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.
ttlSecondsAfterFinished to clean up finished jobs automatically. - https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/#clean-up-finished-jobs-automaticallyjs-yaml <yaml-file> | jq ".topkey.nestedkey" | js-yaml > output.yamlgit show: git show <commithash> ':(exclude)package-lock.json'curl -s checkip.dyndns.orgmockRejectedValue with axios the data should be inside response.data. Eg: mockRejectedValue({ response: { data: {errors: []} }, status: 400, statusText: "Bad Request" })mockRejectedValue with axios the data should be inside response.data. Eg: mockRejectedValue({ response: { data: {errors: []} }, status: 400, statusText: "Bad Request" })echo $PATH | sed -e $'s/\\:/\\\\\
/'g or echo $PATH | tr ":" "\
" to see each entry in $PATH in separate line.:focus-within to keep the parent element visible when focus is on child. Helpful on making dropdown menu.jest.fn we can use mockFn.mockRejectedValue and mockFn.mockResolvedValue instead of mockFn.mockReturnValue(Promise.resolve({data: { message: "Failure", count: 1 }}); https://jestjs.io/docs/en/mock-function-api.html#mockfnmockresolvedvaluevaluedate + Time.zone.utc_offset rather than Time.zone.parse(date.to_s) if you are worried about performance\rssh -R github:80:localhost:4000 serveo.net"\r
# Install telepresence.\r
# Run this command to get an IEx shell connected to the cluster\r
telepresence --run iex --name debug@127.0.0.1 --cookie <cookie here>\r
# Find all nodes which are part of the cluster and their IP addresses\r
:inet_res.getbyname('dockup-headless', :a)\r
# Connect to one of the nodes\r
Node.connect(:"dockup@<ip>")\r
# Start observer locally\r
:observer.start()\rShowing page 30 of 42
AI is everywhere but it's unclear which investments will actually move your metrics and which are expensive experiments.
Most AI projects fail at the data layer. Pipelines, quality, access all need work before LLMs can deliver value.
Your engineers are shipping product. They don't have capacity to also become AI specialists with production-grade experience.
Aging, undocumented codebases make AI integration slow, risky, and expensive. They need to move first.