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Person: "c_valenzuelab"
ChatGPT Codex, OpenAI's first cloud SWE agent
codex-1 openai-o3 codex-mini gemma-3 blip3-o qwen-2.5 marigold-iid deepseek-v3 lightlab gemini-2.0 lumina-next openai runway salesforce qwen deepseek google google-deepmind j1 software-engineering parallel-processing multimodality diffusion-models depth-estimation scaling-laws reinforcement-learning fine-tuning model-performance multi-turn-conversation reasoning audio-processing sama kevinweil omarsar0 iscienceluvr akhaliq osanseviero c_valenzuelab mervenoyann arankomatsuzaki jasonwei demishassabis philschmid swyx teortaxestex jaseweston
OpenAI launched Codex, a cloud-based software engineering agent powered by codex-1 (an optimized version of OpenAI o3) available in research preview for Pro, Enterprise, and Team ChatGPT users, featuring parallel task execution like refactoring and bug fixing. The Codex CLI was enhanced with quick sign-in and a new low-latency model, codex-mini. Gemma 3 is highlighted as the best open model runnable on a single GPU. Runway released the Gen-4 References API for style transfer in generation. Salesforce introduced BLIP3-o, a unified multimodal model family using diffusion transformers for CLIP image features. The Qwen 2.5 models (1.5B and 3B versions) were integrated into the PocketPal app with various chat templates. Marigold IID, a new state-of-the-art open-source depth estimation model, was released.
In research, DeepSeek shared insights on scaling and hardware for DeepSeek-V3. Google unveiled LightLab, a diffusion-based light source control in images. Google DeepMind's AlphaEvolve uses Gemini 2.0 to discover new math and reduce costs without reinforcement learning. Omni-R1 studied audio's role in fine-tuning audio LLMs. Qwen proposed a parallel scaling law inspired by classifier-free guidance. Salesforce released Lumina-Next on the Qwen base, outperforming Janus-Pro. A study found LLM performance degrades in multi-turn conversations due to unreliability. J1 is incentivizing LLM-as-a-Judge thinking via reinforcement learning. A new Qwen study correlates question and strategy similarity to predict reasoning strategies.
Prime Intellect's INTELLECT-2 and PRIME-RL advance distributed reinforcement learning
intellect-2 dreamo qwen gemini-2.5-pro dynamic-byte-latent-transformer gen-4-references mistral-medium-3 le-chat-enterprise primeintellect bytedance qwen gemma meta-ai-fair runwayml mistral-ai google distributed-training reinforcement-learning gpu-clusters model-optimization quantization multimodality agentic-ai video-understanding fine-tuning _akhaliq reach_vb osanseviero aiatmeta c_valenzuelab lmarena_ai adcock_brett
Prime Intellect released INTELLECT-2, a decentralized GPU training and RL framework with a vision for distributed AI training overcoming colocation limits. ByteDance launched DreamO, a unified image customization model on Hugging Face. Qwen released models optimized for GPTQ, GGUF, and AWQ quantization. Gemma surpassed 150 million downloads on Hugging Face. Meta released weights for the Dynamic Byte Latent Transformer and the Collaborative Reasoner framework to improve language model efficiency and reasoning. RunwayML introduced Gen-4 References, a near-realtime model requiring no fine-tuning. Mistral AI released Mistral Medium 3, a strong multimodal model, and Le Chat Enterprise, an agentic AI assistant for business. Google updated Gemini 2.5 Pro Preview with video understanding and UI improvements. "Airbnb for spare GPUs from all over the world" highlights the ongoing challenges and potential of distributed GPU training.
DeepCoder: A Fully Open-Source 14B Coder at O3-mini Level
deepcoder-14b o3-mini o1 gemini-2.5-pro kimi-vl-a3b gpt-4o llama-4-scout maverick behemoth gen-4-turbo imagen-3 together-ai agentica opena bytedance google-deepmind moonshot-ai meta-ai-fair runway open-source reinforcement-learning code-generation multimodality model-training mixture-of-experts l2-normalization image-generation model-performance context-windows philschmid lepikhin reach_vb akhaliq yuchenj_uw epochairesearch danielhanchen c_valenzuelab
Together AI and Agentica released DeepCoder-14B, an open-source 14B parameter coding model rivaling OpenAI's o3-mini and o1 on coding benchmarks, trained with an open-source RL framework from ByteDance and costing about $26,880. Google DeepMind launched Gemini 2.5 Pro with experimental "Flash" versions available to subscribers. Moonshot AI introduced Kimi-VL-A3B, a multimodal model with 128K context outperforming gpt-4o on vision and math benchmarks. Meta AI released Llama 4 Scout and Maverick, with a larger Behemoth model in training, featuring mixture-of-experts and L2 norm techniques. Runway launched Gen-4 Turbo with 10x better results than Gen-3 at the same cost. Google announced Imagen 3, a high-quality text-to-image model now in Vertex AI, enabling easier object removal. The report highlights open-source contributions, reinforcement learning training optimizations, and significant model performance improvements across coding, multimodal, and image generation domains.
OpenAI beats Anthropic to releasing Speculative Decoding
claude-3-sonnet mrt5 openai anthropic nvidia microsoft boston-dynamics meta-ai-fair runway elevenlabs etched osmo physical-intelligence langchain speculative-decoding prompt-lookup cpu-inference multimodality retrieval-augmented-generation neural-networks optimization ai-safety governance model-architecture inference-economics content-generation adcock_brett vikhyatk dair_ai rasbt bindureddy teortaxestex svpino c_valenzuelab davidsholz
Prompt lookup and Speculative Decoding techniques are gaining traction with implementations from Cursor, Fireworks, and teased features from Anthropic. OpenAI has introduced faster response times and file edits with these methods, offering about 50% efficiency improvements. The community is actively exploring AI engineering use cases with these advancements. Recent updates highlight progress from companies like NVIDIA, OpenAI, Anthropic, Microsoft, Boston Dynamics, and Meta. Key technical insights include CPU inference capabilities, multimodal retrieval-augmented generation (RAG), and neural network fundamentals. New AI products include fully AI-generated games and advanced content generation tools. Challenges in AI research labs such as bureaucracy and resource allocation were also discussed, alongside AI safety and governance concerns.