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send this prompt to your OpenClaw to steal ANY writing style from books, articles, tweets, emails...

you can outsource your thinking but you cannot outsource your understanding

📂 SaaS
┃
┣ 📂 Idea

┃ ┣ 📂 Problem Discovery
┃ ┣ 📂 Market Research
┃ ┣ 📂 Niche Selection
┃ ┣ 📂 Competitor Analysis
┃ ┗ 📂 Opportunity Mapping
┃
┣ 📂 Validation
┃ ┣ 📂 Customer Interviews
┃ ┣ 📂 Landing Page Test
┃ ┣ 📂 Waitlist
┃ ┣ 📂 Pre Sales
┃ ┗ 📂 Demand Testing
┃
┣ 📂 Planning
┃ ┣ 📂 Product Roadmap
┃ ┣ 📂 Feature Prioritization
┃ ┣ 📂 MVP Scope
┃ ┣ 📂 Tech Stack
┃ ┗ 📂 Development Plan
┃
┣ 📂 Design
┃ ┣ 📂 Wireframes
┃ ┣ 📂 UI Design
┃ ┣ 📂 UX Flows
┃ ┣ 📂 Prototype
┃ ┗ 📂 Design System
┃
┣ 📂 Development
┃ ┣ 📂 Frontend
┃ ┣ 📂 Backend
┃ ┣ 📂 APIs
┃ ┣ 📂 Database
┃ ┣ 📂 Authentication
┃ ┗ 📂 Integrations
┃
┣ 📂 Infrastructure
┃ ┣ 📂 Cloud Hosting
┃ ┣ 📂 DevOps
┃ ┣ 📂 CI CD
┃ ┣ 📂 Monitoring
┃ ┗ 📂 Security
┃
┣ 📂 Testing
┃ ┣ 📂 Unit Testing
┃ ┣ 📂 Integration Testing
┃ ┣ 📂 Bug Fixing
┃ ┣ 📂 Performance Testing
┃ ┗ 📂 Beta Testing
┃
┣ 📂 Launch
┃ ┣ 📂 Landing Page
┃ ┣ 📂 Product Hunt
┃ ┣ 📂 Beta Users
┃ ┣ 📂 Early Adopters
┃ ┗ 📂 Public Release
┃
┣ 📂 Acquisition
┃ ┣ 📂 SEO Wins
┃ ┣ 📂 Content Marketing
┃ ┣ 📂 Social Media
┃ ┣ 📂 Cold Email
┃ ┣ 📂 Influencer Outreach
┃ ┗ 📂 Affiliate Marketing
┃
┣ 📂 Distribution
┃ ┣ 📂 Directories
┃ ┣ 📂 SaaS Marketplaces
┃ ┣ 📂 Communities
┃ ┣ 📂 Partnerships
┃ ┗ 📂 Integrations
┃
┣ 📂 Conversion
┃ ┣ 📂 Sales Funnel
┃ ┣ 📂 Free Trial
┃ ┣ 📂 Freemium Model
┃ ┣ 📂 Pricing Strategy
┃ ┗ 📂 Checkout Optimization
┃
┣ 📂 Revenue
┃ ┣ 📂 Subscriptions
┃ ┣ 📂 Upsells
┃ ┣ 📂 Add-ons
┃ ┣ 📂 Annual Plans
┃ ┗ 📂 Enterprise Deals
┃
┣ 📂 Analytics
┃ ┣ 📂 User Tracking
┃ ┣ 📂 Funnel Analysis
┃ ┣ 📂 Cohort Analysis
┃ ┣ 📂 KPI Dashboard
┃ ┗ 📂 A/B Testing
┃
┣ 📂 Retention
┃ ┣ 📂 User Onboarding
┃ ┣ 📂 Email Automation
┃ ┣ 📂 Customer Support
┃ ┣ 📂 Feature Adoption
┃ ┗ 📂 Churn Reduction
┃
┣ 📂 Growth
┃ ┣ 📂 Referral Programs
┃ ┣ 📂 Community Building
┃ ┣ 📂 Product Led Growth
┃ ┣ 📂 Viral Loops
┃ ┗ 📂 Expansion Strategy
┃
┗ 📂 Scaling
┣ 📂 Automation
┣ 📂 Hiring
┣ 📂 Systems
┣ 📂 Global Expansion
┗ 📂 Exit Strategy

Best models to run on your hardware level:

---- 8 GB ----

Autocomplete for coding (like Cursor Tab)
- https://huggingface.co/NexVeridian/zeta-2-4bit
- https://huggingface.co/bartowski/zed-industries_zeta-2-GGUF

Tool calling, assistant style
- https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF

---- 16 Gb ----

Here things get better:

Multimodal
- huggingface.co/Qwen/Qwen3.5-9B
- https://huggingface.co/Tesslate/OmniCoder-9B
- https://huggingface.co/unsloth/Qwen3.5-27B-GGUF

---- 24 GB ----

- The best model you can get (thanks Qwen) https://huggingface.co/Qwen/Qwen3.5-27B
- Great model (strong agents) https://huggingface.co/nvidia/Nemotron-Cascade-2-30B-A3B
- Mine hehe https://huggingface.co/0xSero/Qwen-3.5-28B-A3B-REAP

Best models to run on your hardware:

—— 64 GB ——

- Qwen3-coder-next-80B-4bit (coding, Claude code, general agent)
- Qwen3.5-122B-reap: (browser use, multimodal, tool calling, general agent)

—— 96 GB ——

- GLM-4.6V (multimodal and tool calls)
- Hermes-70B (Jailbroken)
- Nemotron-120B-Super: (openclaw)
- Mistral-4-Small (general agent)

—— 192 GB ——

All these are excellent top tier LLMs and approach sonnet in capabilities

- Step-3.5-Flash
- Qwen3.5-397B-REAP
- MiniMax-M2.5 (soon M2.7)
- GLM-4.7-Reap

—— 256 GB ——

#1 MiniMax-M2.5 (M2.7) - 6bit MLX
#2 Qwen3.5-262B-REAP (4-6 bits)
#3 Nemotron-122B (8-9bits)
#4 GLM-5-358B (4bit)

—— 512 GB ——

#1 MiniMax-M2.* - FP16
#2 Qwen3.5-397B - 8bit
#3 Kimi-k2.5-530B-PRISM - 4bit
#4 GLM-5 - 4bit

Here are all the open weight models that can get close frontier level code, and tie for agentic purposes.

GLM-5.*
MiniMax-M2.*
Kimi-K2.5
Deepseek-V3.2
Qwen-3.5-Plus-397B

If you want AI at home for coding agents similar to Claude/Codex the VRAM needed 192GB for Q4 quant + REAP

Weekly best models for your hardware:

~~ 8 to 16gb ~~

Granite models are amazing: [NEW]
- https://huggingface.co/ibm-granite/granite-4.1-8b

Gemma-E4B is a good general QA model
- https://huggingface.co/google/gemma-4-E4B-it

Qwen3.5-9B is the best at this level imo
- https://huggingface.co/Qwen/Qwen3.5-9B

~~ 16 to 64gb ~~

Another larger Granite: This is a general chat model, really dense with world knowledge. [NEW]
- https://huggingface.co/ibm-granite/granite-4.1-30b

- Undisputed kings:

The Qwens at various precisions: (Higher ceiling)
- https://huggingface.co/Qwen/Qwen3.5-9B (and larger variants like 32B/72B)
- https://huggingface.co/Qwen (check latest)

The Gemmas at various precisions: (More efficient)
- https://huggingface.co/google/gemma-4-E4B-it
- https://huggingface.co/google (Gemma family)

~~ 64 to 128gb ~~

- Ling is a new 100B~ contender decent agent [NEW]
https://huggingface.co/inclusionAI/Ling-flash-2.0

- Mistral medium: from my experience their models have been the most consistent! [NEW]
https://huggingface.co/mistralai/Mistral-Medium-3.5-128B

~~ 128gb - 256gb ~~

Undisputed king: DeepSeek-V4-Flash [NEW]

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash

Best models for your hardware this week.

8-12GB

- https://huggingface.co/LiquidAI/LFM2.5-8B-A1B incredible model, so fast, so small

16-32GB

- latest Google model, Gemma 12B: https://huggingface.co/google/gemma-4-12B really solid performance up neck and neck with a model 2x its size from a month ago.

Jetbrains new model, best in class on livecode bench

32-96gb

- Nex-N2-Mini GPT style postrain of Qwen-35B it seems to be its class leader caveman style reasoning https://huggingface.co/Nexdata/Nex-N2-Mini

- Jackrong’s Qwopus is the #1 overall Q4 of Qwen3.6-27B on our benchmark suite of 5 agent + coding benchmarks (1200 samples total) https://huggingface.co/jackrong/QwQ-32B-Preview-Qwopus

192gb

- Step-3.7-Flash is hard to beat, high scores, really fast inference, vision capable, later cutoff dates https://huggingface.co/stepfun-ai/Step-3.7-Flash

384gb

- Nex-N2-Pro GPT style post train of Qwen-3.5-397B incredibly strong and #1 on deepswe if their claims are right https://huggingface.co/Nexdata/Nex-N2-Pro

768gb

- very promising post-train of GLM-5.1 that wins out on 8 benchmarks