Totally agree about the viability of open-source models! The real game-changer isn't just choosing between open vs. closed - it's understanding when each makes sense for your specific use case.
I've been experimenting with both approaches (and honestly failing quite a bit initially D:), and here's what I've learned: Sometimes a smaller, well-tuned open-source model actually outperforms the 'big names' for specific tasks. Especially when you need precise control over data handling and customization.
The key is being realistic about your resources and needs. Not every project needs the latest cutting-edge model - sometimes the 'boring' solution is the right one :)
Want to see some real experiments with AI tools in action? I write about my successes (and failures!)
I agree, SLMs tuned to your needs in many cases is what you really need. What size of models you find best for such tasks? Where's the barrier where to model is 'too small' for the task?
Totally agree about the viability of open-source models! The real game-changer isn't just choosing between open vs. closed - it's understanding when each makes sense for your specific use case.
I've been experimenting with both approaches (and honestly failing quite a bit initially D:), and here's what I've learned: Sometimes a smaller, well-tuned open-source model actually outperforms the 'big names' for specific tasks. Especially when you need precise control over data handling and customization.
The key is being realistic about your resources and needs. Not every project needs the latest cutting-edge model - sometimes the 'boring' solution is the right one :)
Want to see some real experiments with AI tools in action? I write about my successes (and failures!)
thanks for sharing.
I agree, SLMs tuned to your needs in many cases is what you really need. What size of models you find best for such tasks? Where's the barrier where to model is 'too small' for the task?