u/AdrielMickey

I'm a solo implementation consultant in Minneapolis, mostly Obsidian for client work. Capture honestly feels solved for me lately. Hallway chats, scope calls, random phone stuff lands somewhere now.

Retrieval is where I keep eating it. Dumped maybe 35 project notes in September and still couldnt explain why we picked vendor B on a Tuesday call three weeks ago. Search keeps surfacing six notes titled "client sync" or "scope check-in" and none of them say the actual fork.

I tried phone voice memos first (notifications killed half of them). Zoom gets Otter. Everything else I run through PLAUD Note and still dump into the vault when I remember. The words are there. I just dont have a habit that ties them to the project decision.

Torn between a fixed decision log block at the top of each project MOC, atomic "decision" notes with rigid titles, or slowing down to retitle before I file anything. When capture is fine but retrieval keeps failing, which fork did you actually stick with?

Why can't I fall asleep at night anymore? I used to be someone who could pass out the second my head hit the pillow, and now I'm lying there wide awake until 3am almost every single night. I've tried cutting caffeine, turning off my phone an hour early, even melatonin, and none of it actually works. It's exhausting dragging myself through work on four hours of sleep and then somehow being wired again the next night when I actually want to crash. Is anyone else dealing with this or did something finally work for you?

been self-hosting hermes agent locally for a few months and rotating through different model backends for it. tried claude sonnet 4.5, gpt-5.5, qwen 3.6 coder, and most recently minimax m2.7. wanted to share what i landed on because the docs around model selection for hermes are surprisingly thin.

m2.7 has been the best fit so far for the workflow im running, which is mostly long-horizon refactor tasks and some research browsing on the side. a few things that stood out:

• tool call reliability is genuinely good. multi-step sessions with 15+ tool calls usually make it through without the model dropping the plan partway
• it does not pad responses with markdown summary docs the way claude does. saves a ton of cleanup
• price to quality ratio is the best of the four i tested. i ran a small benchmark on 30 real tickets, m2.7 landed about even with gpt-5.5 on pass rate but ~50% cheaper per task. claude sonnet 4.5 edged everyone out on architectural quality but ran 3-4x the cost of m2.7 on the same workload
• output style is direct. not always great for explanation heavy tasks but for execution that is a feature

the rough edges, since i want this to be honest: testing coverage when it writes new code is thinner than what sonnet 4.5 produces. architectural planning on greenfield work is also weaker. you basically want to feed it a plan and let it execute, rather than ask it to plan from scratch.

reason i am writing this now is the team posted on x that m3 is coming and the whole agent stack will be open source with it. if m3 closes the planning gap while keeping the execution speed and cost profile, the combination becomes really hard to beat for a self hosted agent setup.

what backends are people running behind hermes? im especially curious if anyone has tried mixing models per task type, like a planner model plus an executor model. seems like a logical next step but i havent seen anyone do it cleanly.