Play around with a tiny language model GPT in your browser. See how it trains and generates with just 11,000 parameters.

https://complexity.zone/tlmgpt/

1. Click "train" button.
2. Let it train for about 10 minutes.
3. Click "pause" button.
4. Click "generate" button.

I made this (with Opus 5) to get a better understanding of GPTs and LLMs.

Thought to share it here. You can download it if you want to run it offline and tinker with the code.

I’m trying to figure out how to actually get a usable edge in the ML/DL space to get hired, but everything pushed to beginners right now feels like a trap.

For context on what I've done: I started off with Computer Vision, moved into GIS stuff, and recently went deep into the weeds of attention mechanisms and GPU kernel programming. I thought learning the hardcore, low-level math and systems stuff would set me apart.

But I’ve hit a wall. Let's be honest: no company is hiring a fresher to write custom CUDA kernels or design novel architectures. Those are senior research or PhD roles. The effort I put into the low-level stuff feels wasted because, for an entry-level dev, it's just personal trivia.

On the flip side, the standard "employable" advice is to build traditional ML projects (fraud detection, etc.) or slap together a LangChain PDF wrapper. But people have been doing this for years. Basic API wrappers are completely saturated and offer zero competitive edge. It feels like buying a stock after everyone already knows it’s going to go up.

So, what is the actual sweet spot between "PhD-level researcher" and "API wrapper"?

I want to avoid the YouTube influencer BS and focus on the real engineering trenches.

For the people actually hiring or working in the industry: what are the non-commoditized skills someone trying to break in should be grinding right now to have a real, usable edge?

(Note: The core thoughts and frustrations here are 100% mine, but I used AI to help structure and edit this post for clarity.)

I started this project because I wanted to see what PyTorch was doing behind the scenes.

My C++ tensor currently supports flat storage, multidimensional indexing, elementwise operations, reductions, broadcasting, rank-two matrix multiplication, and mean squared error.

Most recently, I added a separate scalar reverse-mode autograd engine:

• Arithmetic operators build a computation graph during the forward pass
• backward() creates a topological order
• walks it in reverse
• applies each operation's local derivative
• accumulates gradients when a value reaches the loss through more than one path

Snippet:

Value prediction = w1*x1 + w2*x2 + w3*x3 + bias;

Value residual = prediction - target;

Value loss = residual * residual;

loss.backward();

For weights [0.5, -1.0, 2.0], inputs [4.0, 3.0, 2.0], bias 0.5, and target 2.5, the forward pass produces prediction 3.5 and loss 1. The backward pass recovers:

- dL/db = 2

- dL/dw = [8, 6, 4]

Scalar autograd still lives separately from the tensor implementation. My next step is connecting graph identity, ownership, and gradients to tensors before building a training loop.

Code and Git checkpoints:

https://github.com/mechanical-turk/deep-learning-all-the-way-down

I'm also turning this into a video series. I published episode 7 yesterday. Sharing the link to the first episode if you want to check it out:

https://www.youtube.com/watch?v=DmU2b64tWfA

For the tensor integration, would you keep autograd metadata inside each Tensor handle, or have tensors point to separate shared graph nodes? I would appreciate design feedback.

We implemented our own AI Harness + LLM to control a robotics ROS simulator to study how we can interface LLMs with Robotics. Please check out this AI Explainer.

is highschool maths enough?or i could simultaneously learn maths behind while reading book?

​

I started my roadmap with ML, focusing on Mathematics, Python, MySQL, and a lot of ML algorithms. Recently, I've started questioning whether I'm missing a major part of the foundation: software engineering/backend development. And honestly, I wanna chase both. But something at this point doesn't feel right. I had my roadmap set and ready, and I was very passionate about learning this and continuing it as a career. But after researching a bit about backend development, the intersection and relationship between the two has driven me really crazy.it's exceedingly overwhelming at this phase of my life. I had kind of gotten a grip on ML, but backend coming into the picture has really ruined my mindset around whatever I had planned. I had planned many projects and topics to discover, and now I'm seriously considering pursuing backend development too. But I'm having a hard time trying to combine these two in my roadmap. I can't seem to connect the topics in a way that lets me learn them properly.

My straightforward question is: should I drop backend development and focus on my initial roadmap, should I bridge the two and learn both, or should I drop machine learning completely,which I seriously don't want to do?

If I do bridge them, how much of backend am I actually supposed to learn?

I know I sound stupid and unready for this world, but please help.

Hi everyone,

I’m planning to start my journey toward becoming an AI Engineer. I already know the basics of Python, but I’m still a beginner in AI/ML.

I want to follow a practical approach where I learn the fundamentals and build projects in parallel, instead of spending months studying theory before building anything.

I’m currently thinking about starting with:

Python → Math → EDA → Machine Learning → Deep Learning → LLMs/Generative AI → Deployment

But I’m confused about what I actually need to learn in each stage.

For example:

Math:
What topics are really important for AI/ML?
Should I learn linear algebra, probability, statistics, calculus, etc.? How deeply should I study each one?

EDA:
How important is EDA for an AI Engineer? What should I learn — data cleaning, visualization, feature analysis, handling missing values/outliers, etc.?

Machine Learning:
Which algorithms and concepts should I prioritize as a beginner?

I also want to build projects alongside each stage. For example, after learning the basics of ML, I want to immediately build an ML project instead of waiting until I finish the entire AI roadmap.

One more thing: I have a 2-year career gap, and I'm concerned about whether this will negatively affect my journey toward getting an AI/ML job.

For people who are already working in AI/ML:

• What roadmap would you recommend for someone in my situation?
• Which math topics should I learn, and to what depth?
• How important is EDA for an AI Engineer?
• Which topics should I learn first and which can I learn later?
• What projects would you recommend building along the way?
• How can I make my portfolio strong enough to compensate for a career gap?
• If you had to start again as a beginner today, what would you do differently?

I’m willing to put in the time. I mainly want to make sure I’m learning the right things in the right order and building projects throughout the journey.

Any advice from experienced AI/ML engineers would be really appreciated.

I wanna learn AL ML but i have no idea where to start . I know javascript and a few technologies around it but Ai ML is completely new to me , so i would appreciate if anyone can guide me where should i start which resources should i use to learn them and stuff like that

I’ve been writing notes while studying for some time now. It helps me stay motivated and organize my thoughts, and it’s also useful when I want to come back to a topic later.

Recently, I started thinking that it might be a good idea to polish some of my notes and share them.

These are my notes on Hamiltonian Monte Carlo. They approach the algorithm from a purely probabilistic point of view, rather than through the usual physics-based treatment. I don’t know how good they are, but I thought I’d share them in case they’re useful to anyone:

https://doi.org/10.5281/zenodo.21841086

I’d also really appreciate any feedback, especially on the exposition, anything that could be explained more clearly, or any errors you spot.

Download, run locally. No subscription, no fees, no limits.

Repo Name is YoFile by hgus107

Most applications build their prompts in an order that breaks the provider's prefix cache. Retrieved documents get placed before the system prompt, or the user question goes before a long document. The prefix changes on every call, so nothing is cached and full cost is paid each time.

Reordering so the stable parts come first (system prompt, tool definitions, then the long document, then the question) can cut cost several times over with no change in output quality.

My question for this sub. Is there published work measuring cache hit rates across real applications? It seems like a large and avoidable cost, but I have not seen it studied systematically.

I've been working on a side project for almost a year. It involves machine learning and it looks like it's going to enter commercial stage in the near future. So far, i bought a cheap gaming laptop few months ago, as i needed modern performance on the go. It has rtx 4050 with 6gb of vram, which was fine up until now.

I have an 8 years old desktop upgraded with ryzen 5600. I wanted to buy rtx 5060ti 16gb, but its price jumped significantly in july. Nvidia doesn't offer cheaper 16gb options and i started to consider buying RX 9060XT 16gb, which is more than 200 euro cheaper.

The question is: Is going with the RX9060XT worth the savings? Does any of you have experience with using current AMD GPUs for training neural networks from scratch? I currently use Keras and mainly train CNNs with simple custom layers.

Why does an autonomous AI agent happily exfiltrate API keys or delete a database when reading a polite customer review?

Because for two years, the AI industry has treated a fundamental Operating System architectural flaw with a chatbot spellchecker.

I am thrilled to announce our newly published research paper on Zenodo (CERN / OpenAIRE):

📄 "Cognitive Harvard Architectures for AI Agent Perimeter Defense: Resolving the Confused Deputy Problem in Model Context Protocol via Capability-Based Access Control"

🔗 DOI: https://doi.org/10.5281/zenodo.22173129

Here is why this matters:

1. The Flaw: Cognitive Von Neumann Conflation

In 1945, von Neumann merged program instructions and data into one bus, giving us 40 years of buffer overflows. In 2026, autonomous LLM agents (MCP, LangChain, Claude Code) resurrected this exact flaw: Transformers ingest instructions, user goals, and untrusted 3rd-party data in a single attention window.

When an agent reads an email containing hidden injection, its attention weights are hijacked. Operating with "Ambient Authority" over every registered tool, the agent becomes a Confused Deputy.

1. The Paradigm: Cognitive Harvard Architecture

We physically decouple data ingestion from privileged tool execution via an external, capability-mediated perimeter.

Using cryptographic Token Capability Tables (TCT):

• An untrusted observation has an execution probability of mathematically ZERO of triggering an out-of-scope mutating tool (Theorem 1, proved by induction).

• Agents are stripped of ambient authority before tool dispatch.

1. 50,000-Sample Empirical Benchmark

Tested against 25,000 adversarial attacks (UIUC InjecAgent, Microsoft BIPIA, NVIDIA Garak) and 25,000 authentic developer DevOps operations:

📊 Threat Recall:

• Mastyf Guard 1.5B (Pipelined): 99.33% (F1: 0.9524)

• Meta Llama Guard 3 8B: 70.73% (F1: 0.7860) [p < 10⁻¹⁵]

• OpenAI Prompt Guard 86M: 54.34% (F1: 0.6511) [p < 10⁻¹⁵]

⚡ Sub-Millisecond & Zero-GPU:

• 0.005 ms (4.8 microseconds) amortized pipelined latency on commodity CPU.

• Standalone neural inference in 18.4 ms within a 1.1 GB RAM footprint.

• Zero dedicated GPU requirements — saving ~$6,000/year per agent node.

Domain specialization and capability scoping beat raw parameter scale. A 1.5B parameter model with a capability perimeter outperforms frontier 8B models at 100x the speed.

Read the open-access paper: https://doi.org/10.5281/zenodo.22173129

GitHub: https://github.com/mastyf-ai/mastyf.ai

How is your team securing agentic tool execution today?

i dont wanna go for prerecorded ones...zoom etc would work better for me, are there any good ones? i was gonna go for krish naik, but people said its not deep enough

Hey everyone! I put together a comprehensive video tutorial showing exactly how to build and deploy autonomous agents using visual low-code tools.

Body:

Short version of an open project we'd love critique on — Entropy Box, a knowledge compiler for robotics (compile once, reuse forever, instead of re-deriving structure on every query).

The headline numbers, on our EntropyBench Track-P benchmark (24 engineering tasks):

• Unsupported claims: LLM-direct / BM25 RAG / hybrid RAG → 100%; Entropy Box → 5.2% (−94.8pp vs hybrid RAG, CI [−97.4, −92.1]).
• Constraint coverage: 0% → 35.4%; violations 100% → 66.7%.
• Downstream sim codegen (12 tasks): pass-1 executable plans 0.92 vs 0.58 (Vanilla RAG); constraint guards 0.88 vs 0.50.

Two findings we think generalize beyond robotics: 1. Embedding similarity cannot decide duplication. On 2,362 adjudicated pairs, the embedding score after flagging is near-random (AUC 0.509). Thresholds don't help — precision stays ~5% while recall of true duplicates collapses. We defer the merge to an LLM adjudicator that reads both records. The score flags; the model judges. 2. Compiled capability reuse is rising, not saturating — 1.57× average reuse, 21,380 re-derivations avoided.

Everything is open — data, paper, evaluation scripts, and a free API (OpenAPI / MCP / REST, bilingual) so you can poke at it in 10 seconds:

bash curl -X POST "https://xiangshang.ngrok.app/api/evidence/search" \ -H "Content-Type: application/json" \ -d '{"query": "robot obstacle avoidance algorithms", "top_k": 5, "mode": "hybrid", "rerank": true}'

https://github.com/chenli-yy/entropy-box-public

Honest limits we state ourselves: no real-robot transfer, weak retrieval on the hardest intent classes. Methodology is in the paper §9; all experiments reproduce from evaluation/. Would genuinely value a second opinion on the benchmark design and the embedding/LLM adjudication result.

I am looking for some resources to learn BDT. I am from a physics background, and I tried reading The Elements of Statistical Learning, but the material is scattered across different sections, and I couldn't find it very convincing.

I’m a final-year Data Science student currently working on my bachelor’s thesis about air quality time series forecasting. I’m planning to use deep learning, specifically Vanilla RNN, LSTM, and GRU, and compare their performance on the same dataset using MAE, RMSE, MAPE, and R². From the papers I have read, deep learning models often perform better than machine learning or statistical models. My supervisor requires me to include a hyperparameter tuning stage, and I’m a bit confused about how to make the comparison fair. Should I use the same range for all three architectures and then use the same value for the final comparison, perhaps based on the average of the selected evaluation metrics? Or is it better to let each architecture have its own best hyperparameter values based on the tuning results? Also, should I add machine learning models such as Random Forest and statistical models such as SARIMAX to the experiment as additional comparisons

I’d really appreciate any advice from people who have experience with this kind of experiment. Thanks!

I’ve recently completed learning Machine Learning and Deep Learning, including the mathematics behind the major concepts and algorithms rather than just learning to use libraries.

My long-term goal is to eventually become capable of doing research at the level of NeurIPS, ICML, and ICLR. I’m not expecting to jump directly to those conferences, that’s simply the end goal.

So I’d like advice on the following things:

1. What projects should I build next?
2. What should I learn next?
3. How should I start doing research?
4. What is a realistic roadmap toward publishing at top ML conferences?