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The Model Context Protocol (MCP) is gaining significant attention in the tech community as a potential game-changer for AI integration. Introduced by Anthropic, MCP aims to standardize interactions between AI agents and external data systems, similar to how USB-C standardizes device connectivity. This protocol promises to reduce repetitive coding, enabling developers to build unified frameworks that can access real-time, domain-specific data securely. Key benefits include reduced redundancy in code, enhanced efficiency through pre-built connectors, and the ability for AI models like AI2SQL to interact with live databases without custom coding. The open-source nature of MCP is likened to 'the HTTP of LLM integrations,' suggesting its potential to become a standard protocol. Developers are encouraged to explore hands-on tutorials and integrate MCP into their workflows to stay ahead in the evolving landscape of AI technology. }

ClickHouse 25.2 introduces several new features including improved support for HTTP event streaming with JSON formats that stream events as they appear, WithProgress variants of output formats, and enhanced Delta Lake integration. It also includes improvements like automatic partition pruning, faster query performance on large datasets, and additional optimizations. The release notes highlight the addition of a Backup table engine, experimental Delta Lake support, and real-time progress bar enhancements in ClickHouse's embedded Web UI.

On March 5th, 2025, the AI model Manus gained widespread attention. Developed by the company behind Monica AI assistant, Manus is described as a general-purpose AI capable of performing various tasks across different applications, potentially outperforming competitors like Deep Research and Claude. The model's viral spread was evident through its high views on social media platforms such as Weibo and Rednote. Its release has sparked discussions among tech enthusiasts and could provide brands with new opportunities for integration into their services.

Replicating analytical data from PostgreSQL to ClickHouse with Postgres CDC is an efficient way to scale your business, enabling real-time analysis of large datasets. By offloading analytical queries to ClickHouse, you can reduce the load on PostgreSQL while leveraging ClickHouse's high-performance capabilities.

This passage provides a detailed overview of the DeepSeek models and their capabilities, including six distilled versions. It outlines their best use cases, reasoning strengths, and compute costs, comparing them to each other and to the original DeepSeek R1 model.

The article discusses DeepSeek's significant advancements in AI inference efficiency, which outperforms many competitors and uses less powerful hardware. Key points include achieving an output of ~1,850 tokens per second per GPU on NVIDIA H800, significantly lower than the H200 benchmarks, and requiring only 2,200 GPUs for inference compared to the massive investments made by hyperscalers. The efficiency allows DeepSeek to reduce capital expenditures, potentially reshaping the AI landscape. The article also questions whether hyperscalers overinvested in current-generation GPUs due to DeepSeek's demonstration of superior performance on fewer resources. Additionally, it explores the implications for GPU vendors if demand decreases and GPUs become underutilized. Overall, the piece highlights how innovation in software efficiency can offer a competitive edge in AI deployment.

The author, who has worked at Google DeepMind for seven years, is leaving to join Anthropic for a year to focus on adversarial machine learning research. He left due to disagreements with leadership over the openness and transparency of scientific publications related to security and privacy in machine learning. Despite the challenges he faced at DeepMind, he believes he can have more impact at Anthropic, where he sees similar values regarding safety and security research. The author expresses hope that his former company will embrace greater collaboration and transparency in addressing these issues. He is optimistic about the potential of language models but emphasizes the need for collective effort to ensure their safe deployment.

The ClickHouse & Vantage teams announced the Billing API for ClickHouse Cloud, which provides programmatic access to billing and usage data. This enables users to integrate cost data into observability tools, automate financial reporting, and implement usage-based billing. A guest post from Vantage showcases how their integration with ClickHouse Cloud's Billing API improves cost visibility, optimizing analytics spend and enabling smarter financial operations. The integration allows for cost alerts, anomaly detection, budget notifications, and detailed cost reports, helping teams manage and optimize cloud spending.

Anthropics, an artificial intelligence company, has raised significant funding, valuing the company at $61.5 billion. This represents a tripling of its valuation since the last funding round.

This article discusses the author's experience with DeepSeek V3 API and compares it to OpenAI. The author found that while DeepSeek is promising, its reliability issues make it less reliable for practical use cases.

The article discusses the rise of distillation techniques in artificial intelligence (AI) development. Distillation involves using a larger 'teacher' model to train smaller 'student' models more efficiently and cost-effectively. This method has gained prominence after Chinese firm DeepSeek used it to build powerful AI models based on open-source systems from competitors Meta and Alibaba, challenging the dominance of US tech giants like OpenAI, Microsoft, and Meta in the market. While distillation can significantly reduce costs for developers and businesses by enabling faster deployment of advanced AI capabilities on devices such as laptops and smartphones, experts note that smaller distilled models may have limited capabilities compared to larger ones. The technique benefits both open-source advocates and those who wish to protect their proprietary large language models against distillation.

Given the daily statistics of 608B input tokens with a 56.3% cache hit rate and 168B output tokens, we can calculate the theoretical daily revenue using DeepSeek-V3's pricing model.

Microsoft has introduced DeepSeek R1 7B and 14B distilled models for Copilot+ PCs via Azure AI Foundry. This advancement aims to bring advanced AI capabilities directly to edge devices, enhancing their performance in real-world applications. The models are optimized for Neural Processing Units (NPUs) to ensure efficient local computation with minimal impact on battery life and resource usage. These models support reasoning tasks that require significant computational power, making them suitable for complex multi-step reasoning scenarios. Developers can access these models through the AI Toolkit VS Code extension and experiment with them using the Playground feature. The integration of these models into Copilot+ PCs is part of Microsoft's broader strategy to make advanced AI accessible on a wide range of devices while leveraging cloud resources when needed, thus creating a new paradigm of continuous compute for AI applications.

DeepSeek, a Chinese AI company, has released its Fire-Flyer File System (3FS) as open-source software. This parallel file system is designed for AI-HPC operations and prioritizes random read speeds over caching. In internal tests, 3FS achieved an aggregate read throughput of 6.6 TB/s on DeepSeek's Fire-Flyer 2 cluster, significantly outperforming competitors like Ceph. The system supports up to 10,000 PCIe Nvidia A100 GPUs and is being made available for free download from the companys Github page.

This article details the author's experience working with Environment Agency flood and river level data using DuckDB for rapid ingest and prototyping and Rill for visualization. The author found that increasing the `maximum_object_size` parameter was necessary to pull in more complete data from the API.