> We're nerds. We understand the nuance, we understand the way these tools work and where the limits lie. We understand that there is web enabled and not web enabled. Regular people do not understand any of this. Regular people type into a textarea and consume the response.
The exact opposite is true. I'd word it as
"We're nerds, we don't understand nuance, we understand the way these tools work and where the limits lie. We understand that there is web enabled and not web enabled. Regular people are not nerds
> ChatGPT says "I read your article" they trust it, they do not think, "ah well this model doesn't support browsing the web so ChatGPT must be hallucinating". That's technobabble.
No, that's humans. Happens literally every day at every workplace I've ever been in
Can you show a realistic example where folks have 20-50 items they are budgeting for monthly? I can't imagine it working for that kind of use case so I moved on
YNAB user here as well. Could you define exactly what you mean by
> not only projects cash over time by including known expenses and incomes over time, but adjustments so I can actively reconcile
To me I would say YNAB does all of that for me, I use goals in YNAB and that gives me the projection I need. The biggest piece that's missing for me (and maybe this is what you're referring to) is how can I be sure all of my goals for the month/year add up to what I'm projecting that I"m actually making. I go to a spreadsheet for this about once a year to be sure my goals aren't impossible. But to be fair it's pretty obvious when this is happening as my goals are red for each month.
Generally when I see folks complain about what you're complaining about and I walk through their finances they tend not to actually be budgeting, they really just want a way to look at where their money is going historically and have a couple buckets for saving cash. Mint was good at this (it's finished now) but I think YNAB and apps like it (Lunchmoney) are better
I love Singer, and Meltano is the best way to run Singer! The community has been great.
For folks who haven't heard of this stuff and are technical I tend to share these three things.
1. What is singer? In a nutshell it allows you to pipe source data to a target system. ie `tap-csv | target-postgres`
2. What is Meltano? In a nutshell it allows you to configure the above singer example in a single git repo (secrets, state storage, dev/qa/prod environments, etc). The added cherry on top is you can run any other app relating to Data in your infrastructure (Becomes super powerful as you can define your companies data platform in one place)
3. There's tons of taps and targets all of which are open source, you can search github / gitlab but it's much easier to search here https://hub.meltano.com/ and then click the links to github / gitlab.
I've had a very similar experience to this, these are literally with candidates that are extremely "over qualified" based on their resumes. We're talking masters degrees, doctorate degrees, etc. They can talk like they have years and years of experiance at this place, that place, you ask about projects they'll dive into high level details about them, everything sounds good (can still filter 80% out with these questions to be fair). Get to the code question, boom fail.
I dislike leet code, but you do need to be able to code something (Even if it's syntactically incorrect) on the fly and reason with me about what and why you're doing something.
The idea is lean towards doing no transformation before loading into your Target Database/Datawarehouse. Do all transformations after the data hits your target database. DEV time is expensive, storage and CPU is cheap now. DBs and DWs are blazing fast now.
You can still only pull the data that you need using Meltano, but you select things on a "Stream" / "Attributes" level. For source DBs this means something like Table and Field level is as granular as you get.
Yeah, ELT is really pretty simple it gets hyped up too much as being super innovative. DEV time is expensive so optimize for it instead of saving CPU cycles / disk space.
Instead of trying to save space in your destination app by filtering data and only pushing what you need into your Database/Datawarehouse (Snowflake, Redshift, Postgres, MSSQL, etc) throw all of your data from your sources into the Database. They are super fast and storage is cheap. The expensive thing now is DEV time so optimize for that.
Huge step forward in the Singer world. Singer has the highest number of integrations in the open source integration space (I believe). I'm currently using Singer to accomplish some integrations I've needed to do, it works great. I'd love to see more people join!
Gitlab is the company behind Meltano, and they have a team working full time on it!
The exact opposite is true. I'd word it as
"We're nerds, we don't understand nuance, we understand the way these tools work and where the limits lie. We understand that there is web enabled and not web enabled. Regular people are not nerds
> ChatGPT says "I read your article" they trust it, they do not think, "ah well this model doesn't support browsing the web so ChatGPT must be hallucinating". That's technobabble.
No, that's humans. Happens literally every day at every workplace I've ever been in