I built an an app that looks at your resume and gives tailored job recommendations rocketjobs.app/landing Thanks to @togethercompute free llama I'm running this for 0$ so this app is free forever no ads

Introducing Claude Sonnet 4.5. The best coding model in the world combined with the best character of any model I've seen.

gpt5's reasoning cot is painfully inefficient

Kimi K2-0905 update 🚀 - Enhanced coding capabilities, esp. front-end & tool-calling - Context length extended to 256k tokens - Improved integration with various agent scaffolds (e.g., Claude Code, Roo Code, etc) 🔗 Weights & code: huggingface.co/moonshotai/Ki… 💬 Chat with new Kimi K2 on: kimi.com ⚡️ For 60–100 TPS guaranteed 100% tool-call accuracy, try our turbo API: platform.moonshot.ai

Good model, but sucks that they didnt realease any benchmark scores. even the system card was meh no real coding benchmarks

sorry for doubting you didnt know you were moving like this

this is about as good as qwen coder imo. but with qwen on cerebras hard to beat it

Introducing Gemini 2.5 Flash Image (aka nano-banana), our SOTA image generation and editing model 🍌 As you might have already seen, this model excels at character consistency, creative edits, and has Gemini's world knowledge!

fridge company mogging apple was not on my bucket list for 2025

Luminal (luminalai.com) is creating PyTorch for Production – an ML compiler that generates blazingly fast CUDA kernels and makes deploying to production one line of code. Congrats on the launch, @stake_jevens, @joefioti, and @matthewjgunton! ycombinator.com/launches/O0g…

windsurf is so back

The recording for this talk is now available! tl;dr - GPUs scale like bandwidth, quadratic w.r.t latency - the key bandwidth they scale is math bandwidth (FLOP/s) - among math bandwidths, low precision matmuls have scaled fastest and some takeaways for data eng/data science

New chatgpt voice mode is so bad. Why does it keep taking breaths? It sounds so unnatural and the voice feels so unstable

No more waitlist – Cerebras inference API is open to all! 1M free tokens/day 20x GPU speed Reasoning in ~1 second It's time to build! inference.cerebras.ai/?utm_s…

🚨 BREAKING: After a years-long battle, Taylor Swift has reclaimed ownership of her entire music catalog for a reported $360 million. Here’s everything that happened:

Today, for the first time, I genuinely want to leave the Apple ecosystem for Google's AI-powered Android. Having worked on Apple's OS for a decade, I know the entire stack intimately. Google's innovation is happening at every level, from smaller to larger models, from cloud to TPUs, from app experiences to search. It's all finally coming to fruition, and the trajectory is clear: Google will be a dominant leader in mobile because of their tight AI integration. Their architecture positions them perfectly to maintain this leadership. What makes this so powerful is the hardware-software integration. Companies without this end-to-end control will struggle to create the same seamless AI experiences across products. Just as OpenAI has leveraged my conversation history to make better decisions with newer models, Google already has access to my data through email. As I use their devices more, their AI will truly understand what I'm trying to accomplish. Yes, privacy remains important, but there are tradeoffs. The conversation about sharing data with companies like Google deserves deeper exploration so we understand what this means for computing's future. When AI algorithms can process and truly understand massive datasets that humans simply cannot manage, we're witnessing what I believe is the biggest breakthrough in computing history. Google's ability to implement this at scale while pivoting so quickly is, without question, a massive win. Their leap in AI intelligence is incredible, and it fundamentally transforms what's possible in personal computing. I'm planning to share more of my experiences on my live streams as I explore this transition. I'll be getting an Android device soon so I can truly understand what this means in practice. As an engineer, I feel it's really important to actually use these products and share my genuine experience in real time, without the hype. I want to see firsthand how Google's AI integration changes the mobile experience and what it might mean for the future of computing.

Introducing Mistral Medium 3: our new multimodal model offering SOTA performance at 8X lower cost. - A new class of models that balances performance, cost, and deployability. - High performance in coding and function-calling. - Full enterprise capabilities, including hybrid or on-premises/in-VPC deployment, custom post-training, and seamless integration into enterprise tools and systems. Check out our blog to learn more:

openai launched gpt-4.5 to great excitement yet under 2 months after launch, they announced it would be deprecated is pre-training over now? @jackgwhitaker and i show: 1. pre-training scaling laws haven't bent 2. why the marginal dollar left it for RL 3. why it'll come back

20 hours of video is insane

Today is the start of a new era of natively multimodal AI innovation. Today, we’re introducing the first Llama 4 models: Llama 4 Scout and Llama 4 Maverick — our most advanced models yet and the best in their class for multimodality. Llama 4 Scout • 17B-active-parameter model with 16 experts. • Industry-leading context window of 10M tokens. • Outperforms Gemma 3, Gemini 2.0 Flash-Lite and Mistral 3.1 across a broad range of widely accepted benchmarks. Llama 4 Maverick • 17B-active-parameter model with 128 experts. • Best-in-class image grounding with the ability to align user prompts with relevant visual concepts and anchor model responses to regions in the image. • Outperforms GPT-4o and Gemini 2.0 Flash across a broad range of widely accepted benchmarks. • Achieves comparable results to DeepSeek v3 on reasoning and coding — at half the active parameters. • Unparalleled performance-to-cost ratio with a chat version scoring ELO of 1417 on LMArena. These models are our best yet thanks to distillation from Llama 4 Behemoth, our most powerful model yet. Llama 4 Behemoth is still in training and is currently seeing results that outperform GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM-focused benchmarks. We’re excited to share more details about it even while it’s still in flight. Read more about the first Llama 4 models, including training and benchmarks ➡️ go.fb.me/gmjohs Download Llama 4 ➡️ go.fb.me/bwwhe9

Introducing our first set of Llama 4 models! We’ve been hard at work doing a complete re-design of the Llama series. I’m so excited to share it with the world today and mark another major milestone for the Llama herd as we release the *first* open source models in the Llama 4 collection 🦙. Here are some highlights: 📌 The Llama series have been re-designed to use state of the art mixture-of-experts (MoE) architecture and natively trained with multimodality. We’re dropping Llama 4 Scout & Llama 4 Maverick, and previewing Llama 4 Behemoth. 📌 Llama 4 Scout is highest performing small model with 17B activated parameters with 16 experts. It’s crazy fast, natively multimodal, and very smart. It achieves an industry leading 10M token context window and can also run on a single GPU! 📌 Llama 4 Maverick is the best multimodal model in its class, beating GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks, while achieving comparable results to the new DeepSeek v3 on reasoning and coding – at less than half the active parameters. It offers a best-in-class performance to cost ratio with an experimental chat version scoring ELO of 1417 on LMArena. It can also run on a single host! 📌 Previewing Llama 4 Behemoth, our most powerful model yet and among the world’s smartest LLMs. Llama 4 Behemoth outperforms GPT4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks. Llama 4 Behemoth is still training, and we’re excited to share more details about it even while it’s still in flight. A big thanks to all of our launch partners (full list in blog) for helping us bring Llama 4 to developers everywhere including @huggingface, @togethercompute, @SnowflakeDB, @ollama, @databricks and many others👏 This is just the start, we have more models coming and the team is really cooking – look out for Llama 4 Reasoning 😉 A few weeks ago, we celebrated Llama being downloaded over 1 billion times. Llama 4 demonstrates our long-term commitment to open source AI, the entire open source AI community, and our unwavering belief that open systems will produce the best small, mid-size and soon frontier models. Llama would be nothing without the global open source AI community & we are so ready to begin this next chapter with you. 🦙 Read more about the release here: llama.com/, and try it in our products today.