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Codemancers
Go doesn't have Functional generics like map, reduce, fold etc because it encourages imperative for loops
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.
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- Iffyuva
https://github.com/mperham/sidekiq/wiki/Signals Sidekiq can't be restarted. It will always be
TERM -t + Start - Published
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- Codemancers
monitoring sneakers via statsd https://github.com/jondot/sneakers/wiki/Metrics
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- Codemancers
was wonderring why google has a seperate email and password page for login https://support.google.com/mail/forum/AAAAK7un8RUoAsE-6wmaSU/?hl=en
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- Codemancers
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- Codemancers
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- Codemancers
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- Iffyuva
Imgix has got face detection https://docs.imgix.com/apis/url/size/crop This is very cool, and interesting :)
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- Iffyuva
If you want to know when a file is removed, run this command
git log -1 -- <path>. In order to get path, you can search for the file content in git log -p- Published
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- Codemancers
Javascript has a
with statement https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/with- Published
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- Iffyuva
Add Lets Encrypt certs to Ruby apps might be easier with https://github.com/sdaubert/letscert
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- Iffyuva
Add Lets Encrypt certs to Ruby apps might be easier with https://github.com/sdaubert/letscert
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- Codemancers
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- Codemancers
subdomain attacks http://resources.infosecinstitute.com/attacks-over-dns/
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- Iffyuva
Elixir/Phoenix framework packages bootstrap 3.3.5, but doesn't say explicitly
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- Iffyuva
testing again
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- Iffyuva
/til In elixir/phoenix, connection, models, etc are all Maps, ie %Map{}. If you understand them, you can go a long way
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- Iffyuva
/til Testing Jekyll
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- Iffyuva
/til Testing jekyll deployments
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- Codemancers
testing hooks
Showing page 41 of 42
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.