A Non Technical Guide to the tech Interview
A Non-Technical Guide to the Tech Interview Your Practical Roadmap to Landing a Job in Tech
Paperback: https://www.amazon.com/Non-Technical-Guide-Tech-Interview-Strategies/dp/B0D98ZNZTT
Kindle: https://www.amazon.com/Non-Technical-Guide-Tech-Interview-Strategies-ebook/dp/B0D9CL9L78/ref=tmm_kin_swatch_0
Introduction Over the years, people from very different backgrounds have reached out to me for advice on tech interviews. New graduates trying to land their first role, experienced engineers aiming for highly competitive companies, career switchers entering tech from non-traditional paths, and even senior professionals preparing for leadership interviews have all asked variations of the same question: How do tech interviews really work, and what are the most important considerations a candidate should keep in mind?
How to Reduce the Cost and Token Usage of Claude Code
How Can I Reduce the Cost and Token Usage of Claude Code? Claude Code is a powerful development assistant, but power comes with a cost: tokens. Whether you are paying via a subscription (such as Claude Max) or through usage-based API billing in the CLI, inefficient context usage can quietly become expensive, slow, and cognitively noisy.
This post is a deep, practical guide to understanding where Claude Code spends tokens, why those costs grow faster than many developers expect, and—most importantly—how to systematically reduce token usage without sacrificing effectiveness.
Best MCP Servers for Claude Code
What Are the Best MCP (Model Context Protocol) Servers to Install for Claude Code Table of Contents Introduction: Claude Code and Why MCP Matters
What Is MCP (Model Context Protocol)?
Open Standard, Interoperability, and Real-World Workflows How MCP Works Technically
Client-Server Interaction Protocol Lifecycle and Transport Setting Up Claude Code: A Quick Primer
Installing Claude Code Getting Your environment ready Basic Configuration Why You Would Use MCP with Claude Code
Building Real-Time Video Intelligence with Gemini
Building Real-Time Video Intelligence with Gemini: A Developer’s Guide to Recreating “Gemini Live” in Google AI Studio Audience: Software engineers, ML engineers, and technical product builders Prerequisites: JavaScript/TypeScript or Python, basic web development, REST/WebSocket APIs
Table of Contents Introduction What “Gemini Live Video” Actually Is Architectural Overview Preparing Your Environment Understanding the Gemini Multimodal API Designing a Real-Time Video Pipeline Capturing Video on the Client Frame Sampling, Encoding, and Transport Building the Gemini Session Layer Sending Visual Context to Gemini Streaming Responses Back to the Client Managing Latency, Throughput, and Cost Security, Privacy, and Compliance Considerations Extending the System: Object Awareness, Guidance, and Actions Testing, Evaluation, and Observability Deployment Patterns Common Pitfalls and How to Resolve Them Conclusion 1.
Mastering stream mode in Google AI Studio
By My Ultimate Guide For Everything
| Jan 7, 2026
| google-ai-studio, stream-mode, gemini-live-api, real-time-ai, multimodal-ai, voice-and-vision-ai, ai-streaming, interactive-ai, developer-tools, ai-product-design
Mastering Stream Mode in Google AI Studio: Voice, Vision, and Real-Time Interaction Table of Contents Introduction What Is Google AI Studio and Why Stream Mode Matters Understanding Real-Time AI: Concepts and Architecture Stream Mode vs. Standard Prompting: What Changes in Practice Enabling Stream Mode: Requirements, Permissions, and Setup Voice Interaction: Microphones, VAD, Turn-Taking, and Natural Conversation Vision in Real Time: Webcam, Screen Share, and Multimodal Context Screen Sharing Deep Dive: What the Model “Sees” and What It Doesn’t Managing Latency: Time-to-First-Token, Network, and Device Constraints Audio Configuration and Troubleshooting Video and Screen Quality: Resolution, Frame Rate, and Token Economics Common Errors and How to Fix Them Security, Privacy, and Data Handling Product Design Patterns for Real-Time AI Experiences Developer Pathways: When to Use the Live API Instead of the UI Performance Optimization: Streaming vs.
Should You Adopt MCP?
Should You Adopt MCP? A Systems-Level Guide to Choosing Between Simple Tool Calls and the Model Context Protocol Abstract As LLM-based systems evolve from prompt-driven prototypes into production-grade platforms, teams increasingly face an architectural decision: Should we integrate external capabilities via simple tool calls, or adopt a structured protocol such as the Model Context Protocol (MCP)?
This article treats MCP not as a feature, but as an interface and governance architecture.
Debugging code with GenAI
Debugging Code with GenAI: What You Need to Know 1. Introduction: How GenAI Facilitates Writing and Debugging Code Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), has rapidly become embedded in modern software development workflows. What began as tools for autocompleting code snippets or answering programming questions has evolved into full-fledged assistants capable of explaining complex codebases, proposing architectural changes, writing tests, and, increasingly, debugging production-grade systems.
At a high level, GenAI helps developers debug code by:
Real-Time Streaming and Camera Input in Google AI Studio: A Comprehensive Guide
Real-Time Streaming and Camera Input in Google AI Studio: A Comprehensive Guide
Introduction
Google AI Studio has revolutionized the way developers and enthusiasts interact with artificial intelligence. One of its standout features is the Stream Realtime capability, which allows users to engage with AI models like Gemini in real-time using multimodal inputs, including text, audio, and video. This feature enables dynamic interactions, making AI more accessible and responsive to human needs.
The AI Video Wars: Veo 3 vs. Runway Gen-3 Alpha vs. Kling AI
The AI Video Wars: Veo 3 vs. Runway Gen-3 Alpha vs. Kling AI The world of generative AI is moving at breakneck speed, nowhere more evident than in the realm of video creation. What was once the exclusive domain of complex software and specialized professionals is now accessible to anyone with a well-crafted text prompt. Among the leading contenders vying for supremacy in this exciting space are Google’s Veo 3, Runway’s Gen-3 Alpha, and the rapidly emerging Kling AI.
MCP Security Risks and Mitigations
By My Ultimate Guide For Everything
| May 10, 2025
| mcp, llm, security-risk, prompt-injection, token-theft, server-compromise, rug-pool, tool-shadowing, tool-poisoning, consent-fatigue
Understanding Model Context Protocol (MCP) Security Risks in LLM Systems As large language models (LLMs) evolve to support more powerful and context-aware applications, new paradigms like the Model Context Protocol (MCP) have emerged. MCP offers a structured way to organize the inputs provided to an LLM, typically encompassing task instructions, memory state, tool documentation, user profiles, historical conversation context, and more. While this protocol enhances the power and usability of LLM-driven systems, it also introduces critical security risks that must be mitigated to ensure user safety and system integrity.