Blog / The engineering notebook
Ideas tested.
Lessons shared.
Field notes from building AI systems. Practical experiments, engineering decisions, and lessons worth sharing.
Blog / The engineering notebook
Field notes from building AI systems. Practical experiments, engineering decisions, and lessons worth sharing.
Amending one commit message is easy; a run of them is not. How to rewrite several past messages at once with interactive rebase, after a batch of commits shipped without their story numbers.
Privacy in AI is a hot topic. I created Llama Assistant to provide a privacy-focused alternative to popular AI assistants like ChatGPT or Claude AI. In this blog post, I'll share my motivation behind creating Llama Assistant and how it differs from other AI assistants.
SegFormer Mix-FFN inserts a depthwise 3×3 convolution between two feed-forward projections, giving image tokens local spatial context without explicit positional embeddings.
LayerScale multiplies each Transformer residual branch by a learned per-channel gain initialized near zero, helping very deep image Transformers begin close to an identity mapping.
Fifteen productivity tips from Dr. Milan Milanovic, covering single-tasking, the 3:3:3 plan, daily deep-work blocks, and the Feynman technique for learning something new.
Where a Django app actually slows down as traffic grows. Benchmark first, then fix the database queries, add the right caching layer, and scale instances with Docker and Kubernetes.
A short note on Vinh Khuat's multi-instrument performance and what it shares with software: love the craft first, practice the tools, then build something whole.
Notes from Minh Vu's session on teaching as a core engineering skill: explaining something is how you consolidate it, which is why leads, founders, and engineers all end up teaching.
A 2023 record of moving this blog to Cloudflare Pages for free bandwidth on a static export, kept for history. The site runs on Vercel Pro again, because API routes, Postgres, the RAG chatbot, and cron jobs all need the compute.
A short review of advancements in YOLO-NAS - a new YOLO architecture born from Neural Architecture Search.
How I built AnyLabeling on top of Labelme: why the tool needed to exist, how Segment Anything and YOLO inference is wired into the labeling loop, and how text OCR labeling works.
Varifocal Loss trains dense detectors to predict a score that combines object confidence with localization quality, while focusing training on difficult negative examples.