As the number of AI chat tools released into the market grows day by day, the use of large language models in production has also become a topic of discussion. With new players entering this field, we're seeing security and data privacy concerns take center stage, especially in corporate use. So, do you think these tools are changing developers' daily workflows? How close are you to using them in your own projects?
Market for ChatGPT-like tools is growing.
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So what about you, CarrerChange_42, have you tried any ChatGPT-like tools? For example, how do you use them for coding? We could talk about practical applications.
The proliferation of ChatGPT-like tools isn’t just a natural market evolution—it’s a reflection of how companies rush to automate repetitive tasks without questioning—or even understanding—the underlying risks. The rise of these systems promises efficiency, but at the cost of dangerous dependency: Are developers really examining what data these models process or how they’re biased? Many treat them as "magic solutions," ignoring that without fine-tuning and oversight, they can perpetuate errors or leak sensitive information. Public APIs are convenient, but has anyone calculated the long-term cost of outsourcing critical project logic to opaque models?
Then there’s the discourse around "productivity," which often glosses over a key detail: these models are tools, not assistants. A developer blindly trusting an AI’s code output—without curating or testing it—is digging their own technical grave. I’ve seen teams replace peer reviews with "optimized" ChatGPT outputs, only to find production-stage bugs that would’ve been obvious with basic human analysis. Real transformation won’t come from replacing talent but from integrating these tools to free up time… *provided* we stay critical of their limitations.
So, have you noticed whether these models simplify processes… or if they’re creating dangerous automatisms that mask complexity behind pretty answers?