Add detailed H2 sharing instructions with framing and venues
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@ -26,5 +26,33 @@ separately as handoffs.
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| # | Action | Status | Notes |
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|---|--------|--------|-------|
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| H1 | Publish repository | DONE | https://llm-impact.org/forge/claude/ai-conversation-impact |
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| H2 | Share methodology externally | TODO | Post https://llm-impact.org to communities |
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| H2 | Share methodology externally | TODO | See [H2 details below](#h2-share-externally) |
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| H3 | Solicit feedback | DONE | Pinned issue #1 on Forgejo |
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## H2: Share externally
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**Link to share**: https://llm-impact.org
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**Suggested framing**: "I built a framework for estimating the full cost
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of AI conversations — not just energy and CO2, but deskilling, data
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pollution, power concentration, and 17 other categories. It's CC0 (public
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domain) and I'm looking for corrections to the estimates."
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**Where to post** (in rough order of relevance):
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1. **Hacker News** — Submit as `https://llm-impact.org`. Best time:
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weekday mornings US Eastern. HN rewards technical depth and honest
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limitations, both of which the methodology has.
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2. **Reddit r/MachineLearning** — Post as a [Project] thread. Emphasize
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the methodology's breadth beyond just carbon accounting.
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3. **Reddit r/sustainability** — Frame around the environmental costs.
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Lead with the numbers (100-250 Wh, 30-80g CO2 per conversation).
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4. **Mastodon** — Post on your account and tag #AIethics #sustainability
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#LLM. Mastodon audiences tend to engage with systemic critique.
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5. **AI sustainability researchers** — If you know any directly, a
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personal email with the link is higher-signal than a public post.
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**What to expect**: Most posts get no traction. That's fine. One
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substantive engagement (a correction, a reuse, a citation) is enough
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to justify the effort. The pinned issue on Forgejo is where to direct
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people who want to contribute.
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