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Part of the “From Vibe to Viable” Series
? Hello Devs!
I’m Anni, a Developer Evangelist here at . I'm super excited to launch this blog series to share some thoughts on , explore the intersection of AI with Data Science, and how this could help you on your Next AI Coding journey.
Generative AI (or GenAI) took us by a storm when Developer workflows have quickly evolved from chat interfaces to AI-enhanced IDEs and now towards fully AI-native environments. With Anthropic's CEO predicting, we face questions around software reliability, efficiency, and trust.
As (Andrej Karpathy's term for when developers "forget code exists") becomes increasingly prevalent, we're also introducing a new challenge—"vibe debugging." How will we effectively review and improve the growing volume of AI-generated code? How do we validate AI-generated code beyond basic functionality? Can we trust AI outputs in production?
From the Code You Have, To the Code You Need
If you’re already using tools like GitHub Copilot, Cursor, ChatGPT, or even custom LLM workflows to speed up coding, awesome. These tools help you move faster and unblock your thinking. But they can also come with new risks—like hallucinated packages, unoptimized code that burns your cloud resource and code that looks right but contains hidden bugs.
That’s where Artemis comes in
Artemis is built to complement and work with both human developers and other GenAI coding tools—not replace them. Unlike simple LLM wrappers, Artemis and its Intelligence Engine uses a mixture of tools (context manager, evaluator, validator, LLMs, etc.) and our data science expertise to take the code you already have (written by a human, Copilot, ChatGPT, or an intern) and helps you evolve it into the code you actually need.
Data science empowers your coding journey at every step, from analyzing prompt effectiveness and selecting task-optimized models to evaluating and scoring AI-generated outputs against rigorous validation criteria. This quantitative foundation ensures AI-generated code meets our real-world requirements and gets us the most optimal results.
Whether you are modernizing legacy code or dealing with AI-generated “vibe code,” Artemis can help score, optimize, and validate your way to better results. Join me in the next "From Vibe to Viable" and learn about how we optimized AI-generated code from GitHub Copilot and improved its performance in Artemis.
Thanks for reading and see you again soon!
?? Anni, Developer Evangelist @ TurinTech
Follow me on or
Part of the “From Vibe to Viable” Series
? Hello Devs!
I’m Anni, a Developer Evangelist here at . I'm super excited to launch this blog series to share some thoughts on , explore the intersection of AI with Data Science, and how this could help you on your Next AI Coding journey.
Generative AI (or GenAI) took us by a storm when Developer workflows have quickly evolved from chat interfaces to AI-enhanced IDEs and now towards fully AI-native environments. With Anthropic's CEO predicting, we face questions around software reliability, efficiency, and trust.
As (Andrej Karpathy's term for when developers "forget code exists") becomes increasingly prevalent, we're also introducing a new challenge—"vibe debugging." How will we effectively review and improve the growing volume of AI-generated code? How do we validate AI-generated code beyond basic functionality? Can we trust AI outputs in production?
From the Code You Have, To the Code You Need
If you’re already using tools like GitHub Copilot, Cursor, ChatGPT, or even custom LLM workflows to speed up coding, awesome. These tools help you move faster and unblock your thinking. But they can also come with new risks—like hallucinated packages, unoptimized code that burns your cloud resource and code that looks right but contains hidden bugs.
That’s where Artemis comes in
Artemis is built to complement and work with both human developers and other GenAI coding tools—not replace them. Unlike simple LLM wrappers, Artemis and its Intelligence Engine uses a mixture of tools (context manager, evaluator, validator, LLMs, etc.) and our data science expertise to take the code you already have (written by a human, Copilot, ChatGPT, or an intern) and helps you evolve it into the code you actually need.
Data science empowers your coding journey at every step, from analyzing prompt effectiveness and selecting task-optimized models to evaluating and scoring AI-generated outputs against rigorous validation criteria. This quantitative foundation ensures AI-generated code meets our real-world requirements and gets us the most optimal results.
Whether you are modernizing legacy code or dealing with AI-generated “vibe code,” Artemis can help score, optimize, and validate your way to better results. Join me in the next "From Vibe to Viable" and learn about how we optimized AI-generated code from GitHub Copilot and improved its performance in Artemis.
Thanks for reading and see you again soon!
?? Anni, Developer Evangelist @ TurinTech
Follow me on or