Language model APIs, custom agents, and AI consulting built for real production environments.
We connect language-model understanding, agent runtime fundamentals, and industrial-grade engineering implementation. The goal is not a demo of model capability, but a workable solution that survives real business workflows, system constraints, and delivery requirements.
3 service lines
API delivery, agent customization, and consulting
Industry-ready
From model principles to deployable systems
End-to-end
delivery
Link model understanding to industrial delivery
An AI partner that works from first principles to final delivery.
The team bridges model understanding, system boundaries, interaction design, and production engineering so that model capability can become an operational product rather than a lab-only prototype.
Agent customization
Tailor agent behaviors around real tasks, toolchains, and operator workflows.
M2Model API delivery
Expose model capabilities through fast APIs with structured outputs and routing control.
UXAI consulting
Connect model understanding, system design, and implementation choices for real delivery.
Compact projects that explain bigger AI system ideas.
Each Nano note turns a narrow prototype into a reusable lesson about models, agents, multimodal systems, or edge deployment.
Nano PD
A compact reading on product-definition thinking for AI systems.
Nano VLM
A multimodal view on small visual-language pipelines and what actually matters.
Nano SG
A lightweight guide to system graphs, orchestration, and agent control surfaces.
LAM
How language-action modeling can move from concept to practical interfaces.
Edge
What changes when model experiences have to run closer to the device.
Auto Research
Patterns for turning research loops into repeatable software workflows.
From model primitives to applied systems.
The delivery loop keeps model capability, runtime control, and product implementation in the same engineering conversation.
Define the system boundary
Map the parts that belong to the model, the agent loop, and deterministic software.
Build the execution layer
Design APIs, prompts, tool routing, observability, and failure handling as one stack.
Deliver the business workflow
Ship interfaces, internal tools, or operator systems that work in real industry environments.
Model principles, systems research, and engineering notes.
The blog now focuses on core architecture research, inference infrastructure, and harness design for serious AI systems.
Principles of the Transformer Architecture
A practical reading of self-attention, token mixing, and the structural reasons Transformer became the base of modern language models.
Deeper Research on Transformer Architecture
Look past the headline design and examine scaling, positional schemes, efficiency variants, and the research directions that made Transformer more useful in practice.
NVIDIA H100 Architecture and Why Inference Needs LPX
A system-level look at H100: compute units, memory hierarchy, and why inference workloads depend on a carefully engineered low-precision execution path.
Research on Harness for Agent Systems
Why agent systems need a harness layer: execution control, tool isolation, observability, retries, and the boundary between model reasoning and production software.
Need AI capability translated into a real production system?
Yuning AI helps teams define the right system architecture, choose the right model interface, and build software that fits operational reality.