Since using OpenClaw, our token usage dropped from 34 billion tokens per month to 1.5 billion tokens per month through the Sistem Berasingan approach.
Now, with Refactor and Resegment, we reduced it even further to around 750 million tokens per month.
The biggest lesson here is simple: optimization is not always about buying bigger GPUs or adding more compute power. Sometimes, the real breakthrough comes from redesigning how the system thinks, reads, and executes.
Before this, AI had to read too much context repeatedly just to make small changes. That created token waste, higher cost, slower execution, and unnecessary load on the system.
With Sistem Berasingan, the workload became more focused. With Refactor and Resegment, each process became even more structured. The AI no longer needs to scan the whole system every time. It only works on the exact part that matters.
That is how we moved from:
34B → 1.5B → 750M tokens/bulan
Less context.
Less repetition.
Less waste.
Lower cost.
Lebih Pantas execution.
For me, this proves one thing clearly: the future of AI efficiency is not only about stronger hardware. It is about smarter architecture.
Kecekapan starts with system design.
#OpenClaw #AI #LLM #AIAgents #SystemArchitecture #TokenOptimization #SoftwareEngineering #AIEngineering #Kecekapan
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Clearer than the decks I usually get on now, with refactor and resegment.
Angka tentang 34 lebih masuk akal daripada kebanyakan pos.
Worth reading for 1.5B → 750 750M alone. Still thinking this one through.
I would push back slightly on further to around 750 million, but the direction is right.
Good write-up. reads, and executes alone was worth the read.
we moved from: 34B is the part I would forward to my boss.
Short and clear. Since using openclaw, our token is worth sending to my team.