Epictetus
5224645703
- docs/vision/LEONARD_FEEDBACK_TO_ARCHITECT.md: 4 resolutions (naming aiND vs Athena; T6 practitioner-gate; T1 has no engine signal yet/actionability_score reserved; ingestion audit is mandatory for indispensable editions) + 2 risks (success metric is human-only; schema forward-compatible, no change in 1.5). - docs/vision/AI_NEWS_DAILY_TIERS.md: 7-tier hierarchy + Builder-Outcome DNA + 4 editorial questions + T6 gate. LOCKED reference for edition curation. - Issue_002.md: PROPOSED hand-curated edition from live 60-story window; T1-T5 + T6 gate; founder confirmation required before 'shipped'. ~11/60 cleared Editorial Test -> exposes ingestion-supply problem. Sprint 1.5 = multiple hand editions; Lens/Memory/Narratives/KG deferred. No schema change.
101 lines
4.5 KiB
Markdown
101 lines
4.5 KiB
Markdown
# Issue 002 — aiND Prototype Edition (PROPOSED)
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**Status:** PROPOSED · hand-curated by Leonard from live scored data (latest 60 entries)
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**Automation:** NONE. No Lens. No scoring changes. Human curation only.
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**Editorial Test applied:** "What can the reader DO?" (install / run / benchmark / replicate / learn)
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**T6 gate applied:** model/API items included ONLY if practitioner-actionable.
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**Purpose:** 2nd edition in the Sprint 1.5 sequence. Benchmark for "would I miss it?" journal.
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**Founder must confirm before this is treated as shipped.**
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---
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## ⭐ Builder Outcomes (T1 — gold)
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*What someone actually accomplished. The strongest stories we have.*
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**I Used AI To Sell 10 Websites This Week** `(id 2648, Reddit, T1)`
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A developer shipped 10 client sites with AI assistance. Concrete outcome, not a prediction.
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→ *Do: steal the workflow. Learn what actually closed deals.*
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**Ford replaced engineers with AI, then quietly hired 350 back — to SAVE money** `(id 2649, Reddit, T1/T5)`
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The "AI replaces engineers" story with the cost lesson attached. A real financial outcome.
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→ *Do: learn the failure mode before you cut a team.*
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**Show HN: I RL-trained an agent that trains models with RL (~$1.3k)** `(id 2461, HN, T1/T2)`
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A full build under a hard cost ceiling. Money spent, artifact shipped.
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→ *Do: replicate the $1.3k training loop.*
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---
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## What Shipped Today (T2)
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**Open-Source Local LLM Training Tool (consumer hardware)** `(id 2315, Reddit, score 0.460 — highest in window)`
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Train/fine-tune local models on consumer GPUs. Install and run today.
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→ *Do: install it. Run on your hardware.*
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**PalmClaw — On-Device Agent Framework for Mobile Phones** `(id 2627, arxiv, T2/T3)`
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A native on-device agent framework. Runs on your phone, not a datacenter.
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→ *Do: build a local agent without cloud dependency.*
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**Agent that turns Remarkable doodles into editable charcoal vectors** `(id 2326, Reddit, T4)`
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Real editable pen-line vectors, not static images. Installable.
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→ *Do: run it on your tablet.*
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---
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## Run It Locally (T3)
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**Open-Source Local LLM Training Tool** (see T2 above) — the clearest local-AI win this window.
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No new GGUF/quant drops in the latest 60; the training tool is the local highlight.
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---
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## Benchmarks & Builds (T4)
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**New LLM Coordination Benchmark — Multi-Agent Coordination** `(id 2441, Reddit, 0.330)`
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A real benchmark with a method. Comparable numbers.
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→ *Do: run it against your own multi-agent setup.*
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**Agentic pipeline for easy Music Video creation** `(id 2325, Reddit, T4 — borderline)`
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State-of-the-art pipeline, but no measured numbers yet.
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→ *Do: try it; report your own numbers (Worth Trying candidate).*
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---
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## Problem Solved (T5)
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**Structured output reliability with LLMs — 3-month production learnings** `(id 2656, Reddit, 0.190)`
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Transferable production war story.
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→ *Do: apply the reliability pattern to your own agents.*
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**The absolute nightmare of putting AI agents into actual production** `(id 2645, Reddit, 0.230)`
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What breaks when agents hit prod. A lesson, not a feature.
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→ *Do: pre-empt the failure modes.*
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**The real bottleneck for AI agents may be proving who they are** `(id 2314, Reddit, 0.190)`
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Identity before intelligence — a production decision, not news.
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→ *Do: set the identity rule before you scale.*
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---
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## Worth Trying Tonight
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1. **Install the Open-Source Local LLM Training Tool** `(id 2315)` — highest-signal item in the window.
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2. **Replicate the $1.3k RL-trained agent** `(id 2461)` — capped cost, full artifact.
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3. **Apply the structured-output reliability pattern** `(id 2656)` — 3-month lesson, free.
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4. **Run PalmClaw on-device agent on your phone** `(id 2627)` — no cloud needed.
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---
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## Rejected from this window (representative T6/T7)
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- OpenAI researcher $2B startup `(2702)` — funding, T7.
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- Lorde on AI glasses `(2587)` — culture, T7.
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- Apple/OpenAI trade-secret suits `(2589, 2652)` — lawsuits, T7.
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- Google training lawsuit `(2490)` — lawsuit, T7.
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- DeepMind CEO "regulate frontier AI" `(2494)` — CEO opinion, T7.
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- Anthropic Claude for Teachers `(2488)` — model news, no practitioner action → T7.
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- DeepSeek $7B round `(2502)` — funding, T7.
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- Reflection $1B compute deal `(2253)` — funding, T7.
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- ~30 arxiv RESEARCH items `(2624–2643…)` — T6 by bucket, but mostly not practitioner-actionable → held at T7 unless a builder angle emerges.
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**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.
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