Field notes
Building at the terminal
A running log of applied AI and backend work — data pipelines, inference services, and the boring infrastructure that keeps them alive.
40+ entries · updated weekly
Ann Naser Nabil — AI/ML & backend engineer
From the lab bench to production.
Engineering0x is the open lab notebook of an AI/ML and backend engineer: applied research, systems that stay up, and the craft of shipping software in the open.
engineering0x — zsh
$ whoami
Ann Naser Nabil
$ git log --oneline -3
d946f45 ci: drop standalone typecheck step; rely on next build's type checking
fbfa05f Add GitHub Pages deploy workflow
07370a9 Add Phase 1 site scaffold and design primitives
$ deploy --status
✓ live at nabil.dev.bd
$
boot complete — static export · 0 runtime deps
Work
A lab bench, not a showcase
A running record of systems, experiments and the occasional yak shave — written where the work happens.
Systems
Backend systems engineered to stay boring
Databases, queues and services designed so that midnight pages never happen. The goal is boring: it means everything is working.
ML
Applied ML, end to end
From data cleaning to production inference, with the trade-offs written down.
12
side projects shipped
99.9%
uptime across the lab stack
About
An open lab notebook
What this site is, what it publishes, and why the goal is boring.
Engineering0x is the open lab notebook of Ann Naser Nabil, an AI/ML & backend engineer. Everything here is written where the work happens — at the same terminal, in the same repos, on the same infrastructure the lab runs on.
What gets published is the transcript of work that actually ran: field notes from applied ML, post-mortems from production, and tutorials that show the whole path — trade-offs, dead ends and all.
The philosophy is straightforward: boring is working. Databases, queues and services are engineered so that midnight pages never happen — the infrastructure is unremarkable on purpose.
$ currently building: applied ML in production, and the infrastructure to keep it boring
Skills
The skill matrix
Three benches — AI/ML, backend, systems — and the tools that live on each.
PyTorchTransformersRAG pipelinesLoRA fine-tuning
Writing
Notes from the lab bench
Field reports, post-mortems and tutorials — every post is a transcript of work that actually ran.
Applied ML
Fine-tuning under 10 GB of VRAM
A practical walkthrough of LoRA fine-tuning on a single consumer GPU: sharding, quantization, and the metrics that actually moved.
LoRAPyTorchshipped
Backend
Postgres at 100k writes per second, without the drama
What it took to keep a write-heavy Postgres cluster boring: partitioning, connection pooling, and knowing when to say no.
PostgreSQLGo
Tooling
A CI pipeline that fails fast and tells you why
Designing a pipeline where a failure is a message, not a mystery — with the exact exit codes and artifacts to prove it.
GitHub Actionsgreen
Contact
Let's build something boring that stays up.
For collaboration, questions, or a good argument about infrastructure.