- Published
- Author
- Mrinmoy
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
- Emil
We can use the command
docker-compose up -d --no-deps --build to do a zero downtime upgrade of services inside a running docker-compose stack.- Published
- Author
- Emil
docker system prune -a will free up significant space on your hard disk if you have a lot of stopped docker containers and dangling volumes etc- Published
- Author
- Emil
Cypher language, the SQL equivalent for neo4j graph DB is kickass . It allows users to fetch complex join queries in an intuitive way using pattern matching. https://neo4j.com/developer/cypher/ . Also, neo4j is the DB that helped journalists uncover interesting data relationships that led to paradise papers. This is because neo4j treats relationships as first class citizens and you can query using paths between different data elements. For example, "what is the shortest path between PersonA and PersonB".
- Published
- Author
- Mrinmoy
- Published
- Author
- Manu
staging hunks with fugitive in vim http://vimcasts.org/episodes/fugitive-vim-working-with-the-git-index/
- Published
- Author
- YuvaCo-founder
This accessibility cheatsheet is pretty good https://bitsofco.de/the-accessibility-cheatsheet/
- Published
- Author
- Harshwardhan
don't use new webpack.optimize.ModuleConcatenationPlugin() in development env, causes memory leak in webpack 3 and build fails
- Published
- Author
- Akshay
rspec-rails will lazy load puma from version 3.7.1
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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.