A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.Awesome Artificial Intelligence A curated collection of must-use, actively maintained resources for building and shipping AI systems. Focus: AI engineering (RAG, agents, evals, guardrails, deploy) plus the best books, guides, papers, and a carefully selected set of tools. 📚 Learn Deep, durable knowledge — still valuable five years from now. Books Modern & Practical — Scalable, maintainable ML pipelines (C... Read more ›
Most software engineering metrics were built for human-paced development. Here’s which ones AI has broken, and the three still worth trusting. Read more ›
Whether you want to know how the brain works, how to learn better, understand human behavior and even brain injury and pathologies, we’ve got you covered. The post appeared first on . Read more ›
This is Day 7 of building a neural network from scratch. Yesterday we ran a whole neural network by hand and got a tidy answer: a 57%… Read more ›
If your team is using Bamboo, you’ve probably seen the news: Bamboo Data Center is being retired as part of Atlassian’s broader Data Center transition strategy. Support will continue for several years, but many teams have already started thinking about the next step. Whether you’re considering Bamboo Cloud or moving to an entirely new CI/CD […] Read more ›
Chemistry isn't something you find. It's something you create through simple neuroscience-backed communication strategies. Read more ›
Accurate prediction of water-quality indicators remains challenging in small tabular environmental datasets because physicochemical variables can exhibit nonlinear interdependencies and modern deep-learning models are sensitive to hyperparameter configuration. In this study, the supervised regression task is defined as predicting dissolved oxygen (mg/L) from the remaining measured physicochemical variables using a modest public dataset of 200 samples. To address this task, the Automatic Featu... Read more ›
处理 Red 状态的 ES 索引 GET _cat/shards?v=true&h=index,shard,prirep,state,node,unassigned.reason&s=state 1 2 ops-pod-loggie-2026.06.11 0 p UNASSIGNED NODE_LEFT ops-pod-loggie-2026.06.11 0 r UNASSIGNED ALLOCATION_FAILED 尝试重新分配 1 POST _cluster/reroute?retry_failed=true 或者直接删除 1 DELETE ops-pod-loggie-2026.06.11 Read more ›
Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Method: We designed a multi-agent peer-reviewed reasoning method in which multiple LLM agents independently generate chain-of-thought reasoning with candidate answers, then act as peer reviewers to evaluate each other's reasoning for factual correctness and logical soundness. The highest-rated reasoning chai... Read more ›
A machine learning-powered simulation is giving researchers a new window into the processes that create some of the universe’s heaviest elements. Where do the gold in jewelry, the uranium in nuclear fuel, and many of the universe’s heaviest elements come from? Scientists believe they are forged in some of the most violent events in the [...] Read more ›
Respect and trust are DevOps engineering disciplines. They shape flow, quality, security, reliability, and adaptation. Read more ›
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field. Read more ›
When you have been programming in .NET for some time, you must be aware of the change already. The use of AI technology is gradually becoming common practice in development, doing all the tedious jobs to allow developers to do their real thinking jobs. The use of AI technology, however, is not limited to what is done by GitHub Copilot. There is much more to the generative AI in relation to .NET development than that. Introduction to Generative AI in Development Generative AI Overview A genera... Read more ›
From pretraining to RLHF/GRPO — every algorithm hand-written in pure PyTorch. Read more ›
The rapid evolution of Quantum Development Kits (QDKs) introduces a specific form of technical debt that compromises code maintainability and hinders software reuse. In the specialized domain of Quantum Software Engineering (QSE), this challenge is intensified by the scarcity of high-quality training data and the high volatility of emerging frameworks, which often lead general-purpose Large Language Models (LLMs) to produce unreliable or halluci... Read more ›
In this Journal Club, M. Jerome Beetz highlights a study published 2021 that examined hippocampal representation of large environments in flying bats. Read more ›
Intel and AMD have released the official specification for AI Compute Extensions, or ACE, a standardized instruction set for future x86 processors. Developed through the x86 Ecosystem Advisory Group, these extensions aim to accelerate matrix multiplication and machine learning workloads directly on the CPU. By establishing a common technical standard, the two rivals intend to prevent market fragmentation and provide a consistent target for software developers. <a href=" Read more ›
With the rapid adoption of LLM-assisted coding, the need to manage the technical debt these systems introduce has become urgent. In this paper, we conduct a multivocal literature review of 104 sources (31 formal, 73 grey) to examine how LLM-assisted development contributes to technical debt and what strategies, metrics, and benchmarks exist to mitigate it. We find that LLMs often amplify traditional forms of technical debt, particularly code, de... Read more ›
A practical guide to machine learning, neural networks, NLP, large language models, prompt engineering, and agentic AI — and how they… Read more ›