3211
When AI promises speed but delivers debugging hell
Lessons learned from building Codescribble with a little help (and a lot of frustration) from an AI
Top posts
Every blog post that took off on Hacker News, ranked by the votes of its best submission. 6,331 posts.
3212
Claude 3 beats GPT-4 on Aider's code editing benchmark
Claude 3 Opus outperforms all of OpenAI’s models on Aider’s code editing benchmark, making it the best available model for pair programming with AI.
3213
What 81,000 people want from AI
Last December, tens of thousands of Claude users around the world had a conversation with our AI interviewer to share how they use AI, what they dream it could make possible, and what they fear it might do.
3214
Google Home: An Insight Into a 4-Year-Old’s Mind
How Google Home provides an interesting insight into the curiosity of a 4-year-old.
3215
The Future of AI Software Development
Detailed report on the Thoughtworks AI retreat, should we write a manifesto for AI-enabled development, is spec-driven development the return of waterfall, LLMs perform better in healthy codebases
3216
Introducing Agents in Haystack: Make LLMs resolve complex tasks
LLMs can now make use of the right tools to resolve complex queries and tasks
3217
What’s the difference between statistics and machine learning?
I had an interesting discussion at work today (among people I think would all call themselves statisticians!) about the distinction(s) between statistics and machine learning. It is something I am …
3218
More Data Is Not Better and Machine Learning Is a Grind
I had an opportunity to sit in on a lecture today by two experts on Machine Learning from Amazon, during a lecture at UNC. Ed Banti is Director of Software Development, Core AI Amazon, and Pat Bajari, was VP and Chief Economist, Core AI, at Amazon as well. Both gentlemen shared their insights on working with AI…
3219
Why I find diffusion models interesting?
HN Discussion
3220
Augmenting Long-term Memory
One day in the mid-1920s, a Moscow newspaper reporter named Solomon Shereshevsky entered the laboratory of the psychologist Alexander Luria. Shereshevsky's boss at the newspaper had noticed that Shereshevsky never needed to take any notes, but somehow still remembered all he was told, and had suggested he get his…
3221
A Mechanistic Interpretability Analysis of Grokking
Neel Nanda reverse engineers neural networks that have "grokked" modular addition, showing that they operate using Discrete Fourier Transforms and tr…
3222
Beyond Diffusion: Inductive Moment Matching
Inductive Moment Matching surpasses diffusion models in speed and sample quality.
3223
Learning to write programs that generate images
Through a human’s eyes, the world is much more than just the images reflected in our corneas. For example, when we look at a building and admire the intricacies of its design, we can appreciate the craftsmanship it requires. This ability to interpret objects through the tools that created them gives us a richer…
3224
GPT-4 gets a B on my quantum computing final exam
Update (April 14): On the suggestion of my TA Justin Yirka, I gave GPT-4 problem 2 from the final exam, along with its own answers and my and Justin’s grading comments, and asked it whether i…
3225
Cruise's 3rd generation self-driving car
Electric self-driving cars will save millions of lives and significantly accelerate the world’s transition to sustainable energy, but only…
3226
3227
Mozilla Is Crowdsourcing Research into YouTube Recommendations
Mozilla has built a browser add-on that transforms YouTube users into YouTube watchdogs YouTube recommendations can be delightful, but they can also be…
3228
Protecting our FLOSS commons from LLMs
In Brief: Two motions regarding
3229
The AI Threat to Open Societies
Good evening and thank you all for coming. I want to use my time tonight to warn the world about an unprecedented danger that’s threatening the very survival of open societies. Last year when I stood before you I spent most of my time analyzing the nefarious role of the IT monopolies. This is what […]
3230
Emacs and Vim in the Age of AI
It’s tough to make predictions, especially about the future. – Yogi Berra
3231
Training is not the same as chatting: LLMs don’t remember everything you say
I’m beginning to suspect that one of the most common misconceptions about LLMs such as ChatGPT involves how “training” works. A common complaint I see about these tools is that …
3232
I sensed anxiety and frustration at NeurIPS 24
last week at NeurIPS’24, one extremely salient thing was the anxiety and frustration felt and expressed by late-year PhD students and postdocs who were confused by the job market that looks and feels so much different from what they expected perhaps when they were applying for PhD programs five or so years ago. and…
3233
The Dual LLM pattern for building AI assistants that can resist prompt injection
I really want an AI assistant: a Large Language Model powered chatbot that can answer questions and perform actions for me based on access to my private data and tools. …
3234
The Speed of Prototyping in the Age of AI
How AI has changed the way I prototype, plan, and ship; and what I'm doing to keep my hands dirty.
3235
The brain's 5-million core, 9 Hz computer
Weekly essays on biology, philosophy and other topics.
3236
OpenGPT-2: We Replicated GPT-2 Because You Can Too
By Aaron Gokaslan* and Vanya Cohen*
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3238
Do you need a vector database?
Spoiler alert: the answer is maybe! Although, my inclusion of the word “actually” betrays my bias. Vector databases are having their day right now. Three different vector DB companies have raised money on valuations up to $700 million (paywall link). Surprisingly, their rise in popularity is not for their “original”…
3239
ChatGPT as a Calculator for Words
One of the most pervasive mistakes I see people using with large language model tools like ChatGPT is trying to use them as a search engine. As with other LLM …
3240
Intellect-2 Release: The First 32B Model Trained Through Globally Distributed RL
We're excited to release INTELLECT-2, the first 32B parameter model trained via globally distributed reinforcement learning. Unlike traditional centralized training efforts, INTELLECT-2 trains a reasoning language model using fully asynchronous RL across a dynamic, heterogeneous swarm of compute contributors.