r/nairobitechies

Over the past few months I've been building an open-source desktop application called Limboo, and I wanted to share the idea behind it because I'm curious whether anyone else has been running into the same problems.

One thing I've noticed with AI coding tools is that they're incredibly good at writing code, but once a project becomes large, the actual engineering workflow still feels fragmented.

The AI is usually in one window.

Git is somewhere else.

The terminal is somewhere else.

Build logs are somewhere else.

Documentation is in another browser tab.

Project decisions are spread across old conversations.

The longer I work on something, the more time I spend rebuilding context instead of actually building software.

That observation is what started Limboo.

The goal isn't to replace coding agents like Claude Code, Codex, or other agent-based tools.

I actually want to use those.

The idea is to build everything around them.

Instead of treating the AI as the entire application, Limboo treats it as one component inside a much larger engineering workspace.

Every task becomes its own isolated session.

Each session has its own conversation history, terminal state, Git branch or worktree, checkpoints, permissions, local memory, search index, execution timeline, and task list.

If I stop working on a feature today and come back in two weeks, I don't want to explain everything again.

I want to reopen the session and continue exactly where I left off.

Another thing I wanted to improve is transparency.

I don't like when an agent runs commands that disappear into a log somewhere.

If it's running a build, I want to watch the build.

If it's modifying files, I want to see the diffs while it's working.

If it wants approval, I don't want a giant modal that interrupts everything—I want approvals to appear naturally inside the conversation stream.

Planning is another area I'm spending a lot of time on.

Instead of generating a plan that disappears after one response, the plan becomes a living task board.

Once it's approved, the agent starts implementing those tasks while updating their progress in real time.

Git is also treated as a first-class part of the workflow instead of an afterthought.

Every change is visible through diffs, checkpoints, snapshots, commit previews, and history before anything gets committed.

The goal is to make it obvious what changed, why it changed, and which conversation produced those changes.

I'm also experimenting with isolated Git worktrees so multiple sessions can work on completely different features without stepping on each other.

Another area I'm investing in is local memory.

Rather than asking the agent to rediscover architecture decisions, coding conventions, and previous implementations every session, the application stores that knowledge locally and retrieves only what's relevant before each request.

Everything is designed around long-running software projects instead of one-off prompts.

The stack is Electron on the desktop, Rust for native services, and AI agents orchestrated through the Claude Agent SDK.

It's still very much a work in progress, and I'm sure there are plenty of design decisions I'll end up changing as I build more of it.

I'd genuinely appreciate feedback from people who use AI coding tools every day.

I'm especially interested in hearing what parts of your workflow still feel disconnected, because that's really the problem I'm trying to solve.

Repository: https://github.com/BotCoder254/limboo

I'd love to hear what you think—both the good and the bad.

I've been reading about how unmanned/unstaffed shops are a normal part of daily life in some countries, and it got me thinking about whether something similar could ever work here in Kenya.

In Japan, unmanned stores are common — some convenience stores now run with AI cameras and self-checkout kiosks instead of cashiers, and honesty-based farm stands (where you drop coins in a box for vegetables) have existed in rural areas for decades. Japan's low crime rate and strong culture of public honesty make this possible; people simply don't take what isn't theirs, even when no one's watching.

South Korea has "unmanned cafes" and snack shops, especially near universities — you grab what you want and pay through a self-service kiosk or an app. Same story: low theft rates and heavy CCTV coverage make it viable.

In parts of Scandinavia (Sweden, for example), there are unmanned grocery stores in small rural towns where the population is too small to justify a full-time cashier. Customers unlock the door with an app and scan their own items.

The common thread: low crime rates, high social trust, and usually some tech (cameras, apps, or digital payment tracking) as a backup deterrent.

So my question is — could this ever work in Nairobi?

Would an unmanned kiosk or shop survive here, even with CCTV and mobile money payments? Is it purely a crime-rate issue, or is it more about trust, infrastructure, and payment habits? I'd love to hear from people who think this could actually work (maybe in a gated estate or a low-traffic area) versus those who think it's wishful thinking in the current environment.

Hi guys, I have these 2 DDR4 RAM sticks 8GB each…
Looking to sell them all @4500. Dm

I launched veralabel.dev about a week ago, and I wanted to share one lesson I've learned as a solo founder.

My original plan was to launch in phases. Phase 1 was focused on medical AI, with the long-term vision always being to build a platform for collecting multimodal datasets and languages for AI training.

As I started talking to people in the industry, reality hit.

Some companies already had internal tools that were years ahead of where I was. One person told me they had an NVIDIA-powered model capable of recognizing diseases across virtually every part of the body. On top of that, I began to appreciate the legal and regulatory hurdles that come with medical AI.

For a moment, it was frustrating.

Then I realized something: this didn't invalidate the vision—it only changed where I should begin.

The end goal had always been the same: help build the datasets that power the next generation of AI, especially data that represents African languages, cultures, and contexts. Starting there makes far more sense than trying to compete immediately in a space where others already have mature internal systems and significant regulatory experience.

So VeraLabel starts with the foundation: data.

I'm still early, and there's a long road ahead, but I'm excited to see where this journey leads.

I'd love to hear from other founders—has the market ever convinced you to change the order of execution without changing your overall vision?