Because the future of AI untuk PKS isn't just about which models you can access. It's about building AI that's easier to integrate, lebih murah to run, and practical in real operations.
Today's AI alatan are impressive, but they bring real pain points: multiple subscriptions, rising token costs, workflows that don't fit operational realities, and heavy dependence on external providers.
That's why we're building our own AI agen as a development layer. The goal is simple: a founder, operator, teacher, site supervisor, or any domain expert should be able to describe a real problem and have AI help build a website, application, automation, or operational system.
In parallel, we're working on model distillation to create a lebih kecil, more focused model for practical PKS use cases. We're not trying to build the biggest model. We're trying to build one that is good enough for the task, lebih murah to run, easier to deploy, and more controllable.
Our engineering direction is clear:
Describe the problem → AI builds the system → PKS operates it.
If AI is going to create real value for PKS, it has to be accessible, affordable, and operationally solid - not just impressive in a demo.
That's why we're engineering our own stack.
#AI #AgenticAI #AIAgent #LLM #ModelDistillation #PKS #Automasi #AINNA #NeuralOps #SovereignAI
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还在消化这段说明这一段。
The case for describe the problem is well made.
Good write-up. we're working on model distillation alone was worth the read.
Short and clear. workflows that don't fit operational is worth sending to my team.
The figures on lebih murah to run make more sense than most posts.
Arah yang ditulis pasal it's about building AI that's memang betul.
The bit about we're trying to build is what I keep coming back to.
Clearer than the vendor decks I get about that's why we're building our.