标签：#AR

🚀 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]

Sigma’s new entry is both a bold design experiment and a pretty decent camera.

These 5- to 6-inch blades are the secret workhorses of the kitchen, whether you’re a smaller person, have smaller hands, or just desire a smaller but still high-functioning knife.

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

Lauf’s sleek new entry feels closer to a traditional mountain bike than anything I’ve ridden before.

After 16 weeks of daily use, our WIRED testers saw visible hair regrowth with these red-light therapy devices.

Data analysis is at the heart of how we spot patterns and improve systems today. Tools like Python, SQL, Power BI, and Tableau are everywhere in the data world, but Excel has held its ground as the starting point for anyone getting into data work, and there is a reason for that. What is Excel? Excel is a spreadsheet built on a grid of rows and columns. You use it to organize, format, and calculate data. For analysts it is where messy raw data gets sorted out, numbers get worked through, and everything gets turned into something that actually makes sense to look at. Ways Excel is Used in Real-World Data Analysis 1. Data Cleaning Raw data is almost never clean. Names are misspelled, IDs get duplicated, spacing is off, values go missing. None of that is unusual, it is just the reality of working with real data. Before any analysis happens the data has to be honest, because if the data is wrong the results will be too. Functions like PROPER() and TRIM() are some of the basic tools that help get data into a state where you can actually work with it. 2. Financial Reporting Every business, big or small, needs to know where the money is going. Excel makes that straightforward. SUM() adds up a range of numbers, AVERAGE() finds the mean, and once the calculations are done the data can be turned into charts and dashboards that tell the story of the business clearly. Not everyone in the room is an analyst, but everyone can read a chart. 3. Business Decision Making Clean data presented well becomes a decision making tool. What do customers want? What is working? What needs to change? Sorting figures from highest to lowest or filtering by region can take thousands of rows and turn them into something focused and answerable. That is really what data is for, helping people make better calls. Excel Features I Have Learned and How They Apply Three features that have stood out to me are conditional formatting, data validation, and cell referencing. Conditional formatting highlights ce

The annual developer event is where Apple announces operating system updates—like iOS 27—and maybe even teases future hardware. Here’s how to see it all.

Based on real software development trends. About a VP of Engineering who believed AI would verify its own output, 47 TODOs that shipped to production, and a $2,800,000 discount calculation error that nobody caught. This story is based on a submission from a community member. If you have a similar story or something you need to get off your chest — reach out. The next one could be yours. Act 1 · The Tech Meeting "Starting today — no more hand-written code." Marcus, the new VP of Engineering, put a slide up on the big screen. Four words: WRITING BY HAND IS OVER. I was sitting in the back row, against the wall. Seven years at this company. Three core modules that I'd built from scratch. Two production systems that ran the company's primary revenue stream. Now someone was telling me — don't write anymore. The room went quiet for about five seconds. Then people started whispering. Someone pulled out a phone and took a picture of the slide. Marcus added: "AI coding isn't optional — it's a mandatory development standard. We benchmarked this. AI writes code 400% faster than humans. Anyone still typing manually is wasting the company's time." I raised my hand. "Who reviews the code?" "AI reviews it." "Who writes the tests?" "AI tests itself." "What if AI writes something wrong?" Marcus laughed. Not a polite laugh. The kind of laugh you give someone whose question you've already decided doesn't matter. "Let me ask you something." He paused. "Do you really think — you, one person — have more training data than Orion-7? " People started laughing. Not supportive laughter. Pile-on laughter. "Or do you think the world's AI companies — hundreds of billions in investment, tens of thousands of GPUs — built something that's less reliable than one backend developer?" Nobody was looking at me anymore. Everyone was watching him, waiting for the kill shot. He didn't take it. He just smiled. "Starting next sprint, it's AI across the board. Anyone who has concerns — my door's open." Act 2 ·

For two years, our Sponsored Placements service booked limited ad inventory through Redis: a counter in Redis, a Redlock around the decrement, and a TTL key per hold. It oversold. Not catastrophically — consistently. 40–60 double-booked placements a month , each one a manual refund and an apology email to an advertiser. The root cause was never one bug. It was the architecture: two sources of truth that could not be made atomic with each other. The count lived in Redis; the ownership lived in SQL. No transaction spans both. The Redlock only ever protected the Redis half. The one mental shift SKIP LOCKED turns a contended table into a concurrent work queue. Instead of every request fighting over one counter, each request grabs different rows and ignores the ones someone else is holding. FOR UPDATE alone serializes — that's the experience that scares people off SQL locking. FOR UPDATE SKIP LOCKED is the opposite: a transaction that would have blocked instead skips the locked row and takes the next free one. One row per reservable unit, then: START TRANSACTION ; SELECT id FROM inventory_unit WHERE placement_id = 42 AND ( status = 'available' OR ( status = 'held' AND hold_expires_at < NOW ( 3 ))) -- self-healing expiry ORDER BY id LIMIT 2 FOR UPDATE SKIP LOCKED ; -- the whole trick UPDATE inventory_unit SET status = 'held' , reservation_id = 'uuid' , hold_expires_at = NOW ( 3 ) + INTERVAL 10 MINUTE WHERE id IN ( 1107 , 1108 ); INSERT INTO reservation (...) VALUES (...); COMMIT ; Two concurrent requests for the same pool lock different rows. Neither waits. The claim, the hold, and the reservation are one transaction — there is nothing to reconcile because there is nothing else. The numbers (8 weeks before vs 8 weeks after) Metric Redis + Redlock MySQL SKIP LOCKED Oversells / month 40–60 0 Reservation p95 210 ms 34 ms Reservation p99 540 ms 61 ms Throughput / instance ~600 RPS 1,400 RPS Lock-wait timeouts / day ~900 <5 Nightly reconciliation 9–14 min deleted Redis cluster

A lot of teams start their LLM stack with one model string in application code. That is fine for prototypes. It becomes painful once multiple products, customers, background jobs, and fallback paths all share the same AI budget. At that point, an OpenAI-compatible gateway should not just be a convenience proxy. It should become a control plane: the place where routing, quotas, cost attribution, keys, and failover are managed consistently. Here is the checklist I use when evaluating whether a gateway setup is production-ready. 1. Keep the SDK surface stable Your application should not need to know every provider-specific header, endpoint, or auth detail. A simple OpenAI-compatible client shape keeps provider changes out of the main code path: from openai import OpenAI import os client = OpenAI ( api_key = os . environ [ " AI_GATEWAY_API_KEY " ], base_url = os . environ . get ( " AI_GATEWAY_BASE_URL " ), ) The app should usually call a logical model or route. Provider-specific decisions should live in gateway configuration where they can be reviewed and changed safely. 2. Route by feature, not by vibes A global default model is easy to start with, but it hides important differences between workloads. A better routing table looks like this: Feature Default tier Fallback tier Budget sensitivity Classification low-cost fast model second low-cost model high Support summary low/mid model mid model high Customer chat mid/frontier model safe fallback medium Coding/analysis strongest reliable model reasoning model low/medium Background enrichment batch/cheap model skip/defer very high The goal is not always to use the cheapest model. The goal is to use the cheapest model that reliably clears the quality bar for that feature. 3. Enforce limits at the gateway boundary Do not rely only on scattered application code for cost control. A shared gateway should enforce: per-API-key quotas per-project or per-customer spend caps per-feature token limits provider and model allow-lists e

I read and collected Arxiv whitepapers starting after the launch of ChatGPT. I copied and pasted excerpts into Word to track them. Then migrated to Obsidian. That vault of some 1700 papers is now online. I figured it was time to see if others would find the collection useful. My whitepapers were organized into some 90 categories, all of which emerged from paper topics. New categories became necessary with the discussion of new methods, techniques, models etc. If I wanted to write about a topic, I'd upload an md file containing research excerpts on that topic to ChatGPT. This worked to a degree but maxxed out context pretty quickly. And I always had related research in multiple categories, according to how the research was framed. (Personas research in Aligment, Psychology, HCI, etc). So I used a plugin to create topic notes that built in and outbound wikilinks across the papers centered on shared concepts. When I ported this all online I added another layer of synthesis: Inquiring Lines as I call them. These cover cross-cutting, tension-surfacing, synthesizing, and frontier-opening research frames. There's 6,000 of them in my collection. Each is a page to itself that's a useful description of a research line of inquiry. These now also have prompts you can run yourself that will find related (and more recent) research - (I can't adequately maintain each topic with new research). It's all at https://inquiringlines.com/inquiring-lines/ if you want to poke around. As is everything in the age of AI, it's a work in progress. But there's a lot of rich material in there. Have a look. submitted by /u/Barton5877 [link] [留言]

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]

For the past year I worked on a remote contract with a US tech company. Paid in USD, ultimately needing Korean won. Simple, right? Then a year in, I actually reconciled what landed in my account. The exchange rate had gone up — and yet my real received amount was lower than I'd expected. I traced it, and money was leaking at every step of the transfer path I hadn't been watching. This is what I learned switching routes over that year: from a direct bank wire to Wise, the real cost difference, and one right buried in my contract. If you're a freelancer or contractor in any country earning USD from abroad, this should save you something. Money leaks in more than one place Getting USD from overseas into local currency looks like one step. It's actually at least four: The wire fee from the US bank, through correspondent banks, to the receiving bank. The exchange rate the receiving bank applies — this is the big one. The receiving fee on the destination side. A hidden "lifting charge" some correspondent banks skim. The largest is the rate. Banks quote two rates, and the "buyer rate" applied when an individual sells dollars is worse than the mid-market reference — typically a 1.5–2% spread . On $1,000, that's $15–20 gone to the rate alone. That number looks small. Accumulated over a year, it stops looking small. Route A — receiving directly through a major US bank My first setup was the simplest: the company wired USD to my US bank account, and I wired it on to my Korean bank. I picked this at contract start without much thought, assuming the client would conventionally cover fees anyway. (Lesson one: specify the transfer method, route, and who pays in the contract. ) The problem was the bank's exchange rate. It applied the buyer rate straight up, with a wider-than-usual spread versus mid-market — plus a send fee, plus the Korean receiving bank's fee. I only noticed months in. Comparing statements, there was a steady 2–3% gap between the won I'd expect at mid-market and t

Imagine you are building a cloud-native backend for a high-frequency trading platform or a core banking ledger. To ensure mathematical immutability and prevent silent data tampering, compliance mandates that every transaction for a specific financial account must be cryptographically chained. This means the signature of Transaction #50 must explicitly include the cryptographic hash of Transaction #49. You cannot sign them out of order, and the backend is strictly responsible for generating and validating this chain. This introduces a massive distributed systems headache: How do you enforce strict, sequential ordering while maintaining the concurrency required to scale a modern cloud architecture? Let's walk through the evolution of this system, deconstruct exactly how the standard event-driven approach fails in production, and examine the Staff-level architecture required to fix it. Phase 1: The MVP & The Database Bottleneck In the early days, traffic is low. An account might see one transaction every few minutes. The "Happy Path" is simple: The API receives a deposit request for Account A. The API queries Postgres for the last_signature_hash . The API computes the new hash in memory: SHA(last_hash + new_transaction_data) . The API writes the new transaction and updates the state. The Pitfall: The Thundering Herd To prevent two concurrent requests from reading the same previous hash, you wrap the database operation in a pessimistic lock: SELECT ... FOR UPDATE . This forces the database to serialize requests at the row level. When a massive partner bank initiates a bulk sync, dumping 5,000 transactions for a single corporate account onto the API in two seconds, 4,999 concurrent threads immediately hit the FOR UPDATE lock and block. The database connection pool is instantly exhausted, latency spikes platform-wide, and the MVP dies. The Insight: The database must be your last line of defense, not your primary queueing mechanism. Contention must be solved upstream in me

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]

Nobody wants to hear this. But the reason your software is hard to change, hard to test, and hard to explain to a new engineer isn't your tech stack. It's that your code doesn't reflect how your business actually works. Your engineers are using one word — "customer," "order," "student," "subscriber" — and meaning six different things depending on which part of the system they're touching. Your domain expert says "order" and means something completely different from what your database schema says "order" is. That gap? That's where complexity lives. That's where bugs are born. That's where senior engineers spend their Fridays. Domain-Driven Design is the discipline of closing that gap. Here's what it actually means, practically, without the academic noise. The Core Problem: One Model Trying to Mean Everything Imagine a map that tried to show subway routes, underwater hazards, hiking trails, and flight paths — all at once. It would be useless. A subway map works because it only shows what matters for navigating trains. A nautical chart works because it only shows what matters for sailing. Each map is an abstraction built for a specific purpose, valid within a specific context. Your software models need to work the same way. The moment you build a single "Customer" class that has to satisfy your billing team, your marketing team, your support team, and your logistics team simultaneously — that class becomes a bloated, ambiguous disaster. Everyone adds their fields. Nobody removes anything. The model stops meaning anything specific to anyone. This is the monolithic model trap. And most large codebases are sitting right inside it. Strategic Design: Understand the Problem Before You Touch Code DDD separates design into two layers. Strategic design comes first — it's the work you do before writing a single line of code. Step 1: Find Your Subdomains A subdomain is a slice of the business problem. Ordering. Shipping. Notifications. Payments. Inventory. These aren't your micro