Using an open model feels surprisingly good
matthewsaltz.com
July 28, 2026
1 min read
🔥🔥🔥🔥🔥
56/100
Summary
Using an open model for AI inference allows users to have greater control over their data and deployment. Setting up an open code inference endpoint can provide a sense of ownership and freedom in managing AI tasks.
Key Takeaways
- The author successfully set up an open model on their own inference endpoint using opencode.
- The experience of using an open model felt liberating and gave the author a sense of ownership over their data.
- The author chose to use Kimi K3 on managed endpoints instead of upgrading their Claude plan for a personal project.
- The author compared the experience of using opencode to the feeling of switching from a complex editor to a simpler one, highlighting a sense of freedom.
Community Sentiment
Positives
- Claude Code's ability to generate extensive, functional code from vague prompts is seen as a game changer for rapid development.
- DeepSeek v4 Flash is impressing users as a solid alternative to big-name models, showing that smaller models can still deliver satisfying performance.
- Frontier models like Opus 5 are praised for their speed and creativity in research applications, significantly reducing the time needed for idea exploration.
- The accessibility of AI tools has lowered barriers for non-software engineers, enabling more people to bring their app ideas to life.
Concerns
- Skeptics point out that smaller models struggle with complex tasks compared to larger ones, emphasizing that they work better as supportive tools rather than standalone solutions.
- There are concerns about the cost and data privacy associated with using proprietary models, with users wanting clearer metrics on pricing and data handling.
