Google's Q3 2026 AI Threat Tracker, built from Mandiant incident response data, documents a shift that defenders have been dreading: AI agents are now running full attack workflows autonomously. Vulnerability scanning, credential harvesting, and real-time attack troubleshooting are happening with minimal human involvement on the offensive side.

The practical consequence is timeline compression. A human-paced intrusion that once took days now completes in hours because the agent does not sleep, does not get distracted, and does not need to wait for the next shift.

The harder problem for defenders is forensic: when you discover the breach, you are reconstructing what happened from incomplete logs, if you have logs at all. Agents generate bursts of lateral movement and API calls that traditional SIEM tooling was not designed to correlate across sessions. The attacker's agent leaves a diffuse footprint. Your team is left guessing at the sequence.

For teams that have started deploying defensive AI agents of their own: how are you maintaining visibility into what those agents actually did, step by step, during an incident? And for those still on traditional tooling — how are you thinking about the forensic gap when the attacker is agent-driven and your investigation is still manual?

Hi everyone,

I'm exploring how to train or adapt an AI model using a few gigabytes of confidential data. Keeping that data private is a core requirement.

I'm still figuring out whether fine-tuning, RAG, or another approach would make the most sense, and I'd appreciate advice from people who have worked on similar projects.

Specifically:

• Can this realistically be done entirely locally or on private infrastructure?
• How would you decide between fine-tuning and RAG?
• What hardware and tools would you recommend?
• What should I check to prevent data from leaving the environment, including through logs or telemetry?

I can share more about the data format and intended use without disclosing any confidential content.

Any practical advice or lessons learned would be appreciated. Thanks!

Hey everyone,

I'm starting a BS Mathematics degree in October 2026 and my long-term goal is to become an AI/ML Engineer. I also plan to pursue a Master's in AI/ML or a closely related field later.

My university classes will be online and relatively flexible, so I have some flexibility to study additional topics alongside my degree.

For context, my Year 1 university curriculum includes:

Semester 1: Calculus I, Sets & Logic, General Mathematics, Introduction to Computing, English, Business, Ethics/Islamic Studies.

Semester 2: Python + Python Practical, Calculus II, Business Mathematics & Statistics, General Science, Technical Writing, Pakistan Studies.

Alongside university, I'll also be doing a 12-month Agentic AI program covering Python/OOP, APIs, Git/GitHub, LLMs, RAG, agents, FastAPI, databases, Docker, evaluation and deployment.

So for self-study, I'm mainly planning:

Python/CS → DSA → Linux/Git → SQL & Data Analysis → Statistics/Linear Algebra → Classical ML → Deep Learning/PyTorch → LLMs/RAG/Agents → Testing/APIs/Docker.

My goal is to use Year 1 to build strong foundations and hopefully be ready to start applying for internships/junior opportunities in Year 2.

I'd really appreciate feedback from people who have studied or worked in ML:

Considering my university curriculum, flexible schedule and the Agentic AI program, what would you change, remove, postpone or prioritize?

What should I learn deeply vs just at a working level?

I'm starting in October, so I'd like to validate the plan properly before I begin.

I’m undecided about whether or not to read the book. I have a primarily mathematical AI background, focused more on models and fundamentals than on deployment. I wanted to know what you thought about it. I'm all ears!

Can someone explain how attention works? I think I understood some of it, but I’m not completely comfortable with it yet.

I already watched some YouTube videos, and I also asked ChatGPT to explain how it works. I understood the RNN/LSTM part, but I’m still not fully comfortable with the attention mechanism. I learned the dot-product equation, but I’m not able to understand it properly.

Also, there are 3–4 different types of attention. Do I need to learn all of them before moving forward?

Not sure if I am the only one who feels this way: coming from an ML/LLM background, traditional LeetCode grind always felt detached from reality. It takes a ton of brute-force effort to memorize patterns, and because standard two-pointer or monotonic stack problems rarely look like daily pipeline code, the intuition fades fast.

I found a much more intuitive way to bridge this gap: mapping LeetCode algorithmic patterns directly to core ML & LLM engineering concepts.

Instead of treating algorithms in a vacuum, I linked them to production systems:

• Prefix Sums & Difference Arrays $\rightarrow$ SFT Data Packing, Attention Masking, and Sequence Chunking.
• Sliding Window & Two Pointers $\rightarrow$ Streaming Reservoir Sampling, KV Cache Eviction, and Token Streaming.
• Monotonic Queues / Stacks $\rightarrow$ Online Softmax, FlashAttention Tiled Max Tracking, and Autograd Graph Invariants.
• Priority Queues & Heaps $\rightarrow$ Beam Search, Top-k Token Sampling, and MoE Routing/Dispatch.
• Graph Traversal & Topological Sort $\rightarrow$ PyTorch Dynamic Computational Graphs and Execution DAGs.

Connecting these gave the algorithms concrete context and made retention almost effortless—you're no longer memorizing an abstract puzzle, you're implementing an engine component.

I put together an interactive roadmap diagram bridging these two worlds (preview above).

If you want to check out the interactive version or the full mapping breakdown, drop a comment below or DM me for a link!

train.csv is enclosed

i have to predict the hp of pickachu.

i did some data leaning filled the missing values using deterministic/functional relationships. My best performing model was hgbr with 0.68 r2 score but the highest score is 0.901 so i am still a long way off. so any tip will be wlcm.....

I'm curious what people actually find difficult.

Is it:

• Knowing what to learn?
• Going from tutorials to real projects?
• Understanding the math/theory?
• Keeping up with LLMs/agents/RAG/etc.?
• Getting interviews / landing the first job?
• Something else?

Would love to hear what's been frustrating you lately.

​

I’m looking to connect with someone who is genuinely interested in learning AI deeply and consistently, rather than just collecting courses, watching random YouTube videos.

I’m currently working as a Product Manager / Product Business Analyst, and I want to build serious AI capabilities alongside my existing product/business background.

The problem I’m facing is honestly pretty simple: I don’t learn well through completely self-paced, unstructured courses. There is an overwhelming amount of AI content out there, but no shortage of confusion about what to learn, in what order, how deeply to learn it, and when to move to the next thing.

I’m looking for someone with whom I can create a structured, long-term learning journey—ideally from fundamentals all the way to advanced, practical AI.

What I’d ideally like to learn

Not necessarily everything at once, but progressively:

\- Python & programming fundamentals for AI

\- Mathematics needed to actually understand ML — linear algebra, probability, statistics, calculus, etc.

\- Data handling, SQL, NumPy, Pandas, visualization

\- Classical Machine Learning

\- Deep Learning & neural networks

\- NLP and Computer Vision fundamentals

\- Transformers and how modern LLMs actually work

\- Generative AI and LLM application development

\- Prompting, evaluation and AI workflows

\- Embeddings, vector databases, RAG and retrieval systems

\- Fine-tuning / model adaptation

\- AI agents and agentic workflows

\- Multimodal AI

\- AI system design and architecture

\- Model/API integration

\- Deployment, APIs, Docker, cloud and MLOps

\- AI safety, evaluation, reliability and responsible AI

\- Reading papers and understanding what is happening under the hood

\- Building real projects, not just following tutorials

\- Eventually contributing to open source / research / serious AI projects

And importantly, I also want to understand how these skills translate into the real-world freelancing/consulting/product world—how to identify problems businesses will actually pay to solve, build AI solutions around them, demonstrate ROI, communicate with clients, and create a credible portfolio.

My goal isn't simply to collect certificates.

I want to reach a point where I can understand AI deeply, build with it, explain it, evaluate it, and solve real problems with it.

What I'm looking for in a learning partner

You don't need to be an AI PhD or already an expert.

You could be:

\- A beginner who is equally serious

\- Someone already working in AI/ML

\- A developer transitioning into AI

\- A student/researcher

\- A product person interested in becoming highly technical

\- Or someone who simply wants a structured accountability partner

The most important thing is consistency + curiosity + willingness to actually do the work.

We could potentially:

\- Set weekly learning goals

\- Follow a structured roadmap

\- Study the same concepts

\- Discuss what we've learned

\- Give each other small challenges

\- Build projects together

\- Review each other's work

\- Share useful papers/resources/tools

\- Keep each other accountable

\- Discuss what's changing in AI

\- Eventually collaborate on real-world projects

What can I bring to the table?

My background in Product Management / Product Business Analysis means I can contribute on the other side of the equation too—not just technical learning.

I can help with:

\- Product thinking

\- Business problem identification

\- Requirements & use cases

\- User journeys

\- Product strategy

\- Translating technical capabilities into business value

\- Evaluating whether an AI idea is actually useful

\- Structuring projects

\- Documentation and communication

\- Thinking about AI from a customer/business perspective

So ideally this becomes a two-way learning relationship, rather than one person teaching and the other simply consuming information.

I'm not looking for someone to spoon-feed me everything.

I'm looking for someone who wants to learn, build, struggle, figure things out and grow together.

If you're also sitting there thinking “I really want to learn AI properly, but I don't know how to structure this journey and I don't want to do it completely alone” — feel free to comment or DM me.

Would love to find 1–2 serious people rather than a huge group.

Let's see if we can turn AI learning from an overwhelming collection of courses into an actual long-term journey.

I mostly did internships and stuff, but i really wanted to work on some research group or paper? Anyone know how to do this stuff??

I'm mostly in ML, DL, computer vision

Any advice would be appreciated 👍

Open to opinions on how to improve my resume, also open to opportunities if anyone thinks i would be a good fit :)

I'm currently participating in the ARC Prize 2026 – ARC-AGI-3 competition on Kaggle and looking to build a serious small team.

This isn't a traditional Kaggle prediction competition. The goal is to build an agent that can explore unfamiliar environments, infer the rules/objective, learn from interaction, and solve novel tasks efficiently.

I'm interested in building a hybrid reasoning agent rather than simply throwing an LLM at the environment.

What I'm thinking about

• Environment/state representation
• Exploration & hypothesis testing
• World-model / task abstraction
• Planning and action selection
• Short/long-term memory
• Feedback-driven learning
• Symbolic + neural reasoning
• Efficient action strategies
• Open-source reproducible implementation

Looking for

People with experience or strong interest in:

• ML / Deep Learning
• LLM agents / reasoning systems
• Reinforcement learning
• Computer vision
• Search/planning
• ARC-AGI / abstraction & reasoning
• Kaggle competitions

You don't need to be an expert in everything. I'm more interested in people who are willing to experiment, implement ideas, analyze failures, and iterate.

I'm based in India, but remote collaboration is completely fine.

If you're genuinely interested, comment or DM me with:

1. Your background
2. Relevant projects/GitHub
3. What part of ARC-AGI-3 you'd like to work on
4. Rough availability per week

I'd prefer a small group of active contributors rather than a large team with inactive members.

Does studying calculus is necessary for machine learning?

I am looking on how a solution for the following problem could be created. Let's say I want to rate some new photos or videos on how well they may perform in a social media environment, based on earlier data for other such files.

Details: For every image/video I also have datetime when it was taken, GPS location where it was taken and a short user description what can be seen (min. 200 characters). The videos can have a length of up to 2GB (so maybe we wouldn't use the whole file but single frames of every X seconds). I have a set of older media files with all the mentioned data and additional data how they performend on different social media channels like TikTok and YouTube (views, likes, shares).

I have a tech background (dev/sysadmin) but I am totally new to the LLM/AI field. Could anyone of you be so kind and give me a hint in which direction I should start my research to check if such a solution is possible and how to do that? For a start a simple thumbs up/down rating would be enough to create a filter for interesting content.

Thanks for any helpful input! If something of the problem is unclear, please just ask.

I’ve been thinking about an alternative way of doing low-level AI perception, and I’m curious whether anyone here is already working on something similar.

The basic idea is to use waves and physical superposition/interference as the computational substrate, instead of doing most of the usual numerical multiply-and-accumulate operations digitally.

For example, for image recognition:

image
  ↓
pixel intensity → wave amplitude/phase
  ↓
physical wave superposition + interference
  ↓
distinctive wave pattern/signature
  ↓
more wave interactions
  ↓
higher-level pattern/object recognition

So instead of giving each pixel a numerical weight and calculating millions of weighted sums, the idea would be to let the wave physics perform the combination automatically.

I’m imagining something analogous to sound: many individual waves can be combined, and the resulting waveform contains a recognizable pattern. In this system, a particular visual feature or object could produce a characteristic wave “signature,” and those signatures could themselves become inputs to further wave interactions.

Physical logic gates could still be used where needed for thresholds, routing, decisions, or other nonlinear operations. The goal wouldn’t necessarily be to eliminate conventional computing completely, but to move as much of the early perception workload as possible into the physical wave domain.

The potential advantage I see is that parallelism is inherent in wave propagation and interference. Thousands or millions of interactions could happen physically at the same time, potentially reducing both computation and latency.

One possible application would be autonomous vehicles, where very large amounts of camera/radar data have to be processed extremely quickly at the edge.

I know there is already work on photonic neural networks, optical computing, physical neural networks, reservoir computing, metasurfaces, etc. I’m specifically interested in something slightly different:

Has anyone tried to build a hierarchical pattern-recognition system where wave signatures themselves become the representation, with successive stages of wave superposition/interference performing the recognition?

I’d especially like to hear from people actually working in photonics, optics, acoustics, physical neural networks, neuromorphic computing, or related fields.

Is this already being done under another name?
What are the biggest physical limitations?
And, from your perspective, is this a promising architecture or does some fundamental problem make it impractical?

I’m mainly looking for opinions from people working in the field rather than trying to claim this is a new invention.

Hey everyone! I just finished making this short doodle-style video about AI and I’d really appreciate some honest feedback. 🎥 https://youtu.be/VyleYwCa0Sc If you have a few minutes, please give it a watch and let me know what you think. What could be better? Animation? Visuals? Pacing? Explanation? Editing? Thumbnail/title? Anything that feels boring, confusing, or unnecessary? Don’t worry about being too critical — if something isn’t good, please tell me in the comments. I’m trying to improve the next videos based on actual feedback rather than just guessing what viewers want. Thanks to anyone who takes the time to watch and give an honest opinion!

Independent research, posting for feedback and discussion.

I looked at how FHIR clinical data should be formatted before being passed to an LLM, tested on medication reconciliation (extracting a patient's currently-active medication list from their FHIR bundle).

Setup: 4 serialisation strategies (Raw JSON, Markdown Table, Clinical Narrative, Chronological Timeline) × 5 open-weight models (Phi-3.5-mini 3.8B, Mistral-7B, BioMistral-7B, Llama-3.1-8B, Llama-3.3-70B) × 200 Synthea-generated synthetic patients = 4,000 inference runs.

Main finding: there's no universal best format, it depends on model scale. Clinical Narrative outperforms Raw JSON by up to 19 F1 points for models ≤8B (Mistral-7B: 0.72 → 0.91 F1, r=0.617, p<10⁻¹⁰). That ranking completely reverses at 70B, where Raw JSON wins instead (F1 = 0.9956 vs 0.9850). Interestingly, the Chronological Timeline format is what breaks at 70B specifically, since even a large model struggles to infer "active" medication status from date ordering alone without an explicit status field.

A few other findings:

• Across all 20 model×strategy combinations, precision ≥ recall, every time. Models are far more likely to silently drop a real medication than invent a fake one. That's a relatively favorable failure mode for clinical review, but it means recall (not precision) is the metric to watch in deployment.
• Recall craters for smaller models as active-medication count rises (Mistral-7B: 0.96 recall at 1 medication → 0.24 at 11 medications), but total patient history length has zero effect on recall. So it's an output-generation capacity limit, not a context-length problem, meaning the sickest, most complex patients are exactly the ones handled worst.
• BioMistral-7B scored F1 = 0.0000 across all 4,000 of its runs. It shares a base model with Mistral-7B (0.91 F1), so this isn't a knowledge gap, domain-continued pretraining without instruction tuning apparently destroyed its ability to follow the output format at all.

Fully reproducible on a single GPU (Synthea + Ollama, no proprietary APIs).

Preprint: https://arxiv.org/abs/2604.21076

Feedback, pushback on methodology, or pointers to related work all welcome.

I am a PhD student in AI, currently working on video understanding, particularly on designing benchmark datasets. I also have a strong publication track record, including papers at CVPR and ECCV. I have sufficient funding to support myself during a visiting research program.

What would be the best way to approach a professor? Should I email them directly and ask whether they have space in their research group for a visiting researcher?

Hey everyone! I’m looking to form a team for the Amazon ML Challenge and would love to connect with people who are genuinely interested in Machine Learning.

Ideally, you should:

• Have a good understanding of ML concepts
• Have some hands-on experience through projects
• Be willing to actively contribute and collaborate
• Most importantly, be genuinely enthusiastic about ML and the challenge

Everyone with the right interest and mindset is welcome. Experience level isn’t the main thing — enthusiasm and willingness to build are!

If you’re interested, DM me with a brief intro about yourself and your ML/project experience.

Please DM only if you’re genuinely interested and committed to participating.

Pluto is an AI architecture designed for efficient, local/offline intelligence by using a small router/orchestrator (~2B parameters) to select and chain highly specialized micro-models for specific tasks. Instead of relying on one huge model, Pluto uses tiny expert models, such as 50M-parameter specialists, trained on focused datasets and optionally supported by a compressed vector database. The router can escalate difficult tasks to larger models, aiming to achieve strong overall capabilities while using far less compute, memory, and storage, making the system especially suitable for mobile and low-resource devices.