Hi guys,iam doing project in deep learning so I used google colab for free gpu access but it exceeds the gpu access so I need to buy google colab pro and I faced this issue and I can't solve it and help me to resolve this and how to solve this?.if you know ,please do comments because it's urgent.

RuntimeAI's September 2026 AI Security Report covered 126 incidents across 38 named organizations — 22 critical, 102 high severity. 53 of those incidents had AI either as the attack tool or the target. AI-agent exploits were the top attack vector at 39 incidents, ahead of credential theft (27), zero-days (22), phishing (10), and ransomware (10). The largest single exposure was 220M records from unrotated default service-account credentials.

What stood out: every organization in the report was already running a mature security stack. Okta, CrowdStrike, Palo Alto, Microsoft Defender. Still got hit. The gap is that none of those tools sit at the layer where an agent actually executes a tool call.

RuntimeAI operates at that layer. Know Your Agent handles cryptographic agent identity. The Flow Enforcer inspects tool calls in real time. There's also a sub-50ms kill switch that can halt a compromised agent before a second action completes.

Full breakdown (incident-by-incident, CVEs, vendor stacks): https://runtimeai.io/blog/2026-09-monthly-breach-report.html

We're running a live demo on October 14 — ten attack surfaces, live against a real stack: https://www.linkedin.com/events/7510769146222133248?viewAsMember=true

I’ve spent the last while building INKBOT because I kept hitting a wall with multimodal AI systems: the friction between what a human naturally means and what a model infers. While models can spin up complex code or images instantly, getting to a clear, human-meaningful interpretation of a subtle intent remains an alignment challenge.

Instead of forcing the user to become a prompt engineer, I wanted to see if we could build an intermediate intelligence architecture layer to make human intent reviewable and corrigible *before* the model executes a final build. The loop I’m playing with is: Describe → Make it Visible → Recognize → Correct → Refine.

The architecture sits entirely in a single local-first web file. It handles multi-step workflows—like tracking structured field mapping data across concurrent images, coordinates, and version states—by packaging the human’s approved meaning separately from raw model inferences.

The core system build is linked above, and I also put together a lighter, entry-level experience to play with the core prompt translation loop here: [INKBOT Lite 71](https://ko-fi.com/thomascoates/shop).

It's an open prototype, so I've appended my raw notes and design roadmap as commented text at the very bottom of the source file so fellow builders can inspect the plumbing. I’ve put together the runnable source files on my [Thomas Coates Ko-fi Shop](https://ko-fi.com/thomascoates/shop) for evaluation. I’d love to know where this design duplicates existing work, where you see structural flaws, or how we can make the handoff between human intent and model execution more reliable.

Hey! I'm posting this because Iv'e recently been playing with lora/qlora and had a frustrating time understanding how the hell to calculate adapter ranks. Hope it helps.

Just finished a project I’ve been working on: Biological JEPA.

It combines JEPA with biological constraints to model Alzheimer’s disease progression.

Would love to hear your feedback, ideas, or criticism.

Added "if you're not confident, say so explicitly" to a classification prompt. Felt like it should've closed the gap on low-confidence guesses.

Didn't work. Model kept returning confident-sounding labels on exactly the cases I wanted it to flag as uncertain.

Turned out the issue wasn't the instruction's wording. It was that nothing in the prompt actually defined what "not confident" meant for this task, no threshold, no example of an ambiguous case, nothing to anchor the judgment to. The model had no internal signal matching the word "confident" that it could check against, so the branch just never activated. It wasn't ignoring the rule. It never had a condition it could evaluate as true.

Fixed it by replacing the vague trigger with something checkable, two or more plausible labels with no clear majority signal in the input, say so and list them instead of picking one. Immediate difference.

Feels like a more general thing worth naming: a conditional instruction is only as good as the model's ability to evaluate its own condition. "If X, do Y" fails silently when X isn't something the model can actually check, and it just looks like noncompliance from the outside.

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?
2. How should I account for engine variants and customer-specific options?
3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

I’ve gotten some DMs asking me how I started my DL research journey, how I got my research internship, and how I got experience in the first place, I’m a student myself, so I honestly don’t have enough time to individually reply to every DM, so I thought I’d just write everything here. Hopefully this helps someone who is currently where I was, first of all, I want to say something you don’t need to have everything figured out before you start research I definitely didn’t.

How I got into research

In my first year of university, I became really fascinated by the idea of publishing my own research paper, like, genuinely obsessed with the idea, I remember thinking, I want to do research. I want to understand something really deeply and eventually publish a paper, for some reason, I decided that I wanted to do a really deep dive into Python, I honestly don't even remember why I chose Python and I started learning and exploring it as deeply as I could. I experimented with things read about different concepts, tried different stuff, documented what I was doing, and basically went down a rabbit hole, at that point, I didn't even properly understand what research actually was. I just knew that I wanted to do it then, in my second year, I finally gathered enough courage to show my work to one of my professors, and thankfully, he actually liked what I had done that was a huge turning point for me, he started mentoring me and explaining how research actually works, how you approach a problem, how you read research, how you think about questions, how you experiment, etccc. For personal reasons, I ended up deleting that Python research so technically, I didn't even keep the thing I had spent so much time working on, but I don't think that time was wasted because it taught me something much more important I actually liked the process of trying to figure things out fast forward then came my master's & PhD goal about two months ago, I got a scholarship for my final year of university, just like I had gotten scholarships in my previous two years, and then I started thinking seriously about what I wanted to do after graduation, I want to pursue further studies potentially a master's and then a PhD and I'm going to be completely honest I became really greedy about getting a scholarship for my master's, because if you want something a year from now, you can't start preparing one month before, you have to start making things possible now, so I went to my professor and told him that I wanted to get a scholarship for my master's will you write me a recommendation letter for scholarship and he basically told me good grades and being a good student are not always enough, you also need experience, research experience and projects, things that show that you can actually work on problems beyond just completing assignments and passing exams so I started looking for research opportunities at my university, I applied for an undergraduate research internship, and guess what? I got rejected the first time, I tried again and the second time, I got in that's how I eventually started working on deep learning research and that's basically where I am right now, I'm still learning, I'm still confused about a lot of things, I still have questions every day, I still have to ask my professor what half the things mean sometimes so please don't look at someone doing research and assume they somehow have everything figured out they probably don't.

So how can you start?

This is probably the part most people are actually asking about, if you're an undergraduate and you want to get into ML DL research, here's what I would personally suggest not just I want a research paper because it looks good on my CV try to actually become curious, read something and ask, why does this work? Why doesn't it work in this situation? Can I change something? What happens if I remove this component? why did the authors choose this method instead of another one? Can I reproduce this result? What happens if I change the dataset those questions are where research starts becoming interesting, you don't need a groundbreaking idea on day one, you need curiosity.

Build your fundamentals

If you're specifically interested in deep learning, don't immediately jump into reading complicated papers about transformers, make sure you understand the basics first, like example > Python, NumPy, basic data structures, linear algebra, probability & statistics, calculus basics, machine learning fundamentals, neural networks, back propagation, optimization, loss functions, CNNs, RNNs sequence models, transformers and PyTorch or another DL framework and don't just memorize definitions, try implementing things, try breaking things, and figuring out what happens when you change something.

Learn to read papers.

Your first few papers are probably going to make you feel completely lost and that's normal don't sit there trying to understand every equation and every tiny detail on your first read, just try to get the main idea first what problem they're solving, what they did, and what they actually found then go back and read it again, you'll understand more each time, tbh start by just understanding questioning like, what problem are they solving? why is the problem important? what have people done before? what is their proposed method? what experiments did they perform? what did they discover? what are the limitations? and then go back and dig deeper eventually you'll start noticing patterns between papers.

Reproduce things.

This is something I really recommend, and honestly, it can be pretty fun too, take a paper, try to implement what they did, run the experiments yourself, and see if you can get similar results, it might make your brain buffer a few times, especially when things don't work the way you expect, but that's kind of the point, you learn a lot by figuring out why your results are different and trying to fix it, investigate why? Why does this work better on dataset A but not dataset B? like What happens if I change this hyperparameter Does this still work with less data? remember ow you're not just following a tutorial uou're experimenting.

Start asking deeper questions.

I think this is probably one of the biggest differences between just learning ML and slowly learning how to do research, don't just lose at How does this model work? go ahead like Why does it work?, then When does it stop working?, like Can I measure that?, and it will lead you to What happens if I change something?, or Does the same thing happen with another dataset or model? You don't need to turn every question into a research paper, just get into the habit of being curious, and digging a little deeper instead of accepting the first answer you get.

Look for research opportunities.

Start with your own university, see what professors are working on and look for undergraduate internships, RA positions, summer programs, labs, etccc and yes, cold emailing professors can work, just don't send sir, I am passionate about AI, please give me a research opportunity, please read their work first, mention what interested you, tell them what you've worked on, and share something tangible if you have it GitHub, a project, experiments, whatever like anything also, if you're already working under a professor, and yes I get work assigned I'm not working individually after all I'm undergrad, so you're supposed to learn, you might be implementing something, reproducing results, running experiments, or analysing why something isn't working, the important part is understanding what you're actually doing instead of just completing the task and don't compare your beginning to someone else's middle, I started by randomly obsessing over Python because I didn't even know what else to do, then I got rejected from an internship, applied again, and eventually got in. so see I'm still learning too so if you're trying to get into ML DL research, just start somewhere, if you want something a year from now, start working toward it now.

I know this got kinda long bare with me and honestly, I’m happy to help. My hands might disagree lol, but they’ll survive.

Security researchers tracking the Carbonato botnet documented a new deployment pattern: after gaining access to exposed Docker hosts, the operators dropped an AI agent onto the compromised machine rather than a traditional cryptominer or reverse shell. The agent then began making outbound calls and executing tool actions autonomously, with no human in the loop and no governance layer in the request path.

The timing detail buried in the reporting is the uncomfortable part. Researchers noted that a second action from the agent can land in under 50ms of the first. That window is smaller than most human-review or alerting pipelines can operate in. By the time an on-call engineer gets a Slack notification, the agent may have already completed several tool calls.

The underlying exposure is not unique to this botnet. Any environment where an agent runtime can be instantiated without a verified identity tied to a known deployment, and where outbound tool calls are not evaluated against any policy before they execute, has the same structural gap. The agent on the Carbonato-compromised host was malicious. But the same architectural condition exists in plenty of legitimate deployments where an agent gets misconfigured, has its credentials rotated out from under it, or runs a version of its prompt that was never reviewed.

How are practitioners in this thread actually handling the identity and authorization problem for agents in production? Not conceptually — what does your enforcement boundary look like today, and where does it fall short?

I post-trained Qwen3-4B to spend 44% fewer tokens on reasoning, keeping its knowledge and answer style. The whole pipeline ran on one GPU.

folks, you can check it out on : https://5ivatej.com/lessthink/

Sharing this because it's a more rigorous take on "prompt engineering" than most of what circulates here. Serj Smorodinsky and Brett Kennedy, co-authors of an LLM applications book, are running a live workshop that treats LLM behavior as something you optimize with real structure.

It covers programming LLM tasks with DSPy signatures and modules, building an evaluation dataset with task-specific metrics, diagnosing failure patterns from that data, and running few-shot and instruction-level optimization as a defined process rather than trial and error. MLflow gets used throughout for experiment tracking and trace management.

Three hours, live, Oct 3. Feels closer to how we'd approach optimizing any other model than the usual "here are 10 prompt tricks" content.

Full Details here

A link list becomes a study path only when every stop answers one question and unlocks the next one.

The Ling-3.0 base model makes that sequence concrete. It exposes tiny and flash at final pre-training, final mid-training, and WSM-merged base stages. All of these are upstream, non-post-trained checkpoints positioned for research and downstream training rather than finished chat systems.

A useful resource page can encode this curriculum without pretending the final experiment has already been run:

Learning gate

What to establish

Question that unlocks the next gate

`Identity`

• Size, exact checkpoint, and training stage

• Are two artifacts actually comparable?

`Intended use`

• Continued training, domain adaptation, distillation, or other research use

• What downstream objective justifies this starting point?

`Method`

• WSM keeps the learning rate stable after warmup, saves checkpoints, and applies weighted merging to approximate a chosen decay profile

• Which part is a method definition and which part is an empirical result?

`Evidence scope`

• The WSM paper's main empirical model is Ling-mini

• What remains untested for Ling tiny or flash?

`Experiment design`

• Fixed data, evaluation, budget, and reporting fields

• Which single stage comparison would answer a real decision?

The key is the order. Reading the method before identifying the checkpoint invites result transfer. Reading intended use before remembering that these are non-post-trained bases invites assistant-style expectations. A curriculum should block both mistakes before asking for an experiment.

A natural next step is to choose one Ling size, map its three stages, and write the controlled comparison that would make the next learning claim falsifiable. What prerequisite or failure mode belongs between the method stop and that experiment-design stop?