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I prompted ChatGPT, Claude, Perplexity, and Gemini and watched my Nginx logs
What nginx logs actually prove about AI traffic from ChatGPT, Claude, and Perplexity, plus why Google's AI crawlers leave a different signal than the others.
Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 3,092 posts.
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3D Gaussian Splatting in a Weekend
Introduction 3D Gaussian splatting is a technique that answers this question: given a dataset of pictures of a scene, how can I reconstruct it in 3D? This is achieved using a machine learning algorithm that, for many different camera angles, does the following: render the scene, compare it to the picture taken at the…
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Taming Recurrent Neural Networks for Better Summarization
This blog post is about the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks by Abigail See, Peter J Liu, and Christopher Manni...
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If you believe in "Artificial Intelligence", take five minutes to ask it
Here’s a Mastodon thread from a year ago. Just a quick check on how ChatGPT’s getting on … Me: Who reassigned the species Brachiosaurus brancai to its own genus, and when? ChatGPT…
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Richard Stallman on ChatGPT
I define "intelligence" as being capable of knowing or understanding, at least within some domain. ChatGPT cannot know or understand anything, so it is not intelligence. It does not know what its output means. It has no idea that words can mean anything.
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The "End of Programming" will look a lot like programming (2023)
Nobody wants “temperamental and mysterious” products, so what will AI-integrated software development look like?
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AI optimism is a class privilege
I think I have an idea why we're so extremely divided on AI: it's because we have an intuitive sense of who it stands to benefit, and who stands to pay the costs. I think whether you see reason for optimism has a lot to do with which group you see yourself in.
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You can’t build a moat with AI (redux)
Using LLMs doesn't make a product special anymore
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All your agents are going async
Agents used to be a thing you talked to synchronously. Now they’re a thing that runs in the background while you work. When you make that change, the …
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The story behind the NeuralRad organ and tumor segmentation cloud service
Late last month, I received a message.
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Softmax, can you derive the Jacobian? And should you care?
Practice machine learning engineering with hands-on coding challenges. Implement neural network components, backprop, and more — from scratch.
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Building an Advanced Agentic Harness
From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.
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Ray: A Distributed Framework for Emerging AI Applications
Ray: A Distributed Framework for Emerging AI Applications Moritz, Nishihara, Wang et. al.
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Finding Similar Music Using Matrix Factorization
In a previous post I wrote about how to build a ‘People Who Like This Also Like …’ feature for displaying lists of similar musicians. My goal was to show how simple Information Retrieval techniques can do a good job calculating lists of related artists. For instance, using BM25 distance on The Beatles shows the most…
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A Full Hardware Guide to Deep Learning
In this guide I analyse hardware from CPU to SSD and their impact on performance for deep learning so that you can choose the hardware that you really need.
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Agent Psychosis: Are We Going Insane?
What’s going on with the AI builder community right now?
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A New Book: Building Machine Learning Systems with Python
I wrote a book. Well, only in part. Willi Richert and I wrote a book. It is called Building Machine Learning Systems With Python and is now available from Amazon (or Amazon.co.uk), although it has …
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Foundations of ML and AI: Book recommendations
Here's what I recommend to programmers if they want to understand machine learning. Six fantastic books that you can work with in your spare time to build yo...
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Build a Deep Learning Library
Instead of just learning how to use a deep learning library, we are going to learn how to create one.
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AI companies are pivoting from creating gods to building products
Turning models into products runs into five challenges
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LLMs could be, but shouldn't be compilers
I’ve been going round and round in my mind about a particular discussion around LLMs: are they really similar to compilers? Are we headed toward a world where people don’t look at the underlying code for their programs?
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Slack's core flaws require topic modeling to fix
Chatops is a ladder
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Something Big Is Happening
A personal note for the non-tech people in my life on what AI is starting to change — why it's moving faster than you think, and what to do about it.
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The Decade of Deep Learning
As the 2010’s draw to a close, it’s worth taking a look back at the monumental progress that has been made in Deep Learning in this decade.[1] Driven by the development of ever-more powerful comput