I wrote a large and thorough article about how to improve specifications in software development. read it here.
I was always confused by two buttons in Google Doc editor: highlight text and highlight background. I constantly made the same mistake again and again clicking on the wrong button. I used this feature several years, but still did wrong clicks…
This year they changed icons and I finally learned them!
The second icon is very good, it clearly shows that background color is here. First icon was unchanged (almost) and it is still wrong. So I learned the second icon finally and rarely click to the wrong one.
However, recently Google changed that and combined both actions into a single button. Am I happy? Hell no! Initial design was good, just with wrong icons. It was a 1-click design. New design is a 2-click design and I don’t like this change.
Interestingly, I never hit wrong icon in MS Word. Maybe it they have good icons? Pages also has a clear solution:
While I think Google Doc Editor is the best online editor, it got slightly worse this week.
Most experienced programmers have encountered projects where an apparently trivial subproblem turns out to be more difﬁcult than the major anticipated problems.
The creation of genuinely new software has far more in common with developing a new theory of physics than it does with producing cars or watches on an assembly line.
To me it doesn’t look like developers should do scope and duration estimates. They can participate and provide meaningful insights, but it’s a crime to ask them for estimates and later make them responsible for these estimates.
We shouldn’t accept a theoretical framework that places a priority on making the model simple over making it accurately reflect reality.
Adaptive behavior might emerge more generally in open thermodynamic systems as a result of physical agents acting with some or all of the systems’ degrees of freedom so as to maximize the overall diversity of accessible future paths of their worlds (causal entropic forcing).
In practice, such agents might estimate causal entropic forces through internal Monte Carlo sampling of future histories generated from learned models of their world. Such behavior would then ensure their uniform aptitude for adaptiveness to future change due to interactions with the environment, conferring a potential survival advantage, to the extent permitted by their strength (parametrized by a causal path temperature, Tc) and their ability to anticipate the future (parametrized by a causal time horizon, ).