Articles

Articles on artificial intelligence, engineering and technology, written in plain language.

• Cloudflare Clef: Decision Models Explained — Clef and Clef-flash do not write sentences. They pick an answer from a fixed list and say how confident they are, in 209 ms and 39 ms. What a decision model is, why it beats an LLM at routing and classification, and how to use one.
• NVIDIA GPU Types: GeForce, L40S and NVIDIA vs AMD vs Apple — What are the different types of NVIDIA GPU, from the GeForce RTX gaming cards to the L4, L40S and H100 you rent in the cloud, with India prices. Plus the AMD Radeon alternative, how Apple competes, how much memory an LLM actually needs, and why running a model is not the same as training one.
• OpenAI Model Names: Sol, Luna, Terra and Astra Explained — Sol, Luna, Terra and Astra are OpenAI's model codenames, and what they rank changed between GPT-5.6 and GPT-6.1. Which is the flagship, which is cheap, and why OpenAI's Astra is not Google's Project Astra.
• Which Gemini Models Do Free, AI Plus and Pro Users Get? — From 9 October 2026 free Gemini users get Flash-Lite only, and AI Plus loses Pro. The full plan-by-plan table of which Gemini model each tier can reach, and what the new effort settings cost you.
• How to Become an AI Engineer in 2026 — What the job actually is, the skills that get tested, a six-month plan that costs nothing, and the courses, simulators and interview preparation to work through in order.
• How to Learn AI in 2026: The Courses Worth Your Time — A checked, structured list of free and paid AI courses from the IITs, Stanford, MIT and the major platforms, with GitHub repositories and a six-month study plan.
• Mistral, a French Company, Launches Mistral Large 4 to Rival China's Open Models — A trillion-parameter open-weight model trained entirely in Europe, with the weights promised by the end of October and a deliberate refusal to block security research.
• A Small AI Learned to Code From One Word per Answer — After a model is trained to code better, even its one-word choices on unrelated questions shift slightly. A new paper shows a copy of the original model can pick up part of that skill just by imitating those single words.
• API vs Subscription: Is the API Included in ChatGPT Plus? — No: API usage is billed separately from every AI subscription, at OpenAI, Anthropic and Google alike. How tokens, rate limits, caching and batch discounts work, and which door to pick.
• Claude Models 2026: Haiku, Sonnet, Opus and Fable Compared — claude-haiku-5-5, claude-sonnet-5-5, claude-opus-5-5 and claude-fable-5-1: what each Claude model is for, its API name, price and context window, and how the subscription differs from the API.
• CLIs and Agent Mode: Claude Code, Codex, Gemini CLI, Cursor and Antigravity Compared — What an AI agent really is, why the command line became its natural home, and how the five big coding agents compare on install, sign-in, memory files, automation and safety.
• What Is Cursor? The AI Code Editor Explained — Cursor is not a model and not a chatbot: it is a VS Code fork with an agent inside that can run Claude, GPT and Gemini. What it does, what it costs, whether it trains on your code, and how it compares with GitHub Copilot, VS Code and Claude Code.
• Gemini, Antigravity and Project Astra: Google's AI Stack Explained — Antigravity vs Project Astra, Gemini vs Gemini CLI: which Google AI name is a model, which is an agent and which is a prototype, plus how the free tier, the AI plans and the API differ.
• Meta's Muse Spark and the Muse Family: The Big Shift from Open to Closed — Meta's first model from its Superintelligence Labs is a multimodal reasoning model you can already use in the Meta AI app, but, unlike Llama, you cannot download it. What it does, how to reach it, and what the name means.
• OpenAI Explained: GPT, ChatGPT and Codex — What GPT actually stands for, how ChatGPT the product differs from the API, what Codex does, and how the subscription ladder compares with per-token billing.
• The AI Tool Map: Models, Apps, Agents and CLIs, Finally Untangled — Claude, Gemini, GPT, Cursor, Antigravity, Muse, Astra: they sound like one crowded category, but they live on different layers. A simple map of what each name really is, and how you pay for it.
• Why experiments teach better than explanations — Reading about backpropagation and watching it happen are very different experiences. Here is how to study with simulators so the idea actually sticks.
• The Lifecycle of Large Language Models: From Pre-training to Efficient Fine-Tuning — Let's understand how an LLM is trained in very less technical way
• The Invisible Engine: Why India Must Move Fast (and Smart) in the Global AI Race — The global AI revolution relies heavily on an invisible supply chain where Taiwan reigns supreme by manufacturing the advanced hardware chips that power major breakthroughs. While India currently trails in frontier model research and semiconductor manufacturing, its true strategic advantage lies in its unmatched capability to deploy complex technology at a population scale. Fueled by a massive $260 billion infrastructure investment from global tech giants, the country has recently surged to rank fourth globally in digital performance. Rather than trying to replicate Silicon Valley by building the next ChatGPT, India's optimal path is to establish sovereign compute power and curate rich, localized datasets. Ultimately, the race is won by capturing economic value, and India can secure an indispensable global position by becoming the world's ultimate engine room for AI adoption and deployment.
• Setting up IOT based garden monitoring system — This article demonstrates the building of plant monitoring system by using ESP8266 chip and a Raspberry pi. This system is a part of experiment where a devotional song is played for a plant and we measure the voltage produced by plants. This also is a part of bigger research project that would tell us the effect on voltage when a decent song is played to a plant.
• The AI Power Shift: From Billion-Dollar Clouds to Your Everyday Laptop — The shift of AI from cloud to personal desktop as people explore intelligence per watt