The Mathematics Search Engine

26+ min ago (1190+ words) Abstract Environmental pollution has become a global problem, and accurate analysis of environmental data and prediction of pollution trends are of great significance for environmental management and pollution control. Environmental data has the characteristics of multi - source, heterogeneous, and large time - space span, which brings challenges to data processing and analysis. This paper studies the application of Python in environmental data analysis and pollution prediction. First, use Python's Pandas, GeoPandas, and Xarray libraries to process multi - source environmental data, including air quality data, water quality data, and meteorological data, realizing data cleaning, integration, and spatial - temporal analysis. Then, build a pollution prediction model based on Python's TensorFlow framework, which combines the long short - term memory (LSTM) network and the attention mechanism to capture the temporal and spatial correlation of pollution data. Finally, verify the model on the air quality data…...

34+ min ago (245+ words) " Table of Contents What is RAG and Why It Matters Instead of relying purely on the model's training data, RAG: " Perfect for chatbots, internal knowledge bases, support tools, and search assistants. High-Level Architecture of a RAG System User Query " Embedding Model " FAISS Vector Search " Relevant Chunks " LLM Prompt Augmentation " Final Answer Key idea: Retrieve first, then generate. Tech Stack & Prerequisites Core Stack " Tip: Use faiss-gpu if you're running on CUDA for large-scale datasets. Step 2: Preparing and Chunking Documents Step 3: Generating Embeddings " Fast " Lightweight " Production-friendly Step 4: Storing Vectors in FAISS python import faiss import numpy as np Step 5: Retrieving Relevant Context " This step is the heart of RAG. Step 6: Augmenting Prompts & Querying the LLM def generate_answer(query): context = retrieve_context(query) prompt = f""" Use the following context to answer the question: " Internal documentation assistant " Customer support chatbot " Codebase Q&A system " Legal or medical…...

35+ min ago (562+ words) A practical way to explain the data engineering process is to walk through a realistic dataset end to end. This blog-style write'up treats the journey from raw data to analytics'ready tables from a data engineer's point of view. From a data engineering perspective, this phase also includes non'functional requirements. These cover data latency (near real'time vs hourly), expected volume, quality SLAs, and regulatory constraints such as retention and PII handling. Clear requirements drive architectural decisions like batch vs streaming, storage layers, and orchestration tools. Consider three core datasets for this project: These sources are messy in practice. Event data may arrive late or out of order, mobile apps may send malformed payloads, and operational teams might change schemas without notice. The data engineer's job is to create a resilient ingestion layer that can tolerate these realities while preserving lineage and…...

40+ min ago (91+ words) Who's shipping vs who's just talking? Here's this week's AI industry intelligence. No high hype alerts this week The AI industry continues to evolve rapidly. Companies that ship consistently rise in our rankings, while those focused on hype alone get flagged by our Hype Gap detector. Methodology: Our leaderboard tracks real product releases, funding events, partnerships, and market traction - not just PR and social media buzz. Want real-time updates? Check out the live leaderboard at sectorhq.co Track specific companies and get instant alerts when they move in the rankings....

41+ min ago (397+ words) This change is a major opportunity for students and professionals. AI-powered problem solving can automate complex calculations, provide instant support, and personalize learning in ways never before possible. This tutorial will discuss the functionality of AI agents, their benefit compared to the classical approach, and the future of the potentially exciting technology. Since time immemorial, the ways people worked out math problems were the same: a pencil, paper, a textbook, and perhaps a calculator. In case you become stuck, you could consult a teacher, ask a classmate or look online and find a similar issue. AI agents such as an AI math problem solver give a more interactive and encouraging experience. These tools can also help you in the entire process and not merely provide you with a final answer. Giving an AI agent a math problem, it will analyse…...

41+ min ago (686+ words) In this blog I want to share a reflection of Co-Learning Mantle. This co-learning is held by Hackquest, a platform to learn about Web3. The participant expected to learn and build a web3 project using Mantle Network. Mantle is a L2 ETH Mantle is a Web3 ecosystem built on Ethereum that positions itself as the "Liquidity Chain of the Future," combining modular blockchain design, zero-knowledge (ZK) proofs, and a large treasury governed by token holders. In the first day there is Town Hall for boarding toward Co-Learning Mantle, we learn about Hackquest platform, what is Mantle and the difference between L1 and L2 ETH network. Layer 1 Eth (L1) is mainnet of ETH networks that base of the blockchain itself, it run the smart-contract directly, provides security through Proof of Stake consensus, and tores all transaction data permanently. But has high gas fees during congestion, Scalability bottlenecks due…...

53+ min ago (699+ words) The brain's timing during speech comprehension matches the stepwise layers of modern, large language models (LLMs), according to a new study. The evidence comes from direct brain recordings collected while people listened to a single, 30-minute story. The brain recordings were analyzed alongside model representations from systems like GPT 2 and Llama 2. The work observed later peaks in language regions when comparing deeper model layers, a pattern that suggests more integrated processing at those points. Collaborators in Jerusalem, Princeton, and industry labs worked on the study, which concentrated on certain brain regions, including Broca's area and the superior temporal gyrus. Researchers used electrocorticography, which involves electrical recording from thin grids placed on the cortex during clinical monitoring. This technology captures fast activity linked to local neural firing." There is longstanding evidence that high-frequency power in these recordings tracks nearby neuronal activity....

1+ hour, 10+ min ago (138+ words) On The Vergecast: What's coming in 2026, in increasing order of hotness. All the way up to Sexy Siri. Here's a thought: what if the next-generation Siri is awesome? Not just awesome for setting timers and dictating text messages (though that would be nice), but so awesome and fun to talk to that people actually start falling in love with their iPhones. We may not be prepared for what happens next. On this episode of The Vergecast, Sexy Siri is just one of the topics at hand. Nilay and David are joined by Joanna Stern, senior tech columnist at The Wall Street Journal, to talk through their most mild, medium, and spicy predictions for the year to come. Subscribe: Spotify | Apple Podcasts | Overcast | Pocket Casts | More See All by David Pierce...

1+ hour, 28+ min ago (667+ words) In the age of AI, the quality of our output is directly proportional to the quality of our input. This concept, often called 'Garbage In, Garbage Out', is the cornerstone of effective interaction with Large Language Models (LLMs). For automation testers, mastering prompt engineering is not just a nice-to-have skill; it's a superpower that can 10x our productivity. This isn't about asking ChatGPT to 'write a test'. It's about architecting your prompts so precisely that the AI becomes an extension of your engineering mind " generating production-ready code, uncovering edge cases you missed, and debugging failures faster than you could manually. A vague request like 'Write a test' will yield a generic result. To get production-ready code, our prompt needs structure. Think of it as CTCO: Context, Task, Constraints, and Output. This framework is the difference between getting 'something that works' and…...

1+ hour, 29+ min ago (106+ words) This year we-Aarne Talman (AMD), Jussi Karlgren (AMD), and myself (TomTom -> Aiven)-have been teaching the LLM and Gen AI course for the second year in a row, for students of different departments of the University of Helsinki. In my 2-week part, I focused again on RAG (Retrieval Augmented Generation) and Applications. One change since 2024 was the addition of Agentic RAG, where the following basic RAG diagram: transforms into an RAG with AI Agents, where each AI Agent has these components: Components of an AI Agent Comparing vanilla RAG and Agentic RAG: How Vanilla RAG compares to Agentic RAG (courtesy: Weaviate blog)...