AskOracle Blog

Integrating an AI chatbot into a website is no longer about building from scratch; it is about orchestrating existing APIs and RAG (Retrieval-Augmented Generation) frameworks. Here is the technical roadmap to deploying a production-grade AI agent. The Tech Stack To minimize overhead and maximize reliability, use this stack: * Brain: OpenAI API (GPT-4o) or Anthropic API (Claude 3.5 Sonnet). * Orchestration: LangChain or Flowise (for visual flow building). * Knowledge Base: Pinecone (Vector Database) for RAG. * Frontend: Custom React widget or a managed service like Voiceflow/Stack AI. * Backend: Vercel Functions or Supabase Edge Functions. Step 1: Knowledge Base and RAG Pipeline You cannot rely on the LLM’s base training. You need RAG to ground the bot in your specific data. Ingestion: Convert your documentation (PDFs, Notion pages, website URLs) into text. Chunking: Split text into 500-character segments with 50-character overlaps. Embedding: Use text-embeddin...

Introduction to Core Web Vitals Core Web Vitals are a set of metrics that measure the performance and user experience of a website. They include Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), and Instantaneous Input Delay (INP). In this guide, we will walk through the steps to measure and improve these metrics using various tools and techniques. Measuring Core Web Vitals with PageSpeed To measure Core Web Vitals, we can use Google PageSpeed Insights. This tool provides a detailed report on the performance of a website, including LCP, CLS, and INP. Open PageSpeed Insights and enter the URL of the website. Click on the "Analyze" button to generate a report. Look for the "Core Web Vitals" section in the report, which provides the metrics for LCP, CLS, and INP. Caching Caching is a technique that stores frequently-used resources in memory or on disk, reducing the need to reload them from the server. This can significantly improve the performanc...

Why Bots Drain Deposits Watch on YouTube: https://www.youtube.com/watch?v=mUQAjLDRItA NEXUS Algo — we teach building trading bots & AI agents. Originally posted at https://www.youtube.com/watch?v=mUQAjLDRItA

Ваш торговый алгоритм – это ребенок, а не робот. Ему нужна забота. Помню, как в начале пути, когда я только-только углублялся в автоматизированный трейдинг, голова кружилась от возможностей. Пишешь код, который сам торгует, пока ты спишь! Это же почти магия, верно? Инновации, новые стратегии, хитрые фильтры, машинное обучение – вот что казалось главным. Мы часами спорили с коллегами, какой алгоритм принесет "альфу", как его оптимизировать, какие индикаторы лучше. Казалось, стоит лишь найти ту самую, уникальную идею, обернуть ее в красивый код, и всё – золотой дождь. Как же сильно я ошибался. Однажды, это было в пятницу вечером, я запустил новый, как мне тогда казалось, гениальный алгоритм. Он показывал отличные результаты на бэктестах, использовал несколько источников данных, хитро обрабатывал новостной фон. Я был уверен, что поймал Бога за бороду. Ушел домой с легким сердцем, предвкушая прибыльные выходные. Утром в субботу проснулся, открыл ноутбук, чтобы проверить... И ...

Stop Chasing the Perfect Backtest: Why Your Trading Bot Is Failing in the Wild It was November 2021. I had just finished building what I was convinced was the holy grail of trading systems. I had run the backtests over a four-year dataset. The win rate was 84%. The Sharpe ratio was so high I felt like a genius. I felt untouchable. I funded the account with $42,000 of my own cash. Three weeks later, I was down $18,000. It wasn't a market crash that killed my balance. It was a stupid, mundane API timeout on a mid-cap altcoin during a sudden liquidity squeeze. The exchange API lagged, my error handler choked, and the bot froze mid-trade. It left a leveraged long position open with no stop-loss active while the market cascaded. I watched the screen in absolute horror as my dashboard spun in an endless loop, completely blind to the liquidation happening in real-time. The math was beautiful. The reality was brutal. That was the day I stopped trying to build the "perfect...