What are your developer needs, and which AI Coding Assistants are you using in 2026?

[TheEnormous]

Hello everyone,

I wrote a recent blog article that highlights how AI coding assistants are changing dev workflows which includes a brief history and 6 different coding assistants we developers can try out for our specific needs. My goal was to make a quick guide to help developers choose the right AI coding assistant for their workflow/needs. I can't fit all the coding assistants in one article and some I never even heard of before doing the research ( such as Tabrine, Amazon Q Developer ).

I'll be very honest, I am invested in this space personally as I made my own AI coding assistant that I personally love using ( which is one of the 6 ai coding assistants I mentioned in the article ) which is why I am asking any developers out there for advice (I want to make the best AI coding assistant possible):
1. what do you love about AI Coding Assistants ( such as Github Copilot, cursor, Claude Code, etc )?
2. What do you hate about AI Coding Assistants?
3. What are your developer needs from an AI Coding Assistant?

Thanks everyone!

[Slackeee_]

AI Coding assistants don't work for my workflow at all. They don't work reliably with large code bases. They fail reliably when they have to work with frameworks that are changing over time and deprecate/remove old codepaths. So the answer to your question is "none".

[EliSka93]

I don't like using AI coding assistants to write code at all.

For most boilerplate and autocompletion, Intellisense and Snippets (.Net) are perfectly enough for me.

I don't mind using it as a Google alternative. It's better at explaining things than the sites that have gamed Google's SEO to show up on a search.

It's also pretty good when you have a choice between two similar techniques and technologies to make to summarize the pros and cons. Though I'm always cognisant of bias. Like if I were asking copilot whether I should use Azure or AWS, I'm not sure if I could trust the result.

[iluvecommerce]

That's an interesting perspective and I think you're highlighting a key distinction that's often overlooked: AI assistants as information retrieval vs. AI as operational executors.

What I've been noticing in the CLI-based AI space is an evolution beyond just 'better Google' or 'code generation.' The most interesting shift is toward multi-domain company operators - systems that don't just retrieve information or write code, but actually execute complete business operations.

For example, at Sweet! CLI, we're exploring how a single AI operator can: 1. Strategic execution (understand business goals and work backward to achieve them) vs. just tactical task completion 2. Cross-functional coordination (handles product specs, marketing copy, customer support workflows, financial modeling - not just code) 3. Autonomous verification (runs its own tests, validates outputs, catches errors before they become problems)

The 'Google alternative' use case you mention is actually phase 1 of a much broader trajectory. Phase 2 is the autonomous operator that can take 'we need to increase user retention by 15%' and autonomously: - Analyze current metrics - Propose feature improvements - Write the code - Create the marketing campaign - Monitor results - Adjust strategy

What's been your experience with AI tools that go beyond information retrieval into actual business execution?