ROUTE
One model is not right for every task.
Routing should consider capability, cost, latency and the sensitivity of each request.
AI LAB / ENGINEERING
Artificial intelligenceis not an API.It is a system.
AI becomes interesting to me when it leaves the demo and enters real software architecture — where models, cost, security, routing, observability and developer experience have to work together.
SYSTEM NOTE
The model is only one component of the system.
MODEL != PRODUCT
AI != MAGIC
SYSTEM > DEMO
AI SYSTEM STACK
From model to product.
06
USER EXPERIENCE
Product
Where users experience AI as a reliable product capability rather than a technical experiment.
InterfacesWorkflowsAgents
05
SOFTWARE
Application
Product logic, APIs, context, domain rules and the integration between AI and software.
APIsContextDomain
04
SEE THE SYSTEM
Observability
Latency, token usage, failures, traces and cost should be observable. Guessing is not monitoring.
TracingCostMetrics
03
CONTROL PLANE
Gateway
A central layer for access, routing, quotas, policy and communication with multiple model providers.
RoutingQuotaPolicy
02
INTELLIGENCE
Models
Choosing the right model for the problem without locking the entire system to one provider.
LLMVisionEmbedding
01
FOUNDATION
Infrastructure
Networking, runtime, storage and security underneath every layer of the AI system.
RuntimeDataSecurity
ENGINEERING PRINCIPLES
AI needs engineering, not magic.
OBSERVE
You cannot operate what you cannot see.
Prompts, responses, latency, failures and cost should be traceable where policy allows.
GOVERN
Access without policy becomes future debt.
Quotas, authorization, usage constraints and data policy belong in the architecture.
ABSTRACT
Providers should not own the architecture.
Applications benefit from stable interfaces that preserve the ability to switch or combine providers.
MEASURE
Quality without measurement is intuition.
Evaluation, model comparison and testing against real workloads should be part of development.
SHIP
A demo is not the finish line.
Value appears when AI can be deployed, observed, maintained and trusted inside a real product.
CURRENT INTERESTS
The problems currently occupying most of my attention.
01
LLM Gateways
Unified model access, routing, failover, policy and cost control.
02
AI Observability
Tracing requests, token consumption, latency, failures and system behavior.
03
Model Routing
Selecting the appropriate model for each task instead of sending everything to the largest model.
04
Governance
Access control, quotas, auditing and AI usage policies at team scale.
05
Developer Experience
Turning AI into infrastructure that genuinely improves engineering speed and quality.
06
AI-native Systems
Products where AI is not an add-on but an intentional part of the system design.
THE LAB
A lab should allow things to break.
A new model, gateway, routing strategy or strange workflow — this is where experiments belong. The useful ones become tools or architecture. The others still teach something.
01
IDEA
02
PROTOTYPE
03
MEASURE
04
BREAK
05
LEARN
06
SHIP
FIELD NOTES
Things worth documenting.
AI experiments, tools and engineering notes will continue to appear in the blog — from model tests to infrastructure architecture and production lessons.
Explore the blog→BOTTOM LINE
The best model in the world is still just an expensive dependency without a good system around it.
What interests me is building that surrounding system — the point where software engineering and artificial intelligence genuinely become one discipline.