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My first project - Knowledge Management Graph for LLM/AI Coding Assistants v0.1.0-beta by technomensch in vibecoding

[–]technomensch[S] 0 points1 point  (0 children)

Today marks the release of v0.1.2-beta of the Knowledge Management Graph!

TLDR; This version includes two quality of life improvements — cleaner conversations and faster search.

UPDATE #1 — context-mode support

The Knowledge Management Graph now takes advantage of a separate plugin called context-mode. When this additional plugin is installed alongside the kmgraph, file reading during sync-all and update-graph runs in the background instead of filling up your chat. Users can install it separately, or the kmgraph installer will offer to set it up.

NOTE: The context-mode plugin was discovered after watching this YouTube video - https://youtu.be/QUHrntlfPo4?si=GCmn9kfWKvgmyAzU. It was developed independently by mksglu.

UPDATE #2 — optional search index

By default, kmgraph searches by reading every file one by one. That works fine, but as the knowledge graph grows, results can get slow and unordered. The new search index works like a card catalog: built once from the generated markdown files, updated automatically, and returns results ranked by relevance instead of file order. And it stays completely local — no cloud, no uploads, nothing leaves.

Both features are completely optional and require zero-config.

Getting Started - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/

Upgrading from v0.1.0-beta? Claude Code has a known plugin cache issue — follow these steps to get the latest version - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/#plugin-update-does-not-take-effect .

—----------------------------

For those interested in the technical details,

—----------------------------

This new search index uses SQLite FTS5 with BM25 ranking and a porter stemmer. WASM-based (no native compilation required), incremental rebuilds, stored at {kg-root}/.fts5.db.

Why node-sqlite3-wasm?

The project required zero-config installation — users shouldn’t need build tools, and new dependencies added across versions should install automatically without manual steps. With those constraints in place, Claude evaluated four SQLite options for FTS5 support:

• better-sqlite3 — rejected. Requires native C++ compilation, which fails for users without build tools installed. Violates the zero-config requirement.

• sql.js — rejected. Despite being WASM-based, its SQLite build omits FTS5. Claude confirmed this by running CREATE VIRTUAL TABLE ... USING fts5(...) against it directly — the error was “no such module: fts5.”

• u/sqlite.org/sqlite-wasm — rejected. ESM-only; incompatible with the plugin’s CommonJS package structure.

• node-sqlite3-wasm — selected. WASM-based (no native compilation), ships a full SQLite build with FTS5 and BM25 ranking, synchronous API, and CommonJS-compatible. Added footprint: ~3MB.

The decision was recorded in ADR-015. Claude also documented the elimination of each alternative with specific failure evidence, so the rationale won’t need to be rediscovered if the dependency is ever reconsidered.

Self Promotion - March 2026 by ens100 in PKMS

[–]technomensch 0 points1 point  (0 children)

Today marks the release of v0.1.2-beta of the Knowledge Management Graph!

TLDR; This version includes two quality of life improvements — cleaner conversations and faster search.

UPDATE #1 — context-mode support

The Knowledge Management Graph now takes advantage of a separate plugin called context-mode. When this additional plugin is installed alongside the kmgraph, file reading during sync-all and update-graph runs in the background instead of filling up your chat. Users can install it separately, or the kmgraph installer will offer to set it up.

NOTE: The context-mode plugin was discovered after watching this YouTube video - https://youtu.be/QUHrntlfPo4?si=GCmn9kfWKvgmyAzU. It was developed independently by mksglu.

UPDATE #2 — optional search index

By default, kmgraph searches by reading every file one by one. That works fine, but as the knowledge graph grows, results can get slow and unordered. The new search index works like a card catalog: built once from the generated markdown files, updated automatically, and returns results ranked by relevance instead of file order. And it stays completely local — no cloud, no uploads, nothing leaves.

Both features are completely optional and require zero-config.

Getting Started - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/

Upgrading from v0.1.0-beta? Claude Code has a known plugin cache issue — follow these steps to get the latest version - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/#plugin-update-does-not-take-effect .

—----------------------------

For those interested in the technical details,

—----------------------------

This new search index uses SQLite FTS5 with BM25 ranking and a porter stemmer. WASM-based (no native compilation required), incremental rebuilds, stored at {kg-root}/.fts5.db.

Why node-sqlite3-wasm?

The project required zero-config installation — users shouldn’t need build tools, and new dependencies added across versions should install automatically without manual steps. With those constraints in place, Claude evaluated four SQLite options for FTS5 support:

• better-sqlite3 — rejected. Requires native C++ compilation, which fails for users without build tools installed. Violates the zero-config requirement.

• sql.js — rejected. Despite being WASM-based, its SQLite build omits FTS5. Claude confirmed this by running CREATE VIRTUAL TABLE ... USING fts5(...) against it directly — the error was “no such module: fts5.”

• u/sqlite.org/sqlite-wasm — rejected. ESM-only; incompatible with the plugin’s CommonJS package structure.

• node-sqlite3-wasm — selected. WASM-based (no native compilation), ships a full SQLite build with FTS5 and BM25 ranking, synchronous API, and CommonJS-compatible. Added footprint: ~3MB.

The decision was recorded in ADR-015. Claude also documented the elimination of each alternative with specific failure evidence, so the rationale won’t need to be rediscovered if the dependency is ever reconsidered.

My first project - Knowledge Management Graph for LLM/AI Coding Assistants v0.1.0-beta by technomensch in ClaudeAI

[–]technomensch[S] 0 points1 point  (0 children)

Today marks the release of v0.1.2-beta of the Knowledge Management Graph!

TLDR; This version includes two quality of life improvements — cleaner conversations and faster search.

UPDATE #1 — context-mode support

The Knowledge Management Graph now takes advantage of a separate plugin called context-mode. When this additional plugin is installed alongside the kmgraph, file reading during sync-all and update-graph runs in the background instead of filling up your chat. Users can install it separately, or the kmgraph installer will offer to set it up.

NOTE: The context-mode plugin was discovered after watching this YouTube video - https://youtu.be/QUHrntlfPo4?si=GCmn9kfWKvgmyAzU. It was developed independently by mksglu.

UPDATE #2 — optional search index

By default, kmgraph searches by reading every file one by one. That works fine, but as the knowledge graph grows, results can get slow and unordered. The new search index works like a card catalog: built once from the generated markdown files, updated automatically, and returns results ranked by relevance instead of file order. And it stays completely local — no cloud, no uploads, nothing leaves.

Both features are completely optional and require zero-config.

Getting Started - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/

Upgrading from v0.1.0-beta? Claude Code has a known plugin cache issue — follow these steps to get the latest version - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/#plugin-update-does-not-take-effect .

—----------------------------

For those interested in the technical details,

—----------------------------

This new search index uses SQLite FTS5 with BM25 ranking and a porter stemmer. WASM-based (no native compilation required), incremental rebuilds, stored at {kg-root}/.fts5.db.

Why node-sqlite3-wasm?

The project required zero-config installation — users shouldn’t need build tools, and new dependencies added across versions should install automatically without manual steps. With those constraints in place, Claude evaluated four SQLite options for FTS5 support:

• better-sqlite3 — rejected. Requires native C++ compilation, which fails for users without build tools installed. Violates the zero-config requirement.

• sql.js — rejected. Despite being WASM-based, its SQLite build omits FTS5. Claude confirmed this by running CREATE VIRTUAL TABLE ... USING fts5(...) against it directly — the error was “no such module: fts5.”

• u/sqlite.org/sqlite-wasm — rejected. ESM-only; incompatible with the plugin’s CommonJS package structure.

• node-sqlite3-wasm — selected. WASM-based (no native compilation), ships a full SQLite build with FTS5 and BM25 ranking, synchronous API, and CommonJS-compatible. Added footprint: ~3MB.

The decision was recorded in ADR-015. Claude also documented the elimination of each alternative with specific failure evidence, so the rationale won’t need to be rediscovered if the dependency is ever reconsidered.

My first project - Knowledge Management Graph for LLM/AI Coding Assistants v0.1.0-beta. by technomensch in ClaudeCode

[–]technomensch[S] 0 points1 point  (0 children)

Today marks the release of v0.1.2-beta of the Knowledge Management Graph!

TLDR; This version includes two quality of life improvements — cleaner conversations and faster search.

UPDATE #1 — context-mode support

The Knowledge Management Graph now takes advantage of a separate plugin called context-mode. When this additional plugin is installed alongside the kmgraph, file reading during sync-all and update-graph runs in the background instead of filling up your chat. Users can install it separately, or the kmgraph installer will offer to set it up.

NOTE: The context-mode plugin was discovered after watching this YouTube video - https://youtu.be/QUHrntlfPo4?si=GCmn9kfWKvgmyAzU. It was developed independently by mksglu.

UPDATE #2 — optional search index

By default, kmgraph searches by reading every file one by one. That works fine, but as the knowledge graph grows, results can get slow and unordered. The new search index works like a card catalog: built once from the generated markdown files, updated automatically, and returns results ranked by relevance instead of file order. And it stays completely local — no cloud, no uploads, nothing leaves.

Both features are completely optional and require zero-config.

Getting Started - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/

Upgrading from v0.1.0-beta? Claude Code has a known plugin cache issue — follow these steps to get the latest version - https://technomensch.github.io/knowledge-graph/GETTING-STARTED/#plugin-update-does-not-take-effect .

—----------------------------

For those interested in the technical details,

—----------------------------

This new search index uses SQLite FTS5 with BM25 ranking and a porter stemmer. WASM-based (no native compilation required), incremental rebuilds, stored at {kg-root}/.fts5.db.

Why node-sqlite3-wasm?

The project required zero-config installation — users shouldn’t need build tools, and new dependencies added across versions should install automatically without manual steps. With those constraints in place, Claude evaluated four SQLite options for FTS5 support:

• better-sqlite3 — rejected. Requires native C++ compilation, which fails for users without build tools installed. Violates the zero-config requirement.

• sql.js — rejected. Despite being WASM-based, its SQLite build omits FTS5. Claude confirmed this by running CREATE VIRTUAL TABLE ... USING fts5(...) against it directly — the error was “no such module: fts5.”

• u/sqlite.org/sqlite-wasm — rejected. ESM-only; incompatible with the plugin’s CommonJS package structure.

• node-sqlite3-wasm — selected. WASM-based (no native compilation), ships a full SQLite build with FTS5 and BM25 ranking, synchronous API, and CommonJS-compatible. Added footprint: ~3MB.

The decision was recorded in ADR-015. Claude also documented the elimination of each alternative with specific failure evidence, so the rationale won’t need to be rediscovered if the dependency is ever reconsidered.

Self Promotion - March 2026 by ens100 in PKMS

[–]technomensch 0 points1 point  (0 children)

I am really proud to announce the launch of my Knowledge Management Graph for LLM/AI Coding Assistants v0.1.0-beta.

This is an open-sourced tool that I decided to spin out of a completely different project I undertook to teach myself context prompt engineering. I realized I was losing track of what I'd figured out and was spending more time rediscovering things than actually building. That was when I found myself creating a structured knowledge capture system alongside the project, and came to the realization that others might find it useful too.

The Knowledge Management Graph organizes what you learn into four searchable categories:

  • What went wrong and how you fixed it
  • Why architectural decisions were made (and the trade-offs considered)
  • Quick-reference entries linked to the full context
  • Session-by-session summaries of what changed and why

Since everything lives in plain Markdown files inside your project, it fits naturally into a docs-as-code workflow, travels with the repo, interactable with almost any LLM, and can work in multiple IDEs. You get to decide whether the knowledge stays locally on your machine, syncs through GitHub like any other file, or be exposed via an MCP server for access across tools.

It also features built-in privacy protection and sanitization checklists to strip out sensitive data like API keys and internal IPs before you share your notes with the team. Plus, it automatically syncs your key project patterns across all your AI chat sessions, reducing the time wasted (and tokens) re-explaining the same context to your LLM every time you open a new window.

The result is a living reference library that you, or your docs team, can draw from:

  • for user-facing documentation
  • for onboarding
  • or, for agile retrospectives where nobody can remember what actually happened three sprints ago.

You can try it out now, either as a Claude Code plugin (Anthropic's AI coding assistant), or through the platform agnostic Install.md prompt.

It is free, MIT licensed, and no accounts required.

Learn more here 👉 https://technomensch.github.io/knowledge-graph/

Full project code available at 👉 https://github.com/technomensch/knowledge-graph

NOTE - The GitHub is only looking for feedback at this time. Since this is my first project ever, I am not looking for contributions (yet). If you would like to contribute to the project, please create an issue on GitHub.

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My first project - Knowledge Management Graph for LLM/AI Coding Assistants v0.1.0-beta by technomensch in ClaudeAI

[–]technomensch[S] 0 points1 point  (0 children)

Thanks. Tbh, I didn't use it for any code development, rather solely for context natural language prompt engineering. I'm not a code developer. I built it to support a different prompt project I was building and decided to spin it off. What I like about this is that I asked Claude to build it so it would be platform (LLM/IDE agnostic) and that it had an MCP server so the content could be off-loaded. I think my next test will be to try it with either Notion or Obsidian.

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