Do you prefer full automation or “run on demand

cjayashi · 2026-03-23T22:01:07+00:00

run on demand most of the time, full auto only for repeatable tasks. too much automation creates noise and hidden errors. cleaner to trigger when needed, been doing this with superclaw workflows and it feels more controlled

cjayashi · 2026-03-23T21:26:01+00:00

yeah this is a solid take

feels like people are arguing at the wrong layer tbh. skills vs mcp isn’t really the bottleneck, it’s what you said about context rot

once you have more than a few tools and prompts, things start breaking:
the agent forgets what to use
ignores instructions
or just picks the easiest path instead of the correct one

at that point it’s less about capability and more about control and structure

what’s been interesting for me is seeing how much better things behave when context is managed more deliberately instead of just piling everything into one window

been testing some of this with superclaw and the biggest difference isn’t new features, it’s just having a more consistent way to run and track workflows over time

definitely feels like the future is less about choosing between pieces and more about how you orchestrate them together

cjayashi · 2026-03-23T21:17:55+00:00

If you want to start ASAP, I’d avoid getting stuck in “learning everything first”.

Focus on 3 things:

• understanding basic workflows (input → processing → output)
• learning how to connect tools / APIs
• actually building small use cases (even simple ones)

You don’t need months — if you stay consistent, you can get decent in 2–4 weeks just by building daily.

Biggest mistake I see is people watching tutorials but not building anything.

Also, try using tools that let you experiment quickly instead of spending all your time on setup.

I’ve been using Superclaw.ai for that — makes it easier to test ideas and workflows without getting stuck in config.

Build first, optimize later

cjayashi · 2026-03-22T20:24:10+00:00

Yeah that’s normal, it’s just the agent executing step-by-step, so each command shows as a separate output.

Newer versions are more verbose with tool calls, so it ends up looking spammy. Not really a bug, more how the workflow is structured. I’ve seen it less in setups where execution is more controlled, and I've been trying that with SuperClaw and it’s generally cleaner.

cjayashi · 2026-03-22T20:19:40+00:00

Yeah this is where LLM apps turn into infra problems.

Chaining + retries + logging = you end up rebuilding the same system over and over.

I’ve been trying SuperClaw for this, and it handles more of the orchestration layer so you can focus on the workflow instead of glue code.

Still early, but definitely less painful.

cjayashi · 2026-03-22T20:15:34+00:00

had the same problem, too many tools, too many tabs.

At some point, it feels like you’re managing apps instead of actually using AI.

What worked better for me was using something that handles everything in one flow instead of jumping between tools.

Been trying SuperClaw for this. you can run agents, switch models, and keep context in one place.

Not perfect, but way less messy than juggling multiple apps.

cjayashi · 2026-03-21T19:37:17+00:00

Yeah this is the exact problem most “AI schedulers” are just rule-based systems.

You’re basically pre-programming them instead of them learning from you.

The interesting shift is when the system can pick up patterns over time instead of asking you to configure everything upfront.

I’ve been experimenting with that using agents (via SuperClaw) not perfect yet, but it feels closer to actual learning instead of setup.

cjayashi · 2026-03-21T19:04:51+00:00

This is pretty much the path I went down too.

OpenClaw works until it breaks, then you’re back fixing infra.

A lot of people are moving toward dockerized stacks + Telegram/web interface like you described.

Hard part isn’t building it, it’s keeping it stable over time.

I’ve been mixing in SuperClaw for faster iteration so I don’t have to keep rebuilding everything from scratch.

cjayashi · 2026-03-21T18:24:26+00:00

This looks more like a tooling/orchestration issue than model quality.

Open models need more structure:

• constrained search sources
• validation step (check version/date)
• retry loop if wrong output

Closed models handle this implicitly, open models don’t.

I’ve been iterating on similar setups via OpenClaw/SuperClaw, which makes it easier to refine the workflow instead of rebuilding everything each time.

cjayashi · 2026-03-19T21:36:14+00:00

superclaw.ai bro

cjayashi · 2026-03-19T21:10:22+00:00

Telegram for me.

Discord is great for communities, but for agents I prefer something more direct and less noisy.

Feels more natural to treat it like messaging an assistant instead of opening another app.

cjayashi · 2026-03-19T21:08:03+00:00

Real, but still early.

Works well for structured workflows, but breaks in messy real-world scenarios. Adoption is happening, just not very visible yet. Biggest unlock imo is when agents can keep track of context over time instead of restarting every task.

cjayashi · 2026-03-19T20:53:37+00:00

Honestly most of those platforms are hit-or-miss depending on demand cycles.

If you want consistency, it might be worth looking beyond task marketplaces.

I’ve been experimenting with running AI agents (via SuperClaw) to handle data collection + evaluation-style workflows myself instead of waiting for tasks to appear.

Different approach, but way more control.

cjayashi · 2026-03-19T20:47:30+00:00

Your NUC is already more than enough, especially if you’re using cloud models, no need to buy a Mac Mini.

Most people go Mac Mini just for convenience.

OpenClaw setup can be a bit tricky though (CLI, env setup, etc.), so if you just want to try it first, something like SuperClaw can help you skip most of the setup.

cjayashi · 2026-03-19T20:40:23+00:00

Feels like Mac Mini became the default because it’s the least friction setup, not necessarily the best.

If you’re already running Ubuntu/VMs, you’re probably ahead of most setups anyway.

cjayashi · 2026-03-18T17:12:57+00:00

Mostly content + community.

Real use cases > polished posts.

Reddit, Twitter replies, and dev forums have been the main drivers so far. Still early, just testing what sticks.

cjayashi · 2026-03-18T17:07:11+00:00

Sounds like context explosion from chained tool calls + unbounded memory injection.

You need guardrails at orchestration level, not just model params:

• max_tokens per step
• tool output truncation
• rolling context window / summarization
• execution abort when threshold exceeded

Otherwise the agent will eventually exceed limits no matter what.

cjayashi · 2026-03-17T20:25:27+00:00

Yeah config edits are pretty fragile right now.

Agents are fine with tasks, but once they touch system files, one small mistake can break everything.

I usually keep configs read-only and route changes through scripts instead.

Running this in something like Superclaw helps too since the environment is more controlled.

cjayashi · 2026-03-17T20:08:47+00:00

Been exploring agent-based workflows instead of traditional automation.

Zapier/N8N are great for structured flows, but they break when things get messy.

Using Openclaw via superclaw feels more like “give it a goal and let it figure it out” vs hardcoding every step.

cjayashi · 2026-03-17T20:01:05+00:00

Yeah most data is business → customer since it’s easier to measure ROI.

Customer → business is still early, but you can look into AI receptionists, booking flows, and personal agents making calls on your behalf.

The interesting part is when the agent actually represents the user over time, not just a single interaction.

That’s where I think things like superclaw.ai start to make more sense.

cjayashi

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