Forem: Sienna

ValRequest - Turn Feelings Into Words

Sienna — Fri, 06 Feb 2026 02:44:38 +0000

Love is a feeling; expressing it is an art. Use ValRequest to craft personalized, heartfelt messages that capture your unique story. Make every word count.

What is ValRequest?

ValRequest is an AI-powered tool that creates short, personalized romantic messages. You choose the recipient, style, and keywords; ValRequest gives you three unique options in seconds.

How to Use ValRequest

ValRequest turns your feelings into words in three simple steps:

1. Choose Recipient & Style

Select who the message is for (partner, crush, or friend) and pick a tone:

💕 Heartfelt - Sincere and emotional
😄 Humorous - Light and funny
🎭 Shakespeare - Poetic and classical
🥰 Cute - Sweet and playful

2. Add Your Keywords

Type a few words that describe your relationship or what you want to say—ValRequest uses them to personalize your greeting.

3. Generate & Copy

Click Generate. ValRequest will create three options. Copy your favorite or save it as an image.

Message Styles & Examples

ValRequest offers various romantic styles:

Classic Romance (Pride & Prejudice style)

"In a world of fleeting moments, you are my forever. My heart knew you before my eyes ever did."

Epic Love (The Notebook style)

"I would cross every ocean, climb every mountain, just to see you smile. You are worth every journey."

Poetic Soul (Shakespeare style)

"You are the poem I never knew how to write, the song my heart always wanted to sing."

Sweet & Playful (Rom-com style)

"You're my favorite notification, my best plot twist, the reason I smile at my phone like an idiot."

Who Is ValRequest For?

ValRequest is perfect for:

Romantics - Anyone who wants to say "I love you" in a way that feels uniquely theirs
Last-minute senders - Need a sincere message fast? ValRequest gives you three options in seconds
Friends & crushes - Perfect for Valentine's notes to friends or that special someone you're still getting to know

Pricing & Credits

ValRequest uses a credit system:

Earn credits by signing up
Generating messages uses credits per request
Additional credits available for purchase

Privacy & Security

ValRequest protects your privacy by:

Only using your inputs (recipient type, style, keywords) to generate messages
Not storing or sharing your generated text for advertising
Using secure API technology for message generation

Try ValRequest now and turn your feelings into words with AI-powered personalized Valentine's messages that sound uniquely like you.

Qwen3-Coder-Next: The Complete 2026 Guide to Running Powerful AI Coding Agents Locally

Sienna — Wed, 04 Feb 2026 12:35:35 +0000

🎯 Core Highlights (TL;DR)

Revolutionary Efficiency: Qwen3-Coder-Next achieves Sonnet 4.5-level coding performance with only 3B activated parameters (80B total with MoE architecture)
Local-First Design: Runs on consumer hardware (64GB MacBook, RTX 5090, or AMD Radeon 7900 XTX) with 256K context length
Open Weights: Fully open-source model designed specifically for coding agents and local development
Real-World Performance: Scores 44.3% on SWE-Bench Pro, competing with models 10-20x larger in active parameters
Cost Effective: Eliminates expensive API costs while maintaining competitive coding capabilities

What is Qwen3-Coder-Next?
Key Features and Architecture
Performance Benchmarks
Hardware Requirements and Setup
How to Install and Run Qwen3-Coder-Next
Integration with Coding Tools
Quantization Options Explained
Real-World Use Cases and Performance
Comparison: Qwen3-Coder-Next vs Claude vs GPT
Common Issues and Solutions
FAQ
Conclusion and Next Steps

What is Qwen3-Coder-Next?

Qwen3-Coder-Next is an open-weight language model released by Alibaba's Qwen team in February 2026, specifically designed for coding agents and local development environments. Unlike traditional large language models that require massive computational resources, Qwen3-Coder-Next uses a sophisticated Mixture-of-Experts (MoE) architecture that activates only 3 billion parameters at a time while maintaining a total parameter count of 80 billion.

Why It Matters

The model represents a significant breakthrough in making powerful AI coding assistants accessible to individual developers without relying on expensive cloud APIs or subscriptions. With the recent controversies around Anthropic's Claude Code restrictions and OpenAI's pricing models, Qwen3-Coder-Next offers a compelling alternative for developers who want:

Data Privacy: Your code never leaves your machine
Cost Control: No per-token pricing or monthly subscription limits
Tool Freedom: Use any coding agent or IDE integration you prefer
Offline Capability: Work without internet connectivity

💡 Key Innovation
The model achieves performance comparable to Claude Sonnet 4.5 on coding benchmarks while using only 3B activated parameters, making it feasible to run on high-end consumer hardware.

Key Features and Architecture

Technical Specifications

Specification	Details
Total Parameters	80B
Activated Parameters	3B (per inference)
Context Length	256K tokens (native support)
Architecture	Hybrid: Gated DeltaNet + MoE + Gated Attention
Number of Experts	512 total, 10 activated per token
Training Method	Large-scale executable task synthesis + RL
Model Type	Causal Language Model
License	Open weights

Architecture Breakdown

The model uses a unique hybrid attention mechanism:

12 × [3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE)]

What makes this special:

Gated DeltaNet: Efficient linear attention for long-range dependencies
Mixture of Experts (MoE): Only activates 10 out of 512 experts per token, dramatically reducing computational cost
Gated Attention: Traditional attention mechanism for critical reasoning tasks
Shared Experts: 1 expert always active for core capabilities

⚠️ Important Note
This model does NOT support thinking mode (<think></think> blocks). It generates responses directly without visible reasoning steps.

Training Methodology

Qwen3-Coder-Next was trained using:

Executable Task Synthesis: Large-scale generation of verifiable programming tasks
Environment Interaction: Direct learning from execution feedback
Reinforcement Learning: Optimization based on task success rates
Agent-Specific Training: Focused on long-horizon reasoning and tool usage

Performance Benchmarks

SWE-Bench Results

Model	SWE-Bench Verified	SWE-Bench Pro	Avg Agent Turns
Qwen3-Coder-Next	42.8%	44.3%	~150
Claude Sonnet 4.5	45.2%	46.1%	~120
Kimi K2.5	40.1%	39.7%	~50
GPT-5.2-Codex	43.5%	42.8%	~130
DeepSeek-V3	38.9%	37.2%	~110

Other Coding Benchmarks

TerminalBench 2.0: Competitive performance with frontier models
Aider Benchmark: Strong tool-calling and file editing capabilities
Multilingual Support: Excellent performance across Python, JavaScript, Java, C++, and more

📊 Interpretation
While Qwen3-Coder-Next takes more agent turns on average (~150 vs ~120 for Sonnet 4.5), it achieves comparable success rates. This suggests it may require more iterations but ultimately solves similar numbers of problems.

Real-World Performance Reports

From community testing:

Speed: 20-40 tokens/sec on consumer hardware (varies by quantization)
Context Handling: Successfully manages 64K-128K context windows
Tool Calling: Reliable function calling with JSON format
Code Quality: Generates production-ready code for most common tasks

Hardware Requirements and Setup

Minimum Requirements by Quantization Level

Quantization	VRAM/RAM Needed	Hardware Examples	Speed (tok/s)
Q2_K	~26-30GB	32GB Mac Mini M4	15-25
Q4_K_XL	~35-40GB	64GB MacBook Pro, RTX 5090 32GB	25-40
Q6_K	~50-55GB	96GB Workstation, Mac Studio	30-45
Q8_0	~65-70GB	128GB Workstation, Dual GPUs	35-50
FP8	~90-110GB	H100, A100, Multi-GPU setup	40-60

Recommended Configurations

Budget Setup (~$2,000-3,000)

Mac Mini M4 with 64GB unified memory
Quantization: Q4_K_XL or Q4_K_M
Expected speed: 20-30 tok/s
Context: Up to 100K tokens

Enthusiast Setup (~$5,000-8,000)

RTX 5090 (32GB) + 128GB DDR5 RAM
Quantization: Q6_K or Q8_0
Expected speed: 30-40 tok/s
Context: Full 256K tokens

Professional Setup (~$10,000-15,000)

Mac Studio M3 Ultra (256GB) OR
Dual RTX 4090/5090 setup OR
AMD Radeon 7900 XTX + 256GB RAM
Quantization: Q8_0 or FP8
Expected speed: 40-60 tok/s
Context: Full 256K tokens

💡 Pro Tip
MoE models like Qwen3-Coder-Next can efficiently split between GPU (dense layers) and CPU RAM (sparse experts), allowing you to run larger quantizations than your VRAM alone would suggest.

How to Install and Run Qwen3-Coder-Next

Method 1: Using llama.cpp (Recommended for Most Users)

Step 1: Install llama.cpp

# macOS with Homebrew
brew install llama.cpp

# Or build from source
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

Step 2: Download the Model

# Using Hugging Face CLI (recommended)
llama-cli -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL

# Or download manually from:
# https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF

Step 3: Run the Server

llama-server \
  -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL \
  --fit on \
  --seed 3407 \
  --temp 1.0 \
  --top-p 0.95 \
  --min-p 0.01 \
  --top-k 40 \
  --jinja \
  --port 8080

This creates an OpenAI-compatible API endpoint at http://localhost:8080.

Method 2: Using Ollama (Easiest for Beginners)

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

# Pull and run the model
ollama pull qwen3-coder-next
ollama run qwen3-coder-next

Method 3: Using vLLM (Best for Production)

# Install vLLM
pip install 'vllm>=0.15.0'

# Start server
vllm serve Qwen/Qwen3-Coder-Next \
  --port 8000 \
  --tensor-parallel-size 2 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

Method 4: Using SGLang (Fastest Inference)

# Install SGLang
pip install 'sglang[all]>=v0.5.8'

# Launch server
python -m sglang.launch_server \
  --model Qwen/Qwen3-Coder-Next \
  --port 30000 \
  --tp-size 2 \
  --tool-call-parser qwen3_coder

⚠️ Context Length Warning
The default 256K context may cause OOM errors on systems with limited memory. Start with --ctx-size 32768 and increase gradually.

Integration with Coding Tools

OpenCode (Recommended)

OpenCode is an open-source coding agent that works excellently with Qwen3-Coder-Next:

# Install OpenCode
npm install -g @opencode/cli

# Configure for local model
opencode config set model http://localhost:8080/v1
opencode config set api-key "not-needed"

# Start coding
opencode

Cursor Integration

Open Cursor Settings
Navigate to "Models" → "Add Custom Model"
Enter endpoint: http://localhost:8080/v1
Model name: qwen3-coder-next

Continue.dev Integration

Edit ~/.continue/config.json:

{
  "models": [
    {
      "title": "Qwen3-Coder-Next",
      "provider": "openai",
      "model": "qwen3-coder-next",
      "apiBase": "http://localhost:8080/v1",
      "apiKey": "not-needed"
    }
  ]
}

Aider Integration

aider --model openai/qwen3-coder-next \
      --openai-api-base http://localhost:8080/v1 \
      --openai-api-key not-needed

💡 Best Practice
Use recommended sampling parameters for optimal results:

Temperature: 1.0

Top-p: 0.95

Top-k: 40

Min-p: 0.01

Quantization Options Explained

Understanding Quantization Levels

Quant Type	Bits	Size	Quality	Speed	Best For
Q2_K	2-bit	~26GB	Fair	Fastest	Testing, limited hardware
Q4_K_M	4-bit	~38GB	Good	Fast	Balanced performance
Q4_K_XL	4-bit+	~40GB	Very Good	Fast	Recommended default
Q6_K	6-bit	~52GB	Excellent	Medium	High quality needs
Q8_0	8-bit	~68GB	Near-perfect	Slower	Maximum quality
MXFP4_MOE	4-bit	~35GB	Good	Fast	NVIDIA GPUs only
FP8	8-bit	~95GB	Perfect	Medium	Production use

Unsloth Dynamic (UD) Quantization

The UD- prefix indicates Unsloth's dynamic quantization:

Automatically upcasts important layers to higher precision
Maintains model quality while reducing size
Uses calibration datasets for optimal layer selection
Typically provides better quality than standard quants at same size

Recommended choices:

General use: UD-Q4_K_XL
NVIDIA GPUs: MXFP4_MOE
Maximum quality: Q8_0 or FP8

Real-World Use Cases and Performance

Community Testing Results

Test 1: Simple HTML Game (Flappy Bird)

Model: Q8_0 on RTX 6000
Result: ✅ One-shot success
Speed: 60+ tok/s
Code quality: Production-ready

Test 2: Complex React Application

Model: Q4_K_XL on Mac Studio
Result: ⚠️ Required 2-3 iterations
Speed: 32 tok/s
Code quality: Good with minor fixes needed

Test 3: Rust Code Analysis

Model: Q4_K_XL on AMD 7900 XTX
Result: ✅ Excellent analysis and suggestions
Speed: 35-39 tok/s
Context: 64K tokens handled well

Test 4: Tower Defense Game (Complex Prompt)

Model: Various quantizations
Result: ⚠️ Mixed - better than most local models but not perfect
Common issues: Game balance, visual effects complexity

Performance vs Claude Code

Aspect	Qwen3-Coder-Next (Local)	Claude Code
Speed	20-40 tok/s	50-80 tok/s
First-time success	60-70%	75-85%
Context handling	Excellent (256K)	Excellent (200K)
Tool calling	Reliable	Very reliable
Cost	$0 after hardware	$100/month
Privacy	Complete	Cloud-based
Offline use	✅ Yes	❌ No

📊 Reality Check
While Qwen3-Coder-Next is impressive, it's not quite at Claude Opus 4.5 level in practice. Think of it as comparable to Claude Sonnet 4.0 or GPT-4 Turbo - very capable but may need more guidance on complex tasks.

Comparison: Qwen3-Coder-Next vs Claude vs GPT

Feature Comparison Matrix

Feature	Qwen3-Coder-Next	Claude Opus 4.5	GPT-5.2-Codex	DeepSeek-V3
Deployment	Local/Self-hosted	Cloud only	Cloud only	Cloud/Local
Cost	Hardware only	$100/mo	$200/mo	$0.14/M tokens
Speed (local)	20-40 tok/s	N/A	N/A	15-30 tok/s
Context	256K	200K	128K	128K
Tool calling	✅ Excellent	✅ Excellent	✅ Excellent	✅ Good
Code quality	Very Good	Excellent	Excellent	Good
Privacy	✅ Complete	❌ Cloud	❌ Cloud	⚠️ Depends
Offline	✅ Yes	❌ No	❌ No	⚠️ If local
Open weights	✅ Yes	❌ No	❌ No	✅ Yes

When to Choose Each Model

Choose Qwen3-Coder-Next when:

You have sensitive code/IP concerns
You want zero marginal costs
You need offline capability
You have suitable hardware ($2K-10K budget)
You're comfortable with 90-95% of frontier model capability

Choose Claude Opus 4.5 when:

You need the absolute best coding quality
Speed is critical (faster inference)
You prefer zero setup hassle
Budget allows $100-200/month
You work on very complex, novel problems

Choose GPT-5.2-Codex when:

You want strong reasoning capabilities
You need excellent documentation generation
You prefer OpenAI's ecosystem
You have enterprise ChatGPT access

Common Issues and Solutions

Issue 1: Out of Memory (OOM) Errors

Symptoms: Model crashes during loading or inference

Solutions:

# Reduce context size
--ctx-size 32768  # Instead of default 256K

# Use smaller quantization
# Try Q4_K_M instead of Q6_K

# Enable CPU offloading
--n-gpu-layers 30  # Adjust based on your VRAM

Issue 2: Slow Inference Speed

Symptoms: < 10 tokens/second

Solutions:

Use MXFP4_MOE on NVIDIA GPUs
Enable --no-mmap and --fa on flags
Reduce context window
Check if model is fully loaded to GPU

Issue 3: Model Gets Stuck in Loops

Symptoms: Repeats same actions or text continuously

Solutions:

# Adjust sampling parameters
--temp 1.0        # Default temperature
--top-p 0.95      # Nucleus sampling
--top-k 40        # Top-k sampling
--repeat-penalty 1.1  # Penalize repetition

Issue 4: Poor Tool Calling with OpenCode/Cline

Symptoms: Model doesn't follow tool schemas correctly

Solutions:

Ensure you're using --tool-call-parser qwen3_coder
Update to latest llama.cpp/vLLM version
Try Q6_K or higher quantization
Use recommended sampling parameters

Issue 5: MLX Performance Issues on Mac

Symptoms: Slow prompt processing, frequent re-processing

Solutions:

Use llama.cpp instead of MLX for better KV cache handling
Try LM Studio which has optimized MLX implementation
Reduce branching in conversations (avoid regenerating responses)

⚠️ Known Limitation
MLX currently has issues with KV cache consistency during conversation branching. Use llama.cpp for better experience on Mac.

FAQ

Q: Can I run Qwen3-Coder-Next on a MacBook with 32GB RAM?

A: Yes, but you'll need to use aggressive quantization (Q2_K or Q4_K_M) and limit context to 64K-100K tokens. Performance will be around 15-25 tok/s, which is usable but not ideal for intensive coding sessions.

Q: Is Qwen3-Coder-Next better than Claude Code?

A: Not quite. In practice, it performs closer to Claude Sonnet 4.0 level. It's excellent for most coding tasks but may struggle with very complex, novel problems that Opus 4.5 handles easily. The trade-off is complete privacy and zero ongoing costs.

Q: Can I use this with VS Code Copilot?

A: Not directly as a Copilot replacement, but you can use it with VS Code extensions like Continue.dev, Cline, or Twinny that support custom model endpoints.

Q: How does quantization affect code quality?

A: Q4 and above maintain very good quality. Q2 shows noticeable degradation. For production use, Q6 or Q8 is recommended. The UD (Unsloth Dynamic) variants provide better quality at the same bit level.

Q: Will this work with my AMD GPU?

A: Yes! llama.cpp supports AMD GPUs via ROCm or Vulkan. Users report good results with Radeon 7900 XTX. MXFP4 quantization is NVIDIA-only, but other quants work fine.

Q: Can I fine-tune this model on my own code?

A: Yes, the model supports fine-tuning. Use Unsloth or Axolotl for efficient fine-tuning. However, with 80B parameters, you'll need significant compute (multi-GPU setup recommended).

Q: How does this compare to DeepSeek-V3?

A: Qwen3-Coder-Next generally performs better on coding agent tasks and has better tool-calling capabilities. DeepSeek-V3 is more general-purpose and may be better for non-coding tasks.

Q: Is there a smaller version for lower-end hardware?

A: Consider Qwen2.5-Coder-32B or GLM-4.7-Flash for more modest hardware. They're less capable but run well on 16-32GB systems.

Q: Can I use this commercially?

A: Yes, Qwen3-Coder-Next is released with open weights under a permissive license allowing commercial use. Always check the latest license terms on Hugging Face.

Q: Why does it take so many agent turns compared to other models?

A: The model is optimized for reliability over speed. It takes more exploratory steps but maintains consistency. This is actually beneficial for complex tasks where rushing leads to errors.

Conclusion and Next Steps

Qwen3-Coder-Next represents a significant milestone in making powerful AI coding assistants accessible to individual developers. While it may not match the absolute peak performance of Claude Opus 4.5 or GPT-5.2-Codex, it offers a compelling combination of:

Strong performance (90-95% of frontier models)
Complete privacy (runs entirely on your hardware)
Zero marginal costs (no per-token pricing)
Tool freedom (use any coding agent you prefer)

Recommended Action Plan

Week 1: Testing Phase

Install llama.cpp or Ollama
Download Q4_K_XL quantization
Test with simple coding tasks
Measure speed and quality on your hardware

Week 2: Integration Phase

Choose your preferred coding agent (OpenCode, Aider, Continue.dev)
Configure optimal sampling parameters
Test with real projects
Compare with your current workflow

Week 3: Optimization Phase

Experiment with different quantizations
Optimize context window size
Fine-tune for your specific use cases (optional)
Set up automated workflows

Future Outlook

The gap between open-weight and closed models continues to narrow. With releases like Qwen3-Coder-Next, GLM-4.7-Flash, and upcoming models from DeepSeek and others, we're approaching a future where:

Most developers can run SOTA-level models locally
Privacy and cost concerns are eliminated
Innovation happens in open ecosystems
Tool diversity flourishes without vendor lock-in

Additional Resources

Official Documentation: Qwen Documentation
Model Repository: Hugging Face - Qwen/Qwen3-Coder-Next
GGUF Quantizations: Unsloth GGUF Repository
Technical Report: Qwen3-Coder-Next Technical Report
Community Discussion: r/LocalLLaMA

Last Updated: February 2026 | Model Version: Qwen3-Coder-Next (80B-A3B) | Guide Version: 1.0

💡 Stay Updated
The AI landscape evolves rapidly. Follow Qwen's blog and GitHub repository for updates, and join the LocalLLaMA community for real-world usage tips and optimization techniques.

2026 Complete Guide: How to Use GLM-OCR for Next-Gen Document Understanding — 0.9B-parameter multimodal OCR model for complex document understanding
The Complete 2026 Guide: Moltworker — Running Personal AI Agents on Cloudflare Without Hardware — Deploy AI agents on Cloudflare with no infrastructure costs
Universal Commerce Protocol (UCP): The Complete 2026 Guide to Agentic Commerce Standards — Open standard for AI-powered commerce and payment processing

Qwen3-Coder-Next Complete 2026 Guide - Running AI Coding Agents Locally

2026 Complete Guide: How to Use GLM-OCR for Next-Gen Document Understanding

Sienna — Wed, 04 Feb 2026 12:24:12 +0000

🎯 Core Takeaways (TL;DR)

GLM-OCR is a 0.9B-parameter multimodal OCR model built on the GLM-V architecture, designed for complex document understanding, not just text extraction.[1][2]
It delivers structure-first outputs (semantic Markdown, JSON, LaTeX), accurately reconstructing tables, formulas, layout, and even handwriting across 100+ languages.[1]
GLM-OCR achieves state-of-the-art performance on OmniDocBench V1.5 (94.62) while remaining lightweight and fast, with ~1.86 PDF pages/second, making it suitable for research, finance, legal, and developer workflows with open Apache-2.0 weights.[1][2][3]

What Is GLM-OCR?
How Does GLM-OCR Work Architecturally?
What Are the Key Features and Technical Specs?
How Well Does GLM-OCR Perform? (Benchmarks & Precision)
Where Can You Use GLM-OCR? Real-World Use Cases
GLM-OCR vs Other OCR Models (PaddleOCR, DeepSeekOCR, VLMs)
How to Deploy and Use GLM-OCR in Practice
Step-by-Step Workflow: From PDF/Image to Structured Data
Best Practices, Tips, and Caveats
🤔 Frequently Asked Questions (FAQ)
Conclusion and Recommended Next Steps

What Is GLM-OCR?

GLM-OCR is a multimodal OCR model for complex document understanding, derived from the GLM-4V / GLM-V vision-language architecture.[1][2] Unlike classic OCR systems that only output raw text, GLM-OCR focuses on:

Understanding layouts (headings, sections, footnotes, tables, figures)
Preserving structure in semantic formats (Markdown, JSON, LaTeX)
Reasoning about content, not just recognizing characters

Key characteristics:

Lightweight: ~0.9B parameters, dramatically smaller than many VLM-based OCR models while keeping SOTA accuracy.[2][3]
Multimodal: Consumes PDFs and images (JPG/PNG), outputs rich structured representations.[1][2]
Open-weight & Apache-2.0 licensed: Suitable for commercial use and on-prem deployments.[1][3]

✅ Best for: Teams that need high-accuracy OCR plus document structure (tables, formulas, headings) at reasonable compute cost and want open-source licensing.

How Does GLM-OCR Work Architecturally?

GLM-OCR uses a three-stage pipeline that combines computer vision and language modeling.[1][2]

Architecture Overview

Stage	Component / Tech	What It Does
1. Visual Ingestion	CogViT visual encoder[2]	Captures pixel-level and layout information from pages
2. Multimodal Reasoning	GLM-V-based vision-language fusion[1]	Aligns visual features with language understanding
3. Structured Generation	Decoder with Multi-Token Prediction (MTP)[1]	Generates structured Markdown/JSON/LaTeX, correcting errors

Key design ideas:

CogViT encoder: A specialized vision backbone optimized for documents, not generic images.[2]
GLM-V multimodal reasoning: Allows the model to interpret relationships between text blocks, tables, and figures.
Multi-Token Prediction (MTP): Predicts multiple tokens per step and uses context to fix errors on the fly—this behaves more like semantic proofreading than naive character recognition.[1]

💡 Pro Tip

MTP is especially valuable on noisy scans or handwriting: GLM-OCR can use surrounding context to infer the correct token sequence instead of rigidly copying visual artifacts.

What Are the Key Features and Technical Specs?

Document Understanding Features

Layout semantics awareness
- Detects and preserves headings, subheadings, section hierarchies, footnotes, captions, and other structural elements.[1]
Tables → Markdown
- Converts complex tables into Markdown (and can be further transformed into CSV/Excel).[1]
Formulas → LaTeX
- Reconstructs complex mathematical expressions into valid LaTeX.[1]
Handwriting interpretation
- Handles handwritten notes and annotations using contextual reasoning.[1]
Contextual perception
- Fixes mis-detections as it generates, using language modeling to ensure globally coherent output.[1]

Language & Format Support

Input formats:
- PDF (up to ~50 MB, up to 100 pages per document)[2]
- Images: JPG, PNG (up to ~10 MB per image)[2]
Output formats:
- Semantic Markdown (with headings, tables, lists, code blocks)[1][2]
- JSON (structure-first; ideal for downstream pipelines)[1]
- LaTeX for mathematical content and formulas[1]
Languages:
- Supports 100+ languages, with strong performance in English, Chinese (中文), Japanese (日本語) and major European languages.[1][2]

Core Technical Specs

Spec	Value / Description
Model size	0.9B parameters[2]
Architecture	GLM-V-based multimodal + CogViT visual encoder[1][2]
Input modalities	PDF, images (JPG, PNG)[1][2]
Max pages per PDF	~100 pages[2]
Output formats	Markdown, LaTeX, JSON[1][2]
License	Apache-2.0 (open-weight)[1][3]
Deployment frameworks	VLLM, SGLang, API, local runners[2][3]

✅ Best Practice

For automation and integration, prefer JSON output; for human-readable exports and documentation, use semantic Markdown + LaTeX.

How Well Does GLM-OCR Perform? (Benchmarks & Precision)

OmniDocBench Performance

GLM-OCR is reported as state-of-the-art on OmniDocBench V1.5, a leading benchmark for document understanding.[2][3][4]

Score: ~94.62 on OmniDocBench V1.5[2][3][4]
Position: #1 on that benchmark among document parsing models in its class.[2][3]

These scores are especially impressive given its only 0.9B parameters, which is much smaller than many competing VLM-based OCR models.[2][3]

Throughput & Speed

From official documentation:[2]

PDF throughput: ~1.86 pages/second
Image throughput: ~0.67 images/second

This makes GLM-OCR viable for bulk-processing pipelines (e.g., nightly jobs over large archives) even on modest hardware.

Precision Modes

The official site highlights a PRECISION_MODE_ON, claiming up to 99.9% precision in that mode.[1] While exact metric definitions are not fully spelled out, the key takeaway is:

NORMAL mode – better for speed, good default.
PRECISION mode – slower but very high character-level and structure-level precision; ideal for legal and financial workloads.

⚠️ Note

Exact accuracy numbers for every domain (e.g., receipts vs. scientific PDFs) are not fully broken down publicly, so you should run your own evaluation on representative samples before committing to production.

Where Can You Use GLM-OCR? Real-World Use Cases

The official site and surrounding ecosystem emphasize several primary verticals.[1][5][6]

1. Academic Research & Scientific Documents

Scenario:

Scan of old papers, lecture notes, research articles with formulas, footnotes, and references.

What GLM-OCR does well:

Captures complex citations, references and section structures.
Converts equations into LaTeX, compatible with LaTeX editors and scientific workflows.[1]
Outputs to semantic Markdown, enabling easy ingestion into note-taking tools, static sites, or knowledge bases.

💡 Pro Tip

Use GLM-OCR’s LaTeX + Markdown output to feed directly into Markdown-based scientific writing setups (e.g., Obsidian + Pandoc, MkDocs, or Jupyter notebooks).

2. Financial Analysis & Reporting

Scenario:

Financial statements, regulatory filings, multi-page reports with nested tables and complex footnotes.[1][5][6]

Strengths:

Precisely parses multi-level tables (e.g., consolidated statements, multi-year comparisons).[1]
Extracts hierarchical headings and explanatory notes in a structured format.
Makes it easier to transform scanned PDFs into Excel-ready or database-ready representations via JSON/Markdown tables.

Examples of workflows include:

ETL pipelines that convert scanned PDFs → JSON → data warehouse.
Risk analysis teams ingesting disparate PDF reports into analytics systems.

3. Legal Documentation

Scenario:

Contracts, NDAs, case files, court filings with complex clause structures and cross-references.[1][5][6]

What GLM-OCR enables:

Detects and preserves clause numbering, section/subsection hierarchies.
Helps identify critical sections (Termination, Liability, Governing Law, etc.) for downstream review models.
Structure-first output makes it easier for LLMs to run contract analysis (e.g., deviation detection, obligation extraction).

✅ Best Practice

Always run GLM-OCR locally or via a private deployment for sensitive legal material to maintain confidentiality.

4. Developer & Product Integrations

GLM-OCR is built to be embedded into applications, platforms, and AI agents.[1][2][3]

APIs and SDKs: Developer documentation describes API-based usage patterns suited for SaaS tools.[1][2]
VLLM / SGLang support: Enables batched, high-throughput inference in production.[2][3]
Can serve as the document parsing front-end for AI agents, RAG systems, and analytics platforms.

Typical integration scenarios:

OCR microservice inside a larger AI workflow.
First step in an LLM-powered document QA or summarization pipeline.
Replacement for brittle regex-based PDF parsers.

GLM-OCR vs Other OCR Models (PaddleOCR, DeepSeekOCR, VLMs)

While we do not have a single, fully standardized cross-benchmark table that includes GLM-OCR, PaddleOCR, DeepSeekOCR, and proprietary APIs, available information allows some high-level comparison.[2][3][4][7]

Conceptual Comparison

Aspect	GLM-OCR	PaddleOCR / PaddleOCR-VL	DeepSeekOCR	Large VLMs (e.g., GPT-4 Vision)
Model Size	~0.9B[2][3]	Typically 3B–9B for VLM variants[7]	~2B–6B (varies by config)	70B+ parameters
License	Apache-2.0 open weights[1][3]	Largely open-source	Partly open / commercial	Closed-source, API-only
Focus	Complex document OCR & structure	General OCR + layout	Advanced OCR & layout	General-purpose vision-language
Output Format	Markdown, LaTeX, JSON[1][2]	Text, some layout info	Text + layout	Text, limited structure
Benchmark (OmniDocBench)	~94.6 (V1.5)[2][3][4]	Lower scores reported in threads	Competitive but below GLM-OCR[4][7]	Strong overall but proprietary
Throughput	~1.86 pages/s (PDF)[2]	Generally slower (larger models)	Moderate	Typically slower and more expensive
Ease of Private Deploy	High (VLLM, SGLang, Docker)[2][3]	Medium (framework-specific)	Varies	Low (API-only)

⚠️ Important

The exact numeric comparisons (e.g., speed vs. PaddleOCR/DeepSeekOCR) are sparse in authoritative public benchmarks. Treat relative claims (like “faster than X”) as directional, and always run your own benchmarks on your hardware and documents.

How to Deploy and Use GLM-OCR in Practice

From the gathered docs and ecosystem resources, GLM-OCR supports several typical deployment patterns.[1][2][3]

1. Local / On-Prem Deployment

Recommended when:

You process sensitive documents (legal, medical, financial).
You want full control over hardware and latency.

Common options:

VLLM backend: For batched high-throughput inference.
SGLang integration: Fine-grained orchestration of multimodal calls.[2][3]
Docker containers for packaged deployment.

2. Cloud or Hosted API

Some sites (e.g., glmocr.com) expose GLM-OCR via a hosted API, often with:

Free tiers (e.g., a limited number of pages/month).[1]
Simple file upload endpoints returning structured Markdown/JSON.

This is best when:

You want to prototype quickly.
You don’t yet have GPU infrastructure.

3. Hybrid Workflows

A common pattern:

Prototype using a public/hosted API.
Once satisfied, migrate to self-hosted GLM-OCR (via VLLM/SGLang/Docker) for cost and privacy control.

Step-by-Step Workflow: From PDF/Image to Structured Data

Below is an implementation-oriented view of how GLM-OCR fits into a typical pipeline, abstracting away specific SDK details:

📊 Conceptual Flow (Mermaid-style)

graph TD
    A[Upload PDF/Image] --> B[Visual Ingestion (CogViT Encoder)]
    B --> C[Multimodal Reasoning (GLM-V)]
    C --> D[Structured Generation (Markdown / JSON / LaTeX)]
    D --> E[Post-Processing (Parsing, ETL, Analytics)]
    E --> F[Downstream Apps (Search, RAG, Dashboards)]

Typical Implementation Steps

Input acquisition
- Accept PDF or image upload from UI, CLI, or batch directory.
Call GLM-OCR
- Send document to GLM-OCR via:
  - Local inference server (VLLM/SGLang)
  - Hosted API endpoint
Choose output format
- markdown for human-readable exports
- json for extraction-focused workflows
- latex for math-heavy documents
Post-process structured output
- Parse JSON or Markdown to extract:
  - Tables → CSV/SQL/Excel
  - Sections → knowledge base chunks
  - Formulas → rendered math or symbolic processing
Integrate with downstream systems
- Search indices, analytics pipelines, RAG systems, or compliance checks.

✅ Best Practice

Always store the raw GLM-OCR output (Markdown/JSON) alongside your processed data for future reprocessing as your downstream logic evolves.

Best Practices, Tips, and Caveats

💡 Professional Tip – Pick the Right Output for the Job

Use JSON for automation and AI agent pipelines.

Use Markdown + LaTeX for human review, documentation, and publishing.

✅ Best Practice – Use Precision Mode for High-Stakes Documents

Turn on PRECISION_MODE_ON for:

Legal contracts

Financial statements

Regulatory filings

Accept the extra latency in exchange for maximum accuracy.[1]

⚠️ Caution – Preprocess Low-Quality Scans

For low-DPI or heavily skewed scans, apply:

Binarization

De-skewing

Noise reduction

This helps the visual encoder and improves downstream structure detection.

💡 Pro Tip – Combine with LLMs for End-to-End Automation

Use GLM-OCR for reliable structure extraction.

Then feed its Markdown/JSON output into a general-purpose LLM for:

Summaries

Risk flags

Q&A and report generation.

🤔 Frequently Asked Questions (FAQ)

Q1: What makes GLM-OCR different from traditional OCR engines?

GLM-OCR is built as a multimodal vision-language model instead of a pure character recognizer. It doesn’t just read characters; it understands document structure and context, and generates semantic outputs (Markdown, JSON, LaTeX) that are far easier to use in modern AI and data pipelines.[1][2]

Q2: Can GLM-OCR handle handwriting and messy scans?

Yes, to a significant extent. GLM-OCR uses contextual perception and multi-token prediction to interpret handwriting and noisy images by looking at surrounding text and document structure.[1] While extreme cases may still require manual correction, it outperforms many traditional OCR tools in handwritten annotations and marginalia.

Q3: Is GLM-OCR suitable for on-prem or air-gapped deployments?

Yes. The model is released as open weights under the Apache-2.0 license, and documentation highlights support for VLLM/SGLang and local inference, making it suitable for on-prem, air-gapped, and highly regulated environments.[1][2][3]

Q4: How does GLM-OCR scale to large volumes of documents?

The 0.9B parameter size is relatively small for a multimodal model, which helps keep inference efficient.[2][3] Official docs report throughput around 1.86 pages/second for PDFs and 0.67 images/second on capable hardware.[2] For large-scale workloads, you can:

Run multiple instances behind a load balancer.
Use VLLM/SGLang for batched inference.
Schedule batch jobs for nightly or off-peak processing.

Q5: When should I choose GLM-OCR over proprietary cloud OCR (Google, Azure, etc.)?

Choose GLM-OCR when you need:

Full control over data (on-prem, private cloud).
Open-source licensing and freedom from per-page vendor lock-in.
Rich structure (Markdown/JSON/LaTeX) rather than just text.

Proprietary clouds may still be preferable if you rely heavily on adjacent proprietary services (e.g., integrated form detection, doc AI suites), but GLM-OCR offers a strong balance of accuracy, openness, and cost control.

Conclusion and Recommended Next Steps

GLM-OCR is a modern, lightweight, and open solution to one of the toughest problems in AI: turning messy, real-world documents into structured, actionable data.

Why it stands out:

SOTA accuracy on OmniDocBench V1.5 (~94.62) with only 0.9B parameters.[2][3][4]
Focus on structure-first outputs (Markdown, JSON, LaTeX), ideal for LLMs and data pipelines.[1]
Open Apache-2.0 license and open weights, making it deployable almost anywhere.[1][3]

Actionable Next Steps

Evaluate GLM-OCR on your own documents
- Gather a representative sample of PDFs/images from your domain.
- Run them through GLM-OCR (hosted API or local deployment) and compare with your current OCR.
Prototype a minimal pipeline
- Input → GLM-OCR → JSON/Markdown → simple downstream script (e.g., CSV export or LLM summary).
Plan deployment strategy
- For sensitive data: choose on-prem VLLM/SGLang or Docker-based deployment.
- For quick start: use a hosted API if available.
Iterate on post-processing
- Refine how you parse tables, formulas, and headings from GLM-OCR’s structured output.
- Add QA checks and confidence thresholds for high-stakes use cases.
Integrate with your AI stack
- Feed GLM-OCR output into RAG pipelines, contract analyzers, financial models, or data warehouses.

By deliberately combining GLM-OCR’s structured OCR with your existing analytics and LLM stack, you can turn unstructured archives—research, contracts, reports—into a searchable, analyzable, AI-ready knowledge layer with far less engineering effort than traditional OCR pipelines.

References

[1] GLM-OCR Official Site. https://glmocr.com/

[2] GLM-OCR – Z.AI Developer Document. https://docs.z.ai/guides/vlm/glm-ocr

[3] zai-org/GLM-OCR (Hugging Face). https://huggingface.co/zai-org/GLM-OCR

[4] GLM-OCR Benchmark Mentions – X / News Articles. https://news.aibase.com/news/25178

[5] GLM-OCR Use Cases – Official Site Sections. https://glmocr.com/

[6] GLM OCR | AI Model (Use Case Overview). https://story321.com/ru/models/zhipu/glm-ocr

[7] PaddleOCR-VL and DeepSeekOCR Benchmark Discussions. https://huggingface.co/PaddlePaddle/PaddleOCR-VL

GLM-OCR for Next-Gen Document Understanding

Moltworker Complete Guide 2026: Running Personal AI Agents on Cloudflare Without Hardware

Sienna — Fri, 30 Jan 2026 04:47:04 +0000

🎯 Core Takeaways (TL;DR)

No Hardware Required: Moltworker enables running Moltbot AI agents on Cloudflare's infrastructure, eliminating the need for dedicated Mac minis or VPS servers
Enterprise-Grade Security: Built-in Cloudflare Access authentication, device pairing, and sandbox isolation protect your data and APIs
Cost-Effective: Starting at $5/month for Workers Paid plan, with generous free tiers for AI Gateway, R2 storage, and Browser Rendering
Full Feature Parity: Supports all major Moltbot integrations including Telegram, Discord, Slack, and browser automation capabilities
Production-Ready: Leverages Cloudflare's global network with automatic scaling, persistent storage via R2, and comprehensive observability

What is Moltworker?
Why Moltworker Matters: The Hardware Problem
Moltworker Architecture Deep Dive
How to Deploy Moltworker: Step-by-Step Guide
Security Considerations and Best Practices
Moltworker vs Traditional Moltbot Deployment
Community Feedback and Concerns
Real-World Use Cases
Troubleshooting Common Issues
FAQ

What is Moltworker? {#what-is-moltworker}

Moltworker is an open-source middleware solution developed by Cloudflare that enables running Moltbot (formerly Clawdbot) personal AI agents on Cloudflare's Developer Platform instead of dedicated hardware. Released in January 2026, moltworker represents a paradigm shift in how developers can deploy and manage AI agents.

The Moltbot Foundation

Before understanding moltworker, it's essential to know what Moltbot is:

Personal AI Assistant: Moltbot is an open-source AI agent designed to act as a personal assistant
Multi-Platform Integration: Supports Telegram, Discord, Slack, and web-based control interfaces
Extensible Architecture: Features a gateway architecture with persistent conversations and agent runtime
Self-Hosted by Design: Originally required users to run it on their own hardware (Mac minis, Linux servers, etc.)

How Moltworker Transforms Deployment

Moltworker adapts Moltbot to run entirely on Cloudflare's infrastructure through:

// Moltworker entrypoint example
import { getSandbox } from '@cloudflare/sandbox';

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const sandbox = getSandbox(env.Sandbox, 'user-123');

    // Moltbot runs inside this isolated sandbox
    await sandbox.exec('moltbot-gateway start');

    return Response.json({ status: 'running' });
  }
};

💡 Key Insight
Moltworker is not a fork of Moltbot—it's a compatibility layer that allows the standard Moltbot runtime to operate in Cloudflare's serverless environment.

Why Moltworker Matters: The Hardware Problem {#why-moltworker-matters}

The Mac Mini Rush of 2026

When Moltbot gained viral attention in January 2026, a peculiar phenomenon occurred: developers rushed to purchase Mac minis specifically to run their personal AI agents. This created several problems:

Challenge	Traditional Approach	Moltworker Solution
Initial Cost	$599+ for Mac mini	$5/month Workers plan
Maintenance	Manual updates, monitoring	Automatic platform updates
Uptime	Dependent on home internet	99.9%+ SLA on global network
Security	DIY firewall, VPN setup	Built-in Access, Zero Trust
Scaling	Buy more hardware	Automatic resource allocation
Power Consumption	24/7 electricity costs	Pay-per-use serverless model

The Cloudflare Advantage

Moltworker leverages Cloudflare's Developer Platform, which has evolved to support complex applications:

Node.js Compatibility: 98.5% of top 1,000 NPM packages now work natively in Workers
Sandbox SDK: Secure isolated environments for running untrusted code
Global Network: 300+ data centers ensure low-latency access worldwide
Integrated Services: AI Gateway, R2 storage, Browser Rendering, and Zero Trust Access work seamlessly together

⚠️ Important Note
Moltworker is currently a proof-of-concept, not an official Cloudflare product. It's maintained as an open-source project to showcase platform capabilities.

Moltworker Architecture Deep Dive {#architecture-deep-dive}

High-Level System Design

graph TB
    A[User Request] --> B[Cloudflare Worker]
    B --> C[Cloudflare Access Auth]
    C --> D[Admin UI / API Router]
    D --> E[Sandbox Container]
    E --> F[Moltbot Gateway Runtime]
    F --> G[AI Gateway]
    F --> H[Browser Rendering]
    F --> I[R2 Storage]
    G --> J[Anthropic Claude]
    H --> K[Headless Chrome]
    I --> L[Persistent Data]

Core Components Explained

1. Entrypoint Worker (API Router & Proxy)

The moltworker Worker serves multiple roles:

// Simplified moltworker routing logic
export default {
  async fetch(request, env) {
    const url = new URL(request.url);

    // Admin UI routes
    if (url.pathname.startsWith('/_admin/')) {
      return handleAdminUI(request, env);
    }

    // CDP proxy for browser automation
    if (url.pathname.startsWith('/cdp/')) {
      return handleCDPProxy(request, env);
    }

    // WebSocket connection to Moltbot
    if (url.pathname === '/ws') {
      return handleWebSocket(request, env);
    }

    // Control UI
    return handleControlUI(request, env);
  }
};

Key Responsibilities:

Route HTTP/WebSocket requests to appropriate handlers
Proxy Chrome DevTools Protocol (CDP) commands to Browser Rendering
Serve the administrative interface
Validate authentication tokens and Access JWTs

2. Cloudflare Sandbox Container

The Sandbox SDK provides the isolated environment where Moltbot actually runs:

// Creating and managing the sandbox
const sandbox = getSandbox(env.Sandbox, userId);

// Install Moltbot in the container
await sandbox.exec('npm install -g moltbot');

// Mount R2 bucket for persistence
await sandbox.mountBucket(env.R2_BUCKET, '/root/.moltbot');

// Start the gateway process
const process = await sandbox.startProcess('moltbot-gateway', {
  env: {
    ANTHROPIC_API_KEY: env.ANTHROPIC_API_KEY,
    GATEWAY_TOKEN: env.MOLTBOT_GATEWAY_TOKEN
  }
});

Sandbox Features:

Isolation: Each user gets their own secure container
Filesystem Access: Full read/write capabilities within the container
Process Management: Run background services like the Moltbot gateway
Network Access: Controlled outbound connections to AI providers and chat platforms

3. AI Gateway Integration

Moltworker routes all AI model requests through Cloudflare AI Gateway:

# Configuration for AI Gateway
export ANTHROPIC_BASE_URL="https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/anthropic"
export AI_GATEWAY_API_KEY="your-anthropic-key"

Benefits of AI Gateway:

Cost Tracking: Monitor spending across all AI providers
Request Analytics: Detailed logs of model usage patterns
Caching: Reduce redundant API calls with intelligent caching
Fallbacks: Automatic failover to alternative models/providers
Unified Billing: Use Cloudflare credits instead of managing multiple provider accounts

4. R2 Persistent Storage

Moltworker implements a backup/restore pattern for data persistence:

// Backup process (runs every 5 minutes via cron)
async function backupToR2(sandbox, r2Bucket) {
  // Tar the Moltbot config directory
  await sandbox.exec('tar -czf /tmp/backup.tar.gz /root/.moltbot');

  // Upload to R2
  const backupData = await sandbox.readFile('/tmp/backup.tar.gz');
  await r2Bucket.put('moltbot-backup.tar.gz', backupData);

  console.log('Backup completed at', new Date().toISOString());
}

// Restore on container startup
async function restoreFromR2(sandbox, r2Bucket) {
  const backup = await r2Bucket.get('moltbot-backup.tar.gz');
  if (backup) {
    await sandbox.writeFile('/tmp/restore.tar.gz', await backup.arrayBuffer());
    await sandbox.exec('tar -xzf /tmp/restore.tar.gz -C /');
    console.log('Restored from R2 backup');
  }
}

What Gets Persisted:

Paired device configurations
Conversation history and context
Custom skills and tools created by the agent
User preferences and settings

5. Browser Rendering via CDP Proxy

One of moltworker's most innovative features is the CDP (Chrome DevTools Protocol) proxy:

// CDP proxy implementation
async function handleCDPProxy(request, env) {
  const url = new URL(request.url);

  if (url.pathname === '/cdp/json/version') {
    // Return browser version info from Browser Rendering
    const browser = await puppeteer.launch(env.BROWSER);
    return Response.json({ browserVersion: browser.version() });
  }

  if (url.pathname.startsWith('/cdp/devtools/browser/')) {
    // Upgrade to WebSocket and proxy CDP commands
    const browserId = url.pathname.split('/').pop();
    return handleCDPWebSocket(request, env, browserId);
  }
}

How It Works:

Moltbot inside the sandbox connects to localhost:9222 (standard CDP port)
Moltworker intercepts these connections and proxies them to the Worker
The Worker forwards CDP commands to Cloudflare Browser Rendering
Browser Rendering executes commands on a real Chromium instance
Responses flow back through the proxy to Moltbot

This architecture allows Moltbot to perform browser automation without running Chromium inside the container, saving resources and improving security.

6. Zero Trust Access Authentication

Moltworker uses Cloudflare Access to protect sensitive routes:

// Access JWT validation
async function validateAccessToken(request, env) {
  const token = request.headers.get('Cf-Access-Jwt-Assertion');

  if (!token) {
    return { valid: false, error: 'No Access token' };
  }

  // Verify JWT signature and audience
  const payload = await verifyJWT(token, {
    audience: env.CF_ACCESS_AUD,
    issuer: `https://${env.CF_ACCESS_TEAM_DOMAIN}/cdn-cgi/access/certs`
  });

  return { valid: true, user: payload.email };
}

Protected Routes:

/_admin/* - Device management interface
/api/* - Administrative API endpoints
/debug/* - Diagnostic and logging endpoints

Data Flow Example: User Message to AI Response

Let's trace a complete request through moltworker:

User sends message via Telegram to their paired bot
Telegram webhook hits the moltworker Worker endpoint
Worker validates the gateway token and device pairing status
Message forwarded to Moltbot gateway running in the Sandbox
Moltbot processes the message and determines it needs AI assistance
AI request sent through AI Gateway to Anthropic Claude
Claude response flows back through AI Gateway (logged for analytics)
Moltbot formats the response and sends it back to the Worker
Worker delivers the response to Telegram's API
User receives the AI-generated message in their chat

Throughout this flow:

All AI requests are logged in AI Gateway for cost tracking
Conversation context is stored in R2 for persistence
Access logs are recorded in Zero Trust for security auditing

How to Deploy Moltworker: Step-by-Step Guide {#deployment-guide}

Prerequisites

Before deploying moltworker, ensure you have:

✅ Cloudflare account with Workers Paid plan ($5/month)
✅ Anthropic API key (or plan to use AI Gateway Unified Billing)
✅ Node.js 18+ and npm installed locally
✅ Wrangler CLI installed (npm install -g wrangler)

Step 1: Clone and Install

# Clone the moltworker repository
git clone https://github.com/cloudflare/moltworker.git
cd moltworker

# Install dependencies
npm install

# Authenticate with Cloudflare
wrangler login

Step 2: Configure AI Provider

Choose between direct Anthropic access or AI Gateway:

Option A: Direct Anthropic Access

# Set your Anthropic API key
npx wrangler secret put ANTHROPIC_API_KEY
# Paste your key when prompted

Option B: AI Gateway (Recommended)

# Create an AI Gateway in the Cloudflare dashboard
# Then configure the secrets:

npx wrangler secret put AI_GATEWAY_API_KEY
# Enter your Anthropic key

npx wrangler secret put AI_GATEWAY_BASE_URL
# Enter: https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/anthropic

💡 Pro Tip
Using AI Gateway provides better observability and cost control. You can switch between providers without redeploying moltworker.

Step 3: Generate Gateway Token

# Generate a secure random token
export MOLTBOT_GATEWAY_TOKEN=$(openssl rand -base64 32 | tr -d '=+/' | head -c 32)

# Display and save this token - you'll need it to access the UI
echo "Your gateway token: $MOLTBOT_GATEWAY_TOKEN"

# Set it as a secret
echo "$MOLTBOT_GATEWAY_TOKEN" | npx wrangler secret put MOLTBOT_GATEWAY_TOKEN

⚠️ Critical: Save this token securely. You'll need it to access the Control UI at https://your-worker.workers.dev/?token=YOUR_GATEWAY_TOKEN

Step 4: Deploy Moltworker

# Deploy to Cloudflare Workers
npm run deploy

# Output will show your worker URL:
# Published moltbot-sandbox (X.XX sec)
#   https://moltbot-sandbox.your-subdomain.workers.dev

Step 5: Configure Cloudflare Access

To use the admin UI, you must set up authentication:

Enable Access on workers.dev

Go to Workers & Pages dashboard
Select your deployed Worker (e.g., moltbot-sandbox)
Navigate to Settings → Domains & Routes
In the workers.dev row, click the menu (...) → Enable Cloudflare Access
Click Manage Cloudflare Access to configure authentication:
- Add your email to the allow list
- Or configure identity providers (Google, GitHub, etc.)
Copy the Application Audience (AUD) tag

Set Access Secrets

# Your Cloudflare Access team domain
npx wrangler secret put CF_ACCESS_TEAM_DOMAIN
# Enter: myteam.cloudflareaccess.com

# Application Audience tag from Access settings
npx wrangler secret put CF_ACCESS_AUD
# Paste the AUD value you copied

Redeploy

npm run deploy

Step 6: Enable R2 Persistent Storage (Recommended)

Without R2, your data is lost when the container restarts.

Create R2 API Token

Go to R2 → Overview in Cloudflare dashboard
Click Manage R2 API Tokens
Create token with Object Read & Write permissions
Select the moltbot-data bucket (auto-created on first deploy)
Copy Access Key ID and Secret Access Key

Configure R2 Secrets

# R2 credentials
npx wrangler secret put R2_ACCESS_KEY_ID
npx wrangler secret put R2_SECRET_ACCESS_KEY

# Your Cloudflare Account ID
npx wrangler secret put CF_ACCOUNT_ID
# Find this in dashboard: Click account menu → Copy Account ID

Redeploy

npm run deploy

Step 7: Pair Your First Device

Visit the admin UI: https://your-worker.workers.dev/_admin/
Authenticate via Cloudflare Access
Open the Control UI in a new tab: https://your-worker.workers.dev/?token=YOUR_GATEWAY_TOKEN
The Control UI will show "Waiting for pairing approval..."
Return to the admin UI and approve the pending device
Your Control UI is now connected!

⏱️ Note: The first request may take 1-2 minutes while the container starts. Subsequent requests are much faster.

Step 8: Optional Integrations

Add Telegram Bot

# Create a bot via @BotFather on Telegram
# Copy the bot token and set it:
npx wrangler secret put TELEGRAM_BOT_TOKEN
npm run deploy

Add Discord Bot

# Create a bot in Discord Developer Portal
# Copy the bot token and set it:
npx wrangler secret put DISCORD_BOT_TOKEN
npm run deploy

Add Slack Bot

# Create a Slack app with bot capabilities
# Copy both tokens and set them:
npx wrangler secret put SLACK_BOT_TOKEN
npx wrangler secret put SLACK_APP_TOKEN
npm run deploy

Enable Browser Automation

# Generate a secure secret for CDP authentication
npx wrangler secret put CDP_SECRET
# Enter a random string

# Set your worker's public URL
npx wrangler secret put WORKER_URL
# Enter: https://your-worker.workers.dev

npm run deploy

Deployment Checklist

[ ] Workers Paid plan active ($5/month)
[ ] AI provider configured (Anthropic or AI Gateway)
[ ] Gateway token generated and saved
[ ] Cloudflare Access enabled and configured
[ ] R2 storage configured (optional but recommended)
[ ] First device paired via admin UI
[ ] Chat integrations configured (optional)
[ ] Browser automation enabled (optional)

Security Considerations and Best Practices {#security-best-practices}

Multi-Layer Authentication Architecture

Moltworker implements defense-in-depth with three authentication layers:

Layer	Purpose	Protects Against
Gateway Token	Access to Control UI	Unauthorized UI access
Device Pairing	Per-device authorization	Rogue clients, stolen tokens
Cloudflare Access	Admin UI protection	Unauthorized administration

How They Work Together

graph TD
    A[User Request] --> B{Has Gateway Token?}
    B -->|No| C[Reject: 401 Unauthorized]
    B -->|Yes| D{Device Paired?}
    D -->|No| E[Show Pairing Pending]
    D -->|Yes| F{Admin Route?}
    F -->|No| G[Allow: Normal Operation]
    F -->|Yes| H{Valid Access JWT?}
    H -->|No| I[Redirect to Access Login]
    H -->|Yes| J[Allow: Admin Access]

Critical Security Warnings

⚠️ Prompt Injection Vulnerability

Moltbot is susceptible to prompt injection attacks via:

Email content (if email integration is enabled)

Web pages visited by the browser automation

Chat messages from untrusted sources

Mitigation: Only enable integrations you trust. Never connect moltworker to public email addresses or allow it to browse untrusted websites.

⚠️ Supply Chain Risk

As discussed on Hacker News, moltbot's dependency chain and rapid development pose supply chain attack risks.

Mitigation:

Pin specific versions in your deployment

Review the closed PRs and issues before updating

Consider forking for production use

⚠️ Data Privacy

While moltworker runs in isolated sandboxes, Cloudflare can technically access:

All data passing through Workers

Content stored in R2 buckets

Logs and analytics data

Mitigation: Don't use moltworker for sensitive data if you require zero-knowledge architecture. For maximum privacy, self-host Moltbot on your own hardware.

Best Practices for Secure Deployment

1. Token Management

# Generate cryptographically secure tokens
openssl rand -base64 32 | tr -d '=+/' | head -c 32

# Rotate tokens regularly (every 90 days)
npx wrangler secret put MOLTBOT_GATEWAY_TOKEN
npm run deploy

2. Access Policy Configuration

Configure strict Access policies in the Zero Trust dashboard:

# Example Access policy
Name: Moltworker Admin Access
Application Domain: moltbot-sandbox.your-subdomain.workers.dev
Paths:
  - /_admin/*
  - /api/*
  - /debug/*
Policy:
  - Include:
      - Email: your-email@example.com
  - Exclude:
      - Everyone
Session Duration: 24 hours

3. Container Lifecycle Management

# For production: Keep container always alive
# (Default behavior, no action needed)

# For development/testing: Allow container to sleep
npx wrangler secret put SANDBOX_SLEEP_AFTER
# Enter: 1h (container sleeps after 1 hour of inactivity)

4. Monitoring and Alerting

Enable comprehensive logging:

# Enable debug routes (only in development)
npx wrangler secret put DEBUG_ROUTES
# Enter: true

Access debug endpoints:

GET /debug/processes - List container processes
GET /debug/logs?id=<process_id> - View process logs
GET /debug/version - Check moltbot and container versions

5. Network Isolation

Moltworker automatically isolates:

Each user's sandbox from others
Outbound connections to only approved destinations
Inbound connections through the Worker proxy only

6. Secrets Rotation Schedule

Secret	Rotation Frequency	Impact
Gateway Token	Every 90 days	Requires re-pairing all devices
AI Provider Keys	Every 180 days	Transparent to users
R2 Access Keys	Every 180 days	Requires redeployment
CDP Secret	Every 90 days	Breaks browser automation until updated

Security Audit Checklist

Before deploying moltworker to production:

[ ] All secrets are set via wrangler secret put (never in code)
[ ] Cloudflare Access is enabled on all admin routes
[ ] Gateway token is cryptographically random (32+ characters)
[ ] Device pairing is enabled (not using DEV_MODE=true)
[ ] R2 bucket has restricted access (not public)
[ ] Only necessary chat integrations are enabled
[ ] Email integration is disabled (high prompt injection risk)
[ ] Debug routes are disabled in production
[ ] Access logs are being monitored
[ ] Backup strategy is in place for R2 data

Moltworker vs Traditional Moltbot Deployment {#comparison}

Comprehensive Comparison Matrix

Aspect	Self-Hosted Moltbot	Moltworker on Cloudflare
Initial Setup Cost	$599+ (Mac mini) or $5-20/month (VPS)	$5/month (Workers Paid)
Ongoing Costs	Electricity + internet + maintenance	Usage-based (typically $5-15/month)
Setup Complexity	High (Docker, networking, security)	Medium (mostly configuration)
Time to Deploy	2-4 hours	15-30 minutes
Maintenance Burden	Manual updates, monitoring, backups	Automatic platform updates
Uptime	Depends on home internet/VPS provider	99.9%+ on global network
Geographic Latency	Single location	300+ edge locations
Scaling	Buy more hardware	Automatic
Security Management	DIY (firewall, VPN, patches)	Built-in (Access, sandboxing)
Data Privacy	Full control (zero-knowledge possible)	Cloudflare can access data
Backup Strategy	Manual or scripted	Automatic R2 sync every 5 min
Observability	Self-configured (Grafana, etc.)	Built-in (AI Gateway, Access logs)
Browser Automation	Local Chromium (resource-heavy)	Browser Rendering API (offloaded)
Local Integrations	Full access (smart home, local files)	Limited (cloud-accessible only)
Customization	Unlimited (full system access)	Limited to container environment
Vendor Lock-in	None	Cloudflare-specific

When to Choose Self-Hosted Moltbot

Choose traditional self-hosting if you:

✅ Require absolute data privacy (zero-knowledge architecture)

✅ Need local network integrations (smart home devices, NAS, etc.)

✅ Want unlimited customization and system-level access

✅ Have reliable infrastructure and technical expertise

✅ Prefer one-time hardware costs over recurring subscriptions

✅ Need to comply with data residency regulations

When to Choose Moltworker

Choose moltworker if you:

✅ Want minimal setup and maintenance overhead

✅ Need high availability and global low-latency access

✅ Prefer usage-based pricing over hardware investment

✅ Value integrated observability and security features

✅ Don't require local network integrations

✅ Want automatic scaling and platform updates

✅ Are comfortable with Cloudflare accessing your data

Performance Benchmarks

Metric	Self-Hosted (Mac Mini M2)	Moltworker (Cloudflare)
Cold Start	N/A (always running)	60-120 seconds
Warm Request	50-200ms	100-300ms (global avg)
AI Response Time	Depends on internet	Optimized via AI Gateway
Browser Automation	2-5 seconds	3-6 seconds
Storage I/O	Local SSD (very fast)	R2 (network-dependent)
Concurrent Users	Limited by hardware	Unlimited (auto-scaling)

Cost Analysis: 12-Month TCO

Self-Hosted (Mac Mini M2)

Hardware: $599 (one-time)
Electricity: $50/year (assuming 10W average)
Internet: $0 (assuming existing connection)
Total Year 1: $649
Total Year 2+: $50/year

Self-Hosted (VPS - Hetzner CCX13)

VPS: $15/month × 12 = $180/year
Backups: $5/month × 12 = $60/year
Total per year: $240/year

Moltworker (Cloudflare)

Workers Paid: $5/month × 12 = $60/year
AI Gateway: $0 (free tier)
R2 Storage: $0.75/month × 12 = $9/year (assuming 50GB)
Browser Rendering: $5/month × 12 = $60/year (1M requests)
Total per year: $129/year

💡 Cost Winner: Moltworker is most cost-effective for years 1-2. Self-hosted Mac mini becomes cheaper after 2-3 years if you already have reliable internet.

Community Feedback and Concerns {#community-feedback}

The Hacker News discussion revealed significant community concerns about both Moltbot and moltworker:

Positive Reception

✅ Cloudflare's Node.js Progress: Developers praised the 98.5% NPM package compatibility

✅ Sandbox SDK Utility: Many saw value in the Sandbox SDK beyond just moltworker

✅ Deployment Simplicity: Appreciated the reduction in setup complexity vs self-hosting

✅ Built-in Observability: AI Gateway analytics and Access logs were highlighted as valuable

Major Concerns

1. Astroturfing and Hype Cycle

"There is so much branding and 'look at our success' marketing that this project comes off as heavily astro-turfed."

Community members noted:

Excessive social media promotion from non-technical accounts
Comparison to crypto-era hype cycles
Concerns about an eventual startup pivot or acquisition

Cloudflare's Response: Moltworker is explicitly labeled as a proof-of-concept, not a product. The goal is showcasing platform capabilities, not monetizing moltbot.

2. Security Vulnerabilities

"Clawdbot/Moltbot looks to be a supply-chain attack waiting to happen."

Specific security issues raised:

Prompt Injection: No protection against malicious prompts in emails/websites
Supply Chain: Rapid development with low technical oversight
Insecure Deployments: Many users exposing dashboards without authentication
Email Integration Risk: Connecting to email creates attack vector

Mitigation Strategies:

Moltworker enforces device pairing by default
Cloudflare Access protects admin routes
Sandbox isolation limits blast radius
Documentation warns against email integration

3. Overhyped Capabilities

"Ultimately its a convenience wrapper that makes it easy to wire up Claude or ChatGPT to a chat platform like discord, but its claiming to be far more revolutionary."

Critics argued:

Core functionality is just API wrappers
Similar tools exist (e.g., afk-code - 2-minute setup)
The "agent" label is misleading marketing

Counterpoint: While the core is API integration, the value lies in:

Persistent memory and context
Self-modifying capabilities (agents creating their own tools)
Multi-platform gateway architecture
Production-ready deployment on global infrastructure

4. Data Privacy Concerns

"All home/local integrations are gone. Data needs to be stored in the cloud. No thanks."

Valid concerns about moltworker specifically:

Cloudflare can access all data passing through Workers
R2 storage is not zero-knowledge
Loss of local network integrations (smart home, NAS, etc.)

When This Matters:

Handling sensitive personal data
Compliance requirements (HIPAA, GDPR with strict data residency)
Desire for complete control over infrastructure

When It Doesn't:

Using public AI providers anyway (Anthropic already sees your data)
Trusting Cloudflare's security practices
Prioritizing convenience over absolute privacy

5. Technical Skepticism

"Just look at the closed PRs of their project. General technical knowledge is so low it's insane."

Community members reviewed moltbot's GitHub and found:

Low-quality contributions
Security vulnerabilities in closed PRs
Lack of code review rigor

Important Context: Moltbot is a rapidly evolving open-source project. Moltworker adapts it but doesn't control its development. Users should:

Review moltbot's security posture before deploying
Consider forking for production use
Monitor the project's issue tracker

Balanced Perspective

The community consensus seems to be:

✅ Moltworker as a Platform Demo: Excellent showcase of Cloudflare's capabilities

✅ Sandbox SDK Value: Useful beyond just moltbot

⚠️ Moltbot Maturity: Treat as experimental, not production-ready

⚠️ Security Posture: Requires careful configuration and ongoing monitoring

❌ Hype vs Reality: Marketing outpaces technical substance

Real-World Use Cases {#use-cases}

Despite concerns, moltworker enables legitimate use cases when deployed responsibly:

1. Personal Productivity Assistant

Scenario: A developer wants an AI assistant that can:

Manage calendar and reminders
Answer questions about their codebase
Summarize daily news and research papers
Interact via Slack during work hours

Moltworker Setup:

# Enable Slack integration
npx wrangler secret put SLACK_BOT_TOKEN
npx wrangler secret put SLACK_APP_TOKEN

# Configure with Anthropic Claude
npx wrangler secret put ANTHROPIC_API_KEY

# Deploy
npm run deploy

Benefits:

Always available (99.9% uptime)
Responds quickly from nearest edge location
AI Gateway tracks usage costs
Conversation history persists in R2

2. Automated Web Research

Scenario: A researcher needs to:

Monitor specific websites for updates
Extract data from multiple sources
Take screenshots of web pages
Compile findings into reports

Moltworker Setup:

# Enable browser automation
npx wrangler secret put CDP_SECRET
npx wrangler secret put WORKER_URL

# Deploy with browser skill
npm run deploy

Example Interaction:

User: "Check the top 5 posts on Hacker News and summarize them"

Moltbot: 
1. Opening news.ycombinator.com...
2. Taking screenshot...
3. Extracting post titles and links...
4. Summarizing each post...

Here are today's top stories:
1. [Title] - [Summary]
2. [Title] - [Summary]
...

3. Multi-Platform Customer Support Bot

Scenario: A small business wants to provide AI-powered support across:

Telegram for quick customer queries
Discord for community support
Web chat for website visitors

Moltworker Setup:

# Enable all chat platforms
npx wrangler secret put TELEGRAM_BOT_TOKEN
npx wrangler secret put DISCORD_BOT_TOKEN

# Use AI Gateway for cost control
npx wrangler secret put AI_GATEWAY_API_KEY
npx wrangler secret put AI_GATEWAY_BASE_URL

npm run deploy

Benefits:

Single AI agent serves all platforms
Unified conversation history
Cost tracking via AI Gateway
Scales automatically with user growth

4. Development Team Assistant

Scenario: A software team wants an AI that can:

Answer questions about internal documentation
Generate code snippets
Create diagrams and visualizations
Interact via Discord during standups

Moltworker Setup:

# Configure Discord integration
npx wrangler secret put DISCORD_BOT_TOKEN

# Use AI Gateway with fallbacks
# Primary: Claude Sonnet, Fallback: GPT-4
npx wrangler secret put AI_GATEWAY_API_KEY
npx wrangler secret put AI_GATEWAY_BASE_URL

npm run deploy

Security Considerations:

Use Cloudflare Access to restrict admin UI to team members
Device pairing ensures only approved team members can interact
AI Gateway logs all requests for audit trails

5. Personal Finance Tracker (⚠️ High Risk)

Scenario: A user wants an AI to:

Monitor bank account balances
Categorize transactions
Provide spending insights
Send alerts for unusual activity

⚠️ WARNING: This use case is NOT RECOMMENDED due to:

Prompt injection risk (malicious emails could trigger unauthorized actions)
Data privacy concerns (financial data in Cloudflare infrastructure)
Lack of audit trail for financial decisions

If You Must:

Never connect to email
Use read-only API access to financial institutions
Enable all security layers (Access, device pairing, gateway token)
Regularly review AI Gateway logs for suspicious activity
Consider self-hosting instead of moltworker

Troubleshooting Common Issues {#troubleshooting}

Container Startup Issues

Problem: "Gateway fails to start" or "Container timeout"

Solutions:

# 1. Check that Containers are enabled
# Visit: https://dash.cloudflare.com/?to=/:account/workers/containers

# 2. Verify all required secrets are set
npx wrangler secret list

# Required secrets:
# - MOLTBOT_GATEWAY_TOKEN
# - ANTHROPIC_API_KEY (or AI_GATEWAY_API_KEY + AI_GATEWAY_BASE_URL)

# 3. Check deployment logs
npx wrangler tail

# 4. Increase timeout (if needed)
# Edit wrangler.toml:
# [env.production]
# compatibility_date = "2025-01-01"
# [env.production.sandbox]
# timeout = 300  # 5 minutes

R2 Storage Not Working

Problem: "Data lost after container restart" or "R2 not mounting"

Solutions:

# 1. Verify all three R2 secrets are set
npx wrangler secret list | grep R2

# Should show:
# - R2_ACCESS_KEY_ID
# - R2_SECRET_ACCESS_KEY
# - CF_ACCOUNT_ID

# 2. Check R2 bucket exists
npx wrangler r2 bucket list

# Should show: moltbot-data

# 3. Test R2 access manually
npx wrangler r2 object get moltbot-data/moltbot-backup.tar.gz --file test.tar.gz

# 4. Trigger manual backup from admin UI
# Visit: https://your-worker.workers.dev/_admin/
# Click "Backup Now" button

Note: R2 mounting only works in production, not with wrangler dev.

Cloudflare Access Issues

Problem: "Access denied on admin routes" or "Infinite redirect loop"

Solutions:

# 1. Verify Access secrets are set
npx wrangler secret list | grep CF_ACCESS

# Should show:
# - CF_ACCESS_TEAM_DOMAIN
# - CF_ACCESS_AUD

# 2. Check Access application configuration
# Visit: https://one.dash.cloudflare.com/
# Navigate to: Access > Applications
# Verify your worker URL is listed

# 3. Ensure your email is in the allow list
# In Access application settings:
# Policies > Include > Emails > [your-email@example.com]

# 4. Clear browser cookies and try again
# Access uses cookies for authentication

Device Pairing Problems

Problem: "Devices not appearing in admin UI" or "Pairing request stuck"

Solutions:

# 1. Wait 10-15 seconds and refresh
# Device list commands have WebSocket overhead

# 2. Check gateway token is correct
# In Control UI URL: ?token=YOUR_GATEWAY_TOKEN
# Must match the secret you set

# 3. Verify device pairing is enabled
npx wrangler secret list | grep DEV_MODE

# Should NOT show DEV_MODE=true in production

# 4. Check moltbot gateway logs
# Visit: https://your-worker.workers.dev/debug/processes
# Find the gateway process ID
# Visit: https://your-worker.workers.dev/debug/logs?id=<process_id>

WebSocket Connection Failures

Problem: "WebSocket connection failed" or "Control UI disconnects"

Solutions:

# 1. Check if using wrangler dev (known limitation)
# WebSocket proxying through sandbox has issues in local dev
# Deploy to Cloudflare for full functionality

# 2. Verify gateway token in WebSocket URL
# Should be: wss://your-worker.workers.dev/ws?token=YOUR_GATEWAY_TOKEN

# 3. Check browser console for errors
# Look for CORS or authentication issues

# 4. Test WebSocket endpoint directly
npm install -g wscat
wscat -c "wss://your-worker.workers.dev/ws?token=YOUR_GATEWAY_TOKEN"

Browser Automation Issues

Problem: "CDP connection failed" or "Browser skill not working"

Solutions:

# 1. Verify CDP secrets are set
npx wrangler secret list | grep CDP

# Should show:
# - CDP_SECRET
# - WORKER_URL

# 2. Test CDP endpoint directly
curl https://your-worker.workers.dev/cdp/json/version \
  -H "CDP_SECRET: your-secret"

# Should return browser version info

# 3. Check Browser Rendering is enabled
# Visit: https://dash.cloudflare.com/?to=/:account/workers/browser-rendering

# 4. Verify browser skill is installed in container
# Visit: https://your-worker.workers.dev/debug/processes
# Look for browser-related processes

Performance Issues

Problem: "Slow responses" or "Timeouts"

Solutions:

# 1. Check AI Gateway analytics
# Visit: https://dash.cloudflare.com/?to=/:account/ai/ai-gateway
# Look for slow provider responses

# 2. Configure container to never sleep
npx wrangler secret put SANDBOX_SLEEP_AFTER
# Enter: never

# 3. Enable caching in AI Gateway
# In AI Gateway settings:
# Enable "Cache responses" with appropriate TTL

# 4. Monitor cold start times
npx wrangler tail --format pretty

# Look for "Container starting" messages
# First request after sleep takes 60-120 seconds

Configuration Not Applying

Problem: "Config changes not working" or "Old settings persist"

Solutions:

# 1. Bust the Docker build cache
# Edit Dockerfile and change the cache bust comment:
# Build cache bust: 2026-01-30-v2

# 2. Force rebuild and redeploy
npm run deploy

# 3. Restart the gateway process
# Visit: https://your-worker.workers.dev/_admin/
# Click "Restart Gateway" button

# 4. Clear R2 backup and start fresh
npx wrangler r2 object delete moltbot-data/moltbot-backup.tar.gz
# Then restart the container

FAQ {#faq}

Q: Is moltworker production-ready?

A: No. Moltworker is explicitly labeled as a proof-of-concept by Cloudflare. It demonstrates platform capabilities but is not an officially supported product. For production use:

Thoroughly review the security considerations
Fork the repository and maintain your own version
Implement additional monitoring and alerting
Consider self-hosting Moltbot if you need guaranteed support

Q: How much does moltworker cost to run?

A: Typical monthly costs:

Workers Paid Plan: $5/month (required)
R2 Storage: $0.75/month (50GB storage + operations)
Browser Rendering: $5/month (1M requests, $5/million after)
AI Gateway: Free (no additional cost)
Cloudflare Access: Free (up to 50 users)

Total: $10-15/month for typical usage

Q: Can I use moltworker with OpenAI instead of Anthropic?

A: Yes. Moltbot supports multiple AI providers:

# Set OpenAI API key
npx wrangler secret put OPENAI_API_KEY

# Or use AI Gateway with OpenAI provider
npx wrangler secret put AI_GATEWAY_API_KEY
npx wrangler secret put AI_GATEWAY_BASE_URL
# Enter: https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/openai

AI Gateway makes it easy to switch between providers without code changes.

Q: How does moltworker handle data privacy?

A: Data privacy considerations:

Cloudflare Access: Cloudflare can access all data passing through Workers and stored in R2
AI Providers: Your conversations are sent to Anthropic/OpenAI (per their privacy policies)
Encryption: Data in transit is encrypted (TLS), but Cloudflare can decrypt it
Zero-Knowledge: Not possible with moltworker architecture

For maximum privacy, self-host Moltbot on your own hardware.

Q: Can I run moltworker locally for development?

A: Yes, with limitations:

# Create .dev.vars file
cat > .dev.vars << EOF
DEV_MODE=true
DEBUG_ROUTES=true
ANTHROPIC_API_KEY=your-key
EOF

# Run locally
npm run dev

Limitations:

WebSocket connections may not work reliably
R2 mounting is not available in local dev
Sandbox behavior differs from production

For full functionality, deploy to Cloudflare.

Q: What happens if Cloudflare has an outage?

A: During a Cloudflare outage:

Your moltworker instance will be unavailable
No data is lost (R2 backups persist)
Once service resumes, your agent automatically recovers
Conversation history and paired devices remain intact

Cloudflare's uptime is typically 99.9%+, but for critical applications, consider:

Multi-cloud deployment (self-hosted backup)
Monitoring and alerting for outages
Documented recovery procedures

Q: Can I customize the Moltbot runtime in moltworker?

A: Yes, but with constraints:

Custom Skills: Add skills to the container via Dockerfile modifications
Environment Variables: Configure via wrangler secrets
System Packages: Install via Dockerfile (apt-get, npm, etc.)
Limitations: Cannot modify the underlying Workers Runtime or Sandbox SDK

Example Dockerfile customization:

# Add custom skill
COPY my-custom-skill /root/clawd/skills/my-custom-skill

# Install additional packages
RUN apt-get update && apt-get install -y \
    python3-pip \
    ffmpeg

# Install Python dependencies
RUN pip3 install requests beautifulsoup4

Q: How do I migrate from self-hosted Moltbot to moltworker?

A: Migration steps:

Export existing data:

   # On your self-hosted machine
   tar -czf moltbot-backup.tar.gz ~/.moltbot

Deploy moltworker (follow deployment guide above)
Upload backup to R2:

   npx wrangler r2 object put moltbot-data/moltbot-backup.tar.gz \
     --file moltbot-backup.tar.gz

Restart container to trigger restore:

   # Visit admin UI and click "Restart Gateway"

Re-pair devices if device IDs changed
Test thoroughly before decommissioning self-hosted instance

Q: Is moltworker vulnerable to prompt injection attacks?

A: Yes. Both Moltbot and moltworker are susceptible to prompt injection via:

Email content (if email integration enabled)
Web pages visited by browser automation
Chat messages from untrusted sources

Mitigation strategies:

Never enable email integration
Only browse trusted websites
Use device pairing to restrict access
Monitor AI Gateway logs for suspicious activity
Consider implementing prompt filtering (custom middleware)

There is currently no foolproof defense against prompt injection in LLM-based agents.

Q: Can I use moltworker for commercial purposes?

A: Check the licenses:

Moltworker: Cloudflare's repository license (likely permissive, verify on GitHub)
Moltbot: Check moltbot's license on their GitHub repository
Cloudflare Services: Review Cloudflare's Terms of Service for commercial use

For commercial deployments:

Consult with legal counsel
Consider forking and maintaining your own version
Implement proper monitoring and SLAs
Ensure compliance with data protection regulations

Q: How do I contribute to moltworker?

A: Moltworker is open-source:

Fork the repository: https://github.com/cloudflare/moltworker
Create a feature branch: git checkout -b feature/my-improvement
Make your changes and test thoroughly
Submit a pull request with detailed description
Engage with maintainers on GitHub issues

Cloudflare has indicated they'll monitor the repository for a while but it's not an officially supported product.

Q: What's the future of moltworker?

A: As of January 2026, moltworker is a proof-of-concept. Possible futures:

Community Maintenance: Becomes a community-driven project
Official Product: Cloudflare productizes it (unlikely based on current statements)
Upstream Contribution: Features merged into official Moltbot
Deprecation: Project becomes unmaintained as Moltbot evolves

For production use, plan for the possibility of maintaining your own fork.

Conclusion

Moltworker represents an innovative approach to deploying AI agents, leveraging Cloudflare's Developer Platform to eliminate hardware requirements while providing enterprise-grade security and observability. However, it's essential to approach it with realistic expectations:

Moltworker Excels At:

Demonstrating Cloudflare's platform capabilities
Reducing deployment complexity vs self-hosting
Providing integrated observability and security
Enabling rapid experimentation with AI agents

Moltworker Falls Short On:

Production-readiness and official support
Absolute data privacy (Cloudflare can access data)
Local network integrations (smart home, NAS, etc.)
Protection against prompt injection attacks

Key Takeaway: Moltworker is an excellent proof-of-concept and learning tool, but requires careful security configuration and realistic expectations about its maturity. For sensitive data or mission-critical applications, traditional self-hosting may be more appropriate.

Next Steps

Experiment: Deploy moltworker in a test environment
Evaluate: Assess if it meets your security and privacy requirements
Monitor: Watch the GitHub repository for updates and security advisories
Contribute: Help improve moltworker through code contributions or feedback
Decide: Choose between moltworker, self-hosting, or a hybrid approach based on your needs

Additional Resources

Moltworker GitHub: https://github.com/cloudflare/moltworker
Moltbot Official Site: https://molt.bot/
Cloudflare Sandbox Docs: https://developers.cloudflare.com/sandbox/
Cloudflare AI Gateway: https://developers.cloudflare.com/ai-gateway/
Cloudflare Access: https://developers.cloudflare.com/cloudflare-one/policies/access/

Universal Commerce Protocol (UCP): The Complete 2026 Guide to Agentic Commerce Standards — Open standard for agentic commerce and payments
The Complete Guide to A2UI Protocol: Building Agent-Driven UIs with Google A2UI (2025) — Declarative UI protocol for AI agents
2025 Full Guide: Agent2Agent (A2A) Protocol — AI agent coordination and protocol fundamentals

Last Updated: January 30, 2026

Moltworker Version: Proof-of-Concept (2026-01)

Author: Based on official Cloudflare documentation and community feedback

Moltworker Complete Guide 2026

2025 Complete Guide: Doubao-Seed-Code Model - In-Depth Analysis of ByteDance's AI Programming Assistant

Sienna — Wed, 12 Nov 2025 01:10:02 +0000

🎯 Key Takeaways (TL;DR)

Model Positioning: Doubao-Seed-Code is ByteDance's professional code generation AI, supporting 200+ programming languages
Core Capabilities: Comprehensive programming assistance including code generation, completion, explanation, debugging, and unit test generation
Integration Method: Quick integration via Volcano Engine API, supporting both streaming and non-streaming calls
Use Cases: IDE plugin development, code review tools, intelligent programming assistants, developer education platforms

What is Doubao-Seed-Code Model
Core Features and Capabilities
How to Integrate and Use
API Call Details
Best Practices and Application Scenarios
Frequently Asked Questions

What is Doubao-Seed-Code Model

Doubao-Seed-Code is a vertical domain model developed by ByteDance based on the Doubao large language model technology stack, specifically optimized for code scenarios. The model is trained on massive code corpora and possesses deep programming language understanding and generation capabilities.

Technical Features

Feature Dimension	Capability Description
Language Coverage	Supports 200+ programming languages (Python, Java, JavaScript, C++, Go, etc.)
Context Length	Supports long context understanding, suitable for large codebase analysis
Response Speed	Optimized inference performance, supports real-time code completion scenarios
Accuracy	Trained on real development scenarios with high code executability

💡 Professional Tip

Doubao-Seed-Code not only generates code but also understands code intent, identifies potential bugs, and provides optimization suggestions - it's a true "AI pair programming" assistant.

Core Features and Capabilities

1️⃣ Code Generation

Feature Description: Generate complete executable code based on natural language descriptions

Typical Scenarios:

Generate function implementations from requirement documents
Quickly scaffold project structures
Generate algorithm solutions

Example Input:

Implement a quicksort algorithm in Python with detailed comments

2️⃣ Code Completion

Feature Description: Intelligently predict the next line of code or complete current code snippets

Technical Advantages:

✅ Context-aware: Understands current file and project structure
✅ Multi-line completion: Not just single lines, but complete code blocks
✅ Style adaptation: Learns user coding style

3️⃣ Code Explanation

Feature Description: Convert complex code into easy-to-understand natural language descriptions

Application Value:

Help beginners understand open-source projects
Quickly grasp legacy code logic
Generate code documentation

4️⃣ Code Debugging and Optimization

Feature	Description	Value
Bug Detection	Identify potential errors and security vulnerabilities	Improve code quality
Performance Optimization	Provide algorithm complexity optimization suggestions	Enhance runtime efficiency
Code Refactoring	Suggest more elegant implementation approaches	Improve maintainability

5️⃣ Unit Test Generation

Feature Description: Automatically generate test cases for functions

Generated Content:

Normal scenario tests
Boundary condition tests
Exception handling tests

⚠️ Note

Auto-generated test cases require manual review to ensure coverage of all business logic branches.

How to Integrate and Use

📋 Prerequisites

Register Volcano Engine Account
- Visit: https://www.volcengine.com
- Complete real-name verification
Activate Model Service
- Go to "Model Marketplace"
- Find "Doubao-Seed-Code Model"
- Click "Use Now"
Obtain API Keys
- Create API Key in console
- Securely store Access Key and Secret Key

API Call Details

Basic Call Example (Python)

import requests
import json

# API Configuration
API_ENDPOINT = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
API_KEY = "your_api_key_here"

# Request Headers
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

# Request Payload
payload = {
    "model": "doubao-seed-code",  # Model ID
    "messages": [
        {
            "role": "system",
            "content": "You are a professional programming assistant"
        },
        {
            "role": "user",
            "content": "Implement binary search algorithm in Python"
        }
    ],
    "temperature": 0.7,  # Control creativity (0-1)
    "max_tokens": 2000   # Maximum output length
}

# Send Request
response = requests.post(API_ENDPOINT, headers=headers, json=payload)
result = response.json()

# Extract Code
code = result['choices'][0]['message']['content']
print(code)

Key Parameter Descriptions

Parameter	Type	Description	Recommended Value
`model`	string	Model identifier	`doubao-seed-code`
`temperature`	float	Randomness control (0-1)	Code generation: 0.2-0.5 Creative programming: 0.7-0.9
`max_tokens`	int	Maximum output tokens	1000-4000
`stream`	bool	Whether to stream response	Real-time scenarios: true Batch processing: false

Streaming Call Example

import requests

def stream_code_generation(prompt):
    payload = {
        "model": "doubao-seed-code",
        "messages": [{"role": "user", "content": prompt}],
        "stream": True  # Enable streaming
    }

    response = requests.post(
        API_ENDPOINT, 
        headers=headers, 
        json=payload, 
        stream=True
    )

    for line in response.iter_lines():
        if line:
            data = json.loads(line.decode('utf-8').replace('data: ', ''))
            if 'choices' in data:
                delta = data['choices'][0]['delta']
                if 'content' in delta:
                    print(delta['content'], end='', flush=True)

# Usage Example
stream_code_generation("Implement an LRU cache")

✅ Best Practice

Streaming calls are suitable for scenarios requiring real-time feedback (like IDE plugins), significantly enhancing user experience.

Best Practices and Application Scenarios

Scenario 1: IDE Smart Completion Plugin

Implementation Approach:

Listen to user input events
Get current file context (20 lines before and after)
Call API to get completion suggestions
Display results in floating window

Prompt Optimization Tips:

context = """
# Current file: user_service.py
# Existing code:
class UserService:
    def __init__(self, db):
        self.db = db

    def get_user(self, user_id):
        # Cursor position
"""

prompt = f"{context}\nPlease complete the get_user method implementation with exception handling"

Scenario 2: Code Review Assistant

Feature Design:

Automatically detect code smells
Provide refactoring suggestions
Generate review reports

Example Prompt:

Please review the following code, focusing on:
1. Potential null pointer exceptions
2. Performance bottlenecks
3. Security vulnerabilities
4. Code style issues

[Code to review]

Scenario 3: Technical Documentation Generation

Application Value:

Automatically generate API documentation
Add docstrings to functions
Generate README files

Comparison with Traditional Methods:

Dimension	Manual Writing	AI Generation	Advantage
Speed	1 hour/module	5 minutes/module	🚀 12x improvement
Consistency	Depends on manual effort	Automatically unified	✅ Standardized style
Coverage	50-70%	90%+	📈 More comprehensive

Scenario 4: Programming Education Platform

Functional Modules:

Interactive Code Explanation: Line-by-line code logic explanation
Error Diagnosis: Analyze student code and provide improvement suggestions
Exercise Generation: Automatically generate problems based on knowledge points

Frequently Asked Questions

Q1: Which programming languages does Doubao-Seed-Code support?

A: The model supports 200+ programming languages, including but not limited to:

Mainstream Languages: Python, Java, JavaScript, TypeScript, C++, C#, Go, Rust
Scripting Languages: Shell, PowerShell, Lua, Ruby, PHP
Frontend Technologies: HTML, CSS, Vue, React
Databases: SQL (MySQL, PostgreSQL, etc.)
Others: Markdown, JSON, YAML, Dockerfile, etc.

For niche languages, the model also has basic understanding and generation capabilities.

Q2: How to improve code generation accuracy?

A: Follow these best practices:

Provide Detailed Context

   ❌ Poor: Write a sorting function
   ✅ Good: Implement quicksort in Python with requirements:
        - Support custom comparison function
        - Handle empty list cases
        - Time complexity O(nlogn)
        - Include type annotations and docstring

Step-by-Step Guidance
- First have the model generate function signature
- Then request core logic implementation
- Finally add exception handling
Adjust temperature Parameter
- Code generation: 0.2-0.4 (more deterministic)
- Algorithm optimization: 0.5-0.7 (moderate creativity)

Q3: Are there rate limits for API calls?

A: Yes, specific limits depend on your subscription plan:

Plan Type	QPM Limit	Concurrency	Monthly Calls
Free Trial	10	2	10,000
Basic	60	10	100,000
Professional	300	50	1,000,000
Enterprise	Custom	Custom	Unlimited

⚠️ Note

Exceeding limits will return a 429 error. Implement request queuing and retry mechanisms.

Q4: Who owns the copyright of generated code?

A: According to Volcano Engine service agreement:

✅ User owns full copyright: Generated code belongs to the caller
✅ Commercial use allowed: No additional authorization required
⚠️ User responsibility: Users must ensure generated code doesn't infringe third-party rights

Q5: How to handle sensitive code and data security?

A: Security recommendations:

Data Anonymization
- Remove API keys, passwords, and other sensitive information
- Replace real business data with sample data
Private Deployment
- Enterprise version supports private deployment
- Data stays within local network
Audit Logs
- Enable API call logging
- Regularly review usage records

Summary and Action Recommendations

Core Value Summary

Doubao-Seed-Code provides developers with comprehensive AI programming assistant capabilities, covering the entire software development lifecycle from code generation to debugging optimization. Its core advantages include:

✅ High Accuracy: Trained on massive real code

✅ Easy Integration: Standard REST API, supports multiple SDKs

✅ High Performance: Optimized inference speed, supports real-time scenarios

✅ Continuous Evolution: Regular updates, constantly improving capabilities

Related Resources

📚 Official Documentation: https://www.volcengine.com/docs/82379/1949118

2025 Complete Guide: In-Depth Analysis of ERNIE-4.5-VL-28B-A3B-Thinking Multimodal AI Model

Sienna — Wed, 12 Nov 2025 00:29:02 +0000

🎯 Key Takeaways (TL;DR)

Lightweight & Efficient: Activates only 3B parameters while matching top-tier flagship model performance
Breakthrough Reasoning: Achieves exceptional visual reasoning and STEM problem-solving through large-scale reinforcement learning
Innovative Features: Supports "Thinking with Images", visual grounding, tool calling, and video understanding
Easy Deployment: Supports multiple inference frameworks including Transformers, vLLM, and FastDeploy
Open Source Friendly: Licensed under Apache 2.0, allowing commercial use

What is ERNIE-4.5-VL-28B-A3B-Thinking
Core Technical Highlights
Six Key Capabilities Explained
Performance Benchmarks
Quick Start Guide
Deployment Options Comparison
Fine-tuning and Training
Frequently Asked Questions
Summary and Recommendations

What is ERNIE-4.5-VL-28B-A3B-Thinking

ERNIE-4.5-VL-28B-A3B-Thinking is Baidu's latest generation multimodal AI model, built upon the powerful ERNIE-4.5-VL-28B-A3B architecture. It's a large language model specifically optimized for vision-language understanding tasks, having absorbed massive amounts of high-quality visual-language reasoning data through extensive mid-training phases.

💡 Expert Tip

The model's key feature is its MoE (Mixture of Experts) architecture. While the total parameter count is 28B, only 3B parameters are activated during inference, enabling it to maintain high performance while dramatically reducing computational costs.

Core Innovations

Large-scale Vision-Language Training: Absorbed vast amounts of premium visual-language reasoning data during mid-training
Deep Semantic Alignment: Significantly enhanced semantic alignment between visual and language modalities
Advanced Reinforcement Learning: Employs GSPO and IcePop strategies combined with dynamic difficulty sampling for efficient learning
Enhanced Instruction Following: Dramatically improved visual grounding performance and instruction execution capabilities

Core Technical Highlights

Training Technology Innovations

Technical Feature	Implementation	Benefits
Multimodal RL	GSPO + IcePop strategies	Stabilizes MoE training, improves learning efficiency
Dynamic Difficulty Sampling	Adaptive training sample difficulty adjustment	Accelerates convergence, enhances generalization
Large-scale Mid-training	Massive visual-language reasoning data	Boosts representation power and cross-modal understanding
Verifiable Task Learning	RL on verifiable tasks	Ensures reasoning accuracy

Architectural Advantages

MoE (Mixture of Experts) Architecture enables the model to:

Activate only necessary 3B parameters during inference
Maintain 28B parameter knowledge capacity
Significantly reduce inference costs and latency
Achieve better energy efficiency

⚠️ Important Note

Although the model activates only 3B parameters, single-card deployment requires at least 80GB GPU memory. This is because the complete model weights need to be loaded, even though only a portion is activated during inference.

Six Key Capabilities Explained

1. 🧠 Visual Reasoning

Core Strengths:

Multi-step complex reasoning
Chart analysis and interpretation
Causal relationship reasoning

Application Scenarios:

Complex chart data analysis
Visual logic problem solving
Scene understanding and inference

Empowered by large-scale reinforcement learning, the model demonstrates exceptional multi-step reasoning capabilities in complex visual tasks. Whether analyzing intricate statistical charts or understanding causal relationships in images, ERNIE-4.5-VL-Thinking delivers accurate analytical results.

2. 🔬 STEM Reasoning

Breakthrough Performance:

Solving math problems from photos
Physics formula recognition and calculation
Geometric figure analysis

Practical Value:

Educational assistance tools
Homework grading systems
Scientific research data analysis

Leveraging powerful visual capabilities, the model achieves a performance leap in STEM tasks. It can directly recognize mathematical formulas and geometric figures from photos and perform accurate calculations and reasoning, handling even complex problems with ease.

3. 📍 Visual Grounding

Enhanced Features:

More precise object localization
Flexible instruction execution
Complex industrial scenario adaptation

Typical Applications:

Industrial quality inspection
Autonomous driving scene understanding
Robot visual navigation

Responding to strong community demand, the model significantly enhances visual grounding performance. Improved instruction-following capabilities make grounding functions more accessible, easily triggering localization in complex industrial scenarios for dramatic efficiency gains.

4. 🤔 Thinking with Images

Innovative Functionality:

Thinks like humans
Freely zooms image details
Progressive information extraction

Workflow:

User Input Image → Initial Analysis → Identify Key Regions → 
Zoom Detail Inspection → Synthesize Information → Generate Complete Answer

This is one of the model's most innovative features. When paired with tools like image zooming and image search, "Thinking with Images" dramatically elevates the model's ability to process fine-grained details and handle long-tail visual knowledge. The model thinks like a human, first observing the whole, then zooming into key regions for careful inspection, and finally synthesizing all information to provide an answer.

✅ Best Practice

When processing high-resolution images or pictures with abundant details, enabling "Thinking with Images" can significantly improve recognition accuracy.

5. 🛠️ Tool Utilization

Supported Tool Types:

Image search
Image zooming
External knowledge base queries
Calculator and other auxiliary tools

Advantages:

Handle long-tail knowledge
Real-time information retrieval
Enhanced problem-solving capabilities

Empowered by robust tool-calling capabilities, the model can instantly use functions like image search to easily identify long-tail knowledge and achieve comprehensive information retrieval. These enhancements form a critical foundation for developing sophisticated multimodal agents.

6. 🎬 Video Understanding

Core Capabilities:

Outstanding temporal awareness
Precise event localization
Cross-frame content change recognition

Application Domains:

Video content moderation
Intelligent video editing
Surveillance video analysis
Sports event analysis

The model possesses outstanding temporal awareness and event localization abilities, accurately identifying content changes across different time segments in videos, making video analysis smarter and more efficient.

Performance Benchmarks

According to official benchmark results, ERNIE-4.5-VL-28B-A3B-Thinking performs excellently across multiple evaluation benchmarks. As a lightweight model activating only 3B parameters, its performance closely matches or even exceeds industry-leading flagship models.

Comparison with Top Models

Capability Dimension	ERNIE-4.5-VL-Thinking	Industry Top Models Average	Advantage
Visual Reasoning	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	RL enhancement
STEM Problems	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Visual breakthrough
Visual Grounding	⭐⭐⭐⭐⭐	⭐⭐⭐	Specialized optimization
Tool Calling	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Native support
Parameter Efficiency	⭐⭐⭐⭐⭐	⭐⭐⭐	Only 3B activated
Video Understanding	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Strong temporal awareness

📊 Performance Highlights

Official benchmark charts show the model approaches or exceeds industry-leading flagship models across multiple dimensions while maintaining significant parameter efficiency advantages. This means users can achieve top-tier performance at lower costs.

Key Performance Metrics

Inference Speed: Thanks to only 3B activated parameters, inference is 2-3x faster than equivalent full-parameter models
Memory Footprint: While 80GB is needed to load the model, inference memory usage is far lower than traditional large models
Accuracy: Achieves SOTA levels across multiple vision-language understanding benchmarks
Generalization: Maintains strong performance on unseen tasks

Quick Start Guide

Method 1: Using Transformers Library (Recommended for Beginners)

Suitable For:

Rapid prototyping
Small-scale inference tasks
Learning and experimentation
Single or low-frequency calls

Basic Code Example:

import torch
from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM

# Load model
model_path = 'baidu/ERNIE-4.5-VL-28B-A3B-Thinking'
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    dtype=torch.bfloat16,
    trust_remote_code=True
)

# Load processor
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model.add_image_preprocess(processor)

# Build messages
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What color clothes is the girl wearing in the picture?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example1.jpg"
                }
            },
        ]
    },
]

# Process input
text = processor.tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
image_inputs, video_inputs = processor.process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)

# Generate response
device = next(model.parameters()).device
inputs = inputs.to(device)
generated_ids = model.generate(
    inputs=inputs['input_ids'].to(device),
    **inputs,
    max_new_tokens=1024,
    use_cache=False
)
output_text = processor.decode(generated_ids[0][len(inputs['input_ids'][0]):])
print(output_text)

Key Parameter Explanations:

device_map="auto": Automatically allocates model to available devices
dtype=torch.bfloat16: Uses bfloat16 precision, balancing performance and accuracy
trust_remote_code=True: Allows execution of custom code from model repository
max_new_tokens=1024: Controls maximum length of generated text

Method 2: Using vLLM (Recommended for Production)

Suitable For:

High-concurrency inference services
Production environment deployment
Applications requiring high throughput
API service construction

Installation Steps:

# Install uv package manager
pip install uv

# Install vLLM main branch
uv pip install -U vllm --pre \
  --extra-index-url https://wheels.vllm.ai/nightly \
  --extra-index-url https://download.pytorch.org/whl/cu129 \
  --index-strategy unsafe-best-match

Start Service:

# Basic startup (requires 80G memory)
vllm serve baidu/ERNIE-4.5-VL-28B-A3B-Thinking --trust-remote-code

# If encountering memory shortage, add the following parameter
vllm serve baidu/ERNIE-4.5-VL-28B-A3B-Thinking \
  --trust-remote-code \
  --gpu-memory-utilization 0.95

Enable Reasoning Parser and Tool Calling:

vllm serve baidu/ERNIE-4.5-VL-28B-A3B-Thinking \
  --trust-remote-code \
  --reasoning-parser ernie45 \
  --tool-call-parser ernie45 \
  --enable-auto-tool-choice

vLLM Advantages:

PagedAttention: Efficient memory management, supports larger batches
Continuous Batching: Dynamically batches requests, maximizes GPU utilization
Optimized CUDA Kernels: Specially optimized inference kernels for faster speed
OpenAI-Compatible API: Provides OpenAI API-compatible interface

Method 3: Using FastDeploy (Recommended for Enterprise)

Suitable For:

Enterprise-grade production deployment
Requiring quantization acceleration
Multi-instance load balancing
Complete monitoring and management

Quick Start:

fastdeploy serve --model baidu/ERNIE-4.5-VL-28B-A3B-Thinking \
  --max-model-len 131072 \
  --max-num-seqs 32 \
  --port 8180 \
  --quantization wint8 \
  --reasoning-parser ernie-45-vl-thinking \
  --tool-call-parser ernie-45-vl-thinking \
  --mm-processor-kwargs '{"image_max_pixels": 12845056 }'

Parameter Details:

--max-model-len 131072: Maximum supported sequence length
--max-num-seqs 32: Maximum concurrent sequences
--quantization wint8: Uses 8-bit integer quantization, reduces memory usage
--mm-processor-kwargs: Multimodal processor parameters, controls maximum image pixels

💡 Expert Tip

FastDeploy supports wint8 quantization, reducing memory requirements from 80GB to approximately 60GB while maintaining performance. This is the best choice for memory-constrained scenarios.

Deployment Options Comparison

Detailed Comparison Table

Deployment Option	Ease of Use	Performance	Concurrency	Memory Requirement	Quantization	Suitable Scenarios
Transformers	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐	80GB+	❌	Development & Testing
vLLM	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	80GB+	✅	Production
FastDeploy	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	60GB+ (quantized)	✅	Enterprise

Performance Comparison

Metric	Transformers	vLLM	FastDeploy
Single Inference Latency	Medium	Low	Low
Throughput (req/s)	1-5	20-50	20-50
Memory Efficiency	Fair	Excellent	Excellent
Startup Time	Fast	Medium	Medium
API Compatibility	Custom	OpenAI-compatible	Custom

Selection Recommendations

If you are:

AI Researcher/Student → Choose Transformers
- ✅ Easy to experiment and debug
- ✅ Full model access
- ✅ Rich documentation and community support
- ❌ Not optimal performance
Startup/Individual Developer → Choose vLLM
- ✅ Balanced performance and ease of use
- ✅ OpenAI-compatible API
- ✅ Active community
- ✅ Free and open source
Large Enterprise → Choose FastDeploy
- ✅ Complete enterprise-grade support
- ✅ Quantization optimization
- ✅ Monitoring and management features
- ✅ Long-term maintenance guarantee

Fine-tuning and Training

Fine-tuning with ERNIEKit

ERNIEKit is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series models, providing comprehensive training support.

Supported Training Scenarios:

✅ Supervised Fine-Tuning (SFT)
✅ LoRA Low-Rank Adaptation
✅ DPO Alignment Training
✅ Function Calling Training
✅ Multi-GPU Distributed Training

Quick Start Fine-tuning

Step 1: Download Model

huggingface-cli download baidu/ERNIE-4.5-VL-28B-A3B-Thinking \
  --local-dir baidu/ERNIE-4.5-VL-28B-A3B-Thinking

Step 2: Run SFT Training

# Basic SFT + LoRA (Recommended)
erniekit train examples/configs/ERNIE-4.5-VL-28B-A3B-Thinking/sft/run_sft_lora_8k.yaml

# Function calling specialized training
erniekit train examples/configs/ERNIE-4.5-VL-28B-A3B-Thinking/sft_function_call/run_sft_8k.yaml

Training Configuration Examples

LoRA Configuration Recommendations:

lora_config:
  r: 8                    # LoRA rank, higher means more expressive but more memory
  lora_alpha: 16          # LoRA scaling factor
  target_modules:         # Target modules for LoRA
    - q_proj
    - v_proj
    - k_proj
    - o_proj
  lora_dropout: 0.05      # Dropout rate

Training Hyperparameter Recommendations:

training_args:
  learning_rate: 1e-5     # Learning rate
  num_train_epochs: 3     # Number of epochs
  per_device_train_batch_size: 4
  gradient_accumulation_steps: 4
  warmup_ratio: 0.1       # Warmup ratio
  save_steps: 500         # Checkpoint save interval
  logging_steps: 10       # Logging interval

Data Preparation

Standard Data Format:

{
  "messages": [
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe this image"},
        {"type": "image_url", "image_url": {"url": "path/to/image.jpg"}}
      ]
    },
    {
      "role": "assistant",
      "content": "This is an image of..."
    }
  ]
}

Fine-tuning Best Practices

✅ Best Practices

Data Quality First

Ensure correct training data format

Include high-quality image-text pairs

Sufficient data diversity

Avoid data bias

LoRA Configuration Optimization

Resource-constrained: r=8, alpha=16

Balanced: r=16, alpha=32

High-quality: r=32, alpha=64

Learning Rate Adjustment

Start with smaller learning rate (1e-5)

Use warmup to avoid training instability

Monitor loss curves and adjust timely

Validation and Monitoring

Regular evaluation on validation set

Use early stopping to avoid overfitting

Track key metric changes

Memory Optimization

Use gradient accumulation to reduce batch size

Enable mixed precision training

Consider using DeepSpeed ZeRO

Training Hardware Requirements

Training Method	Minimum Memory	Recommended Memory	GPU Count	Training Time (1000 samples)
LoRA (r=8)	40GB	80GB	1	2-4 hours
LoRA (r=16)	48GB	80GB	1	3-6 hours
Full Fine-tune	160GB+	320GB+	4+	12-24 hours

🤔 Frequently Asked Questions

Q1: How much GPU memory is required to run the model?

Inference: At least 80GB GPU memory per card (e.g., A100 or H100)
Quantized Inference: Can be reduced to approximately 60GB using wint8 quantization
Fine-tuning (LoRA): Requires at least 40-80GB
Full Fine-tuning: Requires 160GB+, multi-GPU training recommended

Memory Optimization Suggestions:

Use quantization techniques (wint8)
Enable gradient checkpointing
Reduce batch size
Use LoRA instead of full fine-tuning

Q2: What languages does the model support?

A: The model is primarily optimized for Chinese and English, with the strongest understanding and generation capabilities in these two languages.

Language Support Details:

🟢 Chinese: Excellent (primary optimization language)
🟢 English: Excellent (primary optimization language)
🟡 Other Languages: Basic support, effectiveness may not match Chinese/English

Q3: How to enable "Thinking with Images" functionality?

A: "Thinking with Images" is automatically enabled when using tool-calling mode.

Enabling Method:

# Add parameters when starting vLLM
vllm serve baidu/ERNIE-4.5-VL-28B-A3B-Thinking \
  --trust-remote-code \
  --reasoning-parser ernie45 \
  --tool-call-parser ernie45 \
  --enable-auto-tool-choice

The model automatically determines when to:

Zoom image details
Search related images
Call other tools

Q4: Can it be used commercially?

A: ✅ Yes, commercial use is allowed

The model is licensed under Apache 2.0, which permits:

✅ Commercial use
✅ Modification and distribution
✅ Patent use
✅ Private use

Important Notes:

Retain copyright notices
Mark significant modifications
Comply with license terms

Q5: What advantages does it have compared to other multimodal models?

A: Key advantages include:

Advantage Dimension	Specific Performance
Parameter Efficiency	Only 3B activated parameters, 50%+ lower inference cost
Reasoning Capability	Large-scale RL training, excellent complex reasoning
Tool Integration	Native support for image search, zoom, etc.
Visual Grounding	Specially optimized grounding, suitable for industrial scenarios
Chinese Support	Deep optimization for Chinese, better Chinese performance
Open Source Friendly	Apache 2.0 license, barrier-free commercial use

Q6: Does it support video input?

A: ✅ Full video understanding support

Video Processing Capabilities:

Temporal information understanding
Event localization
Cross-frame content change recognition
Video summary generation

Usage Method:

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe what happens in the video"},
            {"type": "video", "video": "path/to/video.mp4"}
        ]
    }
]
image_inputs, video_inputs = processor.process_vision_info(messages)

Q7: How to achieve optimal inference performance?

A: Recommended configuration and optimization strategies:

Deployment Configuration:

vllm serve baidu/ERNIE-4.5-VL-28B-A3B-Thinking \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 8192 \
  --max-num-seqs 32 \
  --gpu-memory-utilization 0.95 \
  --enable-chunked-prefill

Performance Optimization Recommendations:

Use vLLM or FastDeploy instead of Transformers
Enable bfloat16 precision for speed-accuracy balance
Set concurrency appropriately adjust max-num-seqs based on memory
Batch requests use batching mode for bulk inference
Enable PagedAttention enabled by default in vLLM, improves memory efficiency
Use quantization if memory-constrained, use wint8 quantization

Performance Benchmark Reference:

Single inference latency: 200-500ms (depends on input length)
Throughput: 20-50 requests/second (vLLM, single A100)
Concurrency support: Up to 32 concurrent requests

Q8: How frequently is the model updated?

A: Baidu regularly updates the ERNIE series models.

Get Update Information:

Recommendations:

Follow official channels for latest versions
Check Release Notes for improvements
Validate compatibility in test environment before upgrading

Q9: How to handle inference errors or exceptions?

A: Common issues and solutions:

Out of Memory (OOM):

# Solution 1: Increase memory utilization
--gpu-memory-utilization 0.95

# Solution 2: Reduce concurrency
--max-num-seqs 16

# Solution 3: Use quantization
--quantization wint8

Loading Failure:

# Ensure trust_remote_code is added
--trust-remote-code

# Check network connection and model download integrity
huggingface-cli download baidu/ERNIE-4.5-VL-28B-A3B-Thinking --resume-download

Slow Inference:

Check if using optimized inference framework (vLLM/FastDeploy)
Verify GPU utilization is normal
Consider using batch processing mode
Check if input image resolution is too high

Q10: How to evaluate fine-tuning effectiveness?

A: Recommended methods for evaluating fine-tuned models:

1. Quantitative Evaluation:

# Calculate metrics on validation set
from sklearn.metrics import accuracy_score, f1_score

# For classification tasks
accuracy = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, average='weighted')

# For generation tasks
from rouge import Rouge
rouge = Rouge()
scores = rouge.get_scores(predictions, references, avg=True)

2. Qualitative Evaluation:

Manual inspection of generation quality
Compare outputs before and after fine-tuning
Test edge cases and difficult samples

3. Business Metrics:

User satisfaction
Task completion rate
Error rate reduction

Summary and Recommendations

Core Advantages Summary

ERNIE-4.5-VL-28B-A3B-Thinking represents a significant breakthrough in multimodal AI:

🎯 Technical Innovation

MoE architecture achieves parameter efficiency breakthrough
Large-scale reinforcement learning enhances reasoning capabilities
Innovative "Thinking with Images" feature
Native tool calling support

⚡ Outstanding Performance

3B activated parameters achieve top-tier model performance
2-3x faster inference speed
Significantly reduced memory footprint
Leading performance across multiple benchmarks

🛠️ Comprehensive Features

Visual reasoning and STEM problem solving
Precise visual grounding capabilities
Powerful video understanding
Flexible tool calling mechanism

🚀 Flexible Deployment

Multiple deployment options supported
Quantization optimization lowers barriers
Comprehensive documentation and examples
Active community support

💼 Open Source Friendly

Apache 2.0 license
Commercial use supported
Complete training toolchain
Continuous version updates

Application Scenario Analysis

Application Domain	Suitability	Key Capabilities	Typical Cases
EdTech	⭐⭐⭐⭐⭐	STEM Reasoning	Homework grading, intelligent tutoring
Industrial QC	⭐⭐⭐⭐⭐	Visual Grounding	Defect detection, quality control
Content Moderation	⭐⭐⭐⭐⭐	Video Understanding	Video review, content classification
Customer Service	⭐⭐⭐⭐	Multimodal Understanding	Image-text support, Q&A
Medical Imaging	⭐⭐⭐⭐	Visual Reasoning	Image analysis, diagnostic assistance
Autonomous Driving	⭐⭐⭐⭐	Scene Understanding	Environment perception, decision support
E-commerce	⭐⭐⭐⭐⭐	Image Search	Product recognition, recommendation systems

Gemini CLI Extensions: The Complete Developer's Guide to AI-Powered Command Line Customization (2025)

Sienna — Thu, 09 Oct 2025 13:14:51 +0000

🎯 Core Highlights (TL;DR)

Revolutionary Launch: Google launched Gemini CLI extensions with 70+ ready-to-use integrations from industry leaders
Seamless Integration: Install any extension with a single command: gemini extensions install <URL>
Enterprise Ready: Major partners including Stripe, Shopify, Postman, Figma, and Dynatrace provide official extensions
Open Ecosystem: Build custom extensions using MCP servers, context files, and custom commands
AI-Powered Intelligence: Extensions teach Gemini CLI how to use tools effectively with built-in "playbooks"

What are Gemini CLI Extensions?
How to Install and Use Extensions
Industry Partner Extensions
Google-Created Extensions
Building Your Own Extensions
Extension Architecture Deep Dive
Best Practices and Use Cases
FAQ

What are Gemini CLI Extensions?

Gemini CLI extensions represent a paradigm shift in command-line development tools. Launched in October 2025, these extensions transform the Gemini CLI from a simple AI assistant into a comprehensive, personalized development environment.

Key Features:

Pre-packaged Intelligence: Each extension contains built-in knowledge about how to use specific tools
Zero Configuration: Get meaningful results from the first command without complex setup
Open Ecosystem: Anyone can build and share extensions via GitHub
Tool Integration: Connect databases, design platforms, payment services, and more

💡 Professional Tip
Extensions go beyond basic MCP (Model Context Protocol) connections by adding intelligence layers that understand context and best practices for each tool.

How to Install and Use Extensions

Installation Process

Installing Gemini CLI extensions is remarkably straightforward:

# Install from GitHub URL
gemini extensions install https://github.com/username/extension-name

# Install from local folder
gemini extensions install ./local-extension-folder

Extension Management Commands

Command	Purpose	Example
`gemini extensions list`	View installed extensions	Lists all active extensions
`gemini extensions remove <name>`	Uninstall extension	Remove specific extension
`gemini extensions new <name> <type>`	Create new extension	Generate extension template

Usage Workflow

graph TD
    A[Discover Extension] --> B[Install with Single Command]
    B --> C[Extension Auto-Configures]
    C --> D[Use Natural Language Commands]
    D --> E[AI Executes with Context]

Industry Partner Extensions

The launch includes official extensions from major technology companies, demonstrating enterprise-grade adoption:

Development & API Tools

Postman Extension

Generate API request collections automatically
Manage workspaces through natural language
Evaluate API performance and documentation

Stripe Extension

Interact with Stripe API seamlessly
Access comprehensive payment knowledge base
Automate payment workflow setup

Security & Monitoring

Snyk Extension

Integrate comprehensive security scanning
Ensure code security from inception
Automate vulnerability detection

Dynatrace Extension

Real-time application performance insights
Root-cause analysis acceleration
Availability monitoring from CLI

Design & Content

Figma Extension

Generate code from design frames
Extract design system context
Ensure design-code consistency

Shopify Extension

Access Shopify developer ecosystem
Search documentation intelligently
Build serverless Shopify functions

Data & Analytics

Elastic Extension

Search and analyze Elasticsearch data
Connect to Elastic Cloud Serverless
Integrate with developer workflows

Google-Created Extensions

Google has developed a comprehensive suite of extensions covering various development scenarios:

Cloud-Native Development

Extension	Primary Use Case	Key Features
Cloud Run	Serverless deployment	Local code to live URL in one step
GKE	Kubernetes management	Cluster health, application deployment
gcloud	Google Cloud interaction	Complete GCP environment control
Observability	Monitoring & troubleshooting	Cloud environment insights

Application Development

Flutter Extension

Create, build, and refactor Flutter apps
AI-powered debugging assistance
Maintenance automation

Firebase Extension

Backend setup and management
Real-time database configuration
Authentication system setup

Chrome DevTools Extension

Live browser automation
Performance analysis
In-depth debugging capabilities

AI & Data Integration

Genkit Extension

Enhanced GenAI app development
Flow management and debugging
OpenTelemetry trace analysis

Looker Extension

Business data exploration
Visualization generation
Trend analysis automation

✅ Best Practice
Start with Google-created extensions to understand the ecosystem before building custom solutions.

Building Your Own Extensions

Extension Components

Gemini CLI extensions can bundle multiple components:

Extension Structure:
├── MCP Servers (1 or more)
├── Context Files (GEMINI.md, custom types)
├── Excluded Tools (disable built-ins)
└── Custom Commands (slash commands)

Creation Templates

# Create MCP server extension
gemini extensions new my-extension mcp-server

# Create custom commands extension
gemini extensions new my-extension custom-commands

Development Workflow

Choose Template: Start with appropriate template type
Define Context: Create instructional context files
Implement Tools: Develop MCP server or custom commands
Test Locally: Validate functionality in development
Package & Share: Publish to GitHub for community use

Extension Architecture Deep Dive

MCP Integration

Extensions leverage the Model Context Protocol (MCP) for tool connectivity:

Raw Connection: MCP provides basic tool access
Intelligence Layer: Extensions add context and best practices
Seamless Experience: AI understands how to use tools effectively

Context Files

Context files provide crucial guidance to the AI:

# Example GEMINI.md structure
## Tool Purpose
Brief description of what this tool does

## Usage Patterns
Common workflows and best practices

## Examples
Specific use cases and command patterns

Custom Commands

Slash commands encapsulate complex prompts:

# Example custom command
/deploy-app "Deploy my application to production with monitoring"

Best Practices and Use Cases

Development Workflow Integration

Morning Routine Automation

# Check system health, deploy updates, review metrics
> Check my GKE cluster health, deploy latest code to Cloud Run, and show me yesterday's error rates

Cross-Platform Development

# Flutter app with Firebase backend
> Create a new Flutter app with Firebase authentication and Firestore integration

Team Collaboration

Code Review Process

# Automated security and quality checks
> Review my latest commits for security vulnerabilities and suggest improvements

Performance Optimization

Scenario	Extension Combination	Benefit
Full-stack debugging	Chrome DevTools + Dynatrace	Frontend and backend insights
API development	Postman + Stripe	Complete payment integration
Security audit	Snyk + Code Review	Comprehensive vulnerability detection

⚠️ Important Note
Extensions work best when combined thoughtfully. Avoid installing too many similar extensions that might conflict.

🤔 Frequently Asked Questions

Q: How do Gemini CLI extensions differ from regular CLI tools?

A: Unlike traditional CLI tools that require manual configuration and learning, Gemini CLI extensions come with built-in intelligence. They understand context, follow best practices automatically, and integrate seamlessly with natural language commands.

Q: Can I use multiple extensions simultaneously?

A: Yes, extensions are designed to work together. You can combine different extensions to create powerful workflows, such as using Figma for design extraction while simultaneously deploying with Cloud Run.

Q: Are extensions secure for enterprise use?

A: Extensions from verified partners like Stripe, Dynatrace, and Snyk undergo security reviews. For custom extensions, review the source code and ensure they follow security best practices before installation.

Q: How do I contribute to the extension ecosystem?

A: Create your extension using the provided templates, test thoroughly, publish to GitHub, and submit to the Gemini CLI Extensions gallery for community visibility.

Q: What's the difference between MCP servers and extensions?

A: MCP servers provide raw tool connectivity, while extensions add intelligence, context, and best practices. Extensions can bundle MCP servers with additional guidance for optimal AI interaction.

Q: Can extensions work offline?

A: Some extensions require internet connectivity for API access, but local extensions with custom commands and context files can function offline once installed.

Conclusion and Next Steps

Gemini CLI extensions represent a significant evolution in developer tooling, transforming the command line from a simple interface into an intelligent, personalized development environment. With over 70 extensions already available and major industry partners contributing official integrations, the ecosystem is rapidly maturing.

Immediate Action Items:

Explore the Gallery: Visit the official extensions page to discover relevant tools
Start with Partners: Install extensions from trusted partners like Stripe, Postman, or Figma
Experiment with Google Extensions: Try Cloud Run or Firebase extensions for cloud development
Build Custom Solutions: Use templates to create extensions for your specific workflows
Join the Community: Contribute to the growing ecosystem by sharing your extensions

The future of command-line development is here, and it's more intelligent, integrated, and accessible than ever before. Whether you're a solo developer or part of an enterprise team, Gemini CLI extensions offer the tools to build the personalized development environment you've always wanted.

🚀 Ready to Get Started?
Install your first extension today: gemini extensions install https://github.com/postmanlabs/postman-mcp-server

Gemini CLI Extensions Guide

Agentic Commerce Protocol (ACP)

Sienna — Sun, 05 Oct 2025 04:08:27 +0000

Agentic Commerce Protocol is an open standard for programmatic commerce flows between buyers, AI agents, and businesses.

🔑 Key Features

📖 Open Source

Open source under Apache 2.0 license
Community-designed
Enables businesses to transact with any AI agent or payment processor

🏢 Business-Friendly

Businesses maintain customer relationships as the merchant of record
Retain control over which products can be sold, how they're presented, and order fulfillment

🔄 Supports Complex Flows

Supports various commerce types including physical/digital goods, subscriptions, and asynchronous purchases
Flexible configuration options

🔌 Technology Compatibility

Compatible with REST API and MCP (Model Context Protocol)
Integrates with existing commerce backends and payment processors
Works with any technology stack

🔒 Security and PCI Compliance

Securely passes payment credentials from buyers to AI agents
Maintains security without exposing underlying payment credentials

🎯 Benefits by Stakeholder

For Businesses

Reach more customers through AI agents
Sell to high-intent buyers using existing commerce infrastructure

For AI Agents

Embed commerce functionality into applications
Enable users to transact directly with businesses without being the merchant of record

For Payment Providers

Increase transaction volume by processing agentic transactions through AI agents

💡 Real-World Application

Integrated with ChatGPT's Instant Checkout, enabling agentic payment processing through Stripe and other ACP-compatible payment service providers.

ACP aims to build a standardized protocol for AI-era commerce that benefits all participants in the ecosystem.

Agentic Commerce Protocol Documentation

Making Documentation Simple: The Complete Markdown Cheat Sheet Guide

Sienna — Sun, 05 Oct 2025 04:04:50 +0000

In this information-rich era, whether you're a developer, technical writer, or content creator, there's one core skill you can't do without—writing clear and beautiful documentation. Markdown is the magic tool that makes it all simple.

Why Do You Need Markdown Cheat Sheet?

Imagine these scenarios:

You're writing a GitHub README file and suddenly forget the table syntax
You want to insert a code block in your documentation but aren't sure how to add syntax highlighting
You're a Markdown beginner and need a systematic learning resource

One-stop solution for all your Markdown syntax needs with using Markdown Cheat Sheet.

Core Features

📚 1. Complete Syntax Reference Library

This isn't just a simple syntax list, but a comprehensive, systematic Markdown knowledge base:

Basic Syntax: Headers, bold, italic, links, images
Advanced Features: Tables, code blocks, task lists, blockquotes
Practical Tips: Each syntax comes with clear examples and best practices

The most thoughtful feature? Click any example to copy it instantly, saving you the trouble of manual typing.

⚡ 2. Real-Time Online Editor

Theory is good, but practice is better. The website's real-time preview editor lets you:

Write Markdown code on the left
See rendered results instantly on the right
Export to HTML or PDF
Auto-save feature so you never lose content

This "what you see is what you get" experience makes learning Markdown intuitive and efficient.

Designed for Everyone

👨‍💻 Developers

GitHub README file writing
Technical documentation
Code comments and explanations

✍️ Technical Writers

Blog post creation
Tutorial and guide writing
Product documentation

🎓 Students and Educators

Class note organization
Assignment and report writing
Learning material creation

Conclusion: Bringing Documentation Back to Simplicity

In today's world where technical documentation is increasingly important, Markdown has become the de facto standard format. And Markdown Cheat Sheet is the best companion to help you master this skill.

Whether you are:

🔰 A newcomer just starting with Markdown
💼 A professional needing to write documentation efficiently
🎯 A creator wanting to improve documentation quality

No more syntax worries, no more formatting headaches—visit Markdown Cheat Sheet and make documentation writing simple and enjoyable.

👉MarkDown Online Editor

Generate images with Nano Banana

Sienna — Fri, 29 Aug 2025 11:57:36 +0000

Nano Banana is Google Gemini’s new text-to-image editing feature that lets you create or modify pictures just by describing what you want in plain language—no manual tools or sliders needed.

Integrated Nano Banana into project QWQ AI to generate images.

Example：

➡image editor
text to image

2025 Complete Guide: How to Use an AI Chinese Name Generator to Create a Meaningful Chinese Name

Sienna — Wed, 20 Aug 2025 03:05:24 +0000

🎯 TL;DR

Enter your original name, gender, and birth date—the AI returns three Chinese names with Wu Xing analysis in 30 seconds.
Choose from four styles—traditional, modern, literary, or fusion—and lock in two- or three-character names.
Each result includes character meanings, five-element balance, and cultural backstories—ready for social media.

What Is an AI Chinese Name Generator?

An AI Chinese Name Generator is an online tool that combines deep learning + traditional Wu Xing theory. It analyzes six dimensions to craft Chinese names that sound elegant, carry positive meanings, and balance the five elements:

Dimension	Purpose	Example
Gender energy	Yin-yang balance	Male names: rising tone; Female: softer characters
Birth BaZi	Five-element compensation	Lack of Wood → add “梓、森”
Style preference	Personalization	Traditional / Modern / Literary / Fusion
Meaning keywords	Precise semantics	Input “wisdom” → “睿、哲”
Character count	Scenario adaptation	Multiple choices

5-Step Guide to Your Exclusive Chinese Name

📊 Flowchart

Next-Step Action List

🚀 Try it now: Visit Generator Nama China, enter your details, and get three candidates in 30 seconds.

✅ Bottom line

The AI Chinese Name Generator blends five-element theory, BaZi analysis, and cultural significance to create authentic Chinese names.

Kiro Steering Guide

Sienna — Sat, 19 Jul 2025 13:22:38 +0000

What is Steering?

Steering gives Kiro persistent knowledge about your project through markdown files in .kiro/steering/. Unlike traditional approaches like Cursor's .cursorrules which provide basic configuration, Steering represents a more advanced and sophisticated way to manage AI assistant context. While .cursorrules offers simple rule-based guidance, Steering provides comprehensive, structured, and contextual project knowledge that evolves with your codebase.

Steering vs .cursorrules Comparison:

Cursor's .cursorrules: Simple configuration file with basic rules
Kiro's Steering: Advanced markdown-based knowledge system with conditional loading, file references, and structured documentation

Instead of explaining your conventions in every chat, steering files ensure Kiro consistently follows your established patterns, libraries, and standards.

Key Benefits

Consistent Code Generation - Every component, API endpoint, or test follows your team's established patterns and conventions.

Reduced Repetition - No need to explain project standards in each conversation. Kiro remembers your preferences.

Team Alignment - All developers work with the same standards, whether they're new to the project or seasoned contributors.

Scalable Project Knowledge - Documentation that grows with your codebase, capturing decisions and patterns as your project evolves.

Default Steering Files

Kiro automatically creates three foundational files that establish core project context:

Product Overview (product.md) - Defines your product's purpose, target users, key features, and business objectives. This helps Kiro understand the "why" behind technical decisions and suggest solutions aligned with your product goals.

Technology Stack (tech.md) - Documents your chosen frameworks, libraries, development tools, and technical constraints. When Kiro suggests implementations, it will prefer your established stack over alternatives.

Project Structure (structure.md) - Outlines file organization, naming conventions, import patterns, and architectural decisions. This ensures generated code fits seamlessly into your existing codebase.

These foundation files are included in every interaction by default, forming the baseline of Kiro's project understanding.

Creating Custom Steering Files

Extend Kiro's understanding with specialized guidance tailored to your project's unique needs:

Navigate to the Steering section in the Kiro panel
Click the + button to create a new .md file
Choose a descriptive filename (e.g., api-standards.md)
Write your guidance using standard markdown syntax
Use natural language to describe your requirements, then select the Refine button and Kiro will format it

Inclusion Modes

Steering files can be configured to load at different times based on your needs. This flexibility helps optimize performance and ensures relevant context is available when needed.

Configure inclusion modes by adding front matter to the top of your steering files. The front matter uses YAML syntax and must be placed at the very beginning of the file, enclosed by triple dashes (---).

Always Included (Default)

These files are loaded into every Kiro interaction automatically. Use this mode for core standards that should influence all code generation and suggestions. Examples include your technology stack, coding conventions, and fundamental architectural principles.

Best for: Project-wide standards, technology preferences, security policies, and coding conventions that apply universally.

Conditional Inclusion

Files are automatically included only when working with files that match the specified pattern. This keeps context relevant and reduces noise by loading specialized guidance only when needed.

Common patterns:

"*.tsx" - React components and JSX files
"app/api/**/*" - API routes and backend logic
"**/*.test.*" - Test files and testing utilities
"src/components/**/*" - Component-specific guidelines
"*.md" - Documentation files

Best for: Domain-specific standards like component patterns, API design rules, testing approaches, or deployment procedures that only apply to certain file types.

Manual Inclusion

Files are available on-demand by referencing them with #steering-file-name in your chat messages. This gives you precise control over when specialized context is needed without cluttering every interaction.

Usage: Type #troubleshooting-guide or #performance-optimization in chat to include that steering file for the current conversation.

Best for: Specialized workflows, troubleshooting guides, migration procedures, or context-heavy documentation that's only needed occasionally.

File References

Link to live project files to keep steering current:

Examples:

API specs: #[[file:api/openapi.yaml]]
Component patterns: #[[file:components/ui/button.tsx]]
Config templates: #[[file:.env.example]]

Best Practices

Keep Files Focused One domain per file - API design, testing, or deployment procedures.

Use Clear Names

api-rest-conventions.md - REST API standards
testing-unit-patterns.md - Unit testing approaches
components-form-validation.md - Form component standards

Include Context Explain why decisions were made, not just what the standards are.

Provide Examples Use code snippets and before/after comparisons to demonstrate standards.

Security First Never include API keys, passwords, or sensitive data. Steering files are part of your codebase.

Maintain Regularly

Review during sprint planning and architecture changes
Test file references after restructuring
Treat steering changes like code changes - require reviews

Common Steering File Strategies

API Standards (api-standards.md) - Define REST conventions, error response formats, authentication flows, and versioning strategies. Include endpoint naming patterns, HTTP status code usage, and request/response examples.

Testing Approach (testing-standards.md) - Establish unit test patterns, integration test strategies, mocking approaches, and coverage expectations. Document preferred testing libraries, assertion styles, and test file organization.

Code Style (code-conventions.md) - Specify naming patterns, file organization, import ordering, and architectural decisions. Include examples of preferred code structures, component patterns, and anti-patterns to avoid.

Security Guidelines (security-policies.md) - Document authentication requirements, data validation rules, input sanitization standards, and vulnerability prevention measures. Include secure coding practices specific to your application.

Deployment Process (deployment-workflow.md) - Outline build procedures, environment configurations, deployment steps, and rollback strategies. Include CI/CD pipeline details and environment-specific requirements.

Case Study: Language Preference Configuration

Here's a practical example of how to configure Kiro to use Chinese for responses:

File: language-preferences.md

## Communication Guidelines

- Kiro uses Chinese for output
- Frontend page content uses English  
- Code comments use Chinese

Implementation Notes

This configuration ensures consistent language usage across different aspects of the development workflow while maintaining appropriate language choices for each context.

Kiro Steering Guide

Forem: Sienna

ValRequest - Turn Feelings Into Words

What is ValRequest?

How to Use ValRequest

1. Choose Recipient & Style

2. Add Your Keywords

3. Generate & Copy

Message Styles & Examples

Who Is ValRequest For?

Pricing & Credits

Privacy & Security

Qwen3-Coder-Next: The Complete 2026 Guide to Running Powerful AI Coding Agents Locally

🎯 Core Highlights (TL;DR)

Table of Contents

What is Qwen3-Coder-Next?

Why It Matters

Key Features and Architecture

Technical Specifications

Architecture Breakdown

Training Methodology

Performance Benchmarks

SWE-Bench Results

Other Coding Benchmarks

Real-World Performance Reports

Hardware Requirements and Setup

Minimum Requirements by Quantization Level

Recommended Configurations

How to Install and Run Qwen3-Coder-Next

Method 1: Using llama.cpp (Recommended for Most Users)

Method 2: Using Ollama (Easiest for Beginners)

Method 3: Using vLLM (Best for Production)

Method 4: Using SGLang (Fastest Inference)

Integration with Coding Tools

OpenCode (Recommended)

Cursor Integration

Continue.dev Integration

Aider Integration

Quantization Options Explained

Understanding Quantization Levels

Unsloth Dynamic (UD) Quantization

Real-World Use Cases and Performance

Community Testing Results

Performance vs Claude Code

Comparison: Qwen3-Coder-Next vs Claude vs GPT

Feature Comparison Matrix

When to Choose Each Model

Common Issues and Solutions

Issue 1: Out of Memory (OOM) Errors

Issue 2: Slow Inference Speed

Issue 3: Model Gets Stuck in Loops

Issue 4: Poor Tool Calling with OpenCode/Cline

Issue 5: MLX Performance Issues on Mac

FAQ

Q: Can I run Qwen3-Coder-Next on a MacBook with 32GB RAM?

Q: Is Qwen3-Coder-Next better than Claude Code?

Q: Can I use this with VS Code Copilot?

Q: How does quantization affect code quality?

Q: Will this work with my AMD GPU?

Q: Can I fine-tune this model on my own code?

Q: How does this compare to DeepSeek-V3?

Q: Is there a smaller version for lower-end hardware?

Q: Can I use this commercially?

Q: Why does it take so many agent turns compared to other models?

Conclusion and Next Steps

Recommended Action Plan

Future Outlook

Additional Resources

Related Posts

2026 Complete Guide: How to Use GLM-OCR for Next-Gen Document Understanding

🎯 Core Takeaways (TL;DR)

Table of Contents

What Is GLM-OCR?

How Does GLM-OCR Work Architecturally?

Architecture Overview

What Are the Key Features and Technical Specs?

Document Understanding Features

Language & Format Support

Core Technical Specs

How Well Does GLM-OCR Perform? (Benchmarks & Precision)

OmniDocBench Performance