LLMs often fail at the simplest tasks. Just this week I had it fail multiple times where the solution ended up being incredibly simple and yet it couldn’t figure it out. LLMs also seem to „think“ any problem can be solved with more code, thereby making the project much harder to maintain.

LLMs won’t replace programmers anytime soon but I can see sketchy companies taking programming projects by scamming their customers through selling them work generated by LLMs. I‘ve heard multiple accounts of this already happening myself and similar things happened with no code solutions before.

Your anecdote is not helpful without seeing the inputs, prompts and outputs. What you’re describing sounds like not using the correct model, providing good context or tools with a reasoning model that can intelligently populate context for you.

My own anecdotes:

In two years we have gone from copy/pasting 50-100 line patches out of ChatGPT, to having agent enabled IDEs help me greenfield full stack projects.

Our product delivery has been accelerated to while delivering the same quality standards verified by our internal best practices we’ve our codified with determistic checks in CI pipelines.

The power come from planning correctly. We’re in the realm of context engineering now, and learning to leverage the right models with the right tools in the right workflow.

Most novice users have the misconception that you can tell it to “bake a cake” and get the cake ypu had in your mind. The reality is that baking a cake can be broken down into a recipe with steps that can be validated. You as the human-in-the-loop can guide it to bake your vision, or design your agent in such a way that it can infer more information about the cake you desire.

If you’re already good at the SDLC, you are rewarded. Some programmers aren’t good a project management, and will find this transition difficult.

You won’t lose your job to AI, but you will lose your job to the human using AI correctly. This isn’t speculation either, we’re also seeing workforce reduction supplemented by Senior Developers leveraging AI.

Cursor and Claude Code are currently top tier.

GitHub Copilot is catching up, and at a $20/mo price point, it is one of the best ways to get started. Microsoft is slow rolling some of the delivery of features, because they can just steal the ideas from other projects that do it first.

Claude Code is better than just using Claude in cursor or copilot. Claude Code has next level magic that dispells some of the myths being propagated here about “ai bad.”

Cursor hosts models with their own secret sauce that improves their behavior. They hardforked VSCode to make a deeper integrated experience.

Avoid antigravity (google) and Kiro (Amazon). They don’t offer enough value over the others right now.

If you already have an openai account, codex is worth trying, it’s like Claude Code, but not as good.

JetBrains… not worth it for me.