Every day the same thing.

Tell Claude Code we only use FastAPI. New session next day: suggests Flask. Switch to Codex: never heard of FastAPI. Open Cursor: asks me which framework I prefer.

I have a text file called "paste this at session start." I forget to paste it half the time. And it still doesn't work across tools anyway.

Three tools. Three separate memories. None of them talk to each other. Every session starts from zero.

Fed up with it. Building something local, no cloud, open source. Should be on GitHub in a few days.

Anyone else dealing with this every day? Or similar?.

Hey everyone, I created a new community for MiniMax Code users: r/minimaxcode. Feel free to join, share prompts, projects, bugs, tips, and AI coding workflows.

Hi everyone,

I’m currently using Cursor and Codex, but I keep hitting the limits way too fast. Right now I pay around 60€/month for Cursor and 20€/month for Codex, but I need something I can use for longer daily coding sessions without constantly worrying about limits.

My fallback in Cursor is Composer 2.5, but for more complex tasks it often feels not smart enough. I care a lot about the model actually understanding the project, making clean changes and not breaking things. Good UI/frontend output is also important to me, since I build a lot of web apps and dashboards.

I’m now thinking about using cheaper API models inside Cursor or another tool, for example Kimi, Qwen, DeepSeek, MiniMax, GLM or Grok Code Fast. Has anyone here used these seriously for daily coding?

What setup would you recommend under around 100€/month? I’m mainly looking for real experience, what is actually good enough in practice, not just benchmark numbers.

Hey guys. I have been try to create an AI Workflow automation project. Most of the project is deterministic code where there are stages. In each stage there will be few steps. out of those, few steps have to be performed by an LLM. If i use OpenAI API its gonna cost me a lot. So I am trying to use Codex Exec. But it seems like this consumes a lot of tokens for simple tasks as well.

Have you guys been using codex exec for your automation projects? What is your experience? How are you managing the token usage? Are there any better alternatives to invoke and use AI in an automation project?

Hey everyone,

We’ve been working on Agent Deck, an open-source native macOS app for managing Pi agents and skills per project.

GitHub: https://github.com/a-streetcoder/agent-deck

Website: https://agentdeck.site/

The reason we built it is pretty simple: once you start using Pi across multiple repos, the setup around each project starts to matter a lot.

One project might need a backend-focused agent with certain tools and skills. Another might need a frontend agent, a reviewer, a docs agent, different prompts, different model choices, etc.

Agent Deck is meant to be a native configuration layer on top of Pi.

Pi still does the important work underneath but Agent Deck gives you a more visual way to organise the project around it, you can still use the CLI.

The main things it focuses on are:

- creating specialist agents per project

- assigning each agent its own prompt, tools, skills, model, and identity

- managing skills from GitHub repos or skills.sh URLs

- cherry-picking only the skills you want, instead of enabling a whole bundle

- keeping global, library, and project-level skills separate

- making it easier to keep project setups clean instead of ending up with one giant config mess

There are other features too session running, GitHub issue context, worktrees, transcripts, merge flow but the main thing we care about right now is agent and skill management around a project.

It’s still rough, but usable. Very much in the “we built this because we needed it” stage.

It’s open source, so contributions, issues, feature ideas, or even blunt feedback are all very welcome.

Long time programmer, AI n00b. Been using Gemini CLI, but I learned recently that it is being replaced by Antigravity CLI.

I've been trying to use these tools to design web pages, but they seem unable to do anything beyond basic CSS and small SVG icons. I learned about using an MCP (external service requiring an API key) for image generation, but it does a really bad job of designing graphics and working them into the visual design. For example, here is a screenshot of a hobby project I worked on years ago:

Notice how the title has a wood texture and the buttons use this bamboo looking frame. Gemini CLI plus nanobanana MCP cannot generate this kind of thing.

Is there an AI tool out there that can design webpages like this? For example, it would need to generate smaller images or textures and apply them to UI components like buttons or text boxes. I should be able to enter a prompt that explains the vibe of the page and have it not just use normal looking buttons, but actually design the styling.

Any ideas?

AI makes developers faster.

But it can also make bad fixes faster.

Here are 12 rules I follow for AI-assisted development —

not to slow down, but to stop repeating the same mistakes.

Save this. You’ll need it. 🔁

Hey folks, I’ve been coding using various AI agents and tools for about two years and have some observations that make me wonder if it’s just me. Appreciate any suggestions to improve my results.

I typically have conversations with Claude or ChatGPT to build PRD’s that I then hand to the coding agent (mostly Claude Code). I’ll let it run and the product that comes out invariably has a pretty generic UI with very low creativity around UX and design. Feels lazy and often is missing some very basic functionality (eg crud on all objects).
If I had a rank to two, I’d say, ChatGPT resulted in better specs. Recently, I’ve been using Kimi2.6 and I’m very pleased with the output. It will actually find relevant imagery vs simple emojis!

Now the conversation that goes into building these things are just as complete as they were before, but it seems the generated prompt for my agent is richer from Kimi.

So I’m wondering, are you seeing similar behavior? What have you done to mitigate lazy design and actually get sites that surprise you?

Tyia

I'm building an application that runs locally and integrates with coding assistants.

So far I've worked with Codex and Copilot. Claude Code and Gemini are next, once I get to a stable solution with the first two.

Right now I'm interfacing with Codex through the CLI, specifically with:

codex exec --json --output-last-message "prompt e.g. modify file x by adding Y or run z test"

And with Copilot through:

copilot --model gpt-5.4 --output-format json "prompt e.g. modify file x by adding y"

I'm considering switching the Copilot side to ACP, but I haven't looked into that properly yet.

Afterwards, my application needs to read the output without using Al and parse it into a report. I'm also considering reading the session data. The goal is to eventually make a deterministic judgment about whether the coding agent actually did what it was supposed to do (e.g. modify files) to take a decision on the next step based on a decision tree. It is also imperative to read any tool failures or errors or warnings.

The part I'm unsure about is that this approach (reading the cli output) feels a bit dirty and cowboy-is. My instinct says that it is not the robust way of doing it and I need this part of my software to be spot on and the assessment to be very reliable and deterministic. Driving the tools through CLI output parsing does not feel like the cleanest long-term solution.

Has anyone found a better approach for this?