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.
3.1 KiB
SPRINT 1 FINDINGS — Reference Material
Raw evidence from the Sprint 1 manual review (200 stories, athena_review_report.txt). This document is reference material for future decisions — it records what the data actually showed so later tuning has a baseline.
What We Learned
Sprint 1 was pure rules (no embeddings, no LLM, no semantic similarity) by founder directive: discover the taxonomy before adding intelligence. The classifier's job was not accuracy — it was to surface misclassifications as signal. It did.
High Signal (ship)
Stories the editorial test would pass — reader can DO something:
- Local AI — gguf / rtx / quantization / llama.cpp. Highest enthusiast + replication scores.
- e.g. "I benched quad 5060Tis for code generation with Qwen3.6-27B" (final 0.50)
- "Voodoo Quant beats Unsloth Dynamic 2.0 KLD by 95%" (0.45)
- Open-source tools — things to install/run.
- "Show HN: Juggler – open-source GUI coding agent" (0.57, highest in set)
- "Open Source Local LLM Training Tool (consumer hardware)" (0.46)
- "Zer0Fit — MCP server for zero-shot ML, 100% local" (0.30–0.46)
- Benchmarks with real numbers — reproducible, comparable.
- "GPUHedge: cold-start p95 117s→30s" (0.41)
- "Migrating to GPT-5.6: 2.2x faster, 27% cheaper" (0.41)
- Infrastructure with code — fixable, runnable.
- "Your $80 Tesla P100 silently noisy math in llama.cpp — 3-line fix, free" (0.32)
- Production learnings — transferable lessons.
- "The real bottleneck for AI agents may be proving who they are" (0.19)
- "AI agent crawlers now need permission" (0.25)
Low Signal (reject)
Stories the editorial test would fail — reader does NOTHING:
- Funding rounds — PixVerse $439M, DeepSeek $7B. Money moved, no capability changed.
- Lawsuits — Apple/OpenAI trade secret, Google training suit. Spectator content.
- CEO opinions — Altman, Hassabis, Mosseri takes. Someone said something.
- Valuations — PixVerse $2B. Number, no action.
- Corporate announcements — Waze features, Spotify assistant, Siri beta. No reproducible substance.
Taxonomy Gap (the real discovery)
- 45 / 200 (22.5%) UNCATEGORIZED. Mostly Reddit/HN opinion + culture-adjacent pieces with no keyword hit (e.g. "Stop Telling Me to Ask an LLM", "Mesh LLM", "Ghost Font").
- This is not a failure — it's the editorial boundary made visible. The keyword set is narrower than taste. Tuning Sprint 2 (Lens) must start from these 45, not from classifier precision.
Bucket distribution (200 reviewed)
| Bucket | n |
|---|---|
| RESEARCH | 40 |
| INFRASTRUCTURE | 23 |
| UNCATEGORIZED | 45 |
| MODEL RELEASE | 24 |
| PROBLEM SOLVED | 19 |
| SHIPPING | 12 |
| CULTURE | 15 |
| BUSINESS | 13 |
| LOCAL AI | 9 |
Open question for Sprint 2
When the Lens arrives, its first job is not to classify better. It's to explain why a story is or isn't actionable — and to propose a section (§SECTION_DEFINITIONS) for human confirmation. The 45 UNCATEGORIZED are the training set for that judgment, not the 155 that already matched.