aalhour
Joined 11 years ago
Karma
286
Submissions
167
Member since
Jul 28
https://aalhour.com
View on Hacker NewsJoined 11 years ago
Fully remote: Yes (and open to hybrid in Munich)
Willing to relocate: No
LinkedIn: https://www.linkedin.com/in/aalhour/
Website: https://aalhour.com/
GitHub: https://github.com/aalhour
Resumé/CV: available on request (has personal contact info)
Email: a.z.alhour(at)gmail(dot)com
Technologies: Go, Python, Java, k8s, docker, AWS (backend/data infra)
About me: A software engineer with 15 years of experience (ex-Shopify, ex-HubSpot), with prior engineering management experience. I work on the backend, did data engineering in the past and am looking for a role in backend/data infra.
EDIT: typos.
def p(n):
total = 1
for i in range(1, n):
total *= (100-i)/100
return total
for i in range(1, 100):
print(f"p({i}) = {p(i)}")
The first 13 results: p(1) = 1
p(2) = 0.99
p(3) = 0.9702
p(4) = 0.9410939999999999
p(5) = 0.9034502399999998
p(6) = 0.8582777279999998
p(7) = 0.8067810643199997
p(8) = 0.7503063898175998
p(9) = 0.6902818786321918
p(10) = 0.6281565095552946
p(11) = 0.5653408585997651
p(12) = 0.503153364153791
p(13) = 0.44277496045533604
p(12) sits at 50%, of course it will be the mean! :D 1/99 * sum([(i-1)*i for i in range(2, 100)]) # 3266.666666666667
Both the median and mean are around 12 for 10K runs of the loop.