12 Days of OpenAI: o1 pro

OpenAI o1 and o1 pro mode in ChatGPT — 12 Days of OpenAI: Day 1 - YouTube

12 Days of OpenAI: Day 2 - YouTube

12 Days of OpenAI Day 2 LIVE: o1 full is here and every new ChatGPT AI announcement as it happens | Tom's Guide

Day 1 announcements: o1 is fully here

 o1 reasoning model is now fully released and no longer in public preview.

o1 PRO Mode - ChatGPT Pro with Unlimited Compute (Announcement Breakdown) - YouTube

[DAY 2] OpenAI Live Stream | 12 days of OpenAI Releases and Demos 🎅❄️🎄 - YouTube

Day 1 of 12 Days of OpenAI: ChatGPT Pro and o1-Full Model Launched - TechWiser

AI news @ LinkedIn

OpenAI bets you’ll pay $200 a month for ChatGPT | TechCrunch

The biggest reveals so far include a $200 per month subscription tier for power users of ChatGPT and the release of the full version of its “reasoning” o1 model.

OpenAI’s 12 days of ‘shipmas’ include Sora and new reasoning model - The Verge

Sam Altman's new $200 ChatGPT has a big Elon problem... - YouTube

AI: fast embeddings search with SIMD (JS, Python, ...)

usearch - npm
Smaller & Faster Single-File
Similarity Search & Clustering Engine for Vectors


C++ 11 • Python 3 • JavaScript • Java • Rust • C 99 • Objective-C • Swift • C# • GoLang • Wolfram
Linux • MacOS • Windows • iOS • Android • WebAssembly • SQLite3


✅ 10x faster HNSW implementation than FAISS.
✅ Simple and extensible single C++11 header library.
✅ Trusted by giants like Google and DBs like ClickHouse & DuckDB.
✅ SIMD-optimized and user-defined metrics with JIT compilation.
✅ Hardware-agnostic f16 & i8 - half-precision & quarter-precision support.

unum-cloud/usearch: Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍 @GitHub

Unum · USearch 2.16.2 documentation @GitHub

usearch/javascript at main · unum-cloud/usearch

USearch is a high-performance library for building and querying vector search indexes, optimized for Node.js and WASM environments.

Alternative

@sroussey/simsimd - npm

Hardware-Accelerated Similarity Metrics and Distance Functions

• Zero-dependency header-only C 99 library with bindings for Python and JavaScript.
• Targets ARM NEON, SVE, x86 AVX2, AVX-512 (VNNI, FP16) hardware backends.
• Zero-copy compatible with NumPy, PyTorch, TensorFlow, and other tensors.
• Handles f64 double-, f32 single-, and f16 half-precision, i8 integral, and binary vectors.
• Up to 200x faster than scipy.spatial.distance and numpy.inner.
• Used in USearch and several DBMS products.

ashvardanian/SimSIMD: Up to 200x Faster Dot Products & Similarity Metrics — for Python, Rust, C, JS, and Swift, supporting f64, f32, f16 real & complex, i8, and bit vectors using SIMD for both AVX2, AVX-512, NEON, SVE, & SVE2 📐 @GitHub

JavaScript

npm i @sroussey/simsimd

import { dot } from '@sroussey/simsimd';

const vector1 = Float32Array.from([1, 2, 3]);

const vector2 = Float32Array.from([4, 5, 6]);

const dotProduct = dot(vector1, vector2); 

// Calculate cosine similarity using the dot product

const cosineSimilarity = dotProduct / (magnitude(vector1) * magnitude(vector2));

Python

@sroussey/simsimd - npm

pip install simsimd

import simsimd
import numpy as np
vec1 = np.random.randn(1536).astype(np.float32)
vec2 = np.random.randn(1536).astype(np.float32)
dist = simsimd.cosine(vec1, vec2)