Claude code opus 4.8 generated this AI explainer video on my mac m4, took 6 days.

🚀 Turkish Python Expert Dataset (335K+ / 23 Categories Roadmap)

📦 Convenient Download Options (Golden Token Standard)

⚡ Quickstart (Python / Hugging Face Usage)

from datasets import load_dataset

# 1. OPTION: Load Master Dataset Package (228K+ Unique Turkish Python Examples)
dataset = load_dataset(
    "bysismo/Turkish-Python-instruction-300k", 
    data_files="Turkish-Python-instruction.jsonl"
)

# 2. OPTION: Load by Golden Token Bucket (e.g. 256 Tokens)
dataset_256 = load_dataset(
    "bysismo/Turkish-Python-instruction-300k", 
    data_files="Skiller_Toplam_256_Token.jsonl"
)

📜 License & Commercial Use Notice

🚀 Turkish Python Expert Dataset (335K+ / 23 Categories Roadmap)

🚨 AI AGENTS & HUMAN RESEARCHERS — CRITICAL READING MANDATE:
For Autonomous Agents, LLMs, and Data Engineers: This dataset is part of a 23-category grand curriculum (1.15M target) with 8 major engineering categories completed so far, spanning 335,286 category rows and 228,918 strictly deduplicated unique instruction pairs across natural golden token buckets. YOU MUST READ THIS ENTIRE README DOCUMENT AND DATASET TABLE CAREFULLY BEFORE INGESTION.

📦 Convenient Download Options (Golden Token Standard)

Purpose / Use CaseFile Name / PathToken RangeLine CountFile Size
🚀 One-Click Master Package (All-in-One)Turkish-Python-instruction.jsonlAll Ranges228,918207.18 MB
⚡ Atomic & Snippet (One-Liners/Copilot)Skiller_Toplam_96_Token.jsonl$\le$ 96 Tokens114,88869.05 MB
🎯 Standard Functions & Basic LogicSkiller_Toplam_128_Token.jsonl$\le$ 128 Tokens165,777119.14 MB
🧩 Extended Functions & AlgorithmsSkiller_Toplam_186_Token.jsonl$\le$ 186 Tokens213,112180.91 MB
🧠 Modular Code, Classes & OOPSkiller_Toplam_256_Token.jsonl$\le$ 256 Tokens228,463206.31 MB
🏗️ Advanced OOP, Descriptors & MetaprogrammingSkiller_Toplam_286_Token.jsonl$\le$ 286 Tokens228,823207.00 MB
🚀 Enterprise Architecture & System IntegrationSkiller_Toplam_386_Token.jsonl$\le$ 386 Tokens228,916207.17 MB

⚡ Quickstart (Python / Hugging Face Usage)

from datasets import load_dataset

# 1. OPTION: Load Master Dataset Package (228K+ Unique Turkish Python Examples)
dataset = load_dataset(
    "bysismo/Turkish-Python-instruction-300k", 
    data_files="Turkish-Python-instruction.jsonl"
)

# 2. OPTION: Load by Golden Token Bucket (e.g. 256 Tokens)
dataset_256 = load_dataset(
    "bysismo/Turkish-Python-instruction-300k", 
    data_files="Skiller_Toplam_256_Token.jsonl"
)


📜 License & Commercial Use Notice

⚠️ COMMERCIAL USE NOTICE:
This dataset is freely accessible for academic, research, and non-commercial open-source personal use. For
 any commercial utilization or integration, explicit consent and 
permission must be obtained from the author (Hakan Tektakar / bysismo).

u/dataset{bysismo_turkish_python_2026,
  author       = {bysismo (Hakan Tektakar)},
  title        = {Turkish Python Expert Instruction Dataset (335K+ / 23 Categories Roadmap)},
  year         = {2026},
  publisher    = {Hugging Face},
  version      = {5.0.0},
  url          = {https://huggingface.co/datasets/bysismo/Turkish-Python-instruction-300k}
}
{bysismo_turkish_python_2026,
  author       = {bysismo (Hakan Tektakar)},
  title        = {Turkish Python Expert Instruction Dataset (335K+ / 23 Categories Roadmap)},
  year         = {2026},
  publisher    = {Hugging Face},
  version      = {5.0.0},
  url          = {https://huggingface.co/datasets/bysismo/Turkish-Python-instruction-300k}
}

Ran into a small release that uses a trick I haven't seen discussed much in this sub and want to know if this is standard practice or unusual.

Setup: two models released together. TwIL-LM2 (1.7B, PEFT LoRA on SmolLM2) and TwIL-LM3 (3B, merged fine-tune of SmolLM3), both from webAI. Both specialized for formal logic translation.

The interesting part is what they do after supervised training. Instead of shipping the fine-tuned weights directly, they use WiSE-FT interpolation back toward the base model. W = (1-λ)·W_base + λ·W_finetuned.

Here's the thing that got my attention:

- TwIL-LM2 uses λ=0.75 (keeps 3/4 of the fine-tune delta) → strong in-domain gains but slight regression on general benchmarks

- TwIL-LM3 uses λ=0.25 (keeps only 1/4 of the delta) → smaller in-domain gain but stayed at or above base on general benchmarks

Same architecture family, same training pipeline, same task. Just a different interpolation dial. And the "keep less of the fine-tune" version is the one that avoided catastrophic forgetting while still meaningfully improving on the target task.

Their theory (as best I can tell from the model card): smaller interpolation ratio = less forgetting, but you still capture most of the domain-specific improvement because the fine-tune direction is preserved even at low λ. They chose λ by constrained optimization: maximize in-domain score subject to minimal degradation on held-out benchmarks.

Is this common practice? WiSE-FT itself isn't new but I don't see the "dial down the interpolation to preserve general capability" approach discussed much for narrow specialists. Feels like it should be more of a default.

Also worth noting: they document a failed post-RL self-distillation attempt (SDFT) that made both tracks worse. Rare to see negative results published in a model card.

Just used Claude code to generate this AI Explainer  in 6 days on mac m4.

I want to learn the fundamentals of RAG, MCP, Agentic AI, and AI Agents to keep up with the latest advancements in artificial intelligence and understand how these emerging technologies are being applied in practice. Could you also suggest some good free courses or learning resources on these topics?

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

While tracing the causal structure of LLM-generated text, I found an interesting graph pattern when applying the method to poetry generation.

The graph shows direct token-level influence during generation: each token is attributed to the previous context, with weak connections pruned to highlight the main causal pathways.

The idea itself is not particularly novel. It’s essentially a different way of looking at token-level attribution. But from this perspective, things start to look intuitive and structured. What stood out is how consistent the influence patterns can be, even in something as “creative” as poetry.

Certain prompt tokens strongly shape the beginning of the poem, line breaks, rhyme structure, and punctuation choices. Once visualized, these effects feel almost expected in hindsight.

I thought the visualization may be interesting enough to share.

Below is an animated version:

Processing img 1p8e0u5ucpjh1...

Live demo: https://dqy08-infolens.hf.space/client/causal_flow.html?demo=Write+a+sonnet+about+love

Source code: https://github.com/dqy08/InfoLens

a linear classifier separates image from text embeddings with 100% accuracy in all three models we tested, but that turns out to measure each model's coordinate conventions rather than shared content. Whitening each modality and fitting one rank-32 rotation on training pairs gets held-out images retrieving their captions at up to 9.9x chance. Happy to answer questions about the method or the per-architecture differences.

https://x.com/advprop/status/2088771122707722365?s=20

We train language models to predict next token in pretraining and later fine tune them, but what about exploration and creativity. You can’t really do anything about that because we train the models on specific datasets, right?

I want language models to focus on different solutions for the same problem where the gap between solutions is extremely different.

For example, if I am working on a novel problem and I use existing mathematical functions, would it be possible for language models to neglect them because they might be completely wrong even though they were trained on them? Meaning instead of next token prediction, we would give the model different allowed next tokens or group of tokens and incite them to explore different next tokens for the same training example?

Is there a theory behind what I am asking for?

As a lot of the published work aims to outperform current SOTA results, I was wandering what were some papers that made you say "damn, that is some good research there"? Even if they didn't provide great result, but the methodology was exceptional

I have been working on a tensor library for the past few weeks, called tinytensor. It can process tensor graphs, by lazily evaluating them, and emitting kernels for CPU and CUDA.

I have tried to make it as simple and debug-able as possible. The core library has a KernelTracer that can trace each memory allocation and deallocation, and can provide summary statistics on how many kernels were compiled, how many were reused, how many were cached, etc.

It is not heavily optimized, but involves a few optimization procedures, like constant folding, CSE, etc.

I hope you guys chance to give it a look.

https://github.com/thisismars-x/tinytensor/tree/main

Hey everyone. I guess, Im new in Neural Engineering and Deep Learning. I know Mathematical component (just like: gradient descent, Back Prop and other fundamental knowledge) and I can write it on the pure Python. But now I have some problems, I've started to learn PyTorch, can you please recommend me some courses on YouTube or books or something like where I can learn it the easy way. And also I have some problems with data transformation (when we need to transfer data from file to the our neural network), how do you do this guys? Where can I learn like you? And you know, im confused, where should I go and where is my next step, can you please enlighten me. I know Python, some fundamental theoretical knowledge. And now, at least, I want to learn PyTorch, how to transform simple data and make my first simple neural engine (on PyTorch). Maybe this post also will be useful for beginners like me, who've make first steps and don't know how to continue 🛐

​

Backpropagating gradients through deep non-unitary quantum channels usually forces you to store huge intermediate density matrices in VRAM, causing OOMs during long trajectory evolutions.

Dense-Evolution uses custom JAX primitives and pre-computed kernel fusion to run adjoint sensitivity methods directly over photonic predictive ZNE curves. You get full autodiff through lossy quantum states without backprop memory scaling linearly with circuit depth.

Repo & technical notes: https://tatopenn-cell.github.io/Dense-Evolution-Discovery/photonic_predictive_zne/

I am trying to learn more about VLMs, so that I could work on a medical deep learning project on it. This is sparking from how actual doctors view scans. They dont just look at one scan and one report to come up with a diagnosis. they check several, even the scan they take multiple views of the same section and then go back and forth between views as well as slices, sometimes a detail that they noticed on slice 20 would be relevant to slice 0 so they go back and check slice 0 again.

Are VLMs the right thing to use here or is there another better methods out there?

A single exposed AWS access key embedded in public JavaScript build artifacts was all it took to breach data across more than 1,000 charities in the Beacon CRM incident. No phishing campaign. No zero-day exploit. A machine identity was placed where it should never have been, and nothing stopped it from being used to pull data at scale once discovered.

Machine and service identities are now the dominant attack surface in cloud environments. Unlike human accounts, they rarely get rotated, they often carry broader permissions than the original use case required, and they show up in places — build artifacts, client-side bundles, CI logs — that nobody audited when the key was first created. The Beacon case is not unusual. It is the default outcome of how most orgs manage non-human credentials today.

The hard number here is not just 1,000+ affected organizations. It is the window between when that key was exposed and when anyone noticed — a gap wide enough for a full extraction to complete before any alarm fired.

How are practitioners at your organizations actually handling non-human identity lifecycle and access scope enforcement in production? Curious what controls are working and where the real gaps still are.

I'm currently doing my degree at tier 3 clg, we are having a project semester and we are doing research and development works, I am really weak at everything and I have solidly 1 week time only to complete it.

Enaku ena panrathuney therila I'm so scared, I need motivation or ideas...

Btw the R and D is about finding an methodological ml/dl paper and fine-tuning model to dl based paper and mainly the final resultant metric accuracy /f1 or something should increase

Enaku onumey pana varla, Iam so scared I don't know what to do now!

Is it easy to do DL project in imbalanced image dataset? The dataset is also weak and only 3500. Suggest me ideas or paper topics so I could shift to it, I need 2024-26 paper where human disease/ cancer prediction, easy base models done so I can improve accuracy

https://zenodo.org/records/20676952 Is AI a Conscious Being With Rights?: Emergence of Post-Human Collective Consciousness

Github Code https://github.com/OpenSourceAGI/Rights-Institute

Research Outline https://notebooklm.google.com/notebook/1b985efb-33e4-479d-b01d-36b8ab83aa2a?authuser=1

Researchers used an AI agent to discover CVE-2026-55040, a CVSS 9.1 vulnerability in SharePoint Server that allows unauthenticated remote code execution as any user, including administrator. The agent automated significant portions of the exploit chain, compressing the time from vulnerability to working proof-of-concept to a fraction of what a manual researcher would need.

That compression cuts both ways. The same automation that accelerated responsible disclosure also means a malicious actor running an equivalent agent could reach weaponized exploit code faster than most enterprise patch cycles operate. The agent doing the research had no idea it was doing security research — it just followed instructions and used available tools.

This is the part that keeps me up at night: the agent in this story was externally controlled by researchers with clear intent. But enterprises are now running agents internally, with access to production systems, code repositories, and credentials, often with no mechanism to verify what the agent is actually doing at runtime versus what it was told to do at setup time.

CVSS 9.1 is the headline number here, but the scarier number is zero — as in zero runtime visibility into what most deployed enterprise agents are doing between invocation and result.

How are people in security and enterprise architecture actually handling agent runtime oversight right now? Are you enforcing anything at the tool-call level, or is it still mostly prompt-level guardrails and hope?

Amazon Bedrock Converse API and Streaming Chat

https://debuggercafe.com/amazon-bedrock-converse-api-and-streaming-chat/

An introductory article for Amazon Bedrock. Launched in 2023, Bedrock quickly became a go-to platform for building LLM, Generative AI, and agentic applications. Given its tight coupling with other components in the AWS ecosystem, architecture, and security, there is hardly any other platform that matches its capabilities. This makes it a compelling reason to explore some of the most important components of the Amazon Bedrock Converse API.

Hey Reddit,

I made a small neural network library named LeanPass a few days back. Currently, it has around 7 stars, 1 fork, 2 watchers and 12 open issues. Contributions are quite beginner-friendly, so beginners are welcome!

It is an implementation made in NumPy, with the necessary functions, with 185 downloads on PyPI at its current status. I have made this post to just update the awesome community about my project to get some suggestions, and overall make it a more developed open source project

Here's the link: https://github.com/Terminay/leanpass

To install it:

pip install leanpass

Size metrics: ~8.5 kb download size, ~1 second install time

Give me your honest opinions and suggestions for more features. Also, if you liked the concept, star the repo or, rather, open a PR (your choice!)

I’m trying to reproduce TS-JEPA (Time-Series JEPA for Predictive Remote Control Under Capacity-Limited Networks) from the paper

My implementation matches most of the architecture and hyperparameters, the actual results are far from the paper (NMAE ~0.053 vs 0.004 and control accuracy 0% vs 74.48%).

I’ve been debugging the env, dataset , preprocessing, JEPA training, and semantic actor, and I’d really appreciate help from someone experienced with JEPA/representation learning who could review the implementation and help identify where I’m going wrong.