全部 AI 动态

today, Krea 3 makes it to the top of image arenas. - "Fast" pushes the pareto curve of speed and quality. - "Ultra" ranks top-4, only surpassed by OpenAI models. the only company valued under $1T in the top-10! more below 👇

What's new in Grok Bot 00:00 New chat cards 00:31 Speed 00:53 Draft edits 01:08 Email aliases 01:33 X DMs 01:55 Tag @bot 02:31 Connectors 02:48 Voice calls 03:27 Notifications 03:56 Invites and Android Assistant 04:19 Access lists, unread, routines, sign-in This team ships!

Computer built Big Boy No. 4014, the world's largest operating steam locomotive, as a live isometric SVG with its own renderer and a 7-step operating sequence from cold boiler to 60 mph. The live model has 1,324 parts and 15,312 convex faces, built from @UnionPacific's published figures. Try it: http://big-boy-4014.pplx.app

🚀 隆重推出 DeepInfer，我们自研的推理框架，现已完全开源！ 以高吞吐量和低延迟大规模支撑 DeepSeek-V4.1-Flash。我们分享生产环境技术栈，帮助社区降低计算成本、减少能源消耗，让 AI 更可负担。 🔗 http://github.com/deepseek-ai/DeepInfer 1/4

「2Dを3Dに繋ぐ」 生成した動画にアイデアを重ねたらどこまでやれるんだろう、と頑張ってみた軌跡です。 左上が元の生成、右上が完成、下がBlenderの画面。 ■やったこと 1. MidjourneyでKVを作る 2. MiniMax H3で10秒の走りを生成 3. 同じ動きのまま、素材だけ変えて7本に（絵の具／クローム／粉／墨／花／水／糸） 4. 前半の2Dは、Pythonでポーズごとに素材を切り替える 5. 2Dと同じ見え方の3D空間をBlenderに作り、つなぐ 6. 3Dでは、素材の軌跡の中にカメラが入っていく 7. 抜けて引くと、欠片が集まって巨大な走者の絵になる 8. 光の筋や床の映り込みも、Blenderの中で足す ■わかったこと ・生成とツールを組み合わせるとクオリティUP ・2Dを3Dに繋げば多少リッチな表現に AI生成で元からかっこいい素材を出せばいいのかもしれませんが、こういったアプローチもありそうだなと参考まで。

「音と動きをビジュアライズ」 Opus 5.5×Blender×Python ■素材 ・人物：MiniMax H3 ・キービジュアル：Midjourney ・音楽：MiniMax Music 3.0 ■Blenderとコードでやらせたこと ・音が大きいほど、絵が立体になって手前に飛び出す ・速く動いた所ほど、粒になって浮く ・打った瞬間に時間が止まり、カメラだけが回り込む ・止まっている間、空間ごと色と照明が切り替わる ■わかったこと ・生成だけでは出ない絵も組み合わせるとできる ・やりすぎるとだめ ・映像の動きにも反応させたら、映像そのものが鳴っているように見えてきた ・飛び出す量は曲の音と、素材の動きに任せた 実験的な映像ではありますが、何かの表現に使えそうだなと思います。AIをより拡張できる方法を探っていきたいです。 音と動きの両方で絵が動くと、なんか面白いですね。

https://x.com/i/article/2112478059978973184

Spent some time experimenting with MiniCPM-V4.7 35B-A3B and built this Neo-Brutalist portfolio UI, featuring bold colors, chunky layouts, and a slightly unconventional visual style. What makes this experiment interesting is the model behind it. OpenBMB’s MiniCPM-V 4.7 is pushing multimodal AI toward more practical deployment on everyday hardware. With 35B total parameters but only 3B activated at a time, MiniCPM-V 4.7 35B-A3B delivers 17+ tokens/s on iPad Pro M4 (16GB) and 13+ tokens/s on iPhone 17 Pro Max, using just ~3GB of memory in its optimized mobile setup. 🤯 There’s also a compact 1B variant designed for tasks like OCR, chart, math, and general understanding. Both variants support a 256K context window, and the model weights, inference code, and fine-tuning code are open source. For me, the interesting part of multimodal AI is its potential to work with visual information beyond text, and to make those capabilities more accessible through efficient, flexible deployment. Explore MiniCPM-V 4.7 from @OpenBMB: GitHub：http://github.com/OpenBMB/MiniCPM-V Hugging Face：http://huggingface.co/collections/openbmb/minicpm-v-47

I built an automated report generator that runs entirely on my own machine. Input: structured data + charts. Output: a full long-form report — earnings interpretation, weekly summaries, whatever you need. No API. No cloud. No monthly bill. -> Here's MiniCPM-V 4.7 35B-A3B doing exactly that 👇 It read the charts AND the dense tables in one pass, then streamed out analyst-grade markdown: scorecard, segment breakdown, margin drivers, cash flow, even a bull/bear case. The part that got me: it didn't just OCR numbers. It connected them — input costs → margin pressure → guidance risk. That's the jump from "text extraction" to "actual analysis." Why this works locally: → 35B total params, only ~3B active (MoE) → Efficient visual encoding keeps multi-chart inputs cheap → Structured markdown output — goes straight into your doc pipeline Try it GitHub: http://github.com/OpenBMB/MiniCPM-V Hugging Face: https://huggingface.co/collections/openbmb/minicpm-v-47