标签：#AR

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

Over the past few months, I've been working on a high-scale scraping pipeline to aggregate listings directly from company job boards and applicant tracking systems. Mapping over 100,000 distinct companies to their career pages turned out to be a massive engineering headache, but it's finally stable. The result is a unified database of more than 2 million active job postings, which I'm opening up to everyone for free. I am running daily delta refreshes to keep it current. Dataset Overview Scale: 2M+ active job listings across 100,000+ unique companies. Format: Parquet. (To keep storage costs to minimum) Core Fields: job_title, company_name, company_website, job_description, location, post_date, and the original tracking URL. For more detailed info check here . Update Cadence: Refreshed daily straight from the source. View the stats here . (Currently it contains only minimal stats, but I plan on improving it based on the comments) Why I Built This Finding a clean, scaled, and up-to-date job dataset is surprisingly difficult. Most available options are either heavily gatekept by expensive subscription APIs or restricted to a single job board like LinkedIn. By scraping the actual employer sites directly, this collection sidesteps the noise and captures a much cleaner cross-section of the live market. How to Access It I set up a dedicated project space where you can grab the data directly: Open Job data Let me know what kind of analysis or projects you end up running with it. If you have questions about the engineering architecture behind handling this scale, or ideas for specific fields you'd like to see enriched next, let's discuss in the comments. submitted by /u/Invicto_50 [link] [留言]

Why C Is Still the GOAT (And Why People Like Me End Up Hating C++) Disclaimer: This is a personal opinion from a developer who enjoys simple tools, simple languages, and simple debugging sessions. The Eternal Question Every few months, somebody asks: "Why are you still writing C in 2026?" My answer is always the same: Because C knows exactly what it is. No hidden magic. No surprise abstractions. No template metaprogramming black holes. No compiler errors longer than my source code. Just you, a compiler, and a Segmentation Fault waiting patiently around the corner. C Is Honest One thing I love about C is that it never pretends to protect me. If I allocate 8 bytes and write 16 bytes? Boom. If I dereference a bad pointer? Boom. If I forget to free memory? Boom (eventually). But here's the important part: I know exactly why I got cooked. The language didn't hide anything from me. C basically says: "Here's the loaded foot-gun. Try not to shoot yourself." And honestly? I respect that. Meanwhile in C++ C++ often feels like this: template < typename T > concept SomethingComplicated = requires ( T t ) { // 300 lines of magic }; Then the compiler responds with: error: instantiation of recursive template ... required from ... required from ... required from ... required from ... ...followed by 14 pages of diagnostics. At this point, I'm no longer debugging my code. I'm debugging the language itself. My Personal Villain Origin Story I started using Visual C++. You know. The gigantic Microsoft ecosystem. The IDE. The project files. The build settings. The mysterious compiler flags. Eventually I found myself spending more time fighting tools than writing software. And somewhere along the way, I started associating that frustration with C++ itself. Fair or unfair? Probably unfair. But emotions aren't always rational. Modern C++ Feels Like Three Languages Wearing a Trench Coat Old-school C++? Pretty understandable. Modern C++? Sometimes it feels like: C Object-Oriented C++ Template

Most people use these technologies every day. Almost nobody knows they exist. Every time you open YouTube, browse Instagram, join a Zoom meeting, or play an online game, your router is quietly performing a series of networking tricks behind the scenes. Those tricks have names: NAT SNAT DNAT PAT Port Forwarding They sound intimidating. They're actually much simpler than they appear. Let's break them down using something familiar: your home Wi-Fi. The Problem the Internet Had to Solve Imagine a family of five living in one house. Everyone owns a device: Laptop Phone Smart TV Gaming Console Tablet Each device needs internet access. The problem? Your Internet Service Provider usually gives you only one public IP address . Something has to manage all those devices sharing a single internet connection. That's where NAT comes in. NAT: The Receptionist of Your Network NAT stands for Network Address Translation . Think of NAT as a receptionist in an office building. People inside the building have room numbers: Laptop = Room 101 Phone = Room 102 TV = Room 103 But when communicating with the outside world, everyone uses the building's main address. The receptionist keeps track of who sent what. Your router does exactly the same thing. What Happens When You Visit Google? Inside your home: Laptop 192.168.1.10 Your router: Public IP 49.x.x.x When you open Google: 192.168.1.10 ↓ Router ↓ 49.x.x.x ↓ Google Google never sees your private IP. It only sees your router's public IP. That's NAT in action. SNAT: Changing the Sender's Address SNAT stands for Source Network Address Translation . The keyword is: Source It changes the sender's address. Before leaving your network: Source: 192.168.1.10 After SNAT: Source: 49.x.x.x The router replaces your private IP with its public IP. Without SNAT, websites wouldn't know how to send responses back to you. Real-Life Example Imagine mailing a letter. Instead of writing your bedroom number as the return address, you write the house address. Tha

A homelab box that never sleeps runs 8,760 hours a year. So the spec that decides what it costs you is not the one on the box. It is the one nobody prints: how many watts it pulls sitting at the login prompt doing nothing. I kept hitting this while shopping for a Proxmox node, so I put the measured numbers in one place. More on that at the end. First, why the spec sheet lies to you. TDP is a thermal budget, not a power reading TDP is the heat the cooler has to handle at full tilt. It is a design target for the heatsink, not a measurement of what the chip draws, and it says almost nothing about idle. Your homelab box spends 95%+ of its life idle, so the number that runs up the meter is idle wall power, and that number is never on the product page. The arithmetic is unforgiving. One watt running continuously is 8.76 kWh a year. So the gap between a 7 W box and a 35 W box is not 28 watts, it is about 245 kWh a year, every year, for as long as the box is on. Plug in your own rate to get the dollars; the point is the gap compounds. Where TDP actively misleads you A few measured results from the dataset I'll link below, all from third-party wall-meter readings, not vendor claims: The new N100 wave is genuinely low. A Minisforum UN100C measures 5 to 7 W at idle. Beelink, GMKtec and Trigkey N100 boxes land in the 6 to 10 W range. For a Pi-hole, a few containers and some light VMs, this tier is hard to beat on running cost. AMD mini PCs idle far higher than their marketing suggests. A Minisforum UM790 Pro measures 25 to 45 W at idle. A Beelink SER6 Pro lands at 20 to 35 W. These are fast little machines, but if you picked one expecting "small box, small draw," the meter disagrees, and over a year that delta is real money. Newer and higher-TDP is not lower-idle. A Dell OptiPlex 7060 Micro idles just over 18 W on its 65 W-TDP desktop chip. The older 7070 with a six-core part sits around 13 W, and the low-power "T" SKUs lower still. The CPU's TDP class predicted idle better tha

I don't identify as a gamer. I occasionally participate in board games or an amateur football match,...

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submitted by /u/Mo_h [link] [留言]

At Twio we picked pg-boss for our job queue, ran into trouble when we went serverless, looked at Pub/Sub, and ended up on Google Cloud Tasks. This is what each queue got right, what it got wrong for our workload, and the rule we landed on for choosing between them. The workload Twio is an AI SaaS for loan brokers. The piece that needs a job queue is email processing: download an email, parse the body and attachments, OCR, classify with an LLM, write structured data, and index for RAG. One email with five attachments easily becomes 30+ background jobs. A batch upload becomes hundreds. Why pg-boss worked — until it didn't Our database was Postgres on Neon, so pg-boss was the obvious starting point. No extra infrastructure, and one feature we genuinely loved: transactional enqueue . Because jobs live in the same database as business data, you can create a job in the same transaction as the row that triggered it. No dual-write problem, no "DB succeeded but the queue API failed" inconsistency. It also gave us retries, delayed jobs, dead-letter queues, dedup keys, and full SQL visibility into stuck or failed jobs. For a Postgres-first app on always-on infra, it's an excellent tool. Then we moved heavy processing to Cloud Run, and the cracks showed up. pg-boss polls. Neon suspends. They want opposite things. pg-boss runs a query roughly every 1–2 seconds to look for the next job, plus maintenance queries. Neon autosuspends compute when nothing touches the database. If the queue is polling every second, Neon's idle timer never expires — you pay for always-on compute even when the queue is empty. Worse, when Neon did manage to suspend, the next poll had to wake it. That wake-up takes hundreds of ms to a few seconds, and queries that triggered it would fail with Connection terminated , ECONNRESET , or timeouts. Pooled connections made it worse: the pool kept sockets that the server had already closed during suspend, and the next polling cycle picked one up and broke. This isn

Let me start with a confession: I'm a data scientist who's been burned by hype more times than I care to admit. When everyone told me "Model X is the next GPT-killer," I'd run my own benchmarks and find... well, let's just say the results were rarely as advertised. So when I started seeing claims about Chinese AI models catching up to (and sometimes surpassing) Western counterparts, I did what any self-respecting data nerd would do: I put them through my own rigorous testing pipeline. Over the past three months, I've run over 2,000 API calls across four major Chinese model families — DeepSeek, Qwen, Kimi, and GLM — using Global API's unified endpoint (more on that later). I tracked latency, token costs, output quality across multiple benchmarks, and even threw in some real-world tasks that mattered to me personally. Here's what I found, with all the numbers you'd expect from someone who still gets excited about statistical significance. The Testing Methodology (Because Anecdotes Aren't Data) Before we dive into results, let me be transparent about my approach. I ran each model on the following standardized tests: Code Generation : HumanEval (Python) and MBPP (multi-language) — 164 problems total Reasoning : GSM8K (math word problems) and MMLU-Pro (general knowledge) — 1,200 questions Chinese Language : CLUE benchmarks (text classification, NER, reading comprehension) — 3,500 samples English Language : LAMBADA and Hellaswag — 2,000 samples Speed : Average tokens per second over 100 consecutive requests with consistent prompt lengths I also tested vision tasks where applicable, but let's be real — Kimi doesn't support vision at all, and DeepSeek's implementation is... experimental at best. More on that later. All tests were conducted using the same global-apis.com/v1 endpoint, which normalizes API compatibility to OpenAI's format. This isn't an ad — I genuinely found it made my testing easier because I could swap models without rewriting code. The Big Picture: Pricing

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. -- Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. -- Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [留言]

Did you ever just want to see what ChatGPT, Gemini, Claude, etc., would say to your prompt at the same time?!? These guys figured it out. They have all the responses in their own column to the prompt you gave. Its freaking amazing. They offer a discounted rate through one vendor. If you want me to post it let me know. I don't want this post removed so I'm not putting it in this main post. Check it out on their actual site though. AIfiesta.ai I stumbled on this one and am really glad I did. This is not self promotion. I have nothing to do with this app except using it daily. submitted by /u/ActiveUpstairs3238 [link] [留言]