OpenClaw isn't fooling me. I remember MS-DOS

Tons of people called for common sense regulation/guardrails years ago and were shouted down as "luddites obstructing progress." It's funny to see this discussion coming back around.

I was shocked when I saw the guy behind libgdx was also behind pi.dev. Random tech worlds colliding.

But because OpenClaw can just use a web browser like a normal user, you don't need all these APIs and there's no theoretical limitations on the services that can be integrated and automated.

Right now there's a lot of issues/bugs. People have more trust in a deterministic solution like Zapier. But maybe the LLMs and OpenClaw will get there eventually, and if it does, I can see how that's a better solution than a deterministic system.

Which has of course always been the true allure of AI. Do nothing and pretend you did something, when pretending is something you can be bothered to do.

win 3.1 had protected mode. but windows 95 which was dos based did not. 98 i think got protected mode? not 100% sure on that. i know in windows 2000 it was still horrible broken...

Is there something specific that you want that cannot be implemented reasonably on existing systems?

Unless you have a specific compelling benefit that only a rewrite can grant, focused narrow rewrites are the way.

Originally, Betamax increased physical tape length, but cutting tape speed (like how vinyls can play at different RPMs) was a more economical way of cramming more hours onto the same tape by cutting quality.

Both VHS and Betamax went through multiple phases of this. Eventually VHS won by being cheaper.

https://mrbetamax.com/BetaSpeeds.htm

But this wasn't Sony's strength and VHS was consistently cheaper for longer movies.

The counterexamples that convinced me were Grok and Steam VR aren't taking significant marketshare from ChatGPT and Oculus despite having better support for adult content.

Originally, I thought this would kill Oculus given that's the most popular use of VR, but nope.

1. Something like OpenClaw will change the world.

2. OpenClaw is not yet ready.

The heart of OpenClaw (and the promise) is the autonomy. We can already do a lot with the paid harnesses offered by OpenAI and Anthropic, so the secret sauce here is agents doing stuff for us without us having to babysit them or even ask them.

The problem is that OpenClaw does this is an extreme rudimentary way: with "heartbeats." These are basically cron jobs which execute every five minutes. The cron job executes a list of tasks, which in turn execute other tasks. The architecture is extremely inefficient, heavy in LLM compute, and prone to failure. I could enumerate the thousand ways it can and will fail but it's not important. So the autonomy part of the autonomous assistant works very badly. Many people end up with a series of prescriptive cron jobs and mistakenly call that OpenClaw.

Compounding this is memory. It is extremely primitive. Unfortunately even the most advanced RAG solutions out there are poor. LLMs are powerful due to the calculated weights between parametric knowledge. Referring to non-parametric knowledge is incredibly inefficient. The difference between a wheelchair and a rocket ship. This compounds over time. Each time OpenClaw needs to "think" about anything, it preloads a huge amount of "memories" into the query. Everything from your personal details to architecture to the specific task. Something as simple as "what time is it" can chew through tens of thousands of tokens. Now consider what happens over time as the agent learns more and more about you. Does that all get included in every single query? It eventually fails under its own weight.

There is no elegant solution to this. You can "compress" previous knowledge but this is very lossy and the LLMs do a terrible job of intelligently retaining the right stuff. RAG solutions are testing intelligent routing. One method is an agentic memory feedback loop to seek out knowledge which might exist. The problem is this is circular and mathematically impossible. Does the LLM always attempt to search every memory file in the hope that one of the .md files contains something useful? This is hopelessly slow. Does it try to infer based on weekly/monthly summaries? This has proven extremely error-prone.

At this point I think this will be first solved by OpenAI and/or Anthropic. They'll create a clean vectorised memory solution (likely a light LLM which can train itself in the background on a schedule) and a sustainable heartbeat cadence packaged into their existing apps. Anthropic is clearly taking cues from OpenClaw right now. In a couple of years we might have a competent open source agent solution. By then we might also have decent local LLMs to give us some privacy, because sending all my most intimate info to OpenAI doesn't feel great.

Then about $120 in API credits from Anthropic, xAI, openrouter, openAI, and Gemini ( $25 here and there adds up).

Best bang for my buck has been with xAI grok-4.20-beta

OpenClaw is not a CC-only product. You can configure it to use any API endpoint.

Paying $180/month to Anthropic is a personal choice, not a requirement to use OpenClaw.

Like, no one bats an eye at all the people paying $100/mo for Hulu + Live TV, or paying $350/mo for virtual pixels in candy crush / pokemon go / whatever, and I'm having at least that much fun in playing with openclaw.

A lot of people in the Silicon Valley area spend that much ($6/day) on coffee. What they don’t realize is how out of touch they are in thinking makes sense for the rest of the fucking world. $180/mo is about 5% of the median US per capita income. It’s not going to pick your kids up from school, do your taxes, fix your car, or do the dishes. It’s going to download movies and call restaurants and play music. It’s a hobby, high-touch leisure assistant that costs a lot of money.

What a horrible situation.

For comparison, a full time "virtual assistant" with fluent English from the Philippines costs upwards of $700/month nowadays.

The number one goal of AI should be to eliminate human exploitation. We want robots mining the minerals we use for our phones, not children. We should strive to free all of humanity from dangerous labour and the need for such jobs to exist.

If Elon Musk wants Optimus robots to help colonize Mars shouldn’t he be trying to create robots that can mine cobalt or similar minerals from dangerous mines and such?

And you see nothing wrong with that?

I think they do it mostly to feel young and edgy.

There's at least a couple of dozen instances right now, somewhere, getting very close to designing boutique chemical weapons.

The "gave it my Gmail password" problem has a better answer than "don't do that." Security kicks itself out of the room when it only says no. Reserve the no for the worst days. The rest of the time, ship a better way.

That's why I built the platform to make credential leaks hard. It takes more than a single prompt. The credential vault is encrypted. Typed secret wrappers prevent accidental logging and serialization. Per-channel process isolation means a compromise in one adapter does not hand an attacker live sessions in the others.

"Don't do that" fails even for users trying their hardest. Good engineering makes mistakes hard and the right answer easy. Architecture carries the weight so the user does not have to.

On the trifecta being "sorta-kinda solved" by newer models, no. Model mitigations are a layer, not a substitute. Prompt injection has the shape of a confused-deputy problem and the answer to confused deputies has always been capabilities and isolation, not asking the already confused deputy to try harder.

You want the injection to fail EVEN when the model does not catch it.

Existing software is what dumped most of those errands on you in the first place.

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IMHO, the biggest problem with OpenClaw and other AI agents is that the use-cases are still being discovered. We have deployed several hundred of these to customers and I think this challenge comes from the fact that AI agents are largely perceived as workflow automation tools so when it comes to business process they are seen as a replacement for more established frameworks.

They can automate but they are not reliable. I think of them as work and process augmentation tools but this is not how most customers think in my experience.

However, here are a several legit use-case that we use internally which I can freely discuss.

There is an experimental single-server dev infrastructure we are working on that is slightly flaky. We deployed a lightweight agent in go (single 6MB binary) that connects to our customer-facing API (we have our own agentic platform) where the real agent is sitting and can be reconfigured. The agent monitors the server for various health issues. These could be anything from stalled VMs, unexpected errors etc. It is firecracker VMs that we use in very particular way and we don't know yet the scope of the system. When such situations are detected the agent automatically corrects the problems. It keeps of log what it did in a reusable space (resource type that we have) under a folder called learnings. We use these files to correct the core issues when we have the type to work on the code.

We have an AI agent called Studio Bot. It exists in Slack. It wakes up multiple times during the day. It analyses our current marketing efforts and if it finds something useful, it creates the graphics and posts to be sent out to several of our social media channels. A member of staff reviews these suggestions. Most of the time they need to follow up with subsequent request to change things and finally push the changes to buffer. I also use the agent to generate branded cover images for linkedin, x and reddit articles in various aspect ratios. It is a very useful tool that produces graphics with our brand colours and aesthetics but it is not perfect.

We have a customer support agent that monitors how well we handle support request in zendesk. It does not automatically engage with customers. What it does is to supervise the backlog of support tickets and chase the team when we fall behind, which happens.

We have quite a few more scattered in various places. Some of them are even public.

In my mind, the trick is to think of AI agents as augmentation tools. In other words, instead of asking how can I take myself out of the equation, the better question is how can I improve the situation. Sometimes just providing more contextually relevant information is more than enough. Sometimes, you need a simple helper that own a certain part of the business.

I hope this helps.

IoT really comes into its own space though when you pair it up with something that is a real pain to get to. Think somewhere you have to have a crainlift and a 4 hour drive just to touch the 20 year old computer something is hooked up to. Or basically anywhere that takes hours to get to. The space my company typically targeted was high rise air con companies. Or companies where the customer would service out any sort of PLC work to a 3rd party. At that point the savings of having to roll a guy out there vs looking on a computer has the thing pay for itself in 1-2 trips. Also the ability to show up on site with the correct parts. That alone was a huge savings.

IoT's big issues is you have to beat many things that are already dead simple to do.

If they're doing the same thing as the interrupts that the article is talking about, they are low-level.

If they aren't, then the comparison is by name only.