Engineering notes

How I build the systems behind ByteCap.

Deep dives into the technical decisions, trade offs, and lessons behind an AI video product used by thousands of creators.

AI systems Production engineering Honest write-ups

ByteCap build log

Inside the work.

These are working notes, not marketing pages: the messy middle, the constraints, and the choices that made it into production.

01

Building ByteCap’s AI clipping pipeline

A practical look at turning a long video into short clips worth watching: transcription, candidate selection, scoring, rendering, and the systems around it.

AINode.jsFFmpegQueues
Read case study
02

Designing an AI auto B-roll system

How I’m approaching semantic matching, media selection, and timeline composition without making the editor feel unpredictable.

AIVideoProduct
Coming soon
03

Video processing without a huge infrastructure bill

The choices behind async work, temporary media, retries, and keeping a compute-heavy pipeline practical to run.

AWSRedisCost
Coming soon

The goal

Show the thinking, not just the stack.

Each note will cover the problem, approach, architecture, trade offs, and what changed after the system met real users.