Value = doing things that other people don't do, won't do or can't do.
"You get paid in direct proportion to the difficulty of problems you solve" — Elon Musk
Building Principles:
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Value = doing things that other people don't do, won't do or can't do.
"You get paid in direct proportion to the difficulty of problems you solve" — Elon Musk
Service is high-touch result generation (building experience), product is low-touch result generation (scaling experience).
Sell time to buy experience, sell experience to buy time.
“If you don't find a way to make money while you sleep, you will work until you die.” — Warren Buffett
Use free time to build systems that free up time.
"Let systems run the business and people run the systems." — Michael Gerber
More from Jack Butcher
More from Software
Kubernetes vs Serverless offerings
Why would you need Kubernetes when there are offerings like Vercel, Netlify, or AWS Lambda/Amplify that basically manage everything for you and offer even more?
Well, let's try to look at both approaches and draw our own conclusions!
🧵⏬
1️⃣ A quick look at Kubernetes
Kubernetes is a container orchestrator and thus needs containers to begin with. It's a paradigm shift to more traditional software development, where components are developed, and then deployed to bare metal machines or VMs.
There are additional steps now: Making sure your application is suited to be containerized (12-factor apps, I look at you: https://t.co/nuH4dmpUmf), containerizing the application, following some pretty well-proven standards, and then pushing the image to a registry.
After all that, you need to write specs which instruct Kubernetes what the desired state of your application is, and finally let Kubernetes do its work. It's certainly not a NoOps platform, as you'll still need people knowing what they do and how to handle Kubernetes.
⏬
2️⃣ A quick look at (some!) serverless offerings
The offer is pretty simple: You write the code, the platform handles everything else for you. It's basically leaning far to the NoOps side. There is not much to manage anymore.
Take your Next.js / Nuxt.js app, point the ...
Why would you need Kubernetes when there are offerings like Vercel, Netlify, or AWS Lambda/Amplify that basically manage everything for you and offer even more?
Well, let's try to look at both approaches and draw our own conclusions!
🧵⏬
1️⃣ A quick look at Kubernetes
Kubernetes is a container orchestrator and thus needs containers to begin with. It's a paradigm shift to more traditional software development, where components are developed, and then deployed to bare metal machines or VMs.
There are additional steps now: Making sure your application is suited to be containerized (12-factor apps, I look at you: https://t.co/nuH4dmpUmf), containerizing the application, following some pretty well-proven standards, and then pushing the image to a registry.
After all that, you need to write specs which instruct Kubernetes what the desired state of your application is, and finally let Kubernetes do its work. It's certainly not a NoOps platform, as you'll still need people knowing what they do and how to handle Kubernetes.
⏬
2️⃣ A quick look at (some!) serverless offerings
The offer is pretty simple: You write the code, the platform handles everything else for you. It's basically leaning far to the NoOps side. There is not much to manage anymore.
Take your Next.js / Nuxt.js app, point the ...
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.
