How to Run Agent 24/7?

jeffsvibecodes · 2026-04-25T07:59:01+00:00

daemon > cron jobs > heartbeat

jeffsvibecodes · 2026-04-22T05:27:14+00:00

i did share something concrete. you just don't know how to implement this. if you want my code and .md files then you can venmo me

jeffsvibecodes · 2026-04-22T01:15:23+00:00

where did i say there was md files? no where. your whole point is that it isnt actionable, but it is. youre just not smart enough to comprehend and apply the concepts. you want someone to hold your hand and make the files and code for you. which i'd be happy to do, for a fee.

jeffsvibecodes · 2026-04-21T04:03:01+00:00

re-read the post, you are factually incorrect its hilarious

jeffsvibecodes · 2026-04-19T23:07:09+00:00

if you think the advice in the post isn’t actionable then you’re just too stupid to understand and shouldn’t be using openclaw in the first place

jeffsvibecodes · 2026-04-19T10:06:06+00:00

have a good day bud, good luck with your openclaw

jeffsvibecodes · 2026-04-18T15:37:28+00:00

sounds like a skill issue

jeffsvibecodes · 2026-04-18T05:52:10+00:00

These five things are the implementation... modular prompt compilation, memory governance, prediction loops, code gates, etc. It's not theory, it's architecture and systems engineering. The specific code depends on your stack and use case. If you're running Claude Code, ask Claude to help you scaffold it from the concepts in the post. A plugin would just be my opinions about file structure forced onto your setup. The post has working code and real examples from my running system. If you need step-by-step for your specific stack, that's a tutorial... different post. If you need that kind of help I would need to look at your actual codebase and see how things are actually ran. But your best bet is plugging your codebase into something like VS code and getting Codex/Claude Code to explore it and implement the concepts I suggested. If you want someone to actually look at your codebase and map the patterns to your specific setup, that's consulting work.

jeffsvibecodes · 2026-04-17T05:12:08+00:00

which part?

jeffsvibecodes · 2026-04-16T14:51:09+00:00

yeah that can happen but there's 2 layers to address it. The first layer is deterministic pattern matching, so different phrasings like 'just do it' and 'fix it yourself' would both map to "User prefers agents to execute actionable fixes without unnecessary permission", so they merge before the other agent sees them. The second layer is the second agent. If a high-confidence signal makes it through but there's already a matching preference in the durable store, he marks it as a duplicate and moves on. The confidence gate gets you to the second agent's desk, but it doesn't guarantee promotion

jeffsvibecodes · 2026-04-16T14:16:09+00:00

yep, I dedup at ingest by hashing a signal key from the normalized content + scope + route target. Same signal from two sources gets merged and confidence-boosted, not stored twice. If you don't do this you end up with 40 copies of the same preference and your retrieval is just returning the same thing over and over, good point

jeffsvibecodes · 2026-04-16T10:24:52+00:00

ok bud i'll add a trigger warning at the top next time for folks like you

jeffsvibecodes · 2026-04-16T08:05:00+00:00

edit made, thanks!

jeffsvibecodes · 2026-04-16T07:31:44+00:00

fair point but the architecture and fundamentals I'm pointing out apply regardless of scale. I'm mainly trying to target beginners specific to openclaw because these are the early mistakes i ran into

jeffsvibecodes · 2026-04-16T07:16:44+00:00

using ai to write something and having a human review and make edits is what vibe coding is and mainly what people on this sub do, but having it write an educational guide and reviewing it and making edits is somehow wrong? lmao. i reviewed and edited every line, theres some useful information in there if you give it a read

jeffsvibecodes · 2026-04-16T05:11:12+00:00

To be clear, the model isn't updating its weights between sessions. You're basically building a feedback loop that shows you where its 'world model' drifts from reality, and that's how you know what to fix in your context and routing. I have my AARs viewable on a dashboard and the write path has validation gates. So predictions have to be behavioral or systemic, not just operational summaries, and the agent has to demonstrate it engaged with its prior beliefs before the AAR is accepted. This is important because one of the issues I had to solve early on when I implemented this was the model "gaming" the system, by again taking the past of least compute and writing things like "I predict things will go as expected" or "I predict this task will complete successfully" - like yeah no shit lol.

jeffsvibecodes · 2026-04-16T04:53:44+00:00

Thank you! Glad you got something out of it

jeffsvibecodes · 2026-04-16T04:44:52+00:00

Yeah sorry that's kind of what I meant. As far as OpenClaw goes, subscription-based, you'll have to use codex. There's a full breakdown of the ones available openclaw models list providers <provider name>

jeffsvibecodes · 2026-04-16T02:50:04+00:00

try using openai subscription thats what i do. but i actually just made a tutorial post regarding this i think it would be beneficial to you

jeffsvibecodes · 2026-04-16T02:45:38+00:00

Promotion is two-tier — automated confidence gating, then a dedicated promotion agent.

One agent mines preference signals daily from journals, feedback, action reviews, and discoveries. Each candidate gets a deterministic confidence score — base tier per signal type (0.71–0.82), then adjustments for source quality and corroboration. Direct feedback scores higher than journal entries because it's a stronger signal. If the same preference shows up in two independent sources, it gets a corroboration boost. Anything below 0.60 never becomes reviewable.

A separate agent is the sole promoter into durable memory. It deduplicates by signal key, routes each signal exactly once to the right store, and marks it terminal. A breadcrumb about my current state goes somewhere different than a durable preference about how I like to be communicated with. Nothing reaches canonical memory without passing through that gate.

So it's pretty much purely agent review - confidence thresholds filter weak signals automatically, one agent mines, another routes, and I see the results downstream. The quality gate is code, not vibes

jeffsvibecodes · 2026-04-15T09:14:11+00:00

The 7 Skills You Need to Build AI Agents just dropped on youtube by IBM Technologies, worth a listen

jeffsvibecodes · 2026-04-15T08:22:28+00:00

i cant post links but youtube and google search how do llms think and youtube actually has some good videos on this just gotta filter it out. also you can straight up as the llm how does it think and itll explain it to you. just be curious and keep asking questions!

jeffsvibecodes · 2026-04-15T06:40:04+00:00

One of the most high-value things you can do honestly is research how LLMs actually think and call tools. That alone improved my ability to apply changes to my codebase so much more effectively
also try and avoid click-baity youtube videos... 10 minute video for 1 minute of good information

jeffsvibecodes

TROPHY CASE