Lucas F. Costa writes about engineering management, Monte Carlo forecasting, command-line tools, and software planning.
HN links it for his posts on Monte Carlo forecasts and why deadlines fail, which commenters recommend for managers and team planning.
https://en.wikipedia.org/wiki/Delphi_method > https://news.ycombinator.com/item?id=26368011
https://blog.pragmaticengineer.com/yes-you-should-estimate/ > https://news.ycombinator.com/item?id=27006853
https://josephmate.github.io/PowersOf2/ Complexity Estimator
https://earthly.dev/blog/thought-leaders/ > https://news.ycombinator.com/item?id=27467999
https://jacobian.org/2021/may/20/estimation/ > https://news.ycombinator.com/item?id=27687265
https://tomrussell.co.uk/writing/2021/07/19/estimating-large-scale-software-projects.html > https://news.ycombinator.com/item?id=27906886
https://www.scalablepath.com/blog/software-project-estimation/
https://estinator.dk/ > https://news.ycombinator.com/item?id=28104934
https://news.ycombinator.com/item?id=28662856 How do you do estimates in 2021?
https://web.archive.org/web/20170603123809/http://www.tuicool.com:80/articles/7niyym Always Multiply Your Estimates by π > https://news.ycombinator.com/item?id=28667174
https://lucasfcosta.com/2021/09/20/monte-carlo-forecasts.html > https://news.ycombinator.com/item?id=28769331
https://tinkeredthinking.com/index.php?id=833 > https://news.ycombinator.com/item?id=28955154
https://blog.abhi.se/on-impact-effort-prioritization > https://news.ycombinator.com/item?id=28979210
https://www.shubhro.com/2022/01/30/hacks-engineering-estimates/
https://www.paepper.com/blog/posts/monte-carlo-for-better-time-estimates-of-your-work/
https://drmaciver.substack.com/p/task-estimation-101 >…
Guess streams was a mixed analog / digital world thing[0]. Is 'curl' the higher order wave crest of LC_NETWORK (aka 'sane' http protocol reference to type bundled 0,1,2 file descriptors)
One can have curl as a bash loadable routine without the need to recompile bash. Headless or not, much more sane[4] than ctypes.sh[1] as curl binary epilog/prolog handle the non-http protocol items within the context of curl module/program. More relevant logic level related to topic beyond http protocol only curl : socat[2] and gnu netcat[3].
Ah, HN doesn't provide a way to conjure up * data tapes (comments not withstanding). Ebnf being battle tested for years & grepping the ANTLER[7] by the horns:
awk interpeting http stream via http protocol[5] via ebnf of http protocol likely to better handle the different protocal layers more gracefully than bash script. Would need to make sure awk is in bit mode and not character/byte mode to avoid line discipline issues / 'stty sane'[4]. Note:
Perl/python provide better layers of abstraction than shell coding for the different levels of protocols used in/with http protocol/ ebnf,Kleene* coding.[6] In shell, having a different command programm for every ebnf set/protocol level vs. awk, different ebnf for each Kleene* protocol level)
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[0] : Your trerminal is not a terminal: An Introduction to Streams :…
I'm sure you have seen it already, but there has been some sound criticism of the DORA metrics: https://isthisit.nz/posts/2022/state-of-the-dora-devops-metrics/
Though I disagree with Logan about the change failure rate (see https://two-wrongs.com/you-can-reliably-measure-change-failure-rate.html), I still think he is spot on in terms of MTTR.
Strongly seconding Reinertsen for anyone interested in this, though. I think this article of yours is particularly insightful and of high quality: https://lucasfcosta.com/2022/08/31/engineering-metrics.html
A couple of related articles:
https://lucasfcosta.com/2021/09/20/monte-carlo-forecasts.html
https://www.joelonsoftware.com/2007/10/26/evidence-based-scheduling/
I've been trying to find out if there exists any out of the box solutions for getting cycle time for issues etc directly from the github api, and then do forecasting with confidence intervals - but so far I've drawn a blank.
funny given that only a month ago he was expounding the benefits of "Talk To Your Customers" [1] aka "Agile", which is essential artificially and programmatically governing your day via deadlines.
[1] https://lucasfcosta.com/2022/08/03/talk-to-your-customers.html
This is a good example of AI native thinking. Teach AI everything and ask it if it has learnt throughly learnt. The results are surprisingly good.
I am following similar steps from this article
https://www.lucasfcosta.com/blog/backpressure-is-all-you-need