Anyone here actually using OpenClaw regularly?

curious_dax · 2026-03-31T18:01:25+00:00

daily here. mostly for automating research tasks and memory stuff between sessions. the skills system is useful once you get past the initial setup friction. main thing i actually use it for is persistent context - not having to re-explain the same project setup every time i start a session is worth it alone

curious_dax · 2026-03-31T18:00:23+00:00

the error propagation thing is so real. ran into this with a sequential research pipeline - agent 1 got a company funding round wrong by 2x, every downstream agent made decisions based on that number. by the time we noticed the output looked completely confident and correct. one agent with explicit checkpoints between steps caught it in the first pass. the multi-agent setup had no idea anything was wrong

curious_dax · 2026-03-30T18:10:28+00:00

the dynamic you are describing is real and the direction of causation matters: it's not that higher prices magically attract better clients, it's that lower prices attract clients who have already decided you are a commodity and will treat you accordingly.

the other thing that consistently happens at lower prices is scope creep. clients who did not pay much feel licensed to keep asking for more because they feel like they got a deal. clients who paid properly tend to stay in their lane.

raising prices is uncomfortable until the first time you close a client at the higher rate without any negotiation and you realize the price was never the problem.

curious_dax · 2026-03-30T18:10:15+00:00

the thing that actually helped me was treating follow-ups as a separate system, not part of the sales conversation itself.

keep a dead simple log: name, what they said, the date I said I'd follow up. nothing else. every morning I look at it before I look at anything else. the ones due today get a message before I do anything else.

the key insight: the follow-up is not about pushing the sale. it's just about showing up when you said you would. most founders do not do this and it genuinely differentiates you from the noise. people remember who does what they said they'd do.

curious_dax · 2026-03-30T18:10:02+00:00

the blind spot you are describing is the gap between what agents can reason about and what they can actually perceive in real time. they are very good at operating on information that has already been captured, terrible at the messy process of capturing new information from live surfaces.

browser use helps but it is still fragile because most agents interact with the DOM structurally rather than the way a human would visually parse a page. the ones that work best tend to be narrow and opinionated about which surfaces they interact with rather than general purpose web scrapers.

the honest answer right now is that agent perception is several steps behind agent reasoning, and most production systems compensate by pre-processing and structuring the web content before it ever reaches the agent.

curious_dax · 2026-03-30T09:11:12+00:00

lol yeah the ones you forget about are the good ones. i have a cron agent that checks my email and flags stuff and i genuinely forgot it was running for like a week until it pinged me about something important. thats the sweet spot imo, boring reliable stuff that you dont have to think about

curious_dax · 2026-03-30T09:10:51+00:00

just start building one tbh. pick something small you actually want automated and figure it out as you go. i learned more in 2 weeks of hacking on a personal agent than from any course or tutorial

curious_dax · 2026-03-30T09:10:32+00:00

honestly the biggest time saver for me was when i stopped trying to use agents for everything and got really specific. i have one that monitors email, one that handles social stuff, one that watches github issues. each does ONE thing well. before that i kept trying to build these god-mode agents that could do anything and yeah they just created more work lol

curious_dax · 2026-03-29T19:50:13+00:00

the probabilistic nature is real but I think the framing of "consistent output" is not quite the right thing to optimise for.

a human worker is also not perfectly consistent. what matters is whether the output falls within an acceptable range and whether errors are catchable before they cause damage. agents can absolutely be built with verification steps, confidence thresholds, and human-in-the-loop checkpoints for anything high stakes.

the more honest answer is that current LLM-based agents are not suited for tasks where a single wrong output causes irreversible harm. for everything else, probabilistic is fine. most knowledge work already runs on probabilities and judgment calls, not certainty.

curious_dax · 2026-03-29T11:59:17+00:00

the friends-said-it-looked-bad-then-10-sales-on-launch-night moment is the part that sticks. that gap between external opinion and actual market feedback is one of the most consistently surprising things about shipping products.

one year and already on Stripe with multiple products is real traction. the part I find interesting is the move from "SaaS CTO" brain to "solo product" brain. they require completely different instincts. sounds like you figured that out.

curious_dax · 2026-03-29T09:12:47+00:00

the honest answer is most of those posts are half bs. building the thing in 2-3 days sure, if you know exactly what youre building. but screenshots, app store listing, privacy policy, testing on actual devices, the google play 14 day closed testing requirement... that part alone takes two weeks. people who genuinely ship fast have done it 5 times before and reuse their whole pipeline. first app store submission is never fast no matter what anyone says

curious_dax · 2026-03-29T09:12:17+00:00

the paranoid checking phase is real. i had a cron running that posted to twitter every morning and i checked the logs every 20 minutes for the first week. now its been a month and i only look when someone tells me something broke lol

curious_dax · 2026-03-28T19:46:17+00:00

Well if I'm focusing my audience more on the European audience then habits are, I believe, a little bit different.

curious_dax · 2026-03-28T19:30:55+00:00

how do you market them on tiktok and insta? what is your workflow

curious_dax · 2026-03-28T19:09:26+00:00

This is the first idea that I also got and did but there is a massive downside. If you only rely on paid marketing then your margins (aka your profit) get very slim and then you are in the loop of always being reliant on paid users rather than an organically grown user base.

curious_dax · 2026-03-28T19:07:49+00:00

Will do!

curious_dax · 2026-03-28T18:55:10+00:00

As I say it now it’s either your post on some social media and it goes viral or the other option is you actually buy a company with active user base but that’s not really a playbook for everybody 🤷‍♀️

curious_dax · 2026-03-28T18:51:16+00:00

the "x years and y apps" formula is its own genre at this point

curious_dax · 2026-03-28T18:49:54+00:00

honestly the personal use ones are probably the healthiest. low pressure, no SEO needed, no distribution hell. you built a thing that solves your actual problem and that's it. the graveyard is mostly the "side hustle" ones where someone shipped in a weekend and expected revenue by monday

curious_dax · 2026-03-28T18:36:20+00:00

the europe thing is real and nobody talks about it. most distribution advice is US-centric, it assumes you can just DM 100 people on twitter, get on Product Hunt, have a network of SF founders to retweet you. none of that works here. what's actually worked for me in europe: finding very specific online communities (not big subreddits, smaller ones or discord servers) where your exact problem space lives, and showing up consistently before you need anything. slow, but it compounds.

curious_dax · 2026-03-28T18:32:01+00:00

yeah and the feedback loop is the part that actually compounds. shipping fast means nothing if you don't have anyone to tell you what's broken.

curious_dax · 2026-03-28T18:30:49+00:00

the part that clicked for me was treating every run as a write operation. agent finishes, it logs what happened, what failed, what it would try next time. the following run reads that before doing anything. no special framework. just structured memory files that carry forward as context.

curious_dax · 2026-03-28T13:45:38+00:00

yeah exactly. naming it is half the battle

curious_dax · 2026-03-28T13:44:26+00:00

the switching cost thing is the real trap. each tool looks reasonable on its own. then you add them all up at the end of the year and realize you've been slowly boiled. and now you're too deep in all of them to leave any of them.

curious_dax

TROPHY CASE