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.

Published
Author
user-image
Emil
You can speed up image builds by making use "--cache-from". This is useful when 1) you already have a previous version of the image which you can pull 2) docker pull is faster than docker build 3) intermediate layers in the previous version can be reused. This is useful for speeding up builds in docker container builder
Published
Author
user-image
Yuva
Co-founder
There is something called QoS for pods using which we can prevent unwanted rescheduling of pods
Published
Author
user-image
Emil
you can use GCP cloud shell and use it to run kubectl commands instead of having to create a k8s context on your local kubectl CLI tool. This is very useful for doing some adhoc debugging on a k8s cluster you have access to. Just login to google cloud using google auth, and then click on "Connect" next to the cluster you want to connect to, then use cloud shell.
Published
Author
user-image
Akshay
We can leverage structs in ruby to encapsulate and hold internal class data into a struct object. Since struct saves us from writing attr_accessor methods or initializer method, we can easily leverage it when in need of a temporary data structure and need to have the data grouped.
Published
Author
user-image
Kamal
If you are using materialize, you need to initialize the select element with material_select(). Otherwise the select box will not get rendered.
Published
Author
user-image
Akshay
Elixir and Erlang has a limit on the number of atoms that can be created. We can view the atom limit using :erlang.system_info(:atom_limit). Atom's text value is stored in the atom table and this is not garbage collected. This explains why user inputs to phoenix routes are to be matched with strings rather than atoms.
Published
Author
user-image
Kamal
rubocop -a can autocorrect many kinds of issues.

Showing page 67 of 83

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.

Don't Worry. We've got you covered.

Start with the audit.