Software purpose assessment — classify codebases by the human purposes they serve
Project description
High Noon
Hedonics Driven Development (HDD) — classify software, policy, and life decisions by the human purposes they serve.
Five Packages, One Language
| Package | Install | What it does |
|---|---|---|
| hedonics | pip install hedonics |
Shared taxonomy + fungibility calculus — the common language |
| altpath | pip install altpath |
Personal hedonic life assessment — score yourself across 10 domains |
| highnoon | pip install highnoon |
Software purpose assessment — classify code by what it does for humans |
| mainstreet | pip install mainstreet |
Public policy assessment — evaluate policy with real BLS/MIT data |
| frontpage | pip install frontpage |
Hedonic content discovery — YOUR front page, ranked by YOUR needs |
Each works as a CLI, an MCP server (add to Claude/GPT/Cursor), and a Python library.
Quick Start
pip install hedonics
hedonics domains # 10 hedonic life domains (ENDS)
hedonics costs # 9 fungible cost categories (MEANS)
hedonics blockers 07 # What costs block CONNECTION?
hedonics classify "reduce loneliness through community"
The Core Idea
Every piece of software, every policy, every life decision serves human purposes (ENDS) and costs human resources (MEANS).
ENDS — 10 hedonic domains of intrinsic value:
01 NOURISHMENT 02 SHELTER 03 HEALTH 04 CARE 05 MOBILITY
06 GROWTH 07 CONNECTION 08 RECREATION 09 EXPRESSION 10 MEANING
MEANS — 9 fungible cost categories:
T TIME F FINANCIAL A ATTENTION P PHYSICAL S SOCIAL
E ENVIRONMENTAL R REGULATORY K KNOWLEDGE X STATUS
Many ends are locked behind means costs. Someone who wants more CONNECTION (end) might be blocked by TIME burden (means) from a 60-hour work week. The fungibility calculus computes optimal exchanges: trade your FINANCIAL surplus for TIME reduction (hire help, automate) to unlock CONNECTION.
Add to Your LLM
{
"mcpServers": {
"hedonics": {"command": "python", "args": ["-m", "hedonics.mcp"]},
"altpath": {"command": "python", "args": ["-m", "altpath.mcp"]},
"highnoon": {"command": "python", "args": ["-m", "highnoon.mcp"]},
"mainstreet": {"command": "python", "args": ["-m", "mainstreet.mcp"]},
"frontpage": {"command": "python", "args": ["-m", "frontpage.mcp"]}
}
}
Grounded in Empirical Research
- BLS American Time Use Survey — how humans actually spend their 24 hours
- MIT Living Wage Calculator — what humans need across 8 expenditure categories
- BLS Consumer Price Index — hedonic quality adjustment methodology
- Census American Community Survey — housing, insurance, commuting, income by geography
Contributing
See CONTRIBUTING.md. We especially need help with:
- Test coverage (currently zero)
- Data connectors (BLS API, MIT Living Wage, Census ACS)
- Exchange rate calibration from empirical research
- MCP server testing across different LLMs
Status
Pre-alpha. The framework is under active development.
License
MIT
Built by AltPath AI. Solve problems first. Beautify answers second.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file highnoon-0.1.3.tar.gz.
File metadata
- Download URL: highnoon-0.1.3.tar.gz
- Upload date:
- Size: 62.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1724c615471b2d516cf82b0c25f7fd3cfb58f04d563ed3d4cab4593bf3d3af48
|
|
| MD5 |
fa936f7e1447f9ceca03352e24268676
|
|
| BLAKE2b-256 |
a59efbb21ced5fa9ddc0c9eff6ac1ce436e1aeebf3da3e94c4230f814a27df23
|
File details
Details for the file highnoon-0.1.3-py3-none-any.whl.
File metadata
- Download URL: highnoon-0.1.3-py3-none-any.whl
- Upload date:
- Size: 8.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
06e0e65005fddf07458bf3c74bb532cd8a717988993da4b8ef1a3ac8680e08a9
|
|
| MD5 |
30d972f17cd69e35e273a113c9a8d9e8
|
|
| BLAKE2b-256 |
1ddeff0f76ca09aee17ee42869e9820fd57a4272ac4b7c62e74588ccd5dc8ad3
|