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ClickHouse Cloud delivered 1,996× better performance per dollar than Redshift Serverless in CostBench. We trace the gap from fresh data arriving to fast answers coming back.
Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol. I updated my llm-meta-ai plugin for the new model. Here are pelicans for Muse Wonder for the six reasoning levels: minimal, low, medium, high, xhigh, max. This is the max one: For comparison, here are pelicans for Muse 1.3 (released September 2nd) and Muse 1.2 (released August 5th). Meta AI are putting out a new model a month, and the improvements between each model (at least in terms of drawing pelicans) are material. Tags: ai, generative-ai, llms, meta, pelican-riding-a-bicycle, llm-release
iSAM Funds uses ClickHouse Cloud to research full options history, achieving 35–50x compression and 100x faster single-core ingestion while simplifying its data pipelines.
Clera migrated its 500 GB production database and 500+ tables to ClickHouse Managed Postgres overnight, cutting CPU usage from 100% to 10–20%.
Highlights from Postgres Summit US 2026 in New York City, where ClickHouse sponsored the community and demonstrated its growing Postgres integrations.
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 across AWS, GCP, and Azure, with native runtimes, network access, observability, and API and Terraform support.
ClickHouse Managed Postgres uses direct I/O and stripe-sized reads to keep backups fast while protecting the page cache and query latency.
The chDB Durable Layer keeps agent memory fast to query locally while making its analytical state recoverable across laptops, CI jobs, and short-lived sandboxes.
Notes from three days in the Netherlands, featuring a lightning talk on pg_clickhouse and pg_stat_ch at PGDay Lowlands and a session on PostgreSQL 19 monitoring at Percona Live Amsterdam.
UST is bringing Claude to physical AI
More details on Fable 5’s cyber safeguards and our jailbreak framework
QWEN CHAT API DEMO DISCORD It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert (MoE) models. Many critical details regarding this scaling process were only disclosed with the recent release of DeepSeek V3. Concurrently, we are developing Qwen2.
Tech Report HuggingFace ModelScope Qwen Chat HuggingFace Demo ModelScope Demo DISCORD Introduction Two months after upgrading Qwen2.5-Turbo to support context length up to one million tokens, we are back with the open-source Qwen2.5-1M models and the corresponding inference framework support. Here’s what you can expect from this release: Opensource Models: We’re releasing two new checkpoints, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, marking the first time we’ve upgraded our opensource Qwen models to handle 1M-token contexts.
GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction In recent years, Large Language Models (LLMs) have made remarkable advances in mathematical reasoning, yet they can make mistakes, such as miscalculations or logical errors, leading to wrong conclusions. Moreover, even when achieving correct final answers, these powerful models can still regularly make up plausible reasoning steps, where the final answers build upon flawed calculations or derivations, which undermine the reliability and trustworthiness of LLMs’ reasoning processes.
A new set of image generation capabilities for FLUX models, including inpainting, outpainting, canny edge detection, and depth maps.
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.
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we have focused on creating smarter and more knowledgeable language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5. We are announcing what might be the largest opensource release in history!
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In early April, we introduced CodeQwen1.5, which garnered significant attention from the community. Since then, we have been working to enhance the coding model. Today, we are excited to announce the release of the next generation of open-source coding models, Qwen2.5-Coder, and officially rename CodeQwen to Qwen-Coder. We think “Coder” is more human-like and agile, reflecting our vision of it becoming a true coding partner in the future.
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In this blog, we delve into the details of our latest Qwen2.5 series language models. We have developed a range of decoder-only dense models, with seven of them open-sourced, spanning from 0.5B to 72B parameters. Our research indicates a significant interest among users in models within the 10-30B range for production use, as well as 3B models for mobile applications. To meet these demands, we are open-sourcing Qwen2.
