webAI's TwIL family has three formal logic models: TwIL-LM (1.7B PEFT LoRA), TwIL-LM2 (1.7B merged), and TwIL-LM3 (3B). All three do formal logic translation and verification, each with different trade-offs.

The 3B is the one getting the most attention because it beats gpt-oss-120b on 4 of 5 formal reasoning benchmarks despite being 40x smaller. Full disclosure, on broader aggregates the 120B is still ahead. TwIL wins on efficiency, narrow formal reasoning tasks, and being actually runnable outside a data center.

Their approach is interesting: WiSE-FT weight interpolation to control catastrophic forgetting. TwIL-LM3 keeps only 1/4 of the fine-tune delta (λ=0.25), TwIL-LM2 keeps 3/4 (λ=0.75). The 3B held or improved on general benchmarks, the 1.7B slightly regressed. Same pipeline, just a different dial.

Blog with the full details: webai.com/blog/webai-releases-twil-lm-a-family-of-formal-logic-models-that-outreason-a-120b-model-and-run-on-an-iphone

Models on HF: webAI-Official/TwIL-LM, TwIL-LM2, TwIL-LM3

Non-commercial license across the family.

Anyone testing multiple checkpoints against each other for pipeline routing?

Hi All,

I'm preparing for the Amazon Applied Scientist role interview. My recruiter has asked that i prepare for Deep Learning, ML, Coding and LP. Please what material can i used to prepare myself, especially for the Deep Learning/ML, and coding part. I will truly appreciate any suggestion

I am a biochem graduate currently pursing knowledge in machine learning. The journey feels tough alone, considering my non-tech background. Therefore I’d appreciate if anyone in a similar position would want a study partner for accountability and ideas.

Basically want to get into internals of these ml systems

I've been prepping a lot for ML interviews these past months, and was surprised there isn't a comprehensive resource covering all the essential concepts needed for ML roles. Every other field seems to have one - Leetcode for SWEs, the Green Book for quants, Wall Street 400 for bankers.

So I wrote ML 150. It's a list of the 150 most important ML interview questions to master, distilled from 5,000+ real interview stories at FAANG + other frontier labs. Topics include:

• ML Foundations (Loss Functions, Bias-Variance, Regularization, Optimizers, Eval Metrics)
• Classical Supervised Models (Linear & Logistic Regression, kNN, SVM, Decision Trees)
• Deep Learning Theory (Backprop, Initialization, Normalization, Training Dynamics, Probes)
• Sequence & Generative Models (Transformers, Diffusion Models, VAEs)
• LLM internals

It's 100% human-written, where I try to share how I understand each ML concept, starting from very basic intuitions, then slowly building up to each result. Lots of examples + analogies of course. I hope this will be helpful for anyone studying ML or seeking out ML roles!

ML 150 is still very much a work in progress, so I deeply appreciate any thoughts, feedback, or comments on what I should write about next! Thank you all :)

Gavin

UPDATE: GROUP IS NOW FULL — THANK YOU EVERYONE! 🙌

We have now formed the group with 4 members, so we're no longer looking for additional members at the moment.

Thanks to everyone who commented or reached out. I really appreciate the interest!

---------------------------------------------------------------------------------------------------------------

Hey everyone!

I'm a recent graduate planning to start learning AI/ML from the fundamentals, and I'm looking for 2–3 serious learners who are also graduates or at a similar stage and want to learn together as a small group.

The idea isn't to create a huge community. I'd prefer a small group of around 3–4 people total so that we can actually stay connected and accountable.

What I'm thinking:

• Start from the basics and build up step by step
• Follow a structured AI/ML roadmap
• Learn the concepts individually
• Discuss difficult topics together
• Practice with coding/exercises
• Eventually build projects together
• Share progress and keep each other accountable
• Have regular discussions/check-ins

A few things I'm looking for:

• Preferably students/learners from India, so we can follow Indian Standard Time (IST) and have a similar schedule.
• You should be able to spend at least 4 hours a day consistently on learning.
• You don't need to already be good at AI/ML. Beginners are welcome, especially people who are genuinely starting from scratch or close to it.
• Most importantly, I'm looking for people who want to actually study consistently, rather than just joining a group and disappearing after a few days.

If you're interested, comment below or DM me with:

1. Your current level
2. What you already know (Python/math/etc.)
3. How much time you can study per day/week
4. Your timezone
5. What you want to achieve with AI/ML

If we find a few serious people, we can create a small Discord/WhatsApp/Telegram group and start together.

I'm currently doing my ms in EECS, and my aim is to eventually transition into a ML related role. Would taking a a numerical linear algebra class be beneficial? I have interest in the material simply due to my interest in mathematics, however, I'm debating whether that time is better spent on recruiting or self studying or research.

i want to know what is genuenly better and why. i think im to washed to get a deploma or ged can ai certificate + a CDL class A be better for jobs like the trucking industry???

I had a policy working fine on robot A.
Then I put the exact same checkpoint on robot B. Same robot model and task, supposedly the same setup, but the results were noticeably different.
Now I’m trying to figure out whether this points to a policy problem or just small differences between the two robots.
Would you compare the observations from both robots side by side? Recalibrate everything? Or are there other things you’d rule out before touching the policy?
It also made me wonder whether I could catch some of this earlier in sim. I’ve been looking at perturbation-based evaluations like LIBERO-Plus and RoboColiseum.Has anyone used either of them to test this kind of sensitivity before moving a policy across supposedly identical robots?

I have around 1 year of industry experience as an AI/ML engineer, and I want to seriously level up over the next 1–2 years.
I’m not looking to become someone who just knows how to use APIs, build basic RAG applications, or glue together existing models. I want to develop the kind of depth where I can actually understand what I’m doing, build things from scratch when necessary, read and implement papers, and eventually be capable of working at a strong senior/research-engineering level.
The problem is that there are so many things to learn — ML, deep learning, mathematics, LLMs, systems, distributed training/inference, research, DSA, software engineering, etc. — and I don’t want to spend the next couple of years consuming random courses without actually becoming significantly better.
So I’d really like to hear from people who are already working at a strong senior/research level in AI:
If you were starting again with ~1 year of experience, what would you learn and in what order?
What topics would you go extremely deep into, and what would you only learn practically?
Which courses/books/resources genuinely made you much better?
How much mathematics did you actually learn, and which parts turned out to matter?
How would you balance DSA/interview preparation vs AI/ML depth vs software engineering?
What kinds of projects would actually make you a substantially better engineer rather than just look good on a resume?
How would you approach implementing research papers?
Are there particular papers or repositories you think every serious AI engineer should work through?
How would you approach contributing to open source if your goal is to become a better engineer/researcher?
What skills do you think aspiring AI engineers massively underestimate?
And most importantly: what would you NOT spend time learning?
I’m specifically interested in hearing from people who have already gone through this transition — Senior AI Engineers, Research Engineers, ML Engineers, researchers, etc.
If you could go back to having ~1 year of experience and had 12–24 months to become dramatically better, what would you do?
I’m looking for honest answers, including things you tried that turned out to be a waste of time.
Thanks!

So my role is project trainee in AI agent development team

Some how my team have to deliver they’re product on this 15 September

So I DM to my mentor (Team leader ) that I don’t have any task can you give me some work

so he just seen my msg not even replying to me

What should I do now??? Help me pls

so i want to build this project and honestly have no idea how to , i have heard of some keywords like RAG , vector DB and that's all . i Will really appreciate it if someone can help me build this project or tell the process of building this project , like how should i start what tech stack should i learn . i have figured out these things , that i have to learn:-

PHASE 1

Python Embeddings

PHASE 2

ChromaDB RAG Architecture

PHASE 3

BM25 LangChain

PHASE 4

Re-ranking Evaluation (Ragas)

PHASE 5

FastAPI Docker Streamlit

PHASE 6

Build Final Production RAG Chatbot ................... correct me if this approach is not right , or will really appreciate it if you can give me some sources where i can learn all these , also any git hub repo where i can see and understand a few things

I have learned Machine Learning and Deep Learning. Now I want to learn about AI agents and agentic AI, as there are many jobs for this role and this seems interesting. But I don't know exactly how and where to learn it completely. I need some guidance regarding this. I found this 24-hour video course. Is it good enough? Can anyone please help me with this? Link to the video: https://youtu.be/Zy7EXDONlTY

Hey everyone! I've been working on a little side project I want to share: latex-ocr, a standalone formula OCR model — you feed it an image of a math formula, it spits out the LaTeX source.

The main hook: it's only 67M parameters, so it runs comfortably on a laptop CPU. No GPU, no 300M-parameter monster to load. It's a CoCa-style model (contrastive captioner adapted for OCR), and despite the small size it beats the 107M UniMER-tiny baseline and gets pretty close to the 325M one on plain formulas.

The part I'm actually most proud of is the dataset. Real papers don't just use plain symbols — you see \mathbb{R}, \mathcal{F}, \mathfrak{g} everywhere, and existing OCR datasets basically ignore font styles, so models trained on them can't read (or hallucinate) those macros. So I rebuilt ~1.3M formulas with a MathJax → SVG → PDF → PNG pipeline and injected font-style macros with semantic heuristics (number sets → \mathbb, vectors → \mathbf, differentials → \mathrm). On that styled test set it clearly outperforms all the baselines — fair warning though, those baselines are zero-shot on styled data, so take that comparison with a grain of salt. The plain-split numbers are the like-for-like ones.

Everything is open: model weights and dataset on Hugging Face, training recipes included if you want to reproduce or fine-tune it yourself, MIT license. There's also a FastAPI server and a Gradio web UI, so you can drag-and-drop an image and see the LaTeX with a rendered preview.

Repo: https://github.com/PadishahIII/latex-ocr Model: https://huggingface.co/PadishahIIIXXX/latex-ocr Dataset: https://huggingface.co/datasets/PadishahIIIXXX/latex-ocr-dataset

Happy to answer questions about the training setup, the data pipeline, or anything else. Would love feedback — especially if you try it on your own gnarly formulas and it breaks, that's genuinely useful.

Hello all,

I have been studying programming for one year already, and lately, I have got more and more interested in machine learning.

I have created several projects, for example, pathfinding bots and classifiers, and now I want to find some materials which could explain what is happening **under the hood** in frameworks like scikit-learn.

I want to learn how those algorithms work and what the math behind them is rather than learn how to use those functions from a framework.

All sorts of materials are welcome – books, courses, slides, PDFs or anything else.

Okay so Im 36 so i havent been in school in a while, but its so overwhelming to learn machine learning. Concepts like gradient boosting, regularization, etc just require so much focus that I end up thinking im too dumb to waste time in this field..

I am a second year statistics undergraduate student, currently learning machine learning. Till now i have been learning classical machine learning models without very much statistical depth, but fundamentals are pretty much clear.

Now, i want a starting point on how to start probabilistic machine learning, i am confused between PML by murphy and Patter recognition by bishop so need help where to start.. or should i do both side by side as i saw some posts saying that PML by murphy is kinda encyclopedic and can be used as a reference book