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