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Get $1,000 Free Azure Credits for TechLatest Cloud Solutions (Limited-Time Offer)

TechLatest — Tue, 24 Feb 2026 12:59:26 +0000

Cloud infrastructure is often the biggest barrier when developers, startups, cybersecurity learners, and AI builders want to experiment with powerful enterprise-grade tools. High-performance GPUs, scalable compute instances, and persistent storage can quickly become expensive — especially when you are testing multiple environments such as AI model stacks, penetration testing labs, Bitcoin infrastructure, or automation platforms.

To remove this barrier and accelerate innovation, TechLatest is offering eligible users up to $1,000 in free Microsoft Azure credits to deploy ready-to-run, preconfigured cloud solutions. This initiative is designed to help developers, researchers, students, startups, and technology enthusiasts launch production-ready environments without worrying about upfront infrastructure costs.

This is a limited-time, first-come, first-served opportunity, and early applicants have the highest chances of receiving the credits.

Why This Azure Credit Program Matters

Modern workloads such as AI development, blockchain infrastructure, data analytics, and security testing require significant computing power. Even a small experimentation environment may require:

GPU-enabled virtual machines
Persistent storage and high-speed networking
Scalable container or VM infrastructure
Secure access configurations
Long-running compute environments

Setting up these components manually also requires time and deep infrastructure expertise. TechLatest solves this by providing pre-configured, production-ready cloud deployments that can be launched in minutes on Azure. With the additional $1,000 Azure credit support, users can test, build, and scale real-world solutions at almost zero initial cost.

Solutions You Can Deploy Using the Credits

The credits can be applied to multiple TechLatest solutions depending on your use case. These environments are pre-optimized for performance, security, and scalability, allowing users to start working immediately after deployment.

Some of the most popular deployments include:

DeepSeek & Llama powered All-in-One LLM development environments
GPU-accelerated AI model experimentation stacks
Kali Linux penetration testing environments (GUI and browser-based)
Ansible Tower / AWX automation infrastructure
Fully synced Bitcoin Full Node kits
Stable Diffusion environments with AUTOMATIC1111
Jupyter Python notebook environments for data science
Metabase analytics dashboards
BTCPay Server self-hosted Bitcoin payment infrastructure

These solutions eliminate weeks of manual setup and configuration, making them ideal for rapid experimentation, research, training, and production workloads.

Who Should Apply

This program is designed for a wide range of users who want to build real-world infrastructure on Azure:

AI/ML developers building model experimentation environments
Cybersecurity learners creating penetration testing labs
Blockchain developers running full nodes or payment systems
Startups testing scalable SaaS backends
Students building research projects requiring GPU compute
DevOps engineers testing automation and orchestration platforms
Data scientists running analytics pipelines

Whether you are building a prototype, launching a product, or learning advanced infrastructure skills, the credit program significantly reduces the financial barrier.

How the Credit Allocation Works

Once you submit the application, the TechLatest team reviews your request and assigns Azure credits based on eligibility and availability. Because the offer operates on a first-come, first-served basis, early submissions are strongly recommended.

After approval:

Credits are activated for your deployment.
You can launch the selected TechLatest solution directly on Azure
The environment becomes accessible within minutes.
You can begin development, testing, or production usage immediately.

Apply for the Free Azure Credits

You can submit your application using the official form:

Apply here:https://www.techlatest.net/free_azure_credits/
Make sure to select the solution you want to deploy so the credits can be mapped to the correct environment.

Request Credits for a Specific Product

If you are unsure which solution to choose or want to explore the full catalog of TechLatest cloud deployments, you can request a particular product directly through the support portal:

Browse and request products:https://techlatest.net/support/
This page lists all available preconfigured deployments and allows you to request credits for the exact infrastructure stack you need.

Why Early Application Is Recommended

Cloud credit programs typically run for a limited duration and often close once the allocation pool is exhausted. Since this initiative is targeted at enabling real-world deployments, the demand is expected to be high among AI developers, security researchers, and blockchain builders.

Submitting the form early ensures:

Higher chances of approval
Faster credit activation
Immediate infrastructure deployment
Priority access to limited allocation pools

Final Thoughts

Access to scalable infrastructure should not limit innovation. With $1,000 worth of Azure credits, developers and builders can experiment with advanced technologies such as LLM environments, GPU-accelerated AI systems, automation platforms, and blockchain infrastructure without upfront investment.

If you are planning to build, learn, test, or deploy serious workloads in the cloud, this is an excellent opportunity to start with production-grade infrastructure at virtually no cost.

Apply now, select your preferred deployment, and begin building your next project with enterprise-grade Azure resources.

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Top 10 Open-Source User Interfaces for LLMs

TechLatest — Fri, 20 Feb 2026 11:43:38 +0000

Open-source user interfaces for large language models (LLMs) are empowering developers, researchers, and businesses to interact with AI models locally and securely. These UIs offer everything from simple chat interfaces to advanced agent frameworks, supporting a wide range of models and deployment scenarios. In 2025, the landscape is richer than ever, with options tailored for privacy, customization, and rapid prototyping. This guide covers the 10 best open-source UIs for LLMs, their standout features, and direct links to their repositories.

1. Open WebUI

Open WebUI is a self-hosted, extensible AI interface that supports Ollama, OpenAI API, and various LLM runners. It features granular permissions, responsive design, full Markdown/LaTeX support, hands-free voice/video calls, native Python function calling, persistent artifact storage, local RAG integration, web search, image generation/editing, and enterprise authentication. It’s ideal for privacy-focused deployments, local model experimentation, and enterprise use cases.

GitHub Link: https://github.com/open-webui/open-webui

2. LobeChat

LobeChat is a modern, open-source ChatGPT-like UI with support for speech synthesis, multi-modal input, a plugin system, agent marketplace, and one-click deployment. It supports MCP (Model Context Protocol), smart internet search, chain of thought visualization, branching conversations, artifacts, file upload/knowledge base, multi-model providers, local LLMs (Ollama), visual recognition, TTS/STT, text-to-image generation, and custom themes. LobeChat excels in extensibility, user experience, and rapid deployment for both individuals and teams.

GitHub Link: https://github.com/lobehub/lobe-chat

3. Text Generation Web UI

Text Generation Web UI is a popular open-source platform for running and interacting with local LLMs. It supports a wide range of models, offers a clean interface, and includes advanced features like model presets, plugins, and community-driven enhancements. It’s favored by developers and researchers for local experimentation and model benchmarking.

GitHub Link: https://github.com/oobabooga/text-generation-webui

4. Chatbot UI

Chatbot UI is a Next.js-based, open-source chat interface that supports OpenAI and Azure APIs. It’s designed for easy self-hosting, customization, and rapid prototyping, making it a go-to choice for developers building custom chatbot experiences.

GitHub Link: https://github.com/mckaywrigley/chatbot-ui

5. LibreChat

LibreChat is an open-source, multi-provider chat UI with support for MCP, rich UI features, and a focus on privacy. It’s designed for users who want a versatile, self-hosted chat experience with advanced agent orchestration and workflow management.

GitHub Link: https://github.com/danny-avila/LibreChat

6. AnythingLLM

AnythingLLM is a multi-model, MCP-compatible UI that supports local and cloud deployment. It’s known for its flexibility, privacy, and ease of use, making it suitable for both personal and enterprise AI workflows.

GitHub Link: https://github.com/Mintplex-Labs/anything-llm

7. Flowise

Flowise is a visual builder for LangChain-based LLM applications. It enables users to create complex agent workflows and pipelines with drag-and-drop ease, ideal for developers and non-developers alike.

GitHub Link: https://github.com/FlowiseAI/Flowise

8. LangFlow

LangFlow is a visual pipeline builder for LangChain, allowing users to design, prototype, and deploy LLM-powered workflows. It’s perfect for rapid experimentation and building complex agent logic.

GitHub Link: https://github.com/logspace-ai/langflow

9. assistant-ui

assistant-ui is a React/TypeScript-based component library for building custom LLM chat interfaces. It’s highly modular and customizable, making it a favorite for developers building tailored agent experiences.

GitHub Link: https://github.com/assistant-ui/assistant-ui

10. Streamlit

Streamlit is a Python-based framework for quickly building and deploying interactive web apps, including LLM chat interfaces. It’s ideal for rapid prototyping, data science, and AI experimentation.

GitHub Link: https://github.com/streamlit/streamlit

How to Use These UIs

Most UIs can be self-hosted with Docker or via simple npm/pip installs.
They support local models (via Ollama, llama.cpp, etc.) and cloud APIs (OpenAI, Anthropic).
Many offer advanced features like RAG, agent orchestration, multimodal support, and MCP compatibility.

Conclusion

Open-source UIs for LLMs are essential tools for anyone building, experimenting, or deploying AI agents. From simple chat interfaces to full agent frameworks, these projects offer flexibility, privacy, and community-driven innovation. Whether you’re a developer, researcher, or entrepreneur, exploring these UIs can unlock new possibilities for your AI workflows in 2025 and beyond.

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Top 50 Must-Know BlackArch Linux Tools for Penetration Testing

TechLatest — Fri, 20 Feb 2026 11:33:49 +0000

We’ve also put together a completely free course on BlackArch Linux to help you go from zero to confident in using this powerful penetration testing platform. This course, hosted as a curated video playlist on YouTube, walks you through everything from the basics of installing BlackArch Linux to using real tools in real scenarios. Whether you’re just getting started with ethical hacking or looking to deepen your skills, this free resource breaks down complex topics into practical lessons you can follow step by step. You can access the full course here: Watch the free BlackArch Linux course playlist on YouTube

If you’re using BlackArch Linux, you already know one thing:
This is not a beginner-friendly playground. BlackArch is built for people who want control, depth, and serious firepower. With 2800+ tools in its repository, the real challenge isn’t availability — it’s knowing what actually matters.

This guide focuses on 50 essential tools that real penetration testers, red teamers, and security researchers rely on. No fluff. No marketing words. Just tools that are genuinely useful in real-world assessments.

Ready-to-Use BlackArch Linux VM by TechLatest

One of the biggest barriers to adopting BlackArch has always been setup time. Installing Arch Linux, configuring repositories, and managing thousands of tools are not tasks everyone wants to repeat. To solve this, TechLatest provides a ready-to-use BlackArch Linux VM, fully configured and accessible within minutes.

Our BlackArch VM is available for AWS, Azure, and Google Cloud, giving users instant access to a complete penetration testing environment via VNC. No manual installation, no dependency conflicts, and no wasted time — log in and start working.

Information Gathering & Reconnaissance

1. Nmap

The backbone of almost every pentest. Service detection, OS fingerprinting, scripts — Nmap sets the foundation.

2. Masscan

When speed matters. Masscan scans huge IP ranges faster than almost anything else.

3. Amass

Powerful attack surface mapping tool. Excellent for subdomain enumeration and recon automation.

4. theHarvester

Pulls emails, domains, and names from public sources — surprisingly effective for OSINT.

5. Netdiscover

Great for discovering live hosts in a local network using ARP requests.

Web Application Testing

6. Burp Suite

The industry standard for web security testing. Intercept traffic, modify requests, and find logic flaws.

7. Nikto

Quick vulnerability scanner for web servers. Loud, but useful for early discovery.

8. Gobuster

Bruteforces directories, DNS, and virtual hosts with speed and precision.

9. Dirsearch

Focused directory brute-forcing with excellent wordlist support.

10. WhatWeb

Identifies web technologies, CMS, frameworks, and server details in seconds.

Password Attacks & Authentication

11. Hydra

Fast online password brute-forcing tool supporting many protocols.

12. Hashcat

One of the most powerful offline password cracking tools available today.

13. John the Ripper

Classic, reliable, and still very effective for cracking hashes.

14. Medusa

Parallel login brute-forcer — fast and flexible.

15. Crunch

Custom wordlist generator when default lists don’t cut it.

Exploitation Frameworks

16. Metasploit Framework

From exploitation to post-exploitation — this is a full ecosystem, not just a tool.

17. Searchsploit

Offline access to exploit-db. Essential for vulnerability research.

18. BeEF

Browser-based exploitation framework for client-side attacks.

19. Empire

Powerful post-exploitation framework, especially for Windows environments.

20. RouterSploit

Specialized exploitation framework for routers and embedded devices.

Wireless & Network Attacks

21. Aircrack-ng

Complete Wi-Fi security testing suite — capture, crack, analyze.

22. Reaver

Targets WPS-enabled networks. Still effective when misconfigurations exist.

23. Wifite

Automates wireless attacks, great for fast assessments.

24. Kismet

Wireless network detector and sniffer with strong visualization.

25. Bettercap

Modern MITM framework for network attacks and traffic manipulation.

Malware Analysis & Reverse Engineering

26. Ghidra

A professional-grade reverse engineering tool released by NSA.

27. Radare2

Lightweight but extremely powerful reverse engineering framework.

28. Cutter

GUI frontend for Radare2, making analysis more approachable.

29. YARA

Rule-based malware detection tool widely used by researchers.

30. Volatility

Memory forensics framework for analyzing RAM dumps.

Forensics & Anti-Forensics

31. Autopsy

Digital forensics platform for disk analysis and evidence recovery.

32. Sleuth Kit

Low-level forensic tools for file system analysis.

33. Foremost

Recovers deleted files based on headers and signatures.

34. Bulk Extractor

Extracts useful artifacts like emails and URLs from disk images.

35. TestDisk

Excellent for recovering lost partitions and damaged disks.

Sniffing, Spoofing & MITM

36. Wireshark

The most trusted network protocol analyzer in the world.

37. Tcpdump

CLI packet capture tool — simple, fast, effective.

38. Ettercap

Classic MITM attack tool for LAN-based attacks.

39. Dsniff

Collection of tools for sniffing passwords and sessions.

40. Responder

LLMNR, NBT-NS, and MDNS poisoning tool — extremely effective in internal networks.

Privilege Escalation & Post-Exploitation

41. LinPEAS

Automated Linux privilege escalation discovery script.

42. WinPEAS

Windows privilege escalation enumeration tool.

43. GTFOBins

Not a traditional tool, but a critical reference for exploiting Unix binaries.

44. Mimikatz

Extracts credentials from memory — widely used in red team operations.

45. CrackMapExec

Swiss army knife for Active Directory environments.

Automation & Utilities

46. Python

Still the most useful scripting language for custom exploits and automation.

47. Ruby

Used heavily in Metasploit modules and exploit development.

48. SQLmap

Automates detection and exploitation of SQL injection vulnerabilities.

49. Ffuf

Fast web fuzzer for directories, parameters, and APIs.

50. Nuclei

Template-based vulnerability scanner with massive community support.

Final Thoughts

BlackArch Linux is not about convenience. It’s about depth and control. You won’t use all 2800 tools — and you shouldn’t try to. Mastering even 20–30 of these tools is enough to conduct serious, professional-grade penetration tests.

What matters most isn’t how many tools you install — it’s how well you understand:

reconnaissance
attack surface mapping
exploitation logic
and post-exploitation workflows

If you’re running BlackArch through a preconfigured VM or cloud environment, you skip the setup pain and jump straight into learning and execution — which is exactly how it should be.

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From Chat App to AI Powerhouse: Telegram + OpenClaw

TechLatest — Wed, 18 Feb 2026 13:56:44 +0000

In this guide, we’ll walk through how to install OpenClaw on macOS and connect it to Telegram step by step.

OpenClaw is a self-hosted AI assistant that runs locally on your machine while connecting to messaging platforms like Telegram, WhatsApp, Discord, and more. Unlike typical cloud chatbots, OpenClaw gives you full control over your model, workspace, sessions, and integrations.

By the end of this tutorial, you will have:

OpenClaw installed on macOS
An AI model configured (using OpenAI in this example)
Telegram bot connected and approved
Gateway service running in the background
Web dashboard and TUI are working
A fully functioning Telegram AI assistant Let’s get started.

Step 1: Install OpenClaw on macOS

Before connecting OpenClaw to Telegram, we first need to install it locally.

OpenClaw provides a modern installer that automatically detects your operating system and installs the required dependencies (including Node.js if needed).

Run the Installer

Open your terminal and run:

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

This command will:

Detect your operating system
Verify Homebrew (if on macOS)
Check Node.js installation
Install OpenClaw globally via npm
Prepare your environment
Installation Output

If everything is configured correctly, you should see output similar to this:

OpenClaw Installer
Detected: macOS
Install method: npm
Requested version: latest

[1/3] Preparing environment
✓ Homebrew already installed
✓ Node.js v24.6.0 found

[2/3] Installing OpenClaw
✓ Git already installed
INFO Installing OpenClaw v2026.2.15
✓ OpenClaw npm package installed
✓ OpenClaw installed

[3/3] Finalizing setup

OpenClaw installed successfully (2026.2.15)!
Installation complete. Your productivity is about to get weird.

INFO Starting setup
OpenClaw 2026.2.15

This confirms:

Your dependencies are ready
The latest OpenClaw version is installed
Setup has successfully initialized

That means OpenClaw has successfully identified your environment and is preparing the installation.

Step 2: Complete Onboarding & Review Security Warning

After installation, OpenClaw automatically launches its onboarding process.

You’ll be greeted with the OpenClaw interface along with an important Security Warning screen.

Why This Warning Matters

OpenClaw is not a simple chatbot. It is an autonomous AI assistant capable of:

Reading local files
Executing system actions
Running tools and automations
Connecting to external services

Because of this, OpenClaw clearly warns users that:

A bad prompt can trick it into doing unsafe things.

This is expected behavior for agent-style systems.

Step 3: Configure the Model & Authentication Provider

After completing onboarding, OpenClaw will prompt you to select a Model/Auth Provider.

This is where you decide which LLM backend will power your assistant.

During setup, you’ll see a list of available providers. OpenClaw supports multiple options, allowing you to choose based on performance, pricing, availability, or infrastructure preference.

Supported Model Providers

OpenClaw provides flexibility by supporting a wide range of AI providers, including:

Anthropic
OpenAI
OpenRouter
LiteLLM
Amazon Bedrock
Vercel AI Gateway
Moonshot AI
MiniMax
OpenCode Zen
GLM Models
Z.AI
Synthetic
Qianfan

This allows you to run OpenClaw using:

Direct API providers
Gateway aggregators
Enterprise cloud backends
Local or proxy-based model routing

You can choose whichever provider best fits your workflow and infrastructure.

Example Setup: OpenAI

In this tutorial, we’ll use OpenAI as the model provider.

During configuration:

Select OpenAI from the list
Choose the authentication method:
OpenAI API Key
Enter your API key when prompted
You’ll see a prompt similar to:

Enter OpenAI API key

After entering a valid key, OpenClaw verifies the connection and continues setup.

Step 4: Select and Confirm the Default Model

After entering your API key, OpenClaw verifies it and automatically configures a default model.

You’ll see confirmation like:

Saved OPENAI_API_KEY to ~/.openclaw/.env
Model configured
Default model set to openai/gpt-5.1-codex

This means:

Your API key is securely stored in the local .env file
The model provider connection is successful
A default model has been selected

Choosing Your Default Model

OpenClaw gives you the flexibility to:

Keep the current default model
Enter a custom model manually
Select from the available models provided by your chosen backend

For example, with OpenAI, you may see options like:

openai/gpt-5.1-codex
openai/codex-mini-latest
openai/gpt-4
openai/gpt-4-turbo
openai/gpt-4.1
openai/gpt-4.1-mini
openai/gpt-4o and more…

For this tutorial, we’ll use gpt-4o:

openai/gpt-4o

Step 5: Connect OpenClaw to Telegram

After configuring your model, OpenClaw will ask you to select a chat channel.

You’ll see something like:

Select channel (QuickStart)

● Telegram (Bot API)
○ WhatsApp (QR link)
○ Discord (Bot API)
○ Slack (Socket Mode)
...

Step 6: Enter Your Telegram Bot Token

After selecting Telegram (Bot API) as your channel, OpenClaw will prompt you to enter your Telegram bot token.

You’ll see instructions like:

Telegram bot token

1) Open Telegram and chat with @BotFather
2) Run /newbot (or /mybots)
3) Copy the token (looks like 123456:ABC...)

Generate the Token (If You Haven’t Already)

Open Telegram
Search for @botfather
Run:

/newbot

Choose a bot name
Choose a username (must end in bot)

BotFather will return a token similar to:

123456789:AAExampleGeneratedToken

Enter the Token in OpenClaw

Back in your terminal, paste the token when prompted:

Enter Telegram bot token

OpenClaw will validate the token and complete the channel configuration.

If successful, Telegram is now linked to your OpenClaw instance.

Optional: Set via Environment Variable

Instead of pasting it interactively, you can also set it manually:

export TELEGRAM_BOT_TOKEN=your_token_here

Or add it to:

~/.openclaw/.env

This is useful for production or automated deployments.

Step 7: Channel Confirmed & Workspace Initialized

After entering your Telegram bot token, OpenClaw finalizes the channel configuration.

You’ll see confirmation like:

Selected channels

Telegram — simplest way to get started — register a bot with @BotFather and get going.

Updated ~/.openclaw/openclaw.json
Workspace OK: ~/.openclaw/workspace
Sessions OK: ~/.openclaw/agents/main/sessions

This confirms:

Telegram channel is successfully configured
The configuration file has been updated
Workspace directory is initialized
Agent session storage is ready At this point, your assistant is fully wired to Telegram.

What Just Happened?

OpenClaw created and validated:

~/.openclaw/openclaw.json → Main configuration
~/.openclaw/workspace → Working directory
~/.openclaw/agents/main/sessions → Conversation memory & state

This is where your agent:

Stores sessions
Maintains chat history
Tracks tool execution
Manages runtime state
Everything is now persistent and structured.

Skills Status Overview

You’ll also see a skills report:

Skills status

Eligible: 7
Missing requirements: 42
Unsupported on this OS: 0
Blocked by allowlist: 0

What This Means

Eligible → Skills ready to run
Missing requirements → Optional skills requiring extra dependencies
Unsupported on this OS → OS-incompatible tools
Blocked by allowlist → Restricted skills This doesn’t mean something is broken — it simply reflects optional capabilities not yet installed.

You can install additional skills later as needed.

Step 8: Gateway Service Installed & Telegram Connected

After completing the configuration, OpenClaw installs and starts the Gateway service.

You’ll see output similar to:

Installing Gateway service....
Gateway service installed.

Telegram: ok (@yourbotname)
Agents: main (default)
Heartbeat interval: 30m
Session store: ~/.openclaw/agents/main/sessions/sessions.json

What Just Happened?

OpenClaw has now:

Installed the Gateway service (runs in the background)
Created a macOS LaunchAgent
Linked your Telegram bot successfully
Initialized the default agent
Set up persistent session storage The important line is:

Telegram: ok

That means your bot is live.

What is the Gateway?

The Gateway is the control plane of OpenClaw.

It:

Manages chat channels
Handles agent sessions
Maintains WebSocket connections
Routes messages between Telegram and your model
Stores session history
Runs continuously in the background

On macOS, it installs as a LaunchAgent:

~/Library/LaunchAgents/ai.openclaw.gateway.plist

Logs are stored at:

~/.openclaw/logs/gateway.log

Step 9: Hatch the Bot in TUI

When you see:

How do you want to hatch your bot?
Hatch in TUI (recommended)

Do this:

Just press Enter.

That runs:

openclaw tui --ws://127.0.0.1:18789 --agent main --session main

You’ll then see:

connected | idle
agent main | session main
openai/gpt-4o

That means:

Gateway running ✅
Model connected ✅
Session active ✅
Bot alive ✅

You can now type:

Hello

and it responds.

Step 10: Open Dashboard & Start Chatting

Now your Gateway is running.

You can open the OpenClaw WebUI (Dashboard) in your browser:

http://127.0.0.1:18789

If a token was generated during setup, open it like this:

http://127.0.0.1:18789/#token=YOUR_TOKEN

(Use the exact token shown in your terminal.)

What you should see:

Status: Connected
Health: OK
Chat interface ready

To Chat

Go to Chat (left sidebar)
Type your message in the input box
Press Enter or click Send Example test message:

What is 12 × 14?

If it replies → your model, gateway, and agent are fully working.

Step 11: OpenClaw Access Configuration

Run this command to approve Telegram access:

openclaw pairing approve telegram your_code

Step 12: Chat with Telegram Bot

Open Telegram and send a message (e.g., "hello") to your bot.

If it replies, your Telegram bot is successfully connected and working.

Conclusion

You now have a fully working OpenClaw setup connected to Telegram.

In this walkthrough, we:

Installed OpenClaw on macOS
Configured the model provider and API key
Selected a default model
Connected Telegram using BotFather
Installed and started the Gateway service
Launched the TUI and Web Dashboard
Approved Telegram pairing
Successfully tested the bot

At this point, your assistant is live and running locally on your machine. Messages sent to your Telegram bot are routed through the OpenClaw Gateway, processed by your selected model, and returned in real time.

From here, you can explore:

Adding more skills
Connecting additional channels (WhatsApp, Discord, Slack)
Enabling automation workflows
Integrating tools like Notion, GitHub, or file access
Running deeper security audits

OpenClaw turns your local machine into a personal AI control center. Now it’s your move — start building, automating, and experimenting.

Note

We’re excited to offer an exclusive opportunity for our community. You are eligible to receive free Azure credits worth $1,000 to explore, test, and deploy any TechLatest product or solution. Whether you’re building AI agents, experimenting with automation tools, or scaling your infrastructure, these credits give you the flexibility to innovate without upfront costs. This is a limited-time offer available on a first-come, first-served basis, so don’t miss out. To claim your credits, simply fill out the form here: https://www.techlatest.net/free_azure_credits/ and explore our available products and solutions here: https://techlatest.net/support/.

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