Earlier quoted context omitted.
Please elaborate
Almost certainly some kind of scam
From what i have read openclaw seems to be the ideal scam/clickfarm bot or am I completely mistaken?
OpenClaw’s memory is unreliable, and you don’t know when it will break
121–130 of 196 posts
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#122As long as there's no solution to the long-term memory problem, we will have a "country of geniuses in a data center" that are all suffering from anterograde amnesia (movie: Memento), which requires human hand-holding.
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…
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#123If you look at my comment history, you'll see what seems to be someone defending OpenClaw (even though I stopped using it). 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…
> 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?
What you are looking for is an orchestration platform such as n8n or windmill.dev. You can still have a telegram bot and still use LLM for natural language interaction, but it's much more controlled than OpenClaw. I do exactly what you describe, add todos to my todoist account from telegram.
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#124As long as there's no solution to the long-term memory problem, we will have a "country of geniuses in a data center" that are all suffering from anterograde amnesia (movie: Memento), which requires human hand-holding. 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…
You're right to be skeptical. Without a way to actually implement how the human brain processes experiences into a consolidated memory, we won't be able to solve the long term memory problem at all. Not with the current technology.
An LLM context is a pretty well extended short term memory, and the trained network is a very nice comprehensive long term memory, but due to the way we currently train these networks, an LLM is just fundamentally not able to "move" these experiences to long term, like a human brain does (through sleep, among others).
Once we can teach a machine to experience something once, and remember it (preferably on a local model, because you wouldn't want a global memory to remember your information), we just cannot solve this problem.
I think this is probably the most interesting field of research right now. Actually understanding in depth how the brain learns, and figuring out a way to build a model that implements this. Because right now, with backtracking and weight adjustments, I just can't see us getting there.
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#125Earlier quoted context omitted.
> It would call a custom API I had set up that adds items to my TODO list You can use anything to call this API right? I have multiple iPhone shortcut that does this. Heck, I think you can even use Siri to trigger the shortcut and make it a voice command (a bit unsure, it’s been a while since I played with voice)
> You can use anything to call this API right? The API is on my home PC and not exposed to the outside world. Only OpenClaw via Telegram was. So my question is about the infrastructure: How do I communicate with something at home (it could be the API directly) using a messaging app like Telegram? I definitely want an LLM in the mix. I want to casually tell it what my TODO is, and have it: - Craft it into a concise TO…
Expose the API to the outside world using tailscale. Run your telegram bot on n8n or windmill.dev. You can absolutely use an LLM, both n8n and windmill.dev support AI agentic workflows. google "n8n LLM telegram bot" and you'll find tons of examples.
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#126[flagged]
Please don't do this here.
I am sorry. I understand that many of us are heavily invested financially or emotionally into AI but the key insight about OpenClaw remains the same that it really isn't much useful beyond maybe a daily news summary. Nothing that cannot be done otherwise.
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#127I've had a crack at this problem in Agent Kanban for VS Code (https://github.com/appsoftwareltd/vscode-agent-kanban). The core idea is that you converse with the agent in a markdown task file in a plan, todo, implement flow, and that I have found works really well for long running complex tasks, and I use this tool every day. But after a while, the agent just forgets to converse in the task file. The only way to get it to (mostly) reliably converse in the task file is to reference the task file and instructions in AGENTS.md. There is support for git work trees and skipping commits of the agents file so as not to pollute the file with the specific task info. There is also an option for working without work trees, but in this flow I had to add chat participant "refresh" commands to help the agent keep it's instructions fresh in context. It's a problem that I believe will slowly get better as better agents appear, and get cheaper to use, because general LLM capability is the key differentiator at the moment.
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#128I built a special belief-based system recently for my own agent harnesses instead of some similarity based fact storage stuff... which falls flat once conflicting data points enter the system and just increase LLM confusion and make it do weird things. this means learning over time works a bit more like humans do - superseding old beliefs and reconciliating stuff cleanly over time. Also including the building blocks to have a subagent managing it autonomously (with tools/skills/soul). works quite well and very fast given its pure nodejs+sqlite and doesn't eat tokens like crazy or needs any thirdparty embeddings solution. maybe have a look.
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#129[flagged]
Re: OpenClaw’s memory is unreliable, and you don’t know when it will break
#130As long as there's no solution to the long-term memory problem, we will have a "country of geniuses in a data center" that are all suffering from anterograde amnesia (movie: Memento), which requires human hand-holding. 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…
I've used open claw (just for learning, I agree with the author it's not reliable enough to do anything useful) but also have a similar daily summary routine which is a basic gemini api call to a personal mcp server that has access to my email, calendar etc. The latter is so much more reliable. Open claw flows sometimes nail it, and then the next day fails miserably. It seems like we need a way to 'bank' the correct behaviours - like 'do it like you did it on Monday'. I feel that for any high percentage reliability, we will end up moving towards using LLMs as glue with as much of the actual work as possible being handed off to MCP or persisted routine code. The best use case for LLMs currently is writing code, because once it's written, tested and committed, it's useful for the long term. If we had to generate the same code on the fly for every run, there's no way it would ever work reliably. If we extrapolate that idea, I think it helps to see what we can and can't expect from AI.