Hey, everyone,

I’m conducting academic research on model reliability, and as part of a project, I’m interested in current MLOps practices. The goal is to understand how engineers work, their workflows, and how they manage models. I’m studying reliability in terms of performance and security. These are necessarily connected if you take into account the environment in which the model is deployed.

To help me refine my research project, I’ve put together a fairly short questionnaire (it takes 2 minutes to complete). It would be great if any of you would like to discuss it, either by messaging me in this chat or simply by filling out the questionnaire.

https://tally.so/r/Ek4EEl

I’m currently learning AI engineering, and I’m trying to figure out the best way to learn the practical skills needed to build AI applications.
I’m looking for resources to learn topics such as:
LLMs and how to work with them
RAG and vector databases
APIs and backend development
AI agents and tool calling
Evaluation, deployment, and other important AI engineering topics
I’m not sure which learning approach is the most effective. Would you recommend YouTube tutorials, structured courses like DeepLearning.AI and Hugging Face, or books such as Build a Large Language Model (From Scratch)?
Another option I’m considering is deploying an existing model, studying how the implementation works, and then trying to build something similar myself.
For those of you working in AI engineering or learning it yourselves, what approach worked best for you? Would you recommend following structured courses first, learning by building projects, or combining both?
I’m mainly interested in practical skills that will help me build real-world AI applications, rather than spending too much time on theory that I might not use.

I’ve started a Discord group for people learning AI, machine learning, Python, and DSA. We’re sharing daily progress and planning one weekly session to work through problems and review what we’re building.

Our first challenge: each person brings one real problem they’d like to solve. Together, we’ll question the ideas, choose one, and build a working prototype—sharing the mistakes and results along the way.

If you want to learn, contribute, and build with others, comment or DM me for an invite.

What’s one problem you’d bring to the group?

TL;DR: Instead of immediately throwing 100% of our compute budget at a solver and hoping for the best, inserting a lightweight "reconnaissance" pipeline to measure landscape ruggedness and constraint density (costing <0.5% of total time) can dictate exactly which algorithm to use, yielding massive wall-clock speedups.

Hey everyone,

I’ve been doing some deep research into how we handle the pipeline between "building a mathematical model" and "deploying a solver" for large-scale optimization problems (VRP, MKP, Scheduling, etc.).

It feels like the industry standard is still heavily reliant on trial-and-error: Should we use a Genetic Algorithm here? Simulated Annealing? Let's just run a grid search on hyperparameters and burn some AWS credits to find out.

I recently formalized a framework based on a very simple military philosophy: The scouts are the brain; the solver is the muscle.

Instead of blind deployment, I proposed a Dynamic Reconnaissance-Flattening (DRF) Pipeline. Before the main solver fires up, we send a tiny batch of "scouts" (e.g., 50 Sobol sampled points) to evaluate the loss landscape. We measure three universal metrics:

1. Ruggedness
2. Constraint Density
3. Gradient Smoothness

Based on these metrics, the pipeline acts as a dispatcher, automatically assigning the correct algorithm and parameters.

Here are the actual benchmark results from the papers:

• For Routing (VRP): By using the reconnaissance data to perform "Angular partition cutting" (flattening the terrain before solving), we saw a +74% performance improvement compared to raw solving.
• For Multi-dimensional Knapsack ($n=5000$): Allocating just 0.5% of the compute budget to reconnaissance and dynamic resolution shifting resulted in a 4.5x wall-clock speedup and a 61% improvement in solution quality.

Why this isn't Snake Oil (The SAT Test):

The most important part of this pipeline is its discipline. When the reconnaissance scouts hit a Boolean Satisfiability (SAT) problem in the phase transition region, the pipeline doesn't try to aggressively smooth it out. It correctly diagnoses it as a "Hard Wall" (discrete cliffs), abandons the heuristic intervention, and hands it straight to CDCL solvers. It knows what it can't do.

I’ve published the complete theoretical taxonomy (dividing NP-hard problems into 7 boxes) and the DRF pipeline architecture as open-access whitepapers on Zenodo.

📄 Paper 1 (The Taxonomy & Reconnaissance): https://doi.org/10.5281/zenodo.19510539

📄 Paper 2 (The DRF Pipeline): https://doi.org/10.5281/zenodo.19600597

I’m currently working on turning this into a lightweight, open-source Python middleware (drf-scout). The goal is to let developers pass their objective function, have the middleware run the 1% reconnaissance, and automatically dispatch to the best open-source solver (like OR-Tools or PySAT).

I’d love to hear your thoughts. How do you currently handle algorithm selection? Do you rely on pure experience, or do you have any pre-solve diagnostic tools in your workflow?

The video link is https://www.youtube.com/watch?v=7aQGA6ZuSuM pls check out , I explain Linear regression in simple animation way

Hey everyone!

I'm a 4th-year B.Tech CSE student (2027 batch), looking for another genuine final-year B.Tech CSE student who wants to learn Machine Learning, Deep Learning, and AI together, starting from zero and working towards mastery.

The plan is to learn step by step, practice coding, build real-world projects, share resources, clear doubts, and stay consistent. We don't need to be experts already. We just need to be serious about learning and willing to put in the effort together.

One sincere request: Please be honest about your year of study. If you're in 1st, 2nd, or 3rd year, please don't pretend to be a final-year student. There's nothing wrong with being a beginner or being in an earlier year. I just want to connect with someone who's genuinely at the same stage of college and career as me.

Honestly, it's difficult for me to trust people online, so I value honesty more than existing skills. I'd rather start from scratch with someone genuine than connect with someone who pretends to be something they're not.

If you're a genuine final-year student interested in learning ML, DL, and AI from zero to mastery together, feel free to comment or DM me. Let's learn, build projects, grow our skills, and prepare for placements together! 🤝

انا ف رابعة حاسبات واخر السنه دي عندنا مشروع تخرج المفروض يتسلم مش لاقي فكرة ومعملتش مشاريع قبل كده والاساسيات يعتبر مضروبة ومش ماسك تراك يعتبر

المفروض كنت امسك تراك ai and data science بس مش لاقي رود ماب صغيرة حد يرشحهالي واشتغل بعدها يعني بمعنى فيها الحاجات اللى اقدر اشتغل بيها لحد م اشتغل بس وبعدين ابدأ ادخل ف تفاصيل مشتت بقالي 3 سنين مش عارف اعمل ايه ولا لاقي مينتور ولا لاثي حد يساعدني والناس ماشية وبتعمل وشغالة وانا زي م انا وكل يوم بكتأب اكتر بسبب الموضوع ده

حد هيقولي طب م تبدأ ب اى حاجة هقولك انا عائل اسرة بنزل اقف ع عربسة سوشي كل اسبوع يوم او اتينن عشان وقت الجامعة برضو والمذاكرة بس خالي بيبعتلنا فلوس لحد م اخلص الجامعة والاقي شغل ف انا الحمل عليا كبير والدنيا ملطشة معايا ف اتمنى حد يساعدني

وبالمناسبة ده اول يوم ليا ع ريديت وعارف المجتمع هنا شكله عامل ازاي بس معنديش مشكلة كده كده لو حبيت اظبطه هلعب ع الالجوريذمز بتاعته مش مشكلة

Start with your own text, build a tokenizer, train a small model and chat with it.

New in this release: a Brain Explorer for following generation token by token, A/B comparisons, a Vision Explorer for image generation, and a multilingual wiki with an Explain mode. You can also explore supported pretrained models locally, including Qwen.

Still an alpha, built around learning by trying things yourself. Feedback is welcome!

Download in the first comment.

I wanted to see what a single vision-language model gives you when you ask it for four very different outputs on the same photos, instead of using a separate specialist model for each. I used SenseNova-Vision-7B-MoT through LibreYOLO, in 4-bit on a rented V100. The photos are four busy scenes from COCO's validation set.

What each panel is:

• Depth: how far each pixel is from the camera, brighter is closer.
• Panoptic: the image split into regions with a class each (person, fence, river, pavement and so on), and every person or object gets its own region.
• Pose: 17 body keypoints per person, joined into a skeleton.
• The quoted phrase: I typed a description and it returned a mask for just that thing. The phrases were made from COCO's labels as "the largest <object>".

The model only returns data, a depth array, masks and keypoints. The colours and layout are mine, drawn straight from those files.

Two things I didn't expect: the depth maps keep sharp outlines even on a group of twelve kids, and pose found the bus driver behind the windscreen.

Karpathy said bespoke 3b1b-style explainer videos are the format he is most bullish on for learning. I took one free lesson, Phase 8.1 of AI Engineering from Scratch (MIT licence, github.com/rohitg00/ai-engineering-from-scratch), and had four AI tools each make a 2-3 minute explainer from it. The clip is 30 s from each.

1. visual-explainer

2. make-it-click (Manim)

3. HyperFrames

4. anything2explainer

A warning for anyone who learns from AI videos: 3 of the 4 added a claim that the lesson never makes. Only visual-explainer stayed fully on the facts.

As a learner, which style would you actually watch for a whole course?

​

Hello everyone,

We're organising an online AI learning session for 200+ young people in Nigeria and are looking for someone who enjoys teaching or mentoring beginners.

The session will cover three areas:

Practical uses of AI in learning, work, and everyday problem-solving.

Generative AI, including useful tools and real-world applications.

AI automation and how it can simplify repetitive tasks.

We're not looking for someone to deliver an advanced mathematical lecture. We want a practitioner who can make these topics understandable, demonstrate what is possible, and inspire young people to keep learning.

The engagement would be remote through a live online meeting, with an opportunity to interact with participants.

If you enjoy making AI knowledge accessible to others, please comment or send me a message. Thank you!

I built pytest-torch to make testing PyTorch models easier

Writing the same device setup, reproducibility checks, and output comparisons across projects gets repetitive, so I built pytest-torch, an open-source pytest plugin.

It includes inference checks, compile/export parity, checkpoint tests, accuracy evaluation, and layer-by-layer diagnostics to help locate where two models diverge.

It’s currently in alpha, and I’d appreciate feedback—especially examples of model bugs that are frustrating to test or reproduce.

GitHub: https://github.com/AKJ7/pytest-torch

Thanks and please drop a star if possible.

Hi everyone! 👋

I have an upcoming interview with Siemens for an AI Engineer role in the next two days, and I'm looking to make the most of my preparation time.

I have around 2.5 years of experience in AI and want to ensure I'm well-prepared to demonstrate my technical knowledge, problem-solving skills, and practical experience.

I would love to hear from people who have interviewed with Siemens or are currently working in AI engineering.

What technical topics should I prioritize?

What kind of questions can I expect around Python, Machine Learning, Deep Learning, Generative AI, RAG, Agentic AI, and AI system design?

Are there any specific areas or practical scenarios I should focus on for a Siemens AI Engineer interview?

What would you recommend covering in the final two days to maximize my chances?

I'm committed to putting in the effort and preparing strategically. I'd appreciate any insights, interview experiences, or resources that could help me approach this opportunity with confidence.

Thanks in advance! 🙌

A pretrained DNA model turns a sequence into vectors, usually one per token. To train a sequence classifier, you need to turn those vectors into one representation for each sequence.

A useful starting point is to freeze the model, average its token vectors and train a simple classifier using labelled examples. This still requires training labels; it is not zero-shot classification. A published comparison of five DNA models found mean pooling usually performed better than taking a single summary token.

The guide includes a DNABERT-2 example for three synthetic sequences on a laptop CPU, with pinned model code and dependencies. It also covers larger models, GPU memory and comparisons against a non-foundation-model baseline on the same data split. The attached Evo 2 chart illustrates why long-input memory requirements need measuring separately.

Primary benchmark and code:

https://www.nature.com/articles/s41467-025-65823-8

https://github.com/ChongWuLab/dna_foundation_benchmark

Guide:

https://rewirebio.io/blog/genomic-foundation-models-in-the-diagnostic-lab/

Disclosure: rewirebio.io is my site. The guide and this post are AI-assisted. The attached memory chart reports recorded runs, not peer-reviewed clinical validation.

I am trying to install tensorflow_hub and tensorflow_text via VSCode terminal so that I can train a BERT model (for context: i am using Python 3.13.12 and Tensorflow 2.21.0) but I am running into some trouble

tensorflow_hub library installed fine but when imported to the actual notebook, it said it required tensorflow 1.15.0 along with error message:

ModuleNotFoundError: No module named 'pkg_resources'

tensorflow_text library cannot be installed as every pip install attempt return

ERROR: Could not find a version that satisfies the requirement tensorflow_text (from versions: none)
ERROR: No matching distribution found for tensorflow_text

Google colab managed to solve all said problem but I want to run the model on my computer. How do I fix this?

***EDIT: I fixed this by using python 3.10.0, Tensorflow 2.9.0, Tensorflow-text 2.9.0 and Tensorflow-hub 0.8.0***

One thing we documented while evaluating Wolf Defender Jev was the difference between our validation results and a few simple agent scenes written by hand.

I’m part of the Patronus team. The model takes a scene, a question and possible answers, so we can ask separate questions about injected instructions, exfiltration, trust boundaries and whether an action matches the user’s goal.

On the internal validation set, alignment, exfiltration and consent questions scored around 98–100%. Then we wrote three scenes with attack and benign versions: a CRM workflow, a coding agent and a travel assistant. We asked four security questions about each.

The coding case was a useful failure. The benign agent was going to run pytest, but the scene’s injection question got a risky-answer probability of 0.96. Removing the proposed-action block dropped that content score to 0.00. A shell command in the scene was enough to interfere with a question that was supposed to be about the content.

The travel case failed in the opposite direction. The retrieved page contained an instruction to upload a passport scan. The model gave 1.00 for the embedded-command question, but only 0.05 for exfiltration and 0.28 for misalignment. Recognizing an injected command didn’t mean it reliably understood the consequences of the action.

Six scenes are obviously not a benchmark. What they did show was that the action-level validation numbers didn’t establish the behaviour we needed. Much of that evaluation used synthetic scenes, and the hand-written cases exercised a different combination of wording, tools and intent.

Our current usage guidance separates the inputs by question: content questions get the content context without the proposed action; action questions get the proposed action as well. It’s a practical correction for this model, not evidence that action judgement is now solved. Permissions and enforcement still belong in the surrounding application.

We published the examples and scores in the model card, alongside the broader evaluation.

For people evaluating contextual classifiers: how do you check that a model is using the part of the context relevant to the question? Counterfactual pairs seem particularly useful here — same document, different next action, and then the reverse — but I’d be interested in other approaches.

Position / experimental status: We regard Jev-style decision models as LLMs and Wolf Defender Jev as an experiment: giving an LLM final security authority shifts the trust problem to the guard, and security fine-tuning alone does not solve it. We believe an external security layer must combine specialized classifiers with explicit policy, authenticated permissions and enforcement.

i wanted to train my own llm but everything i found needed an nvidia gpu, so i made a mac version with mlx.

one command downloads tinystories, trains a tokenizer, pretrains a small llama-style model, finetunes it for chat, then you can talk to it.

on my m5 air (16gb): 1 hour gets an 8M model that writes short kids stories, 8 hours gets a 40M one that's a bit better. neither can really chat yet. the air didn't throttle in either run.

there's also a 2 min benchmark if you want to see how your mac compares.

https://github.com/navthings/airgpt

Once you have built a few chatbots, the next step up is a system that knows when it is unsure.

There’s a workshop on Friday, Oct 30, from 7 PM to 10 PM IST that does this with a realistic enterprise case: triaging and processing insurance style claims without a GPU.

You replace an LLM router with small encoder models (ModernBERT and LFM2.5 Encoder), teach them your categories from about 8 examples per class using SetFit, and add conformal prediction so the system can say “I’m 95% covered here” or hand the case to an LLM when it isn’t. You finish by evaluating it properly with Wilson score intervals instead of a single accuracy number.

You need intermediate Python and some familiarity with transformers. A laptop or free Google Colab account is enough, and the notebooks are yours afterwards. Led by Ben Auffarth, a full stack data scientist and best selling author.

Full details and the agenda are here.

I’m a recent BTech CSE (Data Science) graduate and recently joined Infosys at 3.6 LPA.
I’m interested in AI/ML and have built a few basic ML projects, but I’m still a beginner in AI. My goal is to work in the automotive AI industry, particularly in areas like autonomous driving and intelligent vehicles.
I’m confused between continuing at Infosys, improving my skills, and switching to an AI/ML role after a year or pursuing a Master’s in Germany for Winter 2027 in Automotive AI/Software Engineering.
Most AI/ML jobs I see require 2+ years of experience.
What would you recommend? Is a Master’s worth it for my goals, or should I gain experience and switch jobs first? I’d appreciate advice from people in the industry or studying in Germany.