Writing

Posts

  1. #xgboost #gradient-boosting

    XGBoost Interview Q&A: Predicting Dublin Rent

    A mock ML interview on XGBoost. One running example: Dublin flat rent, starting from the average of €1,900 and walking toward a real rent of €2,500. Trees, gradients, eta, early...

  2. #human-in-the-loop #approval-fatigue

    The Human in the Loop Can Get Tired

    A human checkbox is not a safeguard by itself. I wrote a one-day toy diary of approving an agent's actions. By evening I had rubber-stamped delete_backups. The loop can fail fro...

  3. #transformers #attention

    From a Sentence to ChatGPT

    Start with four words. Turn them into tokens, embeddings, attention, stacked blocks, and next-word training. That is the same ladder that sits under ChatGPT.

  4. #precision #recall

    Precision vs Recall Is a Product Decision

    Precision and recall are not only model scores. They decide what the product does when it is unsure. Favour the side whose mistake costs more.

  5. #evals #ai-agents

    AI Agent Evals: Golden Examples and the Grader

    You would not hire someone after one good answer. You would give a work sample and a clear bar. One good demo is one good answer. Golden examples are the work sample. The grader...

  6. When you build RAG, Top K and Top P are settings you tune. Recall@K, Precision@K, and cost per request are what you measure. One HR chatbot, one parental-leave question, five nu...

  7. Accuracy can look great when the thing you care about is rare. Precision and recall split the story into two questions. Four colours, one meaning each, used the same way through...

  8. A write first lands in shared buffers. WAL and dirty pages run in parallel, with an LSN gate. Then four Docker runs on a toy lunch_notes table: the wait knob, batching, a few cl...

  9. #ai-papers #transformers

    14 AI Papers: A Lunch-Box Reading Map

    Fourteen famous AI papers as a lunch-box map: what each idea is for, which to read first, and how they fit together.

  10. #system-design #caching

    System Design Patterns: A Lunch-Tray Map

    Seven patterns for system design interviews: read-heavy tricks, write bottlenecks, decoupling, long jobs, failures, spreading data, and real-time updates.

  11. A chatbot answers one token at a time. It should not recompute the whole chat for every new token. The KV cache stores past Keys and Values — and that is why long chats eat GPU ...

  12. #jev #typesafe

    Jev: Stamp the Answer, Don't Write It

    Chat models write text. Decision models return typed fields. You can also wrap the LLM you already run so it answers like a decision model — no extra training.

  13. #floci #aws

    Floci: Any Cloud on Your Laptop

    You do not need a real cloud account to try a bucket. Floci runs AWS, Azure, GCP, and OCI emulators on your laptop — each on its own port.

  14. #clickhouse #olap

    ClickHouse: When Columns Beat Rows

    Postgres is great at one order. It is less great at summing latency for 90 days of events. ClickHouse stores columns together and reads only the ones you name. Keep the ledger i...

  15. #bunnyshell #preview-environments

    Bunnyshell: A Full Stack Per Pull Request

    Shared staging is a queue. Bunnyshell clones the full stack for each pull request — app, database, and the rest — comments a URL, then destroys the clone.

  16. #postgres #sql

    Postgres EXPLAIN: Ask for the Plan

    Ask Postgres for its plan. Read the tree from the inside. JOINs, Index Cond vs Filter, SubPlans, and buffers — explained simply.

  17. A standup claim, a Slack table, and a same-split bake-off on Adult Income. AutoGluon edged AUC. CatBoost won the production shape. TabPFN trained in a blink — on 3,000 rows — an...