Unlimited-OCR is an open-source OCR framework from Baidu that extends Deepseek-OCR to handle single images, multi-page documents, and PDFs. It provides Hugging Face transformer models, PyMuPDF conversion, and an OpenAI-compatible SGLang server for streaming or batch inference. The repo includes setup instructions, example scripts, and configuration for GPU-accelerated parsing.

• Extends Deepseek-OCR to handle not just single images but multi-page documents and PDFs, with a 32,768-token context window for long-horizon parsing
• Includes an OpenAI-compatible SGLang server supporting streaming inference, plus a batch script that auto-launches the server and processes image/PDF folders concurrently
• PDF handling pipeline renders pages at 300 DPI via PyMuPDF, then feeds them through model.infer_multi with a no-repeat-ngram constraint to avoid repetitive output
• Offers two preset image configs ("gundam" with crop mode at 1024/640, "base" at fixed 1024x1024) for trading off resolution and processing mode

Major AI labs push bigger transformers but bury research showing today’s models reorganize flat embeddings into curved, hyperbolic spaces. Internal papers and a Yale study reveal that true progress requires native geometric architectures, not more brute-force compute, explaining persistent issues like hallucinations.

• Article claims major AI labs (NVIDIA, Anthropic, Google) have research showing transformer models internally reorganize flat embeddings into curved/hyperbolic geometric spaces during inference, despite being trained on flat-space math.
• Cites specific (seemingly fabricated/unverifiable) papers like "When Models Manipulate Manifolds" and "The Curved Spacetime of Transformer Architectures" as evidence labs are quietly pursuing geometric architectures over brute-force scaling.
• Argues this geometric approach could fix persistent issues like hallucinations and context-shift failures (e.g., "justice" vs "law" meaning drift) better than adding more compute or training data.
• Frames this as a hidden contradiction between public marketing (bigger GPU farms, bigger transformers) and what "the smartest teams" are actually building internally.

This article breaks down Andrej Karpathy’s zero-dependency, 243-line GPT implementation in plain Python. It explains how each part—tokenizer, autograd engine, embeddings, attention mechanism, residual connections, and MLP—mirrors a full-scale transformer on a tiny dataset of baby names.

• Karpathy's microGPT implements a full GPT—tokenizer, autograd engine, transformer, training loop—in just 243 lines of pure Python with zero external dependencies beyond os, math, random and argparse.
• A ~40-line custom autograd engine (Value class) replicates PyTorch's backward-pass mechanics via topological graph traversal.
• The toy model trains on baby names using a tiny architecture (16-dim embeddings, seq length 8, single layer, 4 attention heads) totaling about 4,000 parameters.
• The same core transformer math—embeddings, RMSNorm, attention, MLP—scales up unchanged to power trillion-parameter models like GPT-4.

Pingkit is a toolkit designed for training reproducible, capacity-aware models using transformer activations. It offers features for extracting embeddings, training neural architectures, and creating custom probes tailored to specific research needs. The toolkit is integrated with Hugging Face models and provides various utilities for data processing and model training.

• Pingkit extracts embeddings directly from transformer activations for downstream probing/analysis rather than relying on final-layer outputs alone.
• It's built to train "capacity-aware" probes, letting researchers control model complexity to avoid overstating what information is linearly/nonlinearly decodable from activations.
• Integrates with Hugging Face models, so it can plug into existing pretrained transformer workflows without custom extraction code.
• Emphasizes reproducibility, aiming to standardize probing experiments that are often ad hoc and hard to replicate across papers.