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Unlock Your Codebase Intelligence: A Private RAG System for Git Repositories Have you ever found yourself diving deep into a large codebase, trying to understand how different components work together or searching for that specific function you know exists somewhere? What if you could simply ask your code repository questions

In an era where privacy concerns are paramount and AI capabilities are becoming essential, finding the sweet spot between powerful AI assistance and data privacy can be challenging. Enter a fascinating open-source project that bridges this gap: the Joplin-Weaviate-Ollama RAG pipeline with Telegram integration. What is This Project? This innovative

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI systems that can access up-to-date information and cite sources. The promise is elegant: combine the reasoning capabilities of large language models with the freshness and specificity of retrieved documents. But there's a fundamental problem lurking beneath this

For clarity, I’m comparing David Kantor’s model with the better-known Myers-Briggs Type Indicator (often mis-heard as “Bristol Mayers”). 1. From Personality Labels to Observable Moves Point of Comparison Kantor Behavioural Propensities Myers-Briggs (MBTI) Unit of analysis In-the-moment speech act (Move, Follow, Oppose, Bystand) Stable trait (e.g., INFP)

LinkedIn, the social network where everyone’s “thrilled to announce” their latest promotion or “grateful for the opportunity” to work 80-hour weeks, might seem like a gleaming showcase of professional success. But if you squint hard enough and read between the lines, those polished posts reveal the dark underbelly of

In late March 2025, Elon Musk announced that his artificial intelligence company, xAI, had acquired his social media platform, X, in an all-stock deal. The transaction valued xAI at $80 billion and X at $33 billion (after accounting for $12 billion in debt). This move, while framed as a synergy