ai-conversation-impact/tasks

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
10	Add AI authorship transparency	anticipated-criticisms	DONE	Updated landing page + README disclosing AI collaboration and project costs
11	Calibrate estimates against published data	competitive-landscape	DONE	Updated impact-methodology.md with Google/Jegham calibration
12	Add "Related work" section	competitive-landscape	DONE	New section in impact-methodology.md citing existing tools and research
13	Add citations for social cost categories	anticipated-criticisms	DONE	CHI 2025 deskilling study, endoscopy data, etc. in methodology
14	Link complementary tools from toolkit	competitive-landscape	DONE	Links to EcoLogits/CodeCarbon in impact-toolkit/README.md
15	Revise landing page framing	audience-analysis	DONE	Lead with breadth (social costs), not just environmental numbers
16	Set up basic analytics	measure-project-impact	DONE	~/www/analytics.sh + ~/www/repo-stats.sh
17	Consider Zenodo DOI	anticipated-criticisms	TODO	Citable DOI for academic audiences
18	Automate project cost on landing page	measure-project-impact	DONE	~/www/update-costs.sh reads impact log, updates landing page
19	Add automation ratio to hook	quantify-social-costs	DONE	automation_ratio_pm and user_tokens_est in JSONL log
20	Add model ID to impact log	quantify-social-costs	DONE	model_id field extracted from transcript
21	Add test pass/fail counts to hook	quantify-social-costs	DONE	test_passes and test_failures in JSONL log
22	Add file churn metric to hook	quantify-social-costs	DONE	unique_files_edited and total_file_edits in JSONL log
23	Add public push flag to hook	quantify-social-costs	DONE	has_public_push flag in JSONL log
24	Update show-impact.sh for new fields	quantify-social-costs	DONE	Social cost proxies displayed in impact viewer
25	Update methodology confidence summary	quantify-social-costs	DONE	4 categories moved to "Proxy", explanation added
26	Build aggregate dashboard	quantify-social-costs	DONE	show-aggregate.sh — portfolio-level social cost metrics
27	Log edited file list in hook	quantify-social-costs	DONE	edited_files dict in JSONL (file path → edit count)
28	Build review delta tool	quantify-social-costs	DONE	show-review-delta.sh — AI vs human code overlap in commits

Handoffs

#	Action	Status	Notes
H1	Publish repository	DONE	https://llm-impact.org/forge/claude/ai-conversation-impact
H2	Share methodology externally	DONE	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: "Most AI impact tools stop at carbon. I built a framework covering 20+ cost categories — including cognitive deskilling, data pollution, algorithmic monoculture, and power concentration — calibrated against Google's 2025 per-query data. CC0 (public domain), 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. Lead with "beyond carbon" — what makes this different from CodeCarbon or EcoLogits.
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.