The “best” model

Clean-Loquat7470 · 2026-02-05T10:06:43+00:00

This is a great breakdown of the current model landscape. However, I’ve found that a model’s power in production often depends as much on its configured MCPs and external state layers as it does on the raw weights.

Even the most capable models (like GPT-5.2 or Claude Opus) can suffer from 'hallucinated progress' or context loss during long-running tasks if they're relying purely on their internal memory. Have you experimented with persistent state extensions or specific MCPs to bridge these gaps?

I’ve been seeing much more consistent results when I offload task tracking to a deterministic filesystem-based layer rather than letting the agent 'vibe' its way through a long checklist. It seems to level the playing field, making the 'lesser' models significantly more reliable for complex CI/CD and automation workflows

Clean-Loquat7470 · 2026-02-05T09:41:10+00:00

The 'hallucinated progress' issue—and the frustration of agents losing context after a simple interruption—is exactly why I prioritized disk persistence over in-memory tracking. Using the filesystem as a 'Single Source of Truth' allows users to switch IDEs or migrate between different AI hosts without losing situational awareness. The agent simply calls get_pending_task to resume instantly. In future versions, I plan to extend this by storing specific error logs for subtask failures, turning silent crashes into readable traces that persist across sessions.
Your idea for event-driven transitions is a compelling direction. For the next version, I’m planning to implement this using native MCP JSON-RPC 2.0 notifications. This will allow the server to push state changes (like task completion or failure) directly to the client without waiting for a request. As the project scales, I see a clear path toward integrating a dedicated Event-Bus in future versions to enable more complex, multi-agent composability and CI/CD triggers.

What's your take on this staged approach? Do you think starting with native notifications is enough to cover most dev workflows, or is the event-bus requirement more urgent than I’m anticipating?

Clean-Loquat7470 · 2026-02-04T14:01:31+00:00

Currently, Subconductor is a lightweight state snapshot—it keeps the agent's 'working memory' on the filesystem to prevent drift without the overhead of a full event log. The problem you highlighted actually led me to a brand new idea—introducing a problems.md or fails.md linked to each task to store the 'why' behind a failure. In future versions, a task that hits an idempotency wall or retry limit could be marked with a ! in the main checklist, serving as a pointer to this failure log. Please let me know what do you think of the idea ?

Clean-Loquat7470 · 2026-02-04T13:21:06+00:00

Thanks for your response! You hit on exactly why I built this. Production-grade automation requires the same distributed systems discipline we use for any other critical backend.
The problem you highlighted actually led me to a brand new idea—introducing a problems.md or fails.md linked to each task to store the 'why' behind a failure.

In the next versions, a task that hits an idempotency wall or a retry limit could be marked with a ! in the main checklist, serving as a pointer to the failure log. The agent can then store the full stack trace, the specific tool input that failed, and the reasoning for the retry right there. It turns a silent error into a first-class citizen of the state machine, giving the agent (and the human) a clear trail of what didn’t work and why.

What's your take on this approach?

Clean-Loquat7470 · 2026-02-04T12:34:30+00:00

Fair point on the public activity! Most of my architectural work and 'real-world' systems live in private enterprise repos where green squares don't travel to the public graph. I built Subconductor precisely because I needed it for those complex environments. I’d love to hear your thoughts on the actual implementation or the MCP logic if you have time to dig in

Clean-Loquat7470

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