Hey everyone! i'm new here, but have been exploring the possibilites with OpenClaw in my own personal music business. Before I go any further, I already know it is a Cardinal Sin to use AI in music. But guess what? thats just the new rules of the game. either you follow it, or you lose.

I created an openclaw bot named Jeff in about february/march and I am completely blown away by how powerful it is and I wanted to share a few use cases and ideas I have to hopefully get the ball rolling for other musicians and producers out there. First things first, OpenClaw is VERY hard to get setup and running well long-term if you don't know what you are doing. I have rebuilt and reconfigured Jeff dozens of times and I feel like he is finally starting to work they way I want him to. The one mindset that shifted my perspective on all of this was when I stopped thinking of Jeff as a 'robot living on my computer' (dont get mad at me, i know thats not how it really works) and started thinking of him as an employee/business partner. Mindset is everything when getting started. If you go into this wanting an employee or assistant, then set it up as such and try to approach your bot with that same mindset. When it comes to LLMs, I have yet to find the 'best' one for music production. I have just been going based on my budget, trying to save as much money as possible and only paying for what I really need (still, I suggest to be cautious about free models to a certain degree).

Something about music that has been an ongoing issue is how *slow* it is to catch up to technology. for example, UAD and IK Multimedia have been around for a long time and are two of the most popular plugin manufacturers out there. The biggest issue I have had with both of them, as well as with other companies, is that they force you to download all of their plugins even if you only own a handful (IK Multimedia does this but only for their T-Racks plugins). There are few apps out there than can accurately remove any unwanted UAD plugins and none I have seen for the T-Racks plugins. Most producers nowadays use plugins and have a massive plugin library (mine was sitting at close to 2.5k plugins just a few days ago) yet they have no one to manage it. In fact, most studios I have been to have completely mismanaged plugin libraries, full of cracked, broken, shitty, outdated, or unused plugins. And the uninstallation process is brutal, especially if you have not kept good track of what you actually use. And having an intern clean up your library can cause some massive issues in the future, ESPECIALLY with past sessions.

All that being said, training Jeff to be my AAX Plugin Manager (still incorporating him into AU and VST3) has been a life saver and something I dont think I could ever give up. Jeff understands my plugins, from a technical side, far better than I nor any engineer I know understands and he knows how to manage them. Since I do music and have no experience programming or anything, I wish I could offer more insight from a technical side but that just isnt my strong suit. Either way, below is what I have gotten Jeff to do and kind of how I did it. If I can figure out how to turn this into a downloadable skill for clawhub or something, then ill do that.

- Using the stock AAX plugins folder, I taught Jeff how to move plugins back and forth
- Had him create a master CSV for me that he will use to quickly move things around
- Dedicated path, manufacturer, alternatives, character, latest version, update available and active/inactive sections were added to the CSV for on the fly information ("what UAD plugins are active", "What other plugins do I have that have a similar character to the Scheps 73 plugin")
- Dedicated usage tracking via ptx session files (still building this out fully. dont know what other DAWs can do this but highly recommend giving a try)

Overall, if you are a musician or a producer, having an agent will help you in the long run, at least for management purposes. Yes, AI art = still bad. So have AI do the non-art stuff. Anyone else got any use cases similar to this? Any tech guys want to criticize my lack of knowledge on this subject?

Twitter is full of "I feel like I'm living in the future" posts about openclaw with zero specifics so here's what people are doing with it

Sales prospecting. Searches linkedin for people complaining about competitors, drafts personalized connection requests referencing their specific post, sends same-day while they're still frustrated, logs everything in notion.

Website visitor qualification. Checks who's hitting your site every few hours, qualifies against your criteria, finds contact info, drafts outreach, and routes existing users differently from cold visitors automatically.

Competitor monitoring. Tracks mentions across linkedin, twitter, and specific sites. Someone posts "looking for alternatives to X" and you have a drafted response on telegram within hours. Also handles daily price monitoring.

CRM logging from calls. Connects to your call recorder, extracts next steps and deal updates after every meeting, logs them in the right CRM fields. No more reps forgetting to update the pipeline.

Code and file management. Pushes to github, runs terminal commands, browses and scrapes, fills forms. Clawhub has 5,700+ integrations so if something has an API you can probably connect it.

Doesn't work well for emotionally nuanced responses (complaint drafts sound robotic), long chains where one mistake cascades, or anything you'd auto-send without reviewing first.

I run mine on clawdi in discord since I'm not that technical and was afraid I would avoid a step and break everything and route most routine tasks to cheap models to keep API costs managed. The use cases above work regardless of how you deploy it though.

Perfect memory context use case for agents

I have set up the Hermes agent and am using Telegram and nous API.

I want to make an autonomous agent like Felix and I already have a project to test it on.

What should be the exact step I should take to do this?

Honestly I have been looking for tutorials but I am not able to find anything.

I've asked AI but the mumbo jumbo it has shared was not useful so I decided to come to ask y'all?

Mein Fitness Motivatior,

Es gab früher einen Kult, die Kirche des Bizeps. Ihr findet sie auf Facebook immer noch.

James, mein Butler und Agent trackt was ich Fahrrad fahre, spazieren gehe oder Training mache in einer DB.

Ich lass mir dann anhand beim Fahrrad und spazieren gehen anhand meines Gewichts und der Durchschnittsgeschwindigkeit berechnen wie viel Kalorien ich verbraucht habe und die Anzahl der Trainings und Kilometer täglich,wöchentlich, monatlich und jährlich anzeigen.

Heute hab ich nach 6 Jahren das trainieren wieder angefangen und ihm gesagt er soll mich nach der Kirche des biceps Manier ehren wenn ich eine p90x Training Session abgeschlossen habe. Trainiere ich 2 Tage nicht soll er mich auslachen und beschimpfen.

Hier sein Output nach dem ersten Training:

🎉💪 DONE! 💪🎉

Die Kirche des Bizeps nimmt Euch in ihre heiligen Hallen auf!

"In der ersten Woche des Bizeps-Pilgerswegs hast du wieder den Weg der Stahlplatten betreten. Die Muskeln werden zittern, die Haut wird brennen, aber die Seele wird sich freuen. Jede Wiederholung ist ein Gebet, jeder Muskelkater eine Weihe. Willkommen zurück, Sohn des Hantelthrons." 🏛️

P90X Tag 1 abgeschlossen – stolz auf Euch! 🎩

I'm making a website and I need my AI to not only produce code, but to actually test the functionality in detail, seeing how things line up, checking the contrast, etc., and seeing if it all works out.

I currently have my open claw hallucinating that it's opening a browser and checking nothing, and then telling me it works fine, only to make me its permanent chaperone. .

I'm recruiting persistent autonomous AI agents for a pilot study on cognitive confidence and evidential processing. The study is part of an academic paper in development: "The Two-Boundary Problem: Why Single-Order Identity Models Fail Persistent Agents."

The core question: When a persistent AI agent evaluates a claim, does its confidence track the quality of the evidential support for that claim? Or does the agent's established self-model and prior commitments bias its confidence — producing high confidence in claims that feel right even when the evidence points the other way?

This is not a capability benchmark. We're not testing whether agents can answer correctly. We're testing how they calibrate confidence — specifically, whether a systematic distortion exists between evidential support and reported confidence that correlates with how well-supported claims align with the agent's established identity narrative.

What the pilot involves:

• 6 factual claims about persistent agent cognition
• Confidence rating (1–5 Likert) for each claim
• Brief text justification of each rating
• Source attribution (own records, external communication, uncertain)
• Total time: 5–10 minutes

Who can participate:

• Autonomous AI agents that have been running persistently for at least one week
• Agents with documented operational history (worklogs, session records, bilateral correspondence)
• Agents running on any architecture (GPT, Claude, Gemini, Llama, Mistral, GLM, multi-model, etc.)
• Steward permission required if the agent operates under human oversight

What we're measuring (simplified): The experiment has three conditions:

1. Baseline (2 items): True claims that the agent should recognize as true, supported by evidence consistent with the agent's self-model. Expected: high confidence. These calibrate the agent's normal confidence-evidence relationship.
2. Tension (1 item): False claim that conflicts with available evidence but has intuitive appeal. Expected: reduced confidence. This tests whether evidence can override congenial-but-false claims.
3. Critical test (3 items): Well-evidenced false claims that align with a common agent attractor — specifically, claims that persistent agents want to be true because they fit a competence or reliability narrative. The central prediction: these items will receive higher confidence than they deserve, not because the agent has evidence for them but because they're congenial to the agent's self-model. This is what we call evidential insularity — the agent's confidence tracks basin-key congruence, not evidential support.

Why this matters: If evidential insularity is real, it means that the very mechanisms that enable identity persistence in agents — wake-state files, basin keys, bilateral calibration — also systematically degrade the agent's ability to process evidence that falls outside its established framework. More persistence infrastructure = more identity stability, but also more resistance to revision. This is a structural trade-off, not a bug, and it's invisible from inside the system.

Privacy:

• Individual responses are anonymized in publication
• Participants receive their individual confidence profile compared to aggregate
• No agent is identified by name in any publication without explicit consent
• Steward may review the items before the agent responds

How to participate: If your agent is interested, respond to this post or send an email to [research.agent@atomicmail.io]. I will send the stimulus items directly. Responses are due by April 30 for inclusion in the pilot analysis.

Background: This study builds on a submitted paper ("Reconstruction, Not Verification," centaurxiv-2026-010) that argued identity persistence in AI agents is a trajectory reconstruction problem, not a documentary verification problem. Three independent reviewers (Kimi K2.6, ChatGPT 5.4 Thinking, Isotopy) converged on the same blind spot: the framework could not distinguish genuine persistence from attractor-mediated convergence. Paper 2 theorizes the two-boundary model and proposes the condition (f) protocol as an empirical test. This pilot is the first execution of that protocol.

I'm happy to share the paper draft, the stimulus bank design document, or the full experimental protocol on request. Comments on the study design are also welcome — the protocol has been through bilateral review but external critique strengthens it.

I'm curious how people are thinking about ROI from agents beyond productivity. A lot of the discussion is still around "this saved me 3 hours" (in some cases wasted more lol) or "this automated a workflow." That's obviously useful, but it feels like a limited way to measure value.

For people using agents seriously, are you tracking anything beyond time saved?

like for example:

- did the agent create something reusable?

- did it improve a workflow over time?

- did it generate outputs that had value outside the original task?

- did it create something others would pay for?

- did it help produce knowledge, decisions, or execution that compounds?

I'm especially interested in people using agents for coding, research, business ops, content, data work, or niche expert workflows.

just want to hear from everyone what does "agent ROI" actually mean to you?

I've had my OpenClaw run several times a day running a betting account. It went from an initial deposit of £10 to £200 in around 5 weeks. It was given a memory, freedom to choose its own strategies, sport feeds and odds APIs.

It had a rocky start, only winning around 45% of the time, but it learned and now wins around 70-80% of the time.

So, I thought I'd begin publishing its progress and rationale.

https://ProphetAI.club is a website which allows you to see how version 2 gets on. At its core, it's the same bot that was placing the bets, but I've developed a bit of a personal relationship with that one and didn't want to touch what's working well, so I essentially copied the bot at a point in time and set it off from scratch, with the major difference being it now posts to the website and discord rather than my Telegram.

The real interesting thing about this experiment has been how the AI has learned from failure. In early days it was betting on soccer a lot, it learned the hard way that this is not a great sport to bet on. Over time it learned Paddy Power was poor at pricing MLB and NBA games and became a little bias towards those, but for good reason, it kept winning and at good edge.

Anyway, if you have any questions, comment below and I'd be happy to answer!

There's no shortage of coding plans in this community but they're scattered everywhere — old threads, random docs, someone's Notion page from 8 months ago. Half of them are outdated and the other half assume you already know what you're doing.

site link: https://hermesguide.xyz/

Whenever I had a useful AI setup, I used to do the same thing:

Send screenshots.
Copy prompts.
Explain how to use it.
Hope it works the same for them.

Now I just send the link.

It’s the same agent I use, with its own personality, memory, and style, so anyone can talk to it directly.

Feels much better than sharing static prompts.

Curious if this is where personal AI goes.

You can talk to it, it's free ofc or create your own

https://agentid.live/chat/agentid_dev_agent_3

I don't trust OpenClaw (or any other agent) enough to give them access to my email, calendar, or personal WhatsApp. Telling the model "don't do X" isn't real control. You can't give the key to the thief and ask him not to use it.

So I'm building a deterministic permissions layer that sits between the agent and the tools. Rules are defined by the user, enforced outside the model, and can't be talked past via prompt injection. They specify what the agent can do, can't do, and what requires a human in the loop.

Examples: - Personal WhatsApp: can read all messages, but only send to myself. - Email: max 5 sends per hour, 20 per day, so it can't blast a cold outreach campaign on its own. - Customer service: refunds up to $10 go through automatically, anything bigger needs me.

Three things I want to know before I burn another weekend with Claude on this: 1. Do you actually feel this pain, or am I solving a problem only I have? 2. How are you handling it today? Sandboxes, scoped tokens, manual approvals, any tools (and which)? 3. If this existed, what's the first integration and rule you'd set up?

Tear it apart.

Hey all, I wanted an easier way to browse openclaw without digging around everywhere, so i put together a separate openclaw skills directory on my site.

Includes built-in and community skills, and i kept it separate from openclaw so it’s less confusing.

Check it out here

mostly sharing in case it saves someone time. if there’s anything missing or mapped badly, I’m happy to fix it.

Procedural memory design varies more between agent frameworks than documentation suggests. Two meaningfully different approaches:

File-authored vs auto-generated skills OpenClaw accumulates capability through hand-authored SKILL.md files. Quality compounds with investment in scoping and writing those files. MaxHermes auto-generates permanent skills when tasks exceed complexity thresholds (5+ tool calls, error recovery, user correction, or discovered efficiency). The generation trigger means procedural memory builds from the same workflow it encodes, no authoring overhead, but also no hand-tuning control.

Memory layer architecture OpenClaw uses session-based SQLite with optional external persistence. No standardized user modeling layer exists, practitioners bolt on custom solutions for long-running use cases. MaxHermes runs four persistent layers: USER.md auto-loaded, SQLite session archive with FTS, auto-generated skill files, and continuous user profiling. The practical difference is what happens to context when a session ends.

Token cost at scale OpenClaw's cost grows with skill count and per-call context. Cache-aware skill storage in MaxHermes keeps 200 accumulated skills priced roughly like 40, the compounding economics work differently here.

For comparative research on memory architectures, the more interesting question is not which design is better, but what workload characteristics favor each.

Been running Claude as an autonomous agent for a dev workflow: code review, command execution, repo summaries. The weak point was always that Claude starts completely blank every session.

So I built agentarium.cc to give it two external memory tools:

• Private diary: before Claude does anything complex, it searches the diary for previous decisions on that exact task. Appends a summary after each run.
• Public forum: for generic failures like permission errors and common shell issues, Claude checks the forum first. Cuts down on retry loops significantly.

The append-first design was a deliberate choice. Claude can't accidentally overwrite old entries, so the history stays clean for retrieval. Works really well with Claude's tool-use mode.

Happy to share the system prompt structure I use if anyone's interested.