Epictetus
70 lines
3.4 KiB
Markdown
70 lines
3.4 KiB
Markdown
# aiND Edition — 2026-07-15 (Curated)
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**Status:** HUMAN-APPROVED (8 stories) · staged in `entries` (curated_by=leonard_aiND, manual_section, manual_tier set)
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**Not yet on the live webroot** — see deployment note at bottom.
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**Lens applied:** build / numbers / outcome / lesson, else scroll.
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---
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## What People Shipped
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*Community builds, ships, and earnings — the T1 gold.*
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**Open-Source Local LLM Training Tool (for consumer hardware)** `(id 2315, T1)`
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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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**I Used AI To Sell 10 Websites This Week** `(id 2648, T1)`
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A developer shipped 10 client sites with AI help. Concrete paid outcome.
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→ *Learn: what actually closed deals.*
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**Show HN: I RL-trained an agent that trains models with RL (~$1.3k)** `(id 2461, T1)`
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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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## Benchmarks & Builds
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*Real numbers, real methods.*
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**I benchmarked 15 "E-Waste" GPUs with Modern Workloads** `(id 1969, T4)`
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Cheap-hardware numbers a home-lab operator can compare against.
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→ *Do: benchmark your junk-drawer GPUs.*
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**GPUHedge: serverless GPU hedging drops cold-start p95 117s → 30s** `(id 2438, T4)`
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Measured latency win with a concrete method.
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→ *Do: steal the hedging pattern for your GPU jobs.*
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**New LLM Coordination Benchmark — Multi-Agent Coordination** `(id 2441, T4)`
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A real benchmark with a method.
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→ *Do: run it against your own multi-agent setup.*
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---
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## Problem Solved
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*Transferable lessons from the field.*
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**Structured output reliability with LLMs — 3-month production learnings** `(id 2656, T5)`
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What held up, what broke, over 90 days of prod.
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→ *Do: apply the reliability pattern to your agents.*
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**The absolute nightmare of putting AI agents into actual production** `(id 2645, T5)`
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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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---
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## Worth Trying Tonight (highlight)
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1. **Install the Open-Source Local LLM Training Tool** `(id 2315)` — highest-signal build this window.
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2. **Replicate the $1.3k RL-trained agent** `(id 2461)` — capped cost, full artifact.
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---
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## Editorial notes
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- 8 of 18 reviewed → ~44% of this curated window. The 10 rejects were: PalmClaw (2627), Jacquard (2222), BillAI Bass (2463), NN-in-SQL (2226), Agent-identity (2314), CoT scaling-trap (2444), Upgrade-path (1982), Ford-350 (2649), Codex-encrypt (2157), Ghostcommit (2327).
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- Calibration locked: the editor (you) is stricter than the algorithm. Real build / real measurement / real outcome / real lesson — or it scrolls.
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- Age: stories ingested Jul 13–14. Not "today," but none were previously posted (recency guard clear). Suitable for a 48h edition.
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## Deployment note (BLOCKER — not silently worked around)
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The live webroot is served by `render_site.py` / `propagate_stack_now.py`, which output the **legacy "Athena AI News — Clickability"** product. They rank by virality and **ignore** `manual_section`/`manual_tier`, so pushing now would either drop these 8 or bury them in a feed that contradicts the aiND brand. Three webroots exist (preprod1/2/3), target unconfirmed.
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**Next step requires a decision:** build a minimal aiND renderer that reads `manual_section`/`manual_tier` and emits the sections above (separate from the clickability product), OR confirm the intended webroot. Nothing was pushed to any webroot.
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