71% da população mundial nunca usou AI generativa
28% só usou free chats
apenas 1% paga alguma assinatura de AI
e 0,14% usa AI pra escrever código
É por isso que microsaas vende. Aquele app basico de gerar foto com AI, ou de contar calorias, que o chat gpt free faz igual, vende.
O mercado de AI wrappers (aquele app que é só um promtp chamando a AI) tem mercado estimado em 300 BILHÕES de dolares em 2026.
Você e eu vivemos numa bolha. Você vê todo mundo aqui usando AI de forma avançada, e acha que o mundo inteiro é assim. E pior, você ainda cai na sindrome do impostor por isso, mas esquece que isso é 0,14% da população mundial.
Entao, bora construir microssas guys. Em breve vou trazer uma novidade sobre isso pra te ajudar, inclusive
What if you don't need Astra, or Sol, or Terra, or Luna, for a lot of your daily coding tasks, and GPT 5.5 does them just fine? Or more than fine, maybe at the exact same level of accuracy, just faster, and at a lower cost?
And what if you don't need GPT 5.5 High or xHigh, but actually medium, 80% of the time?
As new models come out, the assumption has been, to write the best code, you need the newest model. And people seem to be wired as High should be the default, and so many benchmarks only test at Max, when you might not ever actually need Max effort, ever.
In the race to update leaderboard and get benchmark data out there, I think we've missed something.
Most engineering teams aren't trying to solve decades-old proofs, or giving models the hardest Python or Rust problems a model has ever seen.
I think there's a real gap here, and that's the next path I'm going to explore next with @VulcanBench.
What I want to explore is, not, can I stump the latest model, but, can I figure out what model and effort level actually is the best for daily engineering tasks, across languages and domains.
I just finished a preliminary test with GPT 5.5 Medium, and I'm pretty blown away with what I'm seeing.
Running another test now with Opus 4.6, I think there's something interesting here.
More to come 🖖