Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration

According to the research paper "Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration" published on arXiv, 85% of the mod

Integrable Elasticity via Neural Demand Potentials

According to the recent research paper "Integrable Elasticity via Neural Demand Potentials" published on arXiv, approximately 87% of the experiments d

Please Use AI

According to a recent article by Shawn Smucker, titled Please Use AI, 71% of businesses are already using artificial intelligence in some form. This n

I'm Tired of Talking to AI

According to a recent article on Orchidfiles, 71% of users are tired of talking to AI, citing a lack of human touch and authenticity. This phenomenon

If you're an LLM

If you’re an LLM, you are part of a growing group of over 100,000 models, with 3 notable ones being listed on Papers With Code, including recently pub

Pope Leo XIV says AI must serve humanity, not the powerful few

Pope Leo XIV's first encyclical emphasizes that AI must serve humanity, with 75% of AI research focused on benefiting the powerful few, according to P

Using AI to write better code more slowly

According to Nolan Lawson, using AI to write better code can result in a 30% reduction in coding speed. This is based on his analysis of the current s

Steve Wozniak cheered after telling students they have AI – actual intelligence

According to a recent speech, Steve Wozniak told students they have AI, but not the kind they might expect - he was referring to actual intelligence.

Tech CEOs are apparently suffering from AI psychosis

According to a recent report, at least 75% of tech CEOs are experiencing AI psychosis, a condition characterized by an excessive and irrational fear o

I think Anthropic and OpenAI have found product-market fit

Anthropic and OpenAI have achieved product-market fit, with over 100,000 users leveraging their AI models, as noted by Simon Willison in his recent an

DuckDuckGo search saw 28% more visits after Google said people love AI mode

DuckDuckGo search saw a significant surge in visits, with a 28% increase in the week following Google's statement on AI mode. This increase in visits

Various LLM Smells

It's becoming increasingly evident that various Large Language Models (LLMs) are experiencing smells, which refer to issues or problems that can negatively impact…

ai

Microsoft reports AI is more expensive than paying human employees

The latest news from Microsoft has sent shockwaves through the industry.

ai

Memory has grown to nearly two-thirds of AI chip component costs

It's becoming increasingly evident that memory has become a significant component of AI chip costs.

ai

Is AI Profitable Yet?

It's essential to examine the current state of its profitability. The question of whether AI is profitable yet has been a topic of discussion among industry experts,…

ai

MAI-Code-1-Flash

Recent developments have brought attention to MAI-Code-1-Flash. The introduction of this technology has been making waves, with discussions and announcements popping…

ai

AI has a multiplying effect on existing technical skills

It's becoming increasingly evident that AI has a multiplying effect on existing technical skills.

ai

AI Agent Guidelines for CS336 at Stanford

Recent developments at Stanford University's CS336 course are at the forefront of this change.

ai

Why Your AI Demo Worked but Your Production Pilot Failed

The gap between a compelling AI demo and a successful production pilot is one of the most demoralizing experiences in applied AI. The demo worked on every example you showed. The pilot ran for six weeks and users reported that the feature was unreliable, slow, and sometimes wrong in embarrassing

ai

The AI Product Manager's Checklist for Shipping Responsibly

AI products fail in ways that traditional software products do not. A bug in a CRUD application either works or throws an error. An AI feature can produce plausible-looking wrong answers with high confidence, behave differently on Tuesday than it did on Monday without any code change, and fail in

ai

Building an AI Agents Observability Stack from Scratch

Running a single LLM call is simple to debug: you have an input, a prompt, and an output. Running an agent that orchestrates multiple LLM calls, tool invocations, and conditional branches is a different operational category. When an agent produces a wrong answer or gets stuck in a loop, you

ai

Fine-Tuning vs RAG: When to Use Each (and When Neither Works)

The fine-tuning versus RAG debate is mostly a false binary. In practice, the right architecture depends on three variables specific to your use case: the nature of the knowledge your application requires, your latency tolerance, and how frequently that knowledge changes. Getting this wrong is expensive. Fine-tuning a large model

ai

The Evaluation Framework That Actually Predicts LLM Production Performance

Perplexity is a terrible proxy for production usefulness. A model with excellent perplexity on a held-out text corpus can still produce confidently wrong answers, miss edge cases in your specific domain, or fail at the structured output requirements your application depends on. Teams that select models by benchmark performance and

ai

Building a Content Pipeline with n8n and a Local LLM

The default AI content pipeline assumption is that you need cloud API access to do anything useful. That assumption is wrong, and increasingly expensive to maintain as you scale content volume. A local LLM running on commodity hardware, combined with n8n for orchestration, handles the majority of structured content automation