Archive

HackMD is a collaborative online service for sharing and editing markdown documents.

Some nifty features I liked - exporting to github repo. Publishing markdown documents as standalone pages. e.g: this page.

My hackmd page is -- HackMD/@btbytes

Docling: The Document Alchemist from Towards Data Science. Has a good code tutorial on how to use Docling to extract text, tables, and using OCR to extract hard to extract content.

IBM Granite-Docling: End-to-end document understanding

See also: GitHub - bytedance/Dolphin: The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

This dataset was developed using docling: HuggingFaceFW/finepdfs · Datasets at Hugging Face

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Docling + Go + Bob: The Modern Document Stack

desktop application engineered in Go that brings the power of IBM’s Docling service directly the users’ fingertips. Built with the sleek Fyne framework, this application transforms the complex task of document conversion into an intuitive, point-and-click experience. where IBM Bob is a AI chatbot for code IDEs.

How your choice of Optimisers affect your training(and why you should care) - HackMD by Chimay Karkar.

Milvus started out as "Vector Similarity Search", but recently has rebranded as "Vector Database" in-line with current phrasing. Zilliz: Vector Database is the Cloud hosted version of Milvus. Read Vector Database Stories over at Zilliz's blog.

Watched a couple videos of Laser Welding by Rex Alexandre of Handheld Laser Institute

Malloy a modern language for analyzing,
transforming, and modeling data.

run: duckdb.table('../data/airports.parquet') -> {
  select:
    id
    code
    city
  limit: 10
}

is equivalent to SELECT id, code, city FROM airports LIMIT 10 in SQL.

In Malloy, all output fields have names.

One of the main benefits of Malloy is the ability to save common calculations into a data model. The data model is made of sources, which can be thought of as tables or views, but with additional information, such as joins, dimensions and measures.

source: airports is duckdb.table('../data/airports.parquet') extend {
  dimension: county_and_state is concat(county, ', ', state)
  measure: airport_count is count()
  measure: average_elevation is avg(elevation)
}

source can be imported into another using an import statement -- import "airports.malloy"

Sources can also contain named views.

Joins are also declared as part of sources:

source: flights is duckdb.table('../data/flights.parquet') extend {
  join_one: aircraft on tail_num = aircraft.tail_num
}

where is used for filtering. Filtering can be applied to  aggregate calculation (a mesure)

nested views produce with subtables.

Literals of type date and timestamp are notated with an @, e.g. @2003-03-29 or @1994-07-14 10:23:59. Similarly, years (@2021), quarters (@2020-Q1), months (@2019-03), weeks (@WK2021-08-01), and minutes (@2017-01-01 10:53) can be expressed.

There is a special time literal now, referring to the current timestamp, which allows for relative time filters.

Numeric values can be extracted from time values, e.g. day_of_year(some_date) or minute(some_time)

Time ranges -- @2003 to @2005

The output from one stage of a query can be passed into another stage using -> ( Pipeline )

More Reading

• SQL, Malloy, and the Art of the Renaissance
• Data is Rectangular and other Limiting Misconceptions from the creator of Malloy; Jan 2023.

I was interested in learning to read Arabic script and even bought a book with CD on it, in early 2020s. Didn't get around to it, and I left the book in India.

This is an interesting tutorial - Writing Arabic in English.