Built by a creator, not a committee

I didn’t set out to build software. I set out to finish my own project faster.

For eight years I lived inside timelines and keyframes—producing and editing videos across YouTube channels that covered economy and trade news, defense and military, science and space, food, history, and the occasional mystery rabbit hole. Before that, I spent a year at a small advertising startup, watching how “ideas” become campaigns and how messy the world gets between planning and shipping. Those two experiences shaped how I work: ship quickly, keep things practical, and never add friction where speed matters.

When I began prototyping mobile apps—armed with 1–5% basic coding and a stubborn willingness to learn—I ran into a wall I didn’t expect: dataset labeling. I needed clean, consistent annotations to power a private AI workflow for my own LLM‑enabled mobile app. The existing tools were either cloud‑hungry, enterprise‑heavy, or just awkward for a solo builder who wanted to stay offline and move fast, there was no Image annotation software or tool or application for a individual person to use or small team, All were only for enterprise only. I tried several. They were powerful in theory and slow in practice. Too many buttons. Too many walls. Too many nudges to upload my data “for convenience.”

So I built BoundMi—a desktop image annotation tool that behaves like an editor, not a dashboard. It made to work even for Non coder, Pro coder, or even for a person who don't know how to do it, we made the buttons and tools simple so that even beginner or to a person who don't know what is a image annotation is.

The path that got me here

My formal background is in design and communication: a Master’s in Visual Communications and a Bachelor’s in Costume Design & Fashion. Professionally, I’ve worked across Adobe Premiere Pro, DaVinci Resolve, Final Cut Pro, Photoshop, and CorelDRAW. I’ve shipped websites with WordPress and no‑code builders. I’m not a “framework collector.” I’m a maker. If a tool doesn’t help me hit play and move forward, it’s a distraction.

The YouTube years taught me the value of an editor‑first workflow: keep hands on the keyboard, eyes on the frame, and the story moving. Annotation should feel the same. You shouldn’t have to fight the tool to draw a box, adjust a polygon, or move to the next image. And you shouldn’t have to ask permission to keep your data private.

Why BoundMi exists

Because the most common alternatives made me choose between two bad options:

1. Go to the cloud. Upload images, accept latency, accept vendor rules, and pray the export matches your training pipeline.
2. Go enterprise. Install something heavy with a hundred toggles I’ll never use, then wrestle it into a single‑user workflow.

Neither path is creator‑friendly. BoundMi takes a third path: offline‑first by default, AI‑assisted when you want it, and always under your control.

What “editor‑first” means in practice

• Predictable controls. Draw, adjust, re‑label, delete—without wondering if the tool will fight you.
• Local projects. Folder‑based datasets keep everything visible. No mystery storage, no accidental uploads.
• Real‑world speed. Smooth zoom and pan, undo/redo, autosave that quietly does its job. The app gets out of your way.
• Clean exports. COCO, YOLO, and CSV that fit real training pipelines. You shouldn’t need a second tool to “fix” your export.
• Optional AI. If you enable it, detection runs against your imported images, using open‑source models. If you don’t enable it, nothing leaves your machine, Video detection on the way.

The AI stance (short and clear)

AI in BoundMi is a helper, not a hostage taker. You can switch it on or off. Today, the focus is on practical open‑source models—especially OWL‑ViT / OWL2 for class‑agnostic (zero‑shot) object detection. They’re versatile, fast enough on modest hardware, and they don’t force you into a closed ecosystem. I evaluate other model families all the time, but here’s the rule: if it isn’t fast, accurate, and creator‑friendly, it doesn’t ship. No lock‑in, no telemetry circus, no “trust us” black boxes.

What makes BoundMi different

1. Private by default. Your images live on your disk. Period. When AI is on, it talks to a simple endpoint you (or your team) control. No surprise uploads.
2. Made for individuals and small teams. The interface is calm, the features are focused, and the workflow doesn’t assume you have a devops department.
3. Zero shame in being simple. Simple is a feature. The job isn’t to impress you with 40 panels—it’s to help you label 400 images without losing your mind.
4. Open, not open‑ended. Exports are standard (COCO/YOLO/CSV). Inputs are normal folders and class lists. No proprietary maze to lock you in.

From video timelines to annotation lines

People ask why a video person built an annotation tool. The real question is why more editors haven’t. Editors understand precision. We respect time. We expect software to keep up with us. When I label, I think in beats: click, draw, refine, next. BoundMi embraces that rhythm. It’s built to let you move in a straight line from unlabelled to done.

The moment that flipped the switch

I was labeling images for a mobile app experiment—a private LLM that needed crisp, reliable training examples. One particular open source tool I was using kept “helping” me by thing all online, no AI, no auto save, no undo no redo, no delete, no projects, cant open any projects, no way to resume from last work, so list keep going on, but It promised speed. What I got was anxiety: unknown number time i need to rework, waiting, frustration cant save or undo, speed, time and only thing worked was exports that matched the format I’d prepared my pipeline for. That evening I wrote the first outline for BoundMi. By the weekend I had a prototype. I got somethings right on the first try—but I got the direction right: local‑first, editor‑first, export‑honest. Now it took three months to finish and BoundMi (Pronounced as Bound-Me) ready for shipping.

Who BoundMi is for

• Creators and solo founders who want usable, private annotation without a server farm.
• Students and researchers who need clean datasets and predictable exports, not enterprise contracts.
• Small teams and startups that care more about shipping models than managing dashboards.
• Agencies that want to keep client data on‑prem and move quickly between projects.

If you live inside code editors and command lines, BoundMi will feel refreshingly quiet. If you live inside creative tools, it will feel familiar.

What I won’t compromise on

• Privacy and control. Your data isn’t a product. No selling, no sneaking, no telemetry drama.
• Clarity. Features should be clear enough to explain in a sentence. If I can’t do that, I don’t ship it.
• Performance you can feel. If an “AI boost” slows you down, it’s not a boost. It’s bloat.

Design principles

• Calm interface. Fewer modes, fewer surprises. Simple Tools.
• Keyboard first. Power users shouldn’t need the mouse for everything.
• Forgiving by design. Undo is cheap. Autosave is quiet. Nothing catastrophic happens on a mis‑click.
• Honest defaults. Private by default. AI On/Off option. Exports that match what you actually need.

What’s inside today

• Annotation tools that matter: bounding boxes, polygons, and points you can adjust anytime.
• Project structure that stays out of your way: folder‑based datasets and optional class lists from simple text files.
• Real exports: COCO, YOLO, CSV, VGG, JSON, VOC XML—formats your training scripts already understand.
• The little things that add up: zoom, pan, autosave, undo/redo, and sensible keyboard shortcuts.
• Optional AI assist: when enabled, detection runs against a simple endpoint using open‑source models like OWL‑ViT/OWL2 and more models on the way even inhouse models are coming soon.

What’s next (the honest version)

I care about the roadmap, but I care more about stability. The next steps are shaped by real‑world use:

• Better labeling speed through smarter snapping, smarter handles, and smarter defaults.
• Richer class management without turning the app into a spreadsheet.
• Team‑friendly touches that still work for solo users.
• Model upgrades only when they prove real‑world value. Shiny demos don’t count. Sustained accuracy and speed do.

Video annotation on the Roadmap. It’s on my radar, but I won’t ship it until it respects the same principles: local‑first, editor‑first, and actually faster than frame‑by‑frame workarounds.

Why my background matters to BoundMi

Studying visual communication trained my eye for hierarchy, contrast, and focus. Studying fashion taught me discipline, iteration, and how much craft hides behind “simple.” Running YouTube channels taught me to deliver on a schedule, not on a mood. Building websites and tiny apps taught me to avoid architectural drama when a script will do. All of that shows up in BoundMi’s decisions: small, sharp, and useful beats big, slow, and impressive.

The promise

I will keep BoundMi practical. I will keep it private by default. I will treat “simple” as a feature, not a limitation. And I will keep listening—to creators, researchers, students, and small teams who want the tool to disappear so the work can appear.

If you’ve ever opened an app and felt like the app was in charge of you, BoundMi is my answer: a tool that works the way makers actually work.

— BoundMi Founder