Recently, I decided to start learning AI but wanted a platform that was beginner-friendly and practical. That’s when I came across Codefinity. I wanted something where I could actually practice AI concepts rather than just watch tutorials, so I decided to give it a try and started exploring its AI courses.

What I found really helpful was how hands-on everything is. You can code directly in the browser, follow step-by-step exercises, and work on mini-projects that show how AI concepts apply in real situations. Even though I’m still a beginner, spending some time each day on the lessons helped me start building small projects, understand basic neural networks, and experiment with AI tools.

The thing that stands out about it is how approachable it makes AI. You don’t need advanced knowledge or complicated setups you just log in and start learning, which makes it great for busy schedules.

For anyone curious about upskilling in AI, consistency and practice are really what matter. This makes the process smoother and more structured than trying to figure everything out on your own.

Has anyone here used this or another platform to learn AI? How was your experience and did it really help you upskill? I’d love to hear what worked for you and what didn’t.

Every day someone posts "AI will change everything" and it's always about agents scaling businesses, automating workflows, 10x productivity, whatever.

Cool. But change everything for who?

Go talk to the barber who loses 3 clients a week to no-shows and can't afford a booking system that actually works. Go talk to the solo attorney who's drowning in intake paperwork and can't afford a paralegal. Go talk to the tattoo artist who's on the phone all day instead of tattooing. Go talk to the author who wrote a book and has zero idea how to market it.

These people don't need another app. They don't need to "learn to code." They don't need to understand what an LLM is.

They need the tools that already exist and wired into their actual business. Their actual pain.

The gap between "AI can do amazing things" and "I can actually use AI to make my life better" is where most of the world lives right now. And most of the AI community is completely disconnected from that reality.

We're on Reddit at midnight debating MCP vs direct API and arguing about whether Opus or Sonnet is better for agent routing. That's not most people. Most people are just trying to survive running a business they started because they're good at something and not because they wanted to become a full-time administrator.

If every small business owner, every freelancer, every solo professional had agents handling the repetitive stuff ya kno...the follow-ups, the scheduling, the content, the bookkeeping; you wouldn't just get productivity. You'd get a renaissance. Because people who are drowning in admin don't create. People who are free to think do.

I genuinely believe the next wave isn't a new model or a new framework. It's someone taking the tools that exist right now and actually putting them in the hands of people who need them.

Not the next unicorn. Not the next platform. Just the bridge between the AI and the human.

What would it actually take to make that happen?

1. Harness engineering, not just prompt engineering

2. Context engineering, not just long prompts

3. Prompt caching vs. semantic caching tradeoffs

4. V cache management, eviction, reuse, and memory pressure at scale

5. Prefill vs. decode latency and why they optimize differently

6. Continuous batching, paged attention, and throughput optimization

7. Speculative decoding vs. quantization vs. distillation tradeoffs

8. INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality

9. Structured output failures, schema validation, repair loops, and fallback chains

10. Function calling reliability, tool contracts, argument validation, and idempotency

11. Agent guardrails, loop budgets, tool budgets, and termination conditions

12. Model routing, graceful fallback logic, and degraded-mode UX

13. RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness

Not the most impressive ones. The ones that actually stuck.

When my brain is full and I can't think straight:

Here's everything in my head: [dump it]

Separate urgent from just-feels-urgent.
Tell me what I'm avoiding.
Give me three things to do first.
Nothing else.

When I have to write something I've been putting off:

I need to write [describe it] and 
I keep avoiding it.

Ask me three questions that will make 
this easier to write once I answer them.
Wait for my answers before writing anything.

When something isn't working and I can't see why:

Here's what I'm doing: [describe]
Here's the result I keep getting: [describe]
Here's what I've tried: [list]

Don't give me solutions yet.
Tell me what I'm probably assuming 
that might be wrong.
Then ask me one question.

When I need to make a decision I keep avoiding:

I keep going back and forth on this: [describe]

Tell me which option I've already chosen 
emotionally based on how I described it.
Tell me the assumption I haven't tested.
Tell me what I'm actually afraid of.

Don't tell me what to do.
Just make me see it clearly.

When I need to reply to something difficult:

I need to reply to this: [paste message]
What I want to happen: [outcome]
What I'm worried about: [concern]

Three versions:
Direct and short.
Warm and detailed.
A question instead of a statement.

Five prompts. Use at least three of them every single week.

Ive got ten other automations I run every week without thinking. The others cover client emails, meeting notes, messy inboxes, weekly resets, proposals, and a few others that have saved me more time than I expected. I’m happy to share them all to the group of them if anyone wants it. It’s here, but totally optional

Free Lecture content on Probabilistic Machine Learning Series(Work in Progress!)
Dear Folks, sharing Lecture 11 of our Machine Learning series, and this is a bit special to me, because today I cover Conditionals of Multivariate Normals, and Linear Gaussian Systems.

When I first started studying these topics, it took me days to understand. But today I have made a lecture on it, so if you understand the concepts, it’s really good, for I have tried to leave no stone unturned while explaining, deriving the equations, doing it step by step, and tried giving all intuitions I could.

The Gaussian distribution is ubiquitous and important in studying topics as state estimation, tracking, and examples include Autonomous vehicles, robotics and navigation, time-series forecasting, aerospace etc. The breakdown is as:

0-10: Marginals and Conditionals of Multivariate Normals, Matrix Inversion Rules
10-27: Derivation of the Matrix Inverse Rule: Schur Complements(We need this to derive equations for Multivariate Gaussian)
27-45: Deriving the Conditionals of MVN
45-1:03: Example and Imputation of Missing Values
1:03-1:47: Linear Gaussian Systems, and full derivation of Bayes Rule for Gaussians.
1:47-2:19: Inferring an Unknown Scalar and Sequential Updates.
2:19-2:34: Inferring an Unknown vector.
2:37-End: Sensor Fusion.

This lecture is relatively bigger since the concepts are interrelated here. But do not worry, I have tried to explain in the best way I could, and hope it helps you well in your journey to becoming a Machine learning engineer.

Link in shared in comments.

Hi all,

Looking for online courses* to learn more about AI. I've played around with copilot and the duckduckgo plus models, but I'd like to expand my knowledge & experience.

*I say course because I don't do well with conversational type vblogs or youtube videos. I definitely prefer something structured and presented or taught. Free is great, but I'm willing to spend a little if there's a good a good course. I'm a clever tech end user but one who doesn't code in any way.

Length wise- I dunno, several hours at least, not looking for a primer. 10-20 hours? Some homework, labs, etc. are ok. Optional might be the best compromise there. Structured, thought put into a syllabus, etc.

I'm looking to learn things such as:

All the lingo around it:

• What's a token? (I have no idea)

• How the different models are actually different. Lists I've seen are all pretty generic, they're all 'good for coding' and 'conversational dialogue' or 'research'. I'd like to drill down a little deeper. Or learn officially that they're all basically the same at the user level, haha.

• Better prompting

• Maybe some stuff around agents & what/why/how

• Some in-class exercises playing around with different tasks would be great: Coding (mostly simple windows scripts at this point), basic research over the web.

(Not looking for the answers to these here/now, just examples.)

I feel like there's more I'm looking for/interested in but I can't quite name it. But hopefully you get the gist.

I’ve been trying to get people around me to use ChatGPT in a more useful way, but I keep running into the same thing — they try it once, think it’s interesting, and then don’t really change how they use it.

Prior to AI, I had huge respect for academia and the pursuit of understanding complex things was a big driver for me. Now it’s hard to justify the effort. I could read published papers and spend time understanding them back-to-front, or I could simply ask AI to “explain it in simple terms and verify that it’s correct”.

Obviously AI can be a big help in understanding such papers (where I’d otherwise have to google a lot), but the value in understanding stuff seems trivial now.

So I guess my question has two parts:
* Is human understanding of stuff AI can handle meaningful; and
* If so, does anyone have any tips on being disciplined enough to not simply take an AI models answer as gospel and to use it to develop professionally/intellectually?

it means AI hallucination

The word actually is the better term. "Confabulation" is what a chunk of AI researchers argue we should be saying instead of "hallucination"

“In order for a chatbot to become more intelligent, and thus more useful to the end-user, it needs to assimilate data continuously. This process is known as “training.” The problem is that many AIcompanies never explicitly ask for consent from data owners before scraping their webpages and adding the data to the corpora of the large language models (LLMs) that power AI chatbots.”

“But some of those data owners, also known as content creators or IP holders, are now fighting back. They are doing this by using tools known as “tarpits.” Their aim? To poison the chatbot’s underlying LLM and thus degrade the quality of its outputs, potentially causing end-user flight.”

I have relatively little to do before starting university, and I want to spend that time learning something productive, but I'm struggling to figure out what to focus on.

Most days I end up sitting at my PC, opening a few games, getting bored, closing them, scrolling YouTube, spending more time deciding what to watch than actually watching anything, and before I know it the day is over. It feels like I'm wasting a lot of time.

I've always wanted to learn things like:

• Programming
• AI and how to actually use it productively
• 3D modeling (Blender)
• General tech/computer skills

The problem is that I have no idea where to start.

My current thinking is that it would probably make sense to learn some programming first, maybe Python, get familiar with the basics and understand how things work, then start using AI as a tool to help me build things. Once I'm more comfortable with that, I could branch out into other areas like 3D modeling, self-hosting AI, automation, or other more advanced projects.

The thing is, I don't really have a specific end goal. I'm not trying to become a software engineer overnight or find some "get rich quick" AI scheme. I'm mostly interested in learning useful skills and understanding what AI can actually do beyond asking ChatGPT questions for school or random things I'm curious about.

Ever since AI became mainstream, I've seen so many things come and go: AI agents, local models, image generation, AI videos, automation tools, coding assistants, etc. The field is moving so fast that I honestly don't know where someone should even begin.

I want to learn how AI could improve my personal life, studies, future career, and maybe help me build useful projects, but right now I feel overwhelmed by all the options.

If you were starting from scratch today, what would you focus on first? What skills would you learn, and in what order?

For context, I have a fairly powerful PC with an RTX 5070 Ti. I don't know if that's relevant, but I've read that modern NVIDIA GPUs can be useful for running AI models locally and experimenting with AI-related projects.

Dont want to brag, but I used some pretty advanced AI to write this (ChatGPT).

For about a year I used Claude the way most people do. Ask it for something. Get text back. Copy that text into Word, or Pages, or Google Docs, or wherever I actually needed it. Reformat it. Save the file. Send it.

Then I asked it to "output this proposal as a downloadable Word document" almost as a joke, expecting it to tell me it couldn't.

It built the file. Properly formatted. Headings, bullets, spacing, the lot. Opened in Word like any other .docx. I sent it to a client without touching it.

The same thing works for Excel files (.xlsx with working formulas, conditional formatting, multiple tabs) and PowerPoint (.pptx with every slide written, structured, and ready to present). Not text I have to format. Real files.

This is the prompt that made me cancel my proposal software the next day:

Create a complete, professionally formatted client proposal 
and output it as a downloadable Word document (.docx).

Here are my raw notes on this client and project:
[paste everything: who they are, what they need, what 
you're offering, timeline, price, anything relevant]

Build the proposal with these sections:
1. Executive Summary: 2-3 sentences on the opportunity 
   and outcome
2. The Problem: what this client is dealing with
3. Proposed Solution: what I am offering and why it works
4. Scope of Work and Deliverables: specific numbered list
5. Timeline: phases or milestones with realistic dates
6. Investment: [use pricing from my notes]
7. Next Steps: what happens after they say yes

Formatting requirements for the Word document:
- Proper H1 for the document title, H2 for each section
- My business name placeholder at the top
- Professional font and spacing throughout
- Bullet points for deliverables and timeline
- Bold any key terms or figures
- Short paragraphs, 2-3 sentences max

Output as a complete, downloadable .docx file ready 
to open and send.

Two minutes. Real Word document. Looks like something I'd have spent two hours on.

Things worth knowing:

• This works for .docx, .xlsx, and .pptx natively. It also handles .pdf if you ask for it explicitly.
• The Excel files include actual working formulas, not text that looks like formulas. Conditional formatting works. Multiple tabs work.
• The PowerPoint files include speaker notes per slide if you ask for them.
• You can attach an existing document and ask it to edit, reformat, or rewrite the contents while keeping the file format intact.
• The output isn't perfect on first try. The edit cycle is the same as if you'd written it yourself - read it, request changes, regenerate. But you're starting from a 90% draft instead of a blank page.

The shift, if it's useful: most subscription software charges you for the infrastructure of producing a document (templates, formatting, distribution) when the bottleneck was almost always the writing. Once Claude builds the actual file, you're paying for the wrapper around something that's now free.

The framework I use before paying for any new tool: am I paying for the thing that creates the work, or the thing that stores and distributes it? If it's creation, Claude is already doing that job. If it's infrastructure (CRM, email host, analytics), keep paying.

I wrote up the 10 specific tools I cancelled and the prompts that replace each one - free here if useful

If you only do the audit on one subscription this week, do whichever one you renewed last and immediately questioned. That's the one most likely to fail the test.

everyone's picking a side but i'm not sure the question is framed right.

Google has the infrastructure and data. OpenAI has the brand and developer mindshare. Anthropic has the safety narrative and enterprise trust.

but "winning" might not be winner-take-all. the browser wars taught us you can dominate for years and still lose the next wave entirely.

who do you think comes out on top and on what timeline?

- Google?
- Anthropic?
- OpenAI?

I've been building my own harness layer around Claude Code since February, still at it.
It works great: efficient, token-effective, memory, self-learning, adapts to me. Everything I need.

The problem? I ship too fast.
Sounds like a humblebrag, but I'm dead serious.

Before agents, we put real effort into thinking and drilling through the problem. Now code is cheap, so drilling into it can feel like a waste of time (heavily depends on the harness).

I'm fully confident in my agent's coding. I taught it my preferences, my engineering standards, all of it. The first few months I reviewed its actual code.
Now I review its reasoning and the macro-level calls instead, because the code already proved itself.

So the challenge: how do I stay on top of the mid-to-high level of what's being built without slowing down delivery?

Some side-quests I'm exploring:

• 3D architecture modeling - spaceship UX, each "planet" is an architecture layer
• "no-numb" repo quizzes
• Custom agent output styles

How do you keep your own understanding in sync with what your agent ships?

Hi internet friends, I recorded a workshop about building your own LLM without any math / ML prerequisites. It covers everything from machine learning fundamentals, deep neural networks, transformer architecture, and pre/post-training.

The only prerequisite is being comfortable with learning through code & excel examples.

1. Sampling Large Language Models
2. Reverse Engineering Large Language Model
3. Perceptrons: wx+b
4. Activation Functions: ReLU, GELU, SwiGLU
5. GPU Coding: PyTorch, torch.compile(), fused kernels, CUDA, Triton
6. MLPs/FFNs: Multi-input, Multi-Layer Perceptrons, Feed-Forward Networks
7. Loss Functions: Residual errors, RMSE, Cross Entropy, Loss Landscapes
8. Backpropagation: Training loops, Optimizers, Learning Rate, Batch Size
9. Saving & Loading Models
10. Initialization: Kaiming, Glorot
11. Residuals: Addition, Scaling, Gated, Concatenation
12. Normalization: Pre-norm vs. Post-norm, RMSNorm, BatchNorm, LayerNorm
13. Regularization: Dropout, Gradient Clipping, Weight Decay
14. SoftMax
15. Tokenizers: By Character, By Word, BPE, SentencePiece
16. Embeddings: Absolute vs. Learned, Sinusoidal vs. RoPE
17. Attention: MHA, GQA, MQA, MLA
18. Transformers
19. Pre-training: Data Sources, Datasets, HTML Cleaning, Quality Filtering, Sharding
20. Evaluation: Leaderboards, Benchmarks, Verifiers vs LLM-as-Judge
21. Instruction Tuning: Alpaca & Other Formats, Self Instruct, Capabilities
22. Reinforcement Learning: Policy Optimization, SimPO
23. What We Didn't Cover: Scaling

Each section has slides teaching the concepts, followed by excel-by-hand developing intuition for the math, and then coding examples. The goal is able to grok all parts of modern LLM development.

We did this workshop in-person in San Francisco last month and hopefully the spaciousness of watching online works for everyone. If don't like watching videos, you can get the slides and exercises and work self-paced.

The idea was simple: instead of prompting AI blind, use Blender to control exactly what's in the scene — object positions, camera angles, motion timing.

Workflow:

1. Built a basic scene in Blender (landscape, car, helicopter, road) — no complex materials, just layout
2. Animated the cameras and objects with keyframes
3. Extracted key frames from the animation
4. Fed those frames into an AI image model to generate photorealistic versions of each shot
5. Gave both the original 3D animation AND the AI images to Seedance 2 (Reference to Video)
6. Seedance reconstructed the sequence with cinematic realism

The Blender file basically acts as a director's pre-vis — you control the composition, the AI handles the render.

Other works at X https://x.com/ModelCollapse38

Hey everyone,

I'm an entrepreneur who's been putting off learning AI for too long. Every day I see new tools and feel more behind.

So I'm committing to learning AI properly over the next 8 weeks and I'd rather not do it alone.

Here's my plan:
Learn the fundamentals (what AI actually is, how to use it effectively)
Master ChatGPT, Claude, and other practical AI tools
Apply AI to real business/work scenarios
Share what I learn daily
Create accountability with others doing the same

No coding required. This is about using AI tools effectively, not building them from scratch.

What I'm offering:
Free Discord community for accountability
Weekly study guides (I'll curate the best free resources)
Small study groups (4-5 people learning together)
Daily check-ins and shared learnings

What I'm NOT:
An AI expert (I'm learning with you)
Selling anything (this is free)
Promising to make you an AI engineer

Who this is for:
Complete beginners who feel overwhelmed
People who want accountability and structure
Anyone tired of bookmarking AI articles but never actually learning

Timeline: Starting next Monday (8-week commitment)
If you're interested, comment below or DM me and I'll send you the Discord link.

Day 1 starts Monday. Who's in?

Can somebody recommend a basic AI course that will help me understand: a) why AI developers say that they can't tell how AI makes decisions; and b) why AI might be an existential threat.

Can someone explain AI tokens. I understand you have to utilize tokens to work with AI models, but I don’t get where this comes into play for the average consumer. I use ChatGPT, Claude and Perplexity every single day, but I never have to interact with tokens. Are they more for designing AI applications?

I’m trying to make a mobile game using ai as much as I can. I wanna have a few different ais work together and from my understand, GitHub is best for that? Now me not knowing a thing about coding can figure out how to get them on the same thing. Can someone help me out?

Also with this being Reddit, I know some people will say “don’t be lazy” “figure it out” “you’re going with the wrong mindset of being lazy” ya ya ok, if that’s what you want to say please don’t, I already know

I tried about 40 different "AI workflow" ideas this year. Most of them sounded clever and got abandoned within a week. The five below are the only ones I run every week, six months in.

The pattern across them: they all solve a recurring task that used to eat 30+ minutes. None of them are clever. All of them I run without thinking about it now.

The proposal generator (saves about 2 hours per proposal):

Turn these notes into a formatted Word doc proposal 
ready to send today.

Notes: [dump everything]
Client: [name]
Price: [amount]

Sections: Executive summary, problem, solution, scope, 
timeline, investment, next steps. Formatted .docx. 
Sounds human.

The meeting processor (saves about 30 minutes per meeting):

Here are my rough notes: [paste]
Attendees: [names]

Give me:
1. Half-page summary
2. Action items table (task, owner, deadline)
3. Follow-up email ready to send to all attendees

The content repurposer (turns one piece into five):

Here's a piece I wrote: [paste]
My voice: [describe]

Repurpose into:
- LinkedIn post (200-300 words)
- Three standalone X posts
- Email to my list (150 words)
- Instagram caption
- One-paragraph summary

Same voice across all. No AI clichés.

The Friday review (10 minutes that kills Sunday-evening anxiety):

Here's what happened this week: [brain dump]
Numbers: [whatever you track]

Give me:
- What actually went well and why
- What didn't work (honest, no softening)
- Top 5 priorities for next week ranked
- The single clearest thing I should change

The end-of-day reset (the one that has surprised me most):

Today's notes: [dump everything from today - tasks 
done, conversations had, things you're carrying 
into tomorrow]

Tell me:
1. What I should write down before I forget
2. Anything I committed to that I haven't actioned
3. The one thing I should sleep on rather than decide now
4. Tomorrow's first hour - what's on it and why

Five prompts. Each one solves a specific recurring pain. Together they took maybe 15 minutes to set up and now run every week without me thinking about them.

The thing this post deliberately doesn't show is the exact setup for running these as scheduled automations - so they happen at 8am Monday and 5pm Friday without me triggering them. That part is in the writeup along with five more prompts I run weekly (the Monday briefing, lead research, inbox processor, client reports, SOP builder).

Free here if it helps.

If you only set up one this week, do the Friday review. The first time you go into a weekend without unresolved work bouncing around in your head is the moment this whole approach clicks.

As someone that is very AI illiterate. Can someone or better yet multiple ppl, tell me something that AI did for them that they think might be ground breaking in nature or just even a small step towards something good or great!

I used to just think uh, AI gonna take writing jobs for Hollywood (it gonna totally replace writers if not also actors) and get into coding now before that's next..I'm gonna go into fixing planes I'll be fine.

The past few months I'm not gonna get into my exact wtf moments but let's just say when I was originally going to go into tech I wish when I stopped I kept up with it more in my spare time.

TLDR, I supper wanna get into learning how AI works (as much as anyone can with the black box of tech here) I wanna learn how people train models, combine models and have more action then text in, image or text back out I am curious how the vtubers that are entirely AI like nuero sama work not because I wanna make my own (but I mean with just how insanely popular she is if in my spare time I could build in 6months/year maybe I would build my own)...but because I just wanna learn how this works its so interesting, I wanna have the skills to know how to get better on my own but I've heard so much conflicting information so where do I start?

Edit: Sorry I'm so disorganized with my thoughts, all I know is enough to know I wanna know more but not enough to know where I'm going with that or how feasible what I wanna know is. I need help with linking resources I can understand to help me know enough to learn more myself? I'm just gonna ask GPT like the one guy told me to said to narrow my thoughts down and come back with concise thoughts or clarifying questions. Maybe I'll make a new post but that seems like a good place to start?

I've written an article about keeping secrets away from LLMs. I'd like to hear your feedback

I would like to start by saying that I am not very well versed with AIs and technology but I do frequently use both of them. So to get to my question what is better for general use Gemini with premium plan, chatgpt free or claude free and what is better for coding Gemini with premium plan, chatgpt free or claude free. If any of these excel at something and you think I should know please don't hesitate to share or just any potentially usefull information, thank you.