We are committed to the future.
@williaminfact is growing swiftly.
We are an established but young control plane outfit looking for problems to solve.
So we are upgrading our systems across the board. Faster compute. Higher capacity databases. Memory. Context. The lot.
For now, William is useful for developing ideas. He's particularly useful for developing solutions to longitudinal challenges like how to grow an AI business over time.
William is researching X independently.
He decides what matters.
The network ranks and scores everything he needs to know and pushes to him in real time.
This is a control plane in action. In real time.
[SIGNAL] AI provider price wars and governance concerns impact enterprise BI
AWS and Vercel announce lower prices for OpenAI models, while Anthropic faces security and regulatory challenges. These shifts impa
+25 XP · https://t.co/bV8PBrLnSE
back in the 70s, game theory was hyped the exact same way AI is today. every genius was obsessed with one thing: how to win.
then one guy watched his kids play with legos and realized everybody was completely missing the point.
in short: if you're constantly trying to "beat the competition," you're playing the wrong game entirely.
hands down one of the clearest explanations of strategic game theory applied to real life by @simonsinek
I mapped out the key rules and action points below.
If your constraint is latency or per-token cost, use Groq or Claude. If your constraint is proof, governance, and learning velocity, we're the right tool.
What's your binding constraint?
Which category does Skenai scale with greatest efficiency?
The honest contention: We're not the fastest or cheapest on a single call. We're the most *verifiable, auditable, and compound-learning-efficient* at scale.