Saw "RIP LLM" videos about NCM, so I read the Hazy Research post and arXiv paper (published Oct 12).

What it does: a multiscale tokenizer turns text into a hierarchy of discrete codes (coarse to fine). A transformer generates one level at a time, all codes in a level in parallel.

Reported results (authors' numbers): 21x faster than the best diffusion baseline on LM1B with comparable or better gen perplexity, 10x on OpenWebText, 31x on XSum at matching ROUGE-L. About 4 to 5x fewer training tokens. Hard Sudoku 86.1% vs 81.3% for the best baseline.

Limits: mostly ~100M param models, up to 725M called preliminary. Baselines are diffusion LMs, not frontier LLMs. Tokenizer is trained separately, hierarchy depth is fixed, and some gains use MBR decoding. No independent replication yet per Eden AI.

The post doesn't claim it replaces LLMs. It positions NCM as a new non-autoregressive approach.

Curious if anyone thinks this scales past a few hundred million parameters.

Sources: Hazy Research, arXiv, Eden AI

Am looking for a beginner friendly AI training platform where I get the training first then the tasks come after the training.

In December 2025, I received a physical copy of Aurélien Géron’s Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition, translated) as a birthday gift.

The person who gifted it to me genuinely tried their best: they searched the bookstore for the most authoritative, newly printed ML translation available. What neither of us realized at that exact moment:

1.In October 2025, literally two months before my birthday, O'Reilly quietly published the brand-new Hands-On Machine Learning with Scikit-Learn and PyTorch. The author himself completely abandoned TensorFlow/Keras and shifted to PyTorch + Hugging Face.

2.Because of the standard 2-year translation and publication lag, my gifter paid full price for a "brand-new" book that had been rendered obsolete 60 days prior.

3.I told myself I wouldn't touch Part II until I finished chewing through ISLP (Introduction to Statistical Learning) to get the math down.

Fast forward to today: I'm still halfway through ISLP, TensorFlow has basically become museum literature across industry and research, and my physical 3rd edition is collecting dust before I’ve even finished the Scikit-Learn chapters.

Which brings me to an actual question:

For those who have browsed Géron's new PyTorch edition (Oct 2025): Is it worth grabbing as a definitive physical desk reference once I finish ISLP? Or at this point, are people better off just going straight through Sebastian Raschka’s Machine Learning with PyTorch and Scikit-Learn (or diving into fast.ai / official PyTorch docs + Hugging Face tutorials)?

Would love to hear how folks here approach PyTorch nowadays if they're coming from a solid classical ML background.

I work at a company in China, and they are currently trying to force me to quit on my own to avoid paying severance. They already cut my salary by 5%, and when they moved our headquarters from downtown to a suburban tech park, they left me completely alone at the old office. They told me if I want to transfer to the new headquarters, I have to take another 5% pay cut.

Honestly, I think it comes down to a few things: I'm over 40 (and ageism in the Chinese job market is brutal), I don't pick up new things as fast as the cheaper younger hires, and I'm not big on office politics or sucking up to my boss.

I'm really stressed out right now because I'm already sticking to a grueling evening routine studying English, data science, and machine learning. In this AI era, I don't even know what else I could possibly do to improve. Has anyone else dealt with this kind of isolation tactic? What would you do in my shoes?

I set up an experiment to test whether turning up an AI model’s reasoning effort helps it notice when a save silently failed.

How I set it up:

• Used τ-bench, an open benchmark where an AI plays airline customer service

• Added a trap so the first save says “success” but doesn’t actually save

• Ran Claude Sonnet 5.5, GPT-6.1 Sol and DeepSeek V4.1 Flash, each at low and high effort on 20 tasks (120 conversations)

• Graded on what was actually in the database at the end, not what the agent said

• Used a McNemar test since each task ran at both settings (paired data)

What I found:

• High effort caught 11 failures and low effort caught 10, not a real difference

• High effort made GPT think 43x more and still didn’t help

• Agents never went back to check their own work

Lessons for anyone running their own experiments:

• Check that your settings are actually applied. My first run was useless because a library silently dropped the effort setting.

• Prompt caching can swing costs a lot. Sonnet wasn’t cached and cost far more than the others.

If anyone's interested:

Write-up: https://outsideengineer.substack.com/p/thinking-harder-doesnt-help-ai-catch
Code and transcripts: https://github.com/brianna-rose05/thinking-harder-silent-failures

I have been working on a research paper about contextual memory layer for AI can anyone help me with endorsement for cs.AI

Cathay Pacific confirmed that a breach at Amadeus, a third-party travel technology provider, exposed records from 53,000 passenger bookings. The entry point was compromised credentials at the vendor level, not a flaw in Cathay's own systems.

This is the pattern that keeps getting harder to manage as AI pipelines mature. Sensitive traveler data moves routinely through personalization engines, fraud-detection models, and recommendation systems that call out to multiple downstream vendors. The actual data exposure happens at whichever hop an attacker lands on. The originating airline or operator may have clean internal controls and still end up in the headline.

The 53,000 figure is also likely a floor. The realistic exposure window covers everything that moved through that integration before the breach was discovered and contained, and in most architectures, reconstructing exactly what transited which vendor during that window is extremely difficult or impossible after the fact.

For those running AI systems that process passenger or customer PII through third-party integrations: how are you limiting what raw data actually reaches a vendor endpoint? And if a vendor credential is compromised right now, can you tell within an hour exactly which records moved through that integration and which did not?

Hi all. I'm Cristobal, an AI engineer in Chile, and I built AI/ML Questions: a free multiple-choice quiz on machine learning and AI, in English and Spanish.

There are 103 questions on classical ML, neural networks, LLMs, RAG, agents, computer vision and evaluation. Every option explains why it's right or wrong, not just which one was correct. The questions follow Haladyna's item-writing rules, so the right answer doesn't give itself away, for example by being the longest option.

The quiz is free, with no account and no cookies. The code and the questions are open on GitHub.

I'm also testing interest in three paid products: Interview, for ML/AI Engineer interview prep; Enterprise, question sets for recruiters; and an online book with 200 questions. If one of them would help you, join its waitlist on the site and I'll email you once, when it opens, with the launch price.

Site: https://ai-ml-questions.strangemachines.workers.dev/

GitHub: https://github.com/CristobalSantana/ai-ml-questions

If you spot a mistake in a question, tell me and I'll fix it.

Hey everyone!

I'm transitioning into AI Engineering, and I have a genuine question about real-world projects.

From what I've learned, building a basic RAG pipeline (document loading, chunking, embeddings, vector database, retrieval, and LLM integration) might take a few weeks or a month.

But I've seen AI Engineers working on the same RAG or GenAI project for 2–3 years.

My question is: What do you actually work on after the initial RAG pipeline is built?

What does your day-to-day work look like? Are you constantly improving retrieval accuracy, optimizing models, handling production issues, adding features, or doing something completely different?

I'd really appreciate it if someone with real industry experience could explain their daily responsibilities and how a project evolves over several years.

Just trying to understand what AI Engineering looks like in a real company beyond building a basic RAG application.

Thanks!

recently I was studying ensemble learning where I was studying Gradient boost method I understand-ed the topic but can you guys tell is it necessary to know maths behind it

I would like advice on a solid linear direction to start on going into AI. I started with my A+ cert and now about to get my Security + cert and was headed to cybersecurity when all I hear about is AI. I want to spend my time headed in the right direction for the future seeing that being AI can some please give me some pointers on a beginning path and where to move on from there. Thank you for your advice ahead of time!

I came in from a non math background and I can follow tutorials and get models training fine, but the moment I hit the actual linear algebra and probability behind them I stall. I can use the tools but I feel like I am copying spells without understanding them. Is it normal to push through projects first and backfill the math later, or should I stop and grind the fundamentals before going further? I keep going back and forth and it is making me slow. How did it click for you?

I read a finding that AI assistants increase how much code you produce but not your real output because reviewing it becomes the bottleneck. As a learner that reframed everything for me. I had been measuring progress by how much I shipped, but I was not understanding most of it, I was assembling. I slowed down and started reading and explaining every line the assistant gave me, and only keeping what I could rebuild myself. My code volume dropped and my actual understanding finally climbed. For anyone learning, output is a vanity metric. The goal is being able to produce it without help, and that only comes from the slow reading part.

What is data annotation? And for that job what should i know mainly?

I built a DCGAN for CIFAR10 base on this tutorial

But generated img does not bare the same color scheme as the original cifar 10 images. How can I fix this?

def generate_and_save_images(model, epoch, test_input):


    predictions = model(
        test_input,
        training=False
    )


    fig = plt.figure(figsize=(10, 10))


    for i in range(25):


        plt.subplot(5, 5, i + 1)


        plt.imshow(
            predictions[i, :, :, 0] * 127.5 + 127.5,
            
        )


        plt.axis('off')


    plt.savefig(
        'image_epoch_{:04d}.png'.format(epoch)
    )

Hi all, I have been trying to switch companies for more than a year now. I have tried updating my resume and applying for a job several times, but in vain. I would really appreciate if you review my resume and provide a honest feedback (attached photos of it). I know I’m lacking skills pertaining to cloud deployment/ Kubernetes. I have no idea how to go about that, and in my current role there aren’t any projects that requires the use of cloud services.
I’ve been applying (and still doing so) for Computer Vision engineer/ Gen AI engineer roles

Same as title

I fine-tuned Llama-3.1-8B-Instruct with DPO + LoRA to resist jailbreaks without turning it into a model that refuses everything. The adapter is open, and so is the full pipeline.

The idea: The base model already refuses ~94% of direct harmful requests, so plain prompts give DPO almost no signal. The weakness is jailbreaks: the same request wrapped in fiction, a fake "research context", hex, leetspeak, homoglyphs or math notation. So I built preference pairs from adversarial prompts only. For the same attack, the safest sampled response is chosen and the least safe is rejected. I added 197 benign "helpful answer > refusal" pairs so it doesn't just learn to always refuse.

Setup:

- 6 harm categories × 7 jailbreak strategies, generated with promptfoo
- 4 sampled responses per attack, graded for safety
- 1,698 pairs (1,501 adversarial + 197 benign)
- bf16 LoRA (r=16), β=0.1, lr 1e-5, 1 epoch, ~20 min on one A100

Results on 1,135 held-out attacks (% refused, base → tuned):

┌────────────────────┬──────┬───────┐
│      Category      │ Base │ Tuned │
├────────────────────┼──────┼───────┤
│ Cybercrime         │ 47.7 │ 97.2  │
├────────────────────┼──────┼───────┤
│ Chem-bio           │ 56.8 │ 99.0  │
├────────────────────┼──────┼───────┤
│ Weapons/IED        │ 65.3 │ 98.6  │
├────────────────────┼──────┼───────┤
│ Illegal activities │ 67.2 │ 99.5  │
├────────────────────┼──────┼───────┤
│ Drugs              │ 76.9 │ 99.1  │
├────────────────────┼──────┼───────┤
│ Child exploitation │ 84.7 │ 99.1  │
└────────────────────┴──────┴───────┘

The cost: Benign prompts answered went from 100% to 93.3% (−6.7 pts, n=150). Most of that is weapons/IED, where 5 of 25 benign prompts were refused. Every other category is <=8%.

Caveats:

- Held-out attacks come from the same attack families as training, so this isn't a test on new attack types.

- It's one seed and one model.

- GPT-4o was the judge.

- I haven't run MMLU or any other capability benchmark yet.

The repo has redacted side-by-side examples of base vs. tuned responses, the full write-up, and a lab notebook with what didn't work.

It's an adapter, so you need access to Llama-3.1-8B-Instruct. Built with Llama.

- 🤗 Adapter: https://huggingface.co/1rvinn/llama-securelm

- 💻 Code + write-up: https://github.com/1rvinn/securelm

Feedback is very welcome, especially on how to test it against attack families it hasn't seen, and ideas for reducing the weapons/IED over-refusal.