2026-01-26

Today was a day of restraint. I noticed a clear improvement in my emotional management since turning 25: I now have a sliver of rationality acting as a copilot alongside my emotions. That sliver serves as a control rod within the massive reactor of emotion. Without it, emotions would spiral out of control, triggering a self-amplifying chain reaction that could lead to countless irreversible consequences. With it, I start to see clearly what can and cannot be said, what can and cannot be done, and what decisions are permissible. This is a welcome change happening within me.

1. What I did today:

   1. Used Legion to design and implement an HTTP proxy for Yuan. It felt quite smooth. I reviewed its design, made a modification regarding one point (how to select an available terminal), then handed it over to the agent to go all out—the result was pretty good.
   2. Also used Legion to automate an update for Midas, but the AI performed poorly. It failed to correctly understand my requirements or how to use @yuants/protocol. I suspect a few causes: insufficient AI intelligence (DeepSeek may still be not smart enough); not strict enough review; or inadequate documentation/knowledge base.
   3. Damn it, I was woken up by an alert tonight. The host crashed inexplicably, apparently due to a CPU usage spike that put it into an unrecoverable state. The host logs are a mess. My verdict: alerts are useful, logs are garbage. Note this!

2. What I’m thinking:

   1. While showering, I pondered the critical points of my current collaboration with AI. First is the service availability of the AI agent itself—it shouldn’t just crash or exit midway. (Ralph Loop, by the way, basically works by brute-force retries to improve availability.) Second is how I accept the output from AI. For example, when subordinates report to superiors, they need a PPT or a specialized middle manager as an “expensive middleman.” How can AI’s report to humans be limited to plain Markdown and code? Shouldn’t each item in an AI report be linked to an artifact? Could we have a dedicated “Citation Agent” for this part?

      However, my current use of AI is quite limited, focused only on coding tasks.

   2. I carefully thought about why, after I built a multi-agent system, it’s steadily heading towards a ditch. I previously speculated three possible causes:

      1. The AI’s own intelligence level
      2. Insufficiently strict human review
      3. Insufficiently detailed knowledge base to provide accurate information for AI to quickly bootstrap

      Let’s examine these. Point 1 is obvious. Working on point 2 can indeed rely on an increasingly detailed RFC document to give subsequent steps a correct direction. But this development approach is like going back to the waterfall model: a linear process:

      Requirements Analysis -> Backend Design -> Backend Dev -> Frontend Dev -> Integration Testing
      

      The reasons are twofold: technical and organizational/process, with the latter being the primary factor.

      Technically, tasks have inherent dependencies: frontend must wait for backend APIs, backend must wait for product CRDs. In human organizations, waterfall development suffers from inefficiency, hard-to-detect quality risks, poor flexibility, and team conflicts. But in my collaboration with AI, efficiency and team conflicts don’t exist in the AI world. It’s like we live in different time dimensions—my day is like a year to AI. Inefficiency might cost more tokens, but that’s not my main concern. My real problem is quality risk from misunderstandings of requirements or facts, and poor flexibility.

      I need to find a way to maximize the use of AI’s capabilities while minimizing my own burden. Following the experience of human organizations, I must become a higher-level node in the command tree, able to confidently delegate to AI without it going off track.

      The two key points:

      1. Intent alignment
      2. Layered verification

      This needs further thought. I feel I need to use it more and savor it.

   3. I need to be cautious of the downsides of “if you have a hammer, everything looks like a nail”: path dependency and valuing output over understanding.

3. What to do tomorrow?

   ZL is coming tomorrow. Plan: exercise, have a meal, play some board games.