GitHub Discussions — exact posts (title + body)

Live threads (created March 2026):

| # | Topic | URL | |---|--------|-----| | 14 | Share your first AINL workflow | https://github.com/sbhooley/ainativelang/discussions/14 | | 15 | LangGraph → AINL: migration experiences | https://github.com/sbhooley/ainativelang/discussions/15 | | 16 | Enterprise audit use cases with AINL | https://github.com/sbhooley/ainativelang/discussions/16 | | 13 | Welcome (hub reply links to #14–#16) | https://github.com/sbhooley/ainativelang/discussions/13 |

Use these blocks as the canonical copy for edits or reposts. GitHub Discussions do not support tags like issues; category replaces that.

Welcome thread hub reply: A maintainer reply on #13 points newcomers to #14–#16.

Pin #14 as the top discussion (maintainer)

GitHub does not expose a public GraphQL pinDiscussion mutation (verified March 2026). Pinning is UI-only:

1. Open https://github.com/sbhooley/ainativelang/discussions/14
2. In the discussion header, open the ⋯ menu (or use the Pin control if shown in your layout).
3. Choose Pin discussion — you can pin up to four discussions per repository.

Prefer pinning #14 so “Share your first AINL workflow” stays visible at the top of the Discussions index.

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Thread 1 — Share your first AINL workflow

Suggested category: Show and tell

Title

Share your first AINL workflow

Body (markdown)

We're building a library of real `.ainl` patterns — monitoring, digests, chain watchers, MCP bridges — **built by the growing AINL community**.

Reply with:

- What problem you solved (one paragraph)
- Link to a public repo or gist (if you can share)
- Stack notes: OpenClaw, Hermes, bare `ainl run`, emit target, etc.

No workflow is too small. Early examples help the next person ship faster.

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Thread 2 — LangGraph → AINL migration experiences

Suggested category: General

Title

LangGraph → AINL: migration experiences

Body (markdown)

If you've moved (or experimented with moving) orchestration from **LangGraph** (or similar) to **AINL**, share what worked and what didn't:

- What stayed in Python vs what you expressed in `.ainl`
- Token/cost or determinism wins (rough numbers welcome)
- Gaps or feature requests

See also: [`docs/migration/LANGGRAPH_MIGRATION_GUIDE.md`](https://github.com/sbhooley/ainativelang/blob/main/docs/migration/LANGGRAPH_MIGRATION_GUIDE.md).

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Thread 3 — Enterprise audit use cases with AINL

Suggested category: General

Title

Enterprise audit use cases with AINL

Body (markdown)

For **security, GRC, and platform** folks: how are you using (or evaluating) AINL for auditability — JSONL tape, policy gates, strict validation in CI?

- Industry / rough context (no secrets)
- Which controls or narratives you're mapping (e.g., change management, monitoring)
- What evidence you wish the project documented better

Pointers: [`docs/enterprise/SOC2_CHECKLIST.md`](https://github.com/sbhooley/ainativelang/blob/main/docs/enterprise/SOC2_CHECKLIST.md), [Validation deep dive](https://ainativelang.com/docs/validation-deep-dive).

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After posting

URLs are recorded in DISCUSSIONS_SEED_TOPICS.md. Pin #14 via the UI (see Pin #14 above).