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AI 资讯

The proxy industry needs you to never open the network tab

I run 75 scrapers in production. Three of them do any fingerprint spoofing. Maybe five use residential proxies. The rest run on plain datacenter IPs or no proxy at all, and they have been running for months. If you learned scraping from blog posts, that number probably sounds wrong to you. Every tutorial you have read starts the same way: sign up for a residential pool, install a stealth browser, randomize your fingerprint, throttle like a human. Then, on step five, you finally get to look at the actual website. That order is backwards, and it is backwards on purpose. Proxy companies write most of the scraping content on the internet. They were never going to write "you probably do not need us." The scraper with the $80 a month costume Last month my guy sent me his Greenhouse job board scraper to fix. It had everything. Puppeteer with the stealth plugin. Rotating residential proxies. Randomized mouse movements between actions. Human-like typing delays. It still kept dying. So I did the thing nobody had done: opened the page in a normal browser with devtools up. The entire job list was sitting in one XHR request to a public JSON endpoint. No auth. No cookies. A rate limit so loose I never managed to hit it. I deleted basically his entire codebase and replaced it with a fetch call. It has not broken since. He had been paying for proxies for months to hit an endpoint that does not care who you are. This was not a rare lucky case. This is most cases. The 20 minute method What I do on every new target, before writing a single line of code: Open the network tab, filter to XHR/fetch. Reload the page. Click around. Paginate. Search. Find the request that returns the actual data. It is usually JSON and usually obvious. Right click, copy as cURL. Paste it in a terminal and start deleting headers one at a time. Rerun after each delete. Whatever survives step five is your scraper. Most of the time the answer is a user agent header and nothing else. Sometimes a referer. Occasion

2026-08-05 原文 →
AI 资讯

I built skill.md file to stop AI from Generic UI SLOP

Here's the problem. Every AI coding agent (Cursor, Codex, Claude Code, whatever) is trained on millions of websites. Most of those websites are average. So when you prompt "build me a landing page," the model gives you the average of everything it's seen: a centered hero, a purple gradient, three equal feature cards, Inter font, ease-in-out , done. It's not broken. It's just mediocre by default. I'm 17 and I got tired of fighting this in every conversation. So I built VibeCurb : a collection of strict constraint skill files, that force AI agents to actually think about design before they touch code. How it works Every skill follows the same four-phase pipeline: Design Read - The agent reads your reference image, existing codebase, or brief and extracts design signals: typography, palette, layout, focal element, spacing. No code is written here. Quality Gate - The extraction has to pass before the agent is allowed to generate anything. It must prove it understands the design direction, not just spit out defaults. Precise Build - Code generation happens against the extraction, not against the model's built-in idea of what a "website" looks like. Each skill has its own build sequence. Visual Diff - The output is checked against the reference using PASS/FAIL tables across composition, typography, color, motion, and responsiveness. If it drifts, it gets caught. There's also an inline drift rejection layer. It catches known AI defaults (CSS keyword easings like ease-in-out , AI-purple #7c3aed gradients, generic glassmorphic cards, placeholder Lorem ipsum content) and flags them before they make it into the output. The skills Each skill constrains a specific problem space: awwwards-hero - Hero sections only. Six documented architectures (Cinematic Center, Editorial Split, etc.) with implementation blueprints. The agent picks one and commits. awwwards-sections - Pricing tables, bento grids, feature highlights, footers. Same pipeline, different element constraints. awwwards-

2026-08-05 原文 →
开源项目

🔥 didilili / ai-agents-from-zero - 🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对

GitHub热门项目 | 🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化 | Stars: 3,439 | 43 stars today | 语言: Python

2026-08-05 原文 →
AI 资讯

How Much Does It Cost to Self-Host Open Models on AWS?

Your AI bill tripled last quarter. Your CTO forwarded you an article about companies saving 70% by switching to open models. Now someone is asking you to figure out what that would actually look like. I spent the last few weeks digging into this. The numbers, the hardware, the real trade-offs. Here's what I found, with enough specifics that you can actually make a decision rather than just nodding along to another "open source is the future" think piece. What "Open Models" Actually Means When someone says "open model" they mean an AI model where the weights (the learned parameters that make the model work) are publicly downloadable. You grab the file, run it on your hardware, and you don't pay anyone per request. The big names right now: Meta's Llama 4, DeepSeek V4, Zhipu's GLM-5.2, Moonshot's Kimi K3, Alibaba's Qwen 3.5, and Google's Gemma 4. These aren't toys. Some of them genuinely compete with the frontier models on real benchmarks. Chinese open models now handle over 30% of enterprise traffic on OpenRouter, up from 4.5% in early 2025. That's a massive shift in barely a year. The Architecture: What You Actually Need You want your team to use an open model. Here's the stack, from bottom to top. Hardware (The Expensive Part) A model is a giant file. We're talking anywhere from 4 GB (a small 7B model, quantized) to 1.5 TB (Kimi K3, full weights). That entire file needs to sit in GPU memory to run fast. Why GPU memory specifically? Because generating each word in a response requires billions of multiply-and-add operations. GPUs do thousands of these in parallel. A CPU does them one at a time. The practical difference: a 7B model on a CPU generates 2-5 tokens per second (painfully slow for interactive use). The same model on a GPU generates 30-80 tokens per second (feels instant). For one person on a CPU, it might be tolerable. For a team of 10 all hitting the same endpoint? Unusable. Requests queue up and everyone waits 30-60 seconds for responses. Think of it like

2026-08-05 原文 →
AI 资讯

One Rails request, one event: production context for coding agents

Wide Events is a Rails gem that puts the production context a coding agent needs onto one OpenTelemetry root span per request or job. In one production search request, the root event showed 30.0 seconds total duration, 446 ms of Postgres time, and 29.4 seconds of outbound HTTP time. That was enough to focus the investigation on an external dependency. The trace then identified a POST that took 28.9 seconds. The trace contained 82 spans and 20,261 bytes of attribute JSON. The root event contained 40 attributes and 1,420 bytes. This is not a token benchmark, but it shows why the root event is a more compact starting point for an agent. Agents can read the code, but not the running system A coding agent starts with an unusual advantage: it can search every model, controller, job, migration, and test in a few seconds. It also starts with a serious blind spot. The repository cannot tell it: which account experienced the problem which build was running which feature-flag variant was active how many queries the request issued whether a semantic-search leg degraded how much an LLM call cost whether the same symptom appears in one tenant or every tenant Those answers often exist somewhere, but “somewhere” might mean a trace waterfall, application logs, a feature-flag service, product analytics, and a database console. Pulling all of that into a context window is expensive and usually requires several joins that were never designed in advance. A wide event changes the starting point. The app accumulates the context it learns while processing one unit of work, then attaches the completed flat map to the OpenTelemetry root span. The span is marked main=true , so every request or job can be queried as one row. request or job -> Rails and domain context accumulate -> child spans contribute dependency counts and timings -> one flat map is flushed onto the root span -> ClickHouse stores one queryable row The trace still exists. Wide Events gives it an application-shaped index. If y

2026-08-05 原文 →
AI 资讯

I Built a Server Agent Because Uptime Checks Tell You What Failed, Not Why

A status page has a blind spot. It can tell you that your API is returning 502s. It can tell you that a TCP port stopped accepting connections. It can tell you when the incident started. It usually cannot tell you why . Was the application host out of memory? Was disk I/O saturated? Did load climb for 40 minutes before users noticed? Was the server completely healthy and the real problem somewhere else? Those answers often live in a separate monitoring product, disconnected from the incident timeline and disconnected from the status page. That is why I built Servers for StatusPage.me. It is a small, customer-installed host metrics agent and dashboard. You install it on a machine you operate, and it reports CPU, memory, swap, load, disk, and network metrics back to your account. The important part is not “now there are more graphs.” The important part is seeing an outage and the host evidence around it on the same timeline. External checks answer one question. Host metrics answer another. Regular uptime monitoring is still the right tool for the outside-in view: Can users reach the website? Is the API returning the expected response? Does DNS resolve correctly? Is the database port open? Did a scheduled job run? But those checks do not run inside your infrastructure. A healthy HTTP response does not prove that a background worker is about to run out of memory. A timeout does not prove that the app server is overloaded. And an incident can start with a slow disk or growing swap usage long before an endpoint is fully unavailable. The distinction is simple: External monitoring tells you what users can see. Host metrics help explain what the machine was doing when they saw it. You need both. What Servers includes Each registered host gets a dedicated dashboard page with: CPU user, system, and I/O wait utilization Memory use Swap use Load averages Disk use and read/write throughput Network inbound and outbound throughput A human-readable OS description for account owners

2026-08-05 原文 →
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