SsgMshdPotatoes

VibeSchema is a database schema tool with some unique strengths:

- Snapshot timeline for creating versions of your schema and comparing/diffing them

- Ease of dealing with large schemas with diagram views

- Share links (no account needed)

This URL is an example of a share link: https://vibe-schema.com/s/312qXi9Xpqhz7Q2Wub4TuWOXA0c

The free version just went live with

- Up to 100 tables per schema

- Up to 3 projects

- Up to 5 schema snapshots per project

The hope is that these free limits are sufficient to allow many users to experiment with schema ideas, share schema prototypes, store schemas and analyse diffs between schema versions.

To try out the snapshots/diffing, you can save the schema in the URL to a free account, make some changes to it, and create a snapshot.

Have done lots of bug-hunting, but if you find any bugs/anything that could use improvement, please let me know!

Browser tool to get instant database schema images from DBML schemas. There's an SVG version too: change /dbml-to-png to /dbml-to-svg.

Mostly relies on the placement logic, which I'm aiming to keep improving (so if you get a bad placement, I'd be eager to know!).

It has a little DBML reference in the code panel and also a '+' button for quickly inserting tables, relations and enums.

Feedback is welcome!

Browser tool to get instant database schema images from DBML schemas.

There's an SVG version too, just change the /dbml-to-png to /dbml-to-svg.

Mostly relies on the placement logic I've been working on. Should be pretty decent at this point, but if you get ugly placements on your schema, I'd love to know (contact info can be found on the bottom of the page).

A paper that is somewhat related to the question of how well this works is "Do Large Language Models Know What They Don’t Know? Evaluating Epistemic Calibration via Prediction Markets"

There a model gets a prediction market scenario (that in reality has already closed, but not from the model's POV), and it is tasked to predict the outcome AND give its confidence in the prediction.

Conclusion turns out to be "systematic overconfidence across all models". Probably worth keeping an eye on such research, might enable you to make the product better over time as new research comes out etc.

"The entire appeal is that it isn't "human"" - yes, part of the appeal is perhaps that the medium isn't human, but also that the message IS still human, right? And that is the combination I'm talking about.

I guess the question body wasn't clear enough, because that's not what I meant. My bad if so - I just rephrased the part in parentheses to make it clearer.

Maybe I could've worded it a bit better, the question is not whether forums such as HN will continue to exist, but whether/how it is possible to keep them "human", and also what appropriate definitions of that could be.

I thought about this too recently. I guess documenting every consideration along the way would take way too much time (would be longer than the documentation of actual implementations), but one of these days this seems likely to change?

Lol it only took 20 years

True, if you look for all "obvious patterns" and filter those out of the dataset, not much will be left probably. Maybe the best is then to just publish as complete a dataset as possible, so all questions, all user answers, for each user the nr of questions they did, time for each question, etc. Then people using that dataset can draw their own conclusions.

Also for example this one has a giveaway for the human case: "There are lots of great people here at /r/personalfinance" (actually, not sure if that is a giveaway, that was my guess, but depends on how the model was prompted, I guess). And human ones often seem to have two spaces sometimes instead of one, idk why. If you want to get a serious dataset, maybe you could use this one to find all the flaws and perfect it, and then try to get a real dataset from the next one? People will be more eager to help too if they've seen you designed it all very carefully. (Or you could filter the results from this one to make it a good dataset if you get lots of responses.)

Nice idea! Em dashes were giveaways for AIs and typos for human, at least in the ones I did, so those are at least trivial. So might have to do some filtering at least for those.

Some were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me a bit (but this might just be me).

I don't think people are blasted for using AI (mostly), I think people are blasted for low effort work, just like pre-LLMs. LLMs just made it way easier to complete low effort projects, so therefore there is more of it.

I'm hoping to do a show HN soon on something I've been working on, but my account is currently only 6 days old. Tips?

Btw, restricting new accounts (based on karma/age/whatever) could be combined with the option to ask mods for permission somehow, although that'd have to be done in a way that that doesn't become too much work.

Wow, this kinda blew up since first post, what happened :0

Thanks, discovered an H1 issue (JavaScript currently loads it).