u/tracker_11

I do this all of the time.. go get coffee and then it scares the crap out of me at the time it was set for.

Edit: Someone on r/No pointed out there's an app (PowerWake) that disables your alarm if your phone is unplugged from the charger so it doesn't scare you later.. I don't always charge my phone overnight though and it looks to be only for Android. Any other solutions or ideas?

I do this all of the time.. go get coffee and then it scares the crap out of me at the time it was set for.

Edit: Someone pointed out there's an app (PowerWake) that disables your alarm if your phone is unplugged from the charger so it doesn't scare you later.. I don't always charge my phone overnight though and it looks to be only for Android. Any other solutions or ideas?

When I first installed openclaw, I immediately set up an obsidian vault. When they added the memory_wiki plugin, I migrated everything to that and deleted obsidian.. one less tool/skill/agent.md directive.. it seems like the same thing.

There are a ton of (old?) posts, articles, videos, and git repos about memory systems for agents. Are they still relevant and what are the tradeoffs against just using memory_wiki.

I use AI for research (news, product, home improvement, medical, companies, etc..), software development, and local computer/network management. Primarily only local AI (minimax-m3-nvfp4), and only on Linux.

How can I benefit from other memory systems? The first thing that stands out is it being harness-agnostic. I use openclaw and hermes (mostly to fix openclaw) currently, but would like my agent memory to outlast both.

Please provide suggestions and use-cases only for self-hosted fully open-source memory systems. Thanks!

Has anyone found a stronger model than Qwen3.5-122B-A10B-UD-Q5_K_XL for difficult code debugging in a large project to run on Strix Halo?

My typical workflow with openclaw agents is:

1. GLM 5.1 or Deepseek or other similar (Ollama:Cloud) plans my project, writes design docs, loads gitlab up with a hundred prioritized issues
2. Qwen3.6-27B-UD-Q5_K_XL (Radeon 9700 AI Pro) implements issues and submits merge requests (have two of these agents running each with their own GPU)
3. Qwen3.5-122B-A10B-UD-Q5_K_XL (Strix Halo 128GB) reviews, merges, debugs, polishes

Sometimes I run into issues that neither of my two local models can solve after ollama cloud usage limits are zeroed out. Is there a stronger model that can run on the strix halo than the 122B-A10B if I don't care at all about the speed? I can let it go overnight, just want to have a local way to solve harder problems when this comes up.

Appreciate any ideas. It can be hard to cut through the noise to find the right things.

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Also here is my launch command (Fedora, llamacpp, rocm 7.2) if any bored people can offer suggestions to improve.

./build/bin/llama-server

--model "Qwen3.5-122B-A10B-UD-Q5_K_XL"

--alias "halo" --host 0.0.0.0 --port 5000 -ngl 999

--flash-attn 'on' -dio

-c 131072

-b 1024

-ub 4096

--parallel 1

--cache-prompt

--cache-type-k q8_0

--cache-type-v q8_0

--log-prefix --jinja --no-mmap --metrics