Why I Switched from GitHub Copilot to a Self-Hosted AI Solution
I replaced GitHub Copilot with a self-hosted AI and I won’t go back
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Image: Xda-developers
After experiencing limitations with GitHub Copilot due to tightened usage limits, a novice coder decided to switch to self-hosted AI models for coding assistance. Utilizing local large language models (LLMs) has not only enhanced performance but also reduced costs significantly, allowing for greater control and privacy in coding tasks.
- 01The author cites dissatisfaction with GitHub Copilot's tightened usage limits, leading to a search for alternatives.
- 02Local LLMs like Qwen3-Coder-Next and LM Studio provide effective coding assistance without subscription fees.
- 03Using local models allows for a larger context window (256K) compared to GitHub Copilot's offerings.
- 04The transition to self-hosted AI reduces cloud costs and enhances privacy for coding tasks.
- 05The author highlights the ease of integrating local LLMs with VS Code Insiders, eliminating the need for additional plugins.
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Frustrated by the recent restrictions imposed on GitHub Copilot, a novice coder has transitioned to using self-hosted large language models (LLMs) for coding assistance. The author appreciates the flexibility and control offered by local models like Qwen3-Coder-Next and LM Studio, which allow for seamless integration with Visual Studio Code Insiders. This shift not only enhances coding efficiency but also significantly reduces monthly costs associated with cloud subscriptions. The local LLMs provide a larger context window of 256K, which is advantageous for managing extensive codebases. Additionally, the author emphasizes the privacy benefits of running a local AI solution, as it eliminates reliance on cloud services. Overall, the experience has been positive, with the author expressing satisfaction in leveraging their existing hardware to create a more personalized coding environment.
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The switch to self-hosted AI solutions allows coders to save on subscription costs and maintain privacy over their coding practices.
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