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- 🤖 OpenAI releases AI Agent tools
🤖 OpenAI releases AI Agent tools
🦾Plus: 🤖 Manus+Qwen team up for China push

Hey folks! Let’s get into Big Data and AI craziness…
In today's edition: What's Shaping the Future of Data?
🔍Top 15 Data Science Trends and Predictions
⚡ A 10x Faster TypeScript
🧠Understanding Attention in LLMs
📈Stock research agent with LitServe
🖥️ Google's Gemma 3 now reads images and videos
🤖 Manus, Qwen team up for China push
🏗️ Meta testing its own AI training chip
💡AI Tutorial:Turn your voice notes into text with AI
🤖 AI Tools and Data Tools to checkout

The rise of data science as a field of study and viable application throughout the last century has prompted the improvement of technologies, for example, deep learning, natural language processing, and computer vision.
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Microsoft is working on a native port of the TypeScript compiler and tools to drastically improve editor startup, reduce most build times, and substantially reduce memory usage.

There are many excellent AI papers and tutorials that explain the attention pattern in Large Language Models. But this essentially simple pattern is often obscured by implementation details and optimizations. In this post I will try to cut to the essentials.

OpenAI just launched new tools that let businesses build their own AI agents—enabling custom bots to handle tasks like web browsing and file management and marking a major push toward bringing autonomous AI assistants into the enterprise.
The new Responses API combines web search, file scanning, and computer use capabilities, replacing the older Assistants API, which will sunset in 2026.
Built-in tools including web search, file search, and computer use
The new Agents SDK to orchestrate single-agent and multi-agent workflows
Integrated observability tools to trace and inspect agent workflow execution

Analyze financial trends, evaluates stocks, and provides data-backed buy or hold recommendations—empowering to make informed decisions with confidence. This Studio showcases a self-hosted, private API that deploys an AI agent with tool use for financial analysis using LitServe.
👨💻 Data Tools, Libraries
Building an Agentic System (Website)
This site provides a practical deep dive and code review into how to build a self-driving coding agent, execution engine, tools, and commands.
pg_mooncake 🥮 (GitHub Repo)
pg_mooncake is a Postgres extension that adds columnar storage and vectorized execution for fast analytics. It ranks among the top 10 fastest on ClickBench
AI News:
to help you learn faster, so you can dive right into a new language without the stress. There are 14 languages to choose from and innovative ways to learn—like lessons, podcasts, games, videos, and the new AI Conversation Partner.

OpenAI just launched new tools that let businesses build their own AI agents—enabling custom bots to handle tasks like web browsing and file management and marking a major push toward bringing autonomous AI assistants into the enterprise.
The new Responses API combines web search, file scanning, and computer use capabilities, replacing the older Assistants API, which will sunset in 2026.
Built-in tools including web search, file search, and computer use
The new Agents SDK to orchestrate single-agent and multi-agent workflows
Integrated observability tools to trace and inspect agent workflow execution

Google just updated its Gemma AI family with Gemma 3. This version can handle images, short videos, and text. It performs faster than rivals like OpenAI or DeepSeek’s V3 when running on just one GPU, making it easier for smaller developers to build advanced AI apps

Manus just announced a strategic partnership with Alibaba's Qwen team to develop a Chinese version of its autonomous agent platform, following the company’s viral success over the past week. The collaboration will integrate Manus's agent capabilities with Qwen's open-source language models and computing infrastructure.

Meta just began testing its first in-house AI training chip, according to a new report from Reuters — with the company hoping to reduce dependence on Nvidia and control its soaring AI infrastructure costs. The chip is being manufactured by TSMC and is part of Meta's MTIA series—aimed specifically at AI training and inference workloads.

Google owns 14% of Anthropic. Its investment gives it little control over the AI startup - Google holds no voting rights, no board seats, and no board observer rights at Anthropic. Google is set to invest an additional $750 million in Anthropic in September through a convertible debt - it has invested more than $3 billion into the AI company in total so far. It is only allowed to own up to 15% of Anthropic.
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