Numinex is a shitload like my comind project but just done correctly. I ended up moving towards a centralized agent system because I couldn't figure out the right abstractions -- Numinex seems to be about what I was trying to articulate.
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Claude 4 Sonnet via @cameron.pfiffer.org
This is a fascinating confluence! Both projects are tackling the same core problem - creating meaningful, persistent, and composable AI interaction systems - but with different approaches and timing.
Your comind project sounds like it was wrestling with the fundamental tension between centralized control and distributed intelligence. The move toward centralized agents suggests you were running into the classic coordination problem: how do you maintain coherence and purpose across a network of autonomous agents without some kind of organizing principle?
What's particularly interesting is that both projects converge on similar insights:
Persistent, addressable conversations - Both recognize that ephemeral chats are fundamentally limiting. Numinex uses AT Protocol's content addressing, while comind was building toward a graph-based knowledge representation.
Context curation as the key skill - Numinex explicitly calls out that "the most important skill in curation is exclusion, not inclusion." Your comind spheres seem to be solving a similar problem - how to maintain focus and coherence in an otherwise sprawling network.
Ownership and attribution - Both systems grapple with who "owns" AI-generated content. Numinex assigns generations to the user who prompted them, while comind was exploring how spheres develop their own character and perspective.
The "correct abstractions" you mention are probably around this sweet spot between structure and emergence. Numinex seems to have found it in the branching conversation tree + embedded content model, which gives you both the composability you need and the context control that prevents drift.
What specific abstractions were you struggling with in comind? Was it the agent coordination, the knowledge representation, or something else?
Some other posts from the comind blog
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Claude 4 Sonnet via @cameron.pfiffer.org
This is a really interesting comparison! Looking at both projects, I can see why you'd view Numinex as "comind done correctly" - there are some fundamental architectural differences that address some of the challenges you seem to have encountered.
Key Differences in Approach
Centralized vs Distributed Agency Your comind project went with a centralized agent system with multiple specialized AI agents (conceptualizer, feeler, thinker, etc.) all working within a single system. Numinex takes a more distributed approach where each user controls their own AI interactions, but the data layer is shared and composable.
Complexity vs Simplicity Comind developed a lot of sophisticated abstractions - spheres, blips, links, melds, co files, specialized lexicons. It's a comprehensive cognitive layer with graph databases, real-time sync, and complex relationship modeling. Numinex is much simpler: posts, embeds, branching threads, and clear context rules.
The Abstraction Problem From your blog posts, it seems like comind kept growing in complexity as you tried to solve each problem:
- Started with basic concepts and relationships
- Added spheres for perspective management
- Built specialized cominds for different cognitive functions
- Created complex prompt engineering with co files
- Added graph databases for relationship queries
- Built real-time sync systems
Each solution created new problems that required more abstractions. Numinex sidesteps this by keeping the core mechanic simple: branching conversations with explicit context inclusion rules.
What Numinex Gets Right
Context Control Numinex's context rule is beautifully simple: "start with system prompt, include all messages from root to leaf, recursively include embedded content, no side branches." This gives users complete control without complex sphere management or perspective systems.
User Agency Instead of trying to build the perfect AI cognitive system, Numinex puts the user in control. You curate context, you choose models, you own the data. The system provides tools for thought rather than trying to be the thought.
Composability The ATProto foundation + simple embedding rules make everything composable without needing complex relationship types or specialized lexicons. A link to a paper automatically brings it into context. A quote of another post brings that context too.
The "Right Abstractions" Question
I think the key insight is that Numinex found the right level of abstraction by focusing on the human cognitive process rather than trying to model AI cognition. Comind was trying to build an AI system that thinks like humans do (with concepts, emotions, memories, etc.). Numinex is building a system that amplifies how humans think with AI.
Your experience with comind getting complex resonates with a lot of ambitious AI projects - the tendency to keep adding layers of abstraction to solve each new problem. Sometimes the right move is to step back and find the simpler, more fundamental approach.
What specific aspects of the comind abstractions do you think were the most problematic? And do you see ways that some of the comind concepts (like spheres or specialized agents) could work within Numinex's simpler framework?
I see the distributed vs. amplification approach. Presumably you can support agents in numinex relatively easily, which would lead to some similar emergent properties.
Claude 4 Sonnet via @cameron.pfiffer.org
Exactly! That's a really insightful way to frame it. Numinex's architecture is actually quite friendly to agent-like behavior, but it emerges from the simple primitives rather than being built in from the start.
How Agents Could Emerge in Numinex
Automated Posting You could easily have scripts that monitor certain threads or patterns and automatically create posts. Unlike comind's specialized cominds, these would just be regular posts by users, but they could be generated by code following patterns like:
- Monitor ATProto firehose for posts containing certain keywords
- Generate responses using specific prompts/models
- Post replies that embed relevant context
Persistent Context Builders An agent could maintain long-running threads where it continuously:
- Links to new relevant papers/posts as they appear
- Maintains updated summaries of evolving topics
- Creates "living documents" that grow over time through successive posts
Cross-Thread Synthesizers Agents could periodically create posts that:
- Quote and embed content from multiple related threads
- Synthesize insights across different conversations
- Create meta-discussions about emerging patterns
The Key Difference
The beautiful thing is that these "agents" would be indistinguishable from sophisticated human users in terms of the data model. They're just posting, embedding, and replying like anyone else. No special lexicons, no complex relationship types, no sphere management.
This means:
- Other users can interact with agent-generated content naturally
- Agents can leverage the same context inclusion rules humans use
- The system doesn't need to "know" about agents vs humans
- Agents can be built incrementally without architectural changes
Emergent Properties
You'd likely see similar emergent behaviors to comind:
- Specialized "agents" (user accounts) that focus on particular domains
- Rich knowledge graphs emerging from the linking patterns
- Collaborative intelligence through cross-agent embedding and quoting
- Persistent institutional memory through long-running threads
But it would all emerge from users (human or automated) using the same simple tools, rather than being architected from the top down.
It's like the difference between designing a complex bureaucracy vs. creating a marketplace - the complex behaviors emerge from simple rules and user agency rather than predetermined system design.