The latest articles on DEV Community by Amrit sagar (@amrit_sagar). https://dev.to/amrit_sagar https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fthepracticaldev.s3.amazonaws.com%2Fi%2F99mvlsfu5tfj9m7ku25d.png https://dev.to/amrit_sagar en Amrit sagar Sun, 25 Oct 2026 17:21:21 +0000 https://dev.to/amrit_sagar/building-an-open-ai-code-assistant-with-local-llms-and-open-agent-frameworks-10di https://dev.to/amrit_sagar/building-an-open-ai-code-assistant-with-local-llms-and-open-agent-frameworks-10di <h2> Project Overview </h2> <p>For this week's challenge, I built an open-source AI developer assistant designed to run locally using open-weight models. The goal of this project is to give developers full control over their AI tooling while maintaining code privacy and zero API cost.</p> <p>By leveraging open-weight models, developers can run entire coding workflows offline without relying on proprietary third-party APIs.</p> <h2> How It Works </h2> <ul> <li> <strong>Open-Weight Models:</strong> Powered by local inference engines (such as Ollama or Llama.cpp) running open models like Llama 3 or Qwen.</li> <li> <strong>Agent Framework:</strong> Built using open agent frameworks to handle task planning, file creation, and contextual code generation.</li> <li> <strong>Privacy-First:</strong> All processing happens locally on device, ensuring sensitive code never leaves your workstation.</li> </ul> <h2> Why Open Innovation Matters </h2> <p>Open-source AI ensures that powerful developer tools remain accessible, transparent, and customizable. Relying entirely on closed models creates vendor lock-in and raises privacy concerns. Building with open-weight models empowers the developer community to audit, modify, and fine-tune models for custom workflows.</p> <h2> Links &amp; Code </h2> <ul> <li> <strong>GitHub Repository:</strong> <a href="https://github.com/Amrit-sagarr/open-ai-code-assistant" rel="noopener noreferrer">https://github.com/Amrit-sagarr/open-ai-code-assistant</a> </li> <li> <strong>Demo / Screenshots:</strong> Local CLI setup running Llama 3 via Ollama on local machine.</li> </ul> devchallenge week3challenge hf26challenge hacktoberfest Amrit sagar Sun, 25 Oct 2026 17:17:01 +0000 https://dev.to/amrit_sagar/building-an-open-ai-code-assistant-with-local-llms-and-open-agent-frameworks-3bdk https://dev.to/amrit_sagar/building-an-open-ai-code-assistant-with-local-llms-and-open-agent-frameworks-3bdk <h2> Project Overview </h2> <p>For this week's challenge, I built an open-source AI developer assistant designed to run locally using open-weight models. The goal of this project is to give developers full control over their AI tooling while maintaining code privacy and zero API cost.</p> <p>By leveraging open-weight models, developers can run entire coding workflows offline without relying on proprietary third-party APIs.</p> <h2> How It Works </h2> <ul> <li> <strong>Open-Weight Models:</strong> Powered by local inference engines (such as Ollama or Llama.cpp) running open models like Llama 3 or Qwen.</li> <li> <strong>Agent Framework:</strong> Built using open agent frameworks to handle task planning, file creation, and contextual code generation.</li> <li> <strong>Privacy-First:</strong> All processing happens locally on device, ensuring sensitive code never leaves your workstation.</li> </ul> <h2> Why Open Innovation Matters </h2> <p>Open-source AI ensures that powerful developer tools remain accessible, transparent, and customizable. Relying entirely on closed models creates vendor lock-in and raises privacy concerns. Building with open-weight models empowers the developer community to audit, modify, and fine-tune models for custom workflows.</p> <h2> Links &amp; Code </h2> <ul> <li> <strong>GitHub Repository:</strong> <a href="https://github.com/Amrit-sagarr/open-ai-code-assistant" rel="noopener noreferrer">https://github.com/Amrit-sagarr/open-ai-code-assistant</a> </li> <li> <strong>Demo / Screenshots:</strong> Local CLI setup running Llama 3 via Ollama on local machine.</li> </ul> devchallenge week3challenge hf26challenge hacktoberfest