AI

TL;DR - Quick Setup Already know what Serena is? Here’s the fast track: # 1. Install uv and Serena curl -LsSf https://astral.sh/uv/install.sh | sh source ~/.zshrc uv tool install git+https://github.com/oraios/serena # 2. Restart terminal, then connect to your project cd /path/to/your/project claude mcp add serena -- serena-mcp-server --project $(pwd) echo ".serena/" >> .gitignore # 3. Start Claude claude --allowedTools "mcp__serena*" Then tell Claude: “Use Serena to onboard this project.” Want to understand what this does and why? Read on. ...

A Quick Disclaimer This article is primarily a self-note that I keep updating as I learn more about Claude Code. The AI tooling landscape evolves rapidly, so some information might be outdated by the time you read this. If you find something that needs updating, feel free to reach out! Whether you’re a junior developer just getting started or a senior developer looking to boost your workflow, Claude Code has something for everyone. ...

This is an elaborated version of my talk at Google Developer Group Chennai’s DevFest2025, where I shared my journey from rock bottom to becoming an Android Developer at Zoho and how the “Solutionist Mindset” can help us thrive in the age of AI. Presenting the Solutionist Mindset on stage Standing on that stage at DevFest2025 in Chennai, looking out to you all, I asked a simple question: “How many of you are worried about AI taking over your job?” ...

Fair Warning Make sure you are using a capable system — ideally with a powerful CPU, GPU, and adequate cooling — before running large language models locally. LLMs can consume significant resources, generate substantial heat, and may cause system instability or damage if your hardware isn’t up to the task. Please proceed with caution! This guide walks you through setting up Ollama (with deepseek-r1-goose) and Goose CLI. Step 1: Download & Install Ollama curl -fsSL https://ollama.com/install.sh | sh Or, if you prefer to download manually then checkout:https://ollama.com/download Ollama-SetUpGuide ...

When diving into AI/ML, we constantly hear about transformers and their revolutionary impact. But how did we get here? The journey started from Traditional Neural Networks ➡ Recurrent Neural Networks (RNNs) ➡ Attention Mechanisms and finally to Transformers. RNNs were a significant milestone because they addressed sequential data processing but had major limitations. These limitations led to the birth of attention mechanisms and transformers. Interestingly, before GPT became a household name, Google extensively used RNNs in their products, including predictive text, speech recognition, and early recommendation systems. ...