Carina Hong, 24, raised $64 million to build an AI mathematician that discovers new theorems and solves century-old problems.

We treat AI like a search engine, but massive context windows offer more. Stop hugging the coast. Why 2026 is the year to cut ...

You don't need fancy hardware to get started on your coding journey. A low-spec computer will likely be more than you need to ...

You might be staring at your budget, wondering how you’re supposed to cover rent, debt, and everything else on $20–$25 an ...

There is a common misconception that AI applications can be sufficiently tested and derisked by running a pilot in a ...

Overview: Interactive Python courses emphasize hands-on coding instead of passive video learning.Short lessons with instant ...

Saurabh Misra work spans machine learning, large-scale systems, and software performance, with a consistent focus on building faster, more efficient, and more sustainable technology.

Meta’s most popular LLM series is Llama. Llama stands for Large Language Model Meta AI. They are open-source models. Llama 3 was trained with fifteen trillion tokens. It has a context window size of ...

The best new features and fixes in Python 3.14 Released in October 2025, the latest edition of Python makes free-threaded ...

No, Microsoft is not rewriting Windows in Rust. The clarification comes after a LinkedIn post by a Microsoft Distinguished ...

This year, AI continued looming large in the software world. But more than before, people are wrestling with both its amazing ...

在代码大模型（Code LLMs）的预训练中，行业内长期存在一种惯性思维，即把所有编程语言的代码都视为同质化的文本数据，主要关注数据总量的堆叠。然而，现代软件开发本质上是多语言混合的，不同语言的语法特性、语料规模和应用场景差异巨大。如果忽略这些差异，笼统地应用通用的 Scaling Laws，往往会导致性能预测偏差和算力浪费。