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Can we run AI and #ai tools offline? What would it take? These are interesting and important questions.

Some thoguhts on that -- I Want Everything Local — Building My Offline AI Workspace via instavm.io. In this article they talk about:

• #llm s run completely locally
• Code executes inside a lightweight #virtual-machine, not on the host machine
• headless browser for automation and internet access

-Fixie.ai

• turbopuffer

Sources on keeping up with AI.

This is a good start as of July 2025 - How I keep up with AI progress (and why you must too) from Nilenso blog, since I use many of the same.

Via AI Papers Community notes via \@zaph0id; Apr 2024.

• https://aman.ai/papers/
• https://vinija.ai/nlp/GenerativeAI/
• http://vinija.ai/models/LLM/
• https://huyenchip.com/2023/10/10/multimodal.html

--

Ilya's reccommendation to Carmack.. also available as this Arc Folder with links to actual papers and blog posts. See the twitter thread for summaries.

   

via

Segment Anything -- The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.

YavorGIvanov/sam.cpp -- Inference of Meta's SAM in pure C/C++

See also: ggml

8 Google Employees Invented Modern AI. Here’s the Inside Story | WIRED

¶ Transformers from Scratch

Transformers were introduced in this 2017 paper as a tool for sequence transduction—converting one sequence of symbols to another. The most popular examples of this are translation, as in English to German. It has also been modified to perform sequence completion—given a starting prompt, carry on in the same vein and style. They have quickly become an indispensible tool for research and product development in natural language processing.

• Understanding Transformers Using A Minimal Example
• Neural Networks: Zero To Hero by Karpathy

The Python Library

Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models.. save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:

• NLP - text classification, named entity recognition, question answering, language modeling summerization, translation, mulitple choice, and text generation.
• Computer vision - image classification, object detection, and segmentation
• Audio - automatic speech recognition and audio classification
• Multimodal - table question answering, optical character recognition, information extraction from scanned documents, video classification and visual question answering.

transformers support interop between pytorch, tensorflow, and jax. WIth this one can use a diff framework at each stage of a model's lifecycle; train, load, and inference. models can be exported to format like ONNX and torchscript for deployment in prod envs.

Read the documentation here : https://huggingface.co/docs/transformers/index

Sentence Transformers (sbert)

Python module for accessing, using, and training state-of-the-art text and image embedding models. It can be used to compute embeddings using Sentence Transformer models (quickstart) or to calculate similarity scores using Cross-Encoder models (quickstart). This unlocks a wide range of applications, including semantic search, semantic textual similarity, and paraphrase mining.

Hugging Face is a company that focuses on natural language processing (NLP) and artificial intelligence (AI) research. They are best known for their open-source library called "transformers," which provides state-of-the-art general-purpose architectures for NLP tasks. These architectures include BERT, GPT, RoBERTa, T5, and others, which are pre-trained on large datasets and can be fine-tuned for specific tasks such as sentiment analysis, machine translation, question-answering, and more.

Hugging Face's mission is to advance AI research and democratize access to powerful NLP tools for developers, researchers, and organizations. Their platform offers an ecosystem of resources, including pre-trained models, datasets, and community-contributed tools, making it easier for users to implement and experiment with cutting-edge NLP technologies.

Getting started instructions

2023-05-07From their website on creating a new account:¶

Getting started with our git and git-lfs interface

If you need to create a repo from the command line (skip if you created a repo from the website)

$ pip install huggingface_hub
$ huggingface-cli login
$ huggingface-cli repo create repo_name --type {model, dataset, space}

Clone your model or dataset locally

Make sure you have git-lfs installed (https://git-lfs.github.com)

$ git lfs install
$ git clone https://huggingface.co/username/repo_name

Then add, commit and push any file you want, including larges files

# save files via `.save_pretrained()` or move them here
$ git add .
$ git commit -m "commit from $USER"
$ git push

In most cases, if you're using one of the compatible libraries, your repo will then be accessible from code, through its identifier: username/repo_name.

For example for a transformers model, anyone can load it with:

 tokenizer = AutoTokenizer.from_pretrained("username/repo_name") model = AutoModel.from_pretrained("username/repo_name")](https://huggingface.co/welcome)

-- Deploy Evidence on Hugging Face Spaces; see evidence.

2022-11-27

AI Classes often discuss - Deep learning, Machine learning, Probabilistic Graphical Models, Planning algorithms (eg: motion planning), Search algorithms, Knowledge representation (knowledge graphs), Game theory.

Deep learning was much more attractive brand compared to Neural networks.

The DL/ML field has taken off, driven by more data and more computation, and the positive feedback loop by users; lot of Algorithmic innovations.

Software Design Book by John Ousterhout

"collect more data" vs "tune hyper parameters" - systematic approach to making this decision is the distinction between a beginner and a experienced person.