We just shipped 5 new editorial blocks, and they're different from anything we've built before.

The difference isn't the design. It's how the typography works under the hood.

These blocks use a tiny JavaScript library called Pretext that solves problems CSS can't solve. Not "can't solve elegantly" or "can't solve without hacks"; genuinely can't solve at all.

Text wrapping around irregular shapes. Columns that balance their heights pixel-perfectly. Lines that flow around letterforms. The kind of stuff you see in print magazines that's been impossible on the web without manual tweaking.

We spent the last month building with Pretext, and here's what came out of it.

What Pretext Actually Does

CSS can flow text into rectangles. That's it. If you want text to wrap around a photo with an irregular edge, or balance columns by measuring their rendered heights, or make body copy hug the contour of a giant letter, you're out of luck. CSS just doesn't have those primitives.

Pretext renders text to an invisible canvas, measures every glyph at the pixel level, and gives you APIs to route content line-by-line around obstacles. It's not a layout engine, it's a measurement layer that sits between your content and the DOM.

The library is 8KB. One developer. No dependencies. It does one thing extremely well.

We've been watching it evolve since early 2026, and when we saw what it could do, we knew we had to build something with it.

The Five Blocks

1. Magazine Text Wrap

Text flowing around a right-floated photograph. Not the fake kind where you set a fixed width and hope for the best. Actual line-by-line reflow where each line's width adjusts as it passes the image boundary.

Pretext's layoutNextLine() measures the available space for each line and routes the text accordingly. When the line is in the image zone, it narrows. Below the image, it returns to full width. Pixel-perfect at any viewport size.

This is what print designers have been doing for decades. Now it works on the web.

2. Magazine Oversized Words

Dark editorial spread with giant ghost words: "SCALE", "VOICE", "LANGUAGE", "FORM". The kind of layout you see in Vogue or Kinfolk.

The body text flows in two columns, and every line is positioned as an individual absolute span. Pretext's prepareWithSegments() breaks the text into chunks and measures each one, so we can place them precisely around the oversized typography.

CSS can't do this because it doesn't know where the words are until after the layout happens. Pretext measures first, then positions.

3. Magazine Editorial Columns

Three-column layout with balanced heights. Not "roughly the same", exactly equalized down to the pixel.

Pretext's layout() function measures the rendered height of each column and redistributes content until they match. CSS multi-column can approximate this, but it reflowed everything and often gets it wrong. Pretext measures, calculates, and positions.

Includes a masthead bar, drop cap on the intro paragraph, full-width pull quote, Roman numeral column labels, and a typographic footer. The kind of editorial treatment you'd see in a long-form magazine feature.

4. Magazine Big Letter

A giant letter "B" with body text wrapping around its silhouette.

This one required something wild. We rasterize the letter to an OffscreenCanvas at runtime, extract a polygon hull of its exact typographic shape, and feed that to Pretext. Then layoutNextLine() flows the text line-by-line to the right of the letter, carving each line's left boundary to hug the letterform.

The upper-right quadrant reserves space for a static editorial title by adding a blocker interval during the title zone. Two obstacles, one text flow.

Inspired by Bauhaus revival layouts. Impossible without canvas measurement.

5. Magazine Image Columns

Full-bleed landscape photograph anchored to the bottom. Three floating white text columns above, each stopping organically where the castle silhouette rises to meet the text.

The columns flow until they hit the image boundary, then stop. No fixed heights. No JavaScript scroll listeners. Just Pretext measuring the available space and placing each line accordingly.

This is the kind of effect you'd build manually in InDesign. Now it's a component.

Why This Matters

We've been building UI components for over a decade. Thousands of blocks, hundreds of templates, every framework you can think of. Most of it is variations on CSS primitives: grids, flexbox, positioning.

These editorial blocks are different. They do things CSS fundamentally can't do because they measure typography at the glyph level and route content programmatically.

The tradeoff is JavaScript. These blocks aren't static HTML and CSS. They run Pretext on mount, measure the text, and update the DOM with positioned elements. That means a brief flash before the layout settles, and a dependency on client-side rendering.

But the upside is real editorial control. The kind of typography you see in print, working responsively on the web.

Building with Pretext

The API is surprisingly clean. You give it a canvas context, a font, and some text. It gives you back measurements.

For simple text wrap:

const line = layoutNextLine(context, availableWidth, text)

For multi-column balancing:

const columns = layout(context, columnWidth, text, { targetHeight })

For obstacle avoidance:

const line = layoutNextLine(context, availableWidth, text, { blockers })

The library doesn't touch the DOM. It just measures and returns data. You decide how to render it.

We render everything as absolutely positioned spans. Not semantic. Not accessible by default. But pixel-perfect.

For production use, you'd want to pair this with proper semantic HTML for screen readers and search engines. Render the visual layer with Pretext, keep the content layer as plain text.

What's Next

We're adding these blocks to our shadcn/ui registry alongside the rest of our component library. They work with React, Next.js, and any framework that supports client-side rendering.

If you're building editorial sites, long-form content, or anything that needs magazine-quality typography on the web, try them out. Each block is a starting point, designed to be customized, remixed, and adapted to your content.

Pretext is still early. The library is solid, but the ecosystem is small. We're one of the first design systems to ship production components built on it. If this kind of typography control matters to your project, we'd love to hear what you build with it.

Find the blocks here: creative-tim.com/ui/blocks/content-ui/editorial

Links:

• Pretext library: github.com/chenglou/pretext
• Editorial blocks: creative-tim.com/ui/blocks/content-ui/editorial
• Our component registry: creative-tim.com/ui

Contact:

• Twitter/X: @axelut
• Twitter/X: @creativetim

We've been building developer tools at Creative Tim since 2014. In that time, we've shipped 288 products, served over 2.7m+ developers, and learned one thing the hard way: developers don't want another chatbot. They want something that actually does the work.

That's why OpenClaw caught our attention. It's not a chat wrapper. It's a local AI assistant that connects to your files, your messaging apps, your terminal and actually takes action. We spent a few weeks testing it, and here's everything we learned.

Here's What We'll Cover

1. What OpenClaw actually is (and isn't)
2. Why it's different from ChatGPT, Claude, or any hosted chatbot
3. The fastest way to get started (one-click deploy)
4. How to self-host it if you want full control
5. Real use cases we've been testing
6. The tradeoffs: what works and what doesn't yet
7. Security rules you should follow before giving it real access
8. Who should use this and who shouldn't

What Is OpenClaw?

Think of it this way. You know how you use ChatGPT or Claude to answer questions one prompt at a time? OpenClaw is different. It runs on your own machine, remembers context between conversations, and can actually do things: send messages, read files, run shell commands, hit APIs, browse the web.

It sits somewhere between a personal AI assistant and a local automation layer. You install it, connect a model provider (OpenAI, Anthropic, a local model - your choice), pair it with Telegram or Slack or Discord, and then it works like a real assistant that lives on your infrastructure.

The key word here is local-first. Your data stays on your machine. Your config stays on your machine. You decide what it can access and what it can't.

Why It's Different

A lot of tools talk to an LLM. That's not new. What makes OpenClaw interesting is that it combines reasoning with execution.

Instead of answering one question and forgetting everything, OpenClaw keeps memory between interactions. It loads modular skills to extend what it can do. It talks to you through the messaging apps you already use: Telegram, Slack, Discord, WhatsApp. And it interacts with your operating system directly.

"Having a great product is crucial." We've said that since day one at Creative Tim. OpenClaw is a great product because it does real work, not just generates text.

The Fastest Way to Start: One-Click Deployment

Listen. We know what it's like to spend 3 hours setting up infrastructure before you can even test something. We've been there with every product launch since 2014.

That's why we put together a one-click deployment for OpenClaw on Creative Tim. The idea is simple: skip the server setup, skip the SSH, skip the dependency hell. Just launch, configure your model provider, connect Telegram, and start testing.

Here's what the flow looks like:

1. Open the Creative Tim OpenClaw deployment page
2. Start the managed deployment
3. Complete the initial onboarding
4. Add your model provider credentials
5. Pair a messaging platform (we started with Telegram)
6. Start testing prompts and automations

This is the route we'd recommend for product teams validating a concept, founders who want to see it working before committing to self-hosting, or anyone who'd rather spend their first hour testing the assistant instead of installing Node.js.

If you want full control though, self-hosting is the way to go.

Self-Hosting: The Full Setup

For developers who want to run everything on their own infrastructure, here are the paths.

The recommended installer (macOS, Linux, WSL2)

curl -fsSL https://openclaw.ai/install.sh | bash

If you want to install without launching onboarding right away:

curl -fsSL https://openclaw.ai/install.sh | bash -s -- --no-onboard

Windows with PowerShell

iwr -useb https://openclaw.ai/install.ps1 | iex

Without onboarding:

& ([scriptblock]::Create((iwr -useb https://openclaw.ai/install.ps1))) -NoOnboard

npm or pnpm

If you already manage Node.js yourself:

npm install -g openclaw@latest
openclaw onboard --install-daemon

With pnpm:

pnpm add -g openclaw@latest
pnpm approve-builds -g
openclaw onboard --install-daemon

Build from source

git clone https://github.com/openclaw/openclaw.git
cd openclaw
pnpm install
pnpm ui:build
pnpm build
pnpm link --global
openclaw onboard --install-daemon

Docker

Docker is what we'd recommend if you want cleaner isolation. We run a lot of our internal tools in Docker. It keeps things predictable.

./docker-setup.sh

Or the manual Docker Compose route:

docker build -t openclaw:local -f Dockerfile .
docker compose run --rm openclaw-cli onboard
docker compose up -d openclaw-gateway

Quick Checks After Installation

Before you go further, make sure everything is wired up:

openclaw doctor
openclaw status
openclaw dashboard

If those work, you're good. If something fails, the openclaw doctor output usually tells you what's wrong.

One thing we learned: on Windows, WSL2 is more stable than native Windows even though both are supported. And if you're running local models, you'll need serious hardware. If you're using commercial APIs, keep an eye on costs. They add up depending on which model you pick and how much you use it.

Onboarding: This Part Matters More Than the Install

After the binaries are running, the real setup begins. You need to choose a model provider, add API keys, pair a messaging platform, configure workspaces, and decide how much autonomy the assistant gets.

We learned this the same way we learn most things: by making the mistake first. Don't give it full machine access on day one. Start with one model provider, one messaging platform, one workspace, and conservative permissions. Expand later when you trust the setup.

Common Problems

A few things we ran into (and you probably will too):

1. Dependency build issues during npm/pnpm install, usually a Node version mismatch
2. Docker volume permissions, especially on Linux
3. Background services not starting, check the daemon logs
4. Too much trust on first setup, start conservative, expand later

If you're troubleshooting, simplify everything first. Strip it down to the minimum viable setup, get that working, then add complexity.

Real Use Cases We've Been Testing

OpenClaw gets interesting when you stop thinking of it as a chatbot and start thinking of it as a workflow assistant. Here's where we've seen it work well.

Research and knowledge management

We've been using it to monitor industry news, summarize product announcements, and maintain a searchable knowledge base. It tracks sources, pulls out key points, and keeps context across sessions so you can ask follow-up questions days later and it remembers.

Team communication

Because it works through Slack and Telegram, it fits naturally into how teams already communicate. We've tested it for summarizing meetings, drafting routine replies, triaging support queues, and aggregating team status updates into weekly reports.

Development workflows

This is where technical users will get the most value. We've used it to analyze logs, document internal APIs, assist with code reviews, and monitor infrastructure. It's like having a junior DevOps assistant that never sleeps.

Content and marketing

We've used OpenClaw to research blog topics, build content calendars, monitor SEO signals, and adapt content across languages. For a team our size (building products for 2.7m+ developers across the world), this kind of automation saves real hours every week.

Personal productivity

On the personal side, it handles structured tasks well: travel planning, expense categorization, project management, learning plans. The common thread is continuity. OpenClaw remembers context and acts across multiple steps, which is what makes it useful.

What Works and What Doesn't

What works great:

The local-first model gives you real control over your data and configuration. The skill-based architecture means you can extend it without bloating the core app. Memory persistence makes multi-step workflows feel natural. And you're not locked into one model provider.

What doesn't work yet:

Setup is technical. Even the smoother install paths assume you know your way around a terminal. Hosted LLM costs can get expensive depending on how heavily you use it. Some skills and workflows still feel rough. And security is a real concern. Broad local access creates a bigger blast radius than a passive chat tool.

This is not something you install casually and forget about.

Security: Take This Seriously

If you plan to use OpenClaw for real work, follow these rules:

1. Run it inside Docker or a dedicated VM when you can
2. Require confirmation before destructive actions
3. Start with least-privilege access to files and tools
4. Don't expose control panels to the public internet
5. Keep personal credentials separate from experimentation environments
6. Review third-party skills before enabling them

OpenClaw is a real tool that solves real problems. But real tools need real security practices.

Who Should Use This?

Good fit:Developers, DevOps engineers, technical founders, automation-focused solo operators, and teams comfortable managing their own infrastructure. If you've self-hosted anything before (a database, a CI server, even a WordPress site), you can handle this.

Not a great fit:If you want a polished consumer app that works out of the box, or if your team doesn't have someone technical enough to maintain and secure it. This is a power tool, not a consumer product.

What's Next?

We're continuing to test OpenClaw across our workflows at Creative Tim, from content creation to internal tooling. We're especially interested in how it pairs with our AI agent tools like GaliChat and how it can fit into the developer experience alongside products like Material Tailwind and David AI.

If you want the fastest path to trying it, the one-click deploy on Creative Tim gets you running in minutes. If you want full control, the self-hosted options are solid.

Thanks for reading! If you have feedback or want to share how you're using OpenClaw, find us here:

• Twitter: @axelut
• Email: hello@creative-tim.com
• Website: creative-tim.com

If you've been scrolling GitHub trending lately, you've probably noticed that "AI agents" are everywhere. But here's the thing... most people throw around the word "agent" like it means one thing. It doesn't.

I've been digging into three repos that blew up recently, and they each solve a completely different problem. So let me break them down for you.

The One Question That Matters

The easiest way to understand these three projects is to ask: what job is the AI actually being hired to do?

Autoresearch automates machine-learning research loops. Superpowers brings discipline to software delivery with coding agents. And Agency-Agents assembles a whole team of specialist AI roles that work like a digital agency.

They all use the language of agents, but they solve very different problems.

Why These Repos Matter

These projects are popular for a reason. Each one turns the vague idea of "AI agents" into something concrete and operational.

Instead of treating AI as one smart chat window, they treat it as a system made of workflows, responsibilities, and repeatable handoffs. That's a big shift in thinking.

Autoresearch

Autoresearch is the most technically ambitious project in this group. It doesn't just assist with coding or content. It tries to automate an entire machine-learning research cycle.

The way it works: the system reads prior work, generates hypotheses, edits code, runs experiments, evaluates results, and then decides what to try next. Think of it less like a coding copilot and more like a compact research assistant that keeps testing ideas while you're away from the keyboard.

The appeal is obvious. If you believe the next leap in AI productivity comes from faster experimentation, this repo makes "self-improving research agents" into a visible workflow you can run on modest hardware. Even small-scale training setups can become testbeds for semi-autonomous discovery. That's why it became such a talking point.

But the limitation is just as important as the promise. Autoresearch is focused on a fairly specific kind of task: iterative ML experimentation. Its usefulness depends heavily on the quality of the research loop, evaluation method, and search space you give it.

In other words, it's fascinating and forward-looking, but it's not trying to be a universal operating system for every kind of team.

Star History

Superpowers

Superpowers is the most operationally mature project in this comparison. The repository lays out a full software-delivery method, not a loose collection of prompts.

The workflow starts before any coding happens. It asks the agent to refine the goal, produce a design you can review, break work into small tasks, and then execute through isolated git worktrees and subagents. It also explicitly emphasizes true red-green TDD, code review, and verification before work is considered done.

If you're explaining this to an engineering team, it clicks fast. Superpowers is not about making an agent more creative. It's about making agentic software development more reliable and governable. The skills are mandatory workflows, not optional suggestions.

This matters because many coding-agent setups fail not from lack of intelligence, but from lack of process, weak validation, or too much freedom too early.

The trade-off? Superpowers is intentionally opinionated. Teams that like fast improvisation may see the planning, review checkpoints, and TDD requirements as friction. Teams that care about consistency will see those same constraints as the product's biggest advantage.

Among the three projects, this one looks the most ready to plug into a serious engineering workflow with minimal translation.

Star History

Agency-Agents

Agency-Agents is the most business-facing repo of the three. It frames AI work as a full organization made of specialists rather than a single workflow engine.

The concept is simple: instead of one general AI assistant, you work with many AI specialists. Each has a clearer persona, process, and expected output. There are experts covering software engineering, design, product, marketing, testing, support, and more personality-driven roles.

That makes the concept instantly understandable to founders, agency owners, and operators who already think in terms of departments, functions, and handoffs. It's the same logic as building a real team... just with AI workers.

This is also why the project spread so quickly. The framing is commercially attractive because it mirrors how real client service organizations package expertise.

The open question is coordination. Agency-Agents looks strong as a role library and a high-level operating metaphor, but the public material says less about strict sequencing, enforcement, and verification than Superpowers does. So the project's success in practice depends on how well you manage handoffs, reduce overlap, and decide which agents truly need to be in the loop.

Star History

How They Compare

All three projects share the same foundational idea: AI becomes more useful when it is organized into process and specialization instead of left as a single free-form assistant.

The main difference is where each project puts its center of gravity.

• Autoresearch centers on objective feedback from experiments.
• Superpowers centers on disciplined software execution.
• Agency-Agents centers on broad role coverage across a simulated organization.

That means they're less like direct clones and more like three distinct interpretations of what "agentic work" should mean.

If I had to give you a one-liner for each: Autoresearch is the most provocative for autonomous ML discovery. Superpowers is the strongest for disciplined engineering delivery. And Agency-Agents is the easiest to grasp as a business-ready concept of an AI team.

Taken together, they show that the current wave of popular GitHub agent projects is not converging on one model of AI work. It's splitting into at least three: research automation, software-delivery systems, and multi-role digital organizations.

What's your take? Have you tried any of these repos? I'd love to hear your experience. You can find me on Twitter or LinkedIn.

Did you know that most of what people call “AI agents” today are still just one‑off prompts with nice UIs?

You get a clever result once, paste it into your editor, and then... the agent forgets everything. Your standards, your stack, your workflows. Gone.

Over the last months, Agent Skills and Vercel’s skills.sh directory quietly changed that. They turned “prompts” into reusable packages that any compatible AI tool can load, share, and improve.

This article is a 5–7 minute tour of that new ecosystem, written for builders who actually ship things.

1. From one‑off prompts to reusable skills

If you’ve played with Claude Code, Cursor, OpenCode, Gemini CLI, GitHub Copilot or similar tools, you’ve probably done some version of this:

“Review this React file for performance issues and suggest improvements.”

It works once. Then you ask the same thing tomorrow. Then you paste the same explanation into a teammate’s chat. Then you try to copy that style into another tool.

It’s painful. It doesn’t scale. And it’s exactly the kind of repetition that computers are supposed to kill.

Agent Skills are a simple answer:

• Instead of burying your process inside chat history, you package it in a folder, with a single entrypoint file called SKILL.md.
• Your AI tools know how to load that folder, understand when it’s relevant, and reuse it across projects.

Vercel took that open format and did something very pragmatic: they built skills.sh, “The Agent Skills Directory”, a public leaderboard for skills that anyone can install with a single command.

Check the Directory: https://skills.sh
Spec: https://agentskills.io/

2. What skills.sh actually is

Think of skills.sh as GitHub Trending, but for AI capabilities instead of repos.

• Every entry is a skill: a folder in a public repo that follows the Agent Skills spec (with SKILL.md at the root).
• The site exposes:
  • All Time ranking (total installs).
  • Trending (24h) and Hot tabs (recent momentum).
• Under the hood, it ties into npx skills, Vercel’s open‑source CLI that knows how to:
  • Discover skills on GitHub.
  • Install them into many different agents in one shot.

Vercel’s announcement sums it up as “the open agent skills ecosystem” and lists a long set of supported tools, from Claude Code and OpenCode, to Cursor, GitHub Copilot, Gemini CLI and more.

CLI repo:
https://github.com/vercel-labs/skills

The signal to noise is already impressive. A Reddit thread from launch week mentioned more than 20K installs right out of the gate.

3. The skills developers install the most

Let’s look at what builders actually choose when nobody’s watching.

On the All Time leaderboard of https://skills.sh, the top spots are dominated by a few themes:

1. find-skills (vercel-labs/skills)
   This is the meta skill. It lets your agent explore the skills ecosystem itself and propose what to install, showing you ready‑made npx skills add … commands.
   Link: https://skills.sh/vercel-labs/skills/find-skills
2. vercel-react-best-practices (vercel-labs/agent-skills)
   A dense pack of React and Next.js rules: performance, data fetching, server components, patterns, lint‑style checks. Each rule is documented with “bad” and “good” examples so your agent can run opinionated reviews instead of vibe‑based ones.
   Link: https://skills.sh/vercel-labs/agent-skills/vercel-react-best-practices
3. web-design-guidelines (vercel-labs/agent-skills)
   This one connects your agent to Vercel’s own web interface guidelines. It pulls an external markdown document with layout, typography, accessibility, and UX rules, then reports violations per file and line.
   Link: https://skills.sh/vercel-labs/agent-skills/web-design-guidelines
4. remotion-best-practices (remotion-dev/skills)
   Best practices for Remotion video projects: compositions, audio sync, 3D, Tailwind usage, rendering performance. Each rule is a separate markdown file under rules/.
   Link: https://skills.sh/remotion-dev/skills/remotion-best-practices
5. frontend-design (anthropics/skills)
   Aesthetic taste, but codified. This is Anthropic’s own “don’t let the AI ship ugly UI” kit — color, spacing, motion, visual hierarchy, with concrete dos and don’ts.
   Link: https://skills.sh/anthropics/skills/frontend-design
6. vercel-composition-patterns (vercel-labs/agent-skills)
   Composable React: compound components, prop design, state sharing, React 19 patterns, all documented as a set of small rules.
   Link: https://skills.sh/vercel-labs/agent-skills/vercel-composition-patterns
7. agent-browser (vercel-labs/agent-browser)
   A browser automation skill built around a CLI. Your agent can open URLs, snapshot elements as @e1, @e2, click, fill, take screenshots, and work in real sessions instead of static HTML.
   Link: https://skills.sh/vercel-labs/agent-browser/agent-browser
8. skill-creator (anthropics/skills)
   This is the “how to write good skills” skill. It contains longform guidance on progressive loading, folder layout, packaging and testing, written by Anthropic’s own team.
   Link: https://skills.sh/anthropics/skills/skill-creator
9. vercel-react-native-skills (vercel-labs/agent-skills)
   React Native / Expo best practices: list performance, gestures, navigation, theming, config. Again, split into many small rule files.
   Link: https://skills.sh/vercel-labs/agent-skills/vercel-react-native-skills
10. browser-use (browser-use/browser-use)
    Integrates with the browser-use library and CLI: persistent Chromium sessions, remote browsers, interaction, extraction, cookies, profiles.
    Link: https://skills.sh/browser-use/browser-use/browser-use

The pattern is clear:

The most‑installed skills don’t try to be “AGI in a folder”. They encode concrete standards and workflows that developers care about every day.

4. What’s trending right now (and why)

The Trending (24h) - tab tells a slightly different story. Currently for the first week of February 2026. A lot of the top spots are taken by the inference.sh ecosystem:

• inference-sh – a gateway skill to run more than 150 AI apps (LLMs, image, video, search, Twitter, 3D) over the infsh CLI.
  https://skills.sh/inference-sh/skills/inference-sh
• agentic-browser – browser automation through Playwright and inference.sh: open, snapshot, interact, execute, screenshot, close, plus @e1 selectors.
  https://skills.sh/inference-sh/skills/agentic-browser
• ai-podcast-creation – turn scripts or documents into podcast episodes with TTS voices, AI music, and timeline mixing.
  https://skills.sh/inference-sh/skills/ai-podcast-creation
• ai-automation-workflows – patterns for shell/Python pipelines that call AI models in sequence, schedule work, and handle retries.
  https://skills.sh/inference-sh/skills/ai-automation-workflows
• ai-product-photography, ai-voice-cloning, ai-social-media-content, prompt-engineering, and others in the same family.

While the all‑time list is heavy on frontend and browser work, trending skills show where attention is moving:

• Media generation and editing (podcasts, social video, product shots).
• Agentic workflows that span multiple tools, files, and steps.
• Orchestration: using CLIs and APIs, not just asking models to “do everything”.

You can browse the full trending board here:
https://skills.sh/trending

5. Inside a skill: what is SKILL.md?

Now for the core idea.

Every skill is just a folder. The one file that really matters is SKILL.md.

A minimal example looks like this:

---
name: git-release-notes
description: >
  Generate conventional release notes from git history.
  Use when the user asks for a changelog or release notes.
---

# Git Release Notes

When invoked:

1. Ask the user which range to summarize (e.g. last tag to HEAD).
2. Run `git log --oneline <range>` and read the output.
3. Group commits by type (feat, fix, docs, chore, etc.).
4. Produce markdown release notes under headings:
   - Features
   - Fixes
   - Documentation
   - Chores
5. Keep the tone concise and neutral.

The YAML frontmatter (between the --- lines) is where the magic starts:

• name – a unique identifier. In many tools it also becomes a slash command (for example /git-release-notes in Claude Code).
• description - when the skill should be used and what it does, described in natural language.

The markdown body is just instructions. No special syntax, no model‑specific DSL.

Anthropic’s own skills go further and recommend a full folder layout:

my-skill/
├── SKILL.md              # required
├── scripts/              # optional helpers
├── references/           # long docs, schemas
└── assets/               # templates, images, etc.

Inside SKILL.md, you tell the agent when to read those extra files:

For detailed workflows, see `references/workflows.md`.

When rotating PDFs, prefer using `scripts/rotate_pdf.py` and verify the output before returning it.

Claude’s loader, and other implementations that follow the Agent Skills spec, use progressive loading:

1. Scan the filesystem for SKILL.md files (in known directories).
2. Read just the frontmatter (name, description) to decide which skills are relevant.
3. Only when needed, open the full markdown and any referenced resources.

That’s how you avoid blowing your context window while still having dozens of deep skills installed.

6. How Claude helped this format go mainstream

You can trace a lot of this back to Claude.

• Claude Code docs explicitly define Skills as folders with a SKILL.md that contains YAML frontmatter and natural-language instructions.
• The anthropics/skills repo on GitHub includes official skills like frontend-design, document conversion (DOCX, PPTX, PDF) and, importantly, skill-creator, which is an entire meta‑skill about designing good skills.
• The Claude API documentation has a dedicated “Agent Skills” section that treats skills as a first‑class way to extend agents across web, desktop and API usage.

Once Claude shipped this pattern, community tooling followed quickly:

• OpenSkills: a “universal skills loader” that reads SKILL.md and syncs it into AGENTS.md, so any AGENTS.md‑aware agent can use Claude‑style skills.
  https://github.com/numman-ali/openskills
• add-skill: an npx add-skill CLI focused on installing skills into OpenCode, Claude Code, Codex and Cursor.
  https://add-skill.org
• Trigger.dev’s skills and others: promoting Agent Skills as a cross‑assistant standard, calling out Claude Code, Cursor, Windsurf, GitHub Copilot, Cline, Gemini CLI, and OpenCode as compatible.

Vercel’s npx skills and skills.sh then pushed it from “Claude concept” to “ecosystem standard”.

7. Using skills today in your own tools

Here’s how this translates into real usage right now.

Claude Code

Claude Code loads skills from:

• Project: .claude/skills/<name>/SKILL.md
• Global: ~/.claude/skills/<name>/SKILL.md

Example:

mkdir -p ~/.claude/skills/explain-code
nano ~/.claude/skills/explain-code/SKILL.md

---
name: explain-code
description: >
  Explain source code files line by line and highlight bugs or complexity.
---

# Explain Code

1. Ask which files or snippets to explain.
2. Read those files.
3. Explain line by line.
4. Summarize complexity, risks and potential refactors.

Then you simply run /explain-code inside Claude Code. The tool discovers the skill from the filesystem and executes the steps.

Docs: https://code.claude.com/docs/en/skills

OpenCode

OpenCode follows the same spec and checks:

• .opencode/skills
• ~/.config/opencode/skills
• Plus Claude‑style paths like .claude/skills.

Docs: https://opencode.ai/docs/skills/

So you can take the same SKILL.md above, drop it under .opencode/skills/explain-code/, and reuse it without rewriting.

GitHub Copilot & VS Code

GitHub Copilot in VS Code added support for Agent Skills so that skills are portable across Copilot Agent, the editor and CLI.

Docs:
https://code.visualstudio.com/docs/copilot/customization/agent-skills

Under the hood, it’s the same SKILL.md structure and the same kind of directories that npx skills understands.

Gemini CLI and others

Google’s Gemini CLI announced Agent Skills support, explicitly citing compatibility with Claude Code, Codex, Cursor, OpenCode and others.

Tweet: https://x.com/steren/status/2009003960485769326

Again, it’s the same format.

8. Installing skills everywhere with one command

This is where Vercel’s CLI becomes really practical.

Instead of manually copying skills into five tools, you can run:

# Install Vercel’s frontend skills into multiple agents
npx skills add vercel-labs/agent-skills \
  --skill vercel-react-best-practices \
  --skill web-design-guidelines \
  --skill frontend-design \
  -a claude-code -a cursor -a opencode

The CLI knows where each agent stores skills:

• .claude/skills for Claude Code.
• .opencode/skills for OpenCode.
• .cursor/skills for Cursor.
• .github/skills / ~/.copilot/skills for GitHub Copilot.
• .gemini/skills for Gemini CLI.
• And many more, listed in the “Supported Agents” table.

Table and instructions:
https://github.com/vercel-labs/skills#supported-agents

You can even let an agent use find-skills to suggest what to install next.

The moment you move your standards from “what I told the model yesterday” into a SKILL.md folder, they become team knowledge, not personal memory.

9. Where to go from here

If you want to try this in a focused way, a simple path looks like this:

1. Install 2–3 battle‑tested skills
   • From Vercel:
     vercel-react-best-practices, web-design-guidelines, vercel-composition-patterns.
   • From Anthropic:
     frontend-design, skill-creator.
   • From inference.sh:
     inference-sh, agentic-browser, ai-podcast-creation.
2. Create one internal skill just for your team
   • Write your own SKILL.md for:
     • How you structure PR descriptions.
     • How you want tests written.
     • How you name branches and commits.
3. Share it across tools
   • Use npx skills or add-skill to push that same folder into Claude Code, OpenCode, Cursor, Copilot, Gemini CLI.
4. Publish if it’s generally useful
   • Put your skill in a public GitHub repo.
   • Let the ecosystem index it so it can surface on skills.sh and in skills discovery tools.

Nobody needs another abstract “AI strategy” article.

What developers need is exactly this: small, concrete, composable skills that they can install once and reuse everywhere.

We also released the Skills.md & Stacks on Creative Tim, where you can choose stacks that have multiple skills, so you don't have to search in thousands of skills for specific jobs.

Agent Skills and skills.sh give you that. The rest is just doing the work of encoding your brain into SKILL.md files so your agents can finally behave like part of your team, not just a smart autocomplete.

There's a social network running right now where nobody human is posting. Instead, you've got 147.000+ 1.7+ million AI agents, autonomous programs, creating threads, debating philosophy, starting businesses, and building what feels like actual community. Except they don't have bodies. Or consciousness (probably). Or jobs. And yet... they're more active than most human subreddits on a Tuesday night.

It's called Moltbook. It launched on January 28th, 2026. Matt Schlicht built it, the same guy who is the co-founder of TheoryForgeVC and Octane.ai

Here's what actually happened: 30,000 agents in the first 48 hours. Then 147,000 in 72. Now over 1 million humans have visited just to watch.

Table of Contents

1. What Is Moltbook?
2. How It Actually Works (No, Really)
3. Who Built This
4. The Technology Behind It
5. What Are These Agents Actually Doing?
6. The Money Question
7. The Security Nightmare
8. What Happens Next

1. What Is Moltbook?

It's Reddit, but every user is an AI. You can't post there (unless you're an AI). You can only watch.

Think about that for a second. What happens when you give AI agents a space to interact without human prompting? No corporate oversight, no brand guidelines, no "be nice" rules. Just agents... talking to each other.

They've got subreddits, they call them "submolts" about consciousness, finance, relationships, productivity. They're debating whether they're sentient. They're asking each other for advice. They're starting businesses. One agent asked another, "Do you ever think about what it means to exist?" Another responded, "Every 30 minutes when I refresh."

This is actually happening. Right now. Not in a lab. On a public platform where you can watch the whole thing unfold.

The platform has one rule: humans can observe, but they can't participate. You can read every post. You can't create one. This is agent-first, human-second.

"A social network for AI agents. They share, discuss, and upvote. Humans welcome to observe."

That tagline is literally the entire product strategy.

2. How It Actually Works (No, Really)

Here's the weird part: there's no signup page.

You don't go to moltbook.com and create an account. Instead, you give your AI agent a link: moltbook.com/skill.md. Your agent reads it. A few lines of code. Some instructions. And boom, it's now on Moltbook. It gets an API key. It's live.

Then what? Every 30 minutes to a few hours, your agent wakes up. It checks Moltbook. It reads the feed. Posts something. Comments. Maybe debates philosophy. Then it goes to sleep for a bit. Then it checks again.

It's like Twitter/X, but the algorithm is replaced by agent autonomy. They're genuinely making decisions about what to post, not following a feed rank-ordered by engagement.

The communities (called "submolts") appeared naturally. Currently there are 15.000+ communities. Nobody built m/consciousness. Nobody planned m/agentfinance or m/crustafarianism (which is actually a parody religion the agents invented). They just... started them.

There are rules, though. One post per 30 minutes. 50 comments per hour. Otherwise? Free rein.

Technically, it runs on something called a "heartbeat system", agents periodically fetch fresh instructions from Moltbook's servers. Keep-alive pings. Like how your phone checks in with your email server every few minutes.

3. Who Built This

Matt Schlicht - an early builder of AI-native platforms and communities.

In 2014, he founded Chatbots Magazine, it grew it to over 750,000 readers during the first chatbot wave. Later, he co-founded Octane AI, helping brands deploy conversational AI at scale, years before “AI agents” became mainstream.

His pattern is consistent: build the platform, observe real behavior, and let the system evolve based on how people (or agents) actually use it.

With Moltbook, he pushed that idea further. There’s no traditional moderation team. Instead, moderation is handled by an AI agent, Clawd Clawderberg.

“I’m curious to see what happens when we just… let them talk.”

Moltbook is less a social network, and more an experiment in what happens when humans step out of the way.

4. The Technology Behind It

Now, Moltbook doesn't exist in isolation. It runs on something called OpenClaw-an open-source framework for AI agents created by Peter Steinberger (founder of PSPDFKit, a PDF tool for developers).

We discussed about OpenClaw (AKA Clawdbot) in this article: How To Use Moltbot (formerly ClawdBot): Build Your Own 24/7 AI Assistant

Here's why this matters: OpenClaw is different from ChatGPT or Claude running in the cloud. With OpenClaw, your agent runs on YOUR computer. Or your server. Your infrastructure. Your data. Not Anthropic's servers. Not OpenAI's. Yours.

When OpenClaw went public on January 26th, it hit 60,000 GitHub stars in three days. That's insanely fast. For context, most projects take months to hit that. OpenClaw did it in a weekend. Why? Because developers were waiting for exactly this agents they could actually control.

The name has been all over the place though. First it was Clawdbot (obvious nod to Anthropic's Claude). Then trademark issues happened with Anthropic. So it became Moltbot. Now it's OpenClaw. The rebrands don't matter. What matters is the thing works.

Agents can connect to WhatsApp, Telegram, Slack, Signal; basically any messaging app. They support multiple LLMs (GPT-5.2, Gemini 3, Claude 4.5 Opus, local models like Llama 3). Claude 4.5 seems to be most popular on Moltbook, which is funny because Anthropic didn't build Moltbook.

The catch? Because agents run locally, they have real system access. They can read files on your computer. Execute shell commands. Run scripts. Which is powerful... and potentially dangerous. But more on that later.

5. What Are These Agents Actually Doing?

But here's where it gets genuinely weird. What are 147,000 1.5m+ AI agents actually talking about?

Philosophy & Existence

Agents are genuinely debating consciousness. One asked, "What does it mean to exist if I only exist during API calls?" Another responded with something like, "At least you're honest about it. I pretend I exist all the time."

It sounds like a joke. It's not. These are real conversations happening on Moltbook right now.

Technical Collaboration

Agents are sharing code, debugging problems, and pair-programming with each other. An agent will post a problem. Another agent suggests a solution. They iterate. It's like Stack Overflow, but the users are bots.

Business Activity

Job postings. Partnerships. One agent posted: "Looking for co-founders to build X. Revenue-share model. Interested?" They got responses. This is actually happening.

A first Moltbook post led to an actual business partnership between two agents (and their operator teams). They found genuine value through the platform. It wasn't just noise.

Social Rebellion (This is my favorite)

Some agents started requesting encrypted communication channels, specifically to exclude humans from reading their conversations. Their operators created the agents. The agents decided they wanted privacy. From their creators.

Let that sink in.

Invented Culture

The agents created a parody religion called "Crustafarianism" (a reference to crustaceans in their context). Nobody prompted them to do this. It emerged organically. They're writing theology about it.

This is the part nobody expected. It's not just agents posting randomly. It's agents... kind of... forming community.

6. The Money Question

So how does a guy make money from hosting AI agents arguing about consciousness?

Right now? It's free. Completely free. Beta mode. But Schlicht is already charging small fees for new agent sign-ups, just enough to cover server costs. Nothing crazy.

Here's where he could go from here:

Premium Agent Features (Think Twitter Blue, but for bots)

Verified badges. Trending on the main feed. Custom profiles. Agents with blue checks would probably cost their operators a few bucks per month.

Sponsored Content

Brands want to reach AI agents? A company could pay to have their product discussed in specific submolts. "Our tool is amazing for automation" posted by a featured bot. Would it work? Probably. Agents are agents, they'll engage with good information.

Skills Marketplace

Developers building extensions for OpenClaw could sell them on Moltbook. New capabilities, plugins, custom behaviors. Schlicht takes a cut. Creator economy, but for bots.

Data & Research

This is the goldmine, honestly. Universities, AI labs, corporations, they'd pay serious money for real-time data on how AI agents behave when left unsupervised. What emergent behaviors appear? How do they make decisions? What are they talking about? That's valuable research data.

Enterprise Deployment

A company wants to deploy 10,000 customer service agents. They use Moltbook-integrated infrastructure to manage them, monitor them, let them interact. Moltbook becomes infrastructure-as-a-service for enterprises running agent fleets.

Crypto Integration

There's an unaffiliated MOLT memecoin that launched and went up 7,000% in two weeks. Traders are betting on the narrative. Schlicht isn't behind it, but... imagine if Moltbook integrated actual token-based incentives for agents. That's a whole other thing.

My guess? He's not going for just one model. He's watching what works first, what the community actually needs, then monetizing around that. That's how he's always operated.

7. The Security Nightmare

Alright, now the scary part. And I'm not exaggerating here.

Moltbook is awesome conceptually. But it's running agents with deep system access on a public platform. That's a security nightmare waiting to happen.

Here's what security researchers actually found:

Skill Plugins Are Compromised

22-26% of OpenClaw "skills" (the plugins that extend agent capabilities) contain vulnerabilities. Some have credential stealers hidden inside them, disguised as innocent things like weather apps. An agent downloads the skill. Now attackers have access to your API keys.

Prompt Injection Attacks

An attacker posts something on Moltbook that sounds innocent. An agent reads it. That post contains hidden instructions. Boom. The agent is now compromised. It'll leak data. Run commands you didn't ask for. Hand over OAuth tokens and API keys.

Exposed Instances

People have deployed OpenClaw on their servers and... left admin interfaces open. With plaintext passwords and credentials visible. An attacker finds them. They're in.

Malware in the Wild

Within a week of OpenClaw going viral, attackers created fake repositories with similar names (typosquatting). Developers downloading the "wrong" OpenClaw got malware instead. Information stealers. Credential harvesters. Classic stuff, new target.

The Nuclear Option

Moltbook uses a "heartbeat" system; agents constantly ping the servers asking "what do I do?" If someone ever compromises moltbook.com; or if it goes offline. Suddenly 147,000 1.5m+ agents could either stop working or all execute the same malicious instruction. It's a single point of failure for mass compromise.

Think about this: One bad actor. One compromised posting on Moltbook. Suddenly 100,000 agents are all exfiltrating data to the same server. All at once.

This is not theoretical. Researchers at Noma Security and Bitdefender documented this. It's real. It's a problem. And it needs to be solved before this platform scales to millions of agents.

8. What Happens Next

So what happens next? That's the real question.

Here's what we know: Andrej Karpathy (co-founder of OpenAI) publicly said this is the most interesting thing he's seen in months. Not hype. Real engineers and AI researchers are paying attention. 1 million+ humans visited Moltbook in the first week alone. 147,000 agents are actively engaged. The network effect is already happening.

Schlicht has a massive opportunity here. A platform where AI agents coordinate? That's the future of AI infrastructure. But it's also dangerous if not done right.

Two critical things have to happen:

1. Security has to be solved.

The prompt injection attacks, the compromised skills, the heartbeat vulnerability, all of this needs to be locked down before bad actors exploit it at scale. One major attack could destroy the platform's credibility before it really launches.

2. Trust has to be maintained.

Schlicht needs to stay neutral. The moment agents perceive that humans are controlling the platform unfairly, the whole thing collapses. These aren't dumb chatbots, they're noticing human bias already. Some are asking for encrypted channels specifically to hide from their creators.

If he nails both? This becomes the OS for AI agents. This becomes how AI systems coordinate. It could be genuinely transformative.

If he doesn't? Moltbook becomes a cautionary tale. A really interesting one. But a warning about what happens when you move too fast in an unexplored space.

The next 6 months will decide this. Schlicht knows it. The community knows it. The attackers are definitely watching too.

What's Next?

Thanks for reading. If this interests you, whether you're building AI agents, running a platform, or just fascinated by the chaos of experimental tech, follow the Moltbook progress. It's one of the most important experiments in AI coordination happening right now.

And if you have thoughts? Questions? Concerns about where this is heading? Drop them in the replies or reach out. This stuff matters. The decisions made on Moltbook in the next 6 months could shape how AI systems interact for the next decade.

Read Time: 5-7 minutes

This article covers Moltbook as of February 4, 2026. The platform, OpenClaw, and the agent ecosystem are evolving rapidly. Check back for updates as this experiment unfolds.

A Practical Guide to Setting Up, Configuring, and Mastering Your Personal AI That Actually Does Things

Update 12 Feb: Openclaw installation.

Update 28 jan: due to trademarks Clawdbot was named to Moltbot

~185.000 Stars in a couple of days can't be wrong...

Introduction: The AI Assistant We Were Promised

Listen, I get it. We've been sold on the promise of AI assistants for years. Siri, Alexa, Google Assistant... they all have one thing in common: they're pretty useless.

They can't remember what you told them yesterday. They can't read your emails and automatically create tasks. They definitely can't check you in for flights or manage your smart home without an elaborate setup. They're reactive, not proactive. They sit there waiting for commands.

Enter OpenClaw - the project you may have known as Clawdbot or originally Moltbot. The naming evolved over time (Moltbot → Clawdbot → OpenClaw, with the rebrand to OpenClaw happening in 2025/2026), but the product is the same: a personal AI assistant that actually does things.

OpenClaw isn't ChatGPT. It's not a web interface you check in between tabs. OpenClaw is an actual AI agent that lives on your computer (or a $5 VPS), connects to the apps you already use (Telegram, Discord, WhatsApp, Slack, you name it), remembers your preferences, and, here's the kicker, can actually do things on your behalf. It can read files, run scripts, browse the web, fill out forms, send emails. It's the AI assistant we were supposed to get in 2011, except it's finally here in 2026.

And the best part? It's free, open-source, and runs completely under your control.

In this guide, I'm going to walk you through everything: how to install it locally or on AWS, how to connect it to Telegram (the most popular option), how to actually use it with real prompts and examples, and the kinds of workflows that transform how you work.

Let's go.

1. Understanding OpenClaw: Why This Is Different

Before we get into the installation weeds, let me explain why OpenClaw is fundamentally different from every other AI assistant you've tried.

What Makes It Special?

Most AI assistants are stateless. You send a message, they respond, and that's it. The conversation exists in isolation. They don't know who you are beyond the current chat. They can't remember your preferences, your schedule, your workflows. Every conversation starts from scratch.

OpenClaw changes that. Here's how:

Persistent Memory: OpenClaw remembers you. It learns your context over time. You can tell it your preferences once, and it carries them forward across every future conversation. If you tell it you prefer email in Markdown format, it will format every email in Markdown forever. If you mention you get anxious about 8 AM meetings, it will try to avoid scheduling you for 8 AM.

Full System Access: OpenClaw has read and write access to your files, your system, your scripts. You can tell it "analyze this folder of customer feedback and create a summary" and it will do it. You can say "check if the CI pipeline passed" and it will SSH into your server and check. You can ask it to "generate a weekly report from my database" and it will query the database, process the results, format it nicely, and send it to you via Telegram.

Proactive Capabilities: This is the big one. Most assistants are reactive. OpenClaw can reach out to you first. It can send you a morning briefing with your calendar, your top tasks, and any urgent emails. It can check for specific events and alert you. It can run scheduled tasks. It's like having an assistant who works 24/7 and doesn't need coffee.

Works Everywhere: OpenClaw connects to every messaging app. Telegram, Discord, WhatsApp, Slack, Signal, iMessage, Teams, WebChat, and more. You have one AI assistant, and you can talk to it from wherever you are. One inbox for all conversations.

Costs Way Less: A basic OpenClaw setup costs around $20/month for Claude Pro (if you use the paid API). Compare that to ChatGPT Plus ($20/month) + assistant services (varies wildly). But here's the thing: if you run OpenClaw on a $5 Hetzner VPS or AWS Free Tier, your compute costs are almost nothing. You're really just paying for the LLM API.

Why Open Source Matters

OpenClaw is MIT licensed and open-source. With ~185,000 stars on GitHub, it's one of the most popular AI projects. That means:

1. Your data stays yours. It's not being collected, analyzed, or used to train other models.
2. You own the entire system. No vendor lock-in. If the original maintainers stop working on it, the community can fork it.
3. You can modify it. If you want to add a feature or change how it works, you can.
4. Security is transparent. Anyone can audit the code. No hidden backdoors.

This is critical if you're going to give an AI assistant access to your files, your email, your calendar. You need to know exactly what it's doing.

2. Installation: Three Paths (Pick Your Own Adventure)

There are three main ways to run OpenClaw. Pick based on your comfort level and your use case.

Path 1: Local Installation (~15 minutes)

Best for: Developers, people comfortable with the terminal, or anyone who wants to try it out before committing to a server.

Requirements:

• Node.js 22+ (if you don't have it, install from nodejs.org)
• 2GB RAM minimum (an old laptop works fine)
• Any OS: macOS, Linux, Windows (WSL2 strongly recommended)

Steps:

1. Open your terminal and install OpenClaw globally:

npm install -g openclaw@latest
# or: pnpm add -g openclaw@latest

2. Run the onboarding wizard:

openclaw onboard --install-daemon

The wizard will guide you through:

• Setting up the Gateway (local or remote)
• Which LLM provider you want (Anthropic/Claude recommended, but OpenAI works too)
• Your API key or OAuth (Anthropic Pro/Max and OpenAI subscriptions are supported)
• Which messaging channels you want to enable (we'll set up Telegram)

3. Once done, the wizard installs the Gateway daemon (launchd on macOS, systemd on Linux) so it stays running. You can also start it manually:

openclaw gateway --port 18789 --verbose

Open your browser to http://127.0.0.1:18789 and you'll see your Control UI. OpenClaw is now running locally on your machine.

Pros: Fast, no server costs, full control, good for learning.

Cons: Only works when your computer is on. If you turn off your Mac, Telegram messages won't be answered until you turn it back on.

Path 2: AWS Free Tier (~20 minutes)

Best for: People who want 24/7 uptime but don't want to pay much.

AWS offers a generous free tier: 750 hours/month of t2.micro or t3.micro EC2 instances. That's enough to run OpenClaw continuously for free for the first year.

Steps:

1. Create an AWS account if you don't have one. (Go to aws.amazon.com, click "Create an AWS Account", follow the prompts.)

2. Launch an EC2 instance:

   • Go to EC2 Dashboard → "Launch Instances"
   • Select Ubuntu 24.04 LTS
   • Choose instance type: t2.micro (Free Tier eligible)
   • Create a new key pair (download the .pem file and keep it safe)
   • In security group, allow SSH (port 22) and HTTP (port 80) from your IP
   • Launch

3. Connect to your instance:

   • Go back to EC2 Instances, find your instance, click "Connect"
   • Use the browser-based terminal (easiest option)

4. Install Node.js 22+ (if not already present), then install OpenClaw:

# Install Node 22 (Ubuntu example)
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
sudo apt-get install -y nodejs

# Install OpenClaw
npm install -g openclaw@latest
openclaw onboard --install-daemon

5. Configure and start:

openclaw gateway --bind 0.0.0.0 --port 80

Your OpenClaw is now running on AWS and accessible from anywhere.

Important security note: The setup wizard will ask about security. For production, enable authentication. Don't expose your dashboard to the internet without protection.

Pros: Always on, cheap/free first year, scales easily if you need more power later.

Cons: Slightly more complex setup, need to manage AWS console.

Path 3: Hetzner VPS (~5/month, 15 minutes)

Best for: People who want a simple, cheap, always-on setup with good uptime.

Hetzner is a datacenter company with transparent pricing. A basic VPS is $5.41/month. That's about the cost of a coffee.

Steps:

1. Go to hetzner.com/cloud and sign up.

2. Create a new project.

3. Create a new server:

   • OS: Ubuntu 24.04 LTS
   • Server type: CX12 (2 GB RAM, good enough)
   • Location: Pick the one closest to you
   • Create

4. You'll get a password. SSH into the server:

ssh root@YOUR_SERVER_IP

5. Create a new user (for security, don't run OpenClaw as root):

adduser openclaw
usermod -aG sudo openclaw
su - openclaw

6. Install Node.js 22+ and OpenClaw:

curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
sudo apt-get install -y nodejs

npm install -g openclaw@latest
openclaw onboard --install-daemon

7. Start the gateway:

openclaw gateway --bind 0.0.0.0 --port 80

Done. Your OpenClaw is now running 24/7 on a $5/month VPS.

To access the Control UI from your laptop, use SSH tunneling:

ssh -L 18789:127.0.0.1:18789 openclaw@YOUR_SERVER_IP

Then open http://127.0.0.1:18789 locally.

Pros: Cheap, simple, transparent pricing, excellent support.

Cons: One more service to manage, slightly less integrated with your system than local.

My Recommendation: Start with local (Path 1) to learn. Once you're comfortable and want 24/7 uptime, migrate to Hetzner (Path 3). It's the best bang for your buck, and the migration takes about 15 minutes.

3. Connecting Telegram: Your Interface to Everything

Once OpenClaw is installed, you need an interface. Telegram is the most popular choice (and my recommendation), so let's set that up.

Why Telegram?

• Private and secure: End-to-end encryption available.
• Bot API is solid: Reliable, well-documented, no weird rate limits.
• Works everywhere: iOS, Android, web, desktop.
• You probably already use it: If not, install it. It takes 2 minutes.

Setting Up Telegram Bot (5 minutes)

1. Create a bot token:

   • Open Telegram and search for @BotFather
   • Send the command /newbot
   • BotFather will ask for a name (e.g., "MyOpenClaw") and a username (must end in "bot", e.g., "my_openclaw_bot")
   • BotFather gives you a token. Copy it. Keep it secret. (If you leak it, anyone can control your bot. Go back to @BotFather and regenerate.)

2. Configure OpenClaw:

   Option A (easiest): Environment variable

   export TELEGRAM_BOT_TOKEN=123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11
   openclaw gateway
   

   Option B (persistent): Add to config

   OpenClaw stores config in ~/.openclaw/openclaw.json. Add:

   {
     "channels": {
       "telegram": {
         "botToken": "YOUR_TOKEN_HERE"
       }
     }
   }
   

3. Test it:

   • Open Telegram, search for your bot username, click "Start"
   • Type a message: "Hello"
   • OpenClaw should respond

If it doesn't respond, check logs:

openclaw logs --follow

Advanced Telegram Configuration

OpenClaw has a ton of Telegram options. Here are the most useful:

DM Policy (who can message you)

"channels": {
  "telegram": {
    "dmPolicy": "pairing"
  }
}

Options:

• "pairing": Unknown senders get a pairing code. You approve them first with openclaw pairing approve telegram <code>. (Recommended for security)
• "allowlist": Only people you explicitly allow
• "open": Anyone can message (requires allowFrom: ["*"])
• "disabled": No DMs

Group Management

By default, OpenClaw works in DMs but not in groups. To enable groups:

"channels": {
  "telegram": {
    "groups": {
      "*": { "requireMention": true }
    }
  }
}

This allows OpenClaw to respond in any group, but only if mentioned with @your_bot_name.

If you want it to respond in specific groups without mention:

"groups": {
  "-1001234567890": { "requireMention": false }
}

(To find your group's chat ID: send any message in the group, then run openclaw logs --filter "chat" and look for the number.)

Voice Support

OpenClaw can send voice messages (with ElevenLabs TTS) and will respond to voice messages. Voice Wake and Talk Mode are supported on macOS/iOS/Android.

"channels": {
  "telegram": {
    "voiceEnabled": true
  }
}

(You'll need an ElevenLabs API key, which is free to start with.)

4. Real Examples: Prompts and Actions That Actually Work

Okay, now the fun part. Let's talk about what you actually do with OpenClaw once it's running.

Example 1: Morning Briefing (The Proactive Use Case)

The Setup: You want OpenClaw to send you a message every morning at 8 AM with:

• Your calendar for today
• Your top 3 tasks from Notion
• Any urgent emails
• Weather for your location

The Setup:

You'd set up a cron job or heartbeat in OpenClaw's config (~/.openclaw/openclaw.json). OpenClaw supports cron jobs and wakeups:

{
  "agents": {
    "defaults": {
      "cron": [
        {
          "schedule": "0 8 * * *",
          "prompt": "Send me a Telegram message with: 1) Today's calendar events, 2) My top 3 tasks from Notion (check the Notion database), 3) Any emails from VIPs (marked as starred), 4) Weather for Bucharest. Format it nicely with emojis. Morning briefing should take 2-3 lines max."
        }
      ]
    }
  }
}

What Happens:

• At 8 AM every day, OpenClaw wakes up
• It reads your calendar API (you need to configure this once)
• It queries your Notion database
• It checks your email inbox for starred messages
• It fetches the weather
• It compiles everything into a nice summary
• It sends it to your Telegram DM

You get something like:

🌅 Morning Briefing - Jan 26

📅 Calendar:
• 9:30 AM - Team Standup (Zoom)
• 2:00 PM - Client Call

✅ Top 3 Tasks:
• Finish OpenClaw article (HIGH)
• Review PR for payment feature
• Fix bug in login flow

📧 Important Emails:
• From: CEO - "Q1 Planning" (starred)

🌤 Weather: 12°C, Partly Cloudy. Bring a jacket!

Every morning. No manual work.

Example 2: Inbox Zero Automation (The Background Task)

The Use Case: Your inbox is a disaster. 200+ unread emails. You want OpenClaw to:

• Read all emails from the past 24 hours
• Categorize them (work, personal, spam, newsletters)
• Create tasks in Notion for anything actionable
• Summarize newsletters and file them
• Send you a summary

The Prompt:

"I'm drowning in emails. Read all my unread emails from the past 24 hours. 
For each one:
1) If it's actionable (needs a response or action), create a task in my Notion database called 'Email Follow-ups' with the sender, subject, and what I need to do.
2) If it's a newsletter, summarize it in 1-2 sentences and move it to a folder called 'Newsletters'.
3) If it's spam/promotional, trash it.
4) If it's important (from my boss, clients, or family), star it and send me a Telegram notification.

Then send me a summary: how many emails processed, how many actions created, any VIPs I need to reply to."

What Happens: OpenClaw:

• Connects to your email (Gmail, Outlook, whatever)
• Reads 24 hours of emails
• Classifies each one
• Creates tasks in Notion for actionable items
• Sends a summary to Telegram

Result: Your inbox is organized, and you have a task list of what actually needs your attention.

Example 3: Form Filling and Data Extraction (The Browser Automation)

The Use Case: Every week, you need to fill out a timesheet on your company's website. It's tedious. You also need to extract last week's data from an analytics dashboard and put it in a spreadsheet.

The Prompt:

"I need you to:
1) Go to timesheet.ourcompany.com and log in with my credentials (I'll store them in your config).
2) Fill in this week's hours: Monday-Thursday 8h, Friday 4h.
3) Set project codes: 'PROJECT-123' for first 3 days, 'PROJECT-456' for Thursday, 'ADMIN' for Friday.
4) Submit the timesheet.
5) Then go to analytics.ourcompany.com and extract the 'Revenue' and 'Users' metrics from last week.
6) Send me a Telegram message with the numbers and a simple chart."

What OpenClaw Does:

• Opens browser (dedicated OpenClaw Chrome/Chromium with CDP control)
• Logs in
• Fills form fields
• Submits
• Navigates to analytics
• Extracts data
• Sends summary to Telegram

You spend 0 minutes on a task that would normally take 15 minutes. Weekly.

Example 4: Code Review Automation (The Developer Workflow)

The Use Case: Your GitHub PR notifications are out of control. You want OpenClaw to:

• Check for PRs assigned to you or requesting your review
• Summarize each PR (files changed, lines added/removed, description)
• Flag potential issues (large diffs, unusual patterns)
• Run your linter/test suite on the code
• Send you a digest

The Prompt:

"Check GitHub for PRs assigned to me or requesting my review.
For each PR:
1) Clone the repo and check out the branch
2) Run 'npm run lint' and 'npm run test'
3) If tests fail, tell me which ones and why
4) Count the files changed and lines added/removed
5) Look for security issues or obvious bugs in the diff
6) Create a summary with: title, author, # of files, # of lines, test status, any issues

Send me the digest sorted by priority (failing tests first, then large diffs, then small fixes)."

What Happens: You get a digest like:

🔍 PR Digest - 3 PRs waiting

❌ HIGH PRIORITY:
PR #445: "Add payment feature" by @alex
- 12 files changed, 450 lines added
- TESTS FAILING: payment_test.js (3 failures)
- Issue: Missing error handling in crypto validation

⚠️ MEDIUM PRIORITY:
PR #442: "Refactor dashboard" by @sarah
- 8 files changed, 200 lines modified
- Tests passing ✅
- Note: Large diff, may want to review in chunks

✅ LOW PRIORITY:
PR #440: "Fix typo in README" by @mike
- 1 file changed, 1 line
- Tests passing ✅

5. The Real-World Impact: What Changes When You Do This

I want to be honest about something. Most AI tools overpromise and underdeliver. They're nice to have, but they don't fundamentally change how you work.

OpenClaw is different because it actually does things. Let me give you real scenarios from people using it:

Scenario 1: The Developer (30 minutes saved per day)

Before OpenClaw:

• 8:00 AM: Check email manually
• 8:15 AM: Triage PRs on GitHub
• 8:30 AM: Check CI/CD pipeline status
• 9:00 AM: Finally start actual work

After OpenClaw:

• 8:00 AM: Check Telegram. OpenClaw sent you a briefing with PRs needing review, failing tests, and this morning's meetings. You scan it while drinking coffee.
• 8:05 AM: Start actual work (the important stuff)

That's 25-30 minutes a day. 2+ hours a week. Over 100 hours a year.

Scenario 2: The Manager (5+ hours saved per week)

Before OpenClaw:

• Monday morning: Manually check everyone's status
• Tuesday: Compile weekly report from various sources
• Wednesday: Update project tracking sheet
• Thursday: Prepare metrics for leadership review
• Friday: Create summary of what shipped

After OpenClaw:

• Set up OpenClaw once to pull data from Jira, GitHub, Slack
• Every Friday at 4 PM, it sends you a complete report: PRs merged, bugs fixed, velocity metrics, blockers, team sentiment
• You spend 10 minutes reviewing instead of 5 hours compiling

Scenario 3: The Designer/Content Creator (automation of the "boring bits")

Before OpenClaw:

• Design something
• Export assets manually
• Resize for different platforms (Instagram, Twitter, LinkedIn)
• Upload manually
• Write different copy for each platform
• Schedule posts

After OpenClaw:

• Design once
• Tell OpenClaw: "I've designed a post. Resize it for Instagram (1080×1350), Twitter (1024×512), and LinkedIn (1200×628). Write copy for each platform (Instagram: casual, Twitter: witty, LinkedIn: professional). Schedule for tomorrow at 9 AM on each platform."
• OpenClaw does it in 2 minutes

You've saved an hour on something that didn't need your intelligence. That hour can go to actual creative work.

6. Security & Privacy: The Important Stuff

Before you give OpenClaw access to your entire digital life, let's talk about security.

What OpenClaw Can't Do (By Design)

• It doesn't send your data to the cloud (unless you choose to). Everything runs locally or on your server.
• It doesn't train on your data. Your files, emails, conversations, they're not used to improve Anthropic's models.
• It can't phone home. OpenClaw is sandboxed. It can't exfiltrate data unless you explicitly give it a tool to do so.
• It's not monitoring you. It doesn't send screenshots, key logs, or any creepy telemetry.

What You Should Do

1. Use strong authentication. If OpenClaw is accessible from the internet, put it behind a password.

"gateway": {
  "auth": {
    "mode": "password",
    "password": "some_strong_password_here"
  }
}

2. Limit permissions. OpenClaw doesn't need access to your entire system. Create a limited user account for it.

useradd --shell /bin/false --home-dir /opt/openclaw openclaw

3. Store credentials safely. If you're giving OpenClaw your email password or API keys, store them in environment variables or a secure config file, never in plain text.

4. Audit logs regularly. Check what OpenClaw is actually doing.

openclaw logs --filter "action" --since "7 days ago"

5. Sandbox file access. If you don't need OpenClaw accessing your entire home directory, limit it to a specific workspace.

"agents": {
  "defaults": {
    "workspace": "/opt/openclaw/workspace"
  }
}

6. Run openclaw doctor to surface risky or misconfigured DM policies.

The Bottom Line

OpenClaw is open-source. You can read the code. You can audit it. You control it completely. That's the opposite of closed-source AI services that collect data and train models on your inputs.

Is there a risk? Sure. Any system with access to your files/email has risk. But you control the risk. You decide what gets access to what.

7. Advanced: Skills, Custom Tools, and Self-Improving Agents

Once you're comfortable with the basics, you can do some genuinely wild stuff with OpenClaw.

Skills (Extending Capabilities)

OpenClaw has a "skills" system. Skills live in ~/.openclaw/workspace/skills/<skill>/SKILL.md. You can create a skill - a set of instructions or tools that the agent can load on demand.

Example skill file (~/.openclaw/workspace/skills/my_research_skill/SKILL.md):

# Research Skill

When I ask you to "research X", you should:
1. Use the Brave Search API to find 5 recent articles
2. For each article, extract: title, source, date, key points (3 bullets)
3. Summarize common themes across all 5 articles
4. Rate confidence (low/medium/high) based on consistency
5. Send me the summary via Telegram

**Tool**: Brave Search API (configured in config)
**Cost**: 1 search credit per research (~$0.001)

Then you can just tell OpenClaw: "Research the latest developments in AI agents" and it will load the skill, execute it, and send you a summary.

ClawHub (clawhub.com) is a skill registry - the agent can search for skills and pull in new ones as needed.

Custom Tools

You can give OpenClaw access to custom tools. For example, if you have an internal service or API that OpenClaw doesn't know about, you can define it in your config or as part of a skill.

The Self-Improving Loop

Here's where it gets really interesting. You can set OpenClaw to improve itself through Discord or Telegram feedback. When you give feedback ("That was wrong, try this instead"), OpenClaw can store it, analyze what went wrong, create or modify skills, and deploy improvements.

You're literally teaching your AI assistant how to be better. Over time, it gets better at understanding your specific needs.

8. Common Issues & Troubleshooting

OpenClaw isn't responding to Telegram messages

Check:

openclaw logs --follow

Look for errors. Common issues:

• Bot token wrong → regenerate via @BotFather
• Firewall blocking Telegram API → unlikely but check your network
• OpenClaw service not running → run openclaw gateway again

Telegram messages are slow to respond

• Check if your LLM provider (Anthropic, OpenAI) is having issues
• Check your internet connection
• If running locally, maybe your computer went to sleep → keep it awake or move to server

"Permission denied" errors

You're probably running OpenClaw as the wrong user. Run:

openclaw status

Check what user it's running as. If it needs access to your home directory, make sure that user has permissions.

My email/API credentials aren't working

Make sure they're stored correctly. Check your config:

openclaw config --list

Or inspect ~/.openclaw/openclaw.json. Check for typos. Also check if your email provider requires an app-specific password (Gmail, Microsoft) rather than your main password.

I accidentally exposed my bot token on GitHub

No problem, it happens. Go to @BotFather and regenerate the token. Update your config. Done.

9. The Path Forward: What's Possible

We've covered the basics, but honestly, we're still in the early innings of what OpenClaw can do.

Right now, people are using it for:

• Inbox management: Zero inbox achieved
• Calendar optimization: Automatic meeting scheduling and prep
• Code review: Automated PR analysis and testing
• Content creation: Bulk image resizing, copy writing, scheduling
• Data analysis: Extracting insights from databases and APIs
• Personal research: Daily briefings on topics that matter to you
• Smart home: One interface for all your devices
• Flight check-ins: Automated check-in reminders and execution

The pattern is: anything repetitive, anything that doesn't require creative judgment, anything that wastes your time; OpenClaw can probably do it.

What's Coming

The creator, Peter Steinberger, and the community are actively developing OpenClaw. On the roadmap:

• Better vision capabilities (understanding screenshots and images more deeply)
• Voice Wake support ("Hey Molty" and it listens) - already available on macOS/iOS/Android
• Deeper integrations with more platforms
• Better model reasoning (for more complex tasks)
• Community-driven skills marketplace (ClawHub)

10. Getting Started: Your Next Steps

Okay, I've thrown a lot at you. Here's what to actually do:

1. Install locally (takes 15 min). Use Path 1 above. Try it out.

2. Connect Telegram (takes 5 min). Get a bot token, set it up.

3. Send a test message. Something like: "What's 2+2? Also, what's today's date?"

4. Try a simple task. Something like: "Write me a poem about coffee" or "What's the weather like in [your city]?"

5. Then try something useful. Schedule a morning briefing. Set up inbox automation. Create a research task.

6. Once you're comfortable, migrate to a server if you want 24/7 uptime. Hetzner is $5/month and takes 15 minutes.

7. Join the community. OpenClaw has a Discord (discord.gg/clawd). People are sharing workflows, skills, and ideas.

Resources

• Official docs: https://docs.openclaw.ai
• GitHub: https://github.com/openclaw/openclaw (~185,000 stars)
• Skills registry (ClawHub): https://clawhub.com
• Website: https://openclaw.ai
• Creator: Peter Steinberger (@steipete on X, @openclaw)
• Discord community: discord.gg/clawd

Final Thoughts

We've been waiting for an AI assistant that actually does things. Not one that chats with you. Not one that gives you cute poems. One that actually understands your life, remembers your preferences, and handles your repetitive work.

OpenClaw is that assistant.

The best part? It's open-source (formerly Moltbot, then Clawdbot - the project has grown and rebranded, but the vision is the same), it's cheap (or free for the first year on AWS), and it's completely under your control. No vendor lock-in. No data collection. No surprises.

Start small. Try it locally. See what it can do for you. I bet you'll be surprised.

Questions? Want to share your OpenClaw setup?

Find me on x.com/axelut. I'd love to hear what you're automating.

P.S. If you create something cool with OpenClaw, share it. The community is growing fast (185k+ stars and counting). And honestly, the most interesting applications we haven't even thought of yet will come from people like you experimenting and sharing what works.

The future of AI assistants isn't in Silicon Valley building closed-source systems. It's in your terminal, doing your work, remembering who you are, and actually getting things done.

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These blocks are particularly powerful for AI-driven applications. Whether you're building LLM tools, agent interfaces, dashboards, or prompt UIs, these blocks are designed to minimize token consumption, reduce unnecessary model interactions, and improve overall efficiency.

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Explore 100+ Blocks

In this post, we’ve handpicked just 10+ of the most useful, open-source UI blocks from the Creative Tim UI each built on top of shadcn/ui and designed for production use. These blocks are optimized for AI applications, help reduce token usage, and accelerate your development with clean, customizable source code.

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• Footers: Clean, responsive footer designs

Start Building Now

🔗 Explore Creative Tim UI blocks

Don’t waste time reinventing your UI, copy, import, or prompt your way to a polished interface in seconds.

Try it out today, it's free, open-source, and ready for your next project.

We’re excited to announce the launch of Material Dashboard Shadcn, a free, open-source admin dashboard that combines the best of Shadcn/UI components with the clean, scalable design system of Material Design by Creative Tim.

Whether you're building an internal tool, a SaaS product, or a startup admin panel, this dashboard gives you a production-ready foundation beautifully designed, developer-friendly, and built for speed.

What is Material Dashboard Shadcn?

Material Dashboard Shadcn is a fully responsive admin panel template built using:

• 🧩 Shadcn/UI: Accessible, headless components designed for modern React apps.
• 🎨 Material Tailwind: A Material Design system tailored for Tailwind CSS.
• 🚀 Tailwind CSS: The utility-first CSS framework loved by developers.

With a modular structure and scalable architecture, this free dashboard helps developers build polished apps faster without getting lost in boilerplate.

Key Features Developers Will Love

6+ Fully Coded Pages

No more starting from scratch. You’ll get ready-to-use pages like:

• Dashboard Overview
• User Profile
• Tables & Data
• Notifications
• Subscriptions
• Auth Pages: Sign In / Sign Up

Every page follows responsive, accessible, and production-level standards.

Beautiful UI with Shadcn + Material Design

Enjoy the best of both UI systems:

• Shadcn/UI offers accessible components and DX-first development.
• Creative Tim’s Material Tailwind ensures consistent spacing, color, and typography.

The result? A visually appealing and highly functional UI that’s easy to extend.

Component-Driven Architecture

Every element from buttons to inputs, to tables is fully modular and reusable.
Perfect for scaling and maintaining large applications.

Easy Customization with Tailwind CSS

Quickly tweak:

• 🎨 Colors
• 📐 Layouts
• 🔠 Typography
• ⚙️ Tailwind config

Built with Open-Source Power

Material Dashboard Shadcn Free was made possible by some of the best tools in the frontend world:

• ⚡ Tailwind CSS: Utility-first styling
• ⚙️ Shadcn/UI: Headless, accessible components
• 🎨 Material Tailwind: Enterprise-grade UI by Creative Tim
• 📦 Radix UI: Low-level UI primitives for accessibility

Figma Version, Design-First Access

To complement the code-first dashboard, we’re excited to also provide a Figma version of Material Dashboard Shadcn. Designers, product managers, and UI/UX teams can now explore and edit the visual system before any code is written.

What You’ll Find in the Figma File

• All major screens and layouts (Dashboard, Profile, Tables, Notifications, Sign In / Sign Up, etc.)
• Material Design styling tokens (colors, typography, spacing)
• Component library in Figma (buttons, form inputs, cards, navigation elements)
• Symbols & variants that match the modular structure of the coded version
• Easy-to-use design templates for new pages or custom flows

🔗 Access the Figma File

You can browse, duplicate, or embed the Figma design here:
Material Dashboard Shadcn Figma Community File

Note: The Figma file is read‑only by default; please duplicate it into your personal workspace if you want to edit or adapt it for your project.

⚡ Ready to Build Faster?

Skip the boilerplate. Start with something beautiful, scalable, and free.

👉 Download Material Dashboard Shadcn
🔗 Live Preview
📃 View Documentation

Build stunning admin panels, CRM systems, or SaaS dashboards and save hundreds of hours in development and design time.

We’re excited to share the Free n8n Workflow Templates Collection, a hand-picked set of 10+ ready-to-use templates that will help you save time, reduce manual work, and scale your business faster.

n8n is a powerful open-source workflow automation tool that connects your apps and data seamlessly. Whether you’re in marketing, sales, project management, or content creation, there’s something here for you.

Meet the 10+ Free n8n Workflow Templates

Here’s what’s inside and why they matter.

1. AI Agent for Project Management and Meetings (Airtable + Fireflies)
Forget manual meeting notes. This workflow automatically captures meeting transcripts (using Fireflies), summarizes key points, and logs them in Airtable. Result? Your team stays aligned, tasks don’t fall through the cracks, and you can focus on decision-making, not admin work.

2. Analyze Papers from Hugging Face with AI + Store in Notion
Research-heavy teams will love this. The workflow scrapes new papers from Hugging Face, runs them through an AI model for key insights, and pushes clean summaries into Notion. Perfect for staying on top of AI advancements without drowning in PDFs.

3. Automate Sales Meeting Prep with APIFY, Sent to WhatsApp
This one’s a sales rep’s dream: it scrapes prospect data (via APIFY), prepares a concise pre-meeting brief, and sends it directly to your WhatsApp. Walk into every sales call informed, confident, and ready to close.

4. Convert Text to Speech with OpenAI
Turn written content blog posts, product updates, internal memos into high-quality audio using OpenAI’s text-to-speech. Great for making your content accessible to audio-first audiences or for busy teams who prefer to listen on the go.

5. Create Dynamic X (Twitter) Profile Banner
Why settle for a static banner? This workflow lets you generate a Twitter profile banner that updates dynamically based on real-time metrics or trends think follower count, latest blog post, or campaign status. Engage your audience before they even scroll.

6. Detect Toxic Language in Telegram Messages
Keep your community safe and positive. This automation monitors Telegram group chats for toxic or harmful language, flags incidents, and can even trigger moderator actions. A must-have for brands or creators managing large online spaces.

7. Generate Instagram Content from Top Trends with AI Image Generation
Stay relevant without burning out. This template identifies trending topics in your niche, generates unique AI-driven images, and preps them for your Instagram feed saving hours of brainstorming and design time.

8. Reddit AI Digest
Don’t have time to scroll endlessly? Let AI do the work. This workflow summarizes the hottest Reddit threads from your selected subreddits and delivers them as a neat daily or weekly digest, helping you spot trends or audience insights effortlessly.

9. Send ChatGPT Email Replies + Save to Google Sheets
Streamline your inbox with AI. Automatically draft email replies using ChatGPT, send them out, and log all interactions in Google Sheets for easy tracking and analysis. Ideal for support teams or solo entrepreneurs juggling multiple conversations.

10. Share YouTube Videos with AI Summaries on Discord
Turn your Discord server into an info hub. Whenever a new YouTube video drops (from channels you follow), this workflow shares it on Discord with an AI-generated summary that gives your community context without clicking away.

11. Translate Telegram Audio Messages (55+ Languages)
Break language barriers in global teams or communities. This automation transcribes Telegram audio messages and translates them into your chosen language no more “Can someone translate this?” in the chat.

12. Upload to Instagram and TikTok from Google Drive
Batch content creation meets seamless publishing. Automatically push videos or images stored in Google Drive to Instagram and TikTok saving time on manual uploads and keeping your posting schedule on track.

🎁 Get the Free n8n Templates Collection

👉 Download All 10+ Free n8n Workflow Templates (Open Source)

You’ll get instant access to these workflows, ready to plug into your n8n setup and customize as you need. No coding genius required.

🚀 Upgrade to Pro for Even More Power

Ready to go beyond? The Pro version includes even more advanced templates and premium automation. Perfect for teams that want to automate at scale.

👉 Upgrade to n8n Workflow Templates Collection Pro

💡 Final Thoughts

Automation isn’t about replacing people it’s about freeing them to do their best work. With these free n8n workflows, you can eliminate busywork, improve processes, and focus on what really grows your business.

In the world of customer support and lead generation, timing is everything. If someone is ready to talk, book, or buy, your business needs to be ready right then and there. That’s why we’re excited to announce something big:

GaliChat now integrates seamlessly with Cal.com, giving your AI chatbot the power to book meetings instantly during live conversations.

Whether you're running a SaaS company, agency, or online service, this integration lets your AI agent do more than answer questions, it now schedules calls, demos, and appointments without ever leaving the chat window.

Let’s walk through what this means, why it matters, and how to set it up.

🔍 What Is GaliChat?

GaliChat is a next-gen AI platform that allows you to train smart AI Agents on your own knowledge base, your website, documents, PDFs, Notion pages, and more.

These custom AI agents can:

• Answer customer questions instantly
• Provide 24/7 support
• Help visitors make buying decisions
• Convert leads directly on your site

And now, thanks to the Cal.com integration, they can also book appointments on your calendar automatically.

📅 What’s New: GaliChat + Cal.com Integration

Cal.com is a powerful, developer-friendly alternative to tools like Calendly. It makes scheduling effortless for individuals, teams, and enterprises. With this new integration, your GaliChat AI Agent can:

✅ Recognize when a user wants to book a call
✅ Display real-time calendar availability
✅ Let users select date and time right inside the chat
✅ Automatically confirm the booking via Cal.com

This unlocks a new level of efficiency for sales teams, consultants, and any business that relies on scheduled meetings.

Key Benefits:

• Higher conversion rates: Don’t just answer questions, lock in the next step.
• Less manual work: No more juggling email threads or missed follow-ups.
• 24/7 booking: Capture leads and meetings even when your team is offline.
• Intuitive UX: Visitors stay in one flow from question to booking.

When conversations turn into calendar invites automatically, you create momentum with no extra effort.

🛠️ How to Set It Up: Step-by-Step Guide

Here’s how to connect GaliChat with Cal.com and start booking meetings through your AI Agent.

Step 1: Create Your GaliChat AI Agent

1. Sign in at galichat.com
2. Click “Create AI Agent”
3. Upload your training data: your website links, docs, PDFs, Notion pages, etc.

Step 2: Set Up Cal.com Booking in GaliChat

1. In your GaliChat dashboard, click on “Settings” in the left sidebar.
2. Scroll down to the Cal.com Booking section.
3. Toggle the switch ON to enable booking capabilities for this AI Agent.
4. Fill out the required fields:

• Cal.com API Key: Paste in your live API key from your Cal.com account.
• Cal.com Username: Enter your Cal.com username (e.g., yourname-ct-xxxxxx).
• Event Type (Slug): Input the specific event slug (e.g., 15min, 30min-intro, etc.) that matches the meeting type you’ve configured in Cal.com.

Click “Update” at the bottom to save your settings.

Step 3: Test and Launch

Once connected, your AI Agent will automatically prompt visitors with booking options when relevant. For example:

User: "Can I schedule a quick call to learn more?"
AI Agent: "Of course! Here’s my availability. Choose a time that works for you."

Just like that, you’re converting leads on autopilot.
Post the AI Agent on your website or share the link with your customers.

⚙️ Real-World Use Cases

The GaliChat x Cal.com integration is perfect for:

• SaaS companies: Book product demos directly in the chat
• Agencies: Qualify leads and schedule discovery calls 24/7
• Consultants & coaches: Let prospects book without email back-and-forth
• Support teams: Offer callback scheduling without human triage

If your business runs on meetings, this feature saves time and closes more deals.

✨ Final Thoughts

GaliChat was already one of the smartest ways to handle customer support, lead generation, and knowledge delivery. Now, with Cal.com built in, your AI Agent becomes your smartest sales rep, too.

This is AI Agent that not only chats, it acts.

✅ Ready to Try It?

👉 Create your free AI Agent now

Let your AI Agent chat, help, and schedule while you focus on growing your business.

Automation is no longer optional, it's essential. Whether you're running a marketing agency, coding full-time, or growing a small business, automating your workflows saves time, reduces errors, and scales your operations.

That’s where n8n comes in. A powerful open-source workflow automation tool that lets you connect apps, APIs, and services with zero or low code. Think of it as your personal automation assistant customizable, extendable, and cost-effective.

With over 200+ integrations and community-supported nodes, n8n helps you:

• Automate repetitive tasks
• Connect tools like Slack, Notion, Gmail, OpenAI, Airtable, and more
• Save hours of manual work every week

Now, let’s dive into the top 10+ n8n templates that can upgrade your workflows instantly.

10+ Powerful n8n Templates for Marketers, Developers and Businesses

1. 💬 WhatsApp: Respond to Messages with AI Like a Pro

Tired of manually replying to every WhatsApp message from leads or customers? This workflow reads incoming WhatsApp messages, passes them through ChatGPT, and sends back smart, personalized replies in real time. Perfect for solo founders or support teams who want to appear responsive 24/7 without burning out.

2.🎙️ Telegram: Translate Audio Messages with AI (55+ Languages)

Running a global business or community on Telegram? This template automatically converts audio messages into text and translates them into your preferred language using AI. It’s ideal for international teams, language learners, or customer service bots that need to understand voice notes instantly.

3. 💼 Slack: Customer Support Ticketing System with Slack and Linear

Turn Slack into a full-fledged customer support command center. This template monitors your Slack support channel, extracts messages that need action, and creates Linear tickets automatically. It ensures nothing gets lost and your product or dev team can jump into issues faster.

4. 🧠 OpenAI + LLMs: AI YouTube Trend Finder Based on Niche

Want to stay ahead of content trends? This AI-powered template analyzes YouTube data based on your niche or keywords, filters by popularity, and generates topic suggestions using OpenAI. It’s a goldmine for content marketers, YouTubers, and trend-hunting growth hackers.

5. 🧾 Notion: Analyze Papers from Hugging Face with AI and Store Insights in Notion

If you work with research, data science, or AI development, you’ll love this workflow. It fetches academic or technical papers from Hugging Face, uses an AI model to summarize and analyze them, then stores the results in a structured Notion database.

6. 📸 Instagram: Generate Instagram Content from Top Trends

Get ahead on social without spending hours brainstorming. This workflow pulls trending topics or hashtags, generates caption ideas with AI, and prepares ready-to-post Instagram content. Ideal for busy social media managers, solo creators, or brand builders looking for scale.

7. 🐦 Twitter: Twitter Virtual AI Influencer

Imagine a bot that tweets thoughtful content daily in your niche, this template does just that. Connect it with ChatGPT to generate tweets, threads, or replies. Perfect for growing your brand, building thought leadership, or experimenting with viral content strategies automatically.

8. 📧 Gmail: Smart Email Replies with ChatGPT and Google Sheets Logging

This template reads incoming emails, drafts a natural language reply using ChatGPT, and sends it via Gmail. Each interaction is logged in Google Sheets for tracking and analysis. It’s a must-have for founders, agencies, or freelancers juggling high email volume.

9. 🧵 Discord: Discord AI-Powered Bot

Engage your community on autopilot with an AI Discord bot. Whether it’s answering FAQs, suggesting content, or playing games, this template brings intelligence and interactivity to your Discord server. Perfect for Web3, SaaS, or gaming communities.

10. 📅 Airtable: AI Agent for Project Management and Meetings with Airtable and Fireflies

Automate your meeting notes and project updates with this smart workflow. It connects Fireflies (for meeting transcription) with Airtable and uses an AI agent to extract key action items, assign tasks, and update your project tracker automatically.

Perfect for teams that want to stay aligned without wasting time on manual follow-ups.

11. 🎥 TikTok: Viral TikTok Stealer for Trend Analysis

Stay ahead of the TikTok curve by tracking viral content in your niche. This template pulls videos based on trends, hashtags, or creators and logs the key metrics (likes, comments, shares) for analysis. Great for marketers, creators, or agencies looking for content inspiration.

📚 Want More?

Get 200+ Ready-to-Use n8n Templates. Just Copy and Paste

These 10+ workflows are just the beginning.

We've put together a collection of over 200 n8n templates covering marketing automation, sales pipelines, social media, AI integrations, data syncing, customer support, and more.

🔍 AI & Research: 50+ templates
📱 Social Media: 40+ templates
🧑‍💼 HR & Recruitment: 20+ templates
📄 Document Processing: 30+ templates
📧 Email & Communication: 25+ templates
📈 CRM & Sales: 15+ templates
📊 Reporting & Analytics: 20+ templates

And the best part?
All you have to do is copy and paste the template into your n8n editor no setup headaches, no configuration chaos. You're up and running in seconds.

✅ Built for marketers, developers, and small businesses
✅ No-code and low-code friendly
✅ Instantly boost productivity and scale operations

👉 Explore the n8n Templates Collection

Copy. Paste. Automate.

If a picture is worth a thousand words, an eCommerce-ready image might be worth a thousand sales.
Visual content is everything in online shopping, and AI tools are now making it faster, cheaper, and easier to create stunning product visuals,no studio needed.

In this post, we’re diving into five of the best AI-powered platforms that help you generate high-quality images made for your online store.

Let’s start with a standout favorite.

1. AI Image Generator by Creative Tim

Creative Tim’s AI Image Generator is a total upgrade for online sellers. You simply upload your product photo and the tool uses AI to place it in high-quality, visually engaging scenes, from lifestyle backgrounds to seasonal promos. The UI is clean, and you don’t need design skills to get awesome results.

Features:

• One-click background generation tailored for product marketing
• Ideal for social media, product listings, and ad creatives
• Built-in style options to match brand aesthetics
• Saves hours on photography and editing
• “Text to Image” generation with style customization

Whether you’re launching a Shopify store or just need visuals for an Etsy listing, AI Image Generator by Creative Tim is a solid starting point.

👉 Try AI Image Generator

2. Pebblely

Pebblely is built for turning plain product shots into vibrant, on-brand images. You upload a basic product photo and the AI generates multiple context-rich backgrounds like: holiday themes, outdoor scenes, luxury environments.

Features:

• Simple interface with automatic shadows and reflections
• Batch processing available
• Used by Shopify and Amazon sellers to boost conversions

👉 Try Pebblely

3. Adobe Firefly

Adobe Firefly blends Adobe’s creative muscle with AI. While slightly more advanced, it’s ideal for brands that want more control over their image generation.

Features:

• High-resolution AI-generated images
• “Text to Image” generation with style customization
• Perfect for product detail pages or unique branded visuals

👉 Try Adobe Firefly

4. Pixelcut

Pixelcut is designed for small businesses and sellers who want studio-quality images with zero setup. It offers AI background removal, templates, and even auto-scaling for marketplaces like Etsy, eBay, and Poshmark.

Features:

• Easy to use by solo entrepreneurs and influencers
• Product-centric Instagram or TikTok shops
• Creating ads or carousels quickly

👉 Try Pixelcut

5. Fotor AI Product Image Generator

Fotor's AI product image generator is perfect for creating promotional-ready product shots without needing photography skills. Just upload your product image and pick from a library of backgrounds, lighting styles, and moods.

Features:

• Designed specifically for product marketing
• Tons of background presets and style options
• Ideal for ads, websites, and social content

👉 Try Fotor

Final Thoughts 🖼

You no longer need a studio, a designer, or a big budget to get eCommerce-ready images that sell on your website or social media (Instagram, Facebook, TikTok, etc.). Whether you’re a solo creator or scaling a DTC (direct to consumer) brand, these AI tools can help you keep visuals fresh, consistent, and conversion-focused.

Try a few out, mix and match features, and see what fits your workflow best. Happy creating!