标签：#beginners

I build and run one of the tools on this list (AGenO — full disclosure below), and I use every other tool here regularly. This is what "free" actually gets you on each one, including the catches. The AI tool landscape has a dirty secret: almost nothing labeled "free" is free. Most tools give you a taste — ten messages, three images, one song — and then the paywall lands. So instead of another list of forty tools nobody has tried, here are nine that give you real value at $0, organized by what you're trying to make, with the actual limits spelled out. Quick comparison Tool Best for What's actually free The catch ChatGPT General chat & writing ~10 msgs/5h on the flagship model Silently switches you to a weaker model after the limit Claude Long documents, nuanced writing 10–25 msgs/5h, varies with demand Limits shrink when servers are busy Gemini Image generation & editing Generous with a Google account Best features drift to the paid tier Perplexity Research with citations Unlimited basic searches Pro searches are capped Suno AI music ~10 songs/day No commercial use on free; failed generations can eat credits Leonardo AI Stylized art & game assets Daily token allowance Confusing token system; images are public on free Character.AI Roleplay & AI characters Unlimited chat Heavy filters; your chats train their models AGenO All of it in one place Images, songs with vocals, chat, characters, stories, coding problems — daily free allowance One-person project — busy hours can mean a short queue Canva Magic tools Quick social graphics 50 text-to-image uses Design-tool add-on, not a real generator Chat and writing ChatGPT is still the default for a reason — the free tier includes the flagship model and it's good at nearly everything. The catch nobody tells you about: after roughly ten messages in five hours, it quietly downgrades you to a mini model without making it obvious. If your answers suddenly get dumber mid-conversation, that's why. Claude writes the most natural prose

There's a phase almost every developer gets stuck in. You're consuming tutorials, bookmarking articles, finishing courses, and buying books you'll read "eventually." You're learning constantly — but you're not producing anything. You're just... absorbing. That's the learning vacuum. And if you've been there, you know how easy it is to confuse staying busy with making progress. At some point, the shift has to happen. You stop being a sponge and start being a signal. Here's how I started making that turn. Start a Daily or Weekly Code Journal You don't need a blog, a brand, or an audience for this. Just a file. A note. Anything. Write down what you built, what broke, and what you figured out. Even one sentence counts. I like to write a quick sentence and how many hours, just like if you were filling in an invoice for contract work. The act of putting it into words forces you to actually process what you learned instead of letting it blur into the background noise of your brain. Over time, those entries start to look like a roadmap — and you realize you've come further than you thought. Code Something You Actually Want to Build Pick something dumb. Pick something fun. A browser game, a weird UI experiment, a tool that solves exactly one tiny problem in your life. I signed up for DEV Challenges , Summer Bug Challenge and upcoming Weekend Challenge to get my ball rolling. The best projects I've ever worked on had no real-world utility. They were just interesting to me. And that interest kept me showing up even when things got hard. A tutorial can't give you that. Only a project you actually care about can. Find Your People Whether it's here or a Discord server, a local meetup, a dev community on Farcaster or Lens, or just a forum thread you keep coming back to — find somewhere to show up regularly. Lurking is fine at first. But eventually, drop a comment. Answer a question you know the answer to. Share something you built. Community is where isolated learning becomes shar

Every business that has ever typed a web address into a browser owes a small debt to a company most people have never heard of. On March 15, 1985, a computer maker called Symbolics registered Symbolics.com and, in doing so, became the first ever holder of a .com domain name. More than forty years later that address is still registered and still resolves - making it the oldest .com domain on the internet. Who was Symbolics? Symbolics Inc. was a Massachusetts company that built specialized computers called Lisp machines - workstations designed from the silicon up to run the Lisp programming language, then the darling of artificial intelligence research. These were serious, expensive machines aimed at labs and universities, and the company sat right at the cutting edge of 1980s computing. So it was fitting, if a little accidental, that they were first in line when commercial domains became available. The domain name system itself was brand new. DNS had only been introduced in 1983 to replace the unwieldy HOSTS.TXT file that every machine on the early internet had to keep in sync. The now-familiar top-level domains - .com , .org , .net , .edu , .gov - were defined in 1984. When registration opened, .com was meant for commercial entities, and Symbolics grabbed theirs before anyone else did. A slow start for the web's most valuable real estate What is striking today is how little demand there was. In the whole of 1985, only a handful of .com domains were registered - names like BBN, Think, and a few other technology companies trickled in over the following months. There was no gold rush, because there was no web yet. Tim Berners-Lee would not propose the World Wide Web until 1989, and the first website would not appear until 1991. A domain name in 1985 was a technical convenience for reaching a machine, not a brand or a piece of property. That makes Symbolics.com a kind of time capsule. It was registered before the web, before browsers, before e-commerce, and before anyon

The difficulty If you’re using Google Cloud, you’re probably paying for stuff you don’t need anymore—a server someone forgot to turn off, a storage disk someone forgot to delete, or an IP address someone forgot to release. "That waste is costing us money." It consumes electricity. It generates carbon emissions. Almost nobody tracks those alongside the cost. I wanted a tool that could find both problems simultaneously, without me having to think. So I made one. Live demo: https://greenops-dashboard-845589445410.us-central1.run.app/ Code: https://github.com/raghu-putta/greenops-agent How it works: 4 AI agents in a row Imagine an assembly line in which each worker has only one job: Carbon Scout scans your Google Cloud project and reports on everything that looks like it might be unused or forgotten—idle servers, unattached storage disks, and unused reserved IP addresses. GreenOps Analyzer takes that list and adds two numbers to each item: how much it’s costing you in dollars, and how much it’s costing the planet in carbon emissions. Optimization Executor takes each finding and makes it an action: stop this server, delete this disk, release this address. It shows you the plan first; it only changes if you say go. Report Generator rolls up the entire run into a downloadable report so you (or your boss or your sustainability team) have something nice to look at. Powered by: FastAPI (the web framework), Google’s Agent Development Kit (a library for building AI agents), Gemini 2.5 Pro (the AI model doing the reasoning), and Google Cloud Run (where it’s all running). The “bug” that was not a bug: The dashboard has a terminal-style live window that shows you what each agent is doing in real time, using a technique called Server-Sent Events (basically, the server streams updates to your browser one at a time instead of making you refresh). And then at some point that window just stopped showing anything. Nothing. My backend logs told me that the updates were still being sent,

The Shop on the Corner: How I Learned System Design Without Building Amazon Nephew asks his uncle — 10 years deep into building large-scale systems — to explain "system design," and all the scary jargon that comes with it. Uncle refuses to start with the jargon. Instead: "Forget servers and databases for a minute. Just imagine you're running a small shop." What follows is a thought experiment, built one problem at a time — no cloud bills, no fancy stack, just a counter, a storeroom, and a lot of common sense. Part 1: The Counter — Your First "API" 👦 Nephew: Uncle, everyone at work keeps throwing around terms — load balancer, cache, index, sharding. I nod along, but I don't actually get any of it. 👨‍🦳 Uncle: Then don't start there. Close your eyes for a second and forget servers exist. Suppose — just suppose — you're running a small shop. One counter, one small storeroom at the back. A customer walks up and asks for something. What do you do? 👦 Nephew: I'd walk into the storeroom, find it, walk back, hand it over. 👨‍🦳 Uncle: That's it. That's the entire job of a server handling a request. You don't need to understand Amazon's warehouse to understand Amazon's problems. You need a counter and a storeroom, imagined clearly, and the patience to grow them one honest problem at a time. Here's the shape of what you just described, whether you realized it or not: 🧍 Customer 🧑 You (Counter) 📦 Storeroom (Database) | | | |── "Got Maggi?" ───────>| | | |──── Walk in, search ────────>| | |<─────── Found it ────────────| |<── Hand it over, ──────| | | take payment | | 👨‍🦳 Uncle: One customer, one request, one trip to the storeroom, one response. This is fine. This is correct , even, for a shop with five customers a day. Don't let anyone tell you a single counter isn't "scalable" — a shop that small doesn't need two counters, it needs someone to stop worrying and open the shutter. 👦 Nephew: So this is just... a server handling one request at a time? 👨‍🦳 Uncle: Exactly. Customer sen

Terminal-based AI agents have evolved considerably over the past few months, and several changes are significant enough that developers relying on these tools should be aware of them. Most notably, Google has begun retiring Gemini CLI for individual users in favor of Antigravity CLI — a closed-source successor that has drawn some pushback from the community that built out Gemini CLI's open-source ecosystem. Meanwhile, Claude Code has moved to the Opus 4.8 and Fable 5 models with a 1M-token context window, and OpenClaw, the open-source "always-on" agent, has grown into one of the most-starred projects on GitHub — alongside a documented CVE worth knowing about before deployment. I've just published an updated, fact-checked comparison covering: What actually changed with Gemini CLI's retirement, and what it means if you have scripts or CI/CD pipelines depending on it Claude Code's current model lineup, context window, and new Dynamic Workflows feature OpenClaw's architecture, extensibility via ClawHub, and the security considerations that come with deep system access A full feature-comparison table (cost, context window, open-source status, setup complexity) A practical case study walking through how all three tools can work together on a real project Would be curious to hear which of these you're using day-to-day, and whether the Gemini → Antigravity transition has affected your workflow. Full article here: Devlycan - Technology & Programming Insights Devlycan - Technology, programming, AI, lifestyle, and future trends—simple insights for the new digital generation. devlycan.com

When I started learning iOS development, I thought the hardest part would be writing Swift code. I...

We have all been riding the massive vibe coding wave lately. It feels like pure magic to sit back, tell an AI assistant what to build, and watch a full application appear out of thin air. But if you have ever tried to take that exact same web workflow and deploy a smooth, native app onto an iPhone or Android, you know exactly where the frustration sets in. Are you a vibecoder who loves to build applications and you have built many websites? You have built and deployed many websites. Now you really want to make a mobile application that could disrupt the market and go really viral. Have you heard of FlutterFlow ? Have you tried using it? If the answer is no, then I will tell you about FlutterFlow and then you can decide whether you want to check it out and vibe code mobile applications. I will share the app that I created as well. What is FlutterFlow anyway? Have you ever tried building mobile applications and heard of Flutter and Dart? If you haven't, you should definitely check them out. When I was in college looking for a path to choose whether to pursue app development or web development. I explored both options. While exploring app development, I used and built applications using Flutter, an open-source framework created by Google, which uses a programming language called Dart. While Flutter itself is built by Google, FlutterFlow is an independent, visual low-code platform founded by ex-Google engineers. Today, many of us are familiar with AI vibe-coding tools like Cursor and Claude, which allow us to generate code for websites using conversational prompts. FlutterFlow, however, operates differently than vibe-coding: instead of writing code through chat prompts, it provides a visual, drag-and-drop canvas where you can build and design native mobile applications visually while it automatically generates clean Flutter code in the background. I recently had the opportunity to attend a workshop held by the FlutterFlow team and there, I was blown away by the magic of

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Thomas Edison put it plainly: "To have a great idea, have a lot of them." Steve Jobs said something similar. "Creativity is just having enough dots to connect... to connect experiences and to synthesise new things." Both of them are saying the same thing. Your first idea is rarely your best one. The reason why people you call creative can come up with great ideas easily is that they have had more experiences or have thought more about their experiences than other people. So the question becomes: how do you get more ideas, faster? The Most Used Method for Applied Creativity The answer has a name. It was coined by advertising executive Alex Osborn in the 1940s. He called it brainstorming - using the brain to storm a creative problem, with each person in the room attacking the same objective. It sounds simple. Most teams think they already do it. Most of them are wrong. Real brainstorming is a structured process with rules. Break the rules, and you get something that looks like brainstorming but produces far fewer useful ideas. Why Most Brainstorming Sessions Fail Here is what kills a brainstorming session before it even starts. Someone says an idea. Someone else says, "That won't work." The room goes quiet. People stop sharing. That is it. That is the whole problem. When people fear judgment, they self-censor. They only say the safe, obvious ideas. The interesting ones, the ones that could actually lead somewhere, stay locked inside people's heads. Most teams have that one gaffer who has already decided which ideas are worth hearing before anyone has finished their sentence. Or the one who gives you the floor, listens patiently, and then quietly bins everything you said, not because it was bad, but because it was not theirs. Both types do the same damage. The room reads it. People stop sharing. And just like that, the best idea in the session never gets spoken. The goal of brainstorming is to get more ideas. That means the number one rule is: defer judgment . The Rule

I sat down to work for 2 hours. I actually worked for 45 minutes. Sound familiar? You open your...

When I checked Google Search Console after a month, only 2 of my 8 sites were indexed. The other 6 had zero pages in Google's eyes. No penalty, no error banner. Just silence. The bug My build script generated the sitemap by mapping over page objects. Somewhere a URL field was an object, not a string. So the sitemap shipped lines like: <url><loc> https://example.com/[object Object] </loc></url> Google fetched the sitemap, saw garbage URLs, and quietly skipped the whole file. No crawl, no index. How I caught it GSC > Sitemaps > it said "Success" but "Discovered pages: 0". That mismatch is the tell. I opened the raw sitemap.xml in the browser and searched for [object . There it was. Root cause: url: page.url where page.url was itself { path, params } , not a string. The fix // before loc : page . url // -> [object Object] // after loc : `https://livephotokit.com ${ page . path } ` Redeployed, resubmitted the sitemap, and requested indexing on the core pages. Pages started landing in the index within a couple of days. Takeaway A "Success" status on your sitemap does not mean Google read your URLs. Always open the raw XML and eyeball it. One bad [object Object] can silently sink an entire site. I'm building LivePhotoKit and a handful of other small tools solo with AI. Sharing the real bugs as I hit them.

1. The Problem It Solves Logistic Regression is used when the outcome is a category rather than a number . Most commonly, it's used for binary classification , where the answer is either Yes or No , True or False , or 1 or 0 . Typical business problems include: Will a customer churn? Is this transaction fraudulent? Will a customer click an ad? Will a loan default? Is an email spam? Will a machine fail in the next 24 hours? Unlike Linear Regression, we're not trying to predict a continuous value. Instead, we're predicting the probability that an event belongs to a particular class. For example: A customer may have an 82% probability of churning . The business can then decide whether that probability is high enough to trigger an intervention. 2. Core Intuition Imagine you're trying to predict whether a customer will cancel their subscription. Suppose the only feature you have is how many times they opened your app this month. If you use a straight line like Linear Regression, the predictions quickly become unrealistic. A very active customer might end up with a -20% chance of churn . A completely inactive customer could end up with 140% . Probabilities obviously can't work like that. To fix this, Logistic Regression takes the linear equation and passes it through a mathematical function called the Sigmoid Function . Instead of producing a straight line, it creates an S-shaped curve . No matter how large or small the input becomes, the output always stays between 0 and 1 . That makes it perfect for probability estimation. 3. The Mathematical Model The model first calculates a linear score. Instead of using that score directly, it passes it through the Sigmoid function. Where: z = linear score p̂ = predicted probability The final output is always between 0 and 1 . For example: 0.08 → Very unlikely 0.32 → Low risk 0.65 → Moderate risk 0.94 → Very high probability Businesses can then choose a decision threshold. For example: Probability ≥ 0.50 → Predict Churn Probability

As a final-year Software Engineering student, I wanted my Final Year Project to be more than just another CRUD application. That's how Invesmal came to life a Laravel-based platform that connects startups, investors, and mentors using AI-driven matching. The Problem Finding the right investor or mentor is hard. Startups struggle to identify investors whose interests align with their industry, while investors sift through hundreds of pitches manually. I wanted to solve this with smart, automated matching instead of a simple directory listing. What Invesmal Does Invesmal supports four user roles Student, Investor, Mentor, and Admin and includes 12 AI-driven features built on top of a Laravel backend, including: A core matching engine connecting startups with relevant investors Skills and personality analysis for founders Goal-based matching between mentors and mentees Compatibility scoring between startups and investors A funding readiness score to evaluate startup preparedness A startup health score for ongoing progress tracking A recommendation engine surfacing relevant connections Each feature is built as an independent service class connected through dedicated controllers and routes, keeping the codebase modular and easy to extend. Technical Approach The platform is built entirely on Laravel , using: Service-oriented architecture for AI features (separating business logic from controllers) Blade components for dynamic role-based dashboards Livewire for real-time, reactive UI elements without heavy JavaScript A structured chat/messaging system for communication between users One of the more interesting engineering challenges was migrating a working chat and messaging system from an older version of the project into a redesigned Laravel structure while preserving functionality and fixing layout issues (like a tricky sidebar CSS opacity bug) along the way. What I Learned Building Invesmal taught me how to: Structure a large, multi-role Laravel application without the

Here is a question that sounds like a trick: can you build an accurate classifier out of models that are barely better than flipping a coin? Surprisingly, yes. That is the whole idea behind boosting, and AdaBoost is the algorithm that made it famous. I built it from scratch and dropped it into an interactive demo — here's how it actually works, real math, no hand-waving. Play with the live version: https://dev48v.infy.uk/ml/day21-adaboost.html The weak learner: a decision stump AdaBoost's building block is the simplest classifier you can imagine: a decision stump . It is a decision tree with exactly one split. Look at one feature, compare it to one threshold, and call everything on one side "+1" and everything on the other side "−1". That's it. One line, one cut. def stump_predict ( X , dim , thresh , polarity ): pred = np . ones ( len ( X )) if polarity == 1 : pred [ X [:, dim ] <= thresh ] = - 1 else : pred [ X [:, dim ] > thresh ] = - 1 return pred On anything that isn't trivially separable, a single stump is hopeless — on a checkerboard layout it barely passes 55-60%. That is exactly why it's a "weak learner": a model that only beats random guessing by a hair. The magic is in how we combine hundreds of them. Sample weights: a moving spotlight The engine of AdaBoost is a weight on every training point that says "how much does getting this one right matter?" Everything starts equal: n = len ( X ) w = np . full ( n , 1.0 / n ) # uniform: every point weighs 1/n These weights are a probability distribution — they sum to 1. After each round they change: points we got right get lighter, points we missed get heavier. Since we always pick the next stump to minimise weighted error, the heavy points end up dominating the search. The next stump is effectively forced to stare at whatever the committee keeps blowing. Weighted error, not a plain count When we hunt for the best stump each round, we don't count mistakes — we add up the weight of the mistakes: def weighted_error

For years I copied cron expressions off Stack Overflow, pasted them into a config file, crossed my fingers, and moved on. 0 9 * * 1-5 ? Sure, that "looks like weekday morning." */15 * * * * ? "Every 15 minutes, probably." I never actually read them. So I did the thing that always cures this for me: I built a tool that parses a cron expression, explains it in plain English, and shows the next five times it will fire. No library. About 50 lines of real logic. Here's everything I learned. The five fields (and the order that trips everyone up) A standard cron expression is exactly five fields separated by spaces: ┌──────── minute 0 - 59 │ ┌────── hour 0 - 23 │ │ ┌──── day - of - month 1 - 31 │ │ │ ┌── month 1 - 12 │ │ │ │ ┌ day - of - week 0 - 6 ( 0 = Sunday ) * * * * * The order never changes, and the number-one beginner mistake is swapping the first two. Minute comes first. If you write 9 30 * * * thinking "9:30am," you actually get "minute 9, hour 30" — which is invalid, because hours only go to 23. Say it out loud every time: minute, hour, day-of-month, month, day-of-week. Each field answers one question: which values of this unit does the job run on? An * means "every value." Most real schedules pin down a couple of fields and leave the rest as * . Daily at 9am is 0 9 * * * — minute and hour fixed, everything else "every." Lists, ranges, and steps Beyond single numbers, each field understands three operators, and they combine: Comma makes a list: 1,15 in the day field means the 1st and the 15th. Hyphen makes an inclusive range: 1-5 in the day-of-week field means Monday through Friday. Slash makes a step, taking every n-th value: */15 in the minute field means 0, 15, 30, 45 . Steps can apply to a range too, so 0-30/10 means 0, 10, 20, 30 . That's the whole grammar. Number, list, range, step. Once you can expand a field into the concrete set of numbers it matches, you understand cron. Here's the expansion function, which is the heart of the parser: function expandFie

Tree of Thoughts was a genuine leap. Instead of reasoning in one straight line, it branches into several lines, scores them, prunes the dead ends, and searches for the best path — so a puzzle that would sink a single chain of thought becomes solvable. But a tree has one restriction baked right into its shape, and once you see it you can't unsee it: every node has exactly one parent. A branch can be extended or abandoned. It can never be combined with another branch. That matters more than it sounds. Real problems decompose, and when they do, different branches each get part of the answer right. Branch A nails the first half; branch B nails the second half; neither is fully correct on its own. A tree is forced to pick one and throw the other's good half away. Graph of Thoughts (GoT) removes exactly that restriction. 🕸️ Interactive demo (a real merge-sort that branches, merges, and refines — with live-verified scores): https://dev48v.infy.uk/prompt/day21-graph-of-thoughts.html The core idea: thoughts are nodes in a graph GoT reframes reasoning as building a graph . Each vertex is a thought — a partial solution or intermediate result. Each edge is an operation that produced one thought from one or more others. Because it's a general graph and not a tree, a thought is allowed to have several parents, and edges can even loop back on themselves. That single change in the data structure is the entire conceptual leap. Everything else is just the operations you're now free to run on that network. The four operations generate (branch) — the familiar move, straight from Tree of Thoughts. From one thought, produce several different next thoughts. This can also be a split : break the input into independent sub-problems solved on separate branches. Diversity matters here — near-duplicates waste budget. score / rank — turn each thought into a number so the controller can compare them. Objective scorers win: a validator, a test, a metric. In the demo, the scorer is deliberately con

Seriously, LeetCode in 2026? Everyone is saying HackerRank just killed LeetCode, and yet here I am,...

Pernah kepikiran, "Sebenarnya AI agent saya inget apa aja sih soal saya?" Kalau iya, tulisan ini buat kamu. Masalahnya: Memori AI Itu Kotak Hitam Kalau kamu pakai AI agent yang punya memori jangka panjang (persistent memory), kamu mungkin pernah ngerasa gak nyaman karena beberapa hal ini: Gak tahu persis apa yang diingat AI tentang kamu Gak tahu file-nya disimpan di mana Gak bisa edit memori itu tanpa ngetik perintah lewat chat Takut kalau file memorinya rusak, semua informasi hilang begitu saja Studi kasus di tulisan ini pakai Hermes Agent , agent open-source besutan Nous Research. Sebagai konteks buat yang belum familiar: Hermes Agent adalah agent AI open-source yang berjalan sebagai proses (daemon) mandiri di server milikmu sendiri, mengumpulkan memori lintas sesi, menjalankan tugas terjadwal, terhubung ke belasan platform pesan, dan menulis skill-nya sendiri dari pengalaman. Framework berlisensi MIT ini dirilis Februari 2026 dan dengan cepat menarik perhatian komunitas open-source AI. Hermes menyimpan memorinya di dua file utama: USER.md (profil tentang kamu) dan MEMORY.md (catatan agent soal lingkungan kerja, kebiasaan, dan pelajaran yang dipetik), plus satu file lagi SOUL.md untuk "kepribadian" si agent. Semuanya disimpan dalam format teks polos yang dipisah pakai karakter § , seperti ini: Preferensimu: komunikasi singkat dan langsung § Namamu Budi, awal 30-an, tinggal di Surabaya § Penggemar PKM / Building a Second Brain Format ini fungsional, tapi ada beberapa kekurangan: Susah diedit langsung karena bukan format yang ramah manusia Gak ada riwayat versi — sekali salah edit, informasi bisa hilang selamanya Gak ada tampilan visual — susah lihat semua catatan sekaligus Gak ada antarmuka grafis — harus lewat chat agent atau edit file mentah Solusinya: pindahkan memori itu ke Obsidian , aplikasi catatan berbasis markdown yang mendukung riwayat versi lewat git dan bisa diedit bebas. Arsitektur Sistemnya Sistem sinkronisasi ini punya empat lapisan: ┌───────────────

We all use AIs today - From a 5th grader to a retired pensioner, from a small-time business owner to a multimillionaire businessman, from a software engineer to a medical expert. AI exists everywhere! And to be honest its making our lives very simple. Yes, it does!. Response in no time, flexibility, reliability - yes, AI gives all and even more And as Software Engineers, we are getting more inclined towards AI. Back in the days, we used to rely on Stackoverflow to get our queries resolved. Sometimes it did, sometimes it didn't. But, AI changed that landscape completely - asking a query, retrieving data, asking follow-ups and so and on so forth. But, honestly, how many of us have thought - Wow this looks amazing! But how does it actually work! Let's say I type this in Chat GPT or Gemini or Claude etc: "Hi, how is the weather today?". The AI assistant takes the input and processes it and returns the response. But , there is a lot of processing and workflow happening under the hood. As a Software Architect, I struggled a lot to get these answers. Different sources, different suggestions. And the suggestions at some point seemed too overwhelming for me. So, I decided to break it down and start a series which will enable people to understand AI. I want to make people understand AI in the simplest way possible and make every developer leverage AI - not just to get their job done, but also to help in upskilling, so that they don't get lost in the overwhelming world of AI as I did initially! Follow me for more updates!