🤖 Olmo 3: A Transparent Framework for Open-Source AI Models
A landmark in open AI development, Olmo 3 goes beyond open weights to open processes, empowering collaborative, transparent research at scale.
The Allen Institute for AI introduced Olmo 3, a fully open framework for large language models that reveals every step of model development—from data gathering to post-training refinement. It offers several model variants from 7B to 32B parameters with openly available weights and datasets. Designed for transparency and reproducibility, Olmo 3 enables researchers to inspect reasoning traces, retrain at any stage, and explore how data and architecture shape performance. The release also includes new datasets and tools for data curation, training optimization, and reinforcement learning experimentation.
🔗 Read more 🔗
🧮 Solving Fizz Buzz with Cosines
A clever fusion of programming wit and mathematical beauty—turning a beginner’s problem into a work of analytical art.
Susam Pal reinterprets the classic Fizz Buzz challenge as a mathematical exercise using trigonometric and Fourier techniques. He derives a closed-form cosine-based formula that generates the Fizz Buzz sequence, showing how modular logic can emerge from continuous functions. The article concludes with a Python implementation demonstrating this elegant analytical approach.
🔗 Read more 🔗
🗄️ Managing Async Tasks Inside PostgreSQL Tables
A practical and elegant approach—using Postgres as both data store and task manager simplifies systems while boosting robustness and clarity.
This article promotes using PostgreSQL as a native task queue instead of depending on external tools like Redis or Celery. It explains how unifying asynchronous job management within a single database enhances reliability, debugging, and transactional safety. With clear code examples, it shows how to enqueue, process, and retry async tasks effectively while avoiding distributed-state complexity and two-phase commit issues.
🔗 Read more 🔗