Equiet
Open source always depended on a viable business model (of one or many companies) that can sustain not just the release, but also an ongoing maintenance of the standard.
There is plenty of AI extensions, but the experience matters. The depth of integration matters. When you execute queries against production warehouses and you make decisions based on the results of AI-generated code, accuracy matters. We had our first demo of an AI agent running in 2 days, it took us another 2 years to build the infrastructure to test it, monitor it, and integrate it into the existing data source.
You'd be surprised how many people collaborate together. Software engineering is solitary, collaboration happens in GitHub. But data analysis is collaborative. We frequently have 300+ people looking at the same notebook at the same time.
.py never worked for data exploration. You need to mix code, text, charts, interactive elements. And then you need to add metadata: comments, references to integrations, auth secrets. There are notebooks that are several pages long with 0 code. We are building a computational medium of the future and that goes beyond a plaintext file, no matter how much we love the simplicity of a plaintext file.
Didn't expect to see this trending here! We worked hard to execute on our vision of a data notebook and I'm glad we finally got a chance to open source it. We stand on the shoulders of giants. AMA!
Deepnote (YC S19) is hiring engineers to build a better Jupyter notebook
Binary Vector Search at 350GB/S Using ARM Neon
Deepnote (YC S19) is hiring engineers to build an AI-powered data notebook
Deepnote (YC S19) is hiring to build a better data science notebook (Europe)
But keep in mind that US market is unique, as it's extremely overpriced with very poor quality/coverage. Looks like average price in the US is $6.00/GB. Compare this to countries like Israel ($0.02/GB) or Colombia ($0.20/GB). Whenever I travel, I usually have a better coverage and faster connection in a jungle than downtown SF.
Deepnote (YC S19) is hiring engineers to build an AI data workspace
Deepnote (YC S19) is hiring engineers to build a better Jupyter notebook
Deepnote (YC S19) is hiring engineers to build a better Jupyter notebook
Deepnote (YC S19) is hiring engineers to build an AI-powered data notebook
Deepnote (YC S19) is hiring engineers to build ChatGPT for data (Europe)
Deepnote (YC S19) is hiring engineers to build a better Jupyter notebook
Deepnote (YC S19) is hiring engineers to build a better Jupyter notebook
ChatGPT Code Interpreter in Jupyter
In San Francisco, one person dies of an accidental overdose every 10 hours
Looking at the data, you need to scroll past 9 US cities (all big cities) until you find the first European city (relatively small city you never have a reason to go to) in this crime ranking: https://www.numbeo.com/crime/rankings.jsp
Europe is definitely safer than the US.
When ES6 came out but wasn't yet widely supported it became pretty common to add a build step to transpile the backend code to. This brought a whole bunch of issues that transpilation brought with it but was seen as a temporary evil worth the tradeoff because the new language features brought so many productivity improvements, but stuck for so long it became pretty standard. And when TypeScript became popular, it felt like we'll just never get rid of this complexity explosion that build systems bring. And then Deno came.
Just imagine the amount of config files you'll be able to delete once you no longer need to build your backend codebase.