🎨 Sharing AI experiences, insights, and taste 🗺️ Code & Design & KOL
The article explains how to choose suitable large AI models for product use by focusing on three key aspects—model inference, post‑training optimization techniques such as quantization, pruning and distillation, and the role of GPUs, including their kernels, software stack, and computing platforms.
This article walks through constructing an agentic RAG pipeline that combines LangChain, LangGraph, Google Gemini embeddings, and Qdrant vector storage to enable automatic query rewriting, relevance grading, and concise answers to blog‑post questions via a Streamlit UI.
This article defines Context Engineering (CE), contrasts it with traditional Prompt Engineering, explains its components and benefits for Agentic AI, outlines practical steps and best‑practice techniques—including KV‑cache design, masking, file‑system context, and attention manipulation—while also discussing evaluation challenges and future outlook.
The article distills Sam Altman's Startup Playbook, outlining a methodology that emphasizes great ideas, strong teams, outstanding products, and relentless execution, while also describing the founder mindset and illustrating each point with real-world cases such as Airbnb, Facebook, and Pinterest.
The article explains Retrieval‑Augmented Generation (RAG), detailing its three‑step workflow—retrieval, augmentation, generation—along with architecture components, data indexing, vector‑database choices, prompt construction, and challenges such as noise, token limits, and model accuracy, illustrating how RAG enables LLMs to fetch relevant quotes before answering.
The article explains the three‑stage RLHF pipeline—pretraining, supervised fine‑tuning, and reward‑model reinforcement—and draws a detailed analogy to human life phases, showing how early data, personal values, and continual feedback act as a reward function that can be consciously re‑engineered.
Multimodal AI expands beyond single‑modal text, image, or audio by integrating diverse data modes—such as audio‑spectrograms, video, tables, and even tactile formats—enabling models like CLIP (2021) and Flamingo (2022) to achieve superior vision‑language understanding, generation, and retrieval capabilities.
This tutorial walks through why Multi‑Agent Systems, Anthropic's MCP and Google's A2A protocol are crucial for Agentic AI, then demonstrates how to set up two MCP servers, create a hierarchical trio of agents, enable inter‑agent coordination via A2A, and run the complete pipeline with detailed Python code and deployment tips.
The article reviews the emerging AI Agent market in 2025, offering hands‑on evaluations of Manus, Flowith, Lovart and Genspark, analyzes their system prompts and sandbox architectures, compares features, usability and value using a product‑value formula, and discusses the challenges and future prospects of agentic AI.
This article explains why prompt engineering is essential for effective LLM use, outlines core PE formulas and design languages, compares basic and advanced prompting techniques with Retrieval‑Augmented Generation and fine‑tuning, and discusses evaluation strategies and the future role of prompts.
