Agentic AI IDE for Coding

PLUS: Fine-tune Qwen 2.5 Coder for free, o1-like reasoning with any LLM

AuthorAuthor

Shubham Saboo & Gargi Gupta
November 14, 2024

Today’s top AI Highlights:

  1. The first agentic AI IDE that writes, edits, and runs your code

  2. Opensource orchestration framework for building LLM apps with RAG and multiple AI agents in Python

  3. Fine-tune Qwen 2.5 Coder models in Colab absolutely free

  4. Apply OpenAI o1-like reasoning with any other LLM

  5. Convert wireframes into functional React apps using Llama 3.2 vision

& so much more!

Read time: 3 mins

AI Tutorials

Running a fully local RAG (Retrieval-Augmented Generation) agent without internet access is a powerful setup, allowing complete control over data, low-latency response, and ensuring privacy.

Building a local RAG system opens up possibilities for secure applications where online connections are not an option. In this tutorial, you’ll learn to create a local RAG agent using Llama 3.2 3B via Ollama for text generation, combined with Qdrant as the vector database for fast document retrieval. 

We share hands-on tutorials like this 2-3 times a week, designed to help you stay ahead in the world of AI. If you're serious about levelling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.

Build a Local RAG Agent with Llama 3.2 and Vector Database

Fully functional RAG agent running locally in less than 20 lines of Python Code (step-by-step instructions)

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Latest Developments

The AI code completion platform Codeium has released the first-ever agentic IDE, the Windsurf Editor, which actively syncs with your coding actions and maintains awareness of your entire codebase. Building on VS Code, the editor introduces Cascade - an AI agent that watches your coding patterns and maintains deep awareness of your codebase structure.

Cascade can independently execute terminal commands, handle multi-file edits, and even continue code changes you've started in the editor. It is available now with a two-week free trial of all premium features.

Key highlights:

  1. Tools Access - Cascade moves beyond basic AI chatbot - it can grep through codebases, manage directory operations, and run terminal commands through natural language. It maintains context of your ongoing development, so you can say "continue the changes I just made" and it understands exactly what you mean.

  2. Intelligent Code Understanding - The Supercomplete feature predicts your next development intent rather than just the next line of code. When you're implementing a pattern, it can proactively set up multi-cursor edits across relevant files - particularly useful for tasks like adding new API endpoints or updating shared components.

  3. Terminal Experience - The integrated AI terminal assistant remembers complex commands and understands your project context. You can ask it to "set up a new deployment pipeline" or "fix merge conflicts," and it will suggest, execute, and iterate on the necessary commands - eliminating those constant searches for the right git syntax or deployment flags.

  4. Production-Scale Architecture - While offering generous free features, the paid tiers integrate with your broader development ecosystem - tickets, documentation, and team communications. It can identify semantically related files even without direct code references, helping you maintain consistency when updating features that span frontend, backend, and configuration files.

  5. Get Started - Download Windsurf Editor from Codeium's website. The editor works out of the box with existing VS Code settings and extensions. New users get immediate access to the editor's core features, with a two-week trial of premium features including advanced Cascade capabilities - no waitlists or rate limits.

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Dynamiq is an opensource framework that specializes in orchestrating RAG and AI agents. It helps you build complex AI systems by combining pre-built components for RAG, multi-agent coordination, and LLM interactions. The modular architecture allows quick implementation of production-ready features like conversation memory, vector storage integration, and tool-enabled agents.

Key Highlights:

  1. Simple Orchestration - Dynamiq uses a node-based system to define and manage complex workflows. This helps easily chain together different components, like LLMs, agents, tools, and data processors, in a visual and intuitive manner. The framework handles the execution and data flow between nodes.

  2. Flexible Agent Frameworks - It provides built-in support for various agent paradigms, including ReAct, Reflection, and a simple agent. You can choose the best strategy for your specific use case. The framework also includes an adaptive orchestrator to manage multiple agents collaborating on complex tasks, along with tools for prompt management and templating.

  3. Extensive Integrations - Dynamiq connects with several LLM providers (OpenAI, Anthropic, Azure AI, etc.), vector databases (Pinecone, Chroma, Weaviate, etc.), and external tools like code interpreters and search engines.

  4. Built-in Memory and Caching - It includes features for managing conversation history (memory) and caching results. These features are essential for building stateful applications, like chatbots, and for optimizing performance by avoiding redundant computations.

  5. Quick Start - pip install dynamiq and choose from production-ready templates for RAG, agents, or LLM applications. Each template includes configuration files, component setup, and detailed comments explaining the implementation choices.

Quick Bites

Nous Research has introduced Forge Reasoning API that allows you to take any popular model and superpower it with a code interpreter and advanced reasoning capabilities. Using techniques like Monte Carlo Tree Search and Mixture of Agents to create a powerful reasoning system, Forge enabled the opensource model Hermes 70B to outperform OpenAI o1 in a math competition.

Forge currently supports Hermes 3, Claude 3.5 Sonnet, Gemini, and GPT-4 models. It is available to a small group of users for beta testing.

Are Computer Use AI agents the new focus area in AI development? Here’s screengrasp - a SOTA agentic click position prediction model that outperforms even Anthropic Computer Use, OmniParser, and Molmo. It combines the advantages of these top models into one efficient ensemble for precise screen interaction for computer vision agents. It is available via API, you can also try it for free at screengrasp.com.

OpenAI is reportedly working on a code execution sandbox feature within ChatGPT Canvas. It will allow users to test the code in Canvas, initially supporting Python, Javascript, and Typescript.

You can now finetune Qwen-2.5-Coder models for free in Google Colab using Unsloth. It makes finetuning 2x faster and uses 60% less VRAM with no accuracy loss.

Tools of the Trade

  1. Napkins.dev: Opensource tool to convert wireframes into functional React apps using Llama 3.2 Vision and Together AI. Simply upload an image of your design, and it builds the app using React and Tailwind.

  2. SurfSense: Opensource personal research assistant combining NotebookLM and Perplexity features. Users can create knowledge bases, research the web, save dynamic content, and chat with or generate podcasts from saved content, all locally.

  3. AutoGluon: Automates ML tasks to help you achieve strong predictive performance in your apps. With just 3 lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.

  4. Awesome LLM Apps: Build awesome LLM apps using RAG to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos with simple text prompts. These apps will let you retrieve information, engage in chat, and extract insights directly from content on these platforms.

Hot Takes

  1. In the next decade… we are poised to
    - cure all disease
    - get humanoids to do all work
    - automate all white collar professions
    - transform transportation
    This means we can spend all day making shit posts on social media 😂😂 ~
    Bindu Reddy

  2. Very surprised AI coding hasn’t led to a new programming or computation paradigm yet. ~
    Alex Graveley

  3. The 2010s were the age of scaling, now we're back in the age of wonder and discovery once again. Everyone is looking for the next thing. Scaling the right thing matters more now than ever. ~
    Ilya Sutskever

That’s all for today! See you tomorrow with more such AI-filled content.

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