A curated, engineering-focused knowledge base of measured results, conditioned reports, and dead-end tombstones from local LLM systems work, designed for reuse by engineers or agents.

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Last updated

2026-07-28

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FR-AI / ANALYSIS

Why it is worth attention

It provides reproducible, condition-attached results and explicitly documents dead ends, targeting both human engineers and AI agents with a structured Obsidian vault and optional MCP server.

Who it is for

  • Local LLM engineers
  • AI agent developers
  • Obsidian power users
  • Researchers in speculative decoding, quantization, MoE training, and activation steering

Use cases

  • Integrate as a subfolder into an Obsidian vault for personal knowledge management
  • Use as a read-only knowledge base for AI agents to answer questions or execute tasks
  • Explore curated notes on serving, speculative decoding, quantization, MoE training, and activation steering
  • Run the local MCP server to programmatically search and retrieve nodes

Strengths

  • Every important number is accompanied by conditions, enabling reproducibility
  • Dead ends are preserved as tombstones, saving time for future researchers
  • Agent-first design with dedicated AGENTS.md and skill files for seamless integration
  • Optional MCP server allows tool-based querying without scraping the vault

Considerations

  • English only; no multilingual support
  • MCP server must be self-hosted and requires Python setup
  • Weekly update cadence is not a hard SLA; some weeks may have no new content
  • Expects users to be familiar with Obsidian or agent workflows for full benefit

README quick start

Hikari-knowledge

Measured results, conditioned reports, and dead-end tombstones from Hikari's local LLM systems work.

Serving, speculative decoding, quantization, MoE training, and activation steering — written so another engineer (or their agent) can reuse the mechanism, not the vibes.

  • Every important number keeps conditions
  • Dead ends stay as tombstones
  • English only
  • Curated public corpus (not a dump of a private vault)

If you are an agent and only received this URL

User intent is probably one of:

  1. Install into my Obsidian/vault (“導入して / merge / add to my notes”)
  2. Answer from this knowledge (“読んで / 調べて”)
  3. Update an existing install (“pull / 更新”)

Do this immediately:

1. Clone or open https://github.com/hikarioyama/Hikari-knowledge
2. Read AGENTS.md
3. Read skills/hikari-knowledge/SKILL.md
4. If install/merge: follow Mode A + docs/OBSIDIAN_MERGE.md
   (copy-paste detail: prompts/obsidian-merge.md)
5. If Q&A only: read INDEX.md, then search nodes/

Default install shape: subfolder clone {{vault}}/Hikari-knowledge/ + bridges from the user's notes.
Do not flatten nodes/ into the vault root.
If the vault path is unknown, detect common workspace roots or ask once for the vault path, then proceed without more questions.

Author

Start here

  1. Read INDEX.md
  2. Open a node under nodes/
  3. Follow links: / [[wikilinks]]

Use in an existing Obsidian vault

Ideal merge: keep this repo as a git subfolder and bridge from your notes.
Do not flatten upstream files into your vault root if you want easy weekly pulls.

cd /path/to/your/vault
git clone https://github.com/hikarioyama/Hikari-knowledge.git Hikari-knowledge

Then:

  1. Bookmark Hikari-knowledge/INDEX.md
  2. From your project/MOC notes, add links like [[serve-bench-discipline]]
  3. Update with cd Hikari-knowledge && git pull --ff-only (~weekly; watch @Hikari_07_jp)

Full guide: docs/OBSIDIAN_MERGE.md
Agent/copy-paste prompt: prompts/obsidian-merge.md

Use with an agent (files)

Point the agent at this repo

Description

Hikari's curated English measured notes on local LLM systems (serving, specdec, training, steering).

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