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large language models

More than half of developers say ‘LLMs can code better than humans’

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A survey of 800 senior developers reveals rapid adoption of AI tools, but concerns remain around privacy, job loss, and accuracy.

With large language models (LLMs) rapidly becoming an unavoidable tool in the software industry, new research has revealed that over half (53 percent) of senior developers believe LLMs can already code better than most humans. LLMs are AI systems trained on vast text datasets to understand and generate human-like language. Examples include ChatGPT (OpenAI), Claude (Anthropic), and Gemini (Google DeepMind).

The survey, from Clutch, a marketplace for finding business services, polled 800 senior software developers in North America, highlighting how AI is reshaping the future of coding, with 75 percent of respondents saying they expect AI to significantly transform the industry within the next five years. [Read more…] about More than half of developers say ‘LLMs can code better than humans’

Foundation EGI raises $23 million to build ‘world’s first engineering general intelligence platform’

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Foundation EGI, developer of what it describes as “the world’s first Engineering General Intelligence (EGI) platform”, has raised $23 million in oversubscribed Series A funding, raising over $30 million total.

The company is accelerating its vision of turning manufacturing’s biggest bottlenecks into breakthroughs using domain-specific AI.

The latest round was led by Translink Capital with participation from RRE Ventures, McRock Capital, Escape Investment Management (Jim Scapa), Fifth Growth Fund, and returning backers including E14 Fund, UNION, GRIDS Capital, and Henry Ford III. [Read more…] about Foundation EGI raises $23 million to build ‘world’s first engineering general intelligence platform’

RAG vs. Fine Tuning: Which One is Right for You?

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In the world of AI, Large Language Models (LLMs) are at the forefront, revolutionizing how we interact with technology. However, despite their impressive capabilities, LLMs have limitations that must be addressed.

Two prominent methods for enhancing the performance of LLMs are Retrieval Augmented Generation (RAG) and Fine Tuning.

This article explores these methods, their benefits, and their drawbacks, helping you decide which one best suits your needs.

[Read more…] about RAG vs. Fine Tuning: Which One is Right for You?