DEV Community: Soulman

With Clawdi.ai, You Don’t Have to Choose Between Hermes or Openclaw

Soulman — Wed, 15 Apr 2026 22:23:59 +0000

Note: This article is Adapted from the official Clawdi post at clawdi.ai

If you’ve been building AI agents and wondering whether to go with Hermes or Openclaw, Clawdi removes that question entirely. It supports both, so you can work with whichever model fits your use case without locking yourself into one direction. That kind of flexibility matters when you’re still figuring out what works best for a given project.

Skills That Grow and Memory That Actually Works
One of the more practical things about Clawdi is that the skills it comes with don’t stay fixed. They evolve the more you use them, which means the tool gets more useful over time rather than hitting a ceiling early. The memory works the same way you’d want it to. It holds context across sessions, so you’re not starting from scratch every time or repeating information you’ve already given it. For anyone who has dealt with AI tools that forget everything the moment a session ends, this is a meaningful difference.

Private by Default, Ready in 10 Minutes
Getting started takes about 10 minutes and there’s nothing to set up on the infrastructure side. No servers, no complex configuration, you just get in and start working. Everything runs in Phala’s TEE-secured cloud, which means your data is private by default. That’s not a setting buried in a menu somewhere, it’s just how it works out of the box. If you’re building agents that handle sensitive workflows, that baseline matters more than most tools acknowledge.

You can check it out at https://www.clawdi.ai/

Reasons Why You Should Check Agent Stock Expert That’s Recently Launched On Clawdi.ai

Soulman — Wed, 15 Apr 2026 22:15:03 +0000

Note: this article is Adapted from the official Openclaw announcement. Read the Full blog post here: https://www.clawdi.ai/

Openclaw just launched Agent Store on Clawdi, and the first agent available is Stock Expert. If you’ve ever spent 20 minutes jumping between charts, filings, news tabs, and analyst takes just to form a basic view on a stock, this is built for that exact problem. You ask it a question, it does the legwork, and you get back something you can actually work with.

What the Stock Expert Agent Does
You don’t need to think about what’s running under the hood to use it, but here’s what’s actually happening. When you ask it to analyze a stock, it pulls real-time price data, looks at recent filings and announcements, checks technical signals like trend and momentum, evaluates revenue and earnings growth, and reviews financial health. It then puts all of that together into a structured output covering valuation, risks, and a suggested strategy. The goal isn’t to give you more information. It’s to give you a clear picture so you can decide what to do next without having to connect the dots yourself.

Why This Is Worth Paying Attention To
Most research tools either dump data on you or hand you someone else’s opinion. Neither saves you much time. What makes this different is that it runs inside Phala’s TEE infrastructure, meaning the analysis happens in a private, verifiable environment. For anyone building financial tools or working in an institutional context where data handling matters, that’s a meaningful detail. You’re not routing sensitive research through a black box. You’re working inside infrastructure designed to keep that process clean and auditable.

How to Get Started
There’s no setup involved. You sign up on Clawdi, go to the agent catalog, find Stock Expert, click Add, and start asking questions. Prompts like “Analyze AAPL,” “What changed for TSLA this week,” or “Is NVDA overvalued right now” are enough to get something useful back. It won’t make decisions for you and it won’t eliminate risk, but it will cut down the time it takes to go from not knowing what’s going on with a stock to having a clear view of it.

The Agent Store is live now at clawdi.ai.it’s worth checking out

There is More in Clawdi Ai than Just Using the Chat Screen

Soulman — Thu, 09 Apr 2026 21:47:39 +0000

Note: Adapted from the official Clawdi/OpenClaw blog post.

Most people who sign up for Clawdi follow the same pattern. They open the dashboard, start a conversation with their agent, maybe adjust a system message or tweak a prompt, and call it a day. That works well enough as a starting point, but it leaves most of the platform untouched. The features that actually make OpenClaw useful for serious work are sitting one or two clicks away from that chat screen, and the majority of users never find them.

There are three things worth knowing about: the Control UI, agent templates, and per-agent channel bindings. Each one solves a real problem that shows up the moment you try to do something more than basic conversation.

What the Control UI Actually Gives You
The Control UI is a live window into your agent’s environment. Inside it you’ll find a Files tab where you can browse and edit the agent’s workspace in a built-in code editor, a Logs tab that streams your gateway and deployment activity in real time, and for Pro and Max users, a Terminal tab that gives you a full shell directly into the container your agent is running in.
What this means in practice is that you can tail the logs while you’re actively chatting with your agent, so when something feels off you can see exactly what’s happening instead of guessing. You can install tools, run commands, and test things directly inside the environment without rebuilding anything. You can edit files and configuration in place. If you’ve been using Clawdi without ever opening the Control UI, you’ve been working without visibility into what your agent is actually doing, and that makes debugging significantly harder than it needs to be.

Templates and Channel Bindings Clean Up the Rest

Agent templates exist because most people waste time rebuilding the same setup from scratch every time they create a new agent. You pick a model, choose a reasoning mode, decide which skills to include, figure out how the execution layer should be configured, and by the time you’re done you’ve spent twenty minutes on decisions that don’t change much between agents. Templates bundle all of that. You get a pre-configured model and reasoning profile, a default set of skills already connected, and settings that reflect how production agents are actually run. You pick the template closest to what you need, rename it, adjust a couple of things, and you’re live. It removes a lot of friction and reduces the kind of small mistakes that come from manually copying configs.

What Per-agent channel bindings solve

Per-agent channel bindings solve a different problem that shows up once you’re running more than one agent. Each agent can have its own dedicated connection to Telegram, Discord, Slack, or whichever platform your team uses, so they stay separate and observable. There’s also a config side panel inside the chat view that shows you exactly which model the agent is running, which channels it’s connected to, and which skills are attached. You no longer have to dig through configuration files to understand what you’re talking to. You can open the chat, look at the panel, and know immediately, then adjust on the spot if needed.

How to Actually Start Using More of It

The practical shift here is treating your agent as an environment you can see and operate rather than a chat interface you talk at. Open your agent and click into the Control UI. Look at the logs while you have a conversation. If you’re on Pro or Max, open the Terminal and run something simple just to get familiar with it. When you create your next agent, start from a template instead of from scratch. Give each agent its own channel connection so different workflows stay clean and separate.
That progression from chat-only to full environment access is what separates a basic Clawdi setup from one that actually works like infrastructure. The full step-by-step guide covering each of these features is linked below if you want to walk through the setup properly.

Read the full guide here: https://www.clawdi.ai/blog/only-20-percent-of-openclaw-unlocked

Hardening AI agents with hardware level security

Soulman — Tue, 31 Mar 2026 15:46:03 +0000

Most developers recognize the inherent risk in deploying AI agents that handle sensitive API keys or private customer data. Traditional cloud environments often leave this information vulnerable to the infrastructure provider or external breaches. OpenClaw addresses this by running entirely within Phala’s Trusted Execution Environments, which are secure enclaves built directly into the processor. This architectural choice moves security away from "trusted" policies and into the physical hardware, ensuring your agent's execution is isolated and verifiable. Source: [(https://phala.com/posts/erc-8004-launch)]

The power of the Phala TEE stack
By leveraging Phala’s TEE technology, Clawdi.ai creates a secure vault for your data that is invisible even to the host machines. This setup uses memory encryption and isolated execution to ensure that sensitive operations, like managing private keys or processing proprietary datasets, remain completely confidential. It effectively solves the trust issue between the developer and the cloud provider, as the TEE provides cryptographic proof that the code is running exactly as intended without any unauthorized interference. Source: [https://www.panewslab.com/en/articles/d566ft503z4v]
[https://x.com/phalanetwork/status/2038981366126129339?s=46]

Rapid deployment for institutions
For institutions and developers who need to balance speed with high security, this platform offers a streamlined path to production. You can move from a standard Docker application to a hardware-secured deployment in about three minutes for $29 a month. This approach provides a practical way to achieve enterprise-grade privacy and data sovereignty without the complexity of building custom confidential computing infrastructure from scratch.
🔗 Useful links:

Website: https://phala.com/
Clawdi ai: https://www.clawdi.ai/
Telegram: https://t.me/phalanetwork (very active)
X: https://x.com/phalanetwork?
Discord: https://discord.gg/phala-network

WORTH EXPLORING IF DATA PROTECTION MATTERS IN WHAT YOU ARE BUILDING.

Soulman — Thu, 26 Mar 2026 18:43:40 +0000

Throughout March, models running through Phala.com on OpenRouter have been processing over 1 billion tokens per day, all with TEE-GPU guarantees. That means the inference it’s happening inside secure enclaves where your prompts and data stay private and aren’t exposed to the underlying provider.

If you’re building something on OpenRouter you can plug these models into your app with a standard API key. Agents, copilots, data pipelines, automated workflows, it all works the same way you’re used to, just with confidentiality baked in by default. no extra setup, no tradeoffs on privacy.

Worth exploring https://openrouter.ai/provider/phala if data protection matters in what you’re building