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    <title>DEV Community: midasTouch</title>
    <description>The latest articles on DEV Community by midasTouch (@midastouch_54ee2c694ffc3e).</description>
    <link>https://dev.to/midastouch_54ee2c694ffc3e</link>
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      <title>Introducing Oblix – Seamless AI Orchestration Between Local &amp; Cloud Models</title>
      <dc:creator>midasTouch</dc:creator>
      <pubDate>Mon, 17 Mar 2025 03:33:52 +0000</pubDate>
      <link>https://dev.to/midastouch_54ee2c694ffc3e/introducing-oblix-seamless-ai-orchestration-between-local-cloud-models-38od</link>
      <guid>https://dev.to/midastouch_54ee2c694ffc3e/introducing-oblix-seamless-ai-orchestration-between-local-cloud-models-38od</guid>
      <description>&lt;p&gt;🧠 The Problem: Local AI Models Are Powerful but Messy&lt;br&gt;
If you’ve worked with local LLMs like Llama2, Mistral, or Whisper, you’ve likely faced these issues:&lt;/p&gt;

&lt;p&gt;❌ Crippling system slowdowns (Ollama eating up CPU/GPU)&lt;br&gt;
❌ Models crashing mid-inference when overloaded&lt;br&gt;
❌ No seamless fallback to cloud APIs (like OpenAI/Claude)&lt;br&gt;
❌ Manual API juggling when switching models&lt;/p&gt;

&lt;p&gt;This pain point frustrated me while working on AI projects, and I realized there had to be a better way. So I built Oblix.ai—an SDK that automatically routes AI workloads between local and cloud based on system conditions.&lt;/p&gt;

&lt;p&gt;🚀 What is Oblix?&lt;br&gt;
Oblix is a Python SDK that seamlessly orchestrates AI workloads between local models (Ollama, Llama2, Mistral, Whisper) and cloud-based models (OpenAI, Claude, etc.).&lt;/p&gt;

&lt;p&gt;💡 How it works:&lt;br&gt;
✅ Monitors CPU/GPU load and auto-routes AI requests accordingly&lt;br&gt;
✅ Detects network connectivity and falls back to cloud/local as needed&lt;br&gt;
✅ Eliminates manual API switching for AI developers&lt;br&gt;
✅ Runs persistent chat history even across different models&lt;/p&gt;

&lt;p&gt;🔧 How to Use Oblix (Code Example)&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;client = OblixClient(oblix_api_key="your_key")&lt;br&gt;
await client.hook_model(ModelType.OLLAMA, "llama2")&lt;br&gt;
await client.hook_model(ModelType.OPENAI, "gpt-3.5-turbo", api_key="sk-...")&lt;br&gt;
client.hook_agent(ResourceMonitor())&lt;br&gt;
client.hook_agent(ConnectivityAgent())&lt;br&gt;
response = await client.execute("Explain quantum computing")&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;📌 Why This Matters for AI Developers&lt;br&gt;
With Oblix, you don’t have to worry about:&lt;br&gt;
✅ Manual switching between local/cloud models&lt;br&gt;
✅ Slowdowns from resource-heavy local AI&lt;br&gt;
✅ Your AI breaking when offline&lt;/p&gt;

&lt;p&gt;Instead, Oblix handles everything automatically, so you can focus on building instead of debugging AI model execution.&lt;/p&gt;

&lt;p&gt;🚀 Try It Out &amp;amp; Share Your Feedback!&lt;br&gt;
We’re launching Oblix.ai and looking for early adopters to help shape the tool. If you work with local &amp;amp; cloud-based AI models, I’d love to hear your thoughts!&lt;/p&gt;

&lt;p&gt;🔗 Check it out here → &lt;a href="https://www.oblix.ai" rel="noopener noreferrer"&gt;https://www.oblix.ai&lt;/a&gt;&lt;br&gt;
💬 Join our Discord for feedback → &lt;a href="https://discord.gg/QQU3DqdRpc" rel="noopener noreferrer"&gt;https://discord.gg/QQU3DqdRpc&lt;/a&gt;&lt;br&gt;
I will give starbucks giftcard who can give me feedback.&lt;/p&gt;

&lt;p&gt;Let’s make hybrid AI workflows seamless!&lt;/p&gt;

&lt;p&gt;🔥 Bonus: Drop a comment below 👇 if you’ve ever struggled with managing local vs cloud AI models—I’d love to hear how you handle it!&lt;/p&gt;

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      <category>ai</category>
      <category>machinelearning</category>
      <category>python</category>
      <category>cloudai</category>
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