与「SCRAP」相关的搜索结果

Introducing the Firecrawl plugin for Codex. Give Codex access to the most accurate search available (94.7% on SimpleQA) plus tools to scrape, crawl, and interact with any site. Find us on the @OpenAI plugin marketplace today!

Sparks erupted from the tail of a cargo plane as it attempted to land at Cincinnati/Northern Kentucky International Airport. Video shows the Kalitta Air Boeing 777 scraping its tail along the runway before the crew aborted the landing, took back off and circled around for another attempt. The plane landed safely a short time later, and no injuries were reported. Kalitta Air says the aircraft has been removed from service for inspection.

What is ScrapeBadger? Our co-founder explains in 90 seconds 👇 Scraping APIs for Twitter/X, Reddit, Google, Amazon, TikTok, YouTube + 40 more. Built-in anti-bot bypass. MCP server for AI agents. No proxies. No CAPTCHAs. Just clean data. Start FREE 👉

The story of AI in the next few years is going to be compute: an essay on the future of AI. K3 in 2 days is already #10# on OpenRouter with ~140B tok/day, and it’s infra is crumbling. Throughput is down from 30tok/s to 13tok/s, E2E latency is up to 72s and time to first token is >20s! It would cost a minimum of $500k to buy the 8 B300s it would take to serve even quantized Kimi K3 and ~$4M for the more recommended GB300 NVL72 rack. I don’t think Moonshot has the compute available to scale to their demand! In fact, even the US based inference providers will likely not be able to scale capacity as much as they’d like even if they were to host it: a 2.8T model is no joke. GPU providers (neoclouds etc) are doing 3yr and I recently hear 5yr commits with an ungodly 30% down, and customers are chomping it up. Prices continue to go to the moon. The two big labs, hyperscaler clouds, Grok and Meta have compute deals locked in prior, and the rest are fighting for scraps. Tier 1 neoclouds (coreweave/nebius etc) are rumored to not even small “smaller” customers. Meta is the biggest wildcard here. With ~7GW of compute by eoy 2026 and no clear big model ties, they either get to frontier on their own or can host the most Kimi K3 capacity (unless they sell it to the labs). Even though the price of models has fallen over time, it’s worth noting that the price of frontier has not. 3yrs ago, GPT-4 released at $60/M, o1 at $60/M, Opus 4 at $75/M, GPT5 at $10/M, Fable at $50/M and now Sol at $30/M and K3 at $15/M. Even if you consider K3 frontier, that’s only a 4-5x flux in 3yrs. In that time, frontier demand has increased at least 3+ ooms and frontier intelligence performance has gone 32x at least by task time by METR. Essentially, so long as a) the demand for frontier intelligence continues to grow to near infinity, b) the frontier continues to grow in performance, even as c) if the price of frontier declines a little, the value accrued to frontier grows significantly! And there’s a tremendous bull case for those who have locked up compute if you’re bitter lesson pilled and believe larger models will always be smarter models.

the linkedin profile scrape into a sheet every morning. someone opens a browser, finds 10 linkedin profiles matching a search, pulls job title, company, headcount, and pastes it into a sheet. thirty minutes of work. paid $15/hr. done by a human. now: one mcp connector to a search source, one to google sheets, claude sitting in the middle reading the prompt, calling the tools, deciding when it's done. one prompt in → 10 rows populated → no human touching any step in between. that's not the 'claude runs a company' demo. that's a repetitive cognitive task that used to require a hire, now running on a cron job. the part nobody's really clocking: mcp didn't make this possible by being smarter. it made it possible by giving the model hands. read a page, write a cell, loop until done. that's the primitive. you don't replace a task by building a chatbot. you replace it by pointing a pipeline at the tools the task used to touch. the connector layer is what changed. the model just needed somewhere to reach.

The best AI safety grade - a C+. Not from an underdog. From the leader. The Future of Life Institute rated major AI labs on risk management, transparency, and whether they keep their own promises. Anthropic - C+. OpenAI and Google DeepMind - C. Meta - D+. xAI, DeepSeek, and Mistral failed the assessment. Even the leader barely scraped a C+. Meanwhile, several labs have quietly walked back their own earlier safety commitments. And all of this is happening while AI gets trusted with cybersecurity, medical reviews, and autonomous agents. If you’re trusting AI with decisions, don’t ask: “Which model is smarter ?” “Who checked its safety ?”