We build interfaces, data tools, coding systems, and weird intelligent products until they either become useful or get replaced by something sharper.
We build assistants, workflows, and interfaces that have to survive actual use, not just a launch tweet.
A lot of the work starts as experiments. The good ones turn into products. The strange ones keep cooking.
Fast iteration is the culture, but the bar is still usefulness, clarity, and systems that hold up under pressure.
Current lab board
2026Shipping now
Major focus right now is /Slash and Apex.
ACRA, TagPilot, Pulse, and ChatPilot continue shipping in parallel.
Current research
ACRA: Auditable LLM Agents for Evidence-Grounded Clinical Decision Support
Building agents that surface traceable evidence, transparent reasoning steps, and clinician-legible outputs.
In the lab
BrainCrate, Aureus, ACRA, and whatever interesting model behavior survives the week.
Experimental tracks stay small, fast, and disposable until they prove useful.
Focus areas
The common thread is simple: we like systems with strong feedback loops, strong interfaces, and enough edge to turn into real products.
01
AI systems for teaching, tutoring, and reinforcement without sanding off the hard parts of learning.
02
Data labeling, retrieval, and organization tools for the messy information real teams actually work with.
03
Products that make repositories, tools, and internal systems easier to operate, understand, and trust.
04
Long-horizon research into market behavior, decision-making, and model behavior inside financial environments.
Selected work
BlueprintLabs is a focused product lab building practical AI tools, sharp experiments, and internal research systems that can actually ship.
Your AI engineer for GitHub and VPS ops. Repo-aware chat, AI-powered edits, PR reviews, project summaries, and live server monitoring in one mobile-first workspace.
Built to collapse AI chat, code editing, project reporting, and production monitoring into one session instead of five disconnected apps.
A labeling workspace for teams building datasets and training loops without turning annotation into bureaucracy.
This is the strongest product bet in the lab right now: applied ML tooling with a bias toward speed, clarity, and less operational drag.
A GitHub digest that turns repository activity into concise, readable updates instead of another stream of notifications.
Useful when teams want steady signal on what shipped, changed, or started drifting, without spending the week in GitHub tabs.
An AI-powered Solana meme analyzer that automatically scans narratives, token momentum, and social noise for faster signal.
Built for tracking meme coin heat without manually living inside timelines, dashboards, and noisy Telegram chatter all day.
A lightweight assistant widget you can drop into a site with a single script tag and almost no ceremony.
A product experiment in low-friction distribution, simple setup, and getting useful AI into a page fast.
An internal concept around guided learning loops, tutoring, and reinforcement for students and researchers.
An ongoing investigation into market modeling, trading intelligence, and the behavior of language models in economic settings.
Approach
The work should feel experimental and alive, but not messy for the sake of it. We want the final system to carry the same edge as the prototype without feeling half-finished.
01
We start with the ugly workflow, the trust gap, or the broken internal tool instead of a vague AI promise.
02
The first pass should prove the interaction, the loop, or the edge case that actually matters.
03
The final thing should still feel fast and experimental, but clear enough that another team can actually use it.
Contact
Use the work-with-us page for project conversations, browse the public repos on GitHub, or ask Omar in the chat widget if you want the fast version first.
Updates
Launches, experiments, weird findings, and the occasional note when something in the lab becomes real.
