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Tesla这传说之前在抖音刷到过，6月20号，苏州网友实测了一把，时速将近130紧急避险，那一下整个人都做好碰撞准备了。#modely# #紧急避险# #安全驾驶# #天上飞机地下苏E# 视频来源网络，请安全驾驶，请勿模仿。

今天去试驾了极氪007和特斯拉model Y，电车开起来是真的舒服，老婆一定要让我买油车，说不喜欢电车。只有20万预算，选不好哪款。 极氪007 整车6年质保，三电终身质保，车里面装饰比特斯拉高级。丐版裸车209900，不能免息分期。无其他优惠活动。 ModelY 整车4年或者8万公里质保，三电8年质保，毛坯装饰，电池比较耐用。丐版裸车25万，可以5年0息分期，首付8万开回家。

Anyone citing the Lancet study to claim Elon killed 14 million people (or any other ridiculous number) should just be laughed at. The study models a deliberately absurd scenario and it does so with incorrect inputs to the model. The scenario did not come true. Stop citing it!

We’ve been impressed with GLM-5.2 and so are introducing a $9.99/month subscription to give you 2-5x discounted access to it and other open weight models like DeepSeek, Kimi, MiniMax, Mimo, Qwen. Use it on Cline CLI & IDE with $1.99 special promo if sign up via: npm i -g cline

Users have to trust a handful of firms to be instilling appropriate values in their AI models. We investigated 25 frontier AI models’ responses to a big opinion survey. Register for free to explore our findings

QVAC SDK 0.14.0 is live. This release makes the on-device stack faster on mobile, ships the developer-agent path, and takes local text-to-speech to 31 languages. Main highlights: - OpenCode and OpenClaw. The first official OpenCode plugin, plus a maintained OpenClaw compatibility path, both built on managed mode and qvac serve. Point a coding agent at a local model with far less setup and far fewer surprises. - Brain-computer interface transcription, on the SDK. Take recorded neural signal data and decode it into text, fully on-device, no cloud. Stream it in chunks through a simple API. In 0.14 it runs GPU-accelerated on iOS. - Text to Speech in 31 languages with our Supertonic3 upgrade. VOICE AND SPEECH - Supertonic3 multilingual TTS, 5 languages to 31. - Chatterbox and Supertonic now run on the Android GPU, with lower memory use (especially on iOS), quantized s3gen Chatterbox support, and a fix for Chatterbox occasionally emitting random speech. - Whisper transcription now runs on the iOS GPU. Parakeet runs on the Android GPU, with steadier real-time streaming. VISION AND OCR - VLM multi-tile batching: high-resolution Pan and Scan images are encoded in one pass instead of tile by tile, for faster vision throughput. - OCR on ggml (EasyOCR and DocTR) reaches full speed parity with the onnx path, across Metal, OpenCL, and Vulkan. PLATFORM AND RELIABILITY - Dynamic compute backends on Linux: one build picks the right backend at runtime, and opens the door to ROCm and CUDA support without per-backend builds. - Thinking tokens are kept out of the model context, so reasoning no longer fills the KV cache. SDK 0.14.0 is now leaner and faster to start. Let’s build.

My entire AI stack is now Chinese 🇨🇳 87% cheaper. same revenue swaps by task: 1. reasoning / backend brain Opus 4.8 → Kimi K2.7 benchmark gap: ~8% · price: ~11x cheaper 2. code generation GPT-5.5 → Qwen 3.7 Max benchmark gap: ~18% · price: ~7x cheaper 3. agent loops + tool calling Sonnet 4.7 → GLM 5.2 benchmark gap: ~3% · price: ~5x cheaper on input 4. cheap volume / bulk processing GPT-5.5 mini → MiMo V2.5 benchmark gap: ~6% · price: ~12x cheaper 5. image generation GPT-Image-2 → Wan 2.5 benchmark gap: ~5% · price: ~8x cheaper 6. video generation Sora 2 → Kling 3.0 benchmark gap: roughly equal · price: ~6x cheaper [ result after 30 days: ] operating costs dropped 87%, output quality dropped 4% on average, revenue unchanged the most important that these models will be not banned in a month and i can run them locally nobody will steal my data and i can learn them as i need full article drops tomorrow with: > exact routing logic per task type > the 2 cases where I still pay for American > the migration playbook anyone can copy in a weekend VERY IMPORTANT to get migrated now, while it's not too late

A self-evolving agent + a 428B model + 3 Macs = ? Your own AI lab. We ran @MiniMax_AI M3 locally with @tryParallax, right on our desk. Then @GA_agent_ai took over to create a 5-stock portfolio and write it to disk. No cloud. No API bills. Nothing left the machine. Wild to see a ~3K-line agent drive all this with a 400B+ model on local hardware. Thanks to the GenericAgent and MiniMax teams for making local AI feel real.