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Person: "hwchase17"
not much happened today
seedance-1.0 codex claude-code kling-2.1 veo-3 bytedance morph-labs huggingface deeplearning.ai figure-ai langchain sakana-ai video-generation autoformalization ai-assisted-coding api-design context-engineering reinforcement-learning ai-evals hypernetworks model-fine-tuning foundation-models andrew_ng hwchase17 adcock_brett clementdelangue akhaliq jxmnop hamelhusain sh_reya
Bytedance showcased an impressive state-of-the-art video generation model called Seedance 1.0 without releasing it, while Morph Labs announced Trinity, an autoformalization system for Lean. Huggingface Transformers deprecated Tensorflow/JAX support. Andrew Ng of DeepLearning.AI highlighted the rise of the GenAI Application Engineer role emphasizing skills in AI building blocks and AI-assisted coding tools like Codex and Claude Code. Engineering teams are increasingly testing API designs against LLMs for usability. Figure AI's CEO stressed speed as a key competitive advantage, and LangChain introduced the concept of Context Engineering for AI agents. Reinforcement learning on LLMs shows transformative potential, and the community values AI evals and data work. Sakana AI released Text-to-LoRA, a hypernetwork method for generating task-specific LoRA adapters from natural language, enabling efficient model customization. The video generation race heats up with Bytedance's Seed-based model praised for quality, challenging American labs, alongside models like Kling 2.1 and Veo 3.
Titans: Learning to Memorize at Test Time
minimax-01 gpt-4o claude-3.5-sonnet internlm3-8b-instruct transformer2 google meta-ai-fair openai anthropic langchain long-context mixture-of-experts self-adaptive-models prompt-injection agent-authentication diffusion-models zero-trust-architecture continuous-adaptation vision agentic-systems omarsar0 hwchase17 abacaj hardmaru rez0__ bindureddy akhaliq saranormous
Google released a new paper on "Neural Memory" integrating persistent memory directly into transformer architectures at test time, showing promising long-context utilization. MiniMax-01 by @omarsar0 features a 4 million token context window with 456B parameters and 32 experts, outperforming GPT-4o and Claude-3.5-Sonnet. InternLM3-8B-Instruct is an open-source model trained on 4 trillion tokens with state-of-the-art results. Transformer² introduces self-adaptive LLMs that dynamically adjust weights for continuous adaptation. Advances in AI security highlight the need for agent authentication, prompt injection defenses, and zero-trust architectures. Tools like Micro Diffusion enable budget-friendly diffusion model training, while LeagueGraph and Agent Recipes support open-source social media agents.
not much happened today
phi-4 reinforce++ arc-agi-2 ai21-labs ollama langchain togethercompute groq reinforcement-learning ppo model-optimization memory-efficiency python-packages vision text-extraction frontend-code-generation workflow-automation coding-agents compute-cost-reduction ethical-ai agi-benchmarks scam-alerts sebastien-bubeck fchollet tom-doerr arohan_ bindureddy hwchase17 jonathanross321 clementdelangue vikhyatk
Sebastien Bubeck introduced REINFORCE++, enhancing classical REINFORCE with PPO-inspired techniques for 30% faster training. AI21 Labs released Phi-4 under the MIT License, accessible via Ollama. François Chollet announced plans for ARC-AGI-2 and a next-generation AGI benchmark. LangChain launched 10 new integration packages to boost LLM application development. Tom Doerr introduced Ollama-OCR, a Python package for text extraction using vision language models. Arohan optimized Shampoo for memory efficiency, reducing usage from 20 to 6 bytes per parameter. Bindu Reddy showcased CodeLLM's v1 for frontend code generation and highlighted LlamaIndex Workflows for academic summarization and slide generation. Hwchase17 collaborated with Together Compute to enhance WebDev Arena with complex coding agents for LLM coding evaluations. Jonathan Ross detailed Groq's mission to reduce compute costs by 1000x amid rising generative AI spending. Clement Delangue warned about scam alerts involving false claims of association with AI21. Vikhyat K raised concerns about the ethical implications and trade-offs of AGI. Memes and humor included creative AI prompts and critiques of LLM behaviors.
PRIME: Process Reinforcement through Implicit Rewards
claude-3.5-sonnet gpt-4o deepseek-v3 gemini-2.0 openai together-ai deepseek langchain lucidrains reinforcement-learning scaling-laws model-performance agent-architecture software-development compute-scaling multi-expert-models sama aidan_mclau omarsar0 akhaliq hwchase17 tom_doerr lmarena_ai cwolferesearch richardmcngo
Implicit Process Reward Models (PRIME) have been highlighted as a significant advancement in online reinforcement learning, trained on a 7B model with impressive results compared to gpt-4o. The approach builds on the importance of process reward models established by "Let's Verify Step By Step." Additionally, AI Twitter discussions cover topics such as proto-AGI capabilities with claude-3.5-sonnet, the role of compute scaling for Artificial Superintelligence (ASI), and model performance nuances. New AI tools like Gemini 2.0 coder mode and LangGraph Studio enhance agent architecture and software development. Industry events include the LangChain AI Agent Conference and meetups fostering AI community connections. Company updates reveal OpenAI's financial challenges with Pro subscriptions and DeepSeek-V3's integration with Together AI APIs, showcasing efficient 671B MoE parameter models. Research discussions focus on scaling laws and compute efficiency in large language models.
Anthropic launches the Model Context Protocol
claude-3.5-sonnet claude-desktop anthropic amazon zed sourcegraph replit model-context-protocol integration json-rpc agentic-behaviors security tool-discovery open-protocol api-integration system-integration prompt-templates model-routing alex-albert matt-pocock hwchase17
Anthropic has launched the Model Context Protocol (MCP), an open protocol designed to enable seamless integration between large language model applications and external data sources and tools. MCP supports diverse resources such as file contents, database records, API responses, live system data, screenshots, and logs, identified by unique URIs. It also includes reusable prompt templates, system and API tools, and JSON-RPC 2.0 transports with streaming support. MCP allows servers to request LLM completions through clients with priorities on cost, speed, and intelligence, hinting at an upcoming model router by Anthropic. Launch partners like Zed, Sourcegraph, and Replit have reviewed MCP favorably, while some developers express skepticism about its provider exclusivity and adoption potential. The protocol emphasizes security, testing, and dynamic tool discovery, with guides and videos available from community members such as Alex Albert and Matt Pocock. This development follows Anthropic's recent $4 billion fundraise from Amazon and aims to advance terminal-level integration for Claude Desktop.
not much happened today
flux-schnell meta-ai-fair anthropic togethercompute hugging-face audio-generation quantization prompt-caching long-term-memory llm-serving-framework hallucination-detection ai-safety ai-governance geoffrey-hinton john-hopfield demis-hassabis rohanpaul_ai svpino hwchase17 shreyar philschmid mmitchell_ai bindureddy
Geoffrey Hinton and John Hopfield won the Nobel Prize in Physics for foundational work on neural networks linking AI and physics. Meta AI introduced a 13B parameter audio generation model as part of Meta Movie Gen for video-synced audio. Anthropic launched the Message Batches API enabling asynchronous processing of up to 10,000 queries at half the cost. Together Compute released Flux Schnell, a free model for 3 months. New techniques like PrefixQuant quantization and Prompt Caching for low-latency inference were highlighted by rohanpaul_ai. LangGraph added long-term memory support for persistent document storage. Hex-LLM framework was introduced for TPU-based low-cost, high-throughput LLM serving from Hugging Face models. Discussions on AI safety emphasized gender equality in science, and concerns about premature AI regulation by media and Hollywood were raised.
5 small news items
llama-3 xLSTM openai cohere deepmind hugging-face nvidia mistral-ai uncertainty-quantification parameter-efficient-fine-tuning automated-alignment model-efficiency long-context agentic-ai fine-tuning inference-optimization leopold-aschenbrenner will-brown rohanpaul_ai richardmcngo omarsar0 hwchase17 clementdelangue sophiamyang
OpenAI announces that ChatGPT's voice mode is "coming soon." Leopold Aschenbrenner launched a 5-part AGI timelines series predicting a trillion dollar cluster from current AI progress. Will Brown released a comprehensive GenAI Handbook. Cohere completed a $450 million funding round at a $5 billion valuation. DeepMind research on uncertainty quantification in LLMs and an xLSTM model outperforming transformers were highlighted. Studies on the geometry of concepts in LLMs and methods to eliminate matrix multiplication for efficiency gains were shared. Discussions on parameter-efficient fine-tuning (PEFT) and automated alignment of LLMs were noted. New tools include LangGraph for AI agents, LlamaIndex with longer context windows, and Hugging Face's integration with NVIDIA NIM for Llama3. Mistral AI released a fine-tuning API for their models.