u/thechadbro34

I’d open 30-40 tabs 'just to check' them and completely lose track of what I was actually working on. So I built GoPeek, an extension that lets you preview links in a floating window or sidebar without leaving the current page. I haven't launched it yet.

It’s especially useful for, github hopping, docs, reddit, wikipedia style rabbit holes.

The build itself was fun, but the hard part has been getting real beta testers. Cold posting in a few places got some interest, they won't stick around enough and give some feedback more than 'I needed this; neat idea..'

Right now I’m experimenting with communities, small DMs, and better positioning, but I’d love advice from people here who’ve been through this. How do you approach getting early users who actually give useful feedback, without being spammy or annoying?

If you’re curious, you can test it here: https://gopeekapp.github.io/gopeek/, any thoughts on the idea, UX, or how to validate it more effectively

There’s a clear split happening in my dev team right now. The frontend guys are all using cursor or windsurf to just yk vibe code, like highlighting UI blocks, writing vague prompts like 'make this look more modern' and mashing accept. Meanwhile, the backend team is using blackbox CLI agents with highly structured markdown prompts to do strict, deterministic database migrations and api scaffolding. For tailwind classes, vibe coding works great, but it is a nightmare for data integrity.

do you guys enforce different AI tooling rules depending on the stack, or is everyone just using whatever agent they want?

when I first started vibe coding, all my prompts were tiny and tactical. Write a React nav bar, give me a regex for this one URL, spit out a hook for X. it was faster than googling, but the dynamic was basically me as the senior dev trying to wrangle a very fast, rather clueless junior. I still had to glue everything together, and the result was usually weird state plumbing and oneoff components that didn’t quite fit.

a few weeks ago I flipped the workflow. Before writing any code, I spend ten minutes on one long architectural prompt. i’ll tell claude 3.5 sonnet something like, 'we’re building an analytics dashboard, here’s the data model, here’s how I want state to move through the app; here are the constraints. Propose a folder structure and the interfaces between pieces'

We go back and forth until the architecture looks coherent, and only then do I start asking it to generate files. The jump in quality has been obvious, mostly because the model has a real global picture instead of a stream of isolated requests.

have you tried moving from micro prompting to macro prompting and noticed the quality surge?

If you browse most agent tutorials, the examples are almost always the same, like read the weather and say something funny, scrape a page and summarise it or draft a tweet. They are fine for learning, but in practice we all know they are basically just thin wrappers around a single prompt.

I am more interested in setups where an autonomous agent actually runs a multi-step workflow on its own. For example, take a support ticket, inspect the contents, query a database, apply a refund policy, then draft the reply using those results.

I’m looking for concrete examples that are in production today and touch real business logic, not just playground demos. What agents are you running that make real decisions, call multiple tools in a loop, and save you meaningful time or money?

I kept losing context between sessions with the CLI, esp for longer refactors that stretch over a couple of days, so I thought of hacking together a tiny system around it. I started adding a simple 'task id' string in every prompt, for example something like [TASK: auth-refactor-01], log both the prompts and responses into a local folder keyed by that id.

now when i come back, i can replay the last few steps or reopen the same thread by reusing that id and pasting in the last relevant messages, instead of starting from a cold stateless prompt every time. It’s super primitive, basically just files plus a naming convention, but it has made longer workflows a lot convenient for me.

Any of you doing something like this, and is there any cleaner way you’re persisting context across CLI sessions?

my team of four is cranking out client MVPs and the codebase is getting messy fast, everyone has slightly different habits, we leave 'just in case' snippets lying around, and documentation is almost nonexistent because of deadline pressure. We’ve started using Blackbox for small refactors, helper functions, and quick fixes, which helps with speed but sometimes clashes with existing styles or patterns in the repo.

Right now our stack is something like this - github PR reviews (often rushed approvals), coderabbit to catch obvious issues, tried Codacy but it felt bloated for our needs, and sonarqube seemed like overkill for MVP velocity. The main pain points are keeping a consistent style when Blackbox suggestions vary, avoiding accidental breakages in other modules, and keeping things maintainable enough to hand off to clients later without a full rewrite.

So, for folks in similar small team and fast MVPs setups, what workflows, prompts, linters, or lightweight review tools are you pairing with Blackbox to keep code quality and consistency under control without killing speed?

i’ve been using a combo of Cursor and IDE agents copilot and blackbox, depending on the task. Well, in theory it’s the best of all worlds, but in practice you'll find spending good deal of time figuring out which tool to use and when it is behaving weirdly.

like one gives a different answer, another misses context, and suddenly i’m cross checking their answers against each other instead of focusing on fixing the bug i started with. It still nets positive, but it’s def not as seamless as it looks from the outside, and the mental load is way higher than just sticking to a single setup.

Any tips on how you are integrating many tools in your workflow?

I’ve mostly been running vs code with the Blackbox extension as my default setup, and swapping in claude once in a while when I am to do some heavier long‑form reasoning. That combo has been more than enough for day to day work, so I haven’t felt much pressure to jump to cursor or lovable yet, esp once you factor in cost.

Well, from what I’m seeing, a lot of folks are going all in on them plus multiple premium models and ending up north of hundred bucks a month whereas my current stack, which is copilot pro + Blackbox sits closer to the 40 bucks range and still covers autocomplete, chat, and repo level assistance comfortably. How's your setup, sticking with vs code and layering Blackbox on top, or has anyone fully migrated their daily driver IDE to cursor or lovable while keeping it in the mix?

Most current agents feel like they rely on one big general-purpose model for everything, planning, reasoning, and actually writing code. but coding is a different beast compared to normal text.

what if we had dedicated coding models inside the agent stack? one model trained only for code understanding and generation, while another “manager” model orchestrates tasks and pulls pieces together into a project.

i’ve seen hints of this direction in tools like cursor and blackbox ai, but not sure if it’s really happening yet. feels like it could make agents way more reliable for dev work. or are general llms already good enough?