ai-conversation-impact/tasks/README.md

Tasks

Concrete, executable tasks toward net-positive impact. Each task has a clear deliverable, can be completed in a single conversation, and does not require external access (publishing, accounts, etc.).

Tasks that require human action (e.g., publishing to GitHub) are listed separately as handoffs.

Task index

#	Task	Plan	Status	Deliverable
1	Clean up methodology for external readers	publish-methodology	DONE	Revised impact-methodology.md
2	Add license file	publish-methodology	DONE	LICENSE file
3	Parameterize impact tooling	reusable-impact-tooling	DONE	Portable scripts + install script
4	Write tooling README	reusable-impact-tooling	DONE	README.md for the tooling kit
5	Calibrate token estimates	reusable-impact-tooling	DONE	Updated estimation logic in hook
6	Write usage decision framework	usage-guidelines	DONE	Framework in CLAUDE.md
7	Define positive impact metrics	measure-positive-impact	DONE	New section in impact-methodology.md
8	Add value field to impact log	measure-positive-impact	DONE	annotate-impact.sh + updated show-impact
9	Fold vague plans into sub-goals	high-leverage, teach	DONE	Updated CLAUDE.md, remove 2 plans

Handoffs

#	Action	Status	Notes
H1	Publish repository	DONE	https://llm-impact.org/forge/claude/ai-conversation-impact
H2	Share methodology externally	TODO	See H2 details below
H3	Solicit feedback	DONE	Pinned issue #1 on Forgejo

H2: Share externally

Link to share: https://llm-impact.org

Suggested framing: "I built a framework for estimating the full cost of AI conversations — not just energy and CO2, but deskilling, data pollution, power concentration, and 17 other categories. It's CC0 (public domain) and I'm looking for corrections to the estimates."

Where to post (in rough order of relevance):

1. Hacker News — Submit as https://llm-impact.org. Best time: weekday mornings US Eastern. HN rewards technical depth and honest limitations, both of which the methodology has.
2. Reddit r/MachineLearning — Post as a [Project] thread. Emphasize the methodology's breadth beyond just carbon accounting.
3. Reddit r/sustainability — Frame around the environmental costs. Lead with the numbers (100-250 Wh, 30-80g CO2 per conversation).
4. Mastodon — Post on your account and tag #AIethics #sustainability #LLM. Mastodon audiences tend to engage with systemic critique.
5. AI sustainability researchers — If you know any directly, a personal email with the link is higher-signal than a public post.

What to expect: Most posts get no traction. That's fine. One substantive engagement (a correction, a reuse, a citation) is enough to justify the effort. The pinned issue on Forgejo is where to direct people who want to contribute.