As a dev with three decades experience writing code, I use it for stuff I know all about all the time. And these days, the majority of the time it is more correct than I would've been without a few iterations of trial and error.
The correct thing to do in this scenario is to create a new random login alias on your Microsoft account, make it the primary login alias, and disable login for the all other e-mails tied to the account.
I'm skeptical of letting agents run free like this. Even Opus makes decisions I don't always agree with. And I quickly lose my mental model of how the code is evolving.
I get more enjoyment and better results when the coding process is me and the agent working through a plan, at each step sparring over what to do next and how. Then I also catch the bad decisions before they manifest in the code.
China has cheap coal powered electricity and leaders that make things happen. Europe has beaureaucrats that only love talking, high taxes and expensive energy.
Same here. Step 1 is usually a research doc where I simply describe the task and tell it to research the relevant parts of the codebase. This gets refined to a high-level plan, which gets distilled to a detailed step-by-step implementation plan.
When it comes to the actual implementation I prefer to work through it in small steps, where the AI explains to me exactly what it's about to do and why (and I approve) along the way. This enables me to catch it if it's about to do something I disagree with beforehand. And reduces the time I need to spend reviewing in the end.
My agentic workflow probably differs somewhat from the majority of others here, but I can positively guarantee you that both the quality and quantity of my output is significantly higher than it has ever been, in my 20-something years of writing code. And at least 90÷ of the code I've written this year was output by an LLM. You can keep sticking your head in the sand, in the end it will only be to your own detriment.
I'm with you all the way here. I derive zero pleasure from simply typing out the code once the spec is clear. Having a fast forward button to skip that phase is a pure win in my book.
I'd call that irresponsible use. One of the principles I try to stick by is to never offload any major decisionmaking to the LLM without oversight. Because some percentage of the decisions it makes are going to be wrongful (and more often just against my taste).
Not what I do. I'll reformulate the ticket description so that the purpose and as many details as possible about the solution are made clear from the start. Then I tell Opus to go and research the relevant parts of the codebase and what needs to be done, and write its findings to a research.md file. Then I'll review that file, bring answers to any open questions and hash out more details if any parts seem fuzzy. When the research is sound I'll ask Opus to produce a plan.md document that lists all the changes that need to be made as actionable steps (possibly broken into phases). Then I'll let Sonnet execute the steps one by one and quickly review the changes as we go along.
There are many ways to use an LLM to generate a piece of software. I base most of my projects these days around sets of Markdown files where I use AI first to research, then plan and finally track the progress of implementation (which I do step-wise with the plan, always reviewing as I go along). If I was asked to provide documentation for my workflow those files would be it. My code is 99% generated, but I take care to ensure the LLM generates it in a way that I am happy with. I'd argue the result is often better than what I'd have managed on my own.
Anyone test it out for generating 2D art for games? Getting nano banana to generate consistent sprite sheets was seemingly impossible last time i tried a few months ago.
As a AI-aware software engineer currently creating systems that integrate with LLM provider APIs for my company- who also has no idea what an eval is or how a data scientist thinks about RAG. I honestly don't see what value a data scientist would bring to the table for my team. Maybe someone would care to enlighten me?
Your view of what is happening in the neural net of an LLM is too simplistic. They likely aren't subject to any constraints that humans aren't also in the regard you are describing. What I do know to be true is that they have internalised mechanisms for non-verbalised reasoning. I see proof of this every day when I use the frontier models at work.
There must be a mechanism to rate the person submitting the PR. Anyone that wants to submit code to a well-known repo would first need to build a demonstrable history of making high-quality contributions to lesser known projects. I'm not very familiar with the open source scene but I'd find it very surprising if such a mechanism was not already in place. Seems like an obvious solution to the problem of vibe coders submitting slop.