advance for your responses! Have a great day, everyone ☀️ Alex submitted by /u/No_Computer_1247 [link] [留言]

I don’t know if this is the right sub-reddit to ask this type of question. I am quite ignorant about hardcore technical stuff. I want to say that I love the idea of an agnostic approach to AI and being able to understand and decide which model is best suited for a specific task. As well as the ability to have citations, being able to have it look through health research and stuff for queries regarding health, etc. Now I do not know if this is just in a general sense people just complaining or something else entirely, but I am seeing a lot of negative stuff on the Perplexity sub-reddit. In terms of like how the quality has gone down, asking how such a company is still even in business. I was just wondering if any of this holds any water or is overly exaggerated submitted by /u/No-Main6695 [link] [留言]

Cloudflare recently described how a slowdown in its billing pipeline was traced to contention inside the query planning stage of ClickHouse. The team profiled the bottleneck and patched ClickHouse to replace an exclusive lock with a shared lock, drop the per-query copy of the parts list, and improve part filtering. By Renato Losio

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

I've been building a content production tool for my company, which uses AI for things like structure and automatically inserting links with defined anchor text. 2 days ago, I started testing the results in AI text detection scanners and kept getting inconsistent results, even when I knew my articles looked more natural than a previous test. Revision after revision of code, 10 hours spent trying to get it right. And then I decided to pop in a few articles I had personally written, where I knew AI was not involved. Not a single one of the major scanners got it correct. Most of them flagged my original content as having more AI text than the articles my tool was producing. Now that I've gone down this rabbit hole and understand how AI writes and how the detectors work, I'm not sure that any tool is ever going to be able to do this correctly. For obviously written AI articles, sure, it will catch those. But for original content, I just don't see how it's ever going to work. What is everyone's thoughts on this? Has anyone done the same experiment? submitted by /u/Sypheix [link] [留言]

Hi all, Lately I have been working on creating a package for Multi Agent RL based drone environments with different objectives, all bundled into a single GitHub repository: tau-intelligence/MuJoCo-drones-gym. I am currently trying to organize things for RL community people, with a couple more tools coming soon. But right now, I want to make it useful for the community and hence would love some feedback from different people, about how I could improve it, incorporate more things into it or fix some broken implementation. Also everyone is welcome to raise issues on the repo. Thank you for the support. PS: I have some research publications at RL and ML venues regarding work on RL, though I still want to consider myself as a student of the field and hence would love your help here. submitted by /u/MT1699 [link] [留言]

TL;DR — The DIFP protocol was designed to be data-compact and geo-aware from day one. We recently discovered it maps almost perfectly onto the Nostr event format. Here's how, and why it matters for decentralized food infrastructure. Background: What Is DIFP? DIFP (Djowda Interconnected Food Protocol) is an open protocol designed to sync food product data across distributed nodes — compactly, efficiently, and with geo-location awareness built in by default. One of its core design decisions is the PAD system (Preloaded Asset Distribution): Apps ship with a preloaded asset pack — item metadata, compressed images, category structure — all bundled at install time. Only price and availability need to travel over the wire during sync. This means the data footprint per product is tiny. Very tiny. Enter Nostr Nostr is a simple, open protocol for decentralized communication. One of its key specs: events support up to 64 KB of content . When we started exploring Nostr as a potential transport layer, we ran the numbers — and the fit was surprisingly clean. The Math: Products Per Event Baseline encoding A product represented with three fields: { "id" : 500 , "available" : true , "price" : 30000 } At this level of verbosity, a single 64 KB Nostr event can hold approximately: ~1,500 – 2,000 products Already useful. But we can do better. Optimized encoding Two key optimizations: 1. Drop the availability key — If a product entry exists in the JSON, it's available. If it's absent, it's not. No boolean needed. 2. Drop the field names — Instead of {"id": 500, "price": 30000} , just store: 500,30000 Field mapping is handled at the app level, not the protocol level. The device knows position 0 is the product ID, position 1 is the price (in smallest currency unit, e.g. cents). Result ~6,000 – 7,000 products per single Nostr event Possibly more, depending on the price distribution and ID ranges in a given catalog. Geo-Discovery: MinMax99 Cells DIFP uses a geo-cell system called MinMax99 to

The first trillionaire in history won't make their money from banking, oil, or real estate. They'll make it from rockets and algorithms — and the implications of that distinction are genuinely unsettling. The Problem It's Solving (Or Creating) SpaceX is preparing for its IPO. Analysts tracking the raise estimate it will push Elon Musk's net worth past the trillion-dollar threshold, making him not just the richest person on Earth by a wide margin, but something qualitatively different from every billionaire before him. The standard framing treats this as a wealth story. It isn't. A billionaire is powerful because they have money. A trillionaire is powerful because, at that scale, they stop needing permission from anyone — governments, investors, boards, markets. The constraints that keep institutional power in check simply don't apply anymore. How Trillionaire-Scale Power Actually Works There's a clean way to understand the difference. A billionaire can fund political candidates, buy media, lobby aggressively. Another billionaire can fund the opposition. It's expensive, but the system has a counter. A trillionaire doesn't have a counter. They are the counter. They can simultaneously build the communications infrastructure (Starlink), the transportation layer (SpaceX), the compute stack (through xAI), and the political attention economy (via platform ownership). No single democratic institution was designed to regulate someone who owns the pipes that the institution runs on. Arnab Ray's piece in today's Times of India puts it directly: a trillionaire's thoughts and algorithms will shape planetary outcomes. That's not hyperbole. When Musk eventually lands people on Mars, the governance frameworks, the property rights, the social contracts of that colony — those will be engineered by him and his companies, not negotiated through any existing democratic process. What Societies Are Actually Unprepared For Most of the policy debate around billionaires focuses on tax rates

What Ollama actually is Ollama is an open-source runtime for large language models that runs on your own computer — Mac, Windows, or Linux. Think of it as the “Docker for LLMs”: instead of wrestling with Python environments, model weights, and GPU drivers, you type one command and a model is running. The pitch is simple: keep your data on your machine, pay nothing per token, and work offline. When you run ollama run gemma4, Ollama downloads the model, loads it into your GPU’s memory (or system RAM if you don’t have a GPU), and drops you into a chat prompt. That’s it. Behind that simplicity, Ollama is doing a lot of work for you: Model management — pulling, versioning, and storing models from its registry, the way a package manager handles software. Quantization — automatically using compressed (GGUF) versions of models so a 27-billion-parameter model fits in consumer memory. GPU layer allocation — deciding how much of the model lives on your GPU versus CPU, based on the VRAM you have. Context and KV-cache management — handling the memory that grows as a conversation gets longer. A REST API — exposing everything on http://localhost:11434 so your own apps can talk to it. How it works under the hood Ollama is not itself an inference engine. It’s an experience layer wrapped around one. Under the hood it uses llama.cpp, the C++ engine that does the actual math of running a quantized model efficiently on CPUs and GPUs. As of v0.19 (March 2026), Ollama also uses Apple’s MLX backend on Apple Silicon — a change that delivered enormous speedups (on an M5 Max running Qwen 3.5, decode throughput nearly doubled). The workflow looks like this: You run a command — ollama run qwen3 from the terminal, or a request to the API. Ollama resolves the model — if it isn’t already downloaded, it pulls the GGUF weights from the registry. It loads the model into memory — splitting layers between GPU and CPU based on available VRAM. It serves responses — either interactively in your terminal o

So I am trying to figure out what agent OS is. I am a layman and a lot of times when I see the information it comes off as very technical. However, I do like the idea of a dashboard because for my neurodivergent brain, it would be nice to have all of the AI tools in one space. Can you all help me understand what agent OS is? submitted by /u/EducatedBrotha [link] [留言]

https://reddit.com/link/1ty3xhz/video/dzede49lhk5h1/player Link to the replay. What are everyone’s thoughts on this? I know the benchmark has gotten a lot of criticism for being “too difficult” from a scoring perspective, but after watching the replay, it honestly looks like the models just aren’t that close to solving it yet. I’m not saying the benchmark is perfect, but the failures don’t really look like minor scoring issues. They look more like the model still doesn’t understand the task well enough to complete it reliably. submitted by /u/ClickedMoss5 [link] [留言]

Do you think there is a possibility of using sewage water to cool AI servers? submitted by /u/TippaMyClit [link] [留言]

submitted by /u/Godi22kam [link] [留言]

When debates about animal minds, conscious machines, and even fetal awareness spill into public life, the science behind those claims matters as much as the claims themselves. submitted by /u/Brighter-Side-News [link] [留言]

I do web design and my preferred way of getting clients is through cold email because it doesn’t cost money like paid ads, I don’t need to sit there dialing all day, and it allows me to scale my agency while keeping most of it automated. The main thing that helped me stand out in crowded inboxes was changing the way I do outreach. Instead of sending generic emails like “Hey I noticed your website is outdated, I can redesign it for you,” I do something different. I get leads with websites, run full website analysis at scale, and turn issues in design, layout, SEO, and mobile optimization into personalized outreach messages automatically. So instead of sending random spam, the email actually points out things that could be improved on their website without me even needing to manually check every site myself. This method has helped me book way more meetings and scale further than before because the emails actually stand out and feel relevant. I feel like this is a much smarter way to do outreach since it feels personalized while still being fully automated. For anyone wondering, no it’s not some custom built workflow. I use a tool called Swokei for it. I looked for this type of outreach system for a long time and it’s the only tool I found that combines website analysis and personalized outreach in one place. submitted by /u/Murky_Explanation_73 [link] [留言]

This story was shared by a fellow developer on DEV who asked to remain anonymous. If you've got a story to tell — come find me. Your name won't appear anywhere. Based on real microservice security design patterns. About an engineer whose PR got blocked by an AI security system — he thought he was fixing a vulnerability. Turns out, someone had a vested interest in that vulnerability staying open. 1. $1,400,000 All-hands meeting. CTO James stood at the front, a number on the screen: $1,400,000 "This is what we're spending on security this year." He pointed at the number. "The biggest piece — right here." He clicked the remote. VoidSentinel's architecture topology appeared on screen. "VoidSentinel — an AI security platform. Integrated into our CI/CD pipeline. Starting today, every PR involving internal service-to-service calls — it reviews them automatically." The CEO didn't show up today. James didn't mention it. He looked straight at Mark — VP of Security. Mark took the mic. "VoidSentinel has been running in our pre-production environment for three weeks. It's caught 47 high-risk patterns. Zero false positives." He paused. " — Of course, some people might feel uncomfortable when their PR gets blocked. But this isn't personal. This is the security standard. " He wasn't looking at me. But I knew who he was talking about. 2. High Risk. Denied. The story started three weeks earlier. We had a payment service and a user service that talked to each other internally. They shared an old API key — one key across thirty-plus services, unchanged for five years. It wasn't that nobody knew. It just never made it to the top of the backlog. On Day 1, I opened a PR: add independent service-to-service auth between the payment and user services. Not much code — a new token exchange module, three call sites modified. Five minutes later, VoidSentinel's automated comment hit: "High-risk alert: Unauthorized internal access pattern change detected. This PR has been automatically rejected. C

Every weekday, we averaged 10 groups. Every weekend, 15. Same karaoke bar. Same staff. Same songs. For a long time, I just accepted that gap as "normal." Weekends are busier. That's just how hospitality works, right? Wrong. It took me years to realize I wasn't looking at a staffing problem. I was looking at a funnel problem — and I had no idea what a funnel even was. The moment I noticed something was off One Tuesday afternoon, a group of four walked past the front door, looked at the menu board outside, and kept walking. I watched from the counter. I had open rooms. Competitive prices. Cold drinks. Everything they needed. But they left anyway. That one moment stuck with me. Why did they walk in? Why did they look? Why did they leave? I started tracking these moments obsessively. Not with software — just a notebook and a lot of attention. Here's what I found over six weeks: Weekdays : About 40 people walked past who paused at the sign. Of those, maybe 15 came to the door. Of those, 10 groups actually came in and paid. Weekends : About 90 people paused. 30 came to the door. 15 groups booked a room. The conversion rate was almost identical — roughly 25% from "stopped to look" to "became a customer." The difference wasn't that we were worse at converting on weekdays. We just had fewer people at the top. That's a funnel. I didn't know the term at the time. But what I was describing is exactly what marketers call a marketing funnel : Awareness — people notice you exist Interest — they stop to look Consideration — they walk to the door, check the price Action — they book a room and pay Most businesses obsess over the bottom of the funnel. Better sales scripts. Discount campaigns. Loyalty cards. I did the same. I ran Tuesday specials. I trained staff to upsell drinks. I rearranged the menu. None of it closed the gap. Because the gap wasn't at the bottom. It was at the top. On weekdays, I simply had fewer people aware we existed. What I tried instead Once I framed it as a f