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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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