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Person: "awnihannun"
not much happened today
codex claude-4-opus claude-4-sonnet gemini-2.5-pro gemini-2.5 qwen-2.5-vl qwen-3 playdiffusion openai anthropic google perplexity-ai bing playai suno hugging-face langchain-ai qwen mlx assemblyai llamacloud fine-tuning model-benchmarking text-to-video agentic-ai retrieval-augmented-generation open-source-models speech-editing audio-processing text-to-speech ultra-low-latency multimodality public-notebooks sama gdb kevinweil lmarena_ai epochairesearch reach_vb wightmanr deeplearningai mervenoyann awnihannun jordirib1 aravsrinivas omarsar0 lioronai jerryjliu0 nerdai tonywu_71 _akhaliq clementdelangue _mfelfel
OpenAI rolled out Codex to ChatGPT Plus users with internet access and fine-grained controls, improving memory features for free users. Anthropic's Claude 4 Opus and Sonnet models lead coding benchmarks, while Google's Gemini 2.5 Pro and Flash models gain recognition with new audio capabilities. Qwen 2.5-VL and Qwen 3 quantizations are noted for versatility and support. Bing Video Creator launched globally enabling text-to-video generation, and Perplexity Labs sees increased demand for travel search. New agentic AI tools and RAG innovations include LlamaCloud and FedRAG. Open-source releases include Holo-1 for web navigation and PlayAI's PlayDiffusion for speech editing. Audio and multimodal advances feature Suno's music editing upgrades, Google's native TTS in 24+ languages, and Universal Streaming's ultra-low latency speech-to-text. Google NotebookLM now supports public notebooks. "Codex's internet access brings tradeoffs, with explicit warnings about risk" and "Gemini 2.5 Pro is cited as a daily driver by users".
not much happened today
deepseek-r1-0528 o3 gemini-2.5-pro claude-opus-4 deepseek_ai openai gemini meta-ai-fair anthropic x-ai ollama hugging-face alibaba bytedance xiaomi reasoning reinforcement-learning benchmarking quantization local-inference model-evaluation open-weights transparency post-training agentic-benchmarks long-context hallucination-detection teortaxestex wenfeng danielhanchen awnihannun reach_vb abacaj
DeepSeek R1-0528 release brings major improvements in reasoning, hallucination reduction, JSON output, and function calling, matching or surpassing closed models like OpenAI o3 and Gemini 2.5 Pro on benchmarks such as Artificial Analysis Intelligence Index, LiveBench, and GPQA Diamond. The model ranks #2 globally in open weights intelligence, surpassing Meta AI, Anthropic, and xAI. Open weights and technical transparency have fueled rapid adoption across platforms like Ollama and Hugging Face. Chinese AI labs including DeepSeek, Alibaba, ByteDance, and Xiaomi now match or surpass US labs in model releases and intelligence, driven by open weights strategies. Reinforcement learning post-training is critical for intelligence gains, mirroring trends seen at OpenAI. Optimized quantization techniques (1-bit, 4-bit) and local inference enable efficient experimentation on consumer hardware. New benchmarks like LisanBench test knowledge, planning, memory, and long-context reasoning, with OpenAI o3 and Claude Opus 4 leading. Discussions highlight concerns about benchmark contamination and overemphasis on RL-tuned gains.
Mary Meeker is so back: BOND Capital AI Trends report
qwen-3-8b anthropic hugging-face deepseek attention-mechanisms inference arithmetic-intensity transformers model-optimization interpretability model-quantization training tri_dao fleetwood___ teortaxestex awnihannun lateinteraction neelnanda5 eliebakouch _akhaliq
Mary Meeker returns with a comprehensive 340-slide report on the state of AI, highlighting accelerating tech cycles, compute growth, and comparisons of ChatGPT to early Google and other iconic tech products. The report also covers enterprise traction and valuation of major AI companies. On Twitter, @tri_dao discusses an "ideal" inference architecture featuring attention variants like GTA, GLA, and DeepSeek MLA with high arithmetic intensity (~256), improving efficiency and model quality. Other highlights include the release of 4-bit DWQ of DSR1 Qwen3 8B on Hugging Face, AnthropicAI's open-source interpretability tools for LLMs, and discussions on transformer training and abstractions by various researchers.
DeepSeek-R1-0528 - Gemini 2.5 Pro-level model, SOTA Open Weights release
deepseek-r1-0528 gemini-2.5-pro qwen-3-8b qwen-3-235b deepseek-ai anthropic meta-ai-fair nvidia alibaba google-deepmind reinforcement-learning benchmarking model-performance open-weights reasoning quantization post-training model-comparison artificialanlys scaling01 cline reach_vb zizhpan andrewyng teortaxestex teknim1 lateinteraction abacaj cognitivecompai awnihannun
DeepSeek R1-0528 marks a significant upgrade, closing the gap with proprietary models like Gemini 2.5 Pro and surpassing benchmarks from Anthropic, Meta, NVIDIA, and Alibaba. This Chinese open-weights model leads in several AI benchmarks, driven by reinforcement learning post-training rather than architecture changes, and demonstrates increased reasoning token usage (23K tokens per question). The China-US AI race intensifies as Chinese labs accelerate innovation through transparency and open research culture. Key benchmarks include AIME 2024, LiveCodeBench, and GPQA Diamond.
not much happened today
open-code-reasoning-32b open-code-reasoning-14b open-code-reasoning-7b mistral-medium-3 llama-4-maverick gemini-2.5-pro gemini-2.5-flash claude-3.7-sonnet absolute-zero-reasoner x-reasoner fastvlm parakeet-asr openai nvidia mistral-ai google apple huggingface reinforcement-learning fine-tuning code-generation reasoning vision on-device-ai model-performance dataset-release model-optimization reach_vb artificialanlys scaling01 iscienceluvr arankomatsuzaki awnihannun risingsayak
OpenAI launched both Reinforcement Finetuning and Deep Research on GitHub repos, drawing comparisons to Cognition's DeepWiki. Nvidia open-sourced Open Code Reasoning models (32B, 14B, 7B) with Apache 2.0 license, showing 30% better token efficiency and compatibility with llama.cpp, vLLM, transformers, and TGI. Independent evaluations highlight Mistral Medium 3 rivaling Llama 4 Maverick, Gemini 2.0 Flash, and Claude 3.7 Sonnet in coding and math reasoning, priced significantly lower but no longer open-source. Google's Gemini 2.5 Pro is noted as their most intelligent model with improved coding from simple prompts, while Gemini 2.5 Flash incurs a 150x cost increase over Gemini 2.0 Flash due to higher token usage and cost. The Absolute Zero Reasoner (AZR) achieves SOTA performance in coding and math reasoning via reinforced self-play without external data. Vision-language model X-REASONER is post-trained on general-domain text for reasoning. Apple ML research released FastVLM with on-device iPhone demo. HiDream LoRA trainer supports QLoRA fine-tuning under memory constraints. Nvidia's Parakeet ASR model tops Hugging Face ASR leaderboard with MLX implementation. New datasets SwallowCode and SwallowMath boost LLM performance in math and code. Overall, a quiet day with significant model releases and performance insights.
LlamaCon: Meta AI gets into the Llama API platform business
llama-4 qwen3 qwen3-235b-a22b qwen3-30b-a3b qwen3-4b qwen2-5-72b-instruct o3-mini meta-ai-fair cerebras groq alibaba vllm ollama llamaindex hugging-face llama-cpp model-release fine-tuning reinforcement-learning moe multilingual-models model-optimization model-deployment coding benchmarking apache-license reach_vb huybery teortaxestex awnihannun thezachmueller
Meta celebrated progress in the Llama ecosystem at LlamaCon, launching an AI Developer platform with finetuning and fast inference powered by Cerebras and Groq hardware, though it remains waitlisted. Meanwhile, Alibaba released the Qwen3 family of large language models, including two MoE models and six dense models ranging from 0.6B to 235B parameters, with the flagship Qwen3-235B-A22B achieving competitive benchmark results and supporting 119 languages and dialects. The Qwen3 models are optimized for coding and agentic capabilities, are Apache 2.0 licensed, and have broad deployment support including local usage with tools like vLLM, Ollama, and llama.cpp. Community feedback highlights Qwen3's scalable performance and superiority over models like OpenAI's o3-mini.
Qwen 3: 0.6B to 235B MoE full+base models that beat R1 and o1
qwen-3 qwen3-235b-a22b qwen3-30b-a3b deepseek-r1 o1 o3-mini grok-3 gemini-2.5-pro alibaba google-deepmind deepseek mistral-ai mixture-of-experts reinforcement-learning benchmarking model-release model-architecture long-context multi-agent-systems inference dataset-release awnihannun prince_canuma actuallyisaak oriolvinyalsml iscienceluvr reach_vb teortaxestex omarsar0
Qwen 3 has been released by Alibaba featuring a range of models including two MoE variants, Qwen3-235B-A22B and Qwen3-30B-A3B, which demonstrate competitive performance against top models like DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. The models introduce an "enable_thinking=True" mode with advanced soft switching for inference scaling. The release is notable for its Apache 2.0 license and broad inference platform support including MCP. The dataset improvements and multi-stage RL post-training contribute to performance gains. Meanwhile, Gemini 2.5 Pro from Google DeepMind shows strong coding and long-context reasoning capabilities, and DeepSeek R2 is anticipated soon. Twitter discussions highlight Qwen3's finegrained MoE architecture, large context window, and multi-agent system applications.
Reasoning Models are Near-Superhuman Coders (OpenAI IOI, Nvidia Kernels)
o3 o1 o3-mini deepseek-r1 qwen-2.5 openthinker openai nvidia ollama elevenlabs sakana-ai apple reinforcement-learning gpu-kernel-optimization fine-tuning knowledge-distillation scaling-laws chain-of-thought-reasoning model-accessibility alex-wei karpathy abacaj awnihannun
o3 model achieved a gold medal at the 2024 IOI and ranks in the 99.8 percentile on Codeforces, outperforming most humans with reinforcement learning (RL) methods proving superior to inductive bias approaches. Nvidia's DeepSeek-R1 autonomously generates GPU kernels that surpass some expert-engineered kernels, showcasing simple yet effective AI-driven optimization. OpenAI updated o1 and o3-mini models to support file and image uploads in ChatGPT and released DeepResearch, a powerful research assistant based on the o3 model with RL for deep chain-of-thought reasoning. Ollama introduced OpenThinker models fine-tuned from Qwen2.5, outperforming some DeepSeek-R1 distillation models. ElevenLabs grew into a $3.3 billion company specializing in AI voice synthesis without open-sourcing their technology. Research highlights include Sakana AI Labs' TAID knowledge distillation method receiving a Spotlight at ICLR 2025, and Apple's work on scaling laws for mixture-of-experts (MoEs). The importance of open-source AI for scientific discovery was also emphasized.
How To Scale Your Model, by DeepMind
qwen-0.5 google-deepmind deepseek hugging-face transformers inference high-performance-computing robotics sim2real mixture-of-experts reinforcement-learning bias-mitigation rust text-generation open-source omarsar0 drjimfan tairanhe99 guanyashi lioronai _philschmid awnihannun clementdelangue
Researchers at Google DeepMind (GDM) released a comprehensive "little textbook" titled "How To Scale Your Model" covering modern Transformer architectures, inference optimizations beyond O(N^2) attention, and high-performance computing concepts like rooflines. The resource includes practical problems and real-time comment engagement. On AI Twitter, several key updates include the open-sourced humanoid robotics model ASAP inspired by athletes like Cristiano Ronaldo, LeBron James, and Kobe Bryant; a new paper on Mixture-of-Agents proposing the Self-MoA method for improved LLM output aggregation; training of reasoning LLMs using the GRPO algorithm from DeepSeek demonstrated on Qwen 0.5; findings on bias in LLMs used as judges highlighting the need for multiple independent evaluations; and the release of mlx-rs, a Rust library for machine learning with examples including Mistral text generation. Additionally, Hugging Face launched an AI app store featuring over 400,000 apps with 2,000 new daily additions and 2.5 million weekly visits, enabling AI-powered app search and categorization.
TinyZero: Reproduce DeepSeek R1-Zero for $30
deepseek-r1 qwen o1 claude-3-sonnet claude-3 prime ppo grpo llama-stack deepseek berkeley hugging-face meta-ai-fair openai deeplearningai reinforcement-learning fine-tuning chain-of-thought multi-modal-benchmark memory-management model-training open-source agentic-workflow-automation model-performance jiayi-pan saranormous reach_vb lmarena_ai nearcyan omarsar0 philschmid hardmaru awnihannun winglian
DeepSeek Mania continues to reshape the frontier model landscape with Jiayi Pan from Berkeley reproducing the OTHER result from the DeepSeek R1 paper, R1-Zero, in a cost-effective Qwen model fine-tune for two math tasks. A key finding is a lower bound to the distillation effect at 1.5B parameters, with RLCoT reasoning emerging as an intrinsic property. Various RL techniques like PPO, DeepSeek's GRPO, or PRIME show similar outcomes, and starting from an Instruct model speeds convergence. The Humanity’s Last Exam (HLE) Benchmark introduces a challenging multi-modal test with 3,000 expert-level questions across 100+ subjects, where models perform below 10%, with DeepSeek-R1 achieving 9.4%. DeepSeek-R1 excels in chain-of-thought reasoning, outperforming models like o1 while being 20x cheaper and MIT licensed. The WebDev Arena Leaderboard ranks DeepSeek-R1 #2 in technical domains and #1 under Style Control, closing in on Claude 3.5 Sonnet. OpenAI's Operator is deployed to 100% of Pro users in the US, enabling tasks like ordering meals and booking reservations, and functions as a research assistant for AI paper searches and summaries. Hugging Face announces a leadership change after significant growth, while Meta AI releases the first stable version of Llama Stack with streamlined upgrades and automated verification. DeepSeek-R1's open-source success is celebrated, and technical challenges like memory management on macOS 15+ are addressed with residency sets in MLX for stability.
not much happened today
oute-tts-0.3-1b oute-tts-0.3-500m olm-1b qwen-2.5-0.5b hover gpt-4o deepseek-v3 harvey meta-ai-fair stability-ai alibaba deepseek hugging-face text-to-speech zero-shot-learning multilinguality emotion-control motor-control reinforcement-learning local-ai distributed-inference pipeline-parallelism mathematical-reasoning process-reward-models legal-ai education-ai ai-security humor reach_vb drjimfan vikhyatk mervenoyann aiatmeta iscienceluvr alibaba_qwen awnihannun ajeya_cotra emollick qtnx_ designerx
Harvey secured a new $300M funding round. OuteTTS 0.3 1B & 500M text-to-speech models were released featuring zero-shot voice cloning, multilingual support (en, jp, ko, zh, fr, de), and emotion control, powered by OLMo-1B and Qwen 2.5 0.5B. The HOVER model, a 1.5M-parameter neural net for agile motor control, was introduced, leveraging human motion capture datasets and massively parallel reinforcement learning. kokoro.js enables running AI models locally in browsers with minimal dependencies. Meta AI awarded $200K LLM evaluation grants for projects on regional language understanding, complex reasoning, and interactive programming environments. Stability AI's Twitter account was hacked, prompting security warnings. Alibaba Qwen improved Process Reward Models (PRMs) for better mathematical reasoning using a consensus filtering mechanism. DeepSeek V3 uses pipeline parallelism to enhance distributed inference and long-context generation efficiency. Discussions on AI policy in legal frameworks and AI's role in democratizing education were highlighted. Lighthearted AI-related humor was also shared.
not much happened today
helium-1 qwen-2.5 phi-4 sky-t1-32b-preview o1 codestral-25.01 phi-3 mistral llama-3 gpt-3.5 llama-3 gpt-3.5 llmquoter kyutai-labs lmstudio mistralai llamaindex huggingface langchainai hyperbolic-labs replit fchollet philschmid multilinguality token-level-distillation context-windows model-performance open-source reasoning coding retrieval-augmented-generation hybrid-retrieval multiagent-systems video large-video-language-models dynamic-ui voice-interaction gpu-rentals model-optimization semantic-deduplication model-inference reach_vb awnihannun lior_on_ai sophiamyang omarsar0 skirano yuchenj_uw fchollet philschmid
Helium-1 Preview by kyutai_labs is a 2B-parameter multilingual base LLM outperforming Qwen 2.5, trained on 2.5T tokens with a 4096 context size using token-level distillation from a 7B model. Phi-4 (4-bit) was released in lmstudio on an M4 max, noted for speed and performance. Sky-T1-32B-Preview is a $450 open-source reasoning model matching o1's performance with strong benchmark scores. Codestral 25.01 by mistralai is a new SOTA coding model supporting 80+ programming languages and offering 2x speed.
Innovations include AutoRAG for optimizing retrieval-augmented generation pipelines, Agentic RAG for autonomous query reformulation and critique, Multiagent Finetuning using societies of models like Phi-3, Mistral, LLaMA-3, and GPT-3.5 for reasoning improvements, and VideoRAG incorporating video content into RAG with LVLMs.
Applications include a dynamic UI AI chat app by skirano on Replit, LangChain tools like DocTalk for voice PDF conversations, AI travel agent tutorials, and news summarization agents. Hyperbolic Labs offers competitive GPU rentals including H100, A100, and RTX 4090. LLMQuoter enhances RAG accuracy by identifying key quotes.
Infrastructure updates include MLX export for LLM inference from Python to C++ by fchollet and SemHash semantic text deduplication by philschmid.
Moondream 2025.1.9: Structured Text, Enhanced OCR, Gaze Detection in a 2B Model
o1 vdr-2b-multi-v1 llava-mini openai llamaindex langchainai qdrant genmoai vision model-efficiency structured-output gaze-detection reasoning model-distillation multimodality embedding-models gan diffusion-models self-attention training-optimizations development-frameworks api cross-language-deployment semantic-search agentic-document-processing developer-experience philschmid saranormous jxmnop reach_vb iscienceluvr multimodalart arohan adcock_brett awnihannun russelljkaplan ajayj_
Moondream has released a new version that advances VRAM efficiency and adds structured output and gaze detection, marking a new frontier in vision model practicality. Discussions on Twitter highlighted advancements in reasoning models like OpenAI's o1, model distillation techniques, and new multimodal embedding models such as vdr-2b-multi-v1 and LLaVA-Mini, which significantly reduce computational costs. Research on GANs and decentralized diffusion models showed improved stability and performance. Development tools like MLX and vLLM received updates for better portability and developer experience, while frameworks like LangChain and Qdrant enable intelligent data workflows. Company updates include new roles and team expansions at GenmoAI. "Efficiency tricks are all you need."
not much happened today
prime gpt-4o qwen-32b olmo openai qwen cerebras-systems langchain vercel swaggo gin echo reasoning chain-of-thought math coding optimization performance image-processing software-development agent-frameworks version-control security robotics hardware-optimization medical-ai financial-ai architecture akhaliq jason-wei vikhyatk awnihannun arohan tom-doerr hendrikbgr jerryjliu0 adcock-brett shuchaobi stasbekman reach-vb virattt andrew-n-carr
Olmo 2 released a detailed tech report showcasing full pre, mid, and post-training details for a frontier fully open model. PRIME, an open-source reasoning solution, achieved 26.7% pass@1, surpassing GPT-4o in benchmarks. Performance improvements include Qwen 32B (4-bit) generating at >40 tokens/sec on an M4 Max and libvips being 25x faster than Pillow for image resizing. New tools like Swaggo/swag for Swagger 2.0 documentation, Jujutsu (jj) Git-compatible VCS, and Portspoof security tool were introduced. Robotics advances include a weapon detection system with a meters-wide field of view and faster frame rates. Hardware benchmarks compared H100 and MI300x accelerators. Applications span medical error detection using PRIME and a financial AI agent integrating LangChainAI and Vercel AI SDK. Architectural insights suggest the need for breakthroughs similar to SSMs or RNNs.
not much happened today
llama-3-2 llama-3 molmo meta-ai-fair google-deepmind hugging-face on-device-ai multimodality chip-design retrieval-augmented-generation rag benchmarking reliability ai-regulation free-speech pytorch-optimization demis-hassabis clementdelangue svpino awnihannun osanseviero omarsar0 sarahookr ylecun
Meta released Llama 3.2, including lightweight 1B and 3B models for on-device AI with capabilities like summarization and retrieval-augmented generation. Molmo, a new multimodal model, was introduced with a large dense captioning dataset. Google DeepMind announced AlphaChip, an AI-driven chip design method improving TPU and CPU designs. Hugging Face surpassed 1 million free public models, highlighting the value of smaller specialized models. Discussions covered challenges in scaling RAG applications, the future of on-device AI running ChatGPT-level models, reliability issues in larger LLMs, and new Elo benchmarking accepted at NeurIPS 2024. AI ethics and regulation topics included free speech responsibilities and California's SB-1047 bill potentially affecting open-source AI. "AlphaChip transformed computer chip design," and "ChatGPT-level AI on mobile devices predicted within a year."
Qwen 2 beats Llama 3 (and we don't know how)
qwen-2 llama-3 llama-3-70b gpt-4 nllb alibaba groq meta-ai-fair multilinguality benchmarking inference-speed sparse-autoencoders scaling-laws post-training instruction-following rejection-sampling execution-feedback model-release multilingual-models model-training philschmid huybery jonathanross321 awnihannun gdb nabla_theta ylecun
Alibaba released Qwen 2 models under Apache 2.0 license, claiming to outperform Llama 3 in open models with multilingual support in 29 languages and strong benchmark scores like MMLU 82.3 and HumanEval 86.0. Groq demonstrated ultra-fast inference speed on Llama-3 70B at 40,792 tokens/s and running 4 Wikipedia articles in 200ms. Research on sparse autoencoders (SAEs) for interpreting GPT-4 neural activity showed new training methods, metrics, and scaling laws. Meta AI announced the No Language Left Behind (NLLB) model capable of high-quality translations between 200 languages, including low-resource ones. "Our post-training phase is designed with the principle of scalable training with minimal human annotation," highlighting techniques like rejection sampling for math and execution feedback for coding.