We will fine-tune an LLM on your codebase to provide the highest quality code generation, achieving 4.2x the accuracy of Sonnet-3.5.
Fine-tuned code generation
We will fine-tune an LLM on your codebase to provide the highest quality code generation, achieving 4.2x the accuracy of Sonnet-3.5.
Looks great! Good luck with the project!
Amazing work, @Samat! FineCodeX's approach to code generation is impressive, particularly the focus on cost savings and accuracy. The fact that you come from such reputable backgrounds (OpenAI, Anthropic, Asana) certainly adds credibility. What does the price look like?
Congrats Samat, 4.2x accurate than claude 3.5 is really impressive, how did your team do this? It's amazing!
Hey Product Hunt! 👋 We're the FineCodeX team, and we can fine-tune a Llama-3.3-70B on your codebase to provide the highest quality code generation! - 4.2x more accurate code changes compared to Sonnet-3.5 (could be higher depending on the project) - Up to 9x cheaper than large general models - 100% private - model weights or dedicated API that never leaves your infrastructure How it works: - You provide us with the data to fine-tune on - We set up the whole process and give you the model weights
Who on the team worked for OpenAI and Anthropic? This is a compelling pitch but it's unusual to have the affiliations front and center without any of the listed founders or employees being from there.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.