Epictetus
3a42f53333
- docs/vision/VISION_LOCK_V1.md: permanent editorial mission, audience (builders/tinkerers/
local-AI users/home-lab/agent devs), Editorial Test ('what would an enthusiast DO?'),
architectual constraint (editorial vision is primary system), priority order.
- docs/vision/ADR-0007_EDITORIAL_PIVOT.md: accepted pivot record, before/after table,
Sprint 1 evidence (high-signal = actionable, low-signal = spectator content).
- docs/vision/EDITORIAL_GUIDELINES.md: operating rules; score is draft, taste overrides;
hand-before-machine; memory must store 'what can be done' not 'what happened'.
- docs/vision/SECTION_DEFINITIONS.md: 5 sections (What Shipped / Run It Locally /
Benchmarks & Builds / Problem Solved / Worth Trying Tonight).
- docs/vision/SPRINT_1_FINDINGS.md: reference evidence, 45/200 UNCATEGORIZED taxonomy gap.
- Issue_001.md: handcrafted prototype edition from Sprint 1 stories; benchmark for all future automation.
This is the architectural directive. No scoring/narrative/memory work proceeds except in
service of the Vision Lock. Sprint 2 (Lens) stays blocked pending manual review tallies.
98 lines
4.4 KiB
Markdown
98 lines
4.4 KiB
Markdown
# Issue 001 — Prototype Edition: "If We Launched Tomorrow Morning"
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**Type:** Handcrafted prototype edition
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**Automation:** NONE. No new architecture. No additional scoring. Curated by hand from Sprint 1 stories (athena_review_report.txt) against the Editorial Test (VISION_LOCK_V1.md).
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**Purpose:** The benchmark against which all future automation is measured. If a future pipeline can't reproduce the judgment shown here, it isn't ready.
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---
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## What Shipped Today
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**Juggler — open-source GUI coding agent** `(id 2225, HN)`
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A visual, open-source coding agent from the creator of JUCE. Install and drive it.
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→ *Do: install it. GitHub: github.com/juggler-ai/juggler*
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**Open-Source Local LLM Training Tool (consumer hardware)** `(id 2315, Reddit)`
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A tool for training/fine-tuning local models on consumer GPUs.
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→ *Do: run it on your hardware.*
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**Zer0Fit — MCP server for zero-shot ML, 100% local** `(id 2427, Reddit)`
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Wraps Google's TabFM/TimesFM as an MCP server for forecasts/classifications/regressions. Fully local.
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→ *Do: wire it into your agent as an MCP tool.*
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**Hundreds of arXiv papers hit daily — an open-source triage tool** `(id 2430, Reddit)`
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Built because only ~3 of hundreds matter to one researcher's work.
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→ *Do: use it to cut your own paper noise.*
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---
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## Run It Locally
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**Quad 5060Tis benchmarked for code-gen with Qwen3.6-27B** `(id 1558, Reddit, final 0.50)`
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Real numbers, consumer hardware, reproducible method.
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→ *Do: replicate the bench on your GPU.*
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**Voodoo Quant beats Unsloth Dynamic 2.0 KLD by 95% (Qwen3.5 0.8B/2B)** `(id 1977, Reddit, 0.45)`
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Quantization-quality showdown with measured delta.
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→ *Do: try Voodoo on your next quant.*
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**Nemotron Puzzle 75B on a 64GB M2 Max** `(id 1573, Reddit)`
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Runs smoothly — a concrete ceiling number for Apple-silicon home labs.
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→ *Do: compare against your own max-model-on-Mac result.*
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**Your $80 Tesla P100 has been doing silently noisy math in llama.cpp for years** `(id 1975, Infra, 0.32)`
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Three-line fix, free. The most "worth trying tonight" infrastructure post in the set.
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→ *Do: apply the 3-line fix tonight.*
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---
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## Benchmarks & Builds
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**GPUHedge: serverless GPU hedging drops cold-start p95 from 117s → 30s** `(id 2438, Problem Solved, 0.41)`
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Measured latency win with a concrete method.
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→ *Do: steal the hedging pattern for your own GPU jobs.*
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**Migrating a production AI agent to GPT-5.6: 2.2x faster, 27% cheaper** `(id 2089, HN, 0.41)`
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Real cost/latency numbers from a production cutover.
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→ *Do: benchmark your own agent migration against these.*
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**5090 (475/600W) vs 6000 Pro MaxQ / WS (600W) concurrency** `(id 1559, Infra, 0.34)`
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Full compute comparison with methodology.
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→ *Do: map your workload onto their numbers.*
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**Dual-GPU PCIe transfer under llama.cpp (tensor + pipeline)** `(id 1565, Reddit, 0.34)`
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Measured interconnect behavior for multi-GPU home builds.
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→ *Do: check your own PCIe scaling.*
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---
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## Problem Solved
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**The real bottleneck for AI agents may be proving who they are** `(id 2314, Infra, 0.19)`
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Identity before intelligence — a production lesson, not a feature drop.
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→ *Do: learn the pattern before you scale agents.*
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**AI agent crawlers now need permission** `(id 2134, RSS, 0.25)`
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Cloudflare-style rules for agent access. A workflow decision, not news.
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→ *Do: set the same rule for your own agents.*
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**GPUHedge / Migrating** (see Benchmarks) — both are *problem-solved-with-numbers*, the canonical section fit.
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---
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## Worth Trying Tonight
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1. **Apply the Tesla P100 llama.cpp 3-line fix** `(id 1975)` — free, immediate, local.
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2. **Install Juggler and drive one real coding task** `(id 2225)` — open-source, now.
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3. **Replicate the quad-5060Ti Qwen3.6 bench on your GPU** `(id 1558)` — 20 minutes, real numbers.
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4. **Try Voodoo Quant on your next local model** `(id 1977)` — measured 95% KLD win.
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5. **Wire Zer0Fit as an MCP tool in your agent** `(id 2427)` — 100% local forecasting.
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---
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## What This Edition Proves
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Every story above passes the Editorial Test: a reader can **install, run, benchmark, replicate, or learn** something. Nothing here is a lawsuit, a funding round, a CEO take, or a valuation. That's the entire publication in one morning — and the bar every automated edition must clear.
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*Rejected from this prototype (representative): PixVerse $439M raise, Apple/OpenAI trade-secret suit, Altman job-creation take, Spotify AI assistant, Lorde-on-AI-glasses. Reader would do NOTHING with those.*
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