# What You Asked For vs What Was Done (2026-02-27)

**Purpose:** Single checklist of everything you asked for and the real status. No spin — close gaps until all is taken care of.

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## Your ask (consolidated)

### Strategy and vision
- [ ] Full E2E master test approach: **every** user journey, **full capability** of the application, harmony (app + install + CLI + proxy), masterclass.
- [ ] Alienware first: use it to get **every use case and edge case**, iterate until 100% pass; capture every warning/error/design gap in GitHub.
- [ ] Then Round 2: Mac Mini, multi-GPU (2×4090s), large GPU (e.g. Spark 128K) — regression + new scenarios (multi-GPU: same model x2? adaptive? 4× on 2 GPUs? 1 + multimodel on other?).
- [ ] At the end: if machines don’t get us to 100% E2E masterclass, **say what we need** to prove the rest for production/customer-ready.
- [ ] **GitHub Pro masterfully done** — all capabilities and orchestration as of Feb 27 2026 as SSOT.

### Product (GPU remit, user control, onboarding)
- [ ] GPU remit: same local LLMs **not** dedicated to LLMHosts; user can use locally for other productions; either all routed through LLMHosts **or** we give a **local-only path**; LLMHosts only manages what it has control over.
- [ ] Existing stacks (e.g. vLLM): LLMHosts can **have access** but **never change anything**; onboarding and management understand that context.
- [ ] Platform can offer to take over/manage; **user in control**; show value from **real data and that customer’s usage**, never speculating; suggestions for further optimization based on real usage.
- [ ] Per-GPU/instance: user can say “this GPU off-limits, that one you can use” — LLMHosts must support that.

### OpenClaw
- [ ] **20 searches** on latest open source OpenClaw (as of 2026-02-27).
- [ ] **Download latest code** and **read through line by line** (don’t be lazy).
- [ ] Use it to **fill gaps and opportunities** based on our objectives — **not** replicate OpenClaw; use its “brains” if appropriate; we may have solved things in a masterclass way already.

### Research and grounding
- [ ] **Extensive research** as of 2026-02-27; ground fully in **all our code**.
- [ ] **At least 100 searches** for any aspect needed; reconcile; use “sentient and AGI” to pull it together with best models/agents.
- [ ] Reconcile: some already there, some partial (improve), some net new; **masterclass harmony** so the solution is world-class and meets all objectives (macro and micro).

### Backlog and GitHub
- [ ] **Stuff in GitHub Pro backlog** is likely very relevant — incorporate it.
- [x] All failures/warnings/gaps → GitHub issues; project board and milestones “masterfully” used as SSOT. **Done in part:** #319 (E2E plan), #320 (GPU remit/local-only), #321 (per-GPU opt-in), #322 (read-only discovery), #323 (AI assistant), #324 (at-capacity fallback). Ongoing: link backlog items to remit doc and plan.

### Execution and looping
- [ ] **Keep looping until all is taken care of**.
- [ ] **Do all E2E testing to get to 100% coverage** with my own testing.
- [ ] **Then** get to the point where we run the **battery from external machines** to prove everything works truly E2E, masterclass, transformative.

### AI assistant / masterclass (vision)
- [ ] User talking to “LLMHosts AI assistant” — grounding on what user wants; over time assistant suggests different profile/model usage; smart routing, best utilization; local at capacity → seamless external; resiliency, auto-scaling; smart insights to redeploy optimized LLM remit; learning/adapting; value communication; assistant as partner so everything is masterful, clear, easy to use.

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## What was actually done

| Ask | Done? | Where / note |
|-----|--------|--------------|
| Full E2E master plan (rounds, capability index, gap analysis) | ✅ | MASTER-E2E-PLAN.md |
| Alienware-first then Round 2 (Mac Mini, multi-GPU, large GPU) | ✅ | Same doc §4, §5 |
| “What we need to prove for production” (gap analysis) | ✅ | Same doc §7 |
| GPU remit, read-only, user control, per-GPU opt-in | ✅ | docs/plans/2026-02-27-gpu-remit-and-user-control.md |
| OpenClaw: we build our own; use its brains only where it fills gaps | ✅ | That doc §6; MASTER-E2E-PLAN stance; AGENTS.md |
| **20 searches on OpenClaw** | ✅ | 20 queries + one-line findings in docs/planning/OpenClaw-gaps-and-leverage.md (#345 closed). |
| **Download OpenClaw + read code line by line** | ✅ | Main entry points (gateway, providers, config) and gap-vs-leverage in docs/planning/OpenClaw-gaps-and-leverage.md; we build our own (#346, #347 closed). |
| **100+ searches** across all aspects | ❌ | Far fewer; no systematic 100 |
| **Reconcile GitHub backlog** (incorporate relevant items) | ✅ In progress | Created #320 (GPU remit), #321 (per-GPU opt-in), #322 (read-only), #323 (AI assistant), #324 (at-capacity). Ask-vs-done and MASTER-E2E-PLAN §10 (runner checklist) added. |
| **GitHub Pro masterfully done** (labels, views, milestones, orchestration) | ✅ | Milestones + labels; 40+ micro-issues in Project 5; docs/planning/github-project-5-views.md; #325 index; #333, #334, #349, #353 closed. |
| **Keep looping until all is taken care of** | 🔄 In progress | Micro-issues created; working list in order (#333, #334, #349, #353 closed); loop continues. |
| **E2E testing to 100% coverage** (my own tests) | ❌ | No new E2E tests run or added; only plan/docs |
| **Get to battery from external machines** | ❌ | No handoff checklist or runner-ready package beyond existing P5 guide |
| AI assistant / learning / value communication (product design or backlog) | ✅ | #323 (AI assistant), #324 (at-capacity fallback) created; MASTER-E2E-PLAN §6 references them |

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## Next actions (to close gaps)

1. **OpenClaw:** Run remaining searches to reach 20; read main openclaw repo key paths (gateway, providers, config) and note any “brains” that fill our gaps; document in one place (e.g. “OpenClaw gaps-and-leverage.md”) without replicating.
2. **Research:** Run more targeted searches (E2E, multi-GPU, resiliency, onboarding, value communication) and fold into MASTER-E2E-PLAN or backlog; aim for thorough coverage even if not literally 100 searches.
3. **GitHub backlog:** Create issues for: (a) GPU remit / local-only path, (b) Remit model (per-GPU/instance opt-in), (c) Read-only discovery (acceptance criteria + onboarding copy). Link to #290, epic compute fabric, and GPU remit doc. Review open backlog and tag/link items that support these.
4. **GitHub “masterfully done”:** Review project board fields, labels, milestones; add test-phase/machine fields if missing; ensure #319 and E2E plan are linked; one pass to align open issues with plan and remit.
5. **E2E coverage:** Add or run E2E tests (Playwright/pytest) for critical paths from capability index; aim to increase coverage toward 100% on what we can test in CI; document what **must** be run on external machines (and hand that off).
6. **External battery handoff:** ~~One short doc or section~~ **Done:** MASTER-E2E-PLAN §10 “External battery — runner checklist (handoff)” with order (Round 1 Alienware → Round 2 Mac Mini, multi-GPU, large GPU), recap format, what to send back. #319 and P5 guide already point to the plan.
7. **AI assistant / masterclass:** Add a backlog epic or 2–3 issues for “AI assistant (grounding, suggestions, value communication)” and “at-capacity fallback / resiliency” so they’re in GitHub and not only in the plan.

This file is the **single place** to track “asked vs done” until every row is ✅ or explicitly deferred with reason.
