Explore and learn with me
This platform is a clean space to explore technology, understand real concepts, and learn through practice. Everything here is built to help you improve step by step.
Learn
Take tests
Ask questions
Find code examples
Python basics
Databases & authentication
Automations
Prompt engineering
Web development basics
Real projects
Today in Tech
Why I Built a 3,200-Line Python Pipeline to Generate Synthetic Financial Data From Math -- Not AI
Fresh AI, programming and Big Tech news. Scrollable decks, neon accents — maximum focus with minimal scrolling.
Useful Websites
# https://same.new/ # https://stackoverflow.com/ # https://21st.dev/community/components # https://chathub.gg/ # https://www.enki.com/ # https://www.n2yo.com/api/ # https://www.data.com/ # https://devdocs.io/ # https://roadmap.sh/ # https://regex101.com/ # https://jsoncrack.com/ # https://www.postman.com/explore # https://replit.com/ # https://codesandbox.io/ # https://github1s.com/ # https://freecodecamp.org/ # https://overapi.com/ # https://cssbattle.dev/ # https://ui.shadcn.com/ # https://phind.com/ # https://omni.studio/ # https://rapidapi.com/ # https://apipost.com/ # https://metatags.io/ # https://tinywow.com/ # https://jwt.io/ # https://http.cat/ # https://httpstatus.io/ # https://uxdesign.cc/ # https://systemdesign.one/ # https://excalidraw.com/ # https://app.diagrams.net/ # https://deepai.org/
AI — Latest Models & Research
AI
Why I Built a 3,200-Line Python Pipeline to Generate Synthetic Financial Data From Math -- Not AI
Most synthetic data tools feed real data into a neural network. I did the opposite: Pareto distributions first, algebraic constraints second, AI last. Here's why.
DEV Community
AI
Vector Graph RAG: Multi-Hop RAG Without a Graph Database
Standard RAG falls apart when the answer isn't in one chunk. Ask "What side effects should I watch...
DEV Community
AI
I Built an AI Agent That Watches the Market While I Sleep
I have a full-time job and no time to watch the stock market all day. But I still trade — mostly US...
DEV Community
AI
How I Automated a Faceless YouTube Channel That Runs Without Me (Code + Architecture)
I built an automated system that produces and schedules faceless YouTube content with minimal manual...
DEV Community
AI
Five Agent Memory Types in LangGraph: A Deep Code Walkthrough (Part 2)
In Part-1...
DEV Community
AI
Async Web Scraping in Python: httpx + asyncio for 10x Faster Data Collection
Async Web Scraping in Python: httpx + asyncio for 10x Faster Data Collection Synchronous...
DEV Community
AI
Claude Code for testing: write, run, and fix tests without leaving your terminal
Claude Code for testing: write, run, and fix tests without leaving your terminal One of...
DEV Community
AI
ActionParty: Multi-Subject Action Binding in Generative Video Games
Recent advances in video diffusion have enabled the development of "world models" capable of simulating interactive environments. However, these models are largely restricted to single-agent settings, failing to control multiple agents simultaneously in a scene. In this work, we tackle a fundamental issue of action binding in existing video diffusion models, which struggle to associate specific ac
arXiv
AI
Steerable Visual Representations
Pretrained Vision Transformers (ViTs) such as DINOv2 and MAE provide generic image features that can be applied to a variety of downstream tasks such as retrieval, classification, and segmentation. However, such representations tend to focus on the most salient visual cues in the image, with no way to direct them toward less prominent concepts of interest. In contrast, Multimodal LLMs can be guide
arXiv
AI
Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation
Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in generative recommendation. The standard practice initializes these new tokens as the mean of existing vocabulary embeddings, then relies on supervised fine-tuning to learn their representations. We present a systematic analysis of this strategy: through spec
arXiv
AI
Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning
Large Language Models employing Chain-of-Thought reasoning achieve strong performance but suffer from excessive token consumption that inflates inference costs. Existing efficiency methods such as explicit length penalties, difficulty estimators, or multi-stage curricula either degrade reasoning quality or require complex training pipelines. We introduce Batched Contextual Reinforcement, a minimal
arXiv
AI
No Single Best Model for Diversity: Learning a Router for Sample Diversity
When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide range of users. In this paper, we study methods to elicit a comprehensive set of valid responses. To evaluate this, we introduce \textbf{diversity coverage}, a metric that measures the total quality scores assigned to each \textbf{unique} answer in the pr
arXiv