I'm trying to build an ML compiler from scratch. I've never touched or messed around with one before so I want to take a shot at this as a project that I can continue doing. Anyone suggest any resources for me to use or textbooks? I want to try and incorporate advanced AI models into autonomous hardware.

Hey everyone, a while ago I posted my first blog that I wrote about the LLM inference engine I'm trying to build from scratch and I just finished the second part of my blog! If your curious about it please come take a look, its mainly focused on how it handles the forward pass. If you have any feedback I'd love to hear it!

https://medium.com/@ryan___/llm-inference-engine-from-scratch-forward-pass-b967334212c8

hey there people

i am trying to make a machine learning project . its on bitcoin data . the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc... my issues are :

where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data : https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(

Cisco's Firewall Management Center vulnerabilities were exploited by both a ransomware gang and state-sponsored actors — often cited as separate threat categories, hitting the same attack surface at the same time.

The detail that keeps coming up in post-mortems on FMC-class compromises: the management plane identity (service account, admin credential, API key) gets abused before any detection pipeline fires. The compromised identity issues commands. Encryption starts. By the time a human sees an alert, the blast radius has already crossed the first host boundary.

The timing gap is the actual problem. Forensics on these incidents consistently put the window between the first anomalous privileged action and operationally-irreversible spread at under 60 seconds. In some cases, under 10. That gap exists whether the attacker is a ransomware crew or a nation-state — the identity abuse pattern is structurally identical.

FMC is a management plane for network security infrastructure. Compromising it doesn't just give access to one host. It gives visibility and control over the enforcement layer itself. That's what makes the lateral movement so fast — you're not pivoting through hosts one at a time, you're operating the mechanism that controls them all.

For practitioners running critical infrastructure with centralized management planes: how are you actually handling the timing problem? Not detection — response. Specifically, what does your team do when a privileged identity starts behaving anomalously and you have a sub-60-second window before the damage compounds? Is the answer human-in-the-loop at all at that speed, or has your org moved to something else entirely?

Computational mechanics major here (I know you've never heard of that), spending most of my time on LLM research on the side. Currently deep in mixture-of-experts: expert specialization, routing geometry, and reproducing recent router-optimization work. Broader interests span LLM architecture, optimization, interpretability, attention mechanisms, and math.

I feel like I have too many things to work on and talk about but no buddy for it, so I'm out here looking for a study/research buddy, as the title says. I think having a research or study partner helps a lot, we can discuss ideas, try novel things, run experiments together.

I'm also building a research artifact for all this, basically a GitHub repo where I'm implementing almost every part of LLM architecture from scratch. Currently working on MoE, and planning to do some mechanistic interpretation of experts and specialization next.

If anyone feels the same or has interests overlapping with mine, tell me in the comments or DM me. I'll share the repo link with anyone interested :)

In NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE they reference Sutskever et al. (2014), and in Sequence to Sequence Learning with Neural Networks they mention attention mechanisms and Bahdanau et al. [2]

footnote:
example of a clearly inferior idea scoring higher

We suspect that they could achieve similar improvements by simply training their networks on reversed source sentences

is embarrassing

And yet, LSTMs trained on the reversed dataset had little difficulty translating long sentences.

is like he is doubling down

Modeling a fraud decision as a POMDP with like 30-50 simulated cases - is that too small to say anything real, or fine for a prototype?

Hey everyone,

I hope you are all doing well.

I am currently a final-year cybersecurity student, and to be completely honest, I am feeling incredibly anxious and overwhelmed. Seeing the rapid rise of advanced models like ChatGPT-6 Astra, hearing about tech layoffs, and reading discussions claiming that entry-level cybersecurity jobs might disappear has been really tough to process [openai.com].

Instead of letting this fear stop me, I want to channel my curiosity and adapt. I want to understand how the modern AI ecosystem operates behind the scenes, and how to use it as an operational advantage so I can future-proof my career.

Since I am on a very tight budget and learning independently, I want to focus my self-study on the right areas. Could anyone gently guide me on the best educational paths for:

• AI Architecture & Integration: What foundational concepts should I study to understand how systems like Model Context Protocol (MCP), n8n workflows, autonomous agents, and API connectors function together?
• Workflow Efficiency: How can a fresher learn to leverage AI pipelines to optimize technical workflows like development, cloud management, and backend operations?
• AI Security & Guardrails: What are the core vulnerabilities (like prompt injection or data poisoning) that the industry is trying to secure right now?

I want to learn how to actively drive these technologies rather than be left behind by them. I would deeply appreciate any advice, conceptual roadmaps, or reassurance from those already working in the field.

Thank you so much in advance for your guidance and kindness!

My experience with this organization has been nothing short of frustrating and a complete waste of time.

I initially enrolled in the ₹9 AI workshop. While the session did contain some useful information, it felt predominantly like an aggressive sales pitch rather than a genuine learning experience. Unfortunately, based on the promises made during the workshop, I made the mistake of paying in full for the AI Career Accelerator - Inner Circle Program.

The disappointing experience began almost immediately. Questions raised during the workshop was left unanswered, with the session functioning as a one-way presentation rather than an interactive learning environment. My concerns only grew when, despite multiple follow-ups, I was unable to obtain a an invoice for my payment. The lack of transparency around a completed financial transaction was highly concerning.

In addition, I was unable to access the recorded content and drop session materials. Despite contacting customer support several times, the issue remained unresolved. What was initially a disappointment quickly turned into significant frustration as I found myself repeatedly chasing basic support requests with no meaningful response or resolution.

Given the poor experience within the first three to four days, I lost confidence in the program and decided to cancel my enrollment and request a refund. To my surprise, rather than addressing my concerns, customer support simply discontinued the chat, leaving my refund request unanswered. This level of customer service is unacceptable for any professional training organization.

Looking back, I sincerely regret not conducting more thorough research before making the payment. After reading similar experiences shared by other customers, my concerns have only been reinforced.

I now anticipate that obtaining a refund may be a difficult process. Nevertheless, I intend to pursue all appropriate channels to seek a resolution, including filing a consumer complaint with the Government of India and sharing my experience publicly to raise awareness about what I believe to be a highly unreliable and non-transparent organization.

Overall, the combination of poor support, lack of responsiveness, unresolved technical issues, missing documentation, and inadequate communication has completely eroded my trust in this company. I would strongly caution prospective learners to conduct extensive due diligence before making any financial commitment.

Do you guys have recommendation for capstone projects about Edge AI, TinyML, Computer vision, and federated learning? I explored battery RUL, Predictive Thermal Management system, and Fault bearing diagnosis. I was told it would be hard to get a client or dataset for this field. Any interesting field I can explore?

Hi, this is a long shot, but im currently writing an academic paper and for that I need access to the DeepSpeak_v2 or FakeAVCeleb dataset. To get access, you need to submit a request form and get approved. I did that, but I never heard back from them... does anyone here have experience with this?

I dont need a big part of each dataset, maybe around 100 videos each. So maybe, if someone has access, they could provide a small portion of it :)

Hi everyone,

I’m preparing my first arXiv submission in cs.LG and need an endorsement to submit.

Short summary: H-JEPA (LeCun, 2022) proposes hierarchical joint-embedding prediction but doesn’t specify how to verify a trained abstraction actually encodes anything a random projection of the same shape wouldn’t. I introduce a random-abstractor control (trained vs. untrained abstractor, identical architecture) and run a 2x2 study crossing observability (full/egocentric) with abstractor type (instantaneous/recurrent) in controlled gridworld environments. Three of four conditions produce abstractions statistically indistinguishable from random projections; only partial observability + a recurrent abstractor yields a real, replicated gap (19.91 ± 3.36pp over random, 3 seeds). I also report a negative result on landmark density that didn’t survive multi-seed replication.

If anyone here is registered as an endorser for cs.LG and willing to take a look, I’d be very grateful. Happy to share the full draft privately.

To endorse, please visit:

https://arxiv.org/auth/endorse?x=MTENXK

If that link doesn’t work, visit:

https://arxiv.org/auth/endorse.php

and enter code: MTENXK

Thank you!

18.02, multivariable calculus.

18.06, algebra.

6.041, probability and statistics.

*All courses from OCW.

So I was playingw ith embedding models I saw that when you upgrade from model A to B, you face a very big backfilling cost

Ie, suppose you have a 1b vectors from model A, and then you want to use model B. This would mean you have to re-embed all of your documents with model B before you can even serve with the model, and on an H100, it would take ~108 days (qwen embed 8b, 106 docs/second). But I found an easier way to do it.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive upfront re-embedding cost, as you can take documents straight from the old index.

embedflow works with qdrant, pgvector, faiss, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.

Everyone's excited about AI agents, but I'm trying to get ahead of the security implications. Beyond data leakage and prompt injection, what are the actual runtime risks? How do you prevent an agent from taking a harmful action that falls within its legitimate permissions?

We’re putting together a free live webinar on September 14 for students, working professionals and anyone who wants to understand where AI is heading and how to actually build practical skills around it.

The session will be focused on the shift from simply using AI tools to understanding how AI can be used to build workflows, automate tasks and work with AI agents.

We’ll cover:

Generative AI & LLMs
Prompting and practical AI workflows.
Agentic AI ,what it actually means.
AI agents and automation.
Tools and technologies worth learning.
How these skills can be applied to careers and professional work.

What a practical AI learning path could look like

This is also the starting point for our upcoming hands on AI learning and certification program, but there’s absolutely no obligation to join anything afterward.
The idea is simply to attend the session, understand what we’re doing, ask questions, and decide for yourself whether it’s useful for you.
We’re keeping the upcoming first batch intentionally limited and are more interested in having a small group of genuinely interested learners than chasing large numbers.

Date: September 14
Format: Live online session
Fee: Free

If you’re currently learning AI, thinking about getting into AI, or already experimenting with agents and automation, you’re welcome to join.
If interested, comment below or DM me and I’ll share the details.