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Topic: "data-curation"
not much happened today
claude codex langsmith-engine smithdb duet-agent multi-stream-llm delta-mem star-elastic cline langchain notion cursor nous-research nvidia datology agent-infrastructure developer-platforms observability long-running-state streaming orchestration pretraining-efficiency model-architecture external-memory post-training-compression data-curation vision-language-models jonas_geiping siddharth_joshi pratyush_maini
Cline, LangChain, Notion, and Cursor advanced agent infrastructure and developer platforms with innovations like Cline SDK, LangSmith Engine, SmithDB (offering 12–15× faster observability), and Notion's External Agents API integrating third-party agents such as Claude and Codex. Agent UX trends emphasize long-running state, streaming, and orchestration over chat, with tools like Duet Agent and VS Code Agents window enhancing durable execution and inspectable states. Research highlights include Nous Research's Token Superposition Training achieving 2–3× speedup in pretraining, a multi-stream LLM architecture for parallel reasoning by Jonas Geiping et al., and δ-mem external memory improving benchmark scores. NVIDIA's Star Elastic offers post-training model compression at 360× lower cost than pretraining, while Datology focuses on data curation for vision-language models.
Not much happened today
gemini-1.5-flashmodel gemini-pro mixtral mamba-2 phi-3-medium phi-3-small gpt-3.5-turbo-0613 llama-3-8b llama-2-70b mistral-finetune twelve-labs livekit groq openai nea nvidia lmsys mistral-ai model-performance prompt-engineering data-curation ai-safety model-benchmarking model-optimization training sequence-models state-space-models daniel-kokotajlo rohanpaul_ai _arohan_ tri_dao _albertgu _philschmid sarahcat21 hamelhusain jachiam0 willdepue teknium1
Twelve Labs raised $50m in Series A funding co-led by NEA and NVIDIA's NVentures to advance multimodal AI. Livekit secured $22m in funding. Groq announced running at 800k tokens/second. OpenAI saw a resignation from Daniel Kokotajlo. Twitter users highlighted Gemini 1.5 FlashModel for high performance at low cost and Gemini Pro ranking #2 in Japanese language tasks. Mixtral models can run up to 8x faster on NVIDIA RTX GPUs using TensorRT-LLM. Mamba-2 model architecture introduces state space duality for larger states and faster training, outperforming previous models. Phi-3 Medium (14B) and Small (7B) models benchmark near GPT-3.5-Turbo-0613 and Llama 3 8B. Prompt engineering is emphasized for unlocking LLM capabilities. Data quality is critical for model performance, with upcoming masterclasses on data curation. Discussions on AI safety include a Frontier AI lab employee letter advocating whistleblower protections and debates on aligning AI to user intent versus broader humanity interests.