I was playing around with OpenCode on Omarchy over the weekend, working on my Android app, and was struggling to spend a single dollar with DeepSeek. The quality difference from the frontier models I use at work was hardly noticeable (where I typically rack up at least a few hundred dollars per month).

For an MCP used to look up data in any kind of dataset, just having a single tool that accepts an OData Query string has worked well for me.

Preferably, it would ask for help to see the image and only engineer its own workaround if that was not an option. There could also very well be something in the prompt that would cause it not to do so. Opus 4.8 is usually good at asking for clarification for things like this when I'm using it.

Why would it ask to see the image if it was already told it cannot see the image?

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.

Using Mistral or more SOTA models?

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.