My name is Shivam Maurya, and what began as a simple intention to improve gradually evolved into a powerful practice — falling in love with a habit that not only brought success but also transformed the way I live and think.

Hi Everyone,

A few years ago, if you told me a handful of tools could replace 70% of what an entire dev team does, I’d have laughed. But here we are in 2025 — and AI has become a quiet but powerful co-founder for indie hackers, solo devs, and tech creators like you and me.This isn’t some vague futuristic dream. I’ve personally used these tools while building side projects, automating client work, and even brainstorming product ideas. And guess what? These tools are so good, they often feel like a cheat code.Let me walk you through the 10 AI tools I’ve used (or closely followed) that are replacing the heavy lifting usually done by developers, designers, and testers.⚙️ 1. GitHub Copilot — Your AI Pair Programmer

If you’ve ever felt like AI is just “not good enough,” prompting is the fix.It’s a rare skill that’s easy to learn and instantly useful— especially if your work involves teaching, writing, or any kind of thinking.Until we reach general AI (we’re not there yet), your results depend more on your prompts than on the model you use — even with Agentic AI.That makes prompt design a highly valuable meta-skill today.Whether you’re using ChatGPT, DeepSeek, Gemini, or Claude (here’s my breakdown on which tool to use when), your results are only as good as your instructions.Over the past few months, I’ve gone deep into the prompt rabbit hole — taking expert-led courses, testing frameworks, and applying learning science to what works.In this article, I’m sharing what I’ve learned structured into a 3-level guide.You’ll leave with replicable prompts that save you literally hours each week and a clear progression path from beginner to advanced prompt engineer.

Artificial intelligence (AI) refers to the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.^[1] Such machines may be called AIs.

High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."^[2][3]

Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, perception, and support for robotics.^[a] General intelligence—the ability to complete any task performed by a human on an at least equal level—is among the field's long-term goals.^[4] To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.^[b] AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields.^[5]

Artificial intelligence was founded as an academic discipline in 1956,^[6] and the field went through multiple cycles of optimism throughout its history,^[7][8] followed by periods of disappointment and loss of funding, known as AI winters.^[9][10] Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques.^[11] This growth accelerated further after 2017 with the transformer architecture,^[12] and by the early 2020s many billions of dollars were being invested in AI and the field experienced rapid ongoing progress in what has become known as the AI boom. The emergence of advanced generative AI in the midst of the AI boom and its ability to create and modify content exposed several unintended consequences and harms in the present and raised concerns about the risks of AI and its long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

Goals

The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.^[a]

Reasoning and problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.^[13] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.^[14]

Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow.^[15] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.^[16] Accurate and efficient reasoning is an unsolved problem.