End-to-End LLM Stack Trace: From Keystroke to Streamed Token
A software engineer has published a detailed technical document that traces exactly what happens at every layer of the stack when you send a prompt to an LLM like Claude or ChatGPT. Inspired by the classic "what-happens-when" repository for browser navigation, this document provides a production systems perspective on LLM chat interactions.
What the Document Covers
The document follows the full journey in production order:
- Client-side: Live token counting via WASM tokenizers, IME composition events, optimistic UI rendering
- Network: Why SSE wins over WebSockets for chat, UTF-8 boundary problem in streaming
- API Gateway: Edge TLS termination, multi-dimensional rate limiting (RPM vs ITPM vs OTPM)
- Safety classifiers: What runs before and after the model, why prompt injection is structurally unsolved
- Context assembly: What actually goes into the context window (it's not just your messages)
- Tokenization: Why models can't count letters, why leading spaces matter, how special tokens consume budget
- KV cache and prefix caching: GQA vs MHA memory math, PagedAttention, cache hit rate as a cost lever
- Prefill vs decode: Why they're bottlenecked differently (compute vs memory bandwidth)
- Sampling pipeline: The full logit pipeline in order — repetition penalty, temperature, top-k, top-p, softmax, sample
- Streaming: TTFT breakdown, SSE event parsing, incremental markdown rendering
- Tool use and agentic loops: Parallel tool calls, prompt injection resurfacing in tool results
- Billing and observability: TTFT vs TPOT, cache pricing math, what to instrument
Document Details
The document is aimed at engineers who already understand transformers and want to see how production systems actually work. It's released under CC0 license, and contributions are welcome. The author notes several uncovered subsystems at the bottom including speculative decoding, multimodal systems, and multi-agent coordination.
The repository was created to address the gap between high-level "transformers are magic" explanations and academic papers that don't connect concepts to production system behavior.
📖 Read the full source: r/LocalLLaMA
👀 See Also
How to safely run llama.cpp native tools (exec_shell_command) with multi-sandboxing on Linux
A practical guide to enabling llama.cpp native tools, especially exec_shell_command, and running them inside multiple sandboxes (Firejail + tiny Alpine VM) for safe web fetching and command execution via the llama-server web UI.
Scaling Agentic Coding to 150+ PRs/Week: Lessons from $85K in Tokens at Lovable
Alexander Lebedev shares how he scaled from 20–30 PRs/week with one human to 150+ PRs/week with a swarm of AI agents, spending $85K in tokens since January. Key learnings: risk classification, AI review replacing human code review, and the challenge of preserving knowledge diffusion.
Claude Code Structure That Survived Multiple Real Projects
A developer shares a Claude Code setup that held up across 2-3 real projects with multiple skills, MCP servers, and agents. Key findings include using CLAUDE MD for consistency, splitting skills by intent, implementing hooks, and keeping context usage under 60%.
