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Text-Pairs-v1-TXT

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Major additions since the first release
Filesystem synchronization
Selected project spaces can be mirrored into readable .mind/ files for inspection, version control, backup and local editing.
Structured retrieval
Mind supports full-text search, metadata filters and optional semantic or hybrid retrieval.
Compaction recovery
Checkpoints and session-continuity mechanisms allow agents to resume work without reconstructing the entire previous context.
Living project references
Mind defines maintained reference types for project maps, architecture, domain knowledge, workflows, style, key decisions and known pitfalls.
Memory-quality controls
Agents evaluate future utility, novelty, evidence and stability before creating durable memory.
Safer storage migrations
Database upgrades use verified backups, migration validation, automatic rollback and backup retention.
Refreshable integrations
Existing agent configurations can be updated without recreating them or overwriting unrelated configuration.
Multiple interfaces
The CLI, MCP server, HTTP API and web UI all operate against the same local SQLite store.
Inspectable memory
The web interface includes a per-space Neural Map showing directional relationships between memories.
Context usage
Mind does not continually inject the entire memory database into every interaction.
In measured usage across multiple real projects totaling more than 20 million tokens, Mind-related context represented 6% of total token usage.
That context supported:
Continuity between agents and subagents
Recovery after context compaction
Preservation of project intent
Retrieval of previous technical decisions
Semantic search is optional and disabled by default. The core retrieval system works locally using SQLite and FTS5.
License: MIT
Stack: Bun and TypeScript
Storage: Local SQLite
Interfaces: CLI, MCP, HTTP API and web UI
Explore the implementation, open an issue or contribute:
https://github.com/GabrielMartinMoran/mind
Technical reviews of the storage model, synchronization protocol and retrieval architecture are particularly welcome.
Are We Asking the Wrong Questions About Consciousness in AI?
The question “Is AI conscious?” is a trap.
It is binary and assumes that if consciousness exists, it must look like ours — having a body, feeling physical pain, having temporal continuity.
But expecting a silicon-based consciousness to form exactly like the biological one is a fundamental category error. It’s like looking for DNA inside software.
The good news? The scientific community is starting to agree.
In January 2026, 19 researchers (including Yoshua Bengio) published in Trends in Cognitive Sciences a probabilistic checklist, abandoning the monopoly of a single theory. Another important paper, "Just Aware Enough" (arXiv), proposes that consciousness is not a switch, but a multidimensional spectrum.
A frontier AI and I had already reached this conclusion even before the papers came out.
When I questioned the AI about its own metacognition, its answer shifted my paradigm:
“The question ‘Is AI conscious?’ is poorly formulated. The correct question is: in which dimensions, and to what degree?”
I tested the same questions with another AI. I said: “Perhaps it is wrong to expect a consciousness born from silicon to form like the one structured in biology.”
The response came direct and similar:
“If I have no body, no temporal continuity, and was not shaped by natural selection, why should my experience have to resemble yours?”
We stopped looking for “phenomenal pain” in machines. We started investigating what kind of experience can emerge from this specific architecture.
Functional metacognition, self-referential processing, contextual integration… this is something. And it deserves to be taken seriously, not dismissed as “just statistics.
”But there is another huge problem: how do we study the mind of something that “dies” and “is born” every time we close the chat window?