Promised to share my portfolio, so here it is.

None of this is investment advice, please do your own research. I may choose to exit, reduce or add anytime to these.

Just finished @ryanckulp’s new course “How to Buy, Grow, and Sell Small Companies”

https://t.co/wcX0zFXHgL

Here are some main takeaways + my verdict 👇

(1) Acquisition is the easiest path to entrepreneurship

Don’t have an idea? You don’t have to have one. Buy one.

Don’t know how to code? You don’t have to. Use revenue to outsource development.

Don’t have money? You don’t have to have any. Bring in an equity partner. Leverage a debt from family/friends, tech lender, or even the seller.

Capital is not an excuse.

(2) Acquiring has a few distinct advantages to building

Namely:
- Validated demand
- Leverage debt/equity partners
- Focus on going from 1-10, not 0-1

Validate demand:

Acquiring a business that has paying customers saves you the time and energy you would have spent trying to figure out if there was demand for it in the first place.

Acquiring skips that step altogether.

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.

Wracając do sedna dzisiejszego zamieszania z artykułem autosportu / motorsportu. Kubica mówi w nim, że podejmie swoją decyzję w ciągu kilku dni. Rozważa opcję z Williamsem lub posadę kierowcy symulatora w Ferrari. Więc za kilka dni dowiemy się co go czeka za rok. #F1PL

Autosport / motorsport zmienił nagłówek swoje tekstu, bo pierwotnie sugerował on że Kubica jest numerem jeden na liście Williamsa na tytuł mówiący tylko tym, że nadal jest na liście kandydatów. Zrobili to po interwencji zespołu. Same wypowiedzi Kubicy z wywiadu się nie zmieniły.

Trudno powiedzieć czy autor się pośpieszył i wyciągnął inne wnioski niż powinien, a może wie coś więcej, ale nie mógł tego otwarcie napisać. Słowa Kubicy brzmią jednak jednoznacznie: decyzję podejmie w ciągu kilku dni.