标签：#r

Private buyers are increasingly outbidding museums for fossils.

Someone has to bundle all the wires that go into rockets, missiles, and satellites.

Spotify founder Daniel Ek's body-scanning startup, Neko Health, is setting its sights on the United States after raising $700 million from a star-studded group of celebrities, entrepreneurs, and investment firms. It plans to open its first clinic in New York this year before expanding rapidly across the country. Neko operates private clinics offering full-body scans […]

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Companies’ increasing reliance on AI chatbots isn’t making the customer service experience smarter. It’s just making it more infuriating.

The video game industry is in turmoil. Microsoft and Sony are starting to pivot to their next consoles, but it's not looking great: Prices are soaring, Sony is killing the video game disc, and Microsoft is jettisoning studios ahead of the transition. What could entice people to pay? On the Xbox front, we genuinely can't […]

Various interesting things about sorting data: defaults, performance and determinism (or lack thereof) of paging ;) submitted by /u/BinaryIgor [link] [留言]

There's a specific kind of marketer who should be nervous right now. Not the strategist. Not the writer with a point of view. Not the creative director who can look at forty options and know, instantly, which one is alive and which thirty-nine are furniture. The one who should be nervous is the marketer whose entire job was being a slow version of a machine. You know this person. Maybe you've been this person — most of us have, at some point, in some job. The one whose week was resizing banners, rewording the same caption in six formats, pulling a report nobody reads, and calling a meeting to discuss the meeting. Their output was never brilliant, but it was there, and for twenty years, "there" was enough. Volume looked like value. Busy looked like good. AI just ended that arrangement. Quietly, without a memo. The excuse economy is closing For most of modern marketing, mediocrity had excellent cover. A bad campaign could hide behind timelines. A weak idea could hide behind budget. "We didn't have the resources" was the most useful sentence in the industry, and everyone accepted it, because everyone was using it. Now a two-person studio in Amman or Manila or Medellín can produce, in an afternoon, what used to require a floor of people and a quarter of runway. The drafts are instant. The variations are infinite. The production bottleneck — the thing entire careers were built on managing — is basically gone. Which means the only thing left to judge is the thing that was always the actual point: is the idea any good? That question used to arrive at the end of a long process, softened by exhaustion and sunk cost. Now it arrives immediately, naked, on day one. There's nowhere for a bad idea to hide anymore, because there's no longer a six-week production schedule standing in front of it. What the machine actually can't do Here's what gets lost in the panic. AI can generate. It cannot choose. It can write you a hundred taglines. It cannot tell you which one will make a foun

Getting Comfortable with HTML Attributes When I first started learning HTML, attributes felt like tiny details hiding inside the tags. I understood the basic structure of a webpage, but I didn’t fully understand why some elements had extra words like href, src, or alt. Over time, I realized attributes are what make HTML elements useful. They add meaning, behavior, and context. Without attributes, a webpage would still have structure, but it would feel limited and incomplete. What HTML attributes really do An HTML attribute gives extra information about an HTML element. It is written inside the opening tag and usually has a name and a value. In simple words, the tag creates the element, and the attribute explains something about that element. For example: Here, href tells the browser where the link should go. Why attributes matter Attributes may look small, but they make a big difference in how a webpage works. They can: Connect one page to another using links. Display images, videos, and other media. Improve accessibility for users and screen readers. Help CSS and JavaScript identify elements. Control forms, buttons, and user input. Without attributes, HTML would only show content. Attributes help that content become interactive and meaningful. Some attributes I use all the time href for links The href attribute is used with anchor tags. It tells the browser the destination of the link. src for images The src attribute gives the path to an image, video, or audio file. alt for accessibility The alt attribute describes an image. It is helpful when the image does not load and also important for screen readers. id and class for styling id gives a unique name to an element, while class is used when multiple elements share the same styling or behavior. placeholder and required in forms These attributes make forms easier for users to understand and complete. A few habits that helped me Use lowercase attribute names. It keeps the code cleaner and easier to read. Put attribu

Cartography is all about taking the real world and turning it into a picture that people can understand. It’s the process of deciding: what places to show, what details to keep or remove, what colors and symbols to use, how to draw the round Earth on a flat screen or paper Cartography mixes geography (knowing where things are), design (making the map clear and beautiful), and math (flattening the Earth using projections). Every map you see—Google Maps, airport maps, weather maps, D3.js visualizations—is a result of cartography. Line simplification alogorithms are tools used in cartography to reduce the number of points in a geographic shape while keeping the shape recognizable. 🌍 Why do we need line simplification? Real geographic shapes—coastlines, borders, rivers, airport boundaries—are extremely detailed. If you zoom in enough, you can always find more bumps, curves, and tiny wiggles. This is what Lewis Fry Richardson discovered: The more precisely you measure a coastline, the longer it becomes.Because coastlines have infinite detail.But your computer screen does not have infinite detail. It has pixels. If you try to draw a super-detailed coastline - the file becomes huge > the map loads slowly > D3.js rendering becomes slow > zooming becomes laggy > the map looks messy when zoomed out. This is why we need line simplification algorithms. 🎯 What do line simplification algorithms do? They remove unnecessary points from a shape while keeping the overall form. Think of it like: drawing a coastline with fewer squiggles. smoothing a jagged boundary reducing a 10,000‑point shape to 1,000 points. making the map faster and cleaner. The goal is: Keep the important shape, remove the tiny details. 🧩 Why this matters for zoomable maps Zoomable maps (like D3 zoom or Leaflet zoom) need multiple resolutions: When zoomed out → simple shapes When zoomed in → detailed shapes If you use only high‑resolution data: the map becomes slow, too many points are drawn, the user sees clutter

DeepSeek vs Qwen vs Kimi vs GLM: Which One Wins My Freelance Budget? Last Tuesday I spent two hours building a client dashboard that needed AI-powered text summarization. The client is a small e-commerce shop, they get maybe 500 product descriptions a week that need condensing into bullet points. Sounds simple, right? Except when I ran the numbers on my usual OpenAI setup, the bill was going to eat into my margin harder than I'd like. That's when I went down the rabbit hole of Chinese AI models. DeepSeek, Qwen, Kimi, GLM — I've been hearing about these for months from other devs in Discord, but I never actually committed to testing them because, honestly, who has the time? Well, apparently I do, because that Tuesday I decided to run all four head-to-head against my actual workload. Here's what happened. Why I Even Bothered (The Real Math) Before we get into the benchmarks and pricing tables, let me put this in perspective. My hourly rate as a freelance dev sits at $85. Every hour I spend wrestling with a subpar API that hallucinates or charges too much is an hour I'm not billing a client. The "free" model is never free — either it costs me time or it costs me money, and usually both. I was paying roughly $0.60 per 1M output tokens on GPT-4o for the summarization work. For 500 product descriptions, each averaging maybe 150 tokens output, that's about $0.045 per batch. Sounds tiny, right? But multiply that across multiple clients, and suddenly I'm watching $40-60 a month vanish into API costs that I can't really pass along without awkward pricing conversations. So I started shopping. And what I found genuinely surprised me. The Contenders at a Glance All four model families run through Global API's unified endpoint, which means I didn't have to maintain four different SDKs, four different auth setups, four different billing dashboards. Just swap the model name in the request and ship. For a one-person operation, that's huge. Here's the landscape I was working with: Di

Everyone talks about prompts memory RAG But production issues were actually loops false completion replay retries wrong tool non deterministic execution Here are the top 7. That's what led us to build Failproof AI not because we wanted another framework, but because we kept seeing the same reliability problems across every framework.

Connecting an AI agent to a tool is becoming easier. Letting that agent operate a real business system responsibly is still a different problem. Imagine an existing commerce system with APIs for reading orders, changing inventory, creating refunds, and disabling staff accounts. OpenAPI can describe the endpoints. A tool protocol can make them discoverable. An agent framework can select an operation and generate arguments. But those pieces do not, by themselves, answer several business questions: Which operations may be exposed to an agent-facing surface? Which invocation must carry a trusted acting subject? Which operation is high consequence? When does an invocation express approval intent? Which calls need stronger audit handling? Which execution properties should a runtime know before it invokes the API? These questions sit between tool connectivity and final business authorization. That is the layer the Agent Capability Contract, or ACC, is designed to describe. Start with a concrete operation Consider this API operation: paths : /orders/{order_id}/refund : post : operationId : createRefund parameters : - in : path name : order_id required : true schema : type : string requestBody : required : true content : application/json : schema : type : object required : [ amount ] properties : amount : type : number minimum : 0 This is enough to describe how to call the operation. It is not enough to describe how an agent-facing system should treat it. ACC adds a small, machine-readable declaration next to the operation: x-agent-capability : version : 1 enabled : true scope : refund.create risk : level : high subject : required : true approval : required : true when : - param : amount op : " >" value : 1000 audit : sensitive : true execution : readonly : false idempotent : true timeout_ms : 10000 The declaration does not grant the refund. It tells a compatible runtime how the operation should be presented and governed before the business system receives the call. The miss