Inferam

End to end workflow of an LLM agent: User | ▼ Application (builds the context) | ▼ LLM (next-token prediction) | ▼ Application (parses the tool call, executes it) | ▼ Tools (PDF, Search, Python, Image) | ▼ Tool Results (appended to the same context) | |__ loops back to the LLM until no tool call is emitted | ▼ LLM (generates report text) | ▼ Application | ▼ Document Generator (PDF / HTML / DOCX) | ▼ Final File The model never runs a tool. It only predicts tokens. The application does the running.

A clear objective removes more confusion than endless motivation.

How Production AI Systems Actually Work.

AI infrastructure discussions are increasingly about inference throughput, not training FLOPs.

People who learn fastest aren't chasing answers. They're reducing blind spots.

How SGLang Optimizes LLM Serving.

The world rarely rewards what is difficult. It rewards what is useful.

The value of time is measured by what remains after it passes.

Guide to AI precision formats.

The evolution of software.

Continuous batching keeps GPUs busy and LLMs fast.

Something nobody could've imagined 2-3 years back. All ten of the top open-weight models come from China-based labs.

The utilities have two bidders now. Defensive money and datacenter power money are buying the same sector for opposite reasons.

Guide to scalable LLM serving architecture.