hogu

My personal opinion is that outbound is dead, especially if developers are your audience. We are focused on social media -> inbound

We (Saturn Cloud) published a write-up on the architecture and setup process for running our platform on Nebius. The goal was to make it straightforward for teams to use GPU-backed environments without having to spend much time dealing with Kubernetes operations.

The article walks through how resources are provisioned, how environments are created, how GPU jobs are scheduled, and what abstractions we use to keep the system flexible while hiding most of the complexity of the underlying cluster. It also includes some of the design decisions we made along the way and a few of the tradeoffs we ran into.

Since we built the system, I’m happy to answer questions about the architecture, decisions, limitations, or areas we are still iterating on.

Clickhouse also has managed service (https://clickhouse.com/)

Their support is really good to. I've never had customer support help me sort out linux kernel issues before.

We get all developers at our company System 76 linux laptops as their primary dev machine (Lemur Pros with 40 GB of ram)

idea: search engine that only indexes pages that don't have advertisements

What if the mapping between your neck angle and screen angle wasn’t 1-1?

This won’t help if your model won’t fit on a single gpu right? So I’m this example your model has to be under 16gb if you memory?

Why doesn’t this happen with GitHub? GitHub also has very good domain authority.

This has not been true for years. My google ads account was blocked, I had several google engineer friends create internal tickets for my case, and none of it worked out.

Can anyone comment on external displays? The last time I tried this was about 5 years ago. I had a Samsung chromebook which was a great little device, but the font rendering connected to an external display was too terrible.

At saturncloud.io we focus on helping data science teams move to the cloud (either in your AWS account or ours). One component we provide is hosted jupyter lab (along with SSH access so you can use PyCharm/VSCode or anything else.

Hobbyists often use our free tier (30 hrs of GPU per month) or pay for more compute ($0.69/hour) to do a variety of GPU workloads.

We do prohibit things like DDOS attacks and cryptocurrency mining.

Algorithmic trading and quant finance count as pure finance in my book

The area that we've seen Dask shine the most is ML. This is because the Python ecosystem has more advantages when it comes to ML and there is better GPU support.

https://towardsdatascience.com/supercharging-hyperparameter-...

https://towardsdatascience.com/random-forest-on-gpus-2000x-f...

Disclaimer: We produced those benchmark. I'm a founder of Saturn (https://www.saturncloud.io/) and we focus on providing Databricks-like capabilities with Jupyter + Dask + Prefect, so I definitely have strong feelings in this area.

For some context on why this matters, if you're writing a library (like sklearn) and you want to support multiple array types, you might need to do stuff like

  if isinstance(x, ndarray):
      ...
  elif isinstance(x, other_array):
      ...

Just to set some context - this is being driven by Quansight which is led by Travis Oliphant, who united Numeric and Numarray to create NumPy many years ago which lead to the entire python data science ecosystem as we know it. Yes it's ambitious, and they may not succeed, but it's not their first barbecue. Unifying communities and APIs is something they're good at.

S3 buckets are accessible everywhere generally.

We’re a team of 10 and we’ve also found that Cadence replaces our need for stand up meetings. In general, I’ve found that trusting my team to get their work done and unblock themselves has resulted in fewer synchronous meetings

Saturn Cloud | Remote (North/South america) | Senior Software Engineer | Full Time

Saturn Cloud is DataBricks for Dask. We're building an integrated data science platform leveraging Jupyter, Prefect and Dask. Our code base is in Python, Vue/TypeScript, and lots of k8s. We also have some roles that are 50% allocated for contributing to the scientific python ecosystem (mostly Dask, but also sklearn/pandas + friends). We only have 6 engineers right now, so you'll make a big impact.

Right now we're hiring people who can build Python web backends and are somewhat comfortable building UIs. Understanding/empathy of data scientists is a plus, as is understanding of devops/aws/azure/k8s.

The team is fully remote, but we keep to similar time zones. For most regions, our salaries are quite competitive.

Our interview process: Phone Screen, a few 30 minute chats with team members (to give you a sense of what we're like, and help you figure out if you want to work here), 2 hour take home project, 2 hour pair programming session. The take home project is time boxed so that we don't burn your time.

Please apply here:

https://www.saturncloud.io/s/careers/careers-list/?gh_jid=44...

Sorry to approach here, but don't know how else to reach you. I'm looking for remote Vue devs, maybe we can work together? If you'd like to talk, please reach out to hugo@saturncloud.io

There isn't a good way to know for sure that something is going to work, but there is alot of research you can do that will give you good information.

1. Cold email/linkedin message then speak to 100 potential customers. Attempt presales. You don't actually have to get presales, but that process should give you a good indication for willingness to pay / desired functionality, how people are currently working on the problem, and what language they use to describe their pain (quoting that back to customers is good marketing copy)

2. Build a landing page, run facebook / google ads, find out how much it costs to acquire an email address. Your real CAC will be higher of course, but this is a good starting point.

I'm a founder of https://www.saturncloud.io/. I started with #2, and found it cost 70 cents in advertising revenue to get a click, ~7$ for an email address, and ~$100-$200 to get someone to signup. When we started we were attempting a low touch sales model.

Since then we've started focusing on enterprise sales, because the willingness to pay is much higher. We did #1 when moving towards enterprise sales, but should have done that in the beginning.

It's for remote teams? So there is no desk?

I believe the specific incident is xpo logistics

https://www.nytimes.com/interactive/2018/10/21/business/preg...

For example, last October, a 58-year-old woman died of cardiac arrest on the warehouse floor after complaining to colleagues that she felt sick, according to a police report and current and former XPO employees. In Facebook posts at the time and in recent interviews, employees said supervisors told them to keep working as the woman lay dead.

It's a capitalistic market. When everyone does X you have to figure out what Y is which will differentiate yourself. When everyone uses linkedin to target then you have to find other channels.

Everyone should read Traction by Garbriel Weinberg. It's for startup marketing but job searching is basically marketing yourself in a world that has one dominant channel (recruiters). The efficacy of different channels is time varying as they get saturated- You have to find the one that works for you at any given time

Won't there be significant volume from automated trading and market makers who dont need to visit websites in order to trade?

Depends on what your students are doing right? Also I'd bet your running out of ram before CPU. You should try doing the same workload without JH involved - I'd bet you'd still run out of ram. Also - I'd throw hardware at software problems all day long if I could. Hardware is cheap compared to developer time

You haven't had any problems with external monitors? I tried to use a Samsung Chromebook plus as adev machine and terminal fonts on external monitors looked terrible.

Are you running into scaling issues because all students are on a single box? I think the JupyterHub proxying service should be able to handle way more than 70 users, but I could see 70 users on a single machine being problematic. I'm doing a lot of work around JupyterHub - happy to help out if I can, I can be reached at hugo@saturncloud.io

First of all, AWS SageMaker is really a ML system that happens to include Jupyter notebooks as a component. But if you are talking about just the Jupyter notebook part, then I would say - you could use JupyterHub to build your own implementation of SageMaker (you would want to use kubespawner and some deployment of kubernetes if you wanted to scale to multiple nodes). For example, I run https://www.saturncloud.io/, and we orchestrate JupyterHub to do just that.

JupyterHub is more flexible - for example, you could deploy JupyterHub to one beefy server and have Jupyter deployed for many users, which could all read data from a shared filesystem. that kind of thing is not easy to do with SageMaker since everything runs on a separate ec2 instance.

I can't comment on EMR notebooks.

You can charge subscriptions for installed software as well. Oracle is an example, as is every other on premise Enterprise software

It's more skewed by scientific computing vs not