cedricd

In some respects it's similar, in that our table is time-series, but we're fully on the data analysis side. Data flows from production databases (in any format) into a warehouse, and from there we transform it into the activity stream.

Event sourcing is a great way to model production data that would work really well with Narrator, but it's by no means necessary.

That's a great question. No, you don't have to change the any other applications at all. We work from the raw data in a warehouse.

So the typical flow is that your production applications structure their data however they want. From there the data is sent into your warehouse as-is (using an EL tool like Fivetran).

From there you write small sql scripts in Narrator for each activity you'd like to create. Those scripts are responsible from transforming into your format to ours. They're generally fairly short. Our docs describe them here: https://docs.narrator.ai/docs/transformations

Thanks! We've found that this scales fairly well. Tables generated from the activity stream are generally done in seconds or minutes at the worst case.

One reason for this is that most warehouses are column-oriented. This means our table, which is 11 columns wide but pretty deep, is really fast to query.

We had a customer on a 3 Node (tiny) DS large Redshift warehouse dealing with billions of rows. Their Looker queries literally took 8 hours. We moved them to the Narrator structure and they saw the same queries assembled using the activity stream down to minutes (<5 minutes)

Edit: I should also point out that all queries in Narrator are compiled to SQL that runs directly on the warehouse. We don't use any external data stores or anything like that.

Have you seen https://www.listenlively.com/ They're a fair bit below what you've quoted above.

Thanks Roee! Appreciate the support

Yes! We're at https://www.narrator.ai

I agree with you on the latter two points, but for the first one, it sounds like the application is using IE's rendering engine built into all versions of Windows. So the executable is super small given that all the heavy lifting is in libraries already on your system.

Kind of production. It was initially written in Lisp, then translated to C++ to go to production and maintained / extended from there.

That's a great way to prototype -- use what you know and then rewrite (or in this case transpile?)

Narrator | Software Engineer, Designer | New York, NY | Full Time

Narrator provides data science as a service for startups. We've built a platform to allow us to build data analyses extremely quickly and accurately.

We're a small team of 6 (all former data scientists or software engineers) looking for another engineer and our first designer.

check us out: https://narrator.ai email me: cedric@narrator.ai

Say you have 50% of the company and your cofounder has the other 50%. That's all the stock, right?

If you want to raise money you need to sell some stock. So the company creates new stock and sells that -- increasing the total number of shares. You don't get any of that new stock yourself, so your total ownership percentage goes down.

Back when I was at a largish company as an engineering manager I had to negotiate these sorts of contracts all the time. Would have paid for this in a heartbeat. You guys are definitely on to something.

We eventually had an in-house person do it and they didn't love managing it either. The hardest part of their job honestly was tracking all the ones we had, ensuring we renewed on time, and talking to the procurement department to get everything squared away. When we had to move off credit cards to more corporate billing it was a nightmare to update everything.

And even when it was all buttoned up I still had to review that list quarterly to ensure we could drop old stuff, maintain which team(s) owned what service from the technical side, etc.

Next time I'm at a bigger company I'm using you for sure.

But how would you know?

At my previous company data science has become synonymous with data analysis, to the point that the number of data scientists on staff is starting to outnumber data analysts. I think it's more a sign of a maturing field than anything else. The more narrow view of data science as big data, models, and machine learning is probably less a thing now, but data analysis overall is only getting bigger.

The best part of this paper is that they used mannequin challenge videos as their training dataset. That's super clever.

This looks super interesting. We're doing a startup right now that transforms data into time series tables. Building datasets from those in sql has been challenging enough that we built out a UI to do it. This could be another elegant approach.

Do you have a link to some of those articles?

Single page app

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We're growing fast and need help to continue building a team that influences all aspects of the business, from our member-facing social network to the way we manage and design our spaces.

You can apply directly at the listing here https://boards.greenhouse.io/wework/jobs/194515?gh_jid=19451...

We also have openings for software engineers and leads. https://www.wework.com/careers#digital cedric@wework.com

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I had to spend time in embassies too. When you travel for a year it's possible to do that where you are. For example, I got my visas to Myanmar and India in Bangkok. You do have to plan a few days ahead. I meant don't plan it all before leaving.

I quit my job for a year and did this with my wife. We didn't work while traveling -- it would have been too hard / distracting. This might sound odd, but traveling takes as much time as a real job -- the amount you spend just figuring out where to go next and what to do is significant. If you want to work while travel then your best bet is to taking a break from travel and live somewhere for a little while. It's not a bad way to go but it's not what we did.

One interesting thing is the amount we spent for a year of travel was slightly less than the amount we spent simply living at home. If have saved enough where you can spend a year without salary then you can afford to do it.

A few recommendations -- don't plan ahead. You can't. Just plan the first place you want to go to and go from there. You'll have ample time to figure things out and be open to changes in plan. A corollary to this is that if you plan on spending less than 5 months traveling then you may not be able to travel quite that way. It takes a few months just to get into the swing of things.

Lastly, don't stretch your budget thin just to hit an arbitrary length of time. Spend what you need to and leave a few months earlier. You won't enjoy yourself if you have to scrounge for every dime. I'd see people stay in super nasty places for $5-10 / night in places that had simple, clean, and comfortable places for $15 / night.

The elephant in the room here of course is this: https://news.ycombinator.com/x?fnid=b7VO4wED8MRumCeiX5fCnF

Link is from 2006.

I love it. This is creating a benign botnet.

Looks like it's available again

I'm just like you. I don't sub-vocalize or hear words when reading and I can consequently read significantly faster than people speak (I'm assuming most people read at this rate if they're 'hearing' words -- though I'm actually not sure).

I have the exact same experience with character names -- that's a great example.

How long is this supposed to take? It's just showing a spinner for me.

Bit tongue in cheek... but this sets the clock ticking for Microsoft going 'oh, crap, someone is defining a new computing paradigm. Let's get on that before they're way ahead of us again'. Any bets as to when Microsoft announces Windows for Eyewear?

If they're not giving away Android phones for free I highly doubt they'd do the same with glass.

Interesting, but the one comment I haven't seen yet on this thread is 'Why is this useful enough to be a compiler feature?'. I think this is especially relevant with a relatively slim language like C.

In the rare cases you need to do this sort of check why not just write a simple sizeof test?