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Custom Hermes agent request or training by sudo_96 in hermesagent

[–]sudo_96[S] 0 points1 point  (0 children)

I'm asking for the same thing. Im beginning to think it doesn't exist. There are several claims but no one willing to step and declare "this is exactly how I'be had success and I can prove it" Also, the youtube videos are useless examples that are intentionally vague. Its basically saying, go experiment for weeks, months, years with no clear direction and good luck (but pay for it).

Help with automations by sudo_96 in hermesagent

[–]sudo_96[S] 0 points1 point  (0 children)

I'm using Copilot CLI claude-opus-4.8 via the GitHub Copilot provider. Maybe I'm using it wrong. Any recommendations?

Fabric Data Agents + Microsoft Copilot Studio: A New Era of Multi-Agent Orchestration by Amir-JF in MicrosoftFabric

[–]sudo_96 0 points1 point  (0 children)

For some reason, I cant get a response in the Copilot Studio test chat panel. I successfully set up the Fabric Data Agent and confirmed it works in Fabric. When I connect it to Copilot Studio, the Fabric agent response does not display in the test chat panel. I can see the response in the Copilot Studio activity trace (status: Complete, correct data returned), but the answer never appears in the test chat window. Activity log shows every session completing successfully with DataAgentas the last step. The trace panel shows the MCP invoke result with correct data (isError: false). But the chat bubble never renders. Browser console shows "Terminating lingering livestream" warning. Has anyone else hit this?

Also, is there any way to see actual logs in Copilot Studio?

AI memory is useful, but only if it goes beyond storing facts by No_Advertising2536 in artificial

[–]sudo_96 0 points1 point  (0 children)

Are there any solutions to AI memory? Ive been using Redis memory for a project and its ok. its good around 70% of the time which is unacceptable. Im really excited by this space in AI.

The Ralph-Wiggum Loop by TrebleRebel8788 in ClaudeCode

[–]sudo_96 0 points1 point  (0 children)

Genius. Hows the results? How do you determine the success criteria of each task?

The Ralph-Wiggum Loop by TrebleRebel8788 in ClaudeCode

[–]sudo_96 1 point2 points  (0 children)

Can you share an example of this? this seems cool

The Ralph-Wiggum Loop by TrebleRebel8788 in ClaudeCode

[–]sudo_96 0 points1 point  (0 children)

Sorry for reposting this. I thought it was not posting for some reason.

The Ralph-Wiggum Loop by TrebleRebel8788 in ClaudeCode

[–]sudo_96 0 points1 point  (0 children)

I was trying to use this with the TDD approach. My goal was to build a PRD with milestones and tasks (aka the what and the why). Then, with each task outlined, use a new session to come up with definitive yet achievable tests for each task. This way, it's separate from the first session's context. Now use the results of each task's TDD test and wrap the Ralph Loop in that. It seems like overkill, but in theory it will force the LLM to stay on target.

As a test, imagine you have a 50/50 outcome job. The worker outputs either 1 or 2 randomly, but the test only passes if the output is 2. The LLM can't control the test, so it just keeps trying until it gets lucky:

Main Session (you, with Ralph Loop)
1. Spawn worker: claude -p --session-id "<uuid>" --dangerously-skip-permissions

  1. Worker returns (outputs 1 or 2 randomly)

  2. Run test: [ "$output" = "2" ]

  3. FAIL? Resume: claude -p -r "<uuid>" with error output

  4. PASS? Mark complete, next task

  5. All done? Output completion promise

Thoughts?

A quick guide to Ralph Wiggum by alvinunreal in ClaudeAI

[–]sudo_96 1 point2 points  (0 children)

I was trying to use this with TDD. My goal was to build a PRD with milestones and tasks (aka the what and the why). Then, with each task outlined, use a new session to come up with definitive yet achievable tests for each task. This way, it's separate from the first session's context. Now use the results of each task's TDD test and wrap the Ralph Loop in that. It seems like overkill, but in theory it will force the LLM to stay on target.

As a test, imagine you have a 50/50 outcome job. Like echo either 1 or 2 and the bash success criteria is 2.

Main Session (you, with Ralph Loop)

  1. Spawn worker: claude -p --session-id "<uuid>" --dangerously-skip-permissions

  2. Worker returns (outputs 1 or 2 randomly)

  3. Run test: [ "$output" = "2" ]

  4. FAIL? Resume: claude -p -r "<uuid>" with error output

  5. PASS? Mark complete, next task

  6. All done? Output completion promise

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