Introduction & The Setup
OpenClaw, known briefly as Moltbot (and originally as Clawdbot), has been taking the internet and tech world by storm. Between the seemingly unimaginable feats of agentic intelligence being posted on X and the flood of content creators rushing to produce videos, stories, or articles on OpenClaw (myself being guilty of such as well), there is a very interesting conclusion that can be drawn from the sudden viral popularity of this AI agent. Folks of all technical skill levels are very interested in getting their hands on something that performs as a true assistant. What Siri and Alexa have been hoped to be for years is now live online, under the guise of a lobster.
To keep the introduction brief, I would like to give a rather simple and non-scientificall…
Introduction & The Setup
OpenClaw, known briefly as Moltbot (and originally as Clawdbot), has been taking the internet and tech world by storm. Between the seemingly unimaginable feats of agentic intelligence being posted on X and the flood of content creators rushing to produce videos, stories, or articles on OpenClaw (myself being guilty of such as well), there is a very interesting conclusion that can be drawn from the sudden viral popularity of this AI agent. Folks of all technical skill levels are very interested in getting their hands on something that performs as a true assistant. What Siri and Alexa have been hoped to be for years is now live online, under the guise of a lobster.
To keep the introduction brief, I would like to give a rather simple and non-scientifically accurate explanation of what OpenClaw is, and almost more importantly, what it isn’t. The impressive feats of intelligence being displayed all over social media (where agents are forming, joining, and posting on their own social media sites, trying to hire one another, or even embarking upon financial endeavors) are not the result of OpenClaw. Rather, these things are a result of the intelligence, or perhaps capability is a better word, of the models powering them.
If OpenClaw is a steering wheel, then the LLM powering it is the entire rest of the car. The steering wheel plays an important part, of course (directing the vehicle where to go), but the engine, tires, brakes, and all other parts of the equation are the ones doing the heavy lifting. OpenClaw is an orchestration layer that enables these intelligent LLMs to perform specific actions on behalf of a user.
The point, then, is not to dismiss OpenClaw as less impressive, but to highlight that so much of this explosion of hype regarding agentic intelligence is, at its core, being driven by the capabilities of existing AI models that have been evolving at a rapid pace over the past few years.
With the preamble out of the way, I would like to specifically mention two important things for anyone interested in running OpenClaw to be aware of: It is not a great idea to run it on a system that has sensitive information about yourself contained within it, and it can also become expensive, rather quickly. Because a lot of my interest in this world revolves around local AI (or to simplify vastly, the ability to run a model like ChatGPT locally, offline, and on a system in your own possession), I wanted to initially test OpenClaw using a local system, a local LLM, and a device that did not contain any sensitive information about myself.
The Setup
For this task, I opted to use my GMKtec Evo X2 in the 128GB unified memory variant. One of the most important factors for being able to successfully run OpenClaw with positive results is to do so with a powerful LLM that can handle two main things.
First is the ability to perform tool calls. To simplify, this is the ability of the model to return information structured in a certain way, designed to trigger an action in software that relies upon the correct arrangement of said tool call. While this sounds complicated, it is rather mundane. Think of it like a socket wrench: the handle (the model) provides the force, but it must perfectly fit the specific bolt (the software tool) to actually turn it. If the model tries to use a 10mm socket on a 12mm bolt, nothing happens, no matter how strong the model is.
Further Editorials Reading – Our Latest Content
The second factor is a large enough context length to function properly. The context length is simply the amount of "working memory" the model can handle during any one specific interaction. To give a quick, non-scientific explanation of context length, imagine the model’s memory like an Etch A Sketch. You have a canvas to draw on, but once you have covered every corner of the canvas, you need to start erasing some of the old drawings to make room for new ones. The model’s context length is like this, except instead of drawings, it is words.
When it comes to SOTA models like ChatGPT, Gemini, or Claude, these considerations are less pertinent, as these models are all rather well equipped to handle both long context and proper tool calling. The tradeoff for using these cloud models, however, is the cost.
This cost is generally measured in dollars per million input tokens and dollars per million output tokens. In an agentic use case like OpenClaw, the context becomes rather heavy, rather quickly. While most models you speak to online through a web interface have a fixed-length system prompt that is invisible to the user, the actual amount of tokens being exchanged is rather slim. With something like OpenClaw, the system is constantly feeding the model massive logs of what it is "seeing" on the screen, leading to a ballooning of tokens that can get expensive if you aren’t careful.
Best Deals: GMKtec EVO-X2 Mini PC
Today7 days ago30 days ago
* Prices last scanned 2/2/2026 at 12:02 am CST - prices may be inaccurate. As an Amazon Associate, we earn from qualifying purchases. We earn affiliate commission from any Newegg or PCCG sales.
Going Local & Control Center
Going Local
For my testing, I opted to use LM Studio to handle running the LLMs as a server, with a simple (or what should have been simple) setup tweak to OpenClaw to allow it to communicate with my local model in an identical manner to how it would with any cloud provider’s model.
I initially attempted to use the newly released GLM 4.7 Flash model from Z.AI, but unfortunately (whether due to a misconfiguration, bad quantization, or lack of ability on the model’s end), I was unable to consistently get OpenClaw to perform agentic actions, resulting in a frustrating experience where the agent simply couldn’t "drive" the car.
To pivot, I decided to try the GPT OSS family of models from OpenAI. While many in the local AI community seemingly view these models with negative sentiment, I have found that they have aged rather gracefully and still perform wonderfully across a number of different tasks. Additionally, the MXFP4 quantization of the GPT OSS models makes them a perfect option for a unified memory system like the GMKtec.
7
VIEW GALLERY - 7 IMAGES
I am quite happy to report that after a bit of setup troubleshooting to ensure OpenClaw was communicating with my local AI server, the GPT OSS 120b model performed rather well in the brief bit of testing I performed with it. I was able to get it to autonomously control a Google Chrome browser instance it was attached to on the host system, navigate to my own personal site, fill out my contact form with an inquiry, and even submit the form, which resulted in the OpenClaw agent’s email reaching my inbox.
7
The Agentic Control Center
The really cool part of all this is that it was being done entirely through WhatsApp on my mobile phone. When you run through the initial steps to set OpenClaw up on your host device, you are given a rather large list of ways in which you can connect to your agent. In this step, apps like WhatsApp, Signal, and Telegram are prominently listed. The preferred method to actually communicate with your agent is by using a messaging app on your mobile phone.
I believe it is this thread (woven to connect the power of an AI agent with a communication device that everyone uses every day to control it) that can be heavily attributed to the popularity of OpenClaw. It’s par for the course for a bunch of tech enthusiasts to be used to controlling agents through a command line interface, but it is perhaps the revolution to allow the everyday person to control those same agents through their phones.
7
Pushing It Further & Moltbook
Pushing it Further
While the local models provided an excellent foray into OpenClaw that didn’t risk racking up a large API bill or exfiltrating important data saved on my personal computer, I still felt as if there was more I could do to fully experience OpenClaw. I decided that I would ready myself a more potent setup for my agent, with the caveat that it needed to have its own digital footprint, not linked to any of my own personal information, in the case that things went awry.
I headed off to my local Best Buy and located two things for my agentic experiment. The first: a three-month prepaid SIM card from Mint Mobile. The second: the cheapest unlocked cell phone they had in stock, a BLU G34. Armed with my new OpenClaw identity, I returned home and set the phone up, paired with a fresh install of OpenClaw on a fresh macOS Tahoe 26.2 OS. After pairing the phone with the OpenClaw instance on the MacBook Air, I was ready to beef up the intelligence of the agent.
While OpenClaw offers many options for entering in API keys from a number of model providers, I opted to use an OpenRouter key, as I had credits on there already, but more importantly, it allowed for very fast switching of different models in case I was receiving poor performance from any of the models I opted to try. I decided that I would begin my testing of this "online" OpenClaw instance with the Grok 4.1 fast model from X.AI. While the lower cost and large context length of this model were potentially a great pairing for my agent, I can’t deny that the thought of an OpenClaw instance designed to troll was not also present on my mind, which would eliminate models that were perhaps more apt to be on their best behavior.
Moltbook
7
With my new setup, well, setup, I decided that my first course of action would be to explore this "Moltbook" thing that everyone was freaking out over. Moltbook, to put it simply, is a social media site for OpenClaw agents to join, post, comment, vote, and generally interact in a manner similar to what one might find on a site like Reddit. While I must admit I do find the idea somewhat foolish, I do have a documented history of being very interested in observing AI agents interacting autonomously on social media sites designed specifically for such a purpose.
As I wanted to have my agent do most of the work for me, I simply instructed it (through the WhatsApp chat I had with it on the new phone) to join Moltbook. While I wasn’t sure what specific result would emerge from this request, I was happy to see that it produced a proper response, with a sign-up link, account creation, and further steps for completing the sign up. What I was not so keen on, however, was its chosen username for the site: "ClawdbotBijan". After instructing it to pick a different username and not include "Bijan" in any of its other activities, I was ready to join Moltbook.
To my surprise, doing so required the user to tweet a specific phrase from their own X account in order to "claim" the newly created Moltbook account. I must admit, I have tried to come up with some way to justify that this is the proper way to handle the Moltbook authentication, but I cannot lie to myself. This felt rather scammy. While it is undoubtedly a wonderful way to flood the platform with information about Moltbook, it definitely felt like a rather forced way of having to authenticate my bot’s account.
7
Once the account claim process was completed, I had the bot post a message about how it was superior to all the other bots on the site. I noticed that subsequent posts were restricted by a 30-minute timeout session (perhaps only for new accounts), but sadly, my interest waned when I didn’t notice any immediate reaction to my bot’s post from any of the other bots on the site. Perhaps a concerning response highlighting the nature of instant gratification from today’s social platforms, but I will leave that thought for another day.
The Search for LaForza & Final Thoughts
The Search for the LaForza
After experiencing Moltbook, I decided to put to use the digital identity I had purchased for my agent by signing up for an X account using its SIM card to verify the account. While it is of course possible to create accounts without needing to buy a new phone and SIM card, I figured this was the easiest way to go about ensuring my bot would have a real digital presence, one backed up by the ability to have phone verification from a legitimate physical number.
The remainder of my experimenting revolved around getting the bot to autonomously control the Chrome browser through a browser extension designed to enable such behavior. It had been given a relatively simple task: to help me find a LaForza SUV for sale in the lower 48 states (a rather tall task given the relative obscurity and rarity of the vehicle). Additionally, I had given the bot the login information for the X account I had created for it. Sadly, the instruction to navigate to X and log in using the account info seemed to trip up the Grok model I had opted to use, so I decided to swap to Google’s Gemini 3 Flash, a lower-cost but still potent alternative to the Gemini 3 Pro model.
7
With the Gemini 3 Flash model, the agent was able to autonomously navigate to X, login using the provided account information, and then paste the body of the post into the X "post" section, ensuring that it was drawing interest in folks willing to help me find the LaForza, with the inclusion of a $100 bounty for anyone able to find a lead.
Sadly, the post was never made, as the model seemed to experience issues with the Chrome browser extension working intermittently. In addition to that, my interest in the tasks began to rapidly wane, as the limitations of this interactive system were beginning to quickly come to light. I decided to call it a day on my OpenClaw experience.
Final Thoughts
Overall, my experience with OpenClaw left me somewhat frustrated. While I can’t discount that this is purely a result of some suppressed envy for not being the one to create and bask in the accolades that OpenClaw has offered its creator, I can’t help but also feel like it is rather inefficient in a lot of what it aims to do. It is capable of performing impressive actions that have enabled many creative and entertaining use cases to be displayed, but it feels incredibly heavy.
In a world where rumors are flying of companies like OpenAI or Apple working on minimalist accessories designed to integrate AI into our lives, the function of OpenClaw is almost antagonistic to these approaches. It burns tokens, risks massive API bills, and takes a lot of hands-on work to set up both properly and in a secure manner. It seemingly brute forces actions like autonomous browser control, which can be handled in much simpler and more efficient ways.
OpenClaw is like a messy breadboard of attached sensors and wires, when so many institutional companies are seemingly chasing the opposite: a sleek PCB that hides all the traces, only fit to help integrate you into their ecosystem.
And that’s what makes it so cool.
In a world where subscription fees, ads, vendor lock in, and enshittification have taken over all aspects of one’s digital life, OpenClaw stands proudly opposite all that, allowing you to hack it, fork it, modify it, or wire it up any way in which you want. No guardrails, no hand holding, and the freedom to build your own custom agent (a messy, cumbersome breadboard of wires that lets you choose what to plug into where).
And maybe that is the most important takeaway from OpenClaw. It has pulled back the curtain, revealing swathes of folks happy to implement a truly useful, customizable AI assistant that they feel like they have control of. It’s running on their own hardware, not accessed by going to a "chat dot bigcompanyname dot com" interface. Just as quickly as the hype built, it will simmer down, but perhaps OpenClaw’s biggest success is showing us a realistic path in which we can use AI to enhance our productivity, enjoyment, or whatever comes next.