There's a moment in almost every RAG project where someone asks the question that decides your next two years of ops work: "Do we actually need a vector database, or can Postgres just do this?" It's a better question than it sounds, because the honest answer isn't "use Pinecone" or "use Postgres." It's "it depends on numbers you probably haven't measured yet": how many vectors, how aggressively you filter, how much you care about the absolute ceiling of queries per second. Most teams pick bas... Read more ›
Zhipu says GLM-5.2 has reached the top tier of coding models by pairing a 1M-token context window with long-horizon engineering workflows. The more interesting question is whether it holds up outside the leaderboard. Read more ›
Why an M4 Max Mac Studio with 128GB of unified memory is the best local LLM workstation for developers - running Qwen3, Ling and Gemma with llama.cpp and MLX. Read more ›
The General Process of How a Neural Network Processes Information in Its Forward Pass Phase Read more ›
From pretraining to RLHF/GRPO — every algorithm hand-written in pure PyTorch. Read more ›
High-performance, memory-safe Rust implementation of Hugging Face Transformers. TrustformeRS brings the power of transformer models to the Rust ecosystem with zero-cost abstractions, fearless concu... Read more ›
Exploring three stages of Q-learning, a classical machine learning concept: epsilon-constant, decaying epsilon-greedy, and emergent… Read more ›
Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patterns. While deep learning models such as LSTMs and Transformers show promising performance, most existing studies focus on direct absolute load prediction without explicitly addressing target non-stationarity. Motivated... Read more ›
How I combined collaborative filtering and content-based filtering to recommend movies with 63% precision — a step-by-step walkthrough Read more ›
Explore how Retrieval Augmented Generation (RAG) is revolutionizing the precision of responses from large language models (LLMs) such as… Read more ›
SQL is the industry standard for high-performance structured data analysis. However, expressing complex procedural logic, scientific computations, advanced string manipulations, or machine learning workflows in pure SQL can be highly challenging, if not impossible. That kind of work is better done with Python. Data practitioners often take on additional infrastructure management tasks — maintaining custom images and containers, and working with additional compute services — just to run simple... Read more ›
Neurological disorders (ND) impact a significant number of the population all over the world, affecting the brain, spinal cord, and peripheral nerves. These disorders are classified as NeuroDegenerative, NeuroBiological, and NeuroDevelopmental disorders, which vary from common to rare disorders. Traditional deep learning methods often fail to generalize efficiently in these settings and are developed for common or single-label disease classification. Also, the insufficient interpretability in... Read more ›
...rapid digital transformation of consumer‑facing businesses as the paramount driver for market expansion. Global e‑commerce sales surpassed US$ 5 trillion in 2023 and are projected to exceed US$ 7 trillion by 2030, creating an unprecedented volume of... Read more ›
Teaching cellular automata to actually do things Read more ›
Why Are Top Companies Quietly Hiring Agentic AI Engineers? Read more ›
From Model Development to Kubernetes Deployment: Lessons from Building a Full MLOps Portfolio Read more ›
A vector database stores data as vectors (embeddings) and finds items by meaning, not exact match. What it is, how similarity search works, how it differs from a normal database, and why RAG and AI search depend on it. Read more ›
Teaching Computers to Train Together: Building a Distributed Training Platform Across Multiple GPUs…
How I built a lightweight federated machine learning system using PyTorch to distribute training across multiple machines Read more ›
If you are going to go to the bother of fine tuning for trivial problems like subject classification then I think you'll find Scikit Learn with a SGDClassifier on 2-grams will do probably just as well and be under 1MB for the trained classifier. Read more ›
ERRATUM: Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics (2025 J. Stat. Mech. 043401), Komatsu, Hisato Read more ›