Sharing the latest AI-native technologies and real-world implementations.
The article introduces Loop Engineering as the next paradigm in AI development, explaining how autonomous, self‑sustaining loops replace manual prompting, compares it with Prompt, Agent, and Harness engineering, outlines core loop structures, modes, goal design, and provides practical code‑first guidelines.
Over the past four decades AI has undergone five fundamental shifts—from manual feature engineering, through deep neural networks and prompt engineering, to autonomous agents and finally the Harness era—each redefining core technology, scarce talent, and value creation, with the 2026 Harness era emphasizing system‑level scalability over model size.
This guide walks through installing Python, pip, RobotFramework and its HTTP library, then demonstrates a complete RobotFramework test case that defines variables, creates an HTTP context, handles error scenarios, and validates response status and body content.
The article explains the four main machine‑learning categories—supervised, unsupervised, semi‑supervised, and reinforcement learning—detailing how each approach handles data labeling, typical algorithms, and common application scenarios.
The article outlines the seven‑stage machine‑learning pipeline—from data acquisition and preparation through model selection, training, evaluation, parameter tuning, and deployment—highlighting the critical role of feature engineering, which typically consumes over 80% of project time.
The author reflects on six months of learning machine learning, explains that the core techniques are simple while real difficulty lies in applying them to specific problems, and shows how scikit‑learn can quickly address classification, regression, clustering, and dimensionality‑reduction tasks before discussing the need for deeper model and hyper‑parameter tuning.
This article provides a detailed, command‑line walkthrough for installing Java, Elasticsearch, Logstash, and Kibana on Ubuntu, configuring each component with example configuration files, and verifying the ELK stack works by sending test data and querying the REST API.
This article explains how to use netcat (nc) on Linux for TCP/UDP communication, covering command syntax, port scanning, banner grabbing, chat servers, file transfers, directory compression, encrypted streams, cloning devices, remote shells, and advanced piping techniques, all with concrete command examples.
This guide walks through curl’s syntax and options for fetching URLs, saving pages, handling cookies and headers, bypassing SSL verification, using proxies, performing POST requests, looping downloads, and FTP transfers, all illustrated with concrete command examples.
This article provides a comprehensive guide to using tcpdump on Linux, covering command syntax, host and port filters, protocol selection, common and advanced expression syntax, TCP flag and fragment matching, and examples for capturing HTTP, SMTP, and SSH traffic.
