*URLs

It'd take an effort of Herculean scale to pry my RSS feeds away from me. They're fixed more firmly in place than Mjölnir is to the ground when not in Thor's hands. I'm always on the lookout for new feeds or meta feeds, which I describe as a feed, produced by a third-party, that aggregates a bunch of other RSS-based content.

The fine folks over at Browserling publish many free resources for the broader community, and a recent(?) new resource is a set of these meta feeds by topic:

• TechURLs

• DevURLs

• SciURLs

• FinURLs

• MathURLs

• PhysURLs

• HwURLs

• TuxURLs

At present, each site is just some scrollable divs with rendered RSS feeds for each publisher (here's the full list of over one hundred resources). There is also an option to search the feeds.

Browserling has plans to let you:

• filter out links that contain topics you don't like

• pin articles that are interesting to you to the top and receive optional push notifications as soon as these posts get published

• receive a personalized RSS subscription that shows news from the sites you choose

I found some new feeds to add to my RSS collection, and I suspect you will as well.

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* For Beginners

The drop on Amazon's machine learning university in a previous edition turned out to be pretty popular, thanks to pizza bomber Julia Silge.

If you're up for more free (in this case, MIT licensed) AI/ML educational resources, look no further than (of all places) Microsoft, who has two "beginner" courses in artificial intelligence [GH] and machine learning [GH].

These aren't just high-level shills to get you to lay down some coin for Microsoft's Azure-specific services. Fundamental AI/ML literacy is almost a necessity in today's modern workplace, and especially so in the tech industry. Therefore, it is in the best interest of outfits such as Microsoft or Amazon to do what they can to ensure a solid AI/ML-educated hiring pipeline exists.

Microsoft's AI curriculum is based on PyTorch and TensorFlow and covers:

• Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).

• Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.

• Neural Architectures for working with images and text. We will cover recent models, but may lack a little bit on the state-of-the-art.

• Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

The lessons in their ML curriculum contain:

• optional sketchnote

• optional supplemental video

• pre-lecture warmup quiz

• written lesson

• for project-based lessons, step-by-step guides on how to build the project

• knowledge checks

• a challenge

• supplemental reading

• assignment

• post-lecture quiz

and have both R and Python flavors to choose from.

While you're there, check out Data Science for Beginners, Web Development, and IoT for Beginners

ZomboDB

Standalone items have intrinsic value all on their own, but when complementary items are paired together, they can become league more than just the sum of their parts. An example from your supermarket aisle might be that decadently sugary crème filling and circular chocolate cookie wafers are palate magnets on their own; when combined to form an Oreo, they become indistinguishable from crack.

There are similar examples in the tech universe, and one of them — ZomboDB (ZDB) [GH] — is the topic for today's last section.

ZDB's tagline is "Making Postgres and Elasticsearch work together like it's 2022", and is technically described as "a [100% native] Postgres extension that enables efficient full-text searching via the use of indexes backed by Elasticsearch."

ZDB implements Postgres' Index Access Method API, and you can use it in a straightforward fashion with a straightforward CREATE INDEX ... USING zombodb on any existing Postgres table. ZDB fully manages a remote Elasticsearch index and guarantees transactionally-correct text-search query results.

Core features include:

• MVCC-correct text-search and aggregation results

• Managed and queried via standard SQL

• Works with current Elasticsearch releases (no plugins required)

• Query using

  • Elasticsearch's
    Query String Syntax
    via dsl.query_string()

  • ZQL: ZomboDB's custom query language

  • Raw Elasticsearch QueryDSL JSON

  • ZomboDB's type-safe query builder SQL syntax

  • Any combination of the above, even in combination with standard SQL

• Scoring and Highlighting Support

• Support for all Elasticsearch aggregations

• Automatic Elasticsearch Mapping Generation

  • Ability to map custom domains

  • Per-field custom mappings

  • json/jsonb automatically mapped as dynamic nested objects

  • Supports full set of
    Elasticsearch language analyzers

  • Supports Elasticsearch's Similarity Module

• Hot-Standby compatible

• Support for indexing & searching PostGIS geometry and geography types

Eric Ridge (@zoombodb), ZomboDB's creator/maintainer, also has a well-articulated sponsorship framework that I encourage you to check out.

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FIN

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