[100% Off] Claude Haiku 5.5: Cheap, Fast Ai Agents In Python
Cut your Claude API bill: effort tuning, model routing, Haiku subagents, caching and batch, measured with real calls
What you’ll learn
- Know what changed in Claude Haiku 5.5 and fix the code that breaks when you move from Haiku 4.5,Choose the effort and thinking settings with measurements instead of guesses,Route work between Haiku and Opus
- and run Haiku subagents under an Opus orchestrator,Cut costs further with prompt caching
- the Batch API and an eye on the 100K-token price step,Protect quality with regression tests against the real API,Estimate the monthly cost of an AI feature from measured cost per task
Requirements
- Basic Python: functions
- lists and dictionaries
- and running a script from a terminal,An Anthropic API key with a few dollars of credit (running every example costs about two dollars),No machine learning background is needed
Description
This course contains the use of artificial intelligence.
On October 7, 2026 Anthropic released Claude Haiku 5.5: ten cents per million input tokens, a 1M-token context window and, for the first time in a Haiku model, an effort setting. It is built for the high-volume work that makes up most of an AI bill: classifying, routing, extracting and summarizing, and working as a subagent for a larger model.
In this course you build CostCutter, the support backend of PhotoShare, a fictional photo app. Haiku triages every ticket, drafts the routine replies and summarizes the week, while Claude Opus 5.5 only handles the hard cases. A small ledger records the model, tokens, time and cost of every call, so every claim in the course comes with a number.
Everything was recorded with the October 2026 release and checked against a real account. Some of what you will measure:
On 30 labeled support tickets, Haiku matched Opus on accuracy at about 1/29 of the cost.
Higher effort made Haiku think nine times more, without getting more tickets right.
The first version of the support policy got only 23 of 30 tickets right; clearer rules, not a bigger model, fixed it.
What changed from Haiku 4.5 and returns an error now: temperature, prefill and thinking budgets.
Model routing, Haiku subagents, prompt caching, the Batch API, screenshots and the 100K-token price step, each measured.
How this course is made: I designed the project, wrote and ran every line of code against the real API, and reviewed every lesson. The narration uses an AI voice and the animations are generated from my scripts. The complete project, with thirty labeled tickets, a script per lesson and a test suite, is included as a download.
