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TILs

Fueling Curiosity, One Insight at a Time

At Codemancers, we believe every day is an opportunity to grow. This section is where our team shares bite-sized discoveries, technical breakthroughs and fascinating nuggets of wisdom we've stumbled upon in our work.

  1. Codemancers
    To debug your compiled elixir code inside a container you can run the executable file with remote_console argument to get a remote shell.
  2. Codemancers
    To debug your compiled elixir code inside a container you can run the executable file with remote_console argument to get a remote shell.
  3. Codemancers
    ActiveRecord has a warn_on_records_fetched_greater_than config. If you use it “config.active_record.warn_on_records_fetched_greater_than = 1500” then if you use a Model.all and the records are > 1500 it will long a warning. Generally if the row count is high, you would want to use “find_in_batches” rather than “all”. The original issue https://github.com/rails/rails/issues/16463
  4. Codemancers
    use credentials('global-cred-id') to use Jenkins credentials inside pipline Eg: DANGER_GITHUB_API_TOKEN = credentials('78c4b861-f9e8-44b8-8510-4d734ab752a0')
  5. Codemancers
    do not modify instance variables of a singleton class, not thread safe. furthermore no instance variables, only class variables.

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Your competitors are already using AI. The question is how fast you want to unlock the value.

  • Don't know where to start?

    AI is everywhere but it's unclear which investments will actually move your metrics and which are expensive experiments.

  • Your data isn't ready

    Most AI projects fail at the data layer. Pipelines, quality, access all need work before LLMs can deliver value.

  • Internal teams are stretched

    Your engineers are shipping product. They don't have capacity to also become AI specialists with production-grade experience.

  • Legacy systems block everything

    Aging, undocumented codebases make AI integration slow, risky, and expensive. They need to move first.

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