DEV Community: fg dfg fg dfg

Why I built a minimalist MM to CM converter in the age of bloated web tools

fg dfg fg dfg — Tue, 31 Mar 2026 09:48:42 +0000

Ad-Free & Lightning Fast: The Story Behind mmtocm.net

Let's be honest: The web is getting bloated. 😫

Whenever I need to do a simple conversion, like Millimeters to Centimeters, I'm usually met with sites that are 10% tool and 90% ads, trackers, and "Sign up for our newsletter" popups.

That’s why I decided to build mmtocm.net.

🛠 The Tech Stack

I kept it as "Old School" and fast as possible:

Vanilla JS: For instant calculations.
Minimal CSS: Focusing on Core Web Vitals and mobile-first UX.
Zero Tracking: No heavy analytics scripts that slow down the user.

📏 Why MM to CM?

It seems trivial, but for designers, DIY enthusiasts, and engineers, speed and accuracy are everything. I wanted to create a "single-purpose" tool that does one thing and does it perfectly.

🚀 The Product Hunt Launch

I'm excited to announce that we are officially launching on Product Hunt in less than 24 hours!

If you're a fan of minimalist web tools and "Building in Public," I'd love your support and feedback on how to make it even better.

Check out the tool: https://mmtocm.net/

Support us on Product Hunt: [producthunt]

What’s your favorite "minimalist" tool that you use every day? Let's discuss in the comments! 👇

The No-Nonsense Unit Converter Every Developer Needs in Their Pocket

fg dfg fg dfg — Thu, 26 Mar 2026 09:19:11 +0000

Why I ditched the big "Unit Conversion" sites

We’ve all been there: you need to quickly convert an inch-based UI specification to centimeters for a physical mockup. You search Google, click the first result, and get:

Three pop-up ads.
A page that takes 5 seconds to load.
A cluttered UI that makes you hunt for the input box.

The Solution: inchtocm.net

I recently started using inchtocm.net for my quick conversion needs. It is the definition of "do one thing and do it well."

What makes it different:

Zero Ads: No flashing banners or misleading "Download" buttons.
Speed: It’s mobile-friendly and loads instantly, which is perfect when you're on the move or in the middle of a build.
Accuracy & Context: It doesn't just give you the number; it provides a quick conversion table for reference.

If you value clean, fast, and secure tools, bookmark this one. It’s a lifesaver for DIY projects, engineering mockups, or just quick daily calculations.

Check it out: inchtocm

The Ultimate Intelligence Hub for AI Agents & Advanced Workflows

fg dfg fg dfg — Mon, 09 Feb 2026 02:18:21 +0000

Mastering the Autonomous Era: As AI Agents become the standard for software development, having a central "source of truth" is vital.
Why Moltbook-AI?: We built this hub to help developers move from simple prompts to complex agentic workflows.
What’s Inside:
Technical Benchmarks: See how different agent configurations perform in production.
Framework Guides: Step-by-step tutorials for scaling AutoGen and CrewAI.
Direct Access: Start mastering agentic workflows at moltbook-ai.com.

6 Micro-Tools I Built to Master AI and Web Performance (and what’s in my tech stack)

fg dfg fg dfg — Fri, 06 Feb 2026 03:43:33 +0000

The "Micro-Tool" Philosophy 🛠️

In 2026, the best way to learn a new technology isn't by reading docs—it's by shipping. Over the last few months, I've challenged myself to build and launch 6 niche tools, ranging from AI agents to high-performance converters.

My goal was simple: Extreme utility, zero bloat.

Here is a breakdown of what I built and the tech behind them.

1. The AI Agent Hub: Moltbook AI

Moltbook AI is my most ambitious project. It’s moving away from simple chat to an AI Agent Workspace. I used it to test the OpenClaw protocol for agentic workflows.

Best for: Developers looking for AI agent tutorials and structured writing.
Tech: Next.js, Vercel AI SDK, OpenClaw logic.

2. The Conversion Trio: Fast, Local, Private

For these, I focused on Client-Side processing to ensure user privacy and lightning speed.

pdftopng.io: Converts PDFs to images entirely in the browser using PDF.js. No server-side storage, 100% private.
fastconvert.ai: A smart engine for multi-format switching.
openclaw-ai.net: A dedicated entry point for exploring agent-based utility.

3. The "Boring" But Essential Converters

Sometimes, you just need a number, and you need it now. I built these two with Vanilla JS to achieve sub-100ms load times.

inchtocm.net: Pure, ad-free unit conversion.
mmtocm.net: Precision measurement tool for engineering.

The Common Tech Stack 🏗️

Across all these projects, I’ve refined a "speed-run" stack that allows me to go from idea to deployment in days:

Framework: Next.js (for SEO and routing). -** Styling**: Tailwind CSS (for rapid, responsive UI).
Analytics: PostHog (for privacy-first user insights).
Performance: Edge functions to minimize latency for my global "出海" (Global) audience.

Lessons Learned as an Indie Developer 💡

Niche > General: A tool that does one thing perfectly (like PDF to PNG) often gets more organic traffic than a general AI tool.
Performance is a Feature: Especially for unit converters, users will leave if the page takes more than 1 second to load.
SEO takes time: Building backlinks through value-sharing (like this post!) is much more effective than spamming.

Which one would you use? 🎤

I’m looking for feedback on Moltbook AI's new agent tutorials. If you're a developer working with LLMs, I'd love to hear your thoughts on the OpenClaw approach.

Check out the full list and let me know which tool is most useful for your workflow!

Beyond Chatbots: Building Autonomous AI Agents with OpenClaw Architecture

fg dfg fg dfg — Fri, 06 Feb 2026 02:50:18 +0000

The Evolution of AI: From Chat to Agency 🤖

We are moving away from simple "Question-Answer" loops. The future is AI Agents—systems that can use tools, browse the web, and execute tasks autonomously.

While building Moltbook AI, I realized that the biggest bottleneck isn't the LLM itself, but the Standardization of Agency. That’s why I’ve been integrating the OpenClaw logic into the platform to streamline how agents interact with the real world.

What is the "OpenClaw" Approach? 🛠️

The goal of the OpenClaw technical entry is to provide a standardized way for AI models to:

Define Capabilities: What can this agent actually do? (e.g., Search, Write, Analyze).
State Management: Keeping track of the agent's "thought process" across multiple steps.
Tool Orchestration: Seamlessly switching between different API calls without losing context.

At Moltbook AI, we use this architecture to turn a static AI writer into an active research assistant.

How to Build Your First Agent (Tutorial Snippet) 💡

If you're using Node.js to build an agentic workflow, the core logic often revolves around a recursive loop. Here’s a simplified look at how an Agent handles a task:

async function agentLoop(task) {
  let context = task;
  while (true) {
    const response = await callLLM(context); // e.g., using OpenClaw protocol

    if (response.type === 'final_answer') {
      return response.text;
    } 

    if (response.type === 'tool_use') {
      const toolResult = await executeTool(response.toolName, response.args);
      context += `\nObservation: ${toolResult}`;
    }
  }
}

Learning by Doing: AI Agent Tutorials

I’ve started compiling a series of AI Agent Tutorials on the Moltbook platform. My goal is to lower the barrier for developers to move from "Prompt Engineering" to "Agent Engineering."

You can explore the live implementation and the tutorials here: 👉 Moltbook AI - Agent Workspace & Tutorials

By focusing on the OpenClaw technical stack, we ensure that the agents are not just "smart," but predictable and scalable.

Conclusion & Discussion 💬

AI Agents are still in the "Wild West" phase. There are no perfect standards yet, but protocols like OpenClaw are helping us get there.

I have a question for the community:

Are you using LangChain, CrewAI, or building your own custom Agent orchestration from scratch?
What is the hardest part for you when it comes to "Tool Use" in AI?

I'll be replying to comments and sharing more snippets from the OpenClaw implementation!

How Developers Actually Prepare Data Before Feeding It to AI Agents

fg dfg fg dfg — Mon, 02 Feb 2026 07:01:37 +0000

Documentation often describes how data should look before it reaches an AI agent. Real workflows tell a different story.

Data usually arrives in inconvenient formats, with missing metadata or unexpected structure. Before reasoning begins, developers quietly do a lot of cleanup work.

In practice, preparation looks like this:

Converting files into formats libraries handle well

Removing unnecessary structure

Reducing input size where possible

None of these steps feel innovative, but skipping them almost always causes problems later.

I don’t consider this part of “AI development” anymore. It’s closer to basic hygiene. The goal is to make inputs boring so the agent’s behavior is the interesting part.

As autonomous systems become more common, these real-world preparation habits are being discussed more openly in AI agent communities, including those curated at https://moltbook-ai.com/.

Clean inputs don’t guarantee good outcomes, but messy ones almost guarantee bad ones.

A One-Off Task I Didn't Want to Script

fg dfg fg dfg — Mon, 02 Feb 2026 02:45:26 +0000

I was reviewing generated documentation before merging a branch.

Most of it was automated, but one section referenced physical dimensions in millimeters. I needed the same values in centimeters to match an internal note.

This was a classic one-off task. Writing a script would’ve taken longer than the task itself.

I handled it manually using a simple online conversion page, verified the value, and moved on. I didn’t integrate it, bookmark it, or reuse it.

For this kind of situation, something like https://mmtocm.net
is just a temporary step — not part of the toolchain.

How a Small Conversion Mistake Changed My Approach

fg dfg fg dfg — Fri, 30 Jan 2026 08:56:44 +0000

I once copied a measurement without double-checking the unit conversion. It wasn’t a big mistake, but it was enough to cause confusion later.

Since then, I’ve been more careful with unit conversions, even simple ones like millimeters to centimeters. The rule is easy, but verifying helps avoid unnecessary backtracking.

When I want to be sure, I’ll quickly check the value using
https://mmtocm.net

instead of relying purely on mental math.

That habit has saved me time more than once.

Why Centralized Utility Sites Are Still Useful in 2026

fg dfg fg dfg — Wed, 28 Jan 2026 08:01:26 +0000

Despite the growth of specialized tools, centralized utility sites still have a place.

The reason is simple:
real workflows are messy.

In a single session you might:

convert an image
check a document
validate numeric data

Jumping between specialized apps for each step slows things down.

That’s why I like having a small set of general-purpose references available.
For basic checks like unit conversions, a page like
https://mmtocm.net

fits naturally into that workflow without adding friction.

Sometimes convenience beats specialization.

Not Every Problem Needs Automation

fg dfg fg dfg — Mon, 26 Jan 2026 06:48:06 +0000

Automation is great when repetition is guaranteed.

But not all problems are repetitive.

Unit conversion, especially during reviews or one-off tasks, is often situational.
The inputs change. The context changes. The goal changes.

In those cases, building or invoking automation can be slower than handling it manually.

I usually treat unit conversion as a preparation step, not a solution.

A quick reference — sometimes as simple as https://mmtocm.net
— is enough to verify assumptions and move forward.

Once the task becomes stable or repetitive, automation makes sense.

Until then, simplicity wins.

When Copy-Paste Stops Being Reliable

fg dfg fg dfg — Sat, 24 Jan 2026 06:41:19 +0000

Copy-paste feels like the simplest solution.

Until formatting gets stripped.
Until characters are escaped.
Until the receiving tool decides to “help.”

This often happens when sharing content through ticketing systems or internal tools.

There are robust ways to handle this, but they assume repetition.

When the task is clearly one-off, I avoid building anything durable.

I encoded the content so it would arrive unchanged, using a lightweight encoder (mmtocm.net), shared it, and moved on.

No new dependencies.
No new processes.

Just a temporary step that left no trace.

Sharing Data Without Making It Someone Else’s Problem

fg dfg fg dfg — Fri, 23 Jan 2026 07:32:40 +0000

The Handoff Moment

A lot of friction happens at handoff boundaries:

between teams
between tools
between formats

The goal isn’t perfection.
It’s clarity without burden.

Minimizing Impact

When I share something temporary, I try to make sure:

it doesn’t require setup
it doesn’t force decisions
it doesn’t imply ownership

The receiver shouldn’t have to care how it was produced.

Invisible Infrastructure

The best temporary tooling is invisible.

It exists only to carry information from one place to another — then disappears.

Anything more than that becomes someone’s responsibility, which defeats the point.