AI 资讯

According to the latest Pew Research poll, 49 percent of Americans report using chatbots at least occasionally, but 63 percent think the tech is advancing too quickly. Overall, use of AI chatbots has increased dramatically since 2024, when only 33 percent reported using them. Specifically, ChatGPT's usage has doubled since 2023, with 44 percent of […]

Article URL: https://www.ycombinator.com/companies/trellis-ai/jobs/Cg94htp-product-lead Comments URL: https://news.ycombinator.com/item?id=48573221 Points: 0 # Comments: 0

Trump administration officials tell WIRED that if Anthropic wants to rerelease Fable 5, it will need to ensure the model's guardrails can't be circumvented. Security experts say that can't be done.

Clair Health will track inflammation and bloating markers, energy levels, and cycle phase classification to give insights into cycle irregularities and perimenopause, as well as hormonal fluctuations, and how to navigate those changes.

Hey HN, I built a website to watch live baseball games in an 8-bit broadcast. It takes live MLB data streams and converts them into near real-time pixel art gamecasts. Been waiting to share this for when there’s actually a good slate of games happening since the site is pretty bare otherwise. Here is today's schedule: Mets @ Reds - 9:40am PDT https://ribbie.tv/watch/game/824503 Royals @ Nationals - 10:05am PDT https://ribbie.tv/watch/game/822721 Marlins @ Phillies - 10:05am PDT https://ribbie.tv

Google is betting generative AI can breathe new life into the smart speaker. The company's new $99.99 Google Home Speaker replaces the rigid commands of the Google Assistant era with more conversational Gemini interactions.

Hello and welcome to Regulator, an email for Verge subscribers about technology, politics, and what happens when science crashes headlong into self-interest. Not a subscriber? Sign up here today! Got the scoop on a petty feud that's going to somehow fundamentally reshape the entire field of frontier AI development? Send 'em over to tina.nguyen+tips@theverge.com. Back […]

Hey HN! I'm Zach from Adam ( https://adam.new/ ). We're building AI agents for mechanical CAD software. We’ve built the company on two fundamental beliefs: - AI will be the primary medium for creating mechanical designs just like it is in software today. - The best paradigm for CAD generation is to generate CAD as code (text -> code -> CAD). We’re building CADAM, an open source Text to CAD platform. It's a React app (TanStack Start) with a Supabase backend for auth, database, and file storage. T

Yesterday, Snap debuted its new $2,195 Specs glasses. In an interview with CNBC, Snap CEO Evan Spiegel described the Specs as something the company had been working on for more than 12 years, an attempt to "bring computing into the world" and "make it more human." He positioned them as a device to help people […]

Not quite the Grand Theft Auto news everyone's been waiting for, but we'll take it.

Google's new smart speaker is more about Gemini than audio quality.

Rate Limiting and Circuit Breakers in Distributed AI Systems Distributed AI systems are inherently complex, handling massive volumes of requests, variable latency from model inference, and dependencies on external services like GPU clusters, databases, or third-party APIs. Without proper safeguards, a single misbehaving component or a sudden traffic surge can cascade into system-wide failure. Two fundamental patterns— rate limiting and circuit breakers —provide essential protection. This post explores their roles, implementation strategies, and practical Python examples tailored for AI workloads. Why Distributed AI Systems Need These Patterns Consider a typical AI pipeline: a user sends a prompt, which hits a load balancer, then an API gateway, then an inference service (e.g., a large language model), which may call a vector database or a fine-tuning API. Each component has capacity limits: GPU inference servers can handle limited concurrent requests. External APIs (e.g., OpenAI, HuggingFace) impose rate limits. Database connections are finite. Without rate limiting, a single abusive client can exhaust resources. Without circuit breakers, a failing downstream service can cause cascading timeouts and resource exhaustion across the entire system. Rate Limiting: Controlling Request Flow Rate limiting restricts how many requests a client, user, or service can make in a given time window. It prevents resource starvation and ensures fair access. Common Algorithms Algorithm Pros Cons Token Bucket Smooth burst handling, easy to implement Memory per bucket Leaky Bucket Constant outflow rate, simple Less flexible for bursts Fixed Window Simple, low overhead Boundary spikes (reset issues) Sliding Window Smoother than fixed, accurate Slightly more complex For AI systems, token bucket is often preferred because it allows short bursts (e.g., a user sending a batch of prompts) while maintaining a long-term average. Python Implementation: Token Bucket Rate Limiter import time impor