I have experimented with a lot of hacks, like hierarchies of indexed md files, semantic DBs, embeddings, dynamic context retrieval, but none of this is really a comprehensive solution to get something that feels as intelligent as what these systems are able to do within their context windows.

I am als a touch skeptical that adjusting weights to learn context will do the trick without a transformer-like innovation in reinforcement learning.

Anyway, I‘ll keep tinkering…

I’ve had a Whatsapp assistant since 2023, jailbraked as easy assistant. Only thing I kept using is transcription.

https://github.com/askrella/whatsapp-chatgpt was released 3 years ago and many have extended it for more capabilities and arguably its more performant than Openclaw as it can run in all your chat windows. But there’s still no use case.

It’s really classification and drafting.

First of all is not an LLM, you're beholden to an api or local llm limitations. Second of all it's always calendars, email replies, summarizing.

You do not need an LLM for that, and an LLM doesn't make it easier either. It sounds like executive cosplay, not productivity. Everything I see people talking about that's actually productive, it's doing probabilistically when deterministic tools already exist and have for in some cases over 20 years.

You don't need an LLM to put a meeting on a calendar, that's literally two taps with your phone or a single click in gmail. Most email services already have suggestions already built in. Emails have been summarized for 10 years at this point. If you're so busy you need this stuff automated, you probably have an assistant, or you're important enough that actually using general intelligence is critical to being successful at all.

The idea of getting an LLM email response sounds great for someone who has never worked a job in their life.

This comment section is full of llm writen responses too, to the point where its absurd. Noticing how most of them just talk in circles like "But I think many people criticizing the various Claws are missing out on the cronjob aspect. There's value in having your AI do work automatically while you're asleep. You don't even need OpenClaw for that, just a cronjob that runs claude -p in the early morning. If you give your AI enough context about yourself, you get to a point where it just independently works on things for you, and comes to you with suggestions. It doesn't need to be specifically prompted. The environment of data it can access is its own context, its own prompt. With that, it can sometimes be surprising and spooky what you wake up to, without being directly prompted."

This literally isn't even saying anything. This paragraph does not mean anything. It's not saying what its doing, whats happening or what the result is, just "something is happening".

No, you didn't save time using openclaw, you just changed to managing openclaw instead of doing your actual job.

You don't need custom scripts for most things if its actually something that matters, most tools already exist, and if you do openclaw isn't going to help you do it.

* Telegram Health Group, created an agent to help me track sleep, recommend my supplements based on my location, remind me in the morning and evening to monitor my food. I send it images of what I eat and it keeps track of it. * Telegram Career Group, I randomly ask it to find certain kind of job posts based on my criteria. Not scheduled, only when I like to. * Telegram Coder Group, gave it access to my github account. It pulls, runs tests and merges dependabot PRs in the mornings. Tells me if there are any issues. I also ask it to look into certain bugs and open PRs while I'm on the road. * Telegran News Group, I gave it a list of youtube videos and asked it to send me news every day at 10am similar to the videos.

So far, it's a super easy assistant taking multiple personas. But it's getting a bit painful without CC subscription

My teams currently using it for:

- SDR research and drafting

- Proposal generation

- Staging ops work

- Landing page generation

- Building the company processes into an internal CRM

- Daily reporting

- Time checks

- Yesterday I put together proposal from a previous proposal and meeting notes, (40k worth)

https://github.com/GhostPawJS/codex

I’d say it’s like 85% reliable on any given task, and since I supervise it, this is good enough for me. But for something to be useful autonomously, that number needs to be several 9’s to be useful at all, and we’re no world near that yet.

I’m currently watching someone trying and failing to roll openclaw out at scale in an org and they believe in it so much it’s very difficult to convince them even with glaring evidence staring them in the face that it will not work

The other common use case seems to be kicking off an automated Claude session from an email / voicetext / text / Telegram, and getting replies back. I'm emailing Claude throughout the day now, and sometimes it's useful to just forward an email to Claude and ask it to handle the task within it for me.

But I think many people criticizing the various Claws are missing out on the cronjob aspect. There's value in having your AI do work automatically while you're asleep. You don't even need OpenClaw for that, just a cronjob that runs claude -p in the early morning. If you give your AI enough context about yourself, you get to a point where it just independently works on things for you, and comes to you with suggestions. It doesn't need to be specifically prompted. The environment of data it can access is its own context, its own prompt. With that, it can sometimes be surprising and spooky what you wake up to, without being directly prompted.

Give it enough context, long term memory, and ability to explore all of that, and useful stuff emerges.

I have some issues with the article, but I agree with some of the conclusions: It's great tinkering with it if you have time to spare, but not worth using weeks of your time trying to get a perfect setup. It's just not that reliable to use up so much of your time.

I will say, it's still amongst the best tools to do a variety of tasks. Yes, each one of those could be done with just a coding agent, but I found it's less effort to get OpenClaw to do it than you writing something for each use case.

Very honest question: One of the use cases I had with OpenClaw that I'm missing now that I don't use it: I could tell it (via Telegram) to add something to my TODO list at home while I'm in the office. It would call a custom API I had set up that adds items to my TODO list.

How can I replicate this without the hassle of setting up OpenClaw? How would you do it?

(My TODO list is strictly on a home PC - no syncing with phone - by design).

(BTW, the reason I stopped using OpenClaw is boring: My QEMU SW stopped working and I haven't had time to debug).

What seems to be somewhat working for me

1. Karpathy wiki approach

2. some prompting around telling the llm what to store and not.

But it still feels brittle. I don’t think it’s just a retrieval problem. In fact I feel like the retrieval is relatively easy.

It’s the write part, getting the agent to know what it should be memorizing, and how to store it.

"The Claw."

Some of this stuff is starting to look like technologies that worked, looked promising, but were at best marginally useful, such as magnetohydrodynamic generators, tokamaks, E-beam lithography, and Ovonics.

For example, for the invitations in the OP: Have Openclaw write incoming rsvps to a database, probably a flat file here, and use the db as persistent memory: OpenClaw can compose outgoing update emails based on the database. Don't even suggest to OpenClaws that it try to remember the rsvps - its job is just writing to and reading from a database, and composing emails based on the latter. ?

Does that violate the experiment, by using some tool in addition to OpenClaw?

OpenClaw runs Pi in a terminal and exposes the chat thru Telegram or any chatting app. This gave the ah-ha moment to non-coders that coders had had for 6+ months prior.

I've removed it.

The problem is if not carefully designed it will burn through tokens like crazy.

Last I checked, it doesn't!

It's a rather simple framework around an LLM, which actually was a brilliant idea for the world that didn't have it. It also came with its own wow effect, ("My agent messaged me!") so I consider some of the hype as justified.

But that's pretty much it. If you can imagine use cases that might involve emailing an LLM agent and get responses that share context with other channels and resources of yours, or having the ability to configure scheduled/event-based agent runs, you could get some use out of having an Openclaw setup somewhere.

I find the people who push insanity like "It came alive and started making money for me" and the people who label it utterly, completely useless (because it has the same shortcomings as every other LLM-based product) like Mr. "I've Seen Things. Here's the Clickbait" here, rather similar. It's actually hard to believe they know what they're talking about or that they believe what they're writing.

Sure, anything it does can be done better with specialized tooling. If you know that tooling.

The memory thing sounds like an implementation limit rather than something fundamentally unsolvable. Just experiment with different ways of organizing state until something works?

The killer usecase is letting you make whatever you want, instead of being at the mercy of what your OS/platform dictates.

Your idea of a killer idea is a whatsapp summarizer lol.

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.