Eniton

Congrats on the launch @Zai_org 🚀 Day zero on DeepInfra → deepinfra.com/zai-org/GLM-5.2

@leeoxiang @jiamimaodashu 6%的稳定收益？求方案

@jiamimaodashu 除去固定资产外 1000w 现金是相对安全的，1000w 现金放在香港每年能稳定拿到 60w+的现金收益，能覆盖家庭相对高质量的生活开支。

你觉得拥有多少钱才有真正的安全感？说点靠谱的。别说几个亿，几十个亿的扯淡的话。

I'm a very visual person. when I was first getting into ML, I'd try to draw out every concept on pen and paper. back then I couldn't vibe-code a visualization. but now you can! here are my favorite ML visualizations I've been saving for a while. take them as inspo for the next complex topic you want to visualize 🧵

I have a feeling MiniMax will announce that the 50% promotional price is permanent after the first week. openrouter.ai/minimax/minima…

Visiting most of the leading Chinese AI labs, I'm struck by a culture that's extremely well suited to building LLMs with fewer resources, but one happening in a very different ecosystem, more companies at play, almost no data industry, etc. Full report: interconnects.ai/p/notes-from-i…

It's art. she-llac.com/claude-limits

I spent all of Christmas reverse engineering Claude Chrome so it would work with remote browsers. Here's how Anthropic taught Claude how to browse the web (1/7)

🚀 Paper2Slides is now open source! Transform research papers & technical reports into professional presentations with ONE click! We've generated stunning presentation slides from the latest DeepSeek V3.2 paper in diverse styles - check them out and share your feedback! 🔥 Core Features: - 📄 Multi-format support - PDF, Word, Excel, PowerPoint & more - 🎯 Smart content understanding - Captures key insights, figures, formulas, tables & data points. - 🎨 Custom styling - Professional themes with full personalization. - ⚡ Lightning fast - High-quality PPT generation in minutes. GitHub: github.com/HKUDS/Paper2Sl… Never build slides from scratch again! ✨ Come play with it ⭐! #Paper2Slides #AIPPT

Slides: docs.google.com/presentation/d…

Today, we are releasing a new version of K2 (K2-V2), a 360-open LLM built from scratch as a superior base for reasoning adaptation, while still excelling at core LLM capabilities like conversation, knowledge retrieval, and long-context understanding. K2 fills a major gap: highly capable models with no transparency. Instead of releasing only weights, we’re sharing the full training story — dataset recipes, mid-training checkpoints, logs, code, and evaluation tools. That’s 360-open. What’s inside: • 70B dense transformer engineered as a reasoning-enhanced base model • Native 512K context (extendable via RoPE scaling) • Mid-training reasoning phase • Strong tool-use scaffolding What we’re open-sourcing: • 250M+ reasoning traces (math, planning, multi-step logic) • Full pre- & mid-training data compositions • All mid-training checkpoints • Training logs, code, Eval360 Performance: • GPQA-Diamond: 55.1% mid-training → 69.3% after SFT (strongest fully open 70B model) • KK-8 Logic Puzzles: 83% — competitive with DeepSeek-R1 & OpenAI o3-mini-high • ArenaHard V2: 62.1% — close to Qwen3 235B • Outperforms Qwen2.5-72B and approaches Qwen3-235B despite being smaller and fully transparent. 🔗 The Model: bit.ly/3KIYwuo 🔗Technical Report: bit.ly/49V8h2U 🔗Blog: bit.ly/49V7gb6

@immeivise It's a great table. Could you share the link to it?

DeepSeek V3.1の注意！ OpenRouter や Groq 経由でAPI買うと品質が落ちてるかも… 利益優先で精度削ってるケースあり。 DeepSeek V3.1の比較👇 •公式版を100%とすると 　DeepInfra: 97% 　AtlasCloud: 85% 　NovitaAI: 80% 　OpenRouter: 79% 結論👉 ローカルで動かすのが最強🔥 #LLM #AI #DeepSeek

DeepSeek v3.1 のDynamic 1-bitのgguf 量子化がでたぞ！ これは試さねば

@openrouter How does the pricing model for image generation work? It says "$1.238/K input imgs, $0.03/K output imgs" — what does “/K” mean here?

The first-ever image model is up on OpenRouter: 🍌 Gemini 2.5 Image Preview 🍌 Some tests we ran, including our tsunami-edit test:

Introducing SynCity 🌆 SynCity generates entire 3D worlds from a text prompt with no training or optimisation. It leverages pretrained 2D and 3D generators and generates scenes on a grid, tile by tile. The generated 3D environments are diverse, fully coherent, and navigable. 🧵👇

Monk and his giant warsteed traversing a river. Kling 1.6 image to video. 🔊🔊

what?

@kevin_logan @tunguz exactly

@tunguz I can already hear the fan running on that thing.

I mean, I love the new Mac Mini as much as the next guy, but you do know that today you can get those same kinds of specs in a PC for $159?

How can we evaluate LLMs across 1000+ languages? 🌎 The first step towards FineWeb Multilingual was creating FineTasks, a data-driven evaluation framework that helps select reliable evaluation tasks for any language. The @huggingface Team validated it across 9 different languages and evaluated 35 open and closed LLMs. 👀 TL;DR: 🎯 Created FineTasks - a framework for selecting reliable multilingual evaluation tasks 📊 Tasks based on 4 key metrics: monotonicity, low noise, non-random performance, and model ordering consistency 🔍 Tested 185 tasks across 9 diverse languages (Chinese, French, Arabic, Russian, Thai, Hindi, Turkish, Swahili, Telugu) 📋 Selected 96 final tasks covering reading comprehension, general knowledge, language understanding, and reasoning 🧪 Found task formulation matters: Cloze Format better for early training, Multiple Choice Format for later evaluation 📈 Metrics recommendation: Use length normalization for most tasks, PMI for complex reasoning 🔥 Open models are narrowing the gap with closed-source models in multilingual performance. 🏆 Evaluated 35 open and closed-source LLMs; Qwen 2 models dominated high/mid-resource languages: Gemma-2 excelled in low-resource languages 🌐 Framework supports 550+ tasks across various languages

I can't believe I need to say this, but run the code below in your local Jupyter notebook and save 138,830 arXiv papers in multi-markdown format now before they're gone! 😅 Available on @huggingface Datasets: huggingface.co/datasets/neura…

All ~250 YC S24 startups clustered into 20 buckets.