The thing that separates good programmers from OK ones is the ability to which they can develop abstractions. From the best programmers we get libraries, and the rest of us write applications that use those libraries
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.
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, ).
The power of a visualization is that we can have a far more complex concept structure represented externally in a visual display than can be held in visual and verbal working memory
A group of slightly above-average people assigned to do what many considered an unglamorous and thankless task not only achieved success beyond anyone’s wildest expectations, but undoubtedly had a great time doing it and wound up becoming legends in their field.