"with great software comes great devopsability"
cm, july 2023 :-D
"with great software comes great devopsability"
cm, july 2023 :-D
https://graydon2.dreamwidth.org/1597.html :
The Not Rocket Science Rule Of Software Engineering:
automatically maintain a repository of code that always passes all the tests
https://www.reddit.com/r/Terraform/comments/c07dgc/using_external_jsons_as_data_source/
data "http" "example" { url = "..." } locals { example_response = jsondecode(data.http.example.body) }
From there you can manipulate that data structure as you need. If you want to produce a map of lists then probably your next step would be to use two nested for expressions. I’m just guessing what you want the values in those lists to look like, but here’s a starting point:
locals { example_rules = { for k, m in local.example_response : k => [ for k, v in m : { key = k value = v } ] } }
https://github.com/wmazin/Visualizing-Quantum-Computing-using-fractals
https://learn.qiskit.org/course/ch-applications/flexible-representation-of-quantum-images-frqi
https://medium.com/qiskit/exhibition-ready-quantum-image-processing-4bb9fa8b52b5
https://medium.com/qiskit/creating-fractal-art-with-qiskit-df69427026a0
- Google levels:
https://codingrelic.geekhold.com/2018/08/google-software-engineering-levels-and.ht
ml
- Facebook levels:
https://www.quora.com/Facebook-company/What-is-the-expectation-out-of-each-soft
ware-engineering-level-at-Facebook
- Levels at various other companies: https://www.progression.fyi/
Common examples of metrics that can be associated with SLOs are disaster recovery time, application availability, live communication response time, first call resolution rate and application maintenance.
SLI = [ good event / valid event ] * 100%
request / response : Availability, Latency, Quality
Data Processing : Coverage, Correctness, Freshness, Throughput
usually external metrics (i.e. : end service KO time)
--
Appdex : scoring 0 to 1 for user satisfaction
-- source :
- Blog covering AIOps custom automations inside the Cloud Pak for Watson AIOps -> https://developer.ibm.com/articles/supercharged-aiops-with-custom-automations/
- Blog covering Instana’s new remediation framework -> https://www.instana.com/blog/tech-preview-take-action-from-within-instana-using-our-action-framework/
- Demo of Instana’s new remediation framework’s integration with PagerDuty’s Process Automation -> https://www.youtube.com/watch?v=t441dhv7Vtw
And you can connect with Arthur on LinkedIn at: https://www.linkedin.com/in/ademagalhaes
The Bell test demonstrates that measurements of an entangled state cannot be explained by any local hidden variable theory, and that there must be correlations that are beyond classical.
from: https://quantum-computing.ibm.com/composer/docs/iqx/example-circuits/bell
* Quantum Composer
OPENQASM 2.0;include "qelib1.inc";qreg q[2];creg c[2];h q[0];cx q[0],q[1];measure q[0] -> c[0];measure q[1] -> c[1];
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuitfrom numpy import piqreg_q = QuantumRegister(2, 'q')creg_c = ClassicalRegister(2, 'c')circuit = QuantumCircuit(qreg_q, creg_c)circuit.h(qreg_q[0])circuit.cx(qreg_q[0], qreg_q[1])circuit.measure(qreg_q[0], creg_c[0])circuit.measure(qreg_q[1], creg_c[1])
Quantum platforms, libraries, and learning platforms
"Qiskit Machine Learning introduces fundamental computational building blocks - such as Quantum Kernels and Quantum Neural Networks - used in different applications, including classification and regression. On the one hand, this design is very easy to use and allows users to rapidly prototype a first model without deep quantum computing knowledge. On the other hand, Qiskit Machine Learning is very flexible, and users can easily extend it to support cutting-edge quantum machine learning research."
List containers in a precise
$ docker network ls
NETWORK ID NAME DRIVER SCOPE
f732a9b3c5a1 bridge bridge local
7ec614323767 host host local
...
docker network inspect -f '{{ range $key, $value := .Containers }}{{printf "%s: %s\n" $key .Name}}{{ end }}' <NETWORK NAME>
<ID> <Name>