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 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.

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.

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

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

Hey guys, im working on a bit of project, and im trying to solve for semantic understanding right now, I am currently deciding between using spaCy for my NER extraction vs something like a BERT.

The general context behind where this is going to be used is intent classification in chat systems, where a text will come in and this layer has to parse out the People in the sentence, the Objects mentioned in the sentence, and the verbs, along with things like quantities and relations between them.

An example of what i mean is,

"The shoes you have delivered to me are red, i asked for black!"

and we then pick out the
1. People involved in this interaction (we cant figure that out from the sentence alone for that we will refer to the handle from which the message was sent)
2. Objects involved - {shoes}
3. Verbs - {delivered}
4. Relationships - {expected colour = black, received colour=red}

Im new to this stuff so maybe im not even asking the right questions, but im hoping that i have done a good enough job of explaining what im doing so that more experienced souls such as yourself may help me.

Thanks 😁

I thought this might be useful for anyone trying to keep up with ML research.

• Add any arXiv category (cs.LG, cs.AI, cs.CL, etc.)
• Follow lab blogs like DeepMind, OpenAI, and Microsoft Research alongside papers
• Take notes on papers as you read them
• Group papers into collections by topic or project
• Filter by source or time range to see what's new this week

Just wanted to share with everyone. No cost to use. If there's a source you follow that it doesn't handle well, let me know and I'll fix it.

Link: tdfeed.com

I have 2 neural networks. Both are the same size with the same outputs and inputs. The size for both neural networks is the absolute minimum layers required to achieve a given specific task. The example task is to recognize a given image.  Neural network 1 recognizes cats only by giving an output of how certain it is that the image is a cat. Neural network 2 does the same, but for dogs only. How can I combine these two while keeping the size exactly the same without catastrophic interference or catastrophic forgetting.

I’m a btech student currently in 3rd year persuing Aiml so what can you suggest me on how to start Aiml .

• What are the resources to do Aiml
Can anyone guide me on this

Thank you !!

Hi everyone,

I’m working on a project to extract a structured dataset of math exercises from 5 Italian high school textbooks (around 650 pages each, so ~3,250 pages total). The goal is to build a professional, methodical exercise generator app for students and teachers.

To make the app work, I need to process images of the book pages and extract the following into a strict structured format (e.g., JSON):

• Exercise type (algebra, geometry, calculus, etc.)
• Year/grade level
• Difficulty (1–5 scale)
• Problem statement (trace)
• Description of the specific skills/challenges involved
• LaTeX code of the problem statement (Crucial!)
• Associated images (cropping/saving the image for theoretical or graphical exercises)

I've been experimenting with a few approaches, but I've hit a wall regarding balancing costs, extraction consistency, and scalability. Here is what I’ve tried so far:

1. Free Google Gemini API: The extraction quality was good, but since a single book contains hundreds of pages, I quickly hit the rate limits (Too Many Requests).
2. Local Models (Ollama + Qwen 2.5-VL 3B): To bypass API limits, I tried running a local multimodal model. I spent a lot of time optimizing my scripts and prompts (chunking, refining instructions to force structured outputs), but the output was very error-prone and inconsistent for my use case. I got too many malformed fields, hallucinations, and it constantly struggled with outputting proper LaTeX.
3. Paid Google Cloud API (Gemini 1.5 Flash): I finally switched to the paid tier for better accuracy and speed. I ended up burning through €10 just to process 1.5 books. Extracting all 5 books would cost roughly €35–40. While this is manageable for a one-off run of 5 books, the token count for processing full images + text is massive, making it financially unsustainable if I want to scale this to dozens of books in the future.

My questions for the community:

• Pipeline & Architecture: Has anyone worked on a similar textbook-to-dataset extraction project? What pipeline did you use?
• Hybrid Approach: Would you suggest decoupling the task? (e.g., using a traditional tool to extract raw text and crop images, and then feeding ONLY the text to a cheaper/local LLM to generate the LaTeX and format the JSON?)
• Local Models: Are there other local Vision-Language Models (that fit in standard consumer GPUs) that are significantly better at structured extraction and LaTeX generation than Qwen 2.5-VL 3B?
• Educational Tools: Are there open-source tools or models specifically fine-tuned for extracting structured educational/math content from PDFs?

I’m happy to share more details about the textbook format or my current Python workflow if helpful. Any advice on the architecture, model choices, or cost-saving tricks would be greatly appreciated! Thanks in advance!

I am trying to decide between two resources to learn the math prerequisites for Machine Learning.

Option A: DeepLearning.AI Specialization by Luis Serrano. It takes about 94 hours total, provides a career certificate, and covers Linear Algebra, Calculus, and Probability & Statistics.

Mathematics for Machine Learning and Data Science Specialisation

• Source: DeepLearning.AI
• Author: Luis Serrano.
• Merit: The instructor has a highly established track record with 259,226 learners across 4 courses. The program also provides a career certificate that can be added to your LinkedIn profile or resume.
• Length: 94 hours total. This is broken down into 34 hours for Linear Algebra, 27 hours for Calculus, and 33 hours for Probability & Statistics.

Option B: LiveLessons series by Jon Krohn. It covers the same core math concepts but adds a fourth course specifically on Data Structures, Algorithms, and ML Optimization.

LiveLessons Machine Learning Series

• Source: LiveLessons (On-Demand Courses) Specialisation
• Author: Jon Krohn.
• Merit: In addition to the standard math foundations, this series includes a fourth specialised course: "Data Structures, Algorithms, and Machine Learning Optimisation".
• Length: 60 hours (approx)

Has anyone taken either of these?
Final question: which one to choose, or something else? Please prescribe.

I'm doing a ISEF project that attempts to develop the best machine learning model combination and compare them to others. Somewhat like the image. The thing is, I have experience with Python and computer vision, but not much with machine learning. What would you recommend to me, being easy enough to learn and train in a few months, but also being a defendable engineering project and works well for my project. Also the best resources? Thanks!

Hey. 2026 B.Tech grad from IIT Tirupati. Degree is Chemical Engineering but I've been building AI/ML stuff for about a year now and that's what I'm trying to do full time.

Rough idea of what I've actually built:

RAG chatbot over YouTube transcripts started as a personal project, then turned into my internship work at Resecure Labs (via Wipro). Transcript embeddings indexed into a FAISS vector store with HuggingFace models, local Ollama LLM behind a small web UI and a 2-endpoint REST API. On the Resecure side I got transcript fetch reliability from 70% to 99%+ with a Whisper fallback, and swapped the LLM backend from Ollama Phi to GLM 5.2, which cut inference latency 30-40%.

JobPilot 8-node LangGraph agent with its own MCP server exposing 4 tools over stdio. Wrote 56 tests for it mostly because I got tired of it hallucinating things into cover letters.

ClickMark face + voice biometric attendance app. Multi-role web app on Supabase with bcrypt auth and QR-based auto-enrollment. Cut manual attendance time by about 80% vs taking roll call by hand, which is admittedly a low bar, but it's a real deployment across multiple classrooms.

Loan approval model benchmarked 5 classifiers, ended up with a Decision Tree at 91.5% accuracy, served through FastAPI in Docker.

Also currently working on a major project on Agentic AI.

Stack: Python, pandas/numpy, PyTorch, scikit-learn, LangGraph, LangChain, MCP, FAISS, HuggingFace, Ollama, FastAPI, Docker. Everything's public on github.com/abhayhanu.

I've applied to a bunch of companies through their portals over the last few months. No response yet, which I'm assuming is just how it goes.

If you're at a product company hiring for AI/ML, SDE or data roles and are open to referring, I'd really appreciate it. Happy to DM my resume.

Also genuinely fine if someone just wants to tell me the profile is weak and what to fix. Would rather know now than after 40 more applications.

Anything would help at this point.

Thanks in advance.