Chronological feed of everything captured from Andrej Karpathy.
blog / karpathy / Jul 2 / failed
Andrej Karpathy re-implemented t-SNE in JavaScript (tsnejs) to embed high-dimensional TF-IDF vectors of top Twitter users' tweets into 2D for visualization, clustering similar tweeters together. TF-IDF vectors were derived from 200 concatenated tweets per user using scikit-learn's TfidfVectorizer on unigrams and bigrams, yielding 500 x 87k feature matrix with cosine similarities as distances. The pipeline scrapes top users with Kimono, collects tweets via Tweepy, processes with Python/IPython/scikit-learn, and renders interactively with D3.js, emphasizing t-SNE's local structure preservation via KL-divergence on Gaussian-to-Student-t distances.
t-snedimensionality-reductiondata-visualizationmachine-learningjavascriptnlptwitter-analysis
“t-SNE was re-implemented from scratch in JavaScript as tsnejs library”
blog / karpathy / Jul 1 / failed
Jekyll generates static sites from Markdown posts and templates, eliminating PHP vulnerabilities, bloat, and dynamic rendering issues plaguing WordPress. Content lives in plain folders like _posts, enabling easy editing, versioning via Git, and automatic deployment on GitHub Pages. Workflow involves writing Markdown, committing to Git, and pushing for instant live updates with built-in preview via jekyll serve.
jekyllwordpressstatic-site-generatorblogginggithub-pagesweb-developmentstatic-sites
“WordPress blogs are clunky, slow, bloated, and vulnerable to hacks due to dynamic PHP rendering”
paper / karpathy / Jun 22
The model embeds fine-grained fragments—objects from images and typed dependency relations from sentences—into a shared multimodal space, enabling bidirectional image-sentence retrieval. It combines a global ranking objective with a novel fragment alignment objective that directly associates cross-modal fragments. Experiments demonstrate significant performance gains over global-only embeddings, with explicit alignments providing interpretability.
multimodal-embeddingsimage-sentence-retrievalfragment-alignmentcomputer-visionnatural-language-processingmachine-learning
“The model embeds image objects and sentence typed dependency tree relations into a common embedding space.”
blog / karpathy / Apr 26 / failed
Andrej Karpathy shared a link to his ~2-month-old interview from Data Science Weekly. The interview covers ConvNetJS, his professional background, and perspectives on neural network trends, particularly in academia. It provides early insights into the field's direction as of early 2014.
andrej-karpathyconvnetjsdeep-learningneural-netsbrowser-mlinterview
“Karpathy gave an interview about two months prior to April 26, 2014”
blog / karpathy / Nov 27 / failed
Karpathy collected 47 days of Hacker News data by scraping front and new stories pages every minute from August 22 to October 30, storing ~10MB per day as gzipped pickles. BeautifulSoup parsed unstructured HTML tables into JSON with fields like domain, title, points, rank, user, and comments, handling edge cases in a 100-line function. Analysis in an IPython Notebook visualizes upvote trajectories, top domains/users/topics, and optimal posting times, with data/code once publicly available.
hacker-newsdata-analysisweb-scrapingvisualizationpythonbeautifulsoupipython
“Data collected over 47 days from August 22 to October 30”
blog / karpathy / Nov 23 / failed
Chrome extensions require only a manifest file and JavaScript/HTML, deployable in minutes to inject code into any webpage's DOM. They support adding UI elements, persistent storage, and periodic execution, allowing removal of unwanted content, auto-interactions, and novel features like tweet rarity highlighting. Twitter modifications demonstrate hiding promoted tweets, auto-loading new content, and visually prioritizing infrequent posters using scraped user frequencies stored locally.
chrome-extensionsbrowser-hackingjavascriptweb-dom-modificationtwitter-customizationproductivity-hackskarpathy-blog
“Chrome extensions can execute arbitrary JavaScript on any webpage's DOM on load or periodically”
blog / karpathy / Oct 22 / failed
Interpreting a simple image like Obama pranking a scale requires fusing 3D scene structure, physics, human psychology, social norms, and predictive reasoning about mental states—none of which emerge from pixels alone. State-of-the-art CV benchmarks like ImageNet and PASCAL VOC test narrow, disconnected tasks that ignore this iceberg of prior knowledge. Progress demands not just more image data and learning tricks, but structured experiential data, embodiment, and active inference architectures to approximate human-like comprehension.
computer-visionscene-understandingai-challengescommon-sense-reasoningembodimentphysics-inferencehuman-perception
“Recognizing the Obama scale prank requires understanding 3D scene structure, including mirrors creating fake people replicas.”
blog / karpathy / Apr 27 / failed
Andrej Karpathy manually classified 400 CIFAR-10 test images to 94% accuracy, far surpassing the 2011 state-of-the-art of 80% from Coates et al.'s k-means centroids with whitening and SVM. The exercise reveals high intra-class variability in poses, scales, and partial views, plus dataset undersampling causing test images without close training matches. Random clusters yield 70% and random patches 74%, suggesting k-means mainly spreads activations near data manifolds; post-2015 deep learning pushed accuracy to 95%.
cifar-10image-classificationcomputer-visiondataset-analysishuman-vs-machinek-means-clusteringandrej-karpathy
“Human accuracy on 400 CIFAR-10 test images is 94%”