Resources
A curated collection of trusted AI tools and platforms across models, image, video, audio, music, and developer infrastructure.
| Category | Title | Description |
|---|---|---|
| Models | Hugging Face Models | Large hub of open and community models for text, vision, audio, and multimodal tasks with docs and examples. |
| Models | Meta Llama | Open LLM family for chat, coding, and fine tuning across sizes with strong tooling. |
| Models | Mistral AI | Efficient open and hosted models for reasoning, coding, and embeddings with fast inference. |
| Models | Cohere | Enterprise focused LLMs with retrieval, safety tooling, and secure deployment options. |
| Models | Google Gemma | Compact open models from Google for lightweight text tasks and fine tuning. |
| Image | Stable Diffusion | Open image generation ecosystem with SDXL models and community extensions. |
| Image | Midjourney | High quality image generation with stylized control and an active creative community. |
| Image | DALL·E | Text to image model known for detailed generations and prompt following. |
| Video | Runway | Web based video generation and editing with models for text to video and image to video. |
| Video | Pika | Creative video generation platform focused on fast iterations and stylization. |
| Video | Luma AI | Tools for text to video and 3D capture with photorealistic results. |
| Audio | ElevenLabs | Lifelike text to speech, voice cloning, and dubbing tools for content and apps. |
| Speech | Whisper ASR | Open speech recognition model for transcription and translation tasks. |
| Music | Suno | Text to music generation with vocals and styles suitable for demos and content. |
| Music | Udio | Music generation and editing with creative control and fast iteration. |
| Dev | Replicate | Host and run open source models with simple APIs, deployments, and scaling. |
| Dev | Hugging Face Spaces | Deploy demos and apps for models using Gradio or Streamlit with community sharing. |
| Vectors | Pinecone | Managed vector database for semantic search and retrieval augmented apps. |
| Papers | Papers with Code | Research papers linked to code and leaderboards across tasks and datasets. |
How much server power does an AI model Query really need
Every time you type a prompt into an AI model for example: “Write me a marketing summary” or “Help me debug this code”, somewhere, powerful computers burn energy, draw electricity, work hard. AI models, especially large language models (LLMs) are built on math operations that must run very quickly in
Arjus Dashi
What AI promises to our future
AI is having its steam-engine moment. The buildout is visible in cranes over new data centers, invisible in the tools creeping into every spreadsheet, inbox, and factory line. The promise is enormous, but so is the bill. The Generative AI productivity upside is real, but it’s uneven Let’s
Arjus Dashi
RAG System Simplified - AI On Your Data
If you’ve been hearing “RAG” tossed around in tech meetings, investor decks, or even client conversations I’ll break down what Retrieval-Augmented Generation (RAG) is, why it matters, and how you can apply it without getting lost in tech jargon. What is RAG in plain terms RAG, is like
Arjus Dashi
Two Months Across U.S. Startup Hubs - Boston, Austin, San Francisco
This summer, I decided I needed more than meetings and remote calls. I wanted to connect with the raw energy of founders pushing things forward. So, packed my bags and spent two months driving through three U.S. startup hubs: Boston, Austin, and San Francisco. I wanted to emerge with
Arjus Dashi