So, here is a collection of repositories written mainly in Python and Ruby, with a fair amount of JavaScript mixed in.

• 💬 Ask me about designing and building APIs and helping partners and developers use those APIs. Areas of focus: real-time data streaming, event-driven architectures, early-warning systems, and working with social and weather data.

I recently left a Spanish start-up named Tinybird (a fabulous product and team), and am now looking for my next career chapter. The last four chapters have been amazing.

I joined Tinybird in April 2023 as a developer advocate so you will see a set of Tinybird-related projects here (others on the Tinybird account).

Update: Here are some Tinybird projects:

• A collection of unsupervised methods for detecting anomalies in real-time data. These recipes are written in SQL and are applied to streaming data in real-time. With this project, five types of anomalies can be detected:
  • Out-of-range: Identifying data points that fall outside a specified valid range.
  • Rate-of-change: Detecting abrupt changes or spikes in the rate of change of data.
  • Timeout: Flagging data points that cease to arrive within a predefined time interval.
  • Interquartile Range (IQR): Using statistical methods to identify outliers based on the interquartile range.
  • Z-score: Applying standard deviation-based analysis to identify anomalies in data distribution.
• Designed a hands-on workshop for learning to build API endpoints. This project includes an example set of Tinybird Data Source and Pipe definitions.
• A set of weather data API endpoints designed and hosted on Tinybird.
• A Python script that makes requests to the OpenWeatherMap API and streams the data to a Tinybird-hosted project. The Anomaly Detection project includes another example data generator.
  • Includes a Python script for publishing to a Google Pub/Sub stream, Tinybird resources for ingesting that stream and API Endpoints for publishing the data, and a Grafana dashboard JSON Model.

For eight years I was on the Twitter developer relations team, and as such, most of these repositories are focused on collecting, analyzing, and displaying Twitter data. These repositories can be broken up into three types:

• 'Backend' scripts that make API requests, receive the data, and typically store that data somewhere (or if a simple example, just send it off into the ether).

  • https://github.com/twitterdev/search-tweets-python/tree/v2 - Between 2018-2022, I maintained and enhanced this widely used Python client of the Twitter API search endpoints, including a 2021 migration from API 1.1 Enterprise to API 2.0. There is also this Ruby implementation that I developed and updated over the years. It supports all v2 and enterprise Twitter search endpoints (and not 'standard 1.1').

• Web applications in the form of standalone websites or chatbot applications hosted on Twitter.

  • https://github.com/twitterdev/SnowBotDev - Started as a demo of the Twitter Account Activity API, evolved into a full-featured chatbot application. (Since leaving Twitter, this app is no longer functional due to the TwitterDev account on Heroku being abandoned.)
  • https://github.com/jimmoffitt/SocialFlood - In 2016-2017, I went deep into Twitter data use cases related to flood-warning systems. This and other related content led to receiving two national awards for innovation related to public safety and weather communications.

• Customer tools for working with Twitter data.

  • JSON to CSV translator - Many, many customers were not equipped to work with Tweet JSON files and wanted to work instead with simple CSV files for spreadsheets or loading into a database (think of a social studies reseacher who wants to do spreadsheet-hosted analysis). With this tool, you can map the JSON attributes of interest to column fields, point to a collection of JSON files, and generate CSV files.
  • Bounding boxes - Many of the most popular Twitter API endpoints enable searching for Tweets of interest by matching on locations shared by users. The Twitter query language supports a 'bounding box' operator that matches on rectangular areas with sides no larger than 25 miles. This tool is used to take a 'study area' of interest, however large, and output a set of valid queries with bounding box definitions.
  • Query migrator - Twitter API search and filtered stream customers build queries to match on Tweets of interest. Customer can have up to 250,000 queries per real-time stream, and can have even more that are used with search endpoints. This tool was first developed in 2016 to help customers migrate enterprise versions (the "Gnip 2.0" migration). Before I left Twitter, this tool was updated to migrate queries and filters to Twitter API v2 (on a Twitter internal repository).

During the 13 years before Gnip/Twitter, I developed flood-warning software, so you may notice that a certain use case drives most of these efforts...

📫 How to reach me:

• https://www.linkedin.com/in/jimmoffitt/
• @snowman.bsky.social
• https://twitter.com/snowman