NEW Deepseek v3.1 Coder: FULLY FREE AI Coder! Develop a Full-stack App Without Writing ANY Code!

Key Concepts

DeepSeek V3, open-source coding model, MIT license, benchmark scores, Claude 3.7 Sonnet, GPT-4.5, Qwen Max, front-end web development, Deepseek Coder, Llama Coder, Together AI API, local installation, VS Code, environment variables, npm, git, UI/UX cloning, weather dashboard generation, iterations.

DeepSeek V3: A Powerful Open-Source Coding Model

The DeepSeek team released a significant upgrade to their version 3 model, which demonstrates exceptional coding capabilities. This open-source model, licensed under MIT, not only competes with but also outperforms models like Claude 3.7 Sonnet, GPT-4.5, and Qwen Max across various benchmarks, including science, math, and coding. In live benchmark tests, it achieved a score of 49.2, surpassing Claude 3.7 Sonnet by 7 points. Its performance in front-end web development is particularly noteworthy, generating highly executable code and visually appealing web pages with improved layouts, styles, and content quality. The model can generate over 800 lines of code in one shot, producing clean, functional, and production-ready websites.

Introducing Deepseek Coder: Free Local Access to DeepSeek's Coding Power

Deepseek Coder, an open-source tool developed by Hassan (creator of Llama Coder), allows users to harness the full coding potential of DeepSeek locally and for free. It connects through Together AI's free API, eliminating the need for expensive hardware or paid API subscriptions. Users can simply open Deepseek Coder and start building with the model.

Using Deepseek Coder: Features and Examples

The interface of Llama Coder (which also supports Deepseek V3) allows users to input prompts, select the DeepSeek version 3 model (or other models), and choose a quality mode (high quality recommended for better output).

Example 1: SAS Landing Page Generation: A single prompt to create an entire website resulted in over 800 lines of code, generating a clean and functional SAS landing page. The code can be exported and implemented into existing projects. The generated app can be previewed in a new tab for a full visualization.

Example 2: UI/UX Cloning: Providing a UI/UX design of the Airbnb website and requesting a clone resulted in the model generating the main structure and functional components, although color schemes and icons were not fully replicated.

Example 3: Animated Weather Card Dashboard: The model generated a weather card dashboard with animations for different weather conditions. While the initial output was not as high quality as previous examples using Deep Seek V3 with Klein, a second iteration based on the prompt "make it look better" resulted in improvements to the design and animations. Users can switch between iterations to compare the results.

Installing Deepseek Coder Locally: A Step-by-Step Guide

To install Deepseek Coder locally, the following prerequisites are required:

1. Git: Ensure Git is installed on your system.
2. Visual Studio Code (VS Code): Install VS Code for configuring environment variables.
3. Python: Make sure Python is installed.
4. npm: Ensure the npm command is available.
5. Together AI API Key: Obtain a free API key from Together AI.

Installation Process:

1. Clone the Repository: Copy the Git clone command from the GitHub repository and paste it into your command prompt. This will clone the Llama Coder folder to your local computer.
2. Open the Folder in VS Code: Open VS Code, click "File," then "Open Folder," and select the cloned Llama Coder folder.
3. Create an .env File: Create a new file named .env in the Llama Coder folder.
4. Add the API Key: Copy the text TOGETHER_API_KEY= into the .env file and paste your Together AI API key after the equals sign. Save the file.
5. Install Dependencies: Open your command prompt, navigate to the Llama Coder folder using the cd command, and run the command npm install to install all necessary dependencies.
6. Run Locally: Once the installation is complete, run the command npm run dev to start the application locally.
7. Access in Browser: Copy the local host address provided in the command prompt and paste it into your web browser to access Deepseek Coder.

Conclusion

DeepSeek V3 and Deepseek Coder offer a powerful and accessible solution for coding tasks, particularly in front-end web development. The open-source nature, combined with the free Together AI API, makes it an attractive option for developers of all levels. The ability to generate code from prompts, clone UI/UX designs, and iterate on designs provides a flexible and efficient workflow. The step-by-step guide enables users to easily install and run Deepseek Coder locally, unlocking its full potential.