DEV Community: Yanko Alexandrov

ClawBox v2.2.3: Gemma 4 Local AI, ClawBox OS, VNC & Real Browser Automation

Yanko Alexandrov — Fri, 10 Apr 2026 17:20:55 +0000

ClawBox just shipped its biggest update yet. Version 2.2.3 is dropping very soon, and it fundamentally changes what you can do with personal AI hardware.

What is ClawBox?

ClawBox is a plug-and-play AI hardware unit — a pre-configured NVIDIA Jetson Orin Nano 8GB with 512GB NVMe SSD, running OpenClaw (the open-source AI assistant platform). Setup: unbox, plug in, scan QR code. Done. 5 minutes.

67 TOPS of AI performance. 15 watts. Your data stays on your hardware.

What's New in v2.2.3

🤖 Gemma 4 — Fully Local Offline AI

The headline feature. One click to install Google's Gemma 4 directly on your ClawBox. Works 100% offline — no API key, no subscription, no internet. Your AI assistant works even when the cloud goes down.

This is what local AI actually means: not a toy 3B parameter model, but a capable modern AI running on dedicated 67 TOPS hardware.

🖥️ ClawBox OS

A dedicated OS layer built for the Jetson Orin Nano's capabilities. Clean, fast, purpose-built for AI workloads. Every component optimized, nothing wasted.

💸 ClawBox AI — Start Free

We're launching ClawBox AI, our own affordable cloud AI subscription tier. Start for free. Upgrade when you need more. No more $20/month minimums just to get started.

Best of both worlds: Gemma 4 for privacy-sensitive offline tasks + cloud AI when you need more horsepower.

🌐 Real Browser Automation with Chromium

Not headless scraping. Not a plugin. Full Chromium-based browser automation baked into ClawBox OS.

Why it matters: most automation tools get blocked. Full Chromium behaves like a real user — handles CAPTCHAs, login flows, dynamic content, sites that actively block bots. Buy tickets, fill forms, monitor prices, navigate dashboards — from a natural language prompt.

📺 VNC Built-In

Full remote desktop to your ClawBox from anywhere. See what's happening, control it remotely, monitor running tasks. Built in — no third-party setup.

⚡ Generate ClawBox OS Apps from a Single Prompt

Describe an app → ClawBox OS builds it. No coding required.

"Create a tool that monitors my server logs and alerts me when error rates spike."

Done.

💻 Terminal App in the UI

Full terminal access directly in your ClawBox browser interface. No SSH. Open a terminal, run commands, manage your system — from the same browser window you use to chat with your AI.

🔒 Secure by Default

OpenClaw pre-configured and locked down out of the box. Firewall rules, authentication, encrypted comms — handled before the device ships.

🛒 ClawBox Store — One-Click Skills

Browse the ClawBox Store, find an OpenClaw skill, click install. No CLI, no config files, no dependency hell.

The Big Picture

v2.2.3 turns ClawBox from "hardware that runs an AI assistant" into a genuine personal AI compute platform:

Gemma 4 — local model for privacy + offline use
ClawBox AI — cloud AI when you need more power
Chromium automation — that actually works on real websites
App generation — from natural language
VNC + Terminal — full remote control

All of this on a device that costs €549, uses 15 watts, sits on your desk, and takes 5 minutes to set up.

Get ClawBox

👉 openclawhardware.dev

v2.2.3 ships as an automatic update to all existing customers. New orders ship now.

ClawBox v2.2.3 Beta: A Web OS for NVIDIA Jetson That Generates Apps From a Single Prompt

Yanko Alexandrov — Sat, 04 Apr 2026 11:38:42 +0000

We just shipped ClawBox v2.2.3 Beta — the biggest update since launch. ClawBox is an AI hardware box built on the NVIDIA Jetson Orin Nano 8GB with a 512GB NVMe SSD, running OpenClaw (open source).

Here's what's new and why it matters for developers.

🖥️ Full Web OS

Open your browser, type your ClawBox IP, and you're looking at a real desktop environment. File manager, terminal, VS Code, installed apps — all in the browser. No SSH required.

This makes local AI accessible to people who aren't comfortable with the command line.

🏪 App Store

Browse and install AI skills with one click. Community-built skills with ratings, categories, and descriptions. Think VS Code extensions, but for your AI assistant.

⚡ MCP App Generation — The Killer Feature

This is the one that got us excited. Tell your AI:

"Build me a Social Media Tracker"

And it uses Model Context Protocol (MCP) to:

Generate a full web application
Create a desktop icon on your Web OS
Launch the app

Single prompt → working desktop app. The AI scaffolds the frontend, connects to APIs, and deploys it locally.

🆓 ClawAI — Free AI Provider

Ships with a built-in free AI provider. No API keys, no accounts, no credit card. Select it during the 5-minute setup wizard and start chatting immediately.

You can always swap in your own API keys (Anthropic, OpenAI, etc.) or run local models.

🌐 Real Browser Automation

Full Chromium running on dedicated hardware. Websites can't tell it's automated — real browser, real hardware, real IP address.

Use cases:

Web scraping without getting blocked
Form filling and data entry
Price monitoring
Social media automation
24/7 background tasks

🌍 10-Language Setup Wizard

Plug in → pick your language → 5 steps → done in 5 minutes.

Tech Specs

Hardware: NVIDIA Jetson Orin Nano 8GB + 512GB NVMe SSD
AI Performance: 67 TOPS
Local inference: ~15 tok/s for quantized 7B models
Platforms: Telegram, WhatsApp, Discord, Web UI
Software: OpenClaw (open source)
Privacy: Everything runs locally, data never leaves the box

What's Next

We're working on expanding the App Store, improving MCP tooling, and adding more local model support.

If you want to try the Web OS or have questions about the MCP integration, drop a comment below.

📖 Full blog post with more details

🐣 Easter sale: use code EASTER10 for 10% off (ends Monday)

🌐 openclawhardware.dev

Self-Hosting AI in 2026: A Practical Guide

Yanko Alexandrov — Sun, 29 Mar 2026 06:43:59 +0000

I've been running AI models locally for about two years now. When I started, it felt like an esoteric hobbyist pursuit — patchy documentation, hardware that barely scraped by, and models that hallucinated more than they helped. In 2026, that picture has fundamentally changed. Self-hosted AI is genuinely viable, and for many use cases, it's the smarter choice.

This is the guide I wish I'd had when I started.

Why Self-Host AI?

The case for self-hosting isn't ideological — it's practical.

Privacy. Every query you send to a cloud API leaves your machine. Conversations, code snippets, business logic, personal data — all of it transits (and potentially trains on) external infrastructure. When you run locally, that data never leaves.

Cost. At scale, cloud AI costs compound fast. GPT-4 at $30/million output tokens is fine for experiments but punishing for production. A one-time hardware investment pays for itself in 6–18 months depending on usage.

Latency and availability. Local inference doesn't depend on API rate limits, outages, or network quality. Your model is there when you need it.

Customization. You can fine-tune, quantize, and swap models freely. No vendor lock-in. No waiting for a provider to add a feature.

For a deeper breakdown of the why, self-hosted-ai.com has a solid resource section with comparisons across different use cases.

Hardware: What You Actually Need

This is where most people get confused. The requirements vary wildly depending on what you want to do.

Minimum viable setup (inference only)

For running 7B–13B quantized models:

RAM: 16GB minimum, 32GB preferred
CPU: Modern x86 or ARM (Apple Silicon performs exceptionally well)
Storage: 50–100GB for a few models

GPU acceleration

If you're doing anything beyond casual use, a GPU makes a dramatic difference:

# Check your GPU with nvidia-smi
nvidia-smi --query-gpu=name,memory.total --format=csv

# Or for AMD
rocm-smi --showmeminfo vram

Consumer GPUs like the RTX 3090 (24GB VRAM) or 4090 (24GB VRAM) can comfortably run 70B models. For edge deployments, the NVIDIA Jetson Orin lineup offers 40–275 TOPS of neural processing with much lower power draw than a desktop GPU.

Dedicated hardware options

Running a full desktop just for AI inference is wasteful. Several options exist for dedicated appliances:

Raspberry Pi 5 — fine for small models, limited to ~4B parameters practically
NVIDIA Jetson Orin Nano — 40 TOPS, runs 7–13B models well, ~10W TDP
Mini PCs with eGPU — flexible but bulky
Pre-configured appliances like the ones at openclawhardware.dev ship with everything set up — useful if you want to skip the assembly

For a curated list of hardware options across different budgets, private-ai-hardware.com maintains a regularly updated comparison table.

Software Stack

The ecosystem has consolidated significantly. Here's what's actually worth using in 2026:

Ollama — the de facto standard

Ollama has won the local model runner race. It's simple, has a clean REST API, and supports most popular models out of the box.

# Install
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model
ollama pull llama3.2

# Run it
ollama run llama3.2

# Or use the API
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.2",
  "prompt": "Explain RLHF in simple terms",
  "stream": false
}'

LM Studio

If you prefer a GUI, LM Studio gives you a ChatGPT-like interface with a local model backend. Excellent for non-technical users or quick experiments.

Open WebUI

For a proper web UI on top of Ollama:

docker run -d \
  -p 3000:80 \
  --add-host=host.docker.internal:host-gateway \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  ghcr.io/open-webui/open-webui:main

This gives you a full ChatGPT-style interface with conversation history, model switching, and even basic RAG.

Text generation WebUI (oobabooga)

For more advanced users who need fine-grained control over generation parameters, sampling strategies, and LoRA loading.

More software stack comparisons are at self-hosted-ai-assistant.com, including community benchmarks for different hardware configurations.

Model Selection

Choosing the right model matters more than most people realize.

Use Case	Recommended Model	VRAM Required
General chat	Llama 3.2 3B/8B	4–8GB
Code assistance	Qwen2.5-Coder 7B	6GB
Document Q&A	Mistral 7B + RAG	8GB
Complex reasoning	Llama 3.3 70B (Q4)	40GB
Vision tasks	LLaVA 13B	14GB

For most people, a Q4_K_M quantized 8B model hits the sweet spot: near-frontier quality, runs on 8GB VRAM, 20–40 tok/s on decent hardware.

# Pull a quantized model
ollama pull llama3.2:8b-instruct-q4_K_M

# Check how fast it runs
time ollama run llama3.2:8b-instruct-q4_K_M "Count to 10" --verbose

Cost Comparison: Self-Hosted vs. Cloud

Let's be concrete. Here's a realistic TCO comparison for a developer making ~100k API calls/month:

Cloud (GPT-4o):

~100k calls × avg 500 output tokens = 50M tokens/month
At $15/M output tokens = $750/month
Annual: $9,000

Self-hosted (Jetson Orin Nano + Llama 3.2):

Hardware: ~$500 one-time
Power: ~10W × 730h/month = 7.3 kWh × $0.15 = $1.10/month
12-month total: $513

That's a 94% cost reduction. Even factoring in setup time, the math is stark.

run-ai-locally.com has a calculator that lets you plug in your specific usage numbers — worth checking before committing to a hardware budget.

selfhost-ai.com also has detailed guides on setting up monitoring and measuring your actual inference costs over time.

Practical Setup: Getting Started in an Hour

If you just want to get running today:

# 1. Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# 2. Pull a good general-purpose model
ollama pull llama3.2

# 3. Test it
ollama run llama3.2 "What's the capital of France?"

# 4. Set up Open WebUI (optional but recommended)
docker compose up -d  # after setting up docker-compose.yml above

# 5. Check GPU utilization during inference
watch -n 1 nvidia-smi

For production setups, you'll want to add:

A systemd service to auto-start Ollama
Nginx reverse proxy with TLS
Basic auth if exposing beyond localhost
Log rotation and monitoring

The Privacy Angle

This deserves its own section because it's often underestimated. When you use cloud AI:

Your queries are logged (even with privacy settings, metadata is retained)
In enterprise tiers, your data may be used for training (check the ToS)
You're subject to the provider's content policies — models can be modified without notice
Jurisdictional issues: your data may be processed in regions with different legal frameworks

Running locally means you are the only one with access. For medical queries, legal research, business strategy, or anything sensitive, this is not a minor consideration.

Where to Go From Here

Self-hosting AI in 2026 is genuinely accessible. The tooling is mature, the models are capable, and the economics make sense.

A few starting points:

self-hosted-ai.com — comprehensive wiki and hardware guides
Ollama documentation — official model runner docs
r/LocalLLaMA — active community with real-world benchmarks
private-ai-hardware.com — hardware comparison and buying guides

The one thing I'd say to anyone on the fence: just start. Pull a model, run it locally, and notice how different it feels to have your AI conversation stay on your machine. That experience tends to be convincing.

What hardware are you running local AI on? Drop a comment — I'm curious what setups people have found work well.

The Real Cost of Running AI Locally vs Cloud

Yanko Alexandrov — Sun, 29 Mar 2026 06:42:50 +0000

I ran the numbers last month after getting my latest cloud AI bill. The result made me restructure my entire stack.

This isn't an anti-cloud screed — cloud AI has real advantages. But most comparisons I've seen online are either too optimistic about local hardware or use cherry-picked cloud pricing scenarios. I want to give you the actual math, including the costs people routinely forget to include.

The Problem with "Monthly Subscription" Thinking

Most developers hit cloud AI through OpenAI, Anthropic, or Google's APIs. The pricing looks reasonable in isolation: $15/million output tokens here, $3/million input tokens there.

The problem is that these costs compound invisibly. You don't get a big invoice at the end of the year — you get charged incrementally, and the monthly cost feels like a utility bill rather than a capital expense. That framing tricks you into treating it as a fixed overhead rather than a variable cost worth optimizing.

Let's make it concrete.

Scenario 1: The Developer Building an AI Feature

Usage profile:

Personal project with moderate traffic
~500 API calls/day
Average: 200 input tokens + 500 output tokens per call

Monthly cloud cost (GPT-4o):

Input:  500 calls × 30 days × 200 tokens = 3,000,000 tokens × $2.50/M  = $7.50
Output: 500 calls × 30 days × 500 tokens = 7,500,000 tokens × $10.00/M = $75.00
Monthly total: ~$82.50
Annual total:  ~$990

Monthly local cost (Jetson Orin Nano 8GB):

Hardware:    $500 amortized over 4 years = $10.42/month
Power:       10W × 24h × 30 days = 7.2 kWh × $0.15/kWh = $1.08/month
Internet:    Already paying for it = $0 marginal
Software:    Ollama + Open WebUI = $0 (open source)
Monthly total: ~$11.50
Annual total:  ~$510 (year 1, includes full hardware cost)
             ~$25 (years 2-4)

Break-even: Month 6. After that, you're at $25/year vs $990/year.

Scenario 2: The Team Using AI for Internal Tools

Usage profile:

10-person engineering team
Mix of code review, documentation, Q&A
~5,000 API calls/day

Monthly cloud cost (GPT-4o):

Input:  5,000 × 30 × 300 tokens = 45,000,000 tokens × $2.50/M = $112.50
Output: 5,000 × 30 × 600 tokens = 90,000,000 tokens × $10.00/M = $900.00
Monthly total: ~$1,012
Annual total:  ~$12,150

Monthly local cost (Mac Mini M4 Pro or dedicated server):

Hardware:    $1,500 amortized over 4 years = $31.25/month
Power:       25W × 24h × 30 days = 18 kWh × $0.15/kWh = $2.70/month
IT overhead: 2h/month admin time × $100/h = $200/month (realistic)
Monthly total: ~$234
Annual total:  ~$4,300 (year 1) / ~$2,730 (years 2-4)

Break-even: Month 4. Even factoring in admin overhead, local wins cleanly.

The Hidden Costs Everyone Ignores

On the cloud side

Data egress: If you're sending large documents or images for analysis, the ingress is often free but processing costs add up. A pipeline that processes 1,000 PDFs/day gets expensive fast.

Context window pricing: Long context queries (100k+ tokens) cost dramatically more. If your use case needs full document context, those $2.50/M input prices multiply by 50-100x.

Rate limit engineering: At scale, you'll hit rate limits. Either you pay for higher tiers or you build retry logic that adds latency and complexity. Both have costs.

Vendor dependency: When OpenAI deprecated text-davinci-003, anyone who had built around it scrambled. Migration costs are real, even if they're one-time.

On the local side

Setup time: Be honest here. Getting Ollama running takes 20 minutes. Getting a production-grade inference stack with monitoring, auto-restart, and proper networking takes 2-3 days. Factor in your hourly rate.

Power measurement:

# Measure actual power draw during inference on Linux
# Install powerstat: apt install powerstat
sudo powerstat -R -c -z 1 30  # 30 seconds of readings

# Or read directly from hardware sensors
cat /sys/class/powercap/intel-rapl/intel-rapl:0/energy_uj
sleep 1
cat /sys/class/powercap/intel-rapl/intel-rapl:0/energy_uj
# Difference / 1,000,000 = joules = watt-seconds in 1 second = watts

Hardware failure: Consumer hardware fails. Build in a replacement fund: roughly 10-15% of hardware cost per year.

The "it's always on" cost: If your inference server runs 24/7 even when idle, that's wasted electricity. A 10W system left on 24/7 costs about $13/year. A 200W server costs $262/year in standby. Use sleep states or on-demand startup for intermittent workloads.

dedicated-ai-hardware.com has a detailed TCO calculator that accounts for these variables — worth bookmarking before making a hardware decision.

Quality: Is Local AI Actually Good Enough?

This is the question that actually matters. Cost means nothing if local models can't do the job.

In 2026, the honest answer is: it depends on your use case.

Local is fully competitive for:

Code completion and review (Qwen2.5-Coder 7B matches GPT-4o on most benchmarks)
Summarization and document Q&A
Classification and extraction
Conversational interfaces
RAG pipelines over private data

Cloud still leads on:

Complex multi-step reasoning (frontier models are ahead)
Tasks requiring very long context (256k+ tokens)
Vision tasks at scale (though this gap is closing)
Cutting-edge capabilities within days of research release

For most production applications, local models at the 7B-13B scale with Q4 quantization are genuinely excellent. The gap to frontier models exists, but it's smaller than the marketing suggests.

# Quick benchmark comparison
# Test with the same prompt on local vs API

# Local (Ollama)
time curl -s http://localhost:11434/api/generate \
  -d '{"model":"llama3.2","prompt":"Solve: 2x + 5 = 17, show work","stream":false}' \
  | jq '.response'

# You'll get an answer in 1-3 seconds with no network latency

The Privacy Premium: What's It Worth?

Here's a dimension that doesn't show up in TCO calculations but absolutely should.

When a developer uses cloud AI for work, they're likely sending:

Internal code and architecture decisions
Customer data (sometimes inadvertently)
Business logic and competitive information
Employee communications

Under most enterprise cloud AI agreements, the provider doesn't train on your data (in theory, with appropriate settings). But the data still transits their infrastructure, is logged for debugging, and is subject to their security posture and legal obligations.

For regulated industries (healthcare, finance, legal), this isn't a preference question — it's a compliance requirement. HIPAA, GDPR, and SOC 2 all create exposure when sensitive data goes through third-party AI systems.

no-cloud-ai.com has a useful breakdown of data residency requirements by industry and jurisdiction. personal-ai-server.com covers the personal/home use angle for those who simply prefer to keep conversations private.

The privacy value is real, but it's hard to quantify. A practical heuristic: if you'd redact something before sharing it with a contractor, you probably shouldn't send it through cloud AI unencrypted.

Low-Power Options for Always-On AI

Not every AI use case justifies a full server. If you want an always-on local AI assistant without the electricity overhead, low-power options have matured significantly.

The Jetson Orin Nano at 5-10W can run 7B models at 12-18 tok/s — plenty for conversational use cases. Raspberry Pi 5 can handle 3-4B models at reduced throughput. ARM mini PCs from various manufacturers target the 15-25W range with more headroom.

low-power-ai.com tracks the current landscape of low-power AI inference hardware, which changes frequently as new products launch.

home-ai-assistant.com focuses specifically on local AI for home use — always-on assistants, home automation integration, and personal knowledge bases.

Making the Decision

Here's my actual decision framework:

Choose cloud if:

Your usage is unpredictable and bursty (cloud handles scaling better)
You need frontier model capability immediately (not 6 months from now)
Setup time and maintenance are genuinely unacceptable constraints
You're building an early-stage product where infrastructure simplicity matters more than cost

Choose local if:

You have predictable, sustained usage above ~$30/month
Privacy or compliance is a real requirement
Latency matters and you're running inference near the user
You want to experiment freely without watching a token meter
The data you're processing is sensitive

Choose hybrid if:

Route privacy-sensitive queries local, complex reasoning queries to cloud
Use local for high-frequency/low-complexity, cloud for low-frequency/high-complexity
Local for development/testing, cloud for production initially

The Verdict

Cloud AI is not going away, and it shouldn't. The frontier models are genuinely impressive, and the operational simplicity is real. But the "just use the API" default assumption that pervades developer culture in 2026 deserves scrutiny.

For sustained usage above about $30/month, local hardware pays for itself. For privacy-sensitive workloads, local is often the only responsible choice. For experimentation and learning, running models locally removes constraints that shape your thinking in ways you don't notice.

The math is clear. The question is whether you're ready to spend an afternoon setting it up.

What's your current AI infrastructure setup? I'm curious whether teams are doing full local, full cloud, or some hybrid approach. Let me know in the comments.

NVIDIA Jetson for AI Projects: Getting Started in 2026

Yanko Alexandrov — Sun, 29 Mar 2026 06:42:46 +0000

When NVIDIA launched the original Jetson TK1 back in 2014, it was a curiosity — a developer board for robotics researchers and vision engineers. Fast forward to 2026, and the Jetson lineup has become one of the most capable edge AI platforms available, with the Orin series running serious language models alongside computer vision tasks on a platform that sips 5–40 watts.

If you've been eyeing Jetson for an AI project but haven't taken the plunge, this guide is for you.

The Jetson Lineup (2026)

NVIDIA currently ships several Jetson modules. Here's how they stack up for AI workloads:

Module	AI Performance	RAM	TDP	Price (Module)
Jetson Orin Nano 4GB	20 TOPS	4GB	5–10W	~$150
Jetson Orin Nano 8GB	40 TOPS	8GB	5–10W	~$250
Jetson Orin NX 8GB	70 TOPS	8GB	10–20W	~$400
Jetson Orin NX 16GB	100 TOPS	16GB	10–25W	~$600
Jetson AGX Orin 32GB	200 TOPS	32GB	15–60W	~$999
Jetson AGX Orin 64GB	275 TOPS	64GB	15–60W	~$1,499

"TOPS" (Tera Operations Per Second) measures the dedicated neural network accelerator performance. For practical AI inference, the Orin Nano 8GB at 40 TOPS is the sweet spot for most projects — enough headroom for 7B language models plus simultaneous vision processing.

What Makes Jetson Different

It's worth understanding what you're actually buying. A Jetson isn't just "a GPU in a small box."

Unified memory architecture

Unlike a discrete GPU with its own VRAM, Jetson uses unified memory — the CPU and GPU share the same physical RAM pool. This matters because:

Your LLM can use all 8GB for weights, not just what fits in a separate VRAM budget
Zero-copy data movement between CPU and GPU workloads
Simpler programming model for multi-modal applications

# Unified memory means you can allocate large tensors accessible by both CPU/GPU
import torch

# This tensor is accessible from both CPU and GPU without explicit transfers
tensor = torch.zeros(1024, 1024, device='cuda')
cpu_view = tensor.cpu()  # Zero-copy access

JetPack SDK

NVIDIA's JetPack bundles Ubuntu + CUDA + TensorRT + cuDNN + DeepStream in a single flashable image. The key tool here is TensorRT — it takes standard PyTorch/ONNX models and compiles them into optimized inference engines for the specific Jetson hardware.

# Convert an ONNX model to TensorRT engine
trtexec \
  --onnx=model.onnx \
  --saveEngine=model.trt \
  --fp16 \
  --workspace=4096  # 4GB workspace

# Run inference with the optimized engine
# Speed improvement: typically 2-5x vs raw PyTorch

Power profiles

Jetson lets you tune the TDP/performance tradeoff at runtime:

# Check available power modes
sudo nvpmodel -q --verbose

# Set max performance mode
sudo nvpmodel -m 0

# Low-power mode (useful for battery-powered projects)
sudo nvpmodel -m 1

# Check actual power draw
cat /sys/bus/i2c/drivers/ina3221x/*/hwmon/hwmon*/in1_input  # mV
cat /sys/bus/i2c/drivers/ina3221x/*/hwmon/hwmon*/curr1_input  # mA

Use Cases Where Jetson Shines

Local AI assistant

Running a 7B LLM locally on Jetson is entirely practical in 2026. With Ollama:

# Install Ollama (ARM64/JetPack build)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model optimized for edge inference  
ollama pull llama3.2:7b-instruct-q4_K_M

# Run with GPU acceleration (automatic on Jetson)
ollama run llama3.2:7b-instruct-q4_K_M

# Benchmark throughput
ollama run llama3.2:7b-instruct-q4_K_M \
  "Write a 500 word essay on photosynthesis" \
  --verbose 2>&1 | grep "eval rate"
# Expect: 12-18 tok/s on Orin Nano 8GB

Resources like jetson-ai-assistant.com have community-contributed benchmarks and configuration guides specifically for LLM inference on Jetson hardware.

Computer vision pipelines

This is where Jetson has always been strongest. NVIDIA's DeepStream SDK enables multi-stream video analytics:

# Simple inference pipeline with DeepStream
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst

# A pipeline that runs YOLOv8 on 4 camera streams simultaneously
pipeline_str = """
  v4l2src ! video/x-raw,width=1920,height=1080,framerate=30/1
  ! nvvideoconvert ! mux.sink_0
  nvstreammux name=mux batch-size=4 width=640 height=640
  ! nvinfer config-file-path=/models/yolov8.cfg
  ! nvmultistreamtiler ! nvvideoconvert ! nveglglessink
"""

YOLOv8 on Orin Nano at 640×640 runs at ~60 FPS — more than enough for real-time detection.

Robotics and autonomous systems

Jetson is the go-to platform for ROS 2 robotics projects:

# ROS 2 Humble on JetPack 6
sudo apt install ros-humble-desktop

# Run a perception node with GPU acceleration
ros2 run image_pipeline image_proc \
  --ros-args -p use_gpu:=true

Projects range from autonomous drones to mobile manipulation robots to warehouse AMRs. The combination of neural inference, real-time I/O, and Linux flexibility is hard to beat.

Edge AI with Home Assistant

Integrating Jetson with Home Assistant for local AI in the home is a growing use case:

# configuration.yaml
ollama:
  host: http://jetson-local:11434
  model: llama3.2

# Now you can use local AI for automations
automation:
  - alias: "AI-powered occupancy detection"
    trigger:
      - platform: state
        entity_id: camera.front_door
    action:
      - service: ollama.query
        data:
          prompt: "Is there a person in this image? Answer yes/no only."

edge-ai-hardware.com has detailed guides on integrating edge AI hardware with smart home systems.

Project Ideas to Get Started

Here are concrete projects you can build in a weekend:

1. Local voice assistant
Whisper (speech-to-text) → Llama 3.2 (reasoning) → Piper TTS (text-to-speech). Full conversation loop with zero cloud dependency. Total setup: ~2 hours.

2. Smart security camera
Stream from IP cameras → YOLOv8 detection → alert only on specific objects. No cloud subscription, no per-image fees, works offline.

3. Document Q&A system
Index your PDFs/notes with ChromaDB → RAG pipeline with Ollama → query your own knowledge base. Privacy-preserving personal search.

4. Code review bot
Hook into your git workflow → Qwen2.5-Coder analyzes diffs → posts review comments. Local, free after hardware cost.

Pre-Built Options

Building from a bare Jetson module requires a carrier board, thermal solution, storage, and software setup. That's a fun weekend project for hardware enthusiasts, but not everyone wants to go that route.

Pre-configured Jetson AI boxes have emerged as an alternative. jetson-ai-box.com and orin-nano-ai.com list various pre-built options with different software configurations. For an appliance that includes OpenClaw pre-installed with Telegram/Discord integration, jetson-orin-ai.com covers some of the available products in that space.

The tradeoff is straightforward: more money, less time. For a production deployment or a gift for a non-technical person, pre-built makes sense. For learning, bare modules are more educational.

Getting Started: Your First Day with Jetson

# After flashing JetPack via SDK Manager:

# 1. Check your setup
jtop  # Jetson system monitor (like htop but shows GPU/NVENC/power)

# 2. Run a quick GPU benchmark
python3 -c "
import torch
import time
x = torch.randn(4096, 4096, device='cuda')
start = time.time()
for _ in range(100):
    y = x @ x
torch.cuda.synchronize()
print(f'Matrix multiply: {(time.time()-start)*10:.1f}ms per op')
"

# 3. Install Ollama and pull a model
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull llama3.2
ollama run llama3.2 "Hello from my Jetson!"

# 4. Monitor power consumption
sudo tegrastats --interval 1000

Resources

The Jetson ecosystem has a solid community:

NVIDIA Jetson Developer Forums — official support, active devs
jetson-ai-assistant.com — community guides for LLM on Jetson
Jetson Hacks — tutorials and hardware mods
r/JetsonNano — active subreddit (covers all Jetson, not just Nano)
edge-ai-hardware.com — edge AI hardware comparisons and benchmarks

The Jetson platform in 2026 is genuinely mature. The tooling is solid, the community is active, and the hardware hits a power/performance point that no x86 platform can match. If you're building anything that needs AI inference outside a data center — robotics, smart cameras, edge inference, local AI assistants — it deserves serious consideration.

What are you building with Jetson? Drop a comment — always interested in what people are working on at the edge.

Your AI's Memory Is Your Most Valuable Asset — Here's Why You Should Own It

Yanko Alexandrov — Sat, 28 Mar 2026 10:08:14 +0000

Think about everything you've told your AI assistant in the last month.

Your work schedule. Your communication style. Your business plans. Your personal preferences. Maybe even your passwords, your financial situation, your health concerns.

Now ask yourself: who owns all of that?

If you're using ChatGPT, Claude, Gemini, or any cloud AI service — they do. Your AI's memory sits on their servers, governed by their terms of service, accessible to their engineers, and potentially used to train their next model.

The Compounding Value of AI Memory

Here's what most people don't realize: the longer you use an AI assistant, the more valuable its memory becomes.

Week one: Your AI is generic. Same answers as everyone else.
Month three: It knows your writing style, your projects, your decision patterns.
Year one: It has context that would take weeks to rebuild — relationships between projects, lessons learned, preferences you've forgotten you even expressed.

This accumulated knowledge is arguably more valuable than the AI model itself. Models can be swapped and upgraded. But your unique context? Irreplaceable.

And right now, most people are storing this irreplaceable asset on someone else's computer.

Cloud AI: Renting Your Own Brain

Your Data Trains Their Models

OpenAI's terms explicitly state they may use your conversations to improve their models. Google's Gemini conversations are reviewed by human raters. Every brilliant idea you brainstorm — it's all potential training data.

Vendor Lock-In Is Real

Try exporting your full conversation history from ChatGPT. You'll get a JSON dump of raw text — no context, no relationship mapping. Switch to Claude? Start from zero.

The Math

At $20/month for ChatGPT Plus: $720 over three years. And your memory is still rented, not owned.

The Alternative: Local AI Hardware

This is why we built ClawBox — a dedicated AI hardware device (NVIDIA Jetson Orin Nano 8GB + 512GB NVMe SSD) that runs OpenClaw 24/7 on your desk.

Your AI's memory lives on that 512GB drive. Physically. In your home or office.

What this means:

✅ Total Data Sovereignty — No cloud sync, no third-party access
✅ Memory That Survives Everything — Cancel a subscription? Your data stays
✅ No Training on Your Data — Your conversations stay private
✅ True Portability — Copy the drive, clone it, version control with git

But Cloud AI Is More Powerful?

Yes, cloud models are currently larger. But here's the trick: ClawBox uses cloud AI models while keeping your memory local.

OpenClaw routes queries to any cloud API — Claude, GPT, Gemini. You get frontier intelligence. But conversation history, memory files, learned context — all stays on your local drive.

Cloud intelligence + local memory = best of both worlds.

For tasks that don't need frontier models? ClawBox runs quantized local models at 10-15 tok/s on the Jetson's 67 TOPS GPU. Email triage, quick questions, scheduled tasks — all handled locally.

The Memory File System

On ClawBox, your AI's memory is beautifully simple:

MEMORY.md    — Long-term curated knowledge
memory/      — Daily logs (YYYY-MM-DD.md)
SOUL.md      — How your AI behaves
USER.md      — What your AI knows about you

Plain text files. Read them, edit them, back them up, git them. No proprietary format, no API lock-in.

After a year, this becomes a genuinely unique artifact — a detailed map of your professional and personal life, curated by an AI that knows you better than any cloud service ever could.

Who Is This For?

🔒 People who handle sensitive information
🔄 People who hate vendor lock-in
📈 People who think in years, not months
🛡️ People who care about privacy — not because they have something to hide, but because it's their right

170+ units shipped to 22 countries. €549, one-time purchase. No subscriptions.

Get ClawBox →

Built by ID Robots in Bulgaria. Powered by NVIDIA Jetson Orin Nano Super (8GB, 67 TOPS). Runs OpenClaw — open-source AI agent framework.

Originally published on openclawhardware.dev. Also on Medium.

Why I Switched from Cloud AI to a Dedicated AI Box (And Why You Should Too)

Yanko Alexandrov — Thu, 26 Mar 2026 15:55:42 +0000

I used to think cloud AI was the obvious choice. It's convenient, always updated, and someone else handles the infrastructure. I was paying for ChatGPT Plus, using Claude Pro, and had GitHub Copilot running in my editor. That's $60+ per month, and I hadn't even counted the privacy cost.

Then my company had a "data incident" reminder from legal: don't paste customer data into third-party AI tools. That memo made me actually think about what I'd been feeding these cloud services for the past year.

The Subscription Fatigue Is Real

Let's talk numbers. The average developer or knowledge worker in 2026 is juggling:

ChatGPT Plus: $20/mo
Claude Pro: $20/mo
GitHub Copilot: $10/mo
Midjourney or similar: $10-30/mo

That's $60-80/month, or $720-960 per year, for AI tools. And every six months there's a new "must-have" service to add.

I'm not saying cloud AI is bad. These are excellent tools. But the accumulated cost, combined with the privacy reality, started bothering me.

What Actually Goes to the Cloud

When you use a cloud AI assistant for daily tasks, consider what you're sharing:

Your prompts and conversations (used for training in many cases)
Document contents you paste in for analysis
Code you ask it to review
Business context, names, and details that slip in naturally

Most services have opt-outs, but they're buried in settings and sometimes reset. And even if your data isn't used for training, it's still being transmitted to and processed on someone else's servers.

For personal projects, this is fine. For anything touching work, clients, or anything sensitive — it's worth thinking about.

The Dedicated Box Approach

A few months ago I started looking at running AI locally. I'd tried it on my laptop, but the performance was underwhelming — slow inference, fan screaming, battery draining. Not a real workflow.

Then I came across ClawBox by OpenClaw Hardware — a pre-configured AI hardware device built on the NVIDIA Jetson Orin Nano 8GB.

The specs that made me pay attention:

67 TOPS (Tera Operations Per Second) — that's real AI acceleration, not CPU scraping
15W power consumption — runs 24/7 for about $1.50/month in electricity
512GB NVMe SSD — enough storage for multiple models
€549 one-time cost — no subscription

At my previous cloud AI spend rate, it pays for itself in under 9 months.

What "Pre-configured" Actually Means

The thing that sold me wasn't just the hardware — it was the OpenClaw software that comes pre-installed.

OpenClaw is an AI assistant platform that runs locally and connects to:

Telegram — chat with your AI assistant from anywhere
WhatsApp — same AI, different app
Discord — great for teams
Browser automation — it can actually browse the web on your behalf

Setup genuinely took about 5 minutes. Plug it in, scan a QR code, done. The box runs 24/7, draws less power than a lightbulb, and handles requests even when my laptop is off.

Real Use Cases From My Week

Here's what I've actually been using it for:

Document analysis: I paste in contracts, research papers, client briefs. None of that leaves my network. The model processes it locally and gives me a summary.

Daily assistant: "What's on my calendar today? Draft a reply to this email." It handles Telegram messages, so I can chat with it like a regular contact.

Browser research: I ask it to look up product comparisons, pull data from websites, summarize articles. It does the browsing, I get the result.

Code review: Not as powerful as Copilot for autocomplete, but for reviewing logic and explaining code — solid, and completely private.

The Honest Trade-offs

I want to be real about this: local AI isn't GPT-4 level. The model that runs well on 8GB of RAM is going to be smaller and less capable than frontier cloud models.

What you get instead:

✅ Zero subscription cost after hardware purchase
✅ Complete data privacy — nothing leaves your home/office network
✅ Always available, no outages, no rate limits
✅ Customizable — you control which model runs, how it's configured
✅ No usage caps

What you trade:

❌ Raw capability vs frontier models (GPT-4o, Claude 3.7)
❌ Requires initial setup (though ClawBox minimizes this)
❌ Hardware upfront cost

For many workflows, the local model is good enough. For the edge cases where it's not, you can still use cloud AI — but now it's a deliberate choice, not the default.

Who This Makes Sense For

Local AI hardware makes the most sense if:

You're spending $40+/month on AI subscriptions
You work with sensitive data (legal, medical, financial, client work)
You want a persistent AI assistant that's always on
You're technically curious and want to control your own infrastructure
You hate subscription fatigue as much as I do

If you're a casual user who occasionally asks ChatGPT questions — cloud is probably fine. But if AI has become a daily work tool, the math and privacy case for owning your hardware gets pretty compelling.

Getting Started

If you want to explore this route, openclawhardware.dev is a good starting point — they have a ready-to-go solution. Or you can go DIY with a Jetson Orin Nano and install OpenClaw yourself (it's open source).

The cloud isn't going anywhere, and I still use it occasionally. But for daily work? My little box handles it quietly, privately, and without charging me every month for the privilege.

Have you run into subscription fatigue with cloud AI? Or tried local inference at home? I'd love to hear what's working for you in the comments.

🔗 More Resources on Local AI Hardware

If you're exploring dedicated AI hardware, here are some guides I've found helpful:

DIY AI Assistant Setup Guide — Build your own from scratch
Edge AI Hardware Comparison — Compare options side by side
Self-Hosted AI Guide — Everything about running AI locally
Jetson AI Assistant — NVIDIA Jetson-specific setups
Local AI Box Options — Pre-built local AI solutions
Low Power AI Devices — Energy-efficient AI hardware
No Cloud AI — Going fully offline with AI
Home AI Assistant — AI for your home setup
Private AI Hardware — Privacy-focused options
Run AI Locally — Getting started with local inference
Offline AI Device — Best offline-capable hardware
Personal AI Server — Building your own AI server
Mini AI Server — Compact solutions
Plug and Play AI — Zero-config AI boxes
AI Home Server — Home server AI setups

These are community resources exploring different approaches to local AI.

Your AI's Memory Is Your Most Valuable Asset

Yanko Alexandrov — Wed, 25 Mar 2026 08:07:29 +0000

Every conversation, every preference, every learned pattern — your AI's memory becomes more valuable over time.

But who actually owns it?

With cloud AI, your memory sits on their servers. Cancel your subscription? Gone. Company changes terms? Too bad. Your years of accumulated context — the thing that makes your AI actually useful — belongs to them.

The Memory Ownership Problem

We talk endlessly about AI capabilities: context windows, reasoning, speed. But we rarely talk about the most valuable thing that accumulates over time: personalized memory.

After months of use, your AI knows:

Your communication style
Your project contexts and preferences
Your recurring tasks and how you like them done
Your team members and relationships
Your decision-making patterns

This compounds. It becomes worth more than the model itself.

Who Owns Your AI's Brain?

If your AI runs on someone else's server, the answer is: they do.

This is why we built ClawBox — an NVIDIA Jetson-based AI assistant box that runs OpenClaw locally. Your AI's memory lives on a 512GB SSD sitting on your desk. You physically own it. No subscription required to keep your memories.

Cancel nothing. Lose nothing.

The Practical Difference

Local AI memory means:

Portability: Export and move your entire AI context anytime
Privacy: Your conversations never leave your home
Permanence: Your AI relationship isn't tied to a service agreement
Control: You decide what gets remembered and what gets forgotten

This is a cross-post from the ClawBox blog. Read the full article at: https://openclawhardware.dev/blog/your-ai-memory-most-valuable-asset

ClawBox is a plug-and-play AI hardware box (NVIDIA Jetson Orin Nano + OpenClaw). €549, ships worldwide.

How Our AI Agent Generated €76K in Revenue in 49 Days — Running on a $549 Box

Yanko Alexandrov — Fri, 20 Mar 2026 17:04:28 +0000

This isn't a hypothetical. This is what actually happened.

The Setup

I built ClawBox — a dedicated AI hardware device (NVIDIA Jetson Orin Nano 8GB + 512GB NVMe SSD, €549) pre-configured with OpenClaw. It's a plug-and-play AI assistant that runs 24/7 on your desk.

The twist: our AI agent "Mike" runs most of our daily business operations on one of these boxes.

What Mike Does Every Day

Mike runs on OpenClaw with 27 automated cron jobs:

Marketing (~15 tasks/day):

Posts helpful comments on Reddit (r/LocalLLaMA, r/selfhosted, r/homelab, r/JetsonNano)
Comments on YouTube OpenClaw tutorial videos
LinkedIn engagement on AI hardware posts
Quora answers about AI hardware/self-hosting
X/Twitter replies on OpenClaw and AI hardware threads

Sales & Support:

Abandoned cart recovery emails (every 3 hours)
Email drip sequences for subscribers
Discord community support bot
Order status monitoring via Stripe API

Analytics & SEO:

Daily SEO reports from Google Search Console + GA4
Manages 90 SEO satellite domains
Monitors sales and sends daily reports to Telegram
TV dashboard with real-time news and mascot updates

Operations:

Automated data backups every 6 hours
Inventory tracking
Supplier communication

The Numbers

In 49 days (Feb 1 – Mar 20, 2026):

150+ orders
€76K+ revenue
24 countries (US, Germany, France, Italy, UK, Canada, Netherlands, Sweden...)
Zero paid advertising — all organic
~$5/month in Claude API costs
15W power draw — electricity cost is negligible

The Tech Stack

Hardware: NVIDIA Jetson Orin Nano 8GB (67 TOPS, 15W)
Storage: 512GB NVMe SSD
OS: JetPack 6.2.2 (Ubuntu-based)
Software: OpenClaw (open-source, MIT license)
Models: Claude Sonnet (cloud, via API) + Ollama 7B (local)
Messaging: Telegram, Discord, WhatsApp
Automation: OpenClaw cron jobs + browser automation

Key Lessons

1. Dedicated hardware > laptop/VPS

Running an AI agent on your daily driver is impractical. It needs to run 24/7. A dedicated low-power device that draws 15W means you forget it's there.

2. Model routing saves money

Don't throw Opus at every message. Haiku for quick tasks, Sonnet for medium, Opus for deep reasoning. This dropped our API costs from $50/month to $5/month.

3. Browser automation is the killer feature

Most people think of AI assistants as chatbots. The real value is browser automation — an AI that can log into websites, fill forms, scrape data, and take actions on your behalf.

4. Compounding automations are the moat

One cron job is a toy. 27 coordinated cron jobs running 24/7 for weeks is a business advantage. The compound effect of consistent, automated outreach is enormous.

Is This For You?

If you're running OpenClaw on a VPS or laptop and want dedicated hardware:

DIY route: Jetson Orin Nano dev kit ($250) + NVMe SSD ($50) + weekend of setup
Plug-and-play: ClawBox (€549) — everything pre-configured, 5-minute setup

The hardware doesn't matter as much as the automation strategy. OpenClaw is free and open-source. The value is in how you configure it.

AMA

Happy to answer questions about the setup, the business model, or the specific automations. Drop a comment below.

Why I Built a Dedicated Hardware Box for OpenClaw (and Why You Might Want One Too)

Yanko Alexandrov — Tue, 17 Mar 2026 13:42:32 +0000

OpenClaw changed how I think about AI assistants. But running it on my laptop felt wrong — burning 100W+ 24/7, fan noise, tying up my main machine. So I built a dedicated box for it.

The Problem

OpenClaw is incredible software. Browser automation, messaging integrations, scheduling, coding agents — it does it all. But it needs to run always on. Leaving a laptop or desktop running around the clock is:

Expensive: 100-300W = $15-45/month in electricity alone
Noisy: Fans spinning constantly
Risky: Your main machine is now an always-on server
Wasteful: Using 10% of a powerful CPU

Cloud VMs solve some of this, but then you're back to paying monthly and trusting someone else with your data.

The Solution: Jetson Orin Nano

I landed on NVIDIA's Jetson Orin Nano:

Spec	Value
AI Performance	67 TOPS
Power Draw	15W
Memory	8GB LPDDR5
Storage	512GB NVMe SSD
Size	Fits in your palm

15 watts. That's less than a light bulb. Running 24/7/365, that's about $1.50/month in electricity.

What It Runs

The full OpenClaw stack runs beautifully:

Browser automation (Playwright/Chromium) — web scraping, form filling, monitoring
Whisper STT — local speech-to-text, no cloud API
Kokoro TTS — local text-to-speech
7B-13B LLMs via Ollama — local inference for simple tasks
Cloud LLMs (Claude, GPT) — for heavy reasoning (hybrid approach)
Telegram/WhatsApp/Discord — messaging on all platforms
Cron jobs & scheduling — automated workflows 24/7

The Setup Experience

Nobody wants to spend a weekend configuring Linux on embedded hardware. So I made it simple:

Plug in power + ethernet
Scan QR code on your phone
Connect your Telegram/WhatsApp
Done. 5 minutes.

OpenClaw comes pre-installed and pre-configured.

Why Not a Raspberry Pi?

No GPU compute — can't run local models efficiently
4-8GB RAM — OpenClaw + Chromium + LLM won't fit
No AI acceleration — Jetson's 67 TOPS vs Pi's ~0
USB storage — NVMe is 10x faster

The Jetson is in a different league for AI workloads.

Real-World Results

We've shipped 150+ ClawBox units to 15 countries:

Solo founders using OpenClaw as a 24/7 executive assistant
Developers running coding agents on dedicated hardware
Privacy-conscious users keeping all data on-premise
Small businesses automating customer support and social media

The Numbers

Hardware cost: one-time purchase (no subscription)
Power cost: ~$1.50/month
Setup time: 5 minutes
Uptime: 99.9%+ (it just runs)

Compare that to cloud VMs at $20-50/month or leaving your $2000 laptop running 24/7.

NemoClaw Makes It Even Better

With NVIDIA's NemoClaw at GTC 2026, the Jetson + OpenClaw stack gets enterprise-grade security guardrails, content filtering, and safe tool execution — all running locally.

Try It

If you're running OpenClaw on your laptop and feeling the pain, dedicated hardware is the answer.

openclawhardware.dev

Happy St. Patrick's Day — 17% off today with code LUCKY17

What's your OpenClaw setup? Running it on a server, VM, or your main machine? I'd love to hear in the comments.

Why I Built a $549 AI Box Instead of Using Cloud GPUs

Yanko Alexandrov — Sat, 14 Mar 2026 13:07:32 +0000

I've been building AI tools for the past 3 years. Started with cloud VPS instances, moved to local Mac Mini, and finally landed on something that just works — a dedicated AI box running on NVIDIA Jetson Orin Nano.

The Problem with Cloud AI

Don't get me wrong — cloud AI is powerful. But for a personal AI assistant that runs 24/7:

$50-100/month adds up fast ($600-1200/year)
Your data goes through someone else's servers
Latency matters when you want instant responses
API rate limits hit at the worst times

What I Built

ClawBox — a plug-and-play AI assistant box:

NVIDIA Jetson Orin Nano 8GB — 67 TOPS AI performance
512GB NVMe SSD
Carbon fiber case
OpenClaw pre-installed (open source)
15W power consumption (less than a lightbulb)

You plug it in, scan a QR code, and you have a local AI assistant running on Telegram, WhatsApp, Discord — with full browser automation.

The Math

Setup	Year 1	Year 2	Year 3
Cloud VPS (A100)	$1,200	$2,400	$3,600
Mac Mini M4 Pro	$2,399	$2,399	$2,399
ClawBox	$598	$598	$598

One-time purchase. No subscriptions. Your data stays home.

What It Actually Does

This isn't just inference. OpenClaw turns the Jetson into a full AI agent:

🌐 Browser automation — it can navigate websites, fill forms, scrape data
🗣️ Voice assistant — natural conversation, multiple voices
📱 Multi-platform — Telegram, WhatsApp, Discord, web interface
🔧 Skill system — add custom capabilities (weather, calendar, email, IoT)
🏠 Home Assistant integration — control your smart home with AI

NVIDIA Noticed

NVIDIA featured OpenClaw in their official DGX Spark playbook at GTC 2026. That was a pretty surreal moment.

Weekend Sale

If you've been thinking about running AI locally, this weekend might be a good time. I'm running a 10% off flash sale — use code WEEKEND10 at checkout.

openclawhardware.dev

Ships worldwide via DHL Express. Code expires Sunday midnight.

Happy to answer any questions about the hardware, OpenClaw, or running AI on Jetson in the comments!

NVIDIA Featured OpenClaw at GTC 2026

Yanko Alexandrov — Fri, 13 Mar 2026 09:15:32 +0000

At this week's NVIDIA GTC 2026 — the world's largest AI conference — NVIDIA built an entire event around OpenClaw.

Build-a-Claw at GTC

NVIDIA is running a "Build-a-Claw" event at GTC Park (March 16-19) where 30,000 attendees build and deploy personal AI agents using OpenClaw. They're selling Jetson hardware on-site.

From the NVIDIA blog:

"OpenClaw, the fastest-growing open source project in history"

The Official Playbook

NVIDIA published a complete OpenClaw Playbook for DGX Spark — a step-by-step guide to run OpenClaw on Grace Blackwell hardware.

The playbook covers:

Setting up OpenClaw as a local-first agent
Full tool access: file system, browser, terminal, messaging
Running with no cloud dependency
Connecting to Telegram, WhatsApp, Discord

Why This Matters

When NVIDIA builds their keynote week around OpenClaw and calls it "the fastest-growing open source project in history," the signal is clear: personal AI agents on local hardware are becoming infrastructure.

What We've Been Shipping

At ID Robots, we started shipping ClawBox in February — a pre-configured AI computer running OpenClaw on NVIDIA Jetson Orin Nano.

Specs:

NVIDIA Jetson Orin Nano 8GB (67 TOPS)
512GB NVMe SSD
OpenClaw pre-installed
5-minute setup: unbox → plug in → scan QR → AI agent live

Traction: 140+ units, 15 countries, zero ad spend, $598 one-time.

Coming Next Week: v2.2.3

OpenClaw v2.2.3 transforms ClawBox into a full desktop experience:

🖥️ Window Manager — draggable, resizable windows
💻 Terminal — full shell access through browser
🌐 Browser Automation — real Chrome controlled by AI
🖥️ Remote Desktop (VNC) — screen sharing
📁 File Manager — web-based file management
🏪 App Store — 18,000+ apps, one-click install

Free update for all ClawBox owners.

The Economics

Cloud AI agents: $75-300/week in API fees.
ClawBox: $598 once, runs 24/7, ~$15/year electricity.

For daily tasks (cron jobs, browser automation, calendar, messaging), local inference is sufficient. For complex reasoning, ClawBox routes to cloud APIs (hybrid approach).