datascienced

High gross margins feed into costs going into future sales and future development. Zero profit because you are reinvesting. Very tax efficient too!

Also see if there are research tax breaks in your country.

It means being lean and scrappy the whole time (no fluff or big rewrite it in Rust type activity). OTOH the successful companies were not cheap with salaries but not extravagant either. But they aim to get the top 10% cream working for them.

Hate to say it but you need to be strategic and good. Maybe see if you can pay for advice too from someone who has done it in your space.

I haven’t given you an answer! But the main thing I see is a sales system that gets tuned. Sales is done like cattle not pets. And you arrange that or hire someone who can (and as always scrutinise them, learn their job so you can question them).

Good luck!

It is a play on the Go equivalent being Hugo.

Could be good, you need to rethink all the distributed DB stuff.

Does a node deal with certain subset (e.g. certain customers) so you use customerid as a partition key.

CRDTs?

Once a node fills up to say x% capacity do you split that out (using consistent hashing to minimize effect of such spits).

You probably end up reinventing one of the distributed DBs once you make it production ready.

I think it is a lot of work unless you have simplifying requirements e.g. just give each customer a seperate VM and then use a mounted cloud storage to scale up space to some max like 10Tb then maybe cap it there. Write your db log to s3 glacier for a backup.

Second replica for failover.

The “keep jobs” movement sounds wholesome until you realize it also imples keeping that job of the person who waves a flag in front of your car, etc.

The real goal is more like keep people happy, which with our economic system means keep them in money, with our calue system means keep them in jobs… but it doesn’t have to be the same job they had yesterday.

The worry about AI taking over the world is silly. In 1000 years there will be no humans anyway, at least as we recognise today. We would have fast evolved ourselves into something different. We might like being part AI anyway or all AI. It may take a few generations to see that. Hard for us to fathom.

Infact AI might be a way to keep intelligent life going beyond a climate catastrophe, and then explore space and so on.

I sound like a weirdo, as would anyone who said “yeah we’ll all be locked down next year in 2019”

as long as DORA isn’t in your stack ranking review, bonus, or used as a stick to bully people, it is probably OK.

Being good at statistics is more of a knowledge graph of understanding concepts than a statistical model, I think.

Just like understanding a car engine.

Like how HN was recently talking about 8 hours to code the first time, but half hour to retype the same code from memory the next day. The code is almost an small (but critical) artefact.

The reason why I want to learn more about it is I feel it is like a base building block of distributed systems and may be easier to grok and even write a toy version than a bigger thing like kubernetes or a leaderless distributed datastore. I would also learn some go and know how a critical piece of kubernetes works.

What led me there is practicing for a damn system design interview. As much as this whole topic is controversial on HN the grinding has really got me curious about the tech that runs at larger scales and how it works under the hood.