# Issue 002 — aiND Prototype Edition (PROPOSED)

**Status:** PROPOSED · hand-curated by Leonard from live scored data (latest 60 entries)
**Automation:** NONE. No Lens. No scoring changes. Human curation only.
**Editorial Test applied:** "What can the reader DO?" (install / run / benchmark / replicate / learn)
**T6 gate applied:** model/API items included ONLY if practitioner-actionable.
**Purpose:** 2nd edition in the Sprint 1.5 sequence. Benchmark for "would I miss it?" journal.
**Founder must confirm before this is treated as shipped.**

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## ⭐ Builder Outcomes (T1 — gold)
*What someone actually accomplished. The strongest stories we have.*

**I Used AI To Sell 10 Websites This Week** `(id 2648, Reddit, T1)`
A developer shipped 10 client sites with AI assistance. Concrete outcome, not a prediction.
→ *Do: steal the workflow. Learn what actually closed deals.*

**Ford replaced engineers with AI, then quietly hired 350 back — to SAVE money** `(id 2649, Reddit, T1/T5)`
The "AI replaces engineers" story with the cost lesson attached. A real financial outcome.
→ *Do: learn the failure mode before you cut a team.*

**Show HN: I RL-trained an agent that trains models with RL (~$1.3k)** `(id 2461, HN, T1/T2)`
A full build under a hard cost ceiling. Money spent, artifact shipped.
→ *Do: replicate the $1.3k training loop.*

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## What Shipped Today (T2)

**Open-Source Local LLM Training Tool (consumer hardware)** `(id 2315, Reddit, score 0.460 — highest in window)`
Train/fine-tune local models on consumer GPUs. Install and run today.
→ *Do: install it. Run on your hardware.*

**PalmClaw — On-Device Agent Framework for Mobile Phones** `(id 2627, arxiv, T2/T3)`
A native on-device agent framework. Runs on your phone, not a datacenter.
→ *Do: build a local agent without cloud dependency.*

**Agent that turns Remarkable doodles into editable charcoal vectors** `(id 2326, Reddit, T4)`
Real editable pen-line vectors, not static images. Installable.
→ *Do: run it on your tablet.*

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## Run It Locally (T3)

**Open-Source Local LLM Training Tool** (see T2 above) — the clearest local-AI win this window.
No new GGUF/quant drops in the latest 60; the training tool is the local highlight.

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## Benchmarks & Builds (T4)

**New LLM Coordination Benchmark — Multi-Agent Coordination** `(id 2441, Reddit, 0.330)`
A real benchmark with a method. Comparable numbers.
→ *Do: run it against your own multi-agent setup.*

**Agentic pipeline for easy Music Video creation** `(id 2325, Reddit, T4 — borderline)`
State-of-the-art pipeline, but no measured numbers yet.
→ *Do: try it; report your own numbers (Worth Trying candidate).*

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## Problem Solved (T5)

**Structured output reliability with LLMs — 3-month production learnings** `(id 2656, Reddit, 0.190)`
Transferable production war story.
→ *Do: apply the reliability pattern to your own agents.*

**The absolute nightmare of putting AI agents into actual production** `(id 2645, Reddit, 0.230)`
What breaks when agents hit prod. A lesson, not a feature.
→ *Do: pre-empt the failure modes.*

**The real bottleneck for AI agents may be proving who they are** `(id 2314, Reddit, 0.190)`
Identity before intelligence — a production decision, not news.
→ *Do: set the identity rule before you scale.*

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## Worth Trying Tonight

1. **Install the Open-Source Local LLM Training Tool** `(id 2315)` — highest-signal item in the window.
2. **Replicate the $1.3k RL-trained agent** `(id 2461)` — capped cost, full artifact.
3. **Apply the structured-output reliability pattern** `(id 2656)` — 3-month lesson, free.
4. **Run PalmClaw on-device agent on your phone** `(id 2627)` — no cloud needed.

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## Rejected from this window (representative T6/T7)
- OpenAI researcher $2B startup `(2702)` — funding, T7.
- Lorde on AI glasses `(2587)` — culture, T7.
- Apple/OpenAI trade-secret suits `(2589, 2652)` — lawsuits, T7.
- Google training lawsuit `(2490)` — lawsuit, T7.
- DeepMind CEO "regulate frontier AI" `(2494)` — CEO opinion, T7.
- Anthropic Claude for Teachers `(2488)` — model news, no practitioner action → T7.
- DeepSeek $7B round `(2502)` — funding, T7.
- Reflection $1B compute deal `(2253)` — funding, T7.
- ~30 arxiv RESEARCH items `(2624–2643…)` — T6 by bucket, but mostly not practitioner-actionable → held at T7 unless a builder angle emerges.

**Edition signal:** of 60 live stories, ~11 cleared the Editorial Test. That ratio is the ingestion-audit problem noted in the feedback memo — fix supply before judging the edition.