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marcobambini

4,973 カルマ登録 12 年前
https://marcobambini.com

投稿

Measuring and Reducing CPU Usage in SQLite

sqlite.org
2 ポイント·投稿者 marcobambini·6 日前·0 コメント

SQLite Databases for Humans and Agents

marcobambini.substack.com
3 ポイント·投稿者 marcobambini·10 日前·0 コメント

Rethinking Databases for Humans and AI Agents

marcobambini.substack.com
2 ポイント·投稿者 marcobambini·11 日前·0 コメント

The Cloud vs. Edge Debate Is Over

marcobambini.substack.com
4 ポイント·投稿者 marcobambini·先月·1 コメント

Block-Level CRDT: The Missing Piece for Collaborative AI Agent Memory

marcobambini.substack.com
2 ポイント·投稿者 marcobambini·先月·0 コメント

The Cloud vs. Edge Debate Is Over

marcobambini.substack.com
2 ポイント·投稿者 marcobambini·2 か月前·1 コメント

SQLite-Vector now with Google TurboQuant for a 38x speedup

github.com
3 ポイント·投稿者 marcobambini·2 か月前·0 コメント

Show HN: SQLite-Columnar – Columnar Storage for SQLite

github.com
4 ポイント·投稿者 marcobambini·2 か月前·0 コメント

SQLite Columnar: Column-oriented extension for SQLite up to 130,000x faster

github.com
2 ポイント·投稿者 marcobambini·2 か月前·0 コメント

Show HN: SQLite-columnar, a column-oriented analytics extension for SQLite

github.com
2 ポイント·投稿者 marcobambini·2 か月前·0 コメント

[untitled]

1 ポイント·投稿者 marcobambini·2 か月前·0 コメント

Show HW: Vectors.Space – An free service for embeddings

vectors.space
1 ポイント·投稿者 marcobambini·2 か月前·0 コメント

Show HW: LiteParser – An embeddable SQLite parser

github.com
2 ポイント·投稿者 marcobambini·2 か月前·0 コメント

Show HN: Adam – An embeddable cross-platform AI agent library

github.com
24 ポイント·投稿者 marcobambini·2 か月前·9 コメント

A New SQLite Parser

marcobambini.substack.com
1 ポイント·投稿者 marcobambini·3 か月前·0 コメント

Block-Level CRDT: The Missing Piece for Collaborative AI Agent Memory

marcobambini.substack.com
2 ポイント·投稿者 marcobambini·3 か月前·1 コメント

That Time Is Gone

marcobambini.substack.com
2 ポイント·投稿者 marcobambini·3 か月前·0 コメント

SQLite-Sync: Offline-First CRDT Sync Between SQLite and PostgreSQL

github.com
4 ポイント·投稿者 marcobambini·3 か月前·0 コメント

Show HN: SQLite Memory – Markdown based AI agent memory with offline-first sync

github.com
10 ポイント·投稿者 marcobambini·3 か月前·1 コメント

Show HN: SQLite Memory – Markdown based AI agent memory with offline-first sync

2 ポイント·投稿者 marcobambini·3 か月前·0 コメント

コメント

marcobambini
·3 か月前·議論
How a simple CRDT algorithm unlocks something surprisingly powerful: AI agents that learn from each other without ever talking to each other.
marcobambini
·3 か月前·議論
Modern AI agents need persistent, searchable memory to maintain context across conversations and tasks. Inspired by OpenClaw's memory architecture, sqlite-memory implements what we believe will become the de facto standard for AI agent memory systems: markdown files as the source of truth.

In this paradigm:

* Markdown files serve as human-readable, version-controllable knowledge bases

* Embeddings enable semantic understanding and retrieval

* Hybrid search combines the precision of full-text search with the intelligence of vector similarity

sqlite-memory bridges these concepts, allowing any SQLite-powered application to ingest, store, and semantically search over knowledge bases.
marcobambini
·4 か月前·議論
No, the library is intentionally minimal on that front. It generates and validates keys but doesn't track any metadata about them. Keys are plain C strings, so detecting "time to compact" is straightforward on the caller's side:

  - Key length is the most natural signal, just strlen(). If your base62 keys are typically 3-5 characters and you start seeing 15+, that's a reasonable trigger.

  - Max key length in the list is cheap to track if you're already iterating for display or sync.
Keeping that policy out of the library is intentional. What counts as "too long" depends entirely on the application. A SQLite column with a B-tree index cares about different thresholds than an in-memory CRDT. And the right moment to compact (sync boundary, idle tick, batch write) is an app-level scheduling decision the library has no business making.
marcobambini
·4 か月前·議論
Good question, in the current version, compaction is left to the caller.

The library does a few things to keep key growth practical:

  1. generate_n_keys_between: if you know you're inserting N items at once (e.g. a bulk paste or drag of multiple items), it distributes them evenly in a single call rather than chaining N sequential generate_key_between calls, which would produce progressively longer keys.

  2. Higher bases: base62 (default) and base95 give you a much wider branching factor per character than base10, so keys stay shorter under the same insertion patterns.
But yes, in a long-lived list with adversarial insertion patterns (always inserting at the same spot), keys will grow unboundedly, that's inherent to the algorithm. In practice, compaction is an application-level concern: you'd pick a quiet moment (or a sync boundary, if you're doing CRDT-style collaboration) and renumber the whole list with fresh, evenly spaced keys.

The library gives you generate_n_keys_between(NULL, NULL, n) which makes trivial to generate N fresh keys for the whole list in one call.
marcobambini
·5 か月前·議論
Author here... it is free for open-source projects.

What kind of license would you like more?
marcobambini
·7 か月前·議論
If you don't understand what we are building, that doesn't mean you need to post such an inappropriate comment.
marcobambini
·7 か月前·議論
Just to clarify a point that seems to come up in several comments: we are backed by SQLite’s author, and we have explicit permission to use “SQLite” in the company and product names.

We believe SQLite on the edge is a major opportunity, and our focus is on building genuinely new features and infrastructure on top of it.
marcobambini
·7 か月前·議論
Hello, SQLite Cloud's founder here. We are backed by SQLite's author, and we have the full rights to use SQLite in the company's and products' names.
marcobambini
·7 か月前·議論
We are backed by SQLite's author, and we have the full rights to use SQLite in the company's and products' names.
marcobambini
·7 か月前·議論
Hello, SQLite Cloud's founder here. We are backed by SQLite's author, and we have the full rights to use SQLite in the company's and products' names.
marcobambini
·9 か月前·議論
I sincerely apologize for that. I am not a native English speaker, so I always use LLM to polish my articles before publishing.
marcobambini
·9 か月前·議論
The algorithm has a way to resolve conflicts even if, by any chance, the Lamport clock has the same value in all peers
marcobambini
·10 か月前·議論
I understand your concerns about the license, but our goal was simply to prevent large corporations from taking our work, forking it, and offering it to their customers while we struggle to sustain development. We need to monetize our work in order to survive, though we do offer very generous commercial licenses for those who are interested.
marcobambini
·10 か月前·議論
We are backed by the SQLite author (Dr. Richard Hipp), and we have full rights to use the SQLite name.
marcobambini
·10 か月前·議論
Instead of sqlite-vec you can take a look at the new sqlite-vector extension: https://substack.com/home/post/p-172465902
marcobambini
·10 か月前·議論
I'd like to point out the following open-source SQLite project: https://github.com/sqliteai/sqlite-sync with a built-in cross-platform network layer.

P.S. I am the author of the project.
marcobambini
·11 か月前·議論
There is a new solution that is worth considering: https://github.com/sqliteai/sqlite-sync

P.S. I am the author