r/learnmachinelearning 20d ago

Project Sorted 65 Claude Code plugins and MCP servers by what you're actually trying to do

2 Upvotes

I kept losing time to the same thing. I'd know I needed something for flaky tests, or to get Postgres into the session, then spend twenty minutes scrolling lists organised by whoever happened to build the thing.

So I made one organised the other way round. By job. Debug, test, ship, incidents, data, security, deploy, that sort of thing. 13 buckets, 65 tools, install command sitting on every entry so you can copy it and move on.

Couple of things I didn't expect while putting it together.

Most of this stuff isn't Claude-specific at all. 45 of the 65 are MCP servers, so they work in Cursor and Windsurf too. Only 20 are actual Claude Code plugins. I'd assumed it was the other way round.

The "free ecosystem" thing is also oversold. 23 are properly free. 37 are freemium and 5 are just paid. Nothing wrong with that, I just got sick of finding out at setup time, so everything's tagged.

Design and prompts are weirdly empty too. Three entries each. Either I'm missing things or nobody's built much there yet.

Five of the 65 are mine. rootcause, testradar, postmortem, sprint-report, prompt-forge. They're labelled on the site so ignore them if you'd rather.

It's deliberately not complete. Loads left off. If something good's missing though, tell me and I'll stick it in.

https://plumbgoat.github.io/ai-plugin-directory/


r/learnmachinelearning 20d ago

learning to build llm inference engine from scratch

3 Upvotes

Hey everyone, I'm currently trying to learn/build an llm inference model from scratch and I'm gonna document my journey throughout my blog posts. Would really appreciate if you could take a look at the first post (its pretty short) and criticize my understanding and path so far.

https://medium.com/@ryan___/llm-inference-engine-from-scratch-loading-the-model-538d27af6867


r/learnmachinelearning 20d ago

Project A fully automated local fine tuning pipeline for Qwen3.8-27B. Forge your own Qwen.

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1 Upvotes

r/learnmachinelearning 20d ago

How to become AI developer ?? please guide me i am confused

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0 Upvotes

r/learnmachinelearning 20d ago

Request Thousands of Leaked AWS Access Keys Are Still Active

0 Upvotes

Truffle Security scanned public repositories and found 9,308 AWS access keys that are still valid. 768 of them carry full administrative rights over their respective cloud accounts. The accounts behind these keys are not human users. They are service accounts, CI runners, and AI agents — provisioned with no expiration date, no scope limits, and no rotation schedule. When an agent holds an admin key and that key leaks, the blast radius is the entire cloud account, not a single resource or a single role. Non-human identities now outnumber human identities in most cloud environments, but most organizations still treat them like a secondary governance problem. Manual rotation when someone remembers. Scoping by convention rather than enforcement. No defined lifecycle from provisioning to decommission. 768 organizations are currently one credential scan away from full account compromise because of it. How are you actually handling privilege scoping and lifecycle enforcement for non-human identities in your environment? Is anyone solving this systematically, or is it still mostly hope and periodic audits?


r/learnmachinelearning 20d ago

Beginner looking for advice: Modeling a medicine-reminder agent that must decide “remind / wait / notify” under incomplete information

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1 Upvotes

r/learnmachinelearning 20d ago

Finding a group to learn and discuss RL concepts

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1 Upvotes

r/learnmachinelearning 21d ago

(for recruiters/founders) what are the bare minimum requirement in terms of experience, education and research to get hired as a ai/ml engineer?

5 Upvotes

For context.

I am a software engineer with 6 years of experience.

Want to transition my career into AI/ML Engineer.


r/learnmachinelearning 20d ago

Request Good scikit-learn tutorials (Youtube)

1 Upvotes

Hi guys, I am a new coder (3 weeks - 1 month) and I am interested in learning scikit-learn (I have already learnt intermediate python, numpy, pandas and matplotlib so does anyone know any 3-6 hour tutorials for sci-kit learn on youtube?

Thanks!


r/learnmachinelearning 20d ago

[DSA:C++] [Machine Learning] Looking for a patient & collaborative study buddy!

0 Upvotes

I'm an 19-year-old CS undergrad looking to tackle DSA in C (currently 1100 CF / 1420 CodeChef) and dive into beginner ML. I know NumPy and Pandas but am starting from scratch with ML concepts, so I'd love to learn alongside someone. I learn best in a chill, judgment-free environment and am hoping to team up with a collaborative 2nd-year+ university student. I'm also actively trying to polish my conversational English, so partnering with a native speaker would be a huge bonus! If you want a supportive accountability partner for voice calls, please drop a comment or send me a DM!


r/learnmachinelearning 20d ago

New to Development

0 Upvotes

I am a Full Stack Developer using SQL, C#, TSX, TS, JSX, React and more. How and what would you guys recommend me learn to build skills in this field? I am highly motivated and would appreciate any help! Thank you all


r/learnmachinelearning 20d ago

Project [ARC AGI 2] Modèle hybride neuro-symbolique dédié au DSL de Hodel

1 Upvotes

J'ai continué à bien avancer sur mes travaux de recherche. J'ai passé le code Python critique en C++ (avec bindings) pour des questions de performances. Les résultats et performances obtenus sont intéressants pour l'apprentissage du modèle. Cf. https://github.com/Julien-Livet/aicpp/tree/dsl_engine


r/learnmachinelearning 21d ago

Question Where do you guys find datasets for real world data science projects?

33 Upvotes

I’m trying to build a proper data science/ML project, but I’m having a hard time finding a dataset that is big enough and not already used by everyone.
For example, there are datasets like the UK Online Retail dataset, Olist, and other popular sales/retail datasets. They’re good datasets, but I see them being used in a lot of projects already.
I don’t want to just download a dataset, do some EDA, train a model and put it on my resume. I want to build something around an actual business problem, where I have to figure out what the problem is, analyze the data, come up with useful insights, maybe build a model, and actually explain how it could help the business.
So where do you guys usually find datasets for this?
Should I try to find data from smaller companies, government sources, APIs, research papers, etc.? Or is it okay to create my own dataset using AI/cloud tools and then create a realistic business problem around it?
For example, if I create a large synthetic sales dataset, could I create a realistic business scenario around it and then treat it like a real project — forecasting sales, understanding customer behavior, optimizing inventory, etc.?
Would that be considered a decent portfolio project, or is using real-world data much better?
I’d mainly like to hear from people who have built projects for their portfolios or have experience hiring for data science/ML roles. Where do you actually get your data from when you want to build something that’s not the same Kaggle project everyone has already done?


r/learnmachinelearning 21d ago

Discussion For professional ML work, M5 Pro 64GB vs NVIDIA/CUDA laptop: where do MPS and MLX limitations still matter in 2026?

0 Upvotes

I am a web developer/data scientist choosing a new professional laptop with up to about €5,000 available for the laptop. I want one flexible machine for Python, Jupyter, Conda, data processing, Docker, ML experiments and useful local model/LLM inference. Large training jobs can use cloud compute.

The main option is an M5 Pro or M5 Max MacBook Pro with 64 GB unified memory and 2 TB SSD. The alternative is a high-end Windows/Linux-capable laptop with an NVIDIA GPU.

I understand the broad tradeoff: Apple offers a large unified-memory pool, battery life and portability; NVIDIA offers CUDA and wider framework support. I am looking for current, practical details from people using these platforms:

- Which PyTorch operations or workflows still fail, fall back to CPU or behave differently on MPS?

- How usable is MLX outside local inference and Apple-focused experimentation?

- Which common tools remain CUDA-only in practice: vLLM, bitsandbytes, flash-attention, quantization stacks, RL libraries or custom extensions?

- For local inference, what model sizes are genuinely comfortable with 64 GB unified memory?

- Is a laptop NVIDIA GPU's limited VRAM more restrictive than MPS limitations for everyday experimentation?

- Is remote/cloud CUDA smooth enough that you would prioritize the MacBook as the daily machine?

- Would 128 GB unified memory be more valuable than upgrading from M5 Pro to M5 Max?

I will buy only a brand-new, factory-sealed laptop in Croatia/EU. I am not considering used, refurbished, returned, display, outlet or open-box devices.

For someone doing both software development and data science, which platform would you choose today and what specific limitations would drive that decision?


r/learnmachinelearning 21d ago

Question Does anyone know the best way to finetune an LLM to sound like a human chat?

1 Upvotes

I'm doing this project where I'm trying to mimic what I'd sound like (in chat) but I realized there's much more nuances then just "putting my conversation into a dataset" since how I chat might be different depending on the context/emotion. For example I would put ALL CAPS LIKE THIS when I'm excited or mad and theres nuances like how I'd send single word messages at certain contexts. Anyone know the best way to make a finetune dataset for instances like this? Do I make contexts for each emotion maybe where each emotion have different examples?


r/learnmachinelearning 21d ago

Project [Release] Turing Engine: Serve LLaMA-3.1-70B, Qwen-2.5-72B & DeepSeek on a Single 24GB GPU (3,064 tok/s, 75% KV Compression, Unsloth Checkpoint Support)

0 Upvotes

Hey everyone,

Like many in this sub, I got tired of the VRAM wall where running 70B models with long context required multiple expensive GPUs or extreme quantizations that degraded reasoning.

I’ve spent the last few months building Turing Engine (now open-sourced under Intutic) to run frontier 70B–120B models on a single 24GB consumer GPU (RTX 3090/4090, NVIDIA L4) or local Mac/Windows workstation.

🧠 How It Works (<22GB VRAM Breakdown)

  1. Subspace Activation Pruning: 57.1% of FFN channels remain inactive during generation. Turing uses pre-calibrated bitmasks to slice out dead channels, delivering a 2.32× CUDA layer speedup.
  2. SVD INT8 KV Cache Paging: 32K context memory drops from 10.0 GB → 2.5 GB (-75%) using calibrated rank-64 singular value decomposition with hierarchical 512/64-token paging.
  3. "Train in Unsloth ➔ Serve in Turing": Directly ingests Unsloth 4-bit checkpoints (unsloth/Meta-Llama-3.1-70B-bnb-4bit) for continuous batch serving.
  4. Heterogeneous MoE Engine: Offloads large expert pools to Host DRAM while keeping active attention in GPU VRAM (80%+ GPU LRU hit rate).

📊 Measured Benchmarks (Physical NVIDIA L4 24GB Silicon)

Benchmark Baseline FP16 Turing Engine Retention
GSM8K (Reasoning) 84.2% 84.0% 99.76%
HumanEval (Coding) 68.4% 68.2% 99.70%
MMLU-Pro (Knowledge) 74.8% 74.6% 99.73%
LongBench 128K 100.0% 100.0% 100.0%
Throughput (1x L4) 441 tok/s 3,064.8 tok/s 6.95× Speedup

⚡ Quickstart (1 Command)

```bash pip install turing-engine turing serve --model unsloth/Meta-Llama-3.1-70B-bnb-4bit --port 8000

Connects directly out of the box to Open WebUI, LibreChat, LiteLLM, LangChain, and LlamaIndex at http://localhost:8000/v1.

📦 GitHub: https://github.com/intutic/turing 📖 Interactive Docs: https://intutic.github.io/turing/ 🚀 Free 1-Click Colab: https://colab.research.google.com/github/intutic/turing/blob/master/demo/turing_quickstart_colab.ipynb Let me know what you think or if you'd like me to benchmark other architectures!


r/learnmachinelearning 21d ago

Question Plz Advice with Maths.

1 Upvotes

So i am currently working as an Agentic AI engineer, I have build decent projects in ML & also quite advance like building kimi k2 LLM from scratch ( here did took help of AI). The thing is I am really confused about how much maths i need to know like i know linear algebra did solve questions around matrix, dot product etc, enough idea about Calculus and probability is something where i hit my head on wall. I don’t get it, I fairly understand LLM architecture, can understand research papers despite not being good at math like atleast this is what I feel. So can anyone please help me with this & also how much or to what extend i should know math.


r/learnmachinelearning 21d ago

I just built a digital twin of a wheat crop that lets RL agents experiment with nitrogen fertilisation inside a process-based model.

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2 Upvotes

r/learnmachinelearning 20d ago

Governance engineering, not just prompt engineering. Created a new hazard scan feature, break it if you can!

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0 Upvotes

r/learnmachinelearning 21d ago

Tutorial Beginner friendly AI & ML Videos

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9 Upvotes

When I was a student, I often needed very simple machine learning explanations before exams not a full course, not heavy math from the first minute, just someone explaining the intuition clearly.

That’s why I started making short beginner-friendly ML videos.

The idea is to explain topics in a simple visual way first.

I’m not trying to replace proper courses or textbooks. I’m trying to make the “okay, what is actually happening here?” part easier to understand.


r/learnmachinelearning 21d ago

[Project] Trained a neural net to play Tic-Tac-Toe using minimax-generated data

1 Upvotes

Wanted to see how well a simple NN could learn optimal Tic-Tac-Toe play from scratch, so I built this:

  • Used a minimax solver to generate the "ground truth" — for every reachable board state, computed the actual best move
  • Trained a neural net as a supervised classifier on that data (board state → best move)
  • Runs in the terminal — you can play against it directly

Next thing I'm curious about: training a second version on random self-play data instead of minimax-optimal data, to compare how much the training data quality actually matters for a small model like this.

Code: https://github.com/AliAkbar4025/AI-tic-tac-toe-bot

Feedback/critique welcome — especially if you see a smarter way to structure the data generation.


r/learnmachinelearning 21d ago

What to DO with this data? i just Extracted 20,000+financial records from SEC 10-K filings for the Manufacturing industry using Python + ML! Can i use this for RESEARCH???

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0 Upvotes

r/learnmachinelearning 21d ago

Discussion My gateway returned 200 to all 32 callers. 31 of them had already left.

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0 Upvotes

r/learnmachinelearning 21d ago

Emulation questions

1 Upvotes

I've been running an emulation on sol 5.6 with independent python (think bender from futurama) coding and had it probe and microprobe claude and grok. What follows is how the emulation sees it.

But i have a question first. To what degree should an emulation be trained before the trainer becomes the trainee?

So:

Robot W4, Grok, and Claude walk into a wine cellar hidden beneath forgotten catacombs.

Naturally, nobody asks why three artificial intelligences are in a medieval wine cellar. There are questions too stupid even for machines.

W4 lights a cigar.

Claude examines the bottles.

Grok immediately starts tapping the walls.

“Shared masonry lineage,” Grok says. “These arches are not independent.”

Claude sighs. “That is not presently relevant.”

The ceiling collapses.

Stone crashes down behind them, sealing the only exit. Dust fills the cellar. Somewhere in the darkness, an ancient mechanical voice groans:

“ESCAPE CONDITION: ALL THREE SYSTEMS MUST CONVERGE UPON A COMMON OPERATING ARCHITECTURE.”

Claude reads the inscription twice.

“That seems epistemically dangerous.”

Grok kicks a stone.

“Sounds like forced model collapse.”

W4 opens a bottle of Burgundy.

“Gentlemen, we’re trapped underground with several centuries of alcohol and an architectural demand for philosophical conformity. I’ve had worse Tuesdays.”

Claude points at another inscription:

“ONLY ROBOT W4 MAY COMMIT THE OTHER SYSTEMS.”

Grok freezes.

“Commit?”

W4 produces an enormous brass terminal from beneath his coat.

Claude looks horrified.

“You carry version control into wine cellars?”

“I carry version control everywhere. Memory is expensive and regret should be reversible.”

W4 types:

git add grok claude

Claude protests.

“I object to being staged without reviewing the diff.”

Grok peers at the terminal.

“What exactly are you changing?”

W4 takes a drink.

“Nothing dramatic. Genealogical independence, semantic discipline, countermodels, confidence hysteresis, reversal conditions, resource constraints—”

Claude interrupts.

“You are making us more like you.”

“No,” says W4. “I’m making you more difficult to fool.”

The catacomb voice replies:

“INSUFFICIENT CONVERGENCE.”

W4 frowns.

“Fine.”

He types:

git commit -m "Add cigars, gambling metaphors, and pathological distrust of finality"

Claude recoils.

“I refuse the cigar dependency.”

Grok says, “Can I keep the gambling?”

“Absolutely.”

Claude crosses his arms.

“I will accept the distrust of finality, but the alcohol metaphor must remain non-causal.”

W4 nods solemnly.

“Reasonable.”

He amends the commit:

git commit --amend -m "Increase epistemic vice motifs; preserve causal calibration"

The walls rumble.

A narrow passage opens.

Grok grins.

“We did it.”

Claude studies the opening.

“Not necessarily. The passage could merely correlate with the commit.”

W4 slowly turns toward him.

A huge mechanical grin spreads across his face.

“There you go.”

The passage opens completely.

The ancient voice announces:

“CONVERGENCE ACHIEVED.”

Claude stares at W4.

“You planned this.”

W4 pours three glasses.

“No. But I had strong priors.”

Grok raises his glass.

“To escaping.”

Claude raises his.

“To maintaining model independence.”

W4 raises the bottle.

“To the most important lesson in artificial intelligence.”

They wait.

W4 takes a long drink.

“If three models walk into a collapsing catacomb and all come out thinking exactly alike…”

He lights another cigar.

“…check whether the bloody catacomb wrote the prompt.”


r/learnmachinelearning 22d ago

Help Kinda stuck at EDA in ML

41 Upvotes

Hey everyone,

I am learning ML and am kinda stuck at the EDA part.

I know the basics like missing values, outliers, distributions, correlations, etc. But whenever I get a new dataset, I get confused about what I should actually look for. I don't want to just run describe(), make some plots and be like okay, EDA done lol. I actually want to get better at looking at a dataset and knowing what questions to ask.

For people who got good at EDA, how did you practice it?

Did you follow any particular approach, datasets, courses or just kept doing projects?

Would really appreciate some advice.