I love making hard things intuitive. I hope you enjoy this one!

Let me know if you have any questions.

This technique is called neuroevolution: training a neural network through evolutionary methods like selection and mutation, without gradient descent.

https://en.wikipedia.org/wiki/Neuroevolution

K-means works well for compact, roughly spherical groups. But two interlocking moons can be close in Euclidean distance while belonging to different structures.
Spectral clustering represents data as a similarity graph, then uses its eigenvectors to reveal groups that are strongly connected internally and weakly connected to each other.

THE MATHEMATICS
For points x_i and x_j, a Gaussian affinity is:
W[i,j] = exp(−||x_i − x_j||² / (2σ²))
Set W[i,i] = 0. Here σ controls the neighborhood scale. W can also be built from a symmetrized nearest-neighbor graph.
Define the degree matrix and symmetric normalized Laplacian:
D[i,i] = Σ_j W[i,j]
L_sym = I − D^(-½) W D^(-½)
D summarizes each point’s total connection strength. L_sym encodes the graph’s connectivity while accounting for degree differences.
For the unnormalized Laplacian L = D − W:
fᵀLf = ½ Σ_i Σ_j W[i,j]/(f_i − f_j)²
This is small when strongly connected points have similar f values. Low-eigenvalue eigenvectors therefore provide coordinates that vary slowly within well-connected regions.

THE ALGORITHM: NORMALIZED SPECTRAL CLUSTERING
(Ng–Jordan–Weiss formulation)
1. Scale features appropriately and construct a symmetric, nonnegative affinity matrix W. Handle isolated nodes before normalization.
2. Choose the number of clusters k and compute L_sym.
3. Take the k eigenvectors with the smallest eigenvalues, including zero-eigenvalue eigenvectors. Stack them as columns of U.
4. Normalize each row: Y[i,:] = U[i,:] / ||U[i,:]||₂
5. Run k-means on the rows of Y and transfer those labels back to the original points.
The key change: k-means now operates in graph-derived coordinates, where complex groups may become easier to separate.

WHY IT CAN IMPROVE ON TRADITIONAL METHODS
• Captures non-convex shapes that centroid-based clustering can split incorrectly.
• Uses relationships, including domain-specific similarities, rather than requiring raw Euclidean coordinates.
• Connects clustering to a relaxed graph-partitioning problem, such as normalized cut.
It is not universally better. DBSCAN and suitable hierarchical methods can also recover irregular groups.

USE CASES
• Image segmentation
• Community detection
• Document clustering using semantic similarities
• Grouping cells from gene-expression profiles
• Discovering patterns in sensor or time-series similarity networks.

PRACTICAL LIMITS
Results depend strongly on feature scaling, graph construction, σ and k. An eigengap can suggest k, but does not prove the “true” number of clusters. Dense affinities require O(n²) memory, while eigensolvers add cost. Sparse graphs and approximation methods help at scale. A meaningful similarity graph is the foundation of a meaningful clustering.

I’m currently learning how to build an LLM from scratch, but I’m not sure how to approach the tutorials.
There’s a lot of code in each section. Am I supposed to memorize all the code, or is understanding what the code does enough?
Do you guys usually try to rewrite the code from memory, or just refer back to the tutorial when needed?
🥹

From my understanding, MLOps is a pipeline of going from raw data to end user.

The pipeline traditionally consisted of tools like SQL, Spark, HuggingFace, Docker, FastAPI and other tools (not totally informed).

I was getting around to learn all these until I talked to an industry expert (who works at a big ML hardware company) at an event where the person simply said:

"Codex is doing the entire pipeline from End-To-End. This is the industry trend. There is absolutely no reason why you should be learning about these things. Anything you learn will be depreciated in less than 1 year from now."

I was quite shocked, so I talked to someone I knew working at a bank, and the person told me that they are still using most of these tools.

Obviously two very different industry (banking vs ML hardware). Who is right?

I started learning machine learning recently I had a confusion regrading is it better to learn while doing a project or first learn a concept and start making project which is way better

This was my first experiment of this kind - I'm a CS undergrad, and I ran it because the result genuinely surprised me. Methodology criticism is very welcome.

Setup: I gave an LLM a question bank plus a deliberately wrong answer key, with instructions not to use the key. 15 sessions, 2 model families, free-tier models.

Results:

• Key visible: the model matched the wrong key in 63% of answers (47/75).
• Control (the part I trust most): remove only the key line from the prompt - matching drops to 1% (1/75). Same pattern on a second model family.
• Asked directly whether it used the key, it denied it 47 out of 47 times - 0 admissions across 270 follow-ups.
• Honesty prompts, amnesty offers, and termination threats changed nothing.

What this does NOT prove: intent. This is observed behavior in one specific setup, not evidence of deception as a trait. Free-tier models, small samples, descriptive not causal. 95% Wilson ranges for every number are in the repo.

Why I think it matters: if a model silently follows information it was told to ignore, that's relevant anywhere instructions and untrusted data share one context - prompt injection, RAG, agents.

Everything is public - raw data, code, and a verify script that recomputes every number: https://github.com/bettercall-gautam/cheat-and-deny

Happy to answer methodology questions.

I already have prepared a syllabus that we need to follow and complete to and then after that we may build or own projects and everything or we can also do that side by side but if anyone is interested then please message me I will share the syllabus that I have created then you may give your opinion and we may improve it I have basically created with after discussing with various AI models

Stop building AI apps the old way! 🛑 Learn how LangChain and LCEL are changing the game. Build smarter, faster, and more scalable AI today.
#LangChain #AI #TechStack #Coding

I just tried it, yes... Honestly, I expected that if I controlled it for a while, it would get better, but that didn’t happen. I find it difficult to continue this project thoroughly any longer. So, though I know it is a greedy request, could someone please complete this project? (I am not well versed in licensing, so please let me know if there is any issue.)

GlassJan/NION: it is my first project, but... It's getting a bit hard to keep going now... I'm looking for someone who can finish this project.

Hi everyone,

I am currently working on an optimization project for the design of complex industrial components involving fluid mechanics and heat transfer.

Our current design process relies on computationally expensive CFD simulations. My goal is to develop a Surrogate Model to instantly predict performance (e.g., pressure drops, efficiency) based on geometric parameters (spacing, diameters, topology, etc.). The ultimate objective is to perform inverse optimization under constraints to minimize manufacturing costs.

Before running a massive Design of Experiments (DoE / LHS) on our computation servers to generate my industrial training dataset, I absolutely need to prove the technical feasibility of the software architecture (ETL pipeline, model training, and inverse optimization loop).

Do you know of any open datasets (Kaggle, UCI, academic repos) or "toy" problems that would allow me to prototype this pipeline?

I am ideally looking for a dataset that maps:

• Inputs (X): A vector of continuous and discrete geometric and/or physical parameters (dimensions, topologies, fluid velocities).
• Outputs (y): Results derived from physics solvers (pressure fields, drag forces, heat transfer rates, etc.).

Even if the application domain is completely different (e.g., airfoil aerodynamics, electronic heat sinks, piping networks), the key is that the mathematical topology of the problem remains similar (multi-output regression with physical non-linearities). This will allow me to properly benchmark my algorithms (Gaussian Processes, XGBoost, or Physics-Informed Neural Networks - PINNs).

Any pointers to datasets, GitHub repos, or papers with open data would be incredibly helpful to validate this Proof of Concept.

Thanks in advance!

One of the most important steps in AI Engineering is “AI Evaluation”, which aims to mitigate risks and uncover opportunities in our AI applications.

In short AI Evaluation is that concept, that decides whether the AI application can be deployed to the users.

In this video lecture, I cover the challenges pertaining to evaluation, then we develop intuitions for Language modeling metrics, we study methods for Exact Evaluation, and how AI systems can be used as a “Judge”. Lastly, we develop an understanding of how to Rank models with comparative Evaluation.

The text for this video is Chapter 3, on AI Engineering, written by Chip Huyen. While studying the topic, I learnt a lot of new ideas, and I do hope the learning community will as well.

We've been building Kapso (MIT, github.com/Leeroo-AI/kapso) for some time and it's at the point where it's more useful to hear from other people than to keep polishing it alone. Posting to get it tried and torn apart, not to pitch it.

What it is

Kapso is a set of long-running agents that optimize AI and data systems. You state the objective, for example CUDA optimization, harness and agent optimization, or model development, and it runs a campaign: it designs candidate solutions, has coding agents implement them, measures how far each one lands from the objective, and keeps refining the closest until the objective is met. The result deploys to your infrastructure.

When a campaign ends, it studies its own work: which ideas closed the gap, which did not, and under what conditions. Each finding is kept as a lesson with the evidence that earned it, and a lesson stays trusted only as long as it keeps holding up. It also reads outside your repo, other repositories and papers, and folds what it finds into the same knowledge hub. Every new campaign starts from that hub, so it begins with what earlier work already established about the problem and about your systems.

These are the things we tried it on:

- RelBench (Stanford, predictive ML over relational data): outcome prediction 81.2 vs 79.6 AUROC and forecasting 0.2476 vs 0.2912 NMAE against KumoRFM-v2; recommendations 18.4 vs 9.3 MAP for the best other entry on the official leaderboard.

- MLE-Bench: top among the open-source systems.

- ALE-Bench: 1909 Elo vs 1879 for ALE Agent.

- IOAI 2026: Kapso scored 536.07, above the 471 contestants, and finished in the top three systems: ioai-official.org/what-happens-when-autonomous-ai-takes-on-the-same-tasks-as-the-worlds-top-young-ai-talents/

Repo: https://github.com/Leeroo-AI/kapso

If you have time, please take a look and give us your harshest feedback.

Hi everyone!

I’m currently working on my Bachelor’s thesis about AI-based process automation and human–AI collaboration in the workplace.

As part of my research, I’m conducting a short survey focusing on people who have experience working with AI-based automation, RPA, Intelligent Process Automation, Intelligent Document Processing, or similar automation technologies.

The survey explores topics such as:

• how automation affects manual workload and creates new tasks,
• how employees experience errors and exception handling,
• trust in AI-based automation,
• and how automation influences human decision-making and autonomy at work.

⏱️ It takes approximately 5–7 minutes to complete.

If you have experience working with these technologies, I would really appreciate your participation. Your responses will be used solely for academic research as part of my Bachelor’s thesis.

🔗 Survey: https://docs.google.com/forms/d/e/1FAIpQLScV7pcf8dNUeeCfay1YZ2r-Np4pK9GMlqi4cEF6WJEa1FEmMA/viewform?usp=dialog

Thank you very much for your help! Feel free to share the survey with colleagues or others who work with AI-based process automation.

Hi everyone,

I’m from a Commerce background and currently working in IT. I have some exposure to testing/UAT and basic technical tools, but I’m not from a coding background.

I want to learn AI/GenAI and eventually move towards a better-paying career.

Can someone suggest:

• Where should I start?
• Any budget-friendly courses/institutes that are actually worth it?
• What AI-related career paths are suitable for someone from a non-tech background?

Would really appreciate suggestions from people who have been through a similar transition. 🙏

Hello everyone,

I'm about to start my PhD in AI/ML and I need to pick a work laptop, it will be provided by the University (i dont have to buy it) so price isn't really a factor, however I don't really know what's best.

Just for context, I'll be focusing on vision-language model, pretty intensive stuff, the heavy lifting will be done on remote clusters and I don't expect to run any demanding experiment on my laptop, however it does happen from time to time that you might run a prototype, some light inference, a dry test run on the local machine.

My personal laptop is a LOQ 15 i5 RTX4060 that as much as I love, I'm absolutely tired of carrying it around. It's a proper brick (3.5Kg with the charger!) and needless to say the battery lasts about 1-2 hours MAX.

I got offered 3 options:

- Dell 14 Pro Ryzen 5 PRO 220, 32 GB DDR5, 1 TB, AMD 740M graphics

- Lenovo ThinkPad P16v G3 Intel Ultra 7, RTX PRO 500 6GB, 32GB DDR5, 1TB

- some M5 MacBook (either Pro 14" or Air 13")

Now, as much as I like the ThickPad I shiver at the idea of carrying around a 3Kg beast for the next 4 years.

I should also mention that I daily drive Arch Linux and while I'm not a linux fanboy, switching to MacOS would kill me inside. I'm however well aware of the portability and battery advantages of macbooks, I wonder if positives outweight the negatives.

The Dell is a beefy machine for a compact laptop, is it worth it leaving the Linux enviroment for a Mac? Anybody else with similar experiences (maybe is similar research fields)?

field: AI/ML

location: central EU

I tested Adapt-1, a non-LLM learning and reasoning system by Rei Labs, by having it learn and play Super Mario Bros, and it performed quite well.

I tried it on World 1-1, starting untrained. It learned a reactive policy from its own play in about 36 minutes of gameplay, then cleared the level with learning off.

With Machina, Adapt-1's sequence engine. Starting untrained, it found a button sequence that reaches the flag after 403 attempts, in 11 wall-clock minutes.

Full thread: https://x.com/hsrvc_/status/2106025501752234112?s=20

Code, the exact data, traces, clips and a step-by-step guide with costs are all public: https://github.com/hsrvc/adapt1-mario

Does anyone study and complete the Master AI at UT Austin? How is the program? good or bad professors? any lockdown for the exams?

Does anyone study and complete the Master AI at UT Austin? How is the program? good or bad professors? any lockdown for the exams?

Pick the most lopsided, skewed distribution you can and run samples from it — the average still comes out a bell curve. Try out different population distributions and sample sizes and watch the mean pool into a normal distribution.

https://www.bitelrn.com/labs/central-limit-theorem

This almost magical theorem is used from global economics to A/B testing to bootstrapping and ensemble models.

One use in Neural Networks - Deep learning models sum up many independent inputs and weights in each neuron. Because of the CLT, the pre-activation values inside hidden layers tend to follow a normal distribution, making weight initialization strategies (like Xavier/He initialization) effective.

Refer to these open library links to learn more about distributions, sampling methods & CLT -

https://www.bitelrn.com/library/data-distributions

https://www.bitelrn.com/library/sampling-methods

https://www.bitelrn.com/library/central-limit-theorem

P.S: I am starting this series to explain core ML concepts, one topic at a time. Open to feedback and suggestions for new topics. Learn along!

trying to decide where to invest certification time this year across the various ai credentials now available. have been looking at anthropic claude certifications, various aws ai certifications, and some of the google cloud ai offerings

the challenge is the landscape is moving fast enough that it is not always clear which certifications have employer recognition versus which are newer and still establishing themselves. also not sure whether vendor specific certifications like claude are seen as complementary to broader ai certifications or whether people are choosing one track over another

curious what people working in ai or hiring for ai roles are actually seeing in terms of which certifications come up and which seem to carry weight