llm – Prompt Website

AI Assisted Search-based Research Actually Works Now

Apr 22 2025

AI-assisted search-based research is now genuinely effective, evolving from earlier disappointing models. Recent advancements, especially with OpenAI's o3 and o4-mini, allow for real-time searching and reliable, grounded responses. Google and Anthropic lag behind in this area. The ability of AI models to reason through search results marks a significant improvement, enabling tasks like code upgrades seamlessly. As trust in these models increases, concerns about the future of web browsing and economic implications arise, signaling potential shifts in how information is accessed.

https://simonwillison.net/2025/Apr/21/ai-assisted-search/

To Make Language Models Work Better, Researchers Sidestep Language

llm

Apr 15 2025

Researchers are exploring new methods for large language models (LLMs) to enhance efficiency and reasoning by allowing them to process information in mathematical latent spaces instead of converting everything into language. Current LLMs waste computational resources transforming latent representations into words, which can lead to information loss. Two emerging models, Coconut and a recurrent transformer, show promising results by enabling reasoning directly in latent space. While Coconut demonstrated improved efficiency over traditional models, both new approaches indicate potential for better performance albeit with some limitations. This shift could fundamentally change AI reasoning methods.

https://www.quantamagazine.org/to-make-language-models-work-better-researchers-sidestep-language-20250414/

Understanding LLMs: a Simple Guide to Large Language Models

llm

Mar 7 2025

Extremely Short Summary:
Large Language Models (LLMs) like ChatGPT revolutionize AI by predicting next tokens through a transformative architecture called Transformers, leveraging data, similarity, attention, and fine-tuning for diverse applications. Understanding LLMs is crucial for effective usage and advancements.

https://blog.oedemis.io/understanding-llms-a-simple-guide-to-large-language-models

Things We Learned About LLMs in 2024

ai, llm

Feb 9 2025

2024 LLM Highlights:
– GPT-4 Competition: 18 orgs surpassed GPT-4 with new models, including Google’s Gemini 1.5 Pro with 2 million tokens input.
– Local ML Power: Powerful LLMs now run on personal laptops, showcasing incredible efficiency.
– Lower Costs: LLM operational expenses plummeted due to competition, enabling affordable usage (e.g., Google’s Gemini 1.5 Flash pricing).
– Multi-Modal Advances: Most major models adopted multi-modal capabilities (audio, video).
– Voice Integration: Realistic audio input/output was introduced, enhancing interaction.
– App Creation Commoditization: Prompt-driven app development became commonplace, illustrating LLMs’ capabilities.
– Temporary Free Access: Top LLMs were briefly available for free before subscription services resumed.
– Agents Concept Stagnation: The term “agents” remains vague; substantial improvements needed.
– Evaluations Essential: Effective automated evals became crucial for developing impactful LLM applications.
– Apple's ML Progress: Apple’s MLX library aided model efficiency, though their LLM offerings fell short.
– Reasoning Model Advances: New model architectures focus on inference-based reasoning.
– Cost-Effective Training: Major models like DeepSeek v3 trained efficiently under $6 million, indicating potential for sustainable practices.
– Environmental Impact Mixed: Training efficiency improved, but large-scale data center growth poses environmental threats.

https://simonwillison.net/2024/Dec/31/llms-in-2024/