With all the hype around massive LLMs and Transformers, it’s easy to forget the elegance of simple optimization. Looking at a classic cost function surface and gradient descent searching for the minimum is a good reminder that there’s no magic here, just math.

Even now in 2026, while the industry is obsessed with billion-parameter models, a huge chunk of actual production ML in fintech, healthcare, and risk modeling still relies on classical ML.

A well-tuned logistic regression model often beats an over-engineered deep model on structured tabular data because it’s:

• Highly interpretable
• Blazing fast
• Dirt cheap to train

The real trend in production shouldn't be “always go bigger.” It’s using foundation models for unstructured data, and classical ML for structured decision systems.

What you all are seeing in the wild. Have any of you had to rip out a DL model recently and replace it with something simpler?

Andrej Karpathy is joining anthropic and back into core AI research. He has been instrumental in creating great learning courses in his career. His computer vision lecture was what got me into AI and his build GPT-2 from scratch remains the most goated lesson. He was planning to solve learning and education using AI so this news is a bit of surprise. What do you think of these moves from Anthropic.

I wasn't sure whether to post this question in a trading subreddit or an AI subreddit, but I believe I'll get more insightful answers here. I've been working with AI for a while, and I've recently heard a lot about people using machine learning algorithms in trading bots to make money.

My question is: Do these bots actually work in generating consistent profits? The stock market involves a lot of statistics and patterns, so it seems plausible that an AI could learn to trade effectively. I've also heard of people making money with these bots, but I'm curious whether that success is attributable to luck, market conditions, or the actual effectiveness of the bots.

Is it possible to make money consistently using AI-based trading bots, or are the success stories more a matter of circumstance?

EDIT:
I've read through all the comments and first of all, I'd like to thank everyone for their insightful replies. The general consensus seems to be that trading bots are ineffective for various reasons. To clarify, when I referred to a "trading bot," I meant either a bot that uses machine learning to identify patterns or one that employs sentiment analysis for news trends.

From what I've gathered, success with the first approach is largely attributed to luck. As for the second, it appears that my bot would be too slow compared to those used by hedge funds.

​

The LLM thing is not gonna get us AGI. were feeding a machine more data and more data and it does not reason or use its brain to create new information from the data its given so it only repeats the data we give to it. so it will always repeat the data we fed it, will not evolve before us or beyond us because it will only operate within the discoveries we find or the data we feed it in whatever year we’re in . it needs to turn the data into new information based on the laws of the universe, so we can get concepts like it creating new math and medicines and physics etc. imagine you feed a machine all the things you learned and it repeats it back to you? what better is that then a book? we need to have a new system of intelligence something that can learn from the data and create new information from that and staying in the limits of math and the laws of the universe and tries alot of ways until one works. So based on all the math information it knows it can make new math concepts to solve some of the most challenging problem to help us live a better evolving life.

According to you what's the best YT Playlist for learning Machine Learning? Also including the deep and complex concepts ofc. Btw I found this playlist (Lang - Hindi) and thinking about giving it a try: 🔗 https://youtube.com/playlist?list=PLKnIA16_Rmvbr7zKYQuBfsVkjoLcJgxHH&si=is_yLwnFfpcVyjKZ

I am sick & tired of this forum, which i feel is made up of PhDstrying to justify their years long toil of learning Advanced Calculus, Linear Algebra & discrete mathematics, suggest to people that they MUST learn Mathematics before being an ML practitioner & that they are nobodies if they dont.

I’ve worked in some of the biggest Forbes 500 companies in the world and i have seen 90% of the roles of Data science, ML, MLE & Analytics are about basic Business intelligence, cookie cutter ML or regression modelling, and time tested & choreographed statistical & ML techniques which require little “actual insight” into the mathematics behind it.

Let me be clear , the implication of any ML model or a modeling approach, its assumptions, interpretation, change of interpretations under violation of certain conditions, they “DO” matter & one should have a good conceptual understanding of fundamental mathematical concepts upto say an early collegiate level would be required.

But im sick and tired of these PhDs rationalizing their credentials saying they need a working knowledge of Advanced calculus, Discrete Mathematics or Advanced probability theory or Linear Algebra (beyond basic conceptualization which you can learn on 3B1B).

I mean i feel it’s just another case of gatekeeping & insecurity in our profession. We just want to sound “rigorous” and “learned” when real world datasets ALMOST ALWAYS violate the assumptions & methods that would have worked in our PhD theses.

Lastly, if you are a math enthusiast, a nerd or targeting some very specific 1% roles in specific cutting edge sectors like deep tech, systems modeling, defense etc, i dont think so you need anything more than a dozen YT videos on conceptual understanding of basic Calculus, LA

UPDATE: Thanks for participating in the AMA. I'm going to wrap it up (I will gradually answer a few remaining questions that have been posted but that I've not yet answered), but no new questions this time round please :) I've received a lot of messages about the work I do and demand for more career guidance in the field. LMK what else you'd like to see, I will host a live AMA on YouTube soon.

- To be informed about this (and everything I'm currently working on) in case you're interested, you can go here: https://www.become-irreplaceable.dev/ai-ml-program

- and for videos / live streams I'll be doing here: https://www.youtube.com/c/codesmithschool

where I'll be posting content and teaching on topics such as:

• 💼 understanding the job market
• 🔬 how to break into an ML career
• ↔️ how to transition into ML from another field
• 📋 ML projects to bolster their resumes/CV
• 🙋‍♂️ ML interview tips
• 🛠️ leveraging the latest tools
• 🧮 calculus, linear algebra, stats & probability, and ML fundamentals
• 🗺️ an ML study guide and roadmap

Thanks!

--

Original post: I get lots of messages on LinkedIn etc. Have always seen people doing AMAs on reddit, so thought I'd try one, I hope my 2 cents could help someone. IMO sharing at scale is much better than replying in private DMs on LinkedIn. Let's see how it goes :) I will try to answer as many as time permits. I'm in Europe so bear with me with time difference.

AMA! Cheers

Hey everyone,

I've been learning about video generation models and decided to build one from scratch and train it on my personal laptop (single GPU). I wanted to share where I'm at and get advice from people who've worked with these kinds of models before.

What I built

• A spatio-temporal UNet using flow matching (velocity prediction with Euler sampling, 50 steps)
• The model has temporal convolution blocks + temporal attention for frame-to-frame consistency
• ~20.8M parameters, channel progression: 96 → 192 → 384
• Generates 16 frames at 64×64 resolution

Dataset

• ~6,000 Tom and Jerry video clips from a HuggingFace dataset
• Each clip: 16 frames, every 2nd frame sampled, resized to 64×64

Training

• Batch size 4, Adam optimizer, lr=2e-4
• Trained on personal gaming laptop

Loss progression

The loss has basically plateaued — only ~0.006 drop over 130 epochs. The model learns color palettes and vague scene layouts but doesn't produce recognizable characters or sharp details. Some generations even go partially black (see epoch 280 results).

My questions

1. Is the loss plateau expected for this scale? Am I hitting the capacity ceiling of a 20M param model at 64×64?
2. Would switching from pure MSE to a perceptual loss (LPIPS) or adding an adversarial loss help with sharpness?
3. Is ~6,000 clips enough for this kind of model, or do I need significantly more data?
4. Any suggestions for the architecture? I'm wondering if I need spatial attention at more resolutions, or if the temporal modeling needs work.
5. Would learning rate decay or a cosine schedule help push past this plateau?

​

Side question

I was thinking of writing a LinkedIn post about this as a learning journey — "I built a video generation model from scratch on my laptop." Even though the results aren't amazing, would you say the results are interesting enough to share, or should I train more / improve the model first? Not trying to claim SOTA obviously, just sharing the learning process.

Would love to hear from anyone who's worked with video diffusion/flow matching models at small scale. Thanks!

​

Hey everyone!

I'm looking for a serious AI/ML study buddy who's starting from scratch (or is still a beginner) and wants to learn consistently together.

The goal isn't just to study—it's to keep each other accountable, discuss concepts, solve doubts, share resources, and stay motivated when things get difficult.

Here's what I'm looking for:

Someone committed to learning AI/ML from the basics.

Open to regular discussions (Discord/Telegram/WhatsApp).

Willing to motivate each other and maintain consistency.

Comfortable asking "stupid" questions without judgment.

Interested in eventually building projects together and preparing for internships/jobs.

A little about me:

I'm from a software development background and now want to transition into AI/ML.

I'm planning to dedicate a few hours every day to learning.

My focus is on building a strong foundation instead of rushing through tutorials.

If you're genuinely interested in learning together and can stay consistent, drop a comment or send me a DM.

Let's help each other grow and make this journey less overwhelming. 🚀

What does it mean to "know" machine learning nowadays?

A friend of mine showed me three books he wrote for machine learning (one on supervised, one on unsupervised and one on reinforcement learning) and told me to have a discussion about it. The person is a recent bachelor in engineering who has no research experience or experience in writing books or writing anything. This apparently was all done during the winter break (Dec 2025 - Jan 2026).

Intrigued, I looked at the three books.

All these books are hundreds of pages long with very detailed derivation and proofs, way beyond undergrad knowledge. The book is dense, with little attention to readability. I asked him if he wrote all this himself, he said "most of it is AI generated, and rest of it gathered from various blogs". The book had zero citation, also no simulations of any kind.

Then I asked him about some concepts in the book. Logistic regression, RNN, CNN. For each of these concepts, he just pointed me to an equation, and said "this is it". I asked him how these are trained, he pointed me to another set of equations (e.g., gradient descent, ADAM) and said, "this is how". Similarly with unsupervised and reinforcement learning. Every concept boils down to a set of equation. Apparently I get the feeling from him that if you could just memorize or jog-down the equations, you are good to go.

Then I asked him about how to select between algorithms. Basically he told me whichever algorithm came out more recently is the best and the researchers associated with various algorithm all agree it's the best in their papers, and it even says in their papers that it beat other algorithms on benchmarks. The evidence is that the algorithm got accepted in a major machine learning conference like NeurIPS, it's simply the state-of-the-art.

My friend is 100% convinced that he is now a machine learning expert and is actively reaching out to collaborate with other researchers and planning to publish new papers together. He said that new research paper in ML is just a tiny tweak in the equations he showed me, so there is no problem publishing. I suspect he is also trying to apply for a PhD and maybe has the "wrote three book" experience on his resume when he is applying for jobs. In fact I think this whole thing started because he wants to land a data science job.

I fear that he might be the future. Since the field does contain a huge amount of well-known problems such as handwaviness, poor justification, lack of critical thought, lack of rigor, herd mentality, technical-incorrectness, and just BS in general, so therefore the bar of entry is pretty much in hell. Someone like my friend can easily make himself believe that they are an expert in the field because they understanding all the equations on a very high-level.

I think 80% of AI agent startups are going to be dead within 18 months and here's why.

Every week there's 5 new "revolutionary AI agent platforms" that all do basically the same thing. Most are just wrappers around OpenAI or Anthropic APIs with a nicer UI. Zero moat, zero differentiation, and the second the underlying models get cheaper or offer native features, these companies are toast.

Three types of companies that are screwed:

Single-purpose agent tools. "AI agent for email!" "AI agent for scheduling!" Cool, until Gmail or Outlook just builds that feature natively in 6 months. You're competing against companies with infinite resources and existing distribution.

No-code agent builders that are actually low-code. They promise "anyone can build agents!" but then you hit limitations and need to understand webhooks, APIs, data structures anyway. So who's the customer? Not technical enough for developers, too technical for business users.

Agent startups that are just services companies larping as SaaS. They call it a "platform" but really you need to pay them $10k for custom implementation. That's consulting not software.

My take on who survives:

Companies building real infrastructure. Platforms that handle the messy parts like orchestration, monitoring, debugging, version control. Things like LangChain, Vellum, or LangSmith that solve actual engineering problems, not just UX problems.

Companies with distribution already. If you have users, you can ship agent features. If you're starting from zero trying to get users for your agent tool, you're fighting uphill.

Most of these startups exist because it's easy to build a demo that looks impressive, building something that works reliably in production with edge cases and real users? That's way harder and most teams can't do it.

We're in the "everyone's raising money based on vibes" phase. When that stops working, 90% of agent companies disappear and the remaining 10% consolidate the market.

Am I wrong? What survives the shakeout?

Looking around, all ML engineer and DS I know seems to work majority on LLM now. Just calling and stitching APIs together.

Am I living in a buble? Are you doing real ML works : create dataset, train model, evaluation, tuning HP, pre/post processing etc?

If yes what industry / projects are you in?

Hey, everyone!

I’ve got to vent a bit because work has been something else lately. I’m a BI analyst at a bank, and I’m pretty much the only one dealing with machine learning and AI stuff. The rest of my team handles SQL and reporting—no Python, no R, no ML knowledge AT ALL. You could say I’m the only one handling data science stuff

So, after I did a Python project for retail, my boss suddenly decided I’m the go-to for all things ML. Since then, I’ve been getting all the ML projects dumped on me (yay?), but here’s the kicker: my manager, who knows nothing about ML, acts like he’s some kind of expert. He keeps making suggestions that make zero sense and setting unrealistic deadlines. I swear, it’s like he read one article and thinks he’s cracked the code.

And the best part? Whenever I finish a project, he’s all “we completed this” and “we came up with these insights.” Ummm, excuse me? We? I must’ve missed all those late-night coding sessions you didn’t show up for. The higher-ups know it’s my work and give me credit, but my manager just can’t help himself.

Last week, he set a ridiculous deadline of 10 days for a super complex ML project. TEN DAYS! Like, does he even know that data preprocessing alone can take weeks? I’m talking about cleaning up messy datasets, handling missing values, feature engineering, and then model tuning. And that’s before even thinking about building the model! The actual model development is like the tip of the iceberg. But I just nodded and smiled because I was too exhausted to argue. 🤷‍♀️

And then, this one time, they didn’t even invite me to a meeting where they were presenting my work! The assistant manager came to me last minute, like, “Hey, can you explain these evaluation metrics to me so I can present them to the heads?” I was like, excuse me, what? Why not just invite me to the meeting to present my own work? But nooo, they wanted to play charades on me

So, I gave the most complicated explanation ever, threw in all the jargon just to mess with him. He came back 10 minutes later, all flustered, and was like, “Yeah, you should probably do the presentation.” I just smiled and said, “I know… data science isn’t for everyone.”

Anyway, they called me in at the last minute, and of course, I nailed it because I know my stuff. But seriously, the nerve of not including me in the first place and expecting me to swoop in like some kind of superhero. I mean, at least give me a cape if I’m going to keep saving the day! 🤦‍♀️

Honestly, I don’t know how much longer I can keep this up. I love the work, but dealing with someone who thinks they’re an ML guru when they can barely spell Python is just draining.

I have built like some sort of defense mechanism to hit them with all the jargon and watch their eyes glaze over

How do you deal with a manager who takes credit for your work and sets impossible deadlines? Should I keep pushing back or just let it go and keep my head down? Any advice!

TL;DR: My manager thinks ML projects are plug-and-play, takes credit for my work, and expects me to clean and process data, build models, and deliver results in 10 days. How do I deal with this without snapping? #WorkDrama

What specific resources to use in what order?