Coding
A Coding Guide to Implement Zarr for Large-Scale Data: Chunking, Compression, Indexing, and Visualization Techniques
In this tutorial, we take a deep dive into the capabilities of Zarr, a library designed for efficient storage & manipulation of large, multidimensional arrays. We begin by exploring the basics, creating arrays, setting chunking strategies, and modifying values directly on disk. From there, we expand into more advanced operations such as experimenting with chunk…
Vibe Coding Through the Berghain Challenge – Log – nibzard
# Part 1: The Billboard That Started Everything Listen Labs just pulled off a solid growth play. Picture this: You’re driving through San Francisco and spot a cryptic billboard. Five numbers. No explanation. Just: That’s it. SF billboards are basically expensive Reddit posts hoping to go viral online. And this one worked. Someone cracked it…
A Coding Implementation of an Advanced Tool-Using AI Agent with Semantic Kernel and Gemini
In this tutorial, we build an advanced AI agent using Semantic Kernel combined with Google’s Gemini free model, and we run it seamlessly on Google Colab. We start by wiring Semantic Kernel plugins as tools, like web search, math evaluation, file I/O, and note-taking, and then let Gemini orchestrate them through structured JSON outputs. We…
Vibe Coding is Shoot-and-Forget Coding – AI Blog
Vibe coding, the trend of using AI to generate code by describing what you want in natural language, has been hailed as the future of programming. Coined by AI pioneer Andrej Karpathy in early 2025, the term refers to “fully giv[ing] in to the vibes, embrac[ing] exponentials, and forget[ting] that the code even exists”. In…
Vibe coding explained: Plus 7 AI coding tools to get started right away
Back in February 2025, former OpenAI co-founder Andrej Karpathy posted a tweet that would start a revolution in coding and software development. Yes, we’re talking about “vibe coding,” a term that has since spread across the internet like wildfire. Even OpenAI’s latest GPT-5 model aims to please the vibe coding fanatics. So, what is this…
A Coding Guide to Build an Intelligent Conversational AI Agent with Agent Memory Using Cognee and Free Hugging Face Models
In this tutorial, we delve into building an advanced AI agent with agent memory using Cognee and Hugging Face models, utilizing entirely free, open-source tools that work seamlessly in Google Colab and other notebook. We configure Cognee for memory storage and retrieval, integrate a lightweight conversational model for generating responses, and bring it all together…
A Coding Guide to Build a Functional Data Analysis Workflow Using Lilac for Transforming, Filtering, and Exporting Structured Insights
In this tutorial, we demonstrate a fully functional and modular data analysis pipeline using the Lilac library, without relying on signal processing. It combines Lilac’s dataset management capabilities with Python’s functional programming paradigm to create a clean, extensible workflow. From setting up a project and generating realistic sample data to extracting insights and exporting filtered…
Run Multiple AI Coding Agents in Parallel with Container-Use from Dagger
In AI-driven development, coding agents have become indispensable collaborators. These autonomous or semi-autonomous tools can write, test, and refactor code, dramatically accelerating development cycles. However, as the number of agents working on a single codebase grows, so do the challenges: dependency conflicts, state leakage between agents, and the difficulty of tracking each agent’s actions. The…
A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini
In this tutorial, we demonstrate how to build a multi-step, intelligent query-handling agent using LangGraph and Gemini 1.5 Flash. The core idea is to structure AI reasoning as a stateful workflow, where an incoming query is passed through a series of purposeful nodes: routing, analysis, research, response generation, and validation. Each node operates as a…
A Coding Guide Implementing ScrapeGraph and Gemini AI for an Automated, Scalable, Insight-Driven Competitive Intelligence and Market Analysis Workflow
In this tutorial, we demonstrate how to leverage ScrapeGraph’s powerful scraping tools in combination with Gemini AI to automate the collection, parsing, and analysis of competitor information. By using ScrapeGraph’s SmartScraperTool and MarkdownifyTool, users can extract detailed insights from product offerings, pricing strategies, technology stacks, and market presence directly from competitor websites. The tutorial then…
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