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- 🤖 Alibaba Drops Qwen3 AI Model
🤖 Alibaba Drops Qwen3 AI Model
OpenAI drops new shopping feature, Anthropic’s New Study : AI’s Impact on Software Development, Alibaba's Qwen3 AI Model & more!

Hello Everyone,
Here are some interesting updates in AI worth checking out today.
Alibaba Drops Qwen3 AI Model
Anthropic’s New Study : AI’s Impact on Software Development
OpenAI drops new shopping feature!
AI Course of the Day
AI - Word of the Day
🤖 Alibaba Drops Qwen3 AI Model

Alibaba launched Qwen3, a new family of large language models from 0.6B to 235B parameters. The flagship Qwen3-235B-A22B rivals top models like DeepSeek-R1 and Gemini-2.5-Pro, while the smaller Qwen3-30B-A3B outperforms larger models with fewer active parameters. Qwen3 models are now available on Hugging Face, ModelScope, and GitHub, with deployment through tools like SGLang and Ollama.
💡 CXO Corner
⚡ Anthropic’s New Study : AI’s Impact on Software Development

Credits - Anthropic
Anthropic’s latest research shows AI is transforming coding work. Analyzing 500,000 interactions, they found that 79% of Claude Code conversations involved automation, far higher than on standard Claude.ai. Developers mostly use AI for building user interfaces with JavaScript, HTML, and CSS, suggesting UI/UX jobs may face faster disruption. Startups are leading adoption, while enterprises lag. As agentic AI systems grow, developers may shift from writing code to managing AI-driven workflows, raising new questions about the future of software development.
🛍️ OpenAI drops new shopping feature!

OpenAI is rolling out new shopping features in ChatGPT, making it easier to find, compare, and buy products. Users will now see improved product results with visual details, prices, reviews, and direct purchase links — all without ads. The update is gradually launching for Plus, Pro, Free, and even logged-out users worldwide.
Read more here.
🧠 AI Word of the Day: "Overfitting"
Overfitting happens when an AI model learns the training data too well, including the noise, mistakes, or random patterns—making it perform badly on new, unseen data.
Example:
If you train a model to recognize cats and it memorizes only pictures of your cats, it might completely fail when shown a different breed 🐈❌.
That’s all for today!
I hope you liked today’s newsletter edition.
Best,
Harsh