Springfield Oracle
Every Simpsons prediction sourced, scored, fact-checked
189 followers
Every Simpsons prediction sourced, scored, fact-checked
189 followers
Viral Simpsons prediction videos have no sources. Half the clips are AI-generated fakes, and nobody built the actual database until now. Springfield Oracle tracks every prediction with verified episode references, real event citations, and honest fact-checks. The world so far has been relentless, and the Simpsons wrote all of it. Springfield Oracle tells you which claims are real. And which aren't.
Springfield Oracle
Raycast
Website is down?
Springfield Oracle
@chrismessina Hey, yes this morning we've got into a domain level issue. We've updated the link on the PH page to reflect a new working backup. Sorry for the inconvinience. :)
Springfield Oracle
@chrismessina Hi, yes! We had a minor domain-level issue, but it's resolved now. We've temporarily moved to: https://www.springfieldoracle.in/
BrandingStudio.ai
The deepfake problem is what makes this actually necessary rather than just fun. When half the viral clips are fabricated, the whole "Simpsons predicted it" phenomenon becomes impossible to reason about without a sourced database. This is the infrastructure that should have existed years ago.
The scoring system is the right call too. Confirmed with receipts versus Pending versus debunked are very different things and collapsing them all into "the Simpsons predicted X" is where most of the misinformation comes from.
Curious about the methodology for borderline cases. When a prediction is genuinely ambiguous, like something that kind of happened but not quite the way the episode described, how does the scoring handle that nuance? Is there a partial match category or is it binary?
Springfield Oracle
@joao_seabra Exactly the right question and honestly the hardest part of building this.
Right now, the system is deliberately conservative; if it doesn't meet the bar for Confirmed, it goes to Disputed or Pending, no partial credit. The risk of a "partial match" category is that it becomes a catch-all for lazy confirmations. "Kind of happened" is how misinformation starts.
There are genuinely interesting cases where the episode got the concept right but the details wrong, or got the details right, but the framing is contested.
The longer-term plan is a confidence layer that sits beneath the status, essentially a score that reflects how closely the episode maps to the real event across a few dimensions: specificity, timing, and source quality. So you'd see Confirmed at 91% versus 67% and immediately understand that one is a direct match and the other is a reasonable interpretation.
Not live yet. But that's where this goes.
BrandingStudio.ai
@isha_godboley thanks. Looking forward to seeing this live then!
@joao_seabra @isha_godboley How will you expose that confidence layer, separate fields for specificity, timing, and source quality or just one score? An edit log helps too, it'll keep Springfield Oracle hard to game as submissions grow.
Springfield Oracle
@piroune_balachandran I'm still figuring that part out, and yes, working on the edit log as well. I'm planning on keeping it a simple single score so it's easier to understand for now. But that hasn't gone into prod yet.
Product Hunt Regular
The branding / UI / UX here is fantastic. Well done.
This is such a fun and surprisingly useful concept - sourcing and fact-checking every Simpsons prediction adds real credibility to what usually lives as internet folklore. How does the scoring methodology work for predictions that are partially accurate or still unfolding in real time?
Springfield Oracle
@qwang_dazee That usually falls into Pending/Disputed buckets until we have more information to flag it as 'Confirmed' you can find more info into our methodology here: https://www.springfieldoracle.in/about
Honestly, in a sea of AI agent projects, it’s so refreshing to come across something like this! The branding and visual identity really stand out too, it's really nice !
Just out of curiosity, how long did it take you to build this database? And what’s your process for keeping it updated as new viral clips (or new predictions) pop up?
Great work, in any case!
Springfield Oracle
@yoannajuille Thanks Yoanna! It took about half a night to cover this, and down the line, I'm going to use a lot of Claude Code and a little bit of manual effort on keeping the predictions updated, along with a lot of crowdsourcing help.
The concept is brilliant! It's rare to see AI projects with such a distinct and fun personality.
As a fellow maker, I’m impressed by how polished the execution looks even from the landing page.
How long did it take to train the Oracle on all that Springfield lore? Looking forward to trying it!
Springfield Oracle
@linapok Thanks, Lina! It didn't take too long to set up the predictions on the backend, but it takes some effort and time to fact-check everything from multiple reputable sources. I'm planning on making the process faster down the line.
@isha_godboley Thanks for sharing that, Isha! I can definitely see how thorough fact-checking would take time, but it’s what makes the tool so much more reliable. Looking forward to the updates for a faster process! Cheers.