10 Developer Productivity Boosts from Generative AI

Can AI really help speed up code development? In this video, Martin Keen discusses how generative AI enhances software development productivity with tasks like code generation, debugging, and more. Learn about the benefits and limitations of integrating AI into coding workflows.

[mado.madeleine]

One of the most underrated channels out there. Thank you so much for these explainer videos. Always super helpful, clear, on point, and to the point. Please never stop 🙏🏽

[IsGoing]

“Error Explanation” for me. It helps me understand the errors I get in the console much better. Sometimes you get a very long error in the console the AI can just help understand what is going on

[mike-the]

My boss was writing instructions for the customer on how to modify hosts file in windows. I pulled chatgpt and generated *.bat script and verbally expressed instructions: right click => run as admin.
Chatbots are so good at doing bash-like scripts.

[jaffarbh]

Good question (number 10). While I don't use gen AI for code generation, I build RAG systems for the real estate market. This kind of tools is already showing huge promise and will eventually replace the estate agents entirely. I think Gen AI WILL replace programmers in many situations. Eventually, programming will become a niche job, only for the more complex tasks.

[lighteningrod]

I cannot code, and now I can, enough said

[Gutenmorgenside]

More productive brewing using Ai ? I'm also very keen on the way LLM's write the documentation for you 🙂including the markup.

[Fitness-adventure]

Putting all company code and
And business logic on an AI model won’t be a good idea.

[lawrenceemke]

Key assumption over looked: the advancement in the coding language and operational environments in which the code solution operates. Changes in the environment and changes in the language, requires the retraining of the language model.
The understanding of the language model to the operational environment has not been addressed. Consider the hidden assumptions of going from a CPU to a GPU environment. There is a hidden disconnect that must be built into the language
model Mere language syntax does not lead to a better result. The responsibility for the correct system implementation still resided upon the human developer.

Additional consideration is that programs written in earlier language/environment may fail in the new environment and new code may fail in the old environment. This situation brings up the source of errors resulting from hidden (undocumented) assumptions present in each resulting system as it evolves over the life of the system. Additional consideration is the complexity of the tool supply chain and its evolution. A system based upon a tool controlled by an outside source can reek havoc on the ability of the system to adjust to a changing local environment, resulting in failure of the supported function within the local volume. The adjustment to tool changes can lead to time delays and costly system rewrites. When tool chains are controlled by outside vendors, the subsequent systems become dependent upon the tool supplier and changes in the vendor's environment. In real business environments agentic solutions must be apart of a more complex (sometimes unseen) life cycle environment, .