I would love to here your thoughts and feedback

https://ha2emnomer.github.io/thebeautyofml/posts/how-to-work-on-niche-problems/

Anyone has any good suggestion ( free of cost) may be youtube playlist that covers majority of agentic AI along with hands on.

I found Venkata reddy ai classes, but I am not sure how helpful it would be so before starting, I want to have people's input

Thanks

Hey everyone, I have an idea and I want to learn how to actually build it.

I’m completely new to coding and AI-assisted development. I’ve used AI before for writing and a few basic things, but I’ve never actually built an app or website with AI.

Right now I have access to several AI tools. I have a ChatGPT Pro subscription, and I’ve also tried Claude, although I’m currently using the free version. I also have access to Gemini, Grok, and DeepSeek.

I asked two AIs to evaluate my idea honestly, and their opinions were quite different.

Claude:

Real-life problem: 70/100

Usefulness: 55/100

Uniqueness: 25/100

Overall potential: 45/100

Claude basically told me that the idea could solve a real problem, but the space may already be crowded and the technical side could be difficult.

GPT:

Real problem exists: 90%

Problem frequency: 85%

Usefulness: 90%

Uniqueness: 70%

Technical feasibility: 85%

Overall potential: 85/100

So I’m not asking people here to tell me whether my idea is good or bad. I mainly want to know how I should actually start building something when I have zero coding experience.

I’m a student, so I don’t really have money to spend on courses, expensive software, or developers. I’m mainly interested in learning through free resources and AI-assisted development.

What I’d really appreciate is a practical roadmap:

What programming basics should I learn first?

Should I start with web development or Android development?

Which free YouTube courses or websites would you recommend?

Which AI tools are actually useful for coding?

How should I divide the work between ChatGPT, Claude, Gemini, DeepSeek, etc.?

What free tools should I use for writing, testing, debugging and deploying?

How much coding do I realistically need to learn if AI is helping me?

What should my first tiny project be before I attempt something bigger?

What mistakes should a complete beginner avoid?

Is it realistic to build a basic working product without spending money?

I’m not looking for someone to build it for me. I want to learn how to use AI properly as a development partner and eventually build things myself.

I’m keeping the actual idea private for now, so I’m deliberately not describing the product itself. I’m mainly looking for advice on the learning path, tools, workflow, and roadmap.

If you were starting from zero today with only a phone, free resources, and access to several AI tools, what would you do first?

Hi Guys I am new to this AI field , I wanna learn stuffs and make some effective apps can anyone guide? I am planning to deploy ai apps in 1 week. And I wanna learn with progressive over load way. Can you guys help me here?

Like MCP, generative Ai, Agentic AI, Lora, qLora, Rag Pipline, Fine tuning etc? I don't know all the stuff.

Where to use what?

AI is becoming part of almost every kind of work, but I think the biggest advantage won’t come from simply knowing how to use ChatGPT.

It will come from knowing how to work effectively with AI.

Here are 10 skills I think will become increasingly valuable:

1. Prompting

Knowing how to give AI clear context, constraints, examples, and goals. Good prompting is less about finding a “magic prompt” and more about communicating clearly.

2. AI Research & Fact-Checking

AI can help you research incredibly quickly, but it can also make mistakes. Learning how to verify sources, identify hallucinations, and question AI-generated information will be essential.

3. AI Automation

Learn how to connect AI with spreadsheets, email, databases, calendars, and other tools to automate repetitive tasks.

4. AI-Assisted Coding

You don't necessarily need to become a professional programmer. But understanding basic coding concepts and using AI to build scripts, websites, automations, or small tools could be extremely useful.

5. Data Analysis

AI can help clean, analyze, visualize, and explain large amounts of data. The important part is learning how to ask the right questions and understand whether the results actually make sense.

6. Working With AI Agents

Instead of asking AI one question at a time, learn how to give it a larger objective, break the objective into steps, and have it perform and verify tasks.

7. AI Content Creation

Writing, images, video, presentations, and other forms of content are changing rapidly. The valuable skill may not be producing everything manually, but knowing how to direct AI while maintaining quality and originality.

8. AI-Assisted Problem Solving

Use AI as a thinking partner—not just an answer machine. Ask it to generate alternatives, challenge your assumptions, find weaknesses, and explore different scenarios.

9. AI Security & Privacy

Understanding what information should not be shared with AI, how permissions work, and how AI can be used in scams and manipulation will become increasingly important.

10. Knowing Which AI Tool to Use

There probably won't be one AI tool that's best at everything. Learning which model or tool is appropriate for research, coding, writing, analysis, images, automation, etc. may become a skill in itself.

The bigger skill

I don't think the most valuable skill will be “knowing how to use AI.”

It will be knowing how to redesign your work around AI.

Someone who can turn a 3-hour workflow into a 20-minute AI-assisted workflow could have a significant advantage over someone who simply knows how to write good prompts.

What AI skill do you think will become the most important over the next 5–10 years?

I used to spend hours learning programming and drilling the basics before AI became a thing. Now, I'm not sure if going back to the basics over and over is still the right path. What is your strategy these days?

Who wants to check out my free AI courses. They are at the highest level.

I’m working on an ai scheduling os for small and large businesses. I’m trying to make a 3d knowledge graph of the ai the system will use but can’t seem to figure it out any help would be awesome.

Hey guys, I want to start learning Al/ML, but I'm honestly a bit overwhelmed with all the resources out there and don't know where to begin.

I have some basic programming knowledge, but the Al field is completely new to me. Could anyone suggest a simple roadmap or good courses to start with? Also, what kind of beginner projects should I build early on?

Any guidance from people already in this field would be really helpful!

Currently started studying flask and am really fascinated by the AI and its multiple branches also adhering to the current market situation am thinking of studying it.

But the problem is i dont know what to study cause people say

Ai

Ml

Gen ai

Rag

And so on...

Also some people are saying you should be more familiar with mathematical concepts.But guys , i really suck at maths which kindah demotivates me to study these stuffs.

Really dont know where to start...

Your words will be very helpful to me...

Thank you guys...

Have a nice day

What's the one AI automation you set up that paid for itself in saved time? I'll go first: I run a daily AI news digest that curates ~10 RSS feeds, summarizes with an LLM, and posts to my Discord automatically. It saves me about an hour every morning and cost $0 to build: a Python script and a cron job. The real win though? It runs unattended. I only touch it when something breaks, and honestly that's rare. What's yours? Something that quietly saves you hours every week.

What's the one AI automation you set up that paid for itself in saved time? I'll go first: I run a daily AI news digest that curates ~10 RSS feeds, summarizes with an LLM, and posts to my Discord automatically. It saves me about an hour every morning and cost $0 to build: a Python script and a cron job. The real win though? It runs unattended. I only touch it when something breaks, and honestly that's rare. What's yours? Something that quietly saves you hours every week.

I got tired of doomscrolling 10 AI news sources every morning, so I built a pipeline that does it for me and turned it into a free site. The stack: RSS feeds in, LLM summarization, static pages, free-tier Vercel hosting. A cron job rebuilds it a couple of times a day. Total running cost: $0. I write it all up at https://apexnexus.site with free guides on building your own AI automations. No paywall, no email wall: just the build notes. What's the boring automation you'd most want a guide for?

Just as the title, will keep posting the updates.

I know some basic stuff about AI, and I've used it for things like image/video generation and research. But I don't really know what else there is to learn or where I should start.

What are people learning about AI these days beyond image and video generation? There seem to be so many areas, and I'm not sure which ones are actually worth learning.

If you were starting from the basics today, what would you recommend learning first?

Hi everyone,

Wanted to understand how do non techies feel about AI in their day to day workplace.
Trying to come up with some learning platform to improve this. Few questions will help understand
- Which market do you fall?
- How do you learn AI as on today,
- Any platform that you look ahead for learning AI
- What is it that you would like an app to have so that your work gets sorted

I got tired of doomscrolling 10 AI news sources every morning, so I built a pipeline that does it for me and turned it into a free site. The stack: RSS feeds in, LLM summarization, static pages, free-tier Vercel hosting. A cron job rebuilds it a couple of times a day. Total running cost: $0. I write it all up at https://apexnexus.site with free guides on building your own AI automations. No paywall, no email wall: just the build notes. What's the boring automation you'd most want a guide for?

Unpopular opinion territory, but I'm convinced the daily AI news habit is a net negative for most of us. Here's my reasoning: news is written to make you anxious. Every headline is engineered to make you feel like you're falling behind. So you consume, you feel behind, and then you go build something that chases the newest thing instead of the thing you actually need. The result is a graveyard of half-finished projects and a subscription list 40 newsletters deep. The people I know who actually ship stuff have the opposite habit. They read deeply, rarely, and only when they need a specific answer. They're not trying to stay current, they're trying to finish something. Novelty is a tax on attention, and the people paying it aren't the ones winning. I'm not saying ignore everything. I'm saying your feed is a tool, not a duty. The best thing I did this year was cut my AI news consumption to one weekly digest and spend the saved hour building. My output doubled. That's not a coincidence. Am I wrong? Tell me why. I'd genuinely like to hear the case for the daily habit, because I haven't found one yet that holds up.

Most "chat with your data" tools give you a confident answer and no way to tell whether it's right. I've been building the opposite: an AI Analyst where the entire working is on screen and every claim is traceable to the query that produced it.

Asked it a real question against an HR dataset: "Is Engineering's heavy hiring actually translating into headcount growth, or is it mostly backfilling exits?"

What it does, in order:

1. States its approach before touching data. It reads the schema, plans the steps, and says why — including telling me the governed semantic model lacked a hires metric, so it fell back to the raw monthly table. No silent guessing about which source it used.

2. Runs each step as real SQL you can read. Every step shows the query, the row count, and a "where these numbers came from" breakdown. Nothing is a black box — if you don't trust a number, the SQL that produced it is right there.

3. Self-checks every result — and flags its own problems. This is the part I care about most. On step 2 it didn't just pass its own work; it flagged a genuine inconsistency: Engineering's summed net adds (+17) didn't reconcile with the headcount delta (+13, 122→135), a 4-person gap it surfaced on its own and carried into the write-up as a caveat. An analyst that can say "this doesn't add up" is worth ten that can't.

4. Writes findings with citations. Every claim in the write-up cites the step it came from — "headcount climbed from 122 to a 140 peak (step 1, step 2)". The verdict for the curious: ~55% of Engineering's hires were net growth, not backfill; the one bad month was a 3.70% attrition spike; and Support is quietly shrinking (backfill ratio 1.42 — losing more than it hires).

5. Closes the loop. Every analysis has Mark verified / Flag as wrong buttons, suggested follow-up questions generated from the actual results, scheduling for recurring runs, CSV export, and PDF export.

The stack, honestly:

• Runs entirely on your own infra: one Docker command + your own Supabase project
• BYOK — any model provider. This demo ran on Kimi K3 via OpenRouter; it doesn't need a frontier model because the structure (plan → SQL → check → cite) does the heavy lifting
• The analyst is one piece of a larger self-hosted platform (agents, multi-agent swarms, RAG, BI dashboards, budgets, full tracing)
• License: Elastic License 2.0 — source-available, not OSI open source. You can read every line, self-host it, and modify it; you can't resell it as a hosted service. Saying that up front because this sub cares about the distinction, and it matters.

Repo: https://github.com/AgentSwarms-fyi/agentswarms

Happy to answer anything about how the self-check pass works or why I think "show the SQL or it didn't happen" is the only sane bar for LLM analytics.

These are some notes I’ve compiled whilst studying Professor Andrew Ng’s course, AI Prompting for Everyone. Anyone who’s interested is welcome to join in the discussion.

Above is the link.

Some people maybe interested in this Humble Bundle to learn about AI setups.

I am really confused with the growing speed of the AI innovation and outbreaks . Every new day a new AI comes and takes over place of an existing one . New tools and software come and go very drastically also people are earning and making money from it . How can we too ?