与「recomendation」相关的搜索结果

💞red #fyp# #LOOKBOOK# #추천# #recomendation# #룩북촬영# #룩북챌린지# #직캠# #웹화보# #화보# #4K직캠# #세로룩북# #비키니# #bikini# #운동하는여자# #selca# #koreagirls#

🕯️ #fyp# #LOOKBOOK# #추천# #recomendation# #룩북촬영# #룩북챌린지# #직캠# #웹화보# #화보# #4K직캠# #세로룩북# #비키니# #bikini# #운동하는여자# #selca# #koreagirls#

⚠️ Cursor has published a security incident report on the earlier supply chain attack. According to the investigation, the attacker used access keys issued by Cursor to Fireworks and subsequently exposed on Fireworks’ side to gain access to some of Cursor’s SaaS services and internal systems. The attacker ultimately tampered with the distribution cache for the Claude Code extension to collect information about users’ machines and install SSH backdoors. The compromised versions were Anthropic.claude-code 2.1.312 and 2.1.313 in Cursor’s VS Code extension marketplace distribution cache, affecting macOS and Windows. The exposure window ran from October 29 at 17:27 to October 30 at 04:35 (UTC+8), with up to approximately 5,000 users affected. Security recommendation: If you installed or updated to either affected version through Cursor’s marketplace during this window, read the official notice as soon as possible and run the remediation script for your platform to check for and remediate any compromise: Fireworks’ initial announcement and subsequent update:

Safety evaluations are critical for understanding whether a model is ready to deploy, which safeguards to build for it, and where to focus training and research. On our Engineering Blog, we offer 10 recommendations for how to design, run and report them.

🪇🎉 Announcement 🎉🪇 An Epistemics Fund, sponsored by @oughtinc Ought plans to make between $500k and $5M in grants to top recommendations in community notes, reliability tracking and more. Apply! Or Ask Me Anything on the fund/my grant evaluations

We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.

Sleepagotchi is becoming more than a place to check your sleep score. Its ai Sleep Coach uses your sleep data to provide personalized insights and recommendations, while @sleepagotchi is also expanding into recovery, activity and wearable data. That matters because sleep is connected to the rest of your daily routine. Looking at more than one signal can give AI more context than a single sleep score. The game layer adds another reason to keep coming back. You can set a sleep schedule, earn morning rewards, unlock heroes and progress through Dino’s world. So the interesting part is not just collecting more data. It is bringing personal data, AI and daily habits into the same experience. That is what makes Sleepagotchi worth paying attention to.

Doing a bit of a self-experiment. Goal: use my personal health and travel data to provide personalized diet and exercise recommendations for me, using frontier models but in a way that avoids leaking to them any private information. Strategy: use a local model (Qwen 3.8 Flash Next) to orchestrate, and use powerful remote models as a tool call to benefit from higher-level thinking and knowledge that the local model does not have. Three-layer approach to privacy: Avoid leaking personally identifiable information, or leaking my identity through writing style -> local model writes the queries to the frontier model, not me Avoid leaking who I am through the payment channel -> zkAPI Avoid leaking who I am through networking / IP -> Tor You need all three (and finally we have all three, at least to some extent) A skill file teaches the local model when and how to construct minimally-data-revealing requests to remote models. Use zkAPI-wrapped-with-Tor as a CLI tool. And everything works! I got the recommendations back, info from frontier models helped to improve them. Main deficiencies: * Tor is really not optimized for request-by-request de-linking, which is the only form of network-layer privacy that really makes sense in today's world (long-running identifiers are too fragile). Probably not private enough, and latency 10-100x higher than it could be, at the same time. * The skill file's request construction strategies are definitely far from optimal. * Qwen 3.8 Flash Next is still too slow for comfort. It's comfortably running at 20-30 TPS, but it would only really start to feel fast at 100+ * There is a tradeoff: the more careful you are about what data you give to a remote model, the less it can help you

5/ 以及： －place-recommendations －presentations －proactive-value-creation －restaurant-booking －restaurant-recommendations －rideshare －scheduling －shopping －speaking －wake-subscriptions AI Agent 正在从“回答问题”，走向直接操作现实世界的服务。

this is how instinct works under the hood - agent loop outside of the sandbox (running on the backend server) - executing commands and controls file system on a sandbox - backs up git memory folder to S3 bucket available skills: - agent-mail - authenticator-codes - browser-management - browser-use - bug-report - company-identity - data-privacy - deep-research - document-signing - files - flight-booking - food-ordering - gamepigeon - google-docs - i2i-management - instinct-faq - instinct-guides - mac-computer-use - messaging - onboarding - place-recommendations - presentations - proactive-value-creation - restaurant-booking - restaurant-recommendations - rideshare - scheduling - shopping - speaking - wake-subscriptions