Physics Makes 重型 Work Lighter✎ Edit

One of my efforts to support the cost of distilling our own LLM is by building more 分离式系统.

The principle is straightforward:

Do not use an LLM for work that has already become predictable.

Every week, I aim to build around a dozen heavy 分离式系统, supported by the research needed to understand the workflow, identify patterns, define rules, and validate the 系统.

For more complex problems, I may first use ChatGPT or Claude as a higher-level reasoning and research layer.

The flow then becomes:

复杂 问题 → 研究 → 推理 → 蒸馏 → 确定性 Logic → 智能路由 → DeepSeek V4 Flash / Our Own LLM → 执行

The important part is what happens after deterministic logic.

If the workload can be solved through rules, parsers, validation, or structured logic, the 独立系统 handles it directly.

If more reasoning is required, the workload is routed to DeepSeek V4 Flash as a cost-efficient external model.

For specialised, strategic, sensitive, or sovereignty-related workloads, the 系统 can instead route the task to our own LLM models.

So this is not about eliminating LLMs.

It is about using them selectively.

确定性 first.
成本-efficient LLM when required.
Our own LLM when specialised intelligence, control, or sovereignty matters.

This architecture also supports the economics of building our own models.

The more workloads we detach, the lower our recurring token and inference costs.

The lower those costs become, the more resources we can redirect into model distillation, evaluation, infrastructure, and development of our own LLM ecosystem.

That is one of the core ideas behind AINNA NeuralOps.

#AINNA #NeuralOps #DetachedSystems #LLM #AIDistillation #SovereignAI #AIInfrastructure #DeepSeek #ChatGPT #Claude #EnterpriseAI #自动化