How a data scientist at Kilo built the business context layer his AI agents were missing
How a data scientist built an AI context layer for organizational knowledge a dbt project alone can't hold.
AI NEWS JOURNAL
Search AI news
Search headlines, summaries and articles to connect the dots.
Why BigQuery can’t match ClickHouse Cloud for real-time analytics
ClickHouse Cloud delivered 438× better performance per dollar than BigQuery in CostBench. We trace the gap from fresh data arriving to fast answers coming back.
Executable UDFs are now generally available on ClickHouse Cloud
Executable UDFs are now generally available on ClickHouse Cloud across AWS, GCP, and Azure, with native runtimes, network access, observability, and API and Terraform support.
Introducing a way to reflect on how you use Claude
Introducing a way to reflect on how you use Claude
More details on Fable 5’s cyber safeguards and our jailbreak framework
More details on Fable 5’s cyber safeguards and our jailbreak framework
Extending the Context Length to 1M Tokens!
API Documentation (Chinese) HuggingFace Demo ModelScope Demo Introduction After the release of Qwen2.5, we heard the community’s demand for processing longer contexts. In recent months, we have made many optimizations for the model capabilities and inference performance of extremely long context. Today, we are proud to introduce the new Qwen2.5-Turbo version, which features: Longer Context Support: We have extended the model’s context length from 128k to 1M, which is approximately 1 million English words or 1.
Llama 3.2 goes small and multimodal
Ollama partners with Meta to bring Llama 3.2 to Ollama.
Qwen1.5-MoE: Matching 7B Model Performance with 1/3 Activated Parameters
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction Since the surge in interest sparked by Mixtral, research on mixture-of-expert (MoE) models has gained significant momentum. Both researchers and practitioners are keenly interested in understanding how to effectively train such models and assessing their efficiency and effectiveness. Today, we introduce Qwen1.5-MoE-A2.7B, a small MoE model with only 2.7 billion activated parameters yet matching the performance of state-of-the-art 7B models like Mistral 7B and Qwen1.