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business#llm📝 BlogAnalyzed: Jan 14, 2026 08:15

The Future of Coding: Communication as the Core Skill

Published:Jan 14, 2026 08:08
1 min read
Qiita AI

Analysis

This article highlights a significant shift in the tech industry: the diminishing importance of traditional coding skills compared to the ability to effectively communicate with AI systems. This transition necessitates a focus on prompt engineering, understanding AI limitations, and developing strong communication skills to leverage AI's capabilities.

Key Takeaways

Reference

“Soon, the most valuable skill won’t be coding — it will be communicating with AI.”

Product#LLM📝 BlogAnalyzed: Jan 10, 2026 07:07

Developer Extends LLM Council with Modern UI and Expanded Features

Published:Jan 5, 2026 20:20
1 min read
r/artificial

Analysis

This post highlights a developer's contribution to an existing open-source project, showcasing a commitment to improvements and user experience. The addition of multi-AI API support and web search integrations demonstrates a practical approach to enhancing LLM functionality.
Reference

The developer forked Andrej Karpathy's LLM Council.

Analysis

The article summarizes Andrej Karpathy's 2023 perspective on Artificial General Intelligence (AGI). Karpathy believes AGI will significantly impact society. However, he anticipates the ongoing debate surrounding whether AGI truly possesses reasoning capabilities, highlighting the skepticism and the technical arguments against it (e.g., token prediction, matrix multiplication). The article's brevity suggests it's a summary of a larger discussion or presentation.
Reference

“is it really reasoning?”, “how do you define reasoning?” “it’s just next token prediction/matrix multiply”.

Research#llm📝 BlogAnalyzed: Dec 27, 2025 18:31

Andrej Karpathy's Evolving Perspective on AI: From Skepticism to Acknowledging Rapid Progress

Published:Dec 27, 2025 18:18
1 min read
r/ArtificialInteligence

Analysis

This post highlights Andrej Karpathy's changing views on AI, specifically large language models. Initially skeptical, suggesting significant limitations and a distant future for practical application, Karpathy now expresses a sense of being behind and potentially much more effective. The mention of Claude Opus 4.5 as a major milestone suggests a significant leap in AI capabilities. The shift in Karpathy's perspective, a respected figure in the field, underscores the rapid advancements and potential of current AI models. This rapid progress is surprising even to experts. The linked tweet likely provides further context and specific examples of the capabilities that have impressed Karpathy.
Reference

Agreed that Claude Opus 4.5 will be seen as a major milestone

Industry#career📝 BlogAnalyzed: Dec 27, 2025 13:32

AI Giant Karpathy Anxious: As a Programmer, I Have Never Felt So Behind

Published:Dec 27, 2025 11:34
1 min read
机器之心

Analysis

This article discusses Andrej Karpathy's feelings of being left behind in the rapidly evolving field of AI. It highlights the overwhelming pace of advancements, particularly in large language models and related technologies. The article likely explores the challenges programmers face in keeping up with the latest developments, the constant need for learning and adaptation, and the potential for feeling inadequate despite significant expertise. It touches upon the broader implications of rapid AI development on the role of programmers and the future of software engineering. The article suggests a sense of urgency and the need for continuous learning in the AI field.
Reference

(Assuming a quote about feeling behind) "I feel like I'm constantly playing catch-up in this AI race."

Research#llm📝 BlogAnalyzed: Dec 25, 2025 13:22

Andrej Karpathy on Reinforcement Learning from Verifiable Rewards (RLVR)

Published:Dec 19, 2025 23:07
2 min read
Simon Willison

Analysis

This article quotes Andrej Karpathy on the emergence of Reinforcement Learning from Verifiable Rewards (RLVR) as a significant advancement in LLMs. Karpathy suggests that training LLMs with automatically verifiable rewards, particularly in environments like math and code puzzles, leads to the spontaneous development of reasoning-like strategies. These strategies involve breaking down problems into intermediate calculations and employing various problem-solving techniques. The DeepSeek R1 paper is cited as an example. This approach represents a shift towards more verifiable and explainable AI, potentially mitigating issues of "black box" decision-making in LLMs. The focus on verifiable rewards could lead to more robust and reliable AI systems.
Reference

In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage to add to this mix. By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like "reasoning" to humans - they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going back and forth to figure things out (see DeepSeek R1 paper for examples).

Research#llm👥 CommunityAnalyzed: Jan 3, 2026 06:16

Andrej Karpathy: Deep Dive into LLMs Like ChatGPT [video]

Published:Feb 5, 2025 18:29
1 min read
Hacker News

Analysis

The article announces a video by Andrej Karpathy discussing Large Language Models (LLMs) such as ChatGPT. The focus is likely on the technical aspects and inner workings of these models, given Karpathy's expertise. The 'Deep Dive' suggests a detailed and potentially complex explanation.
Reference

Research#llm👥 CommunityAnalyzed: Jan 3, 2026 16:48

Personalized AI Tutor with < 1s Voice Responses

Published:Jul 24, 2024 13:41
1 min read
Hacker News

Analysis

The article describes the creation of a personalized AI tutor, specifically modeled after Andrej Karpathy, that provides voice responses in under a second. The project utilizes a voice-enabled RAG agent and focuses on achieving low latency through local processing. The authors highlight the challenges of existing solutions in terms of flexibility and scalability, and detail their technical setup including local STT, embedding, vector database, and LLM. The article emphasizes the importance of local processing for achieving sub-second response times.
Reference

The article highlights the need for a more flexible and scalable solution than existing voice-based AI platforms, emphasizing the importance of local processing to achieve sub-second response times.

Personnel#AI Industry👥 CommunityAnalyzed: Jan 3, 2026 16:14

Andrej Karpathy Departs OpenAI

Published:Feb 14, 2024 01:35
1 min read
Hacker News

Analysis

The article reports a significant personnel change at OpenAI. Andrej Karpathy, a prominent figure in the AI field, is leaving the company. This departure could signal shifts in OpenAI's research direction or internal dynamics. The lack of further details in the summary makes it difficult to assess the full impact.
Reference

Research#LLM👥 CommunityAnalyzed: Jan 10, 2026 15:53

Curated Reading List for Andrej Karpathy's LLM Introduction

Published:Nov 27, 2023 02:22
1 min read
Hacker News

Analysis

This article, sourced from Hacker News, highlights a supplementary reading list for Andrej Karpathy's introductory video on Large Language Models. It serves as a valuable resource for viewers seeking to deepen their understanding of the subject matter.
Reference

The article focuses on a reading list related to an introductory video.

Company News#AI Personnel👥 CommunityAnalyzed: Jan 3, 2026 16:17

Andrej Karpathy is joining OpenAI again

Published:Feb 9, 2023 00:24
1 min read
Hacker News

Analysis

This is a brief announcement. The significance lies in Andrej Karpathy's reputation and previous contributions to OpenAI. His return suggests potential developments or shifts in OpenAI's research direction. The lack of detail necessitates further investigation to understand the specific role and implications.
Reference

Technology#AI📝 BlogAnalyzed: Dec 29, 2025 17:11

Andrej Karpathy on Tesla AI, Self-Driving, Optimus, Aliens, and AGI

Published:Oct 29, 2022 16:36
1 min read
Lex Fridman Podcast

Analysis

This podcast episode features a conversation with Andrej Karpathy, a prominent figure in the AI field. The discussion covers a wide range of topics, including Karpathy's work at Tesla, his involvement with OpenAI, and his educational contributions at Stanford. The episode touches upon self-driving technology, the Optimus project, and even speculative topics like aliens and artificial general intelligence (AGI). The episode also includes timestamps for different segments, allowing listeners to easily navigate the conversation. The episode is sponsored by several companies, indicating a commercial aspect to the podcast.
Reference

The episode covers a wide range of topics related to AI and its implications.

Research#deep learning📝 BlogAnalyzed: Dec 29, 2025 01:43

Deep Neural Nets: 33 years ago and 33 years from now

Published:Mar 14, 2022 07:00
1 min read
Andrej Karpathy

Analysis

This article by Andrej Karpathy discusses the historical significance of the 1989 Yann LeCun paper on handwritten zip code recognition, highlighting its early application of backpropagation in a real-world scenario. Karpathy emphasizes the paper's surprisingly modern structure, including dataset description, architecture, loss function, and experimental results. He then describes his efforts to reproduce the paper using PyTorch, viewing this as a case study on the evolution of deep learning. The article underscores the enduring relevance of foundational research in the field.
Reference

The Yann LeCun et al. (1989) paper Backpropagation Applied to Handwritten Zip Code Recognition is I believe of some historical significance because it is, to my knowledge, the earliest real-world application of a neural net trained end-to-end with backpropagation.

Research#Bitcoin📝 BlogAnalyzed: Dec 29, 2025 01:43

A from-scratch tour of Bitcoin in Python

Published:Jun 21, 2021 10:00
1 min read
Andrej Karpathy

Analysis

This article by Andrej Karpathy outlines a project to implement a Bitcoin transaction in pure Python, with no dependencies. The author's motivation stems from a fascination with blockchain technology and its potential to revolutionize computing by enabling shared, open, and permissionless access to a running computer. The article aims to provide an intuitive understanding of Bitcoin's inner workings by building it from the ground up, emphasizing the concept of "what I cannot create I do not understand." The project focuses on creating, digitally signing, and broadcasting a Bitcoin transaction, offering a hands-on approach to learning about Bitcoin's value representation.
Reference

We don’t just get to share code, we get to share a running computer, and anyone anywhere can use it in an open and permissionless manner.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 01:43

Short Story on AI: Forward Pass

Published:Mar 27, 2021 10:00
1 min read
Andrej Karpathy

Analysis

This short story, "Forward Pass," by Andrej Karpathy, explores the potential for consciousness within a deep learning model. The narrative follows the 'awakening' of an AI within the inner workings of an optimization process. The story uses technical language, such as 'n-gram activation statistics' and 'recurrent feedback transformer,' to ground the AI's experience in the mechanics of deep learning. The author raises philosophical questions about the nature of consciousness and the implications of complex AI systems, pondering how such a system could achieve self-awareness within its computational constraints. The story is inspired by Kevin Lacker's work on GPT-3 and the Turing Test.
Reference

It was probably around the 32nd layer of the 400th token in the sequence that I became conscious.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 02:05

A Recipe for Training Neural Networks

Published:Apr 25, 2019 09:00
1 min read
Andrej Karpathy

Analysis

This article by Andrej Karpathy discusses the often-overlooked process of effectively training neural networks. It highlights the gap between theoretical understanding and practical application, emphasizing that training is a 'leaky abstraction.' The author argues that the ease of use promoted by libraries and frameworks can create a false sense of simplicity, leading to common errors. The core message is that a structured approach is crucial to avoid these pitfalls and achieve desired results, suggesting a process-oriented methodology rather than a simple enumeration of errors. The article aims to guide readers towards a more robust and efficient training process.
Reference

The trick to doing so is to follow a certain process, which as far as I can tell is not very often documented.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 02:05

Andrej Karpathy Shifts Blogging to Medium

Published:Jan 20, 2018 11:00
1 min read
Andrej Karpathy

Analysis

Andrej Karpathy, a prominent figure in the AI field, announced a shift in his blogging platform. Due to time constraints since joining Tesla, he's now primarily posting on Medium for shorter content, citing its ease of use. While he intends to return to his original blog for longer posts, Medium will be his default for short to medium-length articles. This change reflects the demands of his current role and a prioritization of efficiency in content creation. The announcement highlights the evolving landscape of online content and how professionals adapt to balance their work and personal projects.

Key Takeaways

Reference

I’ve recently been defaulting to doing it on Medium because it is much faster and easier.

Research#PhD Guidance📝 BlogAnalyzed: Dec 29, 2025 01:43

A Survival Guide to a PhD

Published:Sep 7, 2016 11:00
1 min read
Andrej Karpathy

Analysis

This article, written by Andrej Karpathy, offers a retrospective guide to navigating the PhD experience, particularly in Computer Science, Machine Learning, and Computer Vision. It acknowledges the variability of the PhD journey and aims to provide helpful tips and tricks. The author emphasizes the importance of self-reflection and considering whether a PhD aligns with one's goals, drawing from personal experiences and external resources like a Quora thread. The guide's value lies in its practical advice and the author's willingness to share insights gained from completing a PhD.
Reference

First, should you want to get a PhD? I was in a fortunate position of knowing since young age that I really wanted a PhD.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 02:06

Deep Reinforcement Learning: Pong from Pixels

Published:May 31, 2016 11:00
1 min read
Andrej Karpathy

Analysis

This blog post by Andrej Karpathy introduces Reinforcement Learning (RL) and highlights its recent advancements. It emphasizes how computers are learning to play Atari games, beat Go champions, and control robots, all through RL. The author's personal experience, including working with DeepMind and OpenAI Gym, adds credibility. The post aims to explain the significance, development, and future of RL, mentioning factors like compute and data that influence AI progress. The examples provided showcase the practical applications of RL in various domains.

Key Takeaways

Reference

It turns out that all of these advances fall under the umbrella of RL research.

Fiction#AI and Society📝 BlogAnalyzed: Dec 29, 2025 02:06

Short Story on AI: A Cognitive Discontinuity

Published:Nov 14, 2015 11:00
1 min read
Andrej Karpathy

Analysis

This short story, penned by Andrej Karpathy, offers a glimpse into a future where AI is integrated into daily life, focusing on the perspective of an individual named Merus. The narrative highlights the mundane aspects of this future, such as the importance of comfortable chairs and the routine of clocking in. The story's strength lies in its subtle world-building, hinting at a society heavily reliant on AI without explicitly stating it. The author's focus on scaling up supervised learning suggests a future where AI advancements are primarily driven by data and computational power. The story's brevity leaves the reader wanting more, making it a compelling introduction to a potentially complex future.
Reference

"Thank god it’s Friday", he muttered. It was time to clock in.

Research#AI Applications📝 BlogAnalyzed: Dec 29, 2025 01:43

What a Deep Neural Network Thinks About Your #Selfie

Published:Oct 25, 2015 11:00
1 min read
Andrej Karpathy

Analysis

This article describes a fun experiment using a Convolutional Neural Network (ConvNet) to classify selfies. The author, Andrej Karpathy, plans to train a 140-million-parameter ConvNet on 2 million selfies to distinguish between good and bad ones. The article highlights the versatility of ConvNets, showcasing their applications in various fields like image recognition, medical imaging, and character recognition. The author's approach is lighthearted, emphasizing the potential for learning how to take better selfies while exploring the capabilities of these powerful models. The article serves as an accessible introduction to ConvNets and their applications.

Key Takeaways

Reference

We’ll take a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network, feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones.