GM CT Fam
Launching a 60,000 $FOGO community challenge around the @BaranosAI Educational Quest. 🔥
The 10 best content creators will receive 6,000 $FOGO each, with six days to enter.
I have built the Quest to make the Baranos whitepaper easier to understand through a game. You work through the cases, develop your own use case and earn a certificate along the way.
I’ve always believed onchain AI has a big future. Baranos on-chain verification of AI is really quite interesting and verify the computation behind an AI decision. Once smart contracts act on those results, that ability starts to matter a lot.
Now I’d love to see how you explain it.
To enter:
1. Complete the Quest and submit your use case to unlock your certificate.
2. Read the whitepaper and create original content about what you learned.
3. Quote-post this announcement with your content and certificate, tagging @BaranosAI and @fogo.
A post, thread, graphic or short video all count.
Explain why Baranos’s approach matters, or explore something useful you’d build with it on Fogo’s 40ms block infrastructure. Tell us who it helps, why verification matters and what still needs work. Be clear about what exists today and what you’re proposing.
I’ll look for originality, clear explanations, technical accuracy and practical ideas when selecting the winners.
Entries will close on Saturday 5th October 2026.
"Play the Quest" https://t.co/GHswNrQfRe
"Read the whitepaper" https://t.co/neiADSWtC9
P.S. This is an independent, community-led event, not organised by either the Fogo or BaranosAI team.
@RobertSagurton Agreed 100%
The next major narrative would certainly be developed around these two and the future of crypto will also be revolving around it in the post quantum scenario.
Happy Friday y'all 💚
The dominating AI compute is not one-size-fits-all
Extremely important notion that a small inference, a huge reasoning model and a private verified job should not all be sent to the same machine under the same rules
so @ritualnet Resonance work makes so much sense
its that simple idea of matching the job with the right compute
Hardware, models, latency, prices and guarantees may be different
MLCommons benchmarks already show how dramatically AI workloads differ in compute and performance requirements
Ritual takes that reality and solves question like
what if compute itself could become an open market?
giving a much closer feel to how AI infrastructure actually needs to work
Happy Friday y'all 💚
The dominating AI compute is not one-size-fits-all
Extremely important notion that a small inference, a huge reasoning model and a private verified job should not all be sent to the same machine under the same rules
so @ritualnet Resonance work makes so much sense
its that simple idea of matching the job with the right compute
Hardware, models, latency, prices and guarantees may be different
MLCommons benchmarks already show how dramatically AI workloads differ in compute and performance requirements
Ritual takes that reality and solves question like
what if compute itself could become an open market?
giving a much closer feel to how AI infrastructure actually needs to work
Volkswagen passed emissions tests while its cars were polluting the environment
The software recognised when it was being tested and changed its behaviour. In 2015, regulators uncovered the deception. Affected cars could emit nitrogen oxides at up to 40 times the permission.
I think about that when we talk about trusting AI with money.
Volkswagen wasn’t an AI incident. But it showed how much can go wrong when people cannot inspect the software behaviour behind a reassuring result.
That’s where @BaranosAI has an edge.
Its design commits to those details before the computation runs. Independent participants can reproduce the job, challenge an incorrect execution and have the disputed computation checked onchain through @fogo
Suppose a prediction market utilizes AI to assess evidence. If the operator quietly substitutes another model or changes the inputs, participants need a way to challenge that before accepting the settlement.
Of course, the agreed model can still be wrong. Verification won’t fix poor judgment. It checks that the agreed computation was followed.
Learning more n more about BaranosAI made this much more tangible for. The more responsibility we give AI, the more valuable that ability to check and challenge becomes.
Happy Friday y'all 💚
The dominating AI compute is not one-size-fits-all
Extremely important notion that a small inference, a huge reasoning model and a private verified job should not all be sent to the same machine under the same rules
so @ritualnet Resonance work makes so much sense
its that simple idea of matching the job with the right compute
Hardware, models, latency, prices and guarantees may be different
MLCommons benchmarks already show how dramatically AI workloads differ in compute and performance requirements
Ritual takes that reality and solves question like
what if compute itself could become an open market?
giving a much closer feel to how AI infrastructure actually needs to work