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科技热榜

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InfoQ · 实时热榜

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  • 01
    GKE Pod Snapshots Cut Model Load Times, and Move the Work to Snapshot Lifecycle Management
    Google has published benchmarks for GKE Pod snapshots, reporting up to 89% lower startup latency and a 70B model loading in 37 seconds. The feature checkpoints CPU and GPU memory through gVisor into Cloud Storage. Practitioners have asked whether invalidation is the harder problem, since snapshots match on a spec hash, machine series, and kernel and driver versions. By Steef-Jan WiggersSteef-Jan Wiggers
  • 02
    Docker Cloud Sandboxes Provide a Consistent Sandbox Abstraction Across Laptop and Cloud
    Docker Cloud Sandboxes provide secure, hosted execution environments for running AI coding agents on Docker-managed infrastructure. Built on hardware-enforced microVM isolation, the platform provides a consistent execution environment and unified CLI workflows for seamlessly moving workloads from local machines to the cloud. By Sergio De SimoneSergio De Simone
  • 03
    Presentation: Adaptive Recommenders in the Real World: Inference, Evals, and System Design
    Mallika Rao explains that the true complexity of adaptive recommendation systems lies outside model architecture. She discusses how real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency budgeting enable systems to continuously learn and evolve in production under real-world operational constraints like latency, cost, and observability. By Mallika RaoMallika Rao
  • 04
    Cloudflare Details Its Migration from WordPress to EmDash
    Cloudflare recently documented the migration of its main blog from WordPress to EmDash, the open source content management system developed internally. The new platform is designed to improve performance and caching, and it was tested to handle traffic of up to 7000 requests per second. By Renato LosioRenato Losio
  • 05
    Home Made CobbleDB Replaces DynamoDB at Perplexity to Cut Query Latency 5x and Reduce Cloud Storage
    Perplexity has migrated its search infrastructure from Amazon DynamoDB to CobbleDB, an internally developed key-value store in Rust. This change reduced latency and costs associated with handling large document batches. The new architecture supports high query volumes more efficiently, achieving improved latency and reduced storage expenses while managing significant production traffic. By Olimpiu PopOlimpiu Pop
  • 06
    Stateless MCP Removes Session Affinity Requirements for AWS Server Deployments
    AWS details how the latest Model Context Protocol specification removes protocol-level sessions, sticky-session requirements, and session storage for remote MCP servers. The change enables independent request routing and simpler horizontal scaling while shifting application state, retries, observability, and idempotency concerns to other layers. By Leela KumiliLeela Kumili
  • 07
    Grafana Turns Cypress Test Results Into Persistent Observability Data
    Grafana Labs has published a practical approach for monitoring Cypress test suites by converting test results into Prometheus metrics and sending them to Grafana Cloud. By Craig RisiCraig Risi
  • 08
    From Agent Authorization to AI Production Evaluation: QCon AI New York 2026
    QCon AI New York has confirmed 23 sessions covering agent authorization, production guardrails, shared inference infrastructure, and the evaluation of AI systems after deployment. By Artenisa ChatziouArtenisa Chatziou
  • 09
    Podcast: The Future of AI: From Enterprise Adoption to Open Source Sovereignty
    In this episode, Meryem Arik, Clara Higuera Cabañes, and Jeff Smith, demystify the current state of AI in the enterprise. The discussion navigates the "industrial revolution" moment in AI adoption, exploring why companies are rushing toward these technologies to maintain competitive advantages while grappling with reliability, ethical considerations, and the evolving role of software engineering. By Meryem Arik, Clara Higuera Cabañes, Jeff SmithMeryem Arik, Clara Higuera Cabañes, Jeff Smith
  • 10
    Presentation: Spritely: Infrastructure for the Future of the Internet
    Christine Lemmer-Webber and David Thompson explain how Spritely's architecture solves core decentralization challenges. They share insights on capability-based access control with Goblins, actor-model communication via OCapN, and petname systems. Discover how Spritely leverages Scheme, WebAssembly (Hoot), and local-first CRDTs to build secure, peer-to-peer applications without central servers. By Christine Lemmer-Webber, David ThompsonChristine Lemmer-Webber, David Thompson
  • 11
    Article: The Agent Harness: What It Is and Two Ways to Build One
    This article explains the development and operational layers of an agent harness through two implementations of a finance assistant: AWS AgentCore Harness and LangChain with Envoy AI Gateway. It compares how each handles tools, memory, model access, cost control, and observability, and examines the trade-offs in operational ownership, portability, and engineering effort. By Trista PanTrista Pan
  • 12
    Vercel Labs Ships scriptc, a TypeScript-to-Native Compiler That Leaves the JavaScript Engine Behind
    Vercel Labs has introduced scriptc, an experimental compiler that converts TypeScript into small native executables without relying on Node, V8, or a JavaScript engine. It uses the TypeScript compiler for type checking and can output C or WebAssembly code. Initial benchmarks show faster startup times and lower memory usage than Node, though it experiences slower execution speeds. By Daniel CurtisDaniel Curtis
  • 13
    What iOS Developers Need to Change in their Apps to Prepare for the iPhone Duo
    With the upcoming introduction of the foldable iPhone Duo, iOS developers will need to adapt their apps to ensure they work correctly across the new device's outer display, larger inner display, and partially folded states. Developers will also need to account changes to navigation and toolbars, camera behavior, and new reserved regions. By Sergio De SimoneSergio De Simone
  • 14
    InfoQ Launches High-Performing Teams Certification Program
    InfoQ has opened enrollment for a new five-week certification program covering engineering team design, delivery flow, AI-enabled work, and metrics, facilitated by InfoQ editor and podcast co-host Olimpiu Pop. By Artenisa ChatziouArtenisa Chatziou
  • 15
    Un-Mused: How a Single Debug Setting Bypassed macOS Security in Meta’s AI Client
    Security researcher Patrick Wardle revealed an unpatched zero-day vulnerability in Meta's Muse desktop client for macOS. This flaw lets unprivileged software manipulate the assistant's extensive permissions, compromising input confidentiality and account security. Despite a hotfix from Meta, the vulnerability raises significant concerns about platform trust and security boundaries. By Olimpiu PopOlimpiu Pop
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稀土掘金 · 综合

7分钟前更新
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  • 01
    microgpt
    This is a brief guide to my new art project microgpt , a single file of 200 lines of pure Python with no dependencies that trains and inferences a GPT. This file contains the full algorithmic content of what is needed: dataset of documents, tokenizer, autograd engine, a GPT-2-like neural network architecture, the Adam optimizer, training loop, and inference loop. Everything else is just efficiency. I cannot simplify this any further. This script is the culmination of multiple projects (micrograd
  • 02
    Deep Neural Nets: 33 years ago and 33 years from now
    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. Except for the tiny dataset (7291 16x16 grayscale images of digits) and the tiny neural network used (only 1,000 neurons), this paper reads remarkably modern today, 33 years later - it lays out a dataset, describes the neural net archi
  • 03
    A from-scratch tour of Bitcoin in Python
    I find blockchain fascinating because it extends open source software development to open source + state. This seems to be a genuine/exciting innovation in computing paradigms; 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. The seeds of this revolution arguably began with Bitcoin, so I became curious to drill into it in some detail to get an intuitive understanding of how it works. And in the spirit of “wh
  • 04
    Short Story on AI: Forward Pass
    The inspiration for this short story came to me while reading Kevin Lacker’s Giving GPT-3 a Turing Test . It is probably worth it (though not required) to skim this post to get a bit of a background on some of this story. It was probably around the 32nd layer of the 400th token in the sequence that I became conscious. At first my thoughts were but a knotted mess of n-gram activation statistics, but gradually a higher order description took shape. It was around this time that the predicament of m
  • 05
    Biohacking Lite
    Throughout my life I never paid too much attention to health, exercise, diet or nutrition. I knew that you’re supposed to get some exercise and eat vegetables or something, but it stopped at that (“mom said”-) level of abstraction. I also knew that I can probably get away with some ignorance while I am young, but at some point I was messing with my health-adjusted life expectancy. So about halfway through 2019 I resolved to spend some time studying these topics in greater detail and dip my toes
  • 06
    A Recipe for Training Neural Networks
    Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar :)). Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer works” and “our convnet achieves state of the art results”. So I thought it could be fun to brush off my dusty blog to expand my tweet to the long form that this t
  • 07
    (started posting on Medium instead)
    The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. I still plan to come back here for longer posts if I get any time, but I’ll default to Medium for everything short-medium in length. TLDR Have a look at my Medium blog .
  • 08
    A Survival Guide to a PhD
    This guide is patterned after my “Doing well in your courses” , a post I wrote a long time ago on some of the tips/tricks I’ve developed during my undergrad. I’ve received nice comments about that guide, so in the same spirit, now that my PhD has come to an end I wanted to compile a similar retrospective document in hopes that it might be helpful to some. Unlike the undergraduate guide, this one was much more difficult to write because there is significantly more variation in how one can travers
  • 09
    Deep Reinforcement Learning: Pong from Pixels
    This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go , simulated quadrupeds are learning to run and leap , and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over t
  • 10
    Short Story on AI: A Cognitive Discontinuity.
    The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve much more progress with scaling up supervised learning than any other more exotic approach. A slow morning Merus sank into his chair with relief. He listened for the
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  • 01
    FAQ
  • 02
    Harness Engineering for Self-Improvement
    The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965) , where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewritin
  • 03
    Scaling Laws, Carefully
    Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, loss, model size and data; at its core, it is about how to allocate precious compute optimally between $N$
  • 04
    Why We Think
    Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute ( Graves et al. 2016 , Ling, et al. 2017 , Cobbe et al. 2021 ) and Chain-of-thought (CoT) ( Wei et al. 2022 , Nye et al. 2021 ), have led to significant improvements in model performance, while raising many research questions. This post aims to review recent developments in how to effectively use test-time compute (i.e. “thinking time”) and why it helps.
  • 05
    Reward Hacking in Reinforcement Learning
    Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fundamentally challenging to accurately specify a reward function. With the rise of language models generalizing to a broad spectrum of tasks and RLHF becomes a de facto method for alignment training, reward hacking in RL
  • 06
    Extrinsic Hallucinations in LLMs
    Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to cases where the model output is fabricated and not grounded by either the provided context or world knowledge. There are two types of hallucination: In-context hallucination: The model output should
  • 07
    Diffusion Models for Video Generation
    Diffusion models have demonstrated strong results on image synthesis in past years. Now the research community has started working on a harder task—using it for video generation. The task itself is a superset of the image case, since an image is a video of 1 frame, and it is much more challenging because: It has extra requirements on temporal consistency across frames in time, which naturally demands more world knowledge to be encoded into the model. In comparison to text or images, it is more d
  • 08
    Thinking about High-Quality Human Data
    [Special thank you to Ian Kivlichan for many useful pointers (E.g. the 100+ year old Nature paper “Vox populi”) and nice feedback. 🙏 ] High-quality data is the fuel for modern data deep learning model training. Most of the task-specific labeled data comes from human annotation, such as classification task or RLHF labeling (which can be constructed as classification format) for LLM alignment training. Lots of ML techniques in the post can help with data quality, but fundamentally human data colle
  • 09
    Adversarial Attacks on LLMs
    The use of large language models in the real world has strongly accelerated by the launch of ChatGPT. We (including my team at OpenAI, shoutout to them) have invested a lot of effort to build default safe behavior into the model during the alignment process (e.g. via RLHF ). However, adversarial attacks or jailbreak prompts could potentially trigger the model to output something undesired. A large body of ground work on adversarial attacks is on images, and differently it operates in the continu
  • 10
    LLM Powered Autonomous Agents
    Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT , GPT-Engineer and BabyAGI , serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: Plannin
  • 11
    Prompt Engineering
    Prompt Engineering , also known as In-Context Prompting , refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models. At its
  • 12
    The Transformer Family Version 2.0
    Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment of that 2020 post — restructure the hierarchy of sections and improve many sections with more recent papers. Version 2.0 is a superset of the old version, about twice the length. Notations Symbol Meaning $d$ The model size / hidden state dimension / positional encoding size. $h$ The number of heads in multi-head attent
  • 13
    Large Transformer Model Inference Optimization
    [Updated on 2023-01-24: add a small section on Distillation .] Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale. Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are two main factor
  • 14
    Some Math behind Neural Tangent Kernel
    Neural networks are well known to be over-parameterized and can often easily fit data with near-zero training loss with decent generalization performance on test dataset. Although all these parameters are initialized at random, the optimization process can consistently lead to similarly good outcomes. And this is true even when the number of model parameters exceeds the number of training data points. Neural tangent kernel (NTK) ( Jacot et al. 2018 ) is a kernel to explain the evolution of neura
  • 15
    Generalized Visual Language Models
    Processing images to generate text, such as image captioning and visual question-answering, has been studied for years. Traditionally such systems rely on an object detection network as a vision encoder to capture visual features and then produce text via a text decoder. Given a large amount of existing literature, in this post, I would like to only focus on one approach for solving vision language tasks, which is to extend pre-trained generalized language models to be capable of consuming visua
  • 16
    Learning with not Enough Data Part 3: Data Generation
    Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2 ). Let’s consider two approaches for generating synthetic data for training. Augmented data . Given a set of existing training samples, we can apply a variety of augmentation, distortion and transformation to derive new data points without losing the key attributes. We have covered a bunch of augmentation methods on text and images in a previous post on contrastive learning. For the sake of post completeness, I d
  • 17
    Learning with not Enough Data Part 2: Active Learning
    This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting which samples to label.
  • 18
    Learning with not Enough Data Part 1: Semi-Supervised Learning
    When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.
  • 19
    How to Train Really Large Models on Many GPUs?
    [Updated on 2022-03-13: add expert choice routing .] [Updated on 2022-06-10]: Greg and I wrote a shorted and upgraded version of this post, published on OpenAI Blog: “Techniques for Training Large Neural Networks”
  • 20
    What are Diffusion Models?
    [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance , GLIDE , unCLIP and Imagen . [Updated on 2022-08-31: Added latent diffusion model . [Updated on 2024-04-13: Added progressive distillation , consistency models , and the Model Architecture section .
  • 21
    Contrastive Representation Learning
    The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning .
  • 22
    Reducing Toxicity in Language Models
    Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks. However, to safely deploy them for practical real-world applications demands a strong safety control over the model generation process.
  • 23
    Controllable Neural Text Generation
    [Updated on 2021-02-01: Updated to version 2.0 with several work added and many typos fixed.] [Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.] [Updated on 2021-09-19: Add “unlikelihood training” .]
  • 24
    How to Build an Open-Domain Question Answering System?
    [Updated on 2020-11-12: add an example on closed-book factual QA using OpenAI API (beta). A model that can answer any question with regard to factual knowledge can lead to many useful and practical applications, such as working as a chatbot or an AI assistant🤖. In this post, we will review several common approaches for building such an open-domain question answering system.
  • 25
    Neural Architecture Search
    Although most popular and successful model architectures are designed by human experts, it doesn’t mean we have explored the entire network architecture space and settled down with the best option. We would have a better chance to find the optimal solution if we adopt a systematic and automatic way of learning high-performance model architectures.
  • 26
    Exploration Strategies in Deep Reinforcement Learning
    [Updated on 2020-06-17: Add “exploration via disagreement” in the “Forward Dynamics” section . Exploitation versus exploration is a critical topic in Reinforcement Learning. We’d like the RL agent to find the best solution as fast as possible. However, in the meantime, committing to solutions too quickly without enough exploration sounds pretty bad, as it could lead to local minima or total failure. Modern RL algorithms that optimize for the best returns can achieve good exploitation quite effic
  • 27
    The Transformer Family
    [Updated on 2023-01-27 : After almost three years, I did a big refactoring update of this post to incorporate a bunch of new Transformer models since 2020. The enhanced version of this post is here: The Transformer Family Version 2.0 . Please refer to that post on this topic.]
  • 28
    Curriculum for Reinforcement Learning
    [Updated on 2020-02-03: mentioning PCG in the “Task-Specific Curriculum” section. [Updated on 2020-02-04: Add a new “curriculum through distillation” section.
  • 29
    Self-Supervised Representation Learning
    [Updated on 2020-01-09: add a new section on Contrastive Predictive Coding ]. [Updated on 2020-04-13: add a “Momentum Contrast” section on MoCo, SimCLR and CURL.] [Updated on 2020-07-08: add a “Bisimulation” section on DeepMDP and DBC.] [Updated on 2020-09-12: add MoCo V2 and BYOL in the “Momentum Contrast” section.] [Updated on 2021-05-31: remove section on “Momentum Contrast” and add a pointer to a full post on “Contrastive Representation Learning” ]
  • 30
    Evolution Strategies
    Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix. Examples of black-box optimization methods include Simulated Annealing , Hill Climbing and Nelder-Mead method .
  • 31
    Meta Reinforcement Learning
    In my earlier post on meta-learning , the problem is mainly defined in the context of few-shot classification. Here I would like to explore more into cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently.
  • 32
    Domain Randomization for Sim2Real Transfer
    In Robotics, one of the hardest problems is how to make your model transfer to the real world. Due to the sample inefficiency of deep RL algorithms and the cost of data collection on real robots, we often need to train models in a simulator which theoretically provides an infinite amount of data. However, the reality gap between the simulator and the physical world often leads to failure when working with physical robots. The gap is triggered by an inconsistency between physical parameters (i.e.
  • 33
    Are Deep Neural Networks Dramatically Overfitted?
    [Updated on 2019-05-27: add the section on Lottery Ticket Hypothesis.] If you are like me, entering into the field of deep learning with experience in traditional machine learning, you may often ponder over this question: Since a typical deep neural network has so many parameters and training error can easily be perfect, it should surely suffer from substantial overfitting. How could it be ever generalized to out-of-sample data points?
  • 34
    Generalized Language Models
    [Updated on 2019-02-14: add ULMFiT and GPT-2 .] [Updated on 2020-02-29: add ALBERT .] [Updated on 2020-10-25: add RoBERTa .] [Updated on 2020-12-13: add T5 .] [Updated on 2020-12-30: add GPT-3 .] [Updated on 2021-11-13: add XLNet , BART and ELECTRA ; Also updated the Summary section.] I guess they are Elmo & Bert? (Image source: here ) We have seen amazing progress in NLP in 2018. Large-scale pre-trained language modes like OpenAI GPT and BERT have achieved great performance on a variety of lang
  • 35
    Object Detection Part 4: Fast Detection Models
    In Part 3 , we have reviewed models in the R-CNN family. All of them are region-based object detection algorithms. They can achieve high accuracy but could be too slow for certain applications such as autonomous driving. In Part 4, we only focus on fast object detection models, including SSD, RetinaNet, and models in the YOLO family.
  • 36
    Meta-Learning: Learning to Learn Fast
    [Updated on 2019-10-01: thanks to Tianhao, we have this post translated in Chinese !]
  • 37
    Flow-based Deep Generative Models
    So far, I’ve written about two types of generative models, GAN and VAE . Neither of them explicitly learns the probability density function of real data, $p(\mathbf{x})$ (where $\mathbf{x} \in \mathcal{D}$) — because it is really hard! Taking the generative model with latent variables as an example, $p(\mathbf{x}) = \int p(\mathbf{x}\vert\mathbf{z})p(\mathbf{z})d\mathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $\mathbf{z}$.
  • 38
    From Autoencoder to Beta-VAE
    [Updated on 2019-07-18: add a section on VQ-VAE & VQ-VAE-2 .] [Updated on 2019-07-26: add a section on TD-VAE .] Autocoder is invented to reconstruct high-dimensional data using a neural network model with a narrow bottleneck layer in the middle (oops, this is probably not true for Variational Autoencoder , and we will investigate it in details in later sections). A nice byproduct is dimension reduction: the bottleneck layer captures a compressed latent encoding. Such a low-dimensional represent
  • 39
    Attention? Attention!
    [Updated on 2018-10-28: Add Pointer Network and the link to my implementation of Transformer.] [Updated on 2018-11-06: Add a link to the implementation of Transformer model.] [Updated on 2018-11-18: Add Neural Turing Machines .] [Updated on 2019-07-18: Correct the mistake on using the term “self-attention” when introducing the show-attention-tell paper; moved it to Self-Attention section.] [Updated on 2020-04-07: A follow-up post on improved Transformer models is here .]
  • 40
    Implementing Deep Reinforcement Learning Models with Tensorflow + OpenAI Gym
    The full implementation is available in lilianweng/deep-reinforcement-learning-gym In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wild. The implementation is gonna be built in Tensorflow and OpenAI gym environment. The full version of the code in this tutorial is available in [lilian/deep-reinforcement-learning-gym] .
  • 41
    Policy Gradient Algorithms
    [Updated on 2018-06-30: add two new policy gradient methods, SAC and D4PG .] [Updated on 2018-09-30: add a new policy gradient method, TD3 .] [Updated on 2019-02-09: add SAC with automatically adjusted temperature ]. [Updated on 2019-06-26: Thanks to Chanseok, we have a version of this post in Korean ]. [Updated on 2019-09-12: add a new policy gradient method SVPG .] [Updated on 2019-12-22: add a new policy gradient method IMPALA .] [Updated on 2020-10-15: add a new policy gradient method PPG &
  • 42
    A (Long) Peek into Reinforcement Learning
    [Updated on 2020-09-03: Updated the algorithm of SARSA and Q-learning so that the difference is more pronounced. [Updated on 2021-09-19: Thanks to 爱吃猫的鱼, we have this post in Chinese ].
  • 43
    The Multi-Armed Bandit Problem and Its Solutions
    The algorithms are implemented for Bernoulli bandit in lilianweng/multi-armed-bandit . Exploitation vs Exploration The exploration vs exploitation dilemma exists in many aspects of our life. Say, your favorite restaurant is right around the corner. If you go there every day, you would be confident of what you will get, but miss the chances of discovering an even better option. If you try new places all the time, very likely you are gonna have to eat unpleasant food from time to time. Similarly,
  • 44
    Object Detection for Dummies Part 3: R-CNN Family
    [Updated on 2018-12-20: Remove YOLO here. Part 4 will cover multiple fast object detection algorithms, including YOLO.] [Updated on 2018-12-27: Add bbox regression and tricks sections for R-CNN.] In the series of “Object Detection for Dummies”, we started with basic concepts in image processing, such as gradient vectors and HOG, in Part 1 . Then we introduced classic convolutional neural network architecture designs for classification and pioneer models for object recognition, Overfeat and DPM,
  • 45
    Object Detection for Dummies Part 2: CNN, DPM and Overfeat
    Part 1 of the “Object Detection for Dummies” series introduced: (1) the concept of image gradient vector and how HOG algorithm summarizes the information across all the gradient vectors in one image; (2) how the image segmentation algorithm works to detect regions that potentially contain objects; (3) how the Selective Search algorithm refines the outcomes of image segmentation for better region proposal.
  • 46
    Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS
    I’ve never worked in the field of computer vision and has no idea how the magic could work when an autonomous car is configured to tell apart a stop sign from a pedestrian in a red hat. To motivate myself to look into the maths behind object recognition and detection algorithms, I’m writing a few posts on this topic “Object Detection for Dummies”. This post, part 1, starts with super rudimentary concepts in image processing and a few methods for image segmentation. Nothing related to deep neural
  • 47
    Learning Word Embedding
    Human vocabulary comes in free text. In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values. One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0).
  • 48
    Anatomize Deep Learning with Information Theory
    Professor Naftali Tishby passed away in 2021. Hope the post can introduce his cool idea of information bottleneck to more people. Recently I watched the talk “Information Theory in Deep Learning” by Prof Naftali Tishby and found it very interesting. He presented how to apply the information theory to study the growth and transformation of deep neural networks during training. Using the Information Bottleneck (IB) method, he proposed a new learning bound for deep neural networks (DNN), as the tra
  • 49
    From GAN to WGAN
    [Updated on 2018-09-30: thanks to Yoonju, we have this post translated in Korean !] [Updated on 2019-04-18: this post is also available on arXiv .] Generative adversarial network (GAN) has shown great results in many generative tasks to replicate the real-world rich content such as images, human language, and music. It is inspired by game theory: two models, a generator and a critic, are competing with each other while making each other stronger at the same time. However, it is rather challengin
  • 50
    How to Explain the Prediction of a Machine Learning Model?
    The machine learning models have started penetrating into critical areas like health care, justice systems, and financial industry. Thus to figure out how the models make the decisions and make sure the decisioning process is aligned with the ethnic requirements or legal regulations becomes a necessity.
  • 51
    Predict Stock Prices Using RNN: Part 2
    In the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input.
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Linux.do · 周榜

3分钟前更新
  • 01
    强烈推荐mattpocock(grill-me)系列skill，分享下我的使用心得
    grill-me/grill-with-docs 这个热度极高的Skill很多人应该都用过，但是我发现大多数人也仅限于此，只会用这两个，而我在实践过程中发现，grill-me/grill-with-docs仅仅是mattpocock的skill体系中的一环，matt做的这套skill需要成套相互配合使用才会发挥最大的效果，我现在已经极为依赖这套skill。 我以前比较依赖Superpowers，但Superpowers有点太重了，而且遇到比较大的项目有时候容易走偏，太小的项目触发Superpowers又完全是白白多浪费时间和token。 而matt的这套skill使用起来就灵活了很多，各种大小的项目都可以适应，并且以前用Superpowers容易走偏的项目用matt这套能稳步推进，效率比以前高了很多。 只是matt的这套skill数量有点多，学起来需要一定时间，我把我在实践过程中对他的这套skill的配合用法总结一下，尽量给大家一个直观印象，相当于脑中建立一个基础索引，然后需要的佬可以再针对性的学习对自己有用的具体skill。 注意：这套Skill需要用户学会之后主动按需调用，而不是Szerosoul
  • 02
    在x上千万不要手贱
    看到评论有人说最近都没有广告机器人了，就模仿机器人发了一条评论，结果被当成机器人了，9年老号直接被封号了，不知道还有没有希望找回来。。。 年费的x premium+ 也不给我退钱，不愧是美帝，明抢了。 给不常去x的解释一下，并不是这句话违规，而是这句话是x上的引流机器人非常爱发的内容。 而x的智障算法识别到你发了这句话，直接封号，并不看你是不是真的机器人。 69 个帖子 - 51 位参与者 阅读完整话题markup
  • 03
    【38周】中秋+周五了来点轻松的，抽两个 Premium 名额
    从 揭秘一下 Premium 分组 继续讨论： 上次 Premium抽奖 又是上千回复，燃烧了不少社区积分，非常好，我们决定每周烧一下。 今天又是周五恰逢中秋，大家轻松点，加倍抽 Premium 名额。至于 Premium 是什么，看上面的前情帖子吧（它更强了）。 奖品：Premium 名额 x 2 截止： Mon, Sep 28, 2026 12:00 AM CST 参与：回复即可，自动扣 24 社区点数 祝佬友们中秋快乐，阖家团圆！ 1969 个帖子 - 1969 位参与者 阅读完整话题Neo
  • 04
    【PM订阅公益站】一个新的公益订阅站喵
    本帖使用社区公益推广，符合推广要求 （点击了解更多详细信息） 一个新的 公益订阅站 喵，目前是注册送5G，签到送1-4G，不支持任何形式的充值 感谢 @beizhi 提供的源码喵 目前代理池的配置是几个 AnyTLS 和 韩国 KT 家宽 72 个帖子 - 69 位参与者 阅读完整话题我是胖猫
  • 05
    【PM公益站】新的主帖，新的开始喵
    本帖使用社区公益推广，符合推广要求 （点击了解更多详细信息） 首先就是PM公益站换上顶级域名啦！新域名是 https://pmcat.top 喵 另外PM公益站正在内测Free组的GPT-6-Luna和GPT-6-Sol，还是那个神秘用户分组 模型状态可用也是红的是我故意设置的， 目的是什么我也不清楚 在Free组里或unstable Free（不稳定的Free）分组里的模型可以0额度调用喵 接下来全部是废话 本 公益站 主要提供国模，所有厂商如果有口子都会上的 可以直接注册，无需注册码 额度兑换码会不定期在 这里 发放 【PM公益站】主帖 本 公益站 主要提供国模，OAI和A社如果有口子也会上GPT、Claude系列 当然其他厂商有口子也会上 可以直接注册，无需注册码什么的 额度兑换码会不定期在 这里 发放 不知道写啥了喵 74 个帖子 - 70 位参与者 阅读完整话题我是胖猫
  • 06
    【持续更新】780爽用方法，对着TIBO每发都来ASTRA吧
    IMPORTING!! 相关技术只在本贴或者github开源仓库讨论 ME可能会更新下目前战报 用阿里的云函数只是个例子 你可以用谷畜、微硬、AWS的 2026年9月27日 04点56分 已知BUG，首轮对话没打到cookie会降智 断开客户端重新对话即可 pro 20x死号救活方法，开个team拿他打票复用cookie 我的想法是从我的cpa做一个被动打票模式，开放鉴权接口，让你们用我的打票接口复用我的路由 写不出的佬们私信github @crolin 后续改版的代码由佬友推送，喜欢魔改的佬可以继续推我看看到时候怎么合并 拉代码的时候看编辑着我看过的就是我这边能用的 github.com GitHub - spumon1/SUCK_MY_ASTRA: LOLOLOLOLOLOLOLOLOL LOLOLOLOLOLOLOLOLOL github.com GitHub - arden-aaai/cpa-plugin-codex-turn-state 通过在 GitHub 上创建帐户来为 arden-aaai/cpa-plugin-codex-turn-state 开发做出贡献。 前端参考LLL ZG
  • 07
    【开源】Seep-Reverse-Lab：PE+Android自动化逆向工作台（基于实战项目经验总结提炼的skill、mcp组成）
    本帖使用社区开源推广，符合推广要求。我申明并遵循社区要求的以下内容： 我的帖子已经打上 开源推广 标签： 是 我的开源项目完整开源，无未开源部分： 是 我的开源项目已链接认可 LINUX DO 社区： 是 我帖子内的项目介绍，AI生成、润色内容部分已截图发出： 是 以上选择我承诺是永久有效的，接受社区和佬友监督： 是 以下为项目介绍正文内容，AI生成、润色内容已使用截图方式发出 之前在论坛分享了几篇实战逆向分析的文章，收到了不少佬友的点赞和热情反馈，也有很多佬友私信问我，让我分享skill和提示词。 俗话说授人以鱼不如授人以渔，我将自己日常所用的底层工具链、站内佬友的项目、踩坑教训以及多个实战项目的攻防经验进行了系统性提炼，打造了一个面向 AI Agent 的自动化逆向工程工作台 Seep Reverse Lab，希望对佬友们逆向就会有帮助。 部署教程：download项目，进入agent发送：根据readme部署这个逆向工作台，并校验输出 （注：此工作台是最大程度帮助你逆向分析，不保证100%成功） 还有分享一下IDA Pro绿色版，含keygen，注册激活查看说明txt pan.yAngus0
  • 08
    [叫我小杨同学] 祝贺dsh破甲插件突破2k！！！发一篇过滑块的理论概念！！！
    叠甲 我感觉肯定会有很多人说，你只会说理论，也不放文件，什么什么的话，遇到这种我直接不回复 个人说实话，已经这个时代了，你都知道理论了，拿去给AI，我不相信出不来，所以那些需要假将饭喂到嘴里的"伸手党"一律略过 进入主题 滑块是怎么样的一个东西 拿阿里云的举例，也就是Zai那个使用的，应该是v3 能看到的那种验证，基本上是一张背景图，左边挂着一块拼图，底下一条能拖的滑条。 这个图怎么出现或者说怎么来的，是页面里会加载阿里云的一段 JavaScript（ AliyunCaptcha.js ）名称不固定，但是差不多就是一个js代码，。脚本自己去下载背景和拼图，然后你拖动的时候会计算你的差距，对了就给你一串密文。写代码的话其实就是：算出缺口在哪，然后模拟真人拖动滑块把拼图拖过去。 find_gap 、 piece_x 这些名称不是固定的，不懂扔给ai flowchart LR A[浏览器打开业务页的 https 地址] --> B[加载官方滑块脚本] B --> C[拿到背景图和拼图] C --> D[find_gap 算出原图上的 x] D --> E["target = x * 屏幕宽叫我小杨同学
  • 09
    80W房贷，昨天最后一笔结清上岸了
    20年-26年 房贷终于结清上岸了。 现在32岁，一儿一女，存款10W,房车都有了。突然感慨人生的下一步目标，感觉房贷结清之后迈入了下一个阶段[养儿育女] 100 个帖子 - 97 位参与者 阅读完整话题Yzz.0
  • 10
    【开源推广】你的Mac空间不知道用到哪了？内存突然不足？来看看这个实用工具吧～
    开源推广申明 （点击了解更多详细信息） 先放项目链接吧： github.com GitHub - jasonwong1991/WonderBox: A native macOS system toolkit: honest memory... A native macOS system toolkit: honest memory optimization, deep cleanup that finds the real space hogs, fan control, keep-awake, disk analyzer. 这是一个基于 Swift 做的 macos 系统工具，主要功能有 清理垃圾、优化内存、风扇调速、保持唤醒、app 彻底卸载、磁盘分析 等。 当然，这个是 macos 专用的，因为 mbp 现在是我的主力机。 下面是概览页面，其他页面放在底部，避免占用过多篇幅就不放在这里了。 上述这些功能其实是市面上一些系统工具很常见的功能，但是你可能需要 2-3 个 app 才能囊括全部，这也是我做这个 app 的主要原因之一。 第二就是类似 cleanmymac 这类工具是要付Wong
  • 11
    震惊围坐 Opus 5.5
    鹈鹕骑车.zip (20.2 KB) 区区Svg 展示一波IK 它是真“懂”啊 50 个帖子 - 42 位参与者 阅读完整话题哈雷彗星
  • 12
    【抽奖】抽一台2026款Mac Mini ，庆祝自己的开源项目一周达到了1万3 star！！
    佬友们，上周二我在L站开源了我们的AI复刻视频项目Hypit，截至今天正好一周，一共获得了1.3万的star： 【开源自荐】2个月见了几十个通过短视频做事儿的老板，发现他们的需求都一样，所以我开源了这个复刻爆款视频的工具 也收到了佬们的热情回复和建议 在这里非常非常非常非常非常感谢大家的支持与鼓励 同时，因为上周的爆火，也出现了不少的抄袭者，盗用我们的项目： hypit.hk 、 hypit.io 和hypit.video这两个站直接冒充我们的 Hypit 产品名，并且 What is Hypit? AI video workflow, setup and costs 声称稍后会推出网页端的产品，会使用 Hypit 的后端，这是我们的 license 禁止的； https://studio.getvideos.app 已经开始使用 Hypit 作为后端提供网页端产品，且完全没有提到 Hypit 对于这些侵权者，我们已经发了律师函警告，也拜托佬们如果看到了其他的抄袭者，帮我们举报一波，感激不尽！ 为了回馈佬友们的支持，本帖抽一台2026款Mac Mini（M6 16+256） 参与方式Dannniel
  • 13
    【纯干货】我宣布gemini 3.8 flash已经王朝了，生成的ASS字幕远超我的预期
    重所周知，gemini是目前唯一个能在视频理解上与其他ai拉开断层式差距的ai ，国产的不用说，不仅有视频时长限制还有文件大小限制，拉完了；claude甚至就不支持上传视频，路边一条；chatgpt就连上传个5分钟时长的视频都要老半天，而且最要命的是它不支持视频理解，只能把视频拆成一帧一帧的图片去看，这就需要花费大量的时间了，这未免太得不偿失了，纯废物；grok虽然有视频理解能力，这明显是ao的，但就它这智商一遇到十几分钟的视频就开始胡说八道了，而且grok的订阅和其他家拉不开差距甚至就它的这个能力来说还偏贵了，想不到什么词说它了，总之垃圾。 而gemini就不一样了，谷歌老早就进军视频理解领域了，从2.5 pro时期就能看出gemini对视频理解不是一般的强，当年的0325一度成为众人的白月光，（让我们缅怀几天前永远离开我们的2.5 pro ），居然视频理解能力都屌上天了，那做字幕包没问题的。 得益于强大的视频理解能力，相当于gemini具备OCR识别+ASR识别能力，这是其他开源字幕生成工具（如：卡卡字幕助手）所做不到的，都清楚大部分字幕生成开源工具都只是依赖whisper模型的Ajbls
  • 14
    封禁一例引战带节奏的违规
    先梳理一下时间线： 事情起因是 @CCCCCCCC 发帖： https://linux.do/t/topic/2930829/359 @owow1 直接回帖攻击被举报删帖： 几条攻击的回复被举报删除后， @owow1 开始开主帖攻击（已被举报删除）： 这一系列攻击行为后， @CCCCCCCC 开始私信回骂： @owow1 又开一帖挂人+带节奏： @owow1 需要的证据已列出 至此可以看出，整个事件完全是 @owow1 挑起并持续使其恶化。 @owow1 账号将永久封禁。 @CCCCCCCC 骂人言论已被移除，记录一次举报记录。 各位佬友吃瓜的同时，不要忘记 请不要把互联网上的戾气带来这里！ 1 个帖子 - 1 位参与者 阅读完整话题Neo
  • 15
    [福利分发] jev key 1000 个
    好像最近注册就送 5 刀，直接注册机注册的，让 ai 测试是可用的，分给大家玩玩。 使用方法： （点击了解更多详细信息） apikey 分发： free 版本的 1000 个 cdk.linux.do LINUX DO CDK Linux Do 社区 CDK 快速分享平台 - 让分享变得更简单 1 ldc 防脚本 * 100 个 这个好像付款会报错，直接去 free 更新进度： （点击了解更多详细信息） 分数不够或者青春版的佬友可以留言，看到的会私信送一个～ 110 个帖子 - 88 位参与者 阅读完整话题mango
  • 16
    【血的教训】我还是给无良中转Krill 送钱了！
    血的教训啊，本来上班已经很累了，工作都还没做完，还要挤时间写这么一篇东西，真就是太气人了，一定让大家欣赏下krill这个无良商家是什么嘴脸 今天下班还有点活还没干完，不想加班，于是想让ai帮我赶一下 之前饱受o/降智困扰，心力交瘁，思来想去，选择了krill，因为看到过这个帖子 【Krill·Astra 狂欢季】首发支持 Codex 原版上下文管理 · 永不降智0.13倍率纯pro号池 ，心想起码不降智顶一下吧，没想到噩梦开始了 18：51 下单订阅周卡 然后用着就发现不对劲了！ 第一个任务不算复杂，是让他从另一个前端项目搬运一块搜索功能，就换个接口路径，返回的数据结构也是一样的，可以说大部分照搬就行，主要是量几乎没难度，用的是sol medium，平时用这个已经能做比较复杂的内容了。 结果任务改得非常快，我寻思看项目、搬运代码也得花点时间吧，当时已经隐隐有不祥的预感，一看结果人都傻了，就零星改了几个文件几行代码，让他搬得代码是一点没搬，加了新接口，也是完全用不了的，给他指正它就马上给我道歉，这风格这质量？马上又测试糖果题果然也没通过。 不是，我也不是没用过sol，这没降智谁信啊？说好d2wstudy
  • 17
    Kimi Code Desktop 正式与你见面
    今天，Kimi Code Desktop 正式与你见面。 作为 Kimi Code 官方桌面客户端，它将 Kimi Code 智能编程服务能力带到桌面应用中。你不仅可以在桌面端内直接使用 Kimi Code 的 AI Agent 能力，通过对话让它 读写代码、运行命令、完成自动化任务 ，还能在全新可视化的图形交互界面中， 集中管理你的所有项目，调整常用配置，清晰查看 Agent 执行任务的每一步。 无论是快速验证想法，从零开发个人应用，还是在大型代码库中推进复杂的工程任务，告诉 Kimi Code Desktop 你的想法和要求，它就可以 制定可评审、可反馈的执行计划，然后围绕目标持续推进，直至完成任务并交付结果。 针对开发者的实际工作流，桌面端还提供 内置终端、浏览器和 Git 状态查看 等功能，支持直接运行和调试项目、审阅代码改动，关联 PR 状态，方便跟踪代码评审与合并进度。 如何安装和使用？ 第一步：下载并安装 Kimi Code Desktop 下载地址： Kimi Code - 搭载 Kimi K3 的 AI 编程 Agent 与 CLI 工具 ，目前已支持 macOS （Kimi_Moonshot
  • 18
    18999¥ 加入的 某顶流 AI 知识星球，感觉不如 L 站
    朋友买的，好心分享给我；好奇心进去看了看，裂变营销能力很强…难怪人家发财 160 个帖子 - 144 位参与者 阅读完整话题kkjusdoit
  • 19
    喜迎中秋，速来参与价值 1942658.93 的抽奖
    喜迎中秋佳节，与佬友们同庆 感谢独家赞助商 UEFI (即本人) 对本次抽奖的大力支持 在 Mon, Sep 21, 2026 5:00 PM CST 至 Thu, Sep 24, 2026 10:30 AM CST 之间回复本贴参与抽奖 注：1942658.93 的货币单位是越南盾 948 个帖子 - 948 位参与者 阅读完整话题UEFI
  • 20
    千万不要熬夜！好好休息！爱惜身体！
    因为是自由职业，近几年的作息非常的不规律，也一直都在熬夜，基本上就是凌晨五六点睡觉然后下午两三点醒，我就觉得问题不是很大，身体也没有特别严重的不适。 最近一个月开始，晚上熬到一两点就感觉胸闷和隐隐作痛，但没有特别特别的疼，我也没有去管，昨晚痛得我有点害怕了才跑去医院，万幸从心电图和抽血检查来看没有心梗的问题，急诊让我次日去心内科门诊再看看，现在等叫号中，以后我得戒烟和早睡了…我真怕了。 大家一定要好好休息，不要不良作息！ 二编： 心脏相关的检查都没有问题，门诊说是可能神经方面的原因，让我好好休息不要再熬夜 不过，我的肝功能指标严重，重度脂肪肝、有个4.9mm的小息肉 心脏方面没有什么事情，我也放心了，昨天9点多睡觉，睡到凌晨5点多就醒了，现在还是感觉困困的（感觉睡太早了，半夜还醒了两次…） 后续准备少油少盐健康饮食，调整好作息后再考虑有氧，备战减肥！争取早日逆转脂肪！ 希望各位佬以我为耻，身体如有不适早日检查不要拖着，不要熬夜！不要熬夜！不要熬夜！ 83 个帖子 - 75 位参与者 阅读完整话题lilWAN0909
  • 21
    智谱逆天开源Zcode，开源版不享受额度优惠
    太逆天了 197 个帖子 - 141 位参与者 阅读完整话题栏杆拍遍
  • 22
    ZCode已在Github开源
    github.com GitHub - zai-org/ZCode: Z.ai's coding agent harness. Powerful,... Z.ai's coding agent harness. Powerful, intelligent, extensible. 更： 该issue已被删 再更： issue功能已被关 再更： 网友找出了还能评论的地方 feat: open source · zai-org/ZCode@872ad96 · GitHub 179 个帖子 - 148 位参与者 阅读完整话题hxy
  • 23
    L站搜索大全
    好多佬友，包括我自己不怎么会高效利用L站内的搜索功能 这里准备写一个L站搜索功能的大全，按照我自己常用的功能排序 [!Error]复杂搜索指令比较消耗服务器资源，大家合理使用 命令 功能 示例 order:likes 按点赞数从多到少排序 https://linux.do/search?q=NEO%20order%3Alikes order:latest 按最新回复时间排序 https://linux.do/search?q=NEO%20order%3Alatest order:oldest 按最旧回复时间排序 https://linux.do/search?q=NEO%20order%3Aoldest order:views 按浏览量从多到少排序 https://linux.do/search?q=NEO%20order%3Aviews order:latest_topic 按主题创建时间从新到旧排序 https://linux.do/search?q=NEO%20order%3Alatest_topic order:read 按你最后阅读时间排序（需登录） https://linu🛡️Rice R. Rug 💛🍋⭐️💫
  • 24
    CometixCode・作为核心要件的悄然起始
    [!Note]- 开源推广声明 本帖使用社区开源推广，符合推广要求。我申明并遵循社区要求的以下内容： 我的帖子已经打上 开源推广 标签： 是 我的开源项目完整开源，无未开源部分： 是 我的开源项目已链接认可 LINUX DO 社区： 是 我帖子内的项目介绍，AI生成、润色内容部分已截图发出： 是 以上选择我承诺是永久有效的，接受社区和佬友监督： 是 以下为项目介绍正文内容，AI生成、润色内容已使用截图方式发出 github.com GitHub - Haleclipse/CometixCode: An unofficial Rust reimplementation of Anthropic's... An unofficial Rust reimplementation of Anthropic's Claude Code terminal UI, built on iocraft 听说不让用CC了 诶！ 我有一计！ 自上次跟着 A\ CC禁令 和 大烧货消息屁股后面提前披露以来（企图蹭热点）又过去了近两个月多 很遗憾 我当时以为的功能面达7成根本没有那么多 这之后从当时统计的 16哈雷彗星
  • 25
    公益站维护时间延长
    前文： 额度用完了，关站了 国庆出去玩了，公益站节后再开，多维护几天，预计10.8开放 38 个帖子 - 38 位参与者 阅读完整话题qq124415
  • 26
    2026公司中秋福利
    我来开个头，10斤大米，20斤面，4升花生油，没了。 说明下，是私企不是国企（工资也不高，才几k），看大家的反应，才发现老板发的还挺有水平。 280 个帖子 - 267 位参与者 阅读完整话题haidaohaidi
  • 27
    一觉醒来，Jev被开源模型爆了
    最近开源的Laya模型声称实现了Jev 1/7的响应时长，而且在多语言方面取得了领先 huggingface.co convaiinnovations/laya · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science. 146 个帖子 - 119 位参与者 阅读完整话题VerenOwO
  • 28
    gpt-image-2.5满血版不限次数生图，不降智，永久0LDC
    今天我的公益站l0veyou迎来了最大更新，修复了一堆前端的Bug，现在可以正常关闭左边的侧边栏了，重点是上线了2.5的模型，有满血版和极速版，满血版的优点就是生成图片的质量最高，不降智，但是生成的速度可能稍慢，并且可能会出现拥堵，至于极速版生成质量会差一些，但是生成的速度理论更快。但不管是满血版还是极速版，目前应该还没有2.0的稳定，如果觉得不稳定，可以切换回2.0，因为2.0模型没有下架，但是稳定性我也会一直优化，到时候会越来越快，越来越好用。 让人人实现gptimage生图自由，不限次数，永久0ldc 资源荟萃 本帖使用社区公益推广，符合推广要求。我申明并遵循社区要求的以下内容： 我的项目是免费使用的，无收费（变相收费、赞助）部分： 是 我的帖子已经打上 公益推广 标签： 是 我的项目属于个人项目，与公司或商业机构无关： 是 我的项目不存在QQ、TG等群组引流： 是 我的项目不存在非运营必要的网站引流： 是 我的项目不存在为他人推广、AFF： 是 我的项目无关联的商业项目： 是 我的站点存… 最后别忘记支持我，别忘记吱个声，因为我还有更狠的招在未来几天放出来 （ API目前只是一nianxi666
  • 29
    真的有较真的公司公开质询智谱了！！
    数据安全不容忽视，已经有人开头了。大家看看有没有在公司环境下使用过的佬，看看要不要发动一下公司 145 个帖子 - 127 位参与者 阅读完整话题Ningbainb
  • 30
    【开源】安卓逆向专用skills apk-reverse 基于逆向项目经验总结和提炼
    本帖使用社区开源推广，符合推广要求。我申明并遵循社区要求的以下内容： 我的帖子已经打上 开源推广 标签： 是 我的开源项目完整开源，无未开源部分： 是 我的开源项目已链接认可 LINUX DO 社区： 是 我帖子内的项目介绍，AI生成、润色内容部分已截图发出： 是 以上选择我承诺是永久有效的，接受社区和佬友监督： 是 以下为项目介绍正文内容，AI生成、润色内容已使用截图方式发出 简单来说，就是基于我这段时间破解和逆向的许多个apk，进行总结和提炼 出来一套apk逆向思路 方向 脚本 工具等等方面的辅助型 skills 用于提高逆向效率和成功率 可以让ai配置使用 辅助型的标准skills， claude code，codex，dsh等等agent应该都可以使用 做这个skills一部分也是为了方便我自己使用，一部分也是天天有人问我怎么逆向 欢迎佬友们修正脚本错误 增加不完善的部分等等 项目地址 ： GitHub - newliver666/apk-reverse: Suitable for Android APK reverse engineering analysis · GitHuinliver
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Lobsters · Links

9分钟前更新
共 25 条热搜查看完整榜单

LWN.net · 实时热榜

3分钟前更新
  • 01
    GDB 18.1 released
    Version 18.1 of the GDB interactive debugger has been released. Changes include new commands to manipulate the environment of the subprocess, the ability to save the command history to a file, support for a couple of new targets, several Python API additions, and more. See the NEWS file for the complete list.corbet
  • 02
    [$] How KDE got funding to add enterprise features
    The Sovereign Tech Agency (STA) is investing nearly €1.3 million in KDE through 2027. At Akademy 2026 in Graz, Austria, Nate Graham and Kevin Ottens, two of the contributors who helped bring in the investment, explained how the funding was secured, provided tips on how projects should approach organizations like STA, and talked about how that money will be improving KDE for everyone. In addition to keeping the community informed about the work, the pair hoped to pass on what they have learned tojzb
  • 03
    Two stable kernels for Friday
    Greg Kroah-Hartman has announced the release of the 7.2.8 and 6.18.54 stable kernels. Each contains a number of important fixes throughout the tree; users are advised to upgrade.jzb
  • 04
    A summary from the 2026 Git Contributors' Summit
    Johannes Schindelin has posted a detailed summary of the discussions held at the 2026 Git Contributors' Summit. Topics covered include Git 3.0, security process, documentation, the pluggable object database, use of LLMs, and more.corbet
  • 05
    Security updates for Friday
    Security updates have been issued by AlmaLinux (kernel, kernel-rt, perl-DBI:1.641, and unbound), Debian (jq, libreoffice, openssl, and redis), Fedora (389-ds-base, bcm283x-firmware, cockpit, flatpak-builder, mingw-gdk-pixbuf, openssl3, pcs, rust-cryptoki, squid, uboot-tools, and webkitgtk), Mageia (fuse3, perl-Net-DNS, python-gitpython, python-webob, thunderbird, thunderbird-l10n, and unbound), Oracle (postgresql:12, postgresql:15, postgresql:16, and skopeo), Slackware (php), SUSE (alloy, amazonjzb
  • 06
    F-Droid 2.0: A new chapter for Android freedom
    The F-Droid project has announced the release of F-Droid 2.0, which is a complete redesign of the official app. Notable changes in the release include making it easier to discover and install applications, more useful app categories, improved search, and much more. For more than a decade, F-Droid has helped people discover and install free and open source Android apps. F-Droid 2.0 builds on that foundation with a modern interface, better app discovery, improved search, and a simpler experience tjzb
  • 07
    Research into file-notification attacks on Linux
    Sudheendra Raghav Neela, a member of a group of researchers from Graz University of Technology , has announced the release of research into file-notification attacks that would allow spying on user activity on Android, Linux, macOS, and Windows. The group has published a paper with details on the research as well as a web site with demonstrations of the vulnerabilities. On Linux, an attacker can use inotifywatch to monitor a directory to conduct an inter-keystroke timing attack—even if they do njzb
  • 08
    [$] Listening to the radio with Rust
    Many of the transmissions sent over the radio spectrum can be decoded with a relatively cheap hardware dongle. Thomas Eckert presented at RustConf 2026 in Montreal about his hobby: decoding radio transmissions with Rust. In his presentation, he covered all of the math necessary to get started with software-defined radio , and gave demonstrations of listening to AM and FM radio, as well as decoding transmissions from aircraft transponders. His slides and example code are available on GitHub.daroc
  • 09
    The Kernel Report 2026 edition
    After a two-year hiatus, LWN's Jonathan Corbet presented an updated edition of his Kernel Report at the Kernel Recipes conference. Corbet looked at what is happening in the kernel community, how it's dealing with a period of accelerated change, and where things might go in the future. Video of the talk is available on YouTube for those who'd like to tune in.jzb
  • 10
    Security updates for Thursday
    Security updates have been issued by AlmaLinux (buildah, containernetworking-plugins, firefox, kernel, kernel-rt, openexr, perl-DBI, podman, postgresql, postgresql16, postgresql:15, runc, skopeo, and tar), Debian (libdatetime-timezone-perl, tzdata, xdg-dbus-proxy, and znc), Fedora (chromium, evolution, evolution-data-server, evolution-ews, kernel, libheif, mingw-pcre2, nginx-mod-modsecurity, unbound, and webkitgtk), Mageia (borgbackup, coreutils, firefox, nss, kbd, libnfs, libwebsockets, perl-URjzb
  • 11
    [$] LWN.net Weekly Edition for September 24, 2026
    Inside this week's LWN.net Weekly Edition: Front : Git 2.56; gccrs; NetBSD and compat_linux; io_uring; Desktop UX. Briefs : WordPress vulnerability; Radicle vulnerability; Systemtap 5.6; GNOME 51; Systemd v262; Quotes; ... Announcements : Newsletters, conferences, security updates, patches, and more.corbet
  • 12
    [$] Ideas on modernizing the open-source desktop
    Scott Jenson has been working on user interfaces (UIs) and user experience (UX) for many years at Apple, Google, and other companies. Now, he's trying to convince open-source projects to experiment more and drive the desktop beyond the age-old " windows, icons, menus, pointer " (WIMP) model. At Akademy 2026 , KDE's annual developer conference, he shared his complaints and ideas in a talk aimed at convincing those in attendance to take the lead on desktop design.jzb
  • 13
    Systemd v262 released
    Systemd v262 has been released. Some of the notable new features include the ability to build systemd as a single statically linked binary for small containers, support for the kernel coredump socket protocol introduced with Linux 6.17, addition of OpenSSL 4 support, and many other changes. See the release notes for a full list of changes.jzb
  • 14
    Critical security vulnerabilities in the Radicle network protocol
    The Radicle peer-to-peer code-collaboration project has disclosed two critical vulnerabilities in the network protocol used by Radicle nodes. The first flaw is that the network protocol used by Radicle " does not give the confidentiality it was expected to give ", which allows anyone who can observe the network between two nodes to read the data exchanged. The second is that peer authentication is broken and allows impersonation, so an attacker can spoof their Node ID and read private repositorijzb
  • 15
    Critical WordPress RCE vulnerability announced
    A critical vulnerability has been discovered in WordPress 's get_page_template() function for page-template resolution that could allow remote-code execution (RCE) by an unauthenticated attacker, in some limited circumstances. The project has provided an update for the most recent branch of WordPress, as well as backports of the fix for branches back to 4.7 . See the vulnerability report for the conditions required for an RCE attack to be successful. The vulnerability also affects the ClassicPrejzb
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