DEV Community: Kory Becker

The Most Viable OpenClaw Business Models Right Now

Kory Becker — Wed, 25 Mar 2026 16:33:53 +0000

OpenClaw is evolving fast and so are the ways people are turning it into real income.

I just published a breakdown of the 8 business models that builders are actually using today, including real pricing, examples, and workflows pulled from Reddit, Discord, and community threads.

Here are a few that stood out:

• Vertical AIs for niche industries ($500–$1500/mo)
• Wrapper utilities + setup tools ($7K–$35K)
• Viral micro‑apps powered by OpenClaw
• Done‑for‑you OpenClaw installs ($500–$1000 per setup)
• Micro‑SaaS wrappers around a single workflow
• Local business website factories (surprisingly effective)

If you’re exploring OpenClaw as a business tool not just a hobby, this guide will give you a clear starting point.

👉 Read the full article:
The Most Viable OpenClaw Business Models Right Now
https://ai.plainenglish.io/the-most-viable-openclaw-business-models-right-now-8ac5766e222a

My First Month Running OpenClaw Nonstop — Here’s What Everyone Gets Wrong

Kory Becker — Tue, 10 Mar 2026 18:51:17 +0000

I’ve been running OpenClaw on an old Windows 10 desktop (7+ years old, no GPU) for a full month, 24/7. And most people are using it wrong.

I’ve watched users burn money on API calls, expose sensitive data, over‑engineer their setups, and buy hardware they don’t need. So I wrote a deep‑dive on the real‑world setup, mistakes, and fixes that actually matter when running OpenClaw long‑term.

Here’s what I cover in the full post:

• Why you don’t need a Mac Mini or expensive hardware
• Why local LLMs (Ollama, etc.) are a trap for most users
• How OpenClaw quietly sends memory + credentials to remote LLMs
• How to avoid huge API bills caused by bloated memory files
• A simple 3‑tier memory system that actually works
• Why you should use dedicated accounts instead of your personal email
• What OpenClaw gets right — and where it’s still rough around the edges

If you’re experimenting with OpenClaw or planning to run it as a daily driver, this guide will save you time, money, and headaches.

👉 Read the full article

A Beginner‑Friendly Guide to Running OpenClaw for Free

Kory Becker — Sun, 22 Feb 2026 15:32:43 +0000

If you want to experiment with OpenClaw without paying for API calls, this walkthrough shows how to run the entire system using zero‑cost LLMs from OpenRouter and NVIDIA.

OpenClaw can automate your system, run commands, control your browser, and integrate with tools like Slack — all through a chat interface. The best part is that you don’t need a paid model to get started.

What This Guide Covers

• Installing OpenClaw on Windows
• Choosing free LLM providers
• Updating your config.json
• Testing commands and basic automation
• Adding integrations like Slack and browser control
• What free models handle well (and where they struggle)

Why Free Models Work Surprisingly Well

For everyday tasks — file operations, browser automation, running scripts, system commands — free models are more capable than you might expect. They only start to struggle with long reasoning chains or complex multi‑step planning.

A Note on Trust

Giving an AI agent access to your system is a big step. The guide walks through how OpenClaw handles permissions and how to experiment safely while keeping control over what the agent can do.

Read the Full Guide

A Beginner‑Friendly Guide to Running OpenClaw for Free
https://medium.com/towards-artificial-intelligence/a-beginner-friendly-guide-to-running-openclaw-for-free-04459be33ca0

My First Week With OpenClaw: An AI That Actually Runs Your PC

Kory Becker — Sun, 08 Feb 2026 01:24:18 +0000

I spent the past week experimenting with OpenClaw

You can read my full review here.

OpenClaw (Clawdbot / Moltbot) is the open‑source AI agent that can operate your actual computer. And honestly? It's equal parts impressive, terrifying, and a glimpse of where OS‑level AI is heading.

OpenClaw isn't doing anything radically new on its own. What it does get right is packaging everything developers usually duct‑tape together. This includes:

Unified installer
Plug‑and‑play integrations
Browser automation
Mobile control
Low‑friction setup

It's the first time I've seen all of this bundled into something you can install in minutes.

What surprised me most

You can control your PC from Slack or Discord. Yes, really. Tell your desktop to edit files or browse the web while you're away.

Free LLMs work, but only if you give very explicit instructions. Vague tasks often turn into JSON dumps instead of actions.

Browser automation is also still tricky. Navigation works, but form‑filling is hit‑or‑miss on Windows.

Trust is the real question. Giving an AI access to your filesystem and browser is a different psychological leap than using ChatGPT or Copilot.

Should you try it?

If you're curious about where AI‑powered operating systems are headed, OpenClaw is worth a weekend experiment. Just be prepared for some quirks, especially on Windows, and think carefully about how much access you're comfortable granting.

I'm still figuring out how it fits into my workflow, but the potential is undeniable. If you've tried OpenClaw, I’d love to hear what worked (or didn't) for you.

I Wrote "Hello World" to the Cardano Blockchain and Here's How You Can Too

Kory Becker — Sun, 01 Feb 2026 15:49:44 +0000

Read the full article.

Writing text on the blockchain

I’ve always been curious about what it actually takes to write data to a blockchain. Not building a full dApp nor deploying a smart contract, but rather just store a simple message. Something like the classic "Hello World", but on-chain and permanent.

So I decided to try it with Cardano, and honestly, the process was way more approachable than I expected. If you’ve ever wanted to experiment with blockchain development without spending real money or running a full node, this walkthrough might help.

Why store text on a blockchain?

Every blockchain transaction includes metadata. Most people think of blockchains as "sender, receiver, amount", but the metadata field can store arbitrary text or even JSON. That opens the door to things like:

Verifiable logs
On-chain proofs
Timestamped notes
Game data
Audit trails
Tiny decentralized apps
And of course… "Hello World."

Step 1 — Create a Cardano wallet

I used the cardanomsg Python library to generate a wallet. It creates two files:

wallet.addr — your public address
wallet.skey — your private key (keep this safe)

Once the wallet exists, you'll need some ADA to send a transaction. I used a testnet.

Step 2 — Fund the wallet using Cardano's free faucet

Cardano provides a preview testnet and a faucet that sends you free ADA for development. After submitting my wallet address, I received about 10,000 ADA (testnet only). You can verify the balance on Cardanoscan.

Step 3 — Send a transaction with a message

To avoid running a full node, I used Blockfrost, an API that lets you interact with Cardano using a simple key.

With the wallet funded, I sent 1 ADA to myself and attached the message "Hello World" as metadata. The transaction returned a hash, which I could then look up on Cardanoscan to confirm it was included in the chain.

Seeing the message appear on-chain was a pretty cool moment.

A few troubleshooting notes

If you try this yourself, a couple of things might trip you up:

Testnet funds sometimes take a minute or two to appear
Make sure your Blockfrost key is for the Preview network
Transactions may take a bit to index before they show up

Just remember: anything you put on-chain is public and permanent.

If you want to explore further, I’ve shared the full code and steps in the article. It’s a great way to dip your toes into blockchain development without spending real money or setting up heavy infrastructure.

I Tested 6 Copilot Alternatives in VS Code and Here’s What Actually Worked

Kory Becker — Sun, 25 Jan 2026 14:45:59 +0000

I’ve been using GitHub Copilot for a while now, and while it’s great, I started running into some friction: usage quotas, limited model options, and a few privacy concerns. So I decided to see what else is out there.

I tested six different Copilot-style tools inside VS Code, some free, some paid, some local, some cloud-based and ranked them based on:

• Speed
• Accuracy
• Cost
• Privacy
• Ease of setup

Here’s what I tried:

• Ollama with Llama 3.1:8b + Codellama (Free, Local)
• OpenRouter with Devstral 2 2512 (Free, Hosted)
• OpenRouter with Grok 4.1 Fast (Paid)
• GitHub Copilot with GPT-5-Mini (Free)
• Microsoft Copilot in Edge (Free)
• GitHub Copilot with Claude Opus 4.5 (Paid)

I used the Continue extension to unify the experience. It basically turns any LLM into a Copilot-style assistant inside VS Code. Some setups were surprisingly smooth. Others not so much.

My quick take:

• Best for privacy: Ollama (but it’s slow and resource-hungry)
• Best for speed: Copilot + Claude Opus
• Best for budget: Continue + Devstral 2 2512
• Best overall: Copilot + Claude Opus (if you’re okay paying)

If you’re thinking about switching from Copilot or just curious what else is out there, I hope this saves you some time.

Full write-up here:
I Tested the Best Copilot Alternatives for VS Code

Let me know what you’re using or if there’s something I should try next.

vscode #ai #copilot #opensource #devtools #programming #llm

How I Replaced Copilot With a Free AI Model

Kory Becker — Fri, 23 Jan 2026 21:02:16 +0000

A quick practical guide to switching from GitHub Copilot to Continue inside VS Code.

Tutorial

I walk through how to switch from GitHub Copilot to Continue, a powerful VS Code extension that replicates Copilot’s features like autocomplete, chat-based coding, and multi-step agent actions.

The best part? You can pair Continue with any LLM you like including free hosted models like Mistral DevStral or Qwen3 Coder via OpenRouter. No quotas. No cost. Just code.