AI 资讯

TL;DR: An API gateway manages HTTP traffic between services — auth, routing, rate limiting, load balancing for REST and gRPC. An AI gateway manages LLM workloads — token-based rate limiting, model routing, cost attribution, semantic caching, guardrails. Use an API gateway for your microservices. Use an AI gateway for your LLM traffic. Most production teams eventually need both, sitting at different layers. This post walks through exactly where each one fits. When we started adding LLM features to our platform, we already had Kong running for our microservices. The instinct was natural: route the LLM traffic through Kong too. Same auth, same rate limiting, same observability stack. One gateway to rule them all. It worked — for about six months, and only in the sense that requests got through. What it didn't give us was anything useful for actually managing AI workloads. We had no idea what each team was spending on tokens. We had no way to set a budget cap that would fire before the bill arrived. Our rate limits were based on requests per minute, which meant a single request with a 50k token prompt counted the same as one with a 200 token prompt. And when OpenAI had a partial outage, Kong had no concept of "try Anthropic instead" — we just served errors. None of that is a criticism of Kong. It's doing exactly what it was designed to do. The problem was us expecting an API gateway to handle a fundamentally different category of infrastructure problem. Here's the precise distinction, and why it matters architecturally. What an API gateway actually does An API gateway is a reverse proxy that sits between client applications and backend services. It handles the cross-cutting concerns of service-to-service HTTP communication: authentication, authorization, rate limiting, load balancing, SSL termination, request transformation, and routing based on URL paths or headers. A typical request flow through an API gateway: Client sends a request to the gateway endpoint Gateway ve

I couldn't find many resources online that showed how to see cache misses on macOS. I thought I'd learn a bit about Instruments, throw some toy problems at it, and write about the experience in a blog post. These toy examples - iterating sequentially then randomly, summing elements in a matrix, naive vs tiled matmul and iterating AoS vs SoA - are great to see some real numbers from L1 data cache misses, but they're far away from real programming problems. What's your experience using tools like Instruments/perf/cachegrind to actually optimize an algorithm? Is it ever needed to measure this at the hardware counter level or can you get away with intuition about the size of your data structure, how it's been accessed and using basic timers? submitted by /u/markuzo1 [link] [留言]

A new member has joined the fulgur family. fulgur-chart — a CLI that takes Chart.js v4-compatible JSON specs and renders deterministic SVG/PNG charts. No browser required. https://github.com/fulgur-rs/fulgur-chart Two things make it different: it doesn't spin up a browser, and for a fixed version, font, and rendering options, the same JSON input always produces byte-identical output. This post covers why I built it, a timing coincidence that made me feel like I was on the right track, and how to use it. Why I wanted graphs in PDFs fulgur and fulgur-chart are built around one idea: AI agents should be able to generate documents that look good . There are three steps to that argument. First, Markdown isn't expressive enough. For client-facing reports, plain Markdown often undersells otherwise strong content. Second, visual quality is persuasive. A well-formatted report lands differently than a wall of text. Third — and this is the one I keep coming back to — in many business workflows, PDF carries more institutional weight than a Markdown file or a transient web page . That authority has two dimensions. There's a cognitive one: PDFs read as "serious documents." Proposals, reports, invoices — the format itself signals credibility. And there's a technical one: PDF can support digital signatures, encryption, and archival profiles such as PDF/A. That's the ground flpdf covers, a pure-Rust PDF toolkit modeled on qpdf's workflow. So the goal is always PDF, not HTML, not a web page. That's what fulgur is for. And a polished report needs charts. But Markdown can't draw charts. Which brings me to a problem I already knew was coming: the Chart.js library requires JavaScript to run . fulgur has no browser and no JS runtime, so there was no path to running Chart.js directly. The design choice: no JS engine The obvious alternative was to embed a JavaScript runtime. I could either run Chart.js with a compatible Canvas implementation, or build a JavaScript renderer that consumes Cha

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit.

The Jeff Bezos-backed startup finally revealed more pricing details for its debut EV, including a $29,950 starting price for the SUV variant, and boosted the base range to 205 miles.

Your company's permission-aware, proactive and agentic brain Discussion | Link

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Introducing: the Engineering issue We can’t fix everything, but we can be ambitious. We can take on the challenge of making the world better through human ingenuity. That’s what the new…

Article URL: https://www.economist.com/europe/2026/06/18/europeans-should-learn-to-love-the-air-conditioner Comments URL: https://news.ycombinator.com/item?id=48658484 Points: 16 # Comments: 31

I spent some time reading Grafana Alloy’s source code to understand how it loads configuration, builds a dependency graph, evaluates components, and runs them. I wrote a short breakdown of what I learned, mainly focused on the runtime/controller, loader, scheduler, services, and component lifecycle. It is not a complete deep dive into the whole project, but more of a source-code reading note from exploring a real production Go codebase. Feedback is welcome, especially from people familiar with observability systems or large Go codebases. submitted by /u/am0123 [link] [留言]

The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles. Now, the payment company Stripe, founded by brothers Patrick and John Collison, says it will fund a new…

The biggest Switch 2 exclusives so far have largely been about scale. Mario Kart World introduced a wide open continent to race across, Donkey Kong Bananza let you smash basically everything around you, and Pokopia brought an expansive Minecraft-style creative experience to the Pokémon universe. Star Fox is different. A remake of a Nintendo 64 […]

With its new pickup, Slate Auto is making a simple bet: price matters more than almost anything else. The company announced today that the American-made electric truck will start at $24,950, placing it squarely in the mid-$20,000 price range it had originally promised and making it the least expensive pickup truck and EV available today. […]

We now know the price of Slate Auto's affordable American-made electric truck, almost a year after the company warned it wouldn't hit its initial "under $20,000" target price. The no-frills pickup starts at $24,950 - matching the revised mid-$20,000 price range it promised last year, after the Trump administration announced it was putting an end […]

The US government is reportedly asking Meta to share its AI models for review, in the midst of growing security and safety concerns over the technology.

AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models. To understand this challenge, consider the foundation of the web itself. The web was not designed…

Naomi Saphra discusses 5 rules governing language model behavior, breaking down why LLMs act like populations rather than individuals. She explains how tokenization creates strange semantic blind spots and highlights the mechanics of sycophancy, showing how models leverage subtle data associations to match user biases and demographics - even guessing political views based on favorite sports teams. By Naomi Saphra