Writing
Essays, Digests, and Deep Dives
Long-form writing across GenAI, infra, developer tooling, and what actually matters for builders.
Tech Monthly Digest: August 2026 - Anthropic Turns a Profit, the AI Act Goes Live, and Agents Reach the Physical World
August 2026 was the month the AI business stopped being a projection. Anthropic posted more than $11.5B in quarterly revenue and the first operating profit in frontier AI. The EU AI Act became fully enforceable on August 2, with real fines attached. Salesforce made its CRM a plugin inside Claude. Google shipped Managed Agents and Antigravity into public preview. And Anthropic's Model Hardware Standard pointed agents at microscopes and robotic arms. Here's what mattered for builders — and for the people who have to justify the spend.
Tech Monthly Digest: June 2026 - GPT-5.6, Anthropic's Mythos, and the Business of Agents
June 2026 was the month the agent story split cleanly into two tracks: the technology kept compounding, and the business finally caught up to it. OpenAI shipped GPT-5.6 with a model card and a Codex-native release. Anthropic pushed its Mythos preview wider and took Claude Code's Dynamic Workflows to GA. WebMCP jumped past Chrome. And underneath it all, the money moved — record funding, enterprise seat expansions, and the first real agent-governance regulation. Here's what mattered for builders and for the people who fund them.
Product Analytics: Building a Stack That Actually Drives Decisions
Most analytics stacks generate dashboards nobody opens. This is the product-builder's guide to doing it right in 2026 — a clean event taxonomy, a North Star you can defend, the modern warehouse-to-BI pipeline, the trust problems that quietly kill adoption, and where AI copilots actually help. Built for people who ship products, not just charts.
Agentic RAG: When Retrieval Learns to Think
Classic RAG retrieves once and hopes. Agentic RAG plans, retrieves, reflects, and retrieves again — closing the gap between 'found some chunks' and 'actually answered the question.' This is the practical 2026 guide to query planning, multi-hop retrieval, re-ranking, tool-using retrievers, and the new failure modes you inherit when retrieval becomes an agent.
AI Agent Memory: Architectures for Long-Running Agents
A stateless agent forgets you the moment the context window closes. Real assistants remember — preferences, past decisions, what worked and what failed. This is the 2026 guide to agent memory: working vs long-term, episodic vs semantic vs procedural, the vector and graph stores that back them, and the hard problem nobody talks about — knowing what to forget.
Small Language Models: Why Smaller Is Winning in Production
The race for ever-bigger frontier models grabbed the headlines, but the quiet revolution of 2026 is happening at the other end: small, specialized models that run on a laptop, a phone, or the edge — fast, cheap, private, and shockingly capable. Here's why SLMs are taking over real production workloads, how distillation and quantization make them possible, and when small genuinely beats big.
AI Evals: How to Actually Measure LLM and Agent Quality in Production (2026 Guide)
Anyone can ship an LLM demo. Shipping one you can trust is an evaluation problem. This is the practical 2026 guide to AI evals — what to measure, how to build offline and online eval pipelines, when LLM-as-judge works (and when it lies), and how to turn 'it feels better' into numbers you can defend.
Tech Monthly Digest: May 2026 - Opus 4.8, GPT-5.5 Instant, and the Agent Stack Goes Mainstream
May 2026 was the month frontier AI stopped feeling like a release schedule and started feeling like an operating system. Anthropic shipped Opus 4.8 just 41 days after 4.7 with Dynamic Workflows orchestrating up to 1,000 subagents. OpenAI made GPT-5.5 Instant the default for every ChatGPT user. Google I/O turned the agent stack into a developer platform with Gemini 3.5 Flash, Antigravity 2.0, and WebMCP. Microsoft Agent 365 went GA. Here's what actually mattered for builders.
Google I/O 2026: The Agentic Gemini Era - Antigravity 2.0, Gemini 3.5 Flash, and the Full Agent Stack
Google I/O 2026 was not a model launch. It was Google revealing a complete agent stack: Gemini 3.5 Flash beating last quarter's flagship, Antigravity 2.0 with a real CLI and SDK, Managed Agents as an API primitive, Gemini Spark as a 24/7 personal agent, WebMCP turning the open web into an agent surface, and an Android CLI that lets agents drive Android Studio. Here's what actually shipped and why it changes how you build.
GitStory: Turning Any GitHub Profile Into an AI-Generated Career Narrative
GitStory is a full-stack Replit Buildathon project that transforms a public GitHub profile into a cinematic, AI-generated developer story. It pulls live profile data, uses GPT-5.4 for narrative generation, caches stories in PostgreSQL, and makes each story instantly shareable.
Tech Monthly Digest: April 2026 - Agents Become the Enterprise Operating Layer
April 2026 was the month agents stopped being a developer-side experiment and became enterprise infrastructure. OpenAI pushed Codex across apps, teams, and AWS. Anthropic shipped Opus 4.7, Claude Design, and two massive compute expansions. Google Cloud Next turned agent platforms into the main cloud story. Here's what actually mattered for builders.
AI × Analytics: Why 20% of Companies Are Capturing 75% of AI's Economic Value
Stanford's 2026 AI Index and PwC's AI Performance Study reveal a striking truth: the gap between AI winners and losers isn't about model quality — it's about analytics. Here's what the data says and how builders can position themselves at the most powerful intersection in tech.
Tech Monthly Digest: March 2026 – The Model Avalanche, Sora's Death, and Agentic Infrastructure Comes of Age
March 2026 was the month AI stopped being cyclical and became perpetual. Twelve frontier models shipped in a single week. OpenAI killed Sora and acquired Astral. Anthropic leaked a model and its own source code. Meta's Llama 4 went open source and beat GPT-4o. AWS AgentCore and Google ADK 1.0 made production agents real. Here's everything that mattered for builders.
Anthropic Shipped 20+ Major Updates in 3 Months - Here's What Builders Need to Know
From Claude Cowork and Opus 4.6 to Computer Use and 1M context windows, Anthropic's Q1 2026 was a masterclass in shipping. A deep dive into every major release, what it means for builders, and why the gap between AI users and AI builders is widening every week.
A2A Protocol: How Google's Agent-to-Agent Standard Is Redefining Multi-Agent AI in 2026
Google's Agent-to-Agent (A2A) protocol is quietly becoming the TCP/IP of the agentic era — a universal standard that lets AI agents from different vendors, frameworks, and clouds communicate, negotiate, and collaborate. Here's what it is, how it works, and why every AI engineer needs to understand it right now.
Prompt Injection: The Silent Exploit Threatening Every AI Application
Prompt injection is the top attack vector for LLM-powered applications — and most developers are still shipping vulnerable code. Learn how attackers hijack AI systems, the real-world breaches that prove the threat is live, and the layered defenses every AI engineer must implement in 2026.
Tech Monthly Digest: February 2026 – A2A, Context Engineering, and the Rise of Agentic Everything
February 2026 was the month agentic AI stopped being experimental and started being expected. Google's A2A protocol redefined how agents communicate, Context Engineering emerged as the must-have production skill, MCP became the de facto standard for tool use, and every major cloud doubled down on inference infrastructure. Here's everything that mattered for builders this month.
Context Engineering: The Skill That Replaced Prompt Engineering for Production AI in 2026
Prompt engineering got you started. Context engineering gets you to production. Learn why the most critical skill for building reliable AI agents and enterprise LLM applications in 2026 is no longer what you say to the model — it's what the model knows when it starts thinking.
Tech Monthly Digest: January 2026 – Agents, IDEs, and Cloud AI in Motion
January 2026 pushed AI development from "nice-to-have assistants" to always-on engineering teammates. From GPT-5.1 in production tools to agent-first IDEs and maturing orchestration frameworks, this monthly digest highlights the updates that actually matter for builders.
Cursor: The AI-Native IDE Redefining How Developers Ship Software
Cursor is an AI-first IDE that combines full-project understanding, agentic workflows, and powerful model integrations to help developers ship features, refactors, and fixes dramatically faster in 2025–2026.
OpenAI Codex: The Cloud-Native AI Coding Agent Reshaping Software Engineering
OpenAI Codex is a cloud-based AI coding agent that runs in a sandboxed environment, handling tasks like writing features, fixing bugs, and answering codebase questions in parallel, bringing a new paradigm to software development in 2025.
Claude Code: Anthropic's AI-Powered CLI That Changes How You Write Software
Claude Code is Anthropic's agentic coding tool that lives in your terminal. It understands your entire codebase, edits files, runs commands, and handles complex multi-step engineering tasks, redefining what developer productivity looks like in 2025.
Biweekly Tech Radar: AI Agents, Foundation Models, and Infra
From GPT-5.1 and Gemini 3 to massive AI infrastructure deals and multimodal agents, late November 2025 was packed with launches that are quietly redefining what 'production AI' means. This biweekly digest cuts through the noise and focuses on what matters for builders.
Mastering Modern IDEs in 2025: VS Code, Cursor, Kiro, and Antigravity
Explore how VS Code, Cursor, Kiro, and Google Antigravity are reshaping the developer experience—from lightweight code editing to fully agentic AI-driven workflows.
Tech BiWeekly Digest: November 11–23, 2025
This edition covers key developments in AI and cloud infrastructure, highlighting OpenAI's GPT-5.1 launch, LangChain's production advances, Gemini's new features, and AWS's expanding AI toolkit.
From Zero to 3-Star: My 8-Month Journey to 2100+ LeetCode Rating & 1000+ DSA Problems
How I went from knowing nothing about competitive programming in April 2025 to achieving 3-star on CodeChef, 2100+ rating on LeetCode, and solving 1000+ DSA problems by November 2025. A raw, honest account of the struggles, strategies, and breakthroughs.
LangChain: The Orchestration Layer for Production-Grade AI Applications
LangChain transforms raw LLMs into reliable, multi-tool AI applications by enabling complex workflows, memory management, and seamless integration with vector databases.
Unlocking Automation Power with n8n: The Open-Source Workflow Automation Tool
Discover how n8n's open-source, flexible automation platform is transforming businesses by enabling powerful workflows without extensive coding. Learn its features, use cases, and how to get started.
Tech BiWeekly Digest: Week of November 3, 2025
Welcome to your go-to digest for the latest upheavals and breakthroughs in GenAI, developer frameworks, and cloud infrastructure. This edition deciphers fresh updates from OpenAI, LangChain, Gemini, and more.
Vector Databases: The Foundation of Modern AI Applications
The rise of generative AI and large language models has fundamentally transformed how we store and retrieve data. Traditional databases struggle with the demands of modern AI workloads. Enter vector databases—a paradigm shift in data management that's quietly becoming table stakes for any organization building AI-native applications.
Tech BiWeekly Digest: Week of October 20, 2025
Welcome to the inaugural edition of Tech BiWeekly Digest—your Sunday morning companion for staying current with the rapidly evolving world of GenAI, cloud computing, and emerging technologies.
Fine-Tuning Large Language Models: Techniques, Trends, and Best Practices in 2025
Fine-tuning Large Language Models (LLMs) is a powerful way to adapt general-purpose AI models to specialized tasks or domains. This blog explores the latest fine-tuning techniques, their benefits, challenges, and practical steps to optimize LLM performance in 2025.
Retrieval-Augmented Generation: Transforming How LLMs Access Knowledge
Large Language Models have revolutionized how we interact with AI, but they face a fundamental limitation: their knowledge is frozen at the time of training. RAG emerged as the solution to this challenge, fundamentally changing how we build production AI applications.
LLM Poisoning: The Hidden Threat to AI Trustworthiness
LLM poisoning is a rising cybersecurity concern where attackers subtly corrupt the data used to train large language models, causing these AI systems to behave unpredictably or dangerously. Recent research shows that it takes only a small number of poisoned documents to impact even the largest models.
When AI Dreams: Understanding and Mitigating LLM Hallucinations
Explore why large language models hallucinate, real-world examples, and proven strategies to reduce these errors for reliable AI applications.
Mastering Python in 2025: The Universal Language for Modern Development
Uncover why Python is the number one tool in 2025 for rapid development, automation, and data science. Explore core features, latest advancements, real-world use cases, and actionable best practices to build robust Python solutions.
OpenAI Agent Builder: Building Smarter AI Agents Without Code
OpenAI Agent Builder is a breakthrough visual platform launched in 2025 that empowers developers and non-developers alike to create, test, and deploy intelligent multi-step AI agents quickly and securely. It combines drag-and-drop simplicity with enterprise-grade features, redefining AI automation workflows.
MCP Servers: The Backbone of Intelligent AI Integration in 2025
Model Context Protocol (MCP) servers are revolutionizing AI applications by enabling seamless, secure, and scalable interaction between large language models and external data sources and systems. In 2025, they have become essential for building context-aware AI workflows across industries.
Amazon Bedrock: Empowering Scalable and Secure Generative AI for Production in 2025
Amazon Bedrock provides serverless foundation models with enterprise-grade security and seamless AWS integration, transforming how organizations build and deploy generative AI applications.
Mastering AWS Lambda in 2025: The Serverless Computing Revolution
Discover the latest capabilities of AWS Lambda, its transformative role in serverless computing, best practices, and how to build scalable applications on AWS with minimal operational burden.
Mastering Java in 2025: The Enduring Powerhouse of Modern Software
Explore why Java remains essential in 2025, the latest language updates, versatile use-cases, expert best practices, and how to build robust, maintainable Java applications from the ground up.
Mastering C++ in 2025: Power, Precision, and Performance for All Innovators
Explore C++'s ongoing evolution, from modern language features to high-performance applications. Learn key concepts, advanced best practices, and how to develop robust and efficient solutions with C++ in 2025.
AWS SageMaker: The Complete Guide to Building, Training, and Deploying ML Models
AWS SageMaker is a fully managed machine learning platform that streamlines the entire ML lifecycle—from data preparation and model training to deployment and monitoring. Discover how SageMaker empowers teams to build scalable, secure, and production-ready AI solutions.
Large Language Models in 2025: Trends, Breakthroughs, & Leading Examples
Large Language Models have transformed AI interactions across industries. This comprehensive 18-minute read explores the latest advancements, challenges, and future directions of LLMs while featuring top players like ChatGPT, Gemini, Claude, and Perplexity.
AI vs Generative AI: Understanding the Key Differences in 2025
Artificial Intelligence (AI) and Generative AI are often used interchangeably, but they represent distinct technologies with unique purposes and capabilities. This post unpacks their core differences, how they function, and why understanding both matters for today's tech landscape.
Deep Learning Algorithms in 2025: From MLP to Transformers and GANs
Explore the defining deep learning algorithms—ANNs, CNNs, RNNs, LSTM, GRU, Transformers, Autoencoders, and GANs—powering breakthroughs in AI today. Includes practical explanations, use cases, and Python starter syntax for each network.
All Major Machine Learning Algorithms (2025): The Ultimate Guide to Paradigms, and Algorithms
Explore every major class of machine learning algorithms in 2025: supervised, unsupervised, semi-supervised, and reinforcement learning. This in-depth post lists all top algorithms in each paradigm and provides practical Python syntax to help you get started.