Fantastic article. It started like a joke but then added some pretty interesting notes (still using a funny tone).
2,175 karma · joined November 8, 2016
It comes from previous posts I’ve come across, but I haven’t considered exactly what you mentioned. That’s on me.
I usually write about Startups, Agile, and CLIs.
This is a great paragraph:
> If you want to, you can just decide to shift gears at this point, and no one's going to tell you you can't. You can just decide to be more curious, or more responsible, or more energetic, and no one's going to go look up your college grades and say, "Hey, wait a minute, this person's supposed to be a slacker."
I've often seen people get too attached to an unproductive "identity" instead of looking at things as they are. It's way too common for people to fail once and think they're a failure, rather than thinking that they just failed at that particular time.
By the way, I remember meeting you during the S23 batch and how genuinely excited you were to meet us, young founders who were just getting started. It does seem like you found your people!
I used to use em dashes before they were cool. I actually learned about them when I emailed a guy who's a software engineer at Genius and also writes for The New Yorker and The Atlantic.
I asked him for tips on how to write well and he recommended that I read Steven Pinker's "The Sense of Style", which uses em dashes exhaustively, and explains when and why one should use them.
It also pains me that I can't use them anymore or else people will think an AI did the writing.
The main issues with other libraries is that they're either:
(a) ugly (b) difficult to use (i.e. having to do things imperatively) (c) not flexible enough
Apache ECharts solve these 3 problems. It's pretty by default, it allows us to mount/calculate the declarative spec for the graphs in the back-end and then only send the desired spec to the front-end so it can render, and it's also extremely flexible to the point we can support everything that traditional BI tools can do.
We've never had to extend the lib to do anything new, everything we need is already there.
Glad to see this great piece of work on top of HN.
Financial literacy itself is quite simple: spend less than you make.
Everything else is an optimization and it’s pretty easy to learn with a few days of research.
I know this is the classical HackerNews type comment, but I honestly can’t understand why it’s so hard when there’s so much information available and so few pre-requisites (almost none) to learn about it.
Lan parties were probably the best part of my teenage years.
Also, the terrace part is amazing.
I miss the good old days of playing DotA (the old one) the whole night while drinking coke and eating pizza with friends.
When you explained how you had just reimplemented the CSS spec I was mindblown.
You guys rock. Repaint is amazing.
We've already seen hacky solutions starting to be replaced and that makes me quite happy.
Thanks for reporting it, it helps us understand what people want!
I haven't yet had time to implement it because I was working on the core pieces of functionality, but as a dark mode user I want to get to that soon.
It's not as much of a direct competitor because Count's approach seems more like "Miro for Data" while ours is more like "Notion for Data".
Still, I do think people could end up comparing both our tool and theirs when evaluating a few types use cases.
We want the free tier to be so good it becomes the new notebook standard - and then we'll only charge for features that companies need, like Slack integrations or a large number of seats.
Regarding AI, we currently go with GPT-4o by default, but you can change it on the settings panel. Currently, we have only enabled GPT-4o and Mistral though, but we want to add more - it's just not our priority right now because the OpenAI model is just incredibly good.
I do believe we should bring software best practices to the data world, not only regarding code design but regarding infrastructure and tooling too (like versioning everything).
Still, I get where they're coming from. A software engineer would also be frustrated if they had to learn everything a data scientist knows (probably even more).
I think the tooling itself can solve this issue by encouraging best practices though.
Still, I think that we can get to a place where everyone uses the same tool to collaborate on data matters, like a "Retool for data/BI". At a high-level, that's the direction we're going, and we're starting with notebooks and dashboards.
Regarding self-hosting: Yes, we have discussed that with other potential customers.
If you're interested in a self-hosted offering, please reach out to me at lucas.costa [at] briefer [dot] cloud and I'll help you out there and walk you through what that would look like.
In Briefer you can hide the blocks you don't want others to see, like the ones in which you're just manipulating data. Instead, you can show only the results so that your analysis is really easy to understand.
You can also display your analyses as dashboards or build small data apps with inputs and dropdowns for them to use.
Still, I wouldn't recommend completely non-technical users to create content within Briefer - only to read it and interact with the apps/dashboards there.
You can see the response here: https://news.ycombinator.com/item?id=41055495
The products are similar in the sense that both are cloud-based notebooks, but we have different approaches to building them.
I answered a question about Hex below, and the general outline applies here. To summarize:
1. We want to go beyond just notebooks. We want to centralize all data tasks in Briefer, including traditional BI so we’re building specific features for that, like dashboarding and everything that comes with it.
2. We’ll enable people to use their own compute for the notebooks.
3. We want to allow people to manage notebooks, dashboards, and apps “as code” so they can version it whenever necessary.
We unfortunately didn’t finish items 2 and 3 yet because we’ve only started working on Briefer six months ago, but we’re fast and we’ll get there soon.
Also, we’re working on something new that will make the distinction clearer, but I can’t talk about that yet.
The AI also helps folks do queries without having to explore the schema for a while.
That’s possible. For it to work you’d have to create a “dropdown block” and select the “dynamic” option in its settings. Then, you will be able to select a dataframe and the column from that dataframe you want to use as dropdown options.
By the way, every query automatically becomes a dataframe so you can use either a query result’s column or a raw Python dataframe.
As a side note, I’m putting together detailed product documentation this week too and I’ll make sure to include it there.
To pull data from the API you can use the requests package which is already within your Python environment.
If you have sensitive API keys you can add them to your environment variables list and then read them with Python too so they’re not apparent in the code.
We do not support running notebooks locally right now, but offline access is coming soon.
With regards to running code on high-end GPUs: currently, we manually allocate GPUs to our customers when they need that. Still, we do want to eventually allow people to connect to their cloud providers to spin-up compute instances there.
Would love to hear more about what you mean regarding the CLI and connectors. Can you give me a few examples of what these would look like?
By the way, I didn't know Calca. Looks neat, I'll definitely have a look.
We do a lot of things to keep the credentials safe that go beyond just putting everything behind a VPN and setting pods' SECCOMP profiles. That obviously includes encrypting credentials and putting in place tight access controls both to the credentials themselves and the encryption keys.
Also, even though we're not SOC2 compliant (yet) we do run pen tests.