Coding
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…
A Coding Guide for Building a Self-Improving AI Agent Using Google’s Gemini API with Intelligent Adaptation Features
In this tutorial, we will explore how to create a sophisticated Self-Improving AI Agent using Google’s cutting-edge Gemini API. This self-improving agent demonstrates autonomous problem-solving, dynamically evaluates performance, learns from successes and failures, and iteratively enhances its capabilities through reflective analysis and self-modification. The tutorial walks through structured code implementation, detailing mechanisms for memory management,…
Anthropic’s new AI model resorted to blackmail during testing, but it’s also really good at coding
So endeth the never-ending week of AI keynotes. What started with Microsoft Build, continued with Google I/O, and ended with Anthropic Code with Claude, plus a big hardware interruption from OpenAI, the week has finally come to a close. AI announcements from the developer conferences jockeyed for news dominance this week, but OpenAI managed to…
A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization
Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA (Low-Rank Adaptation). In this tutorial, we walk through a practical implementation…
A Coding Guide to Asynchronous Web Data Extraction Using Crawl4AI: An Open-Source Web Crawling and Scraping Toolkit Designed for LLM Workflows
In this tutorial, we demonstrate how to harness Crawl4AI, a modern, Python‑based web crawling toolkit, to extract structured data from web pages directly within Google Colab. Leveraging the power of asyncio for asynchronous I/O, httpx for HTTP requests, and Crawl4AI’s built‑in AsyncHTTPCrawlerStrategy, we bypass the overhead of headless browsers while still parsing complex HTML via…
A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow
In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and…
A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs
In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision of model parameters, typically from 32-bit floating point values to lower bit-width representations, thus yielding smaller models that can run faster on hardware with limited resources. This tutorial introduces…
“Vibe Coding” vs Reality
Published Mar 19, 2025 – 11 min read – Text Only There’s a trend on social media where many repeat Andrej Karpathy’s words (archived): “give in to the vibes, embrace exponentials, and forget that the code even exists.” This belief — like many flawed takes humanity holds — comes from laziness, inexperience, and self-deluding imagination….
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