- Published
- Author
Iffyuva
blocking browser call to https://login.deutschebank.co.in/corp/L001/consumer/theme/new_style_HW.css makes Deutsche Bank website load faster
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
https://saaspages.xyz is a website that collects best practices for SaaS websites, like hero sections, pricing, faq, etc
- Published
- Author
- Emil
https://saaspages.xyz is a website that collects best practices for SaaS websites, like hero sections, pricing, faq, etc
- Published
- Author
- Emil
https://saaspages.xyz is a website that collects best practices for SaaS websites, like hero sections, pricing, faq, etc
- Published
- Author
- Iffyuva
https://github.com/sl1pm4t/k2tf : Nifty tool to convert yaml to hcl. Useful when incorporating kubernetes provider
- Published
- Author
- Atul
To debug your compiled elixir code inside a container you can run the executable file with
remote_console argument to get a remote shell.- Published
- Author
- Atul
To debug your compiled elixir code inside a container you can run the executable file with
remote_console argument to get a remote shell.- Published
- Author
- Goromlagche
\p{} matches a character’s Unicode script. You can use \p{Tibetan}] to match for tibetan scripts https://ruby-doc.org/core-2.6.3/Regexp.html
- Published
- Author
- Atul
Kubernetes secret management - Store your AWS or any other keys, secret in kubernetes secret(
https://kubernetes.io/docs/concepts/configuration/secret
kubectl get secret) base64 encoded and define an environment variable in kubernetes config to access them in your app.https://kubernetes.io/docs/concepts/configuration/secret
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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.