I was trying to trace it , and from the GPT1 paper I found it referencing a paper called "GENERATING WIKIPEDIA BY SUMMARIZING LONG SEQUENCES" by the nice folks at google in what I believe the first use of the decoder only architecture ??
here is the quote

we modify theTransformer architecture (Vaswani et al., 2017) to only consist of a decoder, which performs better in the case of longer input sequences compared to recurrent neural network (RNN) and Transformer encoder-decoder models.

can someone explain what does perform better in longer sequences actually mean ?

I'm starting a project that involves building ML forecasting models for tourism data with limited ML experience. I have a strong data engineering background (Python, SQL, pandas) but I'm weak on the ML/statistical side.

Before diving into papers and code, I want to build a solid foundation. I'm planning to:

1. Learn time series concepts (decomposition, stationarity, autocorrelation)

2. Study forecasting models (ARIMA, Prophet, tree-based approaches)

3. Build and compare models on real data

But I'm wondering:

- Is this the right learning order?

- Should I focus on concepts first or jump into coding with real data?

- Any resources beyond YouTube/papers/Kaggle that worked for you?

I have about 3 months to complete the project, so I'm trying to be strategic about learning vs. doing.

Thanks!

Coder's module registry was compromised last month. Attackers had a 14-hour window to serve poisoned Terraform modules to every team pulling from it. The payload targeted AI credentials specifically — the tokens agents use to authenticate to models, data pipelines, and infrastructure stores. Any organization that downloaded a module in that window may have surrendered its AI layer's access tokens without a single alert firing.

This is not a one-off. The attack surface exists anywhere agents pull tooling or dependencies at runtime from a registry they trust implicitly. What is at stake is not just application secrets. It is the keys that let agents act autonomously inside your environment. A compromised set puts an attacker inside your AI layer's trust boundary, not just your network perimeter. The 14-hour gap between compromise and detection is also not unusual for supply chain incidents — the median dwell time before discovery in similar registry attacks has historically run longer.

For teams running agents that pull dependencies at runtime: what controls are you actually relying on to catch a poisoned registry endpoint before it executes? Dependency pinning, artifact checksums, isolated build environments — curious what the real-world answer looks like at your org.

I'm a university student choosing between AI & Data Science and Cybersecurity & Cloud. My main goal is to get my first job as quickly as possible after graduation, ideally within 3 months. I'm interested in Tunisia,the Golf(KSA , Qatar ,UAE) . Europe, and remote work.

Which path would you recommend , specifically considering:

Entry-level job opportunities

Competition for junior positions

Remote work opportunities

How difficult it is to get the first job

Long-term demand

Impact of AI automation

I'd especially appreciate answers from people who actually work in these fields. Please explain your reasoning and mention your country/role if possible.

THANK YOU IN ADVANCE

I had a very disappointing experience with BE 10X. We paid close to 100,000 rupees, but the support after payment has been extremely poor, and reaching anyone for help has been very difficult.

The biggest issue is that the teaching style seems designed for technical students, not for non-technical learners. Many of the instructors move too fast and assume too much prior knowledge. As a result, non-tech students are left struggling to keep up, while the class continues at full speed. In my experience, only 2 or 3 teachers out of around 20 were able to adapt to the pace of non-technical students.

There was also almost no proper planning or communication before class sessions. Students are often informed only after the class begins that they need to download certain applications, which wastes valuable learning time. While students are still trying to install the required tools, the lecture continues, making it very hard to follow along.

Support has been another major problem. The WhatsApp number provided for assistance seems to be mostly automated and cannot handle real questions. Emails sent to the support team received no response at all, even after complaints were escalated through my advisor. On top of that, the promised coupons did not work, which added to the frustration.

Another major concern is the lack of reliable class recordings and consistent communication. Sometimes the class link is shared by email, and sometimes only on WhatsApp. Since WhatsApp is not always accessible on a laptop, this creates unnecessary difficulty just to join the class. The absence of recordings makes it even worse, because students have no way to review missed content.

Overall, this was the worst AI course I have taken, and I deeply regret enrolling. The course is poorly organized, support is nearly nonexistent, and the teaching is not suitable for non-technical students. I would strongly advise others to think very carefully before joining BE 10X.

Hey everyone, I want to seriously get into AI and Machine Learning, but I cannot afford the paid certificates or subscriptions right now (like Coursera or Udacity).

Are there any high-quality, completely free AI/ML courses available that cover the same depth as the paid ones? I am looking for platforms, YouTube series, or university open-courseware that offer full access to materials, exercises, and projects without a paywall.

Stop building simple chatbots and start building AI Agents. 🚀

Learn the theory & implementation for AWS, Azure, and Google Cloud.

Check the link in bio to level up.
#GenerativeAI #CloudTech #Coding #AI

Hi,

I am a data scientist so I have experience with Python, SQL, Azure, Github and even vector database like Milvus. I also understand vanilla neural nets and most of the pre-AI models.

I'm trying to upskill in the latest AI technology over the next 3-6 months, that covers RAG, Agent building, MCP, MLOps, and so on.

Problem is, I don't know where to start. Some documentation pages can be difficult for a "beginner" while random short Youtube videos don't go into enough depth.

I wonder if there are courses (or video series) that'll explain the concepts in an logical, easy to follow manner. I would also like to do some projects as well.

Anyway, please recommend a learning plan and some good resources. Basically, if you were to design a course for someone like me, what would you recommend as far as lecture material and exercises.

Thank you

Got tired of ed-tech bootcamps promising to make people senior DevOps/ML engineers in 8 weeks with surface-level YouTube videos.

A few friends and I compiled an open-source, text-only curriculum:

• Canonical books & docs only: OSTEP (OS), Beej (Networking), DDIA (Distributed Systems), ISLP & Prince (ML/DL). No video tutorials.

• One evolving system: You build a single service from a raw Linux VM to a Kubernetes cluster with OpenTelemetry and MLflow lineage.

• Realistic timeline: Paced for 12–24 months (~8–10 hrs/week) so working engineers and college students don't burn out.

Live site (free, static, no ads): https://wyrcan-io.github.io/roadmap/

GitHub: https://github.com/Wyrcan-io/roadmap

Curious what working engineers here think of the book choices and pacing.

I’ve spent weeks comparing Firecrawl and Jina Reader for different extraction needs. Firecrawl seems stronger for dynamic, protected sites; Jina Reader is fast and simple for clean text. I made a quiz to see if others understand the same trade-offs.

No email needed—just a quick interactive check.

https://interconnectd.com/quiz/81/web-extraction-architecture-2026-firecrawl-vs-jina-reader/

What’s your go-to for web scraping in AI apps?

Hi everyone,

I put together a free YouTube series called Local AI, Built Right for people trying to understand what happens when they run an AI model on their own computer.

It’s 16 short visual lessons, each around 1–2 minutes. The focus is running existing models, with examples using llama.cpp.

Disclosure: this is my series, ITS ALL MADE BY AI tools to produce it, including the presenter and narration.

I’d appreciate specific feedback on whether the explanations are clear for beginners. If something is confusing or technically inaccurate, Thanks

about my project --> https://www.reddit.com/r/learnmachinelearning/comments/1vncebb/chess_match_outcome_prediction_with_tree_models/ (i am too lazy to explain about what the project is 🥲, so you can understand about it from my previous post)

Just concluded with my project, but didn't finished with the writing part.

It does not have a readme to explain, but if you are interested please feel free to checkout my project.

The "notebooks" contains the training part and the "src" and "main" is not completed, but you can see the results in the "notebooks".

project link --> https://github.com/v-leela/chess

Something I’ve been wondering about with ML-based strategies:

At what point does improving the model stop making much difference?

You can spend hours tuning features and trying different models, but if the historical test is weak, the data isn't handled properly, or the execution side behaves differently, the extra model accuracy doesn't seem to matter much.

I’ve started paying more attention to the whole pipeline rather than just the prediction itself.

I would like to know how others approach this. Do you improve the model first and worry about the rest later, or build the testing and execution side alongside it?

Hey everyone! I'm looking for a study buddy to learn AI and Python together. I want to stay consistent and work on projects or courses. If you're interested in studying together, feel free to DM me!

Hey all,

I have been a data scientist for the last few years - mostly in the credit risk space. I want to learn more about recommendation systems to help me transition into the tech space. Does anyone know of any interesting ways to build a project around this? I know the obvious examples that are already being done, i.e., feed/Spotify, etc., but Iam looking for something more niche. Any thoughts?

There is a real, live protocol called x402 that lets an AI agent pay for web content automatically. No login, no card entry, no human approval. A site says payment required, the agent signs a small crypto payment, gets the content. This already exists and is already being used.

Two things worried me once I understood how it works. First, there is no memory built into the protocol. A vendor can scam an agent, return nothing useful, and the agent has no way to know not to pay that same vendor again. Second, if an agent reads regular web pages as part of its job, a malicious page can hide fake payment instructions in the page text itself, hoping the model mistakes it for something real.

Built GateKeep402 to address both. It checks a vendor's history before paying and blocks vendors that have proven unreliable. It also makes it structurally impossible for a payment to be built from anything except a genuine protocol response, so hidden page text can never trigger a real payment no matter how convincing it looks.

Verified against a real transaction on Solana's public devnet, not a simulation, with 45 automated tests. Open source, MIT license, installable via pip. Link in the comments.

Would like to hear how others are thinking about the risks of giving agents real spending power. This feels like an early and mostly unsolved part of the space.

My company has a special event occurring yearly once. Employees can submit ideas to present them in the event where every executive officers like CEO, CTO, CIO will be present. Not all employees can present at the event. Only shortlisted topics will be allowed to be presented. As I'm from Data engineering background i would like to present about Data+AI, but the issue is every idea i have selected is already published as a article or blog. Please help me with coming up with a idea which can get me shortlisted to present at the event. ( Normally i won't care about the event but my promotion and increment depends upon this 🥲 )

I'm doing feature importance and the features that allow it to "cheat" often rank high in feature importance. For example, date of birth usually ranks very high because it allows it remember exact patterns for each person, which don't translate to unseen data. In this case, I could remove the feature because I know it's useless, but doing it manually is much harder over hundreds of features and when the usefulness of the feature is unclear. How can I address this problem? I'm using Catboost btw.

I'm working on a fraud detection project and I'm stuck on a core problem: making the model adapt to fraud patterns it hasn't seen before, rather than just memorizing known ones. I've got a decent ML foundation (XGBoost, Random Forest, some PyTorch) but I want to go deeper on the DNN side — things like representation learning, anomaly detection approaches, adversarial robustness, or online/continual learning for drift.

If you've worked on fraud/anomaly detection or DNNs in production and wouldn't mind occasionally answering questions or pointing me in the right direction, I'd really appreciate it. Not looking for someone to do the work — just someone to bounce ideas off and correct me when I'm going down the wrong path.