1/ In early 2004 we were heads down executing on Edit and Continue across a large contingent of teams. There had been several iterations of scoping, redesigns, and customer feedback.
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@MSEdgeDev @EdgeDevTools @ChromiumDev
#tools #accessibility #browsers
Also, a thread: 👇🏼

Issues pane, powered by @webhintio, listing accessibility issues with explanations why these are problems, links to more info and direct links to the tools where to fix the problem. https://t.co/4K5RynHhbg

The inspect element overlay showing accessibility relevant information of the element, including contrast information, ARIA name, role and if it can be focused via keyboard.

Colour picker with contrast information offering colours that are AA/AAA compliant. You can also see compliant colours indicated by a line on the colour patch.
Note: the current algorithm fails to take font weight into consideration, that's why there will be a new one.

Vision deficit ("colour blindness") emulation. You can see what your product looks like for different visitors.
https://t.co/bxj1vySCAb

Software architecture is in crisis, and the way to fix it is a hefty dose of anarchy.
Some lay the blame for this on @boicy with the whole microservices thing.
(Admittedly, @nicolefv, @jezhumble and @realgenekim didn’t help when they statistically proved that he might have been onto something with all that de-coupling and team-alignment…)
However I don’t blame him at all.
I think he saved us; bringing us back to the path of value-delivery and independent services, but now with added independent teams.
But one thing is clear. Microservices need more architecture, not less (as do other forms of #Accelerate-style software organisation).
(See https://t.co/B2hWmXhIqe if you need convincing)
I mean, all those pesky slices we need to carve up our monoliths (or were they big balls of mud?) That’s a significant amount of work right there…

Some lay the blame for this on @boicy with the whole microservices thing.
(Admittedly, @nicolefv, @jezhumble and @realgenekim didn’t help when they statistically proved that he might have been onto something with all that de-coupling and team-alignment…)
However I don’t blame him at all.
I think he saved us; bringing us back to the path of value-delivery and independent services, but now with added independent teams.

But one thing is clear. Microservices need more architecture, not less (as do other forms of #Accelerate-style software organisation).
(See https://t.co/B2hWmXhIqe if you need convincing)
I mean, all those pesky slices we need to carve up our monoliths (or were they big balls of mud?) That’s a significant amount of work right there…

@JuliaLMarcus @Iplaywithgerms This paper gives documentation on software (with causal reasoning, assumptions reviewed in appendix) for a parametric approach to estimating either "total effects" or "controlled direct effects" with competing events and time-varying
@Iplaywithgerms Total effects capture paths by which treatment affects competing event (e.g. protective total effect of lifesaving treatment on dementia may be wholly/partially due to effect on survival). Controlled direct effects do not capture these paths
@Iplaywithgerms More detailed reasoning on the difference and tradeoffs between total and controlled direct effects and causal reasoning in the point treatment context provided here along with description of some estimators and
@Iplaywithgerms If you are familiar with more robust approaches like IPW or even better TMLE for time-varying treatment, these are trivially adapted to go after the controlled direct effect by simply treating competing events like loss to follow-up (censoring). e.g.
@Iplaywithgerms Examples of IPW estimation of the total effect of a time-varying treatment described in Appendix D of this paper:
https://t.co/RNhcgTBMkb
And here
https://t.co/rMWmwFBWwV
Others in reference lists of above papers.
@Iplaywithgerms Total effects capture paths by which treatment affects competing event (e.g. protective total effect of lifesaving treatment on dementia may be wholly/partially due to effect on survival). Controlled direct effects do not capture these paths
@Iplaywithgerms More detailed reasoning on the difference and tradeoffs between total and controlled direct effects and causal reasoning in the point treatment context provided here along with description of some estimators and
@Iplaywithgerms If you are familiar with more robust approaches like IPW or even better TMLE for time-varying treatment, these are trivially adapted to go after the controlled direct effect by simply treating competing events like loss to follow-up (censoring). e.g.
@Iplaywithgerms Examples of IPW estimation of the total effect of a time-varying treatment described in Appendix D of this paper:
https://t.co/RNhcgTBMkb
And here
https://t.co/rMWmwFBWwV
Others in reference lists of above papers.