Tutorials Build an LLM Spend Governor: Budget Caps in Python
A runnable Python governor that caps LLM spend per user and auto-downgrades models.
How-to content for builders, indie hackers, and AI engineers. Less theory, more shipped code.
Tutorials A runnable Python governor that caps LLM spend per user and auto-downgrades models.
Tutorials Stream Gemini's thought summaries live, control reasoning effort, and track thinking-token cost.
Tutorials Cut MCP agent context up to 99% by exposing tools as a code API the model calls in code.
Tutorials Build real-time agents on the first reasoning diffusion LLM: OpenAI-compatible, 1000 tok/s.
Tutorials Build a plan-act-verify agent loop with an external check, retry budget, and clear stop rules.
Tutorials Reuse a huge codebase prefix across every Fable 5 call and pay ~90% less.
Tutorials Let Claude write code that calls your tools in a loop — 20–40% fewer tokens, same accuracy.
Tutorials Claude Fable 5 always thinks. Use effort, display and max_tokens to control reasoning cost.
Tutorials Use DeepSeek V4 Pro's auto KV cache to run huge-context jobs for cents.
Tutorials MiniMax M3 hands-on: MSA sparse attention plus real 1M-token long context, with runnable Python.
Tutorials Tune token spend on Opus 4.8 with the effort parameter. Runnable Python, real I/O, real numbers.
Security Harden MCP servers: kill tool poisoning, validate tokens, sandbox tools