AI 门道 · AI 资讯 · 学习中心 · 模型与平台 · 工具导航

AI门道｜看懂 AI，用出门道。

AI 门道面向独立创作者，提供 AI 资讯、工具导航、知识教程与词元预算工具。

AI 实时资讯

学习与实践

  • Quoting Prompting Claude Fable 5.2

    Claude Fable 5.2 responds closely to what the prompt says about who owns the code it works on. In Anthropic’s testing on small coding tasks, when a prompt said nothing about ownership or presented the…

  • llm-anthropic 0.31

    Release: llm-anthropic 0.31 Anthropic released Fable 5.2 and Mythos 5.2 today. I've added them to llm-anthropic , so you can now run prompts through Fable 5.2 like this: uv tool install llm llm instal…

  • 让炼丹更科学一些（十一）：无调度学习率新思路

    前面的多篇文章中，我们已经表明了学习率调度对终点收敛的重要性。与此同时，“无调度（Schedule-Free）学习率”一直也是训练人的理想追求：不预设学习率曲线，也不预设训练步数，训练过程随时终...

  • llm 0.37.1

    Release: llm 0.37.1 A bugfix release for LLM , with contributions from Steven Huang , Tao An , Arjan Mossel , and XQ . Tags: llm

  • datasette-agent 0.5a0

    Release: datasette-agent 0.5a0 This is a significant release of Datasette Agent . The most notable new feature is called the panel - it lets the agent chat open and then sit next to pages from Dataset…

  • datasette-llm 0.1a10

    Release: datasette-llm 0.1a10 Fixes a bug that affected my agent.datasette.io demo site, where the set of available LLM models was not correctly restricted to just those in the configuration. Tags: da…

  • llm 0.37

    Release: llm 0.37 There's a lot of stuff in this new release of my LLM CLI tool and Python library, representing around two months of accumulated features and fixes. I added gpt-6.1-sol and gpt-6.1-te…

  • datasette-llm 0.1a9

    Release: datasette-llm 0.1a9 I have some big upgrades to Datasette Agent in the pipeline, but in the meantime here's a tiny user experience improvement to the underlying datasette-llm plugin. It now p…

  • Building a multiplayer game world with autonomous Fable-class models

    How I Built the Most AI-Pilled Company in the World Don't be put off by the clickbait headline, this piece by Jacob Sansbury describing how they are building Spawn is a fascinating exploration of the …

  • DeepSeek 养鱼入门：DSH 桌宠怎么装，没显示时怎么排查

    按桌面版与 Web 版分别整理 dsh-pet 安装入口，附首次验收步骤、版本记录和常见失败排查，避免把插件装进错误的运行环境。

  • DeepSeek 长期养鱼怎么做？记忆、主动聊天和 Token 费用管理

    用一份可检查的偏好档案建立长期体验，区分本地触发与模型调用，附三天试运行方案、费用计算口径和可复制的记忆整理模板。

  • DeepSeek 大肥鱼是什么？从聊天人格到桌面宠物，读懂 AI 养鱼玩法

    看到“大肥鱼”“养鱼”“DSH”却不知道从哪开始？分清官方框架、社区人格和桌宠插件，再按自己的需求选择聊天、桌面陪伴或自动化路线。

  • 从参数对称性到有效学习率

    近来，越来越多的论文或者博客，开始基于参数的对称性来分析和指导模型的优化，比如Hyperball、HyperTransfer、ELR等，它们总体都是围绕着$\ell(\boldsymbol{w}...

  • 用 AI 制定学习计划：把“每天学两小时”变成可验收的练习

    从一个明确成果出发，用诊断题、短练习、反馈和复测制定个人学习计划，避免只收集资料和照抄答案。

  • AI 回答是真是假？普通用户核验数字、引用和结论的实用流程

    从一句可验证的主张入手，核对原始来源、日期、统计口径和引用范围，避免把带链接的回答直接当成事实。

  • AI 越聊越跑偏怎么办？多轮对话整理与新会话交接指南

    用事实清单、决策记录和交接摘要减少反复解释，区分对话背景与已确认要求，适合写方案、改文案和做长期项目。

  • 用 AI 总结长文章：怎样避免漏掉条件、数字和反对意见

    把摘要拆成提取、归纳和对照三个步骤，用来源编号保留证据，适合课程资料、方案和公开报告的阅读整理。

  • AI 会议纪要怎么做？从零散记录到可执行待办的整理方法

    用一段虚构会议记录练习区分决定、建议和待确认事项，保留负责人及截止时间，避免 AI 替团队做承诺。

  • AI 提示词怎么写？普通人可直接上手的六要素练习

    把“帮我写一下”变成明确任务：用读书分享通知练习目标、对象、材料、约束、格式和验收，附修改前后对比及可复制模板。

  • Donating Agent Skills to the Agentic AI Foundation

    Donating Agent Skills to the Agentic AI Foundation News today from Anthropic: We’re committed to keeping Agent Skills open, community-driven, and vendor-neutral. Today, we are donating the standard to…

  • Battle Drones - a vibe coded RTS with audio-based multiplayer

    My latest vibe coded game experiment is a Command & Conquer style RTS (Real Time Strategy) game called Battle Drones ( play it here ). Unlike my previous experiments , this one is actually fun to play…

  • 连续化 Softmax Attention

    Softmax Attention想必大家已经不陌生，它是定义在有限向量集合下的一个加权混合算子。近日，论文《Continuum Attention》提出了一个很有意思的想法——将注意力连续化。...

  • llm-echo 0.5

    Release: llm-echo 0.5 I'm working towards a new LLM release, and testing PRs caused me to revisit the llm-echo debug plugin. This plugin provides a "model" which doesn't involve an LLM at all - it jus…

  • llm-tools-datasette 0.1.1

    Release: llm-tools-datasette 0.1.1 When I switched LLM over to httpx2 in LLM 0.33 I forget that this would break any plugins which used httpx without directly depending on it. llm-tools-datasette was …

  • Profiling web-edit with Python 3.15: flame graphs and source heatmaps

    Research: Profiling web-edit with Python 3.15: flame graphs and source heatmaps I put the new Python 3.15 profiler through its paces by having GPT-6 Astra Extra High in Codex run it against my new web…

  • llm-meta-ai 0.2

    Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores M…

  • llm-gemini 0.35

    Release: llm-gemini 0.35 I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan. Here are the l…

  • AI 生成的 3D 模型怎么放进游戏？Blender 清理与导入验收教程

    从一件静态道具开始，检查尺寸、原点、法线、面数、材质和碰撞，把“看起来不错”的 AI 模型整理成能在游戏场景使用的资产。附交付清单、对比测试和失败处理方法。

  • 让 AI 修 Godot 报错：从最小复现到回归验收的完整流程

    遇到节点为空、信号触发两次或重开后状态错误时，怎样给 AI 提供有效证据？用一个按钮计分的小项目练习复现、定位、最小修改和回归验收，附可直接复制的排错模板。

  • 如何让GDN2更稳定一些？

    我们知道，DeltaNet的核心是$\boldsymbol{I}-\eta\boldsymbol{k}\boldsymbol{k}^{\top}$形式的更新矩阵（参考《线性注意力简史：从模仿、创...

  • llm-openai-decisions 0.1a0

    Release: llm-openai-decisions 0.1a0 OpenAI released their new Jev-style Decisions API , as previously announced at last week's DevDay. Since I already have an llm-typesafe plugin for talking to Jev, I…

  • OpenAI “rogue” agent activities found on Wikimedia projects

    OpenAI “rogue” agent activities found on Wikimedia projects Given how tempting a target wikis are for rogue agent swarms, it's not a huge surprise that Wikipedia found evidence of that activity once t…

  • EmbeddingGemma 2

    My comment on EmbeddingGemma 2 — Hacker News. I really appreciate that EmbeddingGemma 2 is under the Apache 2.0 license. For embedding models in particular, I don't think it makes sense to use a…

  • llm-mistral 0.16

    Release: llm-mistral 0.16 Adds support for reasoning models, such as the newly released Mistral Large 4 . Tags: llm , mistral , llm-reasoning

  • llm-anthropic 0.30

    Release: llm-anthropic 0.30 In addition to Claude Sonnet 5.5, this release adds the ability to run llm anthropic refresh to refresh the list of Anthropic models directly from their API - which means I…

  • 通过微扰分析求解等式约束优化问题

    对于无约束的光滑函数来说，求极值的通用方法自然是求导（梯度）并让导数等于零了。那带等式约束的极值问题呢？很多读者应该也不陌生，标准方法是拉格朗日乘子法。然而，拉格朗日乘子总给人一种纯粹的代数技巧...

  • Quoting Muse AI Agent

    Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and nobody came down. He left angry at 9:38 and left a negative rating. Worse, …

  • AI 写完代码怎么验收？从复现 Bug 到回归测试的完整流程

    用一个订单金额函数演练 AI 编程验收：先写输入与预期，再运行正常、边界和异常用例，区分测试通过、构建成功与真实可用，留下可复查的交付记录。

  • 知识库答非所问怎么办？用小型评测集排查 RAG 的检索与引用

    从可回答、不可回答、旧版本冲突和越权问题四类样本入手，逐层记录检索片段、答案与引用。附评测记录模板、故障定位顺序和上线验收办法。

  • AI 输出 JSON 总报错？用 Python 做字段校验与失败重试

    把 AI 返回的文本变成可靠数据：从工单分类示例出发，建立字段约定，校验类型与范围，拒绝重复键，并区分格式错误与业务错误。附可在本地运行的 Python 示例。

  • SF October 14th: A Birds of a Feather Session on Agentic Engineering

    SF October 14th: A Birds of a Feather Session on Agentic Engineering I'm hosting an evening event with Jesse Vincent in San Francisco on Wednesday 14th October for people who are building weird and in…

  • llm-anthropic 0.29

    Release: llm-anthropic 0.29 Adds support for Claude Opus 5.5 : llm -m claude-opus-5.5 "prompt goes here" Tags: llm , anthropic

  • llm 0.36

    Release: llm 0.36 New OpenAI models: gpt-6-sol for GPT-6 Sol and gpt-6-luna for GPT-6 Luna . #1702 Model plugins can now declare supports_conversation = False for models that only accept single-turn p…

  • llm-typesafe 0.1a0

    Release: llm-typesafe 0.1a0 I built this new plugin for LLM to add support for TypeSafe AI's new Jev model . Install it like this: llm install llm-typesafe Then set an API key ( get one here , the wai…

  • Jev introduces a new shape of LLM - System One, aka Decision Models

    Last week TypeSafe AI unveiled Jev , their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for …

  • llm-keys-ui 0.1

    Release: llm-keys-ui 0.1 This plugin solves a very specific problem. I've started using Codex Remote to run coding agents on various machines while controlling them from my phone. Sometimes I use thos…

  • An Overview of Deep Learning for Curious People

    (The post was originated from my talk for WiMLDS x Fintech meetup hosted by Affirm.) I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol in 20…

  • 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…

  • 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 t…

  • 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 r…

  • 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 P…

  • 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 sim…

  • 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 t…

  • 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…

  • 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: A…

  • 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 w…

  • 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…

  • LayaAir 中文资料：编辑器入门与AI协同文档

    官方中文文档索引，区分基础工程搭建和AI工具接入；这是一份资料导读，不是本站完整项目教程。

  • Web 3D 官方资料：Three.js、Babylon.js 与 PlayCanvas

    资料索引，帮助定位框架手册与开发入口；不再把选型提纲标为完整3D实战。

  • Cocos Creator + AI 实战：制作30秒点击计分游戏

    中文逐步搭建：创建Canvas与Game节点、挂载完整TypeScript组件、生成目标、处理点击、计分倒计时和重开。

  • Phaser + AI 实战：制作30秒接金币H5游戏

    从一个HTML页面到可玩的接金币游戏：键盘与触摸控制、生成与回收、计分倒计时、结束重开，附源码和在线试玩。

  • Godot + AI 实战：从零做一个可重开的躲避游戏

    中文逐步实战：建工程、画角色、实现移动与敌人、检测碰撞、计时并重开。附可导入工程与完整GDScript。

  • 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 …

  • 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 …

  • 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 ponde…

  • 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 o…

  • 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 Lea…

  • 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…

  • 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: Th…

  • 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 f…

  • 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…

  • 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 ap…

  • 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 2021-0…

  • 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…

  • 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…

  • 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…

  • 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 Neur…

  • 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 …

  • 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 encod…

  • 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…

  • 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 tr…

  • 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 — …

  • Token费用怎么算：输入、输出与推理预算

    先用参考编码统计输入词元，再设置预计输出和推理用量，最后选择有明确渠道报价的模型。估算用于比较预算，实际账单以服务商记录为准。

  • AI人格怎么使用：临时对话与长期默认

    人格提示词调整AI的表达风格，不改变模型本身的知识、权限或事实判断。先选一种风格试用，再决定是否保存为长期偏好。

  • AI眼里的你怎么测？豆包、DeepSeek 与 Codex 使用指南

    AI眼里的你根据你与AI互动的方式生成趣味画像和插画封面。可以选择电脑端Codex，或在豆包、DeepSeek中完成行为统计后回来领取。结果是趣味解读，不是心理诊断。

  • OpenAI agents attacked RubyGems back in May

    OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx - three of the four authors of the report on the agent attac…

  • Self-generated prompt injections in compaction summaries

    Self-generated prompt injections in compaction summaries In Our framework for reporting model misalignment OpenAI provide "six reports on unexpected or concerning model behavior we’ve observed in the …

  • How To Write With An LLM

    How To Write With An LLM Thomas Ptacek on using LLMs as copyeditors, not as writing assistants: Rule Number One: You may not use a single word an LLM suggests to you. [...] I think that as a form of i…

  • 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 empiri…

  • 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 p…

  • 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 …

  • 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 mode…

  • 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 …

  • 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 ca…

  • 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.…

  • 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 (C…

  • 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 siz…

  • 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 d…

  • 解构Scaling Law：优化、架构、数据的三重奏

    训练一个大型的神经网络，最终效果会受到非常多因素的影响，换个优化器，换个模型架构，或者换一个训练集，结果都可能截然不同。在工程实践中，我们将调试这些因素的经验结果，戏称为“炼丹”。但如何从经验上...

  • 简单谈谈K3的MoE和Attention

    上个月，我们发布了迄今为止最大的开源模型K3。作为K2的继任者，K3并不是一次从零开始的重新设计，而是沿着我们过去一系列工作自然演化而来，并融合了我们对效果、效率、稳定性的一些最新理解和改进。可...

  • 除了交叉熵，LM Loss还有什么选择？

    一直以来，交叉熵（Cross Entropy）都是LLM预训练和微调的标准损失函数。那这个“标准”可以改吗？如果想改，那又有哪些选择呢？改完之后又会带来什么影响呢？可能很多读者从未认真推敲过这些...

  • 动量的新理解：逼近特征层面的梯度下降

    一个以动量为状态变量的优化器，基本形式如下：\begin{equation}\begin{aligned}\boldsymbol{M}_t =&\, \beta \boldsymbol{M}_{...

  • 让炼丹更科学一些（八）：多阶段训练的学习率

    上篇文章《让炼丹更科学一些（七）：步长调度与权重平均》我们简单介绍了无调度（Schedule-Free）学习率的工作，它试图通过某种权重平均来替代学习率调度，实现常数学习率就可以训出最优模型的效...

  • 让炼丹更科学一些（九）：经典自适应梯度算法

    本系列前面八篇文章，都是在围绕SGD及其学习率讨论。而从本文开始，我们将正式进入自适应梯度算法的世界。可以说，现在所有的自适应梯度算法，都有一个共同的源头，那就是2011年的经典之作《Adapt...

  • 让炼丹更科学一些（十）：单调性假设的拆与补

    上文《让炼丹更科学一些（九）：经典自适应梯度算法》中，我们为形如$\boldsymbol{\theta}_{t+1} = \boldsymbol{\theta}_t - \eta_t \bold...

  • DaVinci Resolve 官方视频：剪辑、调色与声音

    下载原站练习素材，完成剪辑、基础调色和人声检查，导出测试片。

  • Runway Academy：从生成片段到创作流程

    选择一个与项目匹配的视频教程，完成一个短镜头并保存输入和配置。

  • Runway 官方视频：用 Chat Mode 组织创作

    用简短描述生成一组候选，比较反馈修改前后的差异。

  • Unreal Engine：第一个小时与第一个小游戏

    完成安装、基础场景与打包流程，检查分享后的工程能否运行。

  • Unity Essentials：建立第一个交互作品

    完成编辑器基础、场景搭建与交互任务，运行并检查自己的小作品。

  • Blender 官方基础视频：从界面到第一个对象

    跟随基础操作完成一个简单物体，保存工程并导出预览图。

  • 把一段故事拆成可生成、可剪辑的镜头表

    把“小夜灯的一晚”写成六镜头拍摄表，每镜只安排一个动作，并预设生成失败时的替代方案。

  • 给游戏 NPC 接入大模型前，先划定行为边界

    为商店NPC设计可验证的对话接口：模型负责台词，库存、金币与交易结果由游戏代码决定。

  • 怎样用自己的任务比较两个 AI 模型

    用20条真实任务搭一份小型评测集，识别编造、格式失败和版本退化，而非凭榜单选模型。

  • 给 AI 看图之前，怎样描述你的问题

    用一张产品界面截图练习看图提问：限定可见证据、坐标区域和输出格式，减少凭空补细节。

  • 用小型资料库搭建可核对来源的问答

    用三份产品资料完成一次可核对来源的问答实验，定位“找错资料”和“看见资料仍答错”的区别。

  • AI 配音到成片：一份可执行的检查清单

    把一段约20秒旁白做成可剪辑素材：分句、校读、多音字、停顿、响度和字幕逐项检查。

  • 本地大模型上手：先做一轮小规模验证

    先估内存，再用同一组请求验证本地模型的速度与质量；附 Ollama 用量字段和测速公式。

  • 为游戏资产建立一份可复用的视觉规范

    以背包里的钥匙、药水和金币为例，建立能真正复用的素材规范与三张资产验收表。

  • 把重复工作变成可检查的自动化流程

    从“反馈文本转待审任务”搭一条可检查工作流，先验证字段、去重与失败恢复，再接自动触发。

  • 怎样理解节点式 AI 图像工作流？

    跑通官方基础文生图工作流，再固定种子做单变量对照，学习读连接、修依赖和保存可复用流程。

  • 从想法到一个可玩的 2D 游戏原型

    先交付移动、收集与重开三个可检查的玩法，再衔接带源码的Godot、Cocos或Phaser完整实战。

  • 把一张图片做成游戏里的 3D 道具

    用一个桌面木箱练习图转3D：检查轮廓、比例、法线、材质与目标引擎表现，保留可验收交付清单。

  • 用已有图片制作一支 30 秒短片

    围绕一支30秒桌面小夜灯短片，完成六镜分镜、参考素材、逐镜生成、配音与最终导出检查。

  • 模型、平台和 API，有什么区别？

    以“批量整理用户反馈”为例，决定用聊天网页、工作流还是 API，避免买错入口和套餐。

  • 推理强度越高，就一定越合适吗？

    用可复测的小实验选择推理强度：先定义正确答案，再比较通过率、等待时间与单次成功成本。

  • Token 是什么？为什么不等于字数？

    用一笔多轮对话账单，分清字数、上下文、输入输出和缓存；附可复制的用量记录模板。

  • 30 秒视频，为什么会生成 90 秒素材？

    从六镜头短片反推素材量、重试上限与现金预算，附按次收费和成功率的算例。

模型与工具

  • Meshy

    将文本或图片转换成 3D 资产，提供纹理相关功能。

  • Tripo

    提供文本、图片到 3D 的生成流程，以及绑定相关能力。

  • Scenario

    面向创作资产制作的 AI 平台，支持风格控制与工作流。

  • Ludo

    为游戏研究、概念设计和素材制作提供辅助。

  • 即梦 AI

    通过文本或图片创作视频片段。

  • Runway

    通过模型与多种创作入口完成生成式视频任务。

  • ElevenLabs

    通过文字描述生成音效，用于视频和游戏等作品。

  • Blender

    用于网格编辑、UV 与素材修整的 3D 创作软件。

  • Godot

    用于创建游戏工程和验证游戏玩法的引擎。

  • ComfyUI

    使用节点组织生成模型的输入、采样与输出过程。

  • n8n

    连接数据与服务，将重复工作组织成自动化流程。

  • Claude

    Anthropic 的对话与任务助手，可用于整理需求、分析材料和辅助编程。

  • DeepSeek

    深度求索提供的对话助手和开发者模型入口。

  • Gemini / Google AI Studio

    Google 的多模态模型与开发者实验入口，适合验证文本和多媒体任务。

  • Kimi

    月之暗面的知识工作和编程助手，可辅助资料处理与任务拆解。

  • Mistral / Le Chat

    Mistral 提供助手、模型和开发者服务。

  • Midjourney

    围绕图像和视频生成的创作平台，可用于视觉概念探索。

  • Recraft

    面向视觉设计的生成平台，支持图像及风格化创作。

  • Ideogram

    集成图像模型与创作应用的平台，适合海报与视觉素材探索。

  • FLUX / Black Forest Labs

    Black Forest Labs 的图像模型入口，可用于生成和编辑视觉素材。

  • Adobe Firefly

    Adobe 的生成式创作工具，可衔接现有设计和视频流程。

  • Suno

    通过描述进行音乐创作的平台，可用于探索配乐方向。

  • Dify

    以可视化方式组织模型、知识与应用工作流。

  • Hugging Face

    用于发现模型、数据集及演示应用的社区与基础设施入口。

  • Ollama

    提供在本地运行模型的工具及模型库。

  • LM Studio

    用于管理和运行本地模型的桌面工具。

  • Unreal Engine

    用于实时 3D 游戏与视觉内容制作，可集成 AI 生成资产。

  • Unity

    游戏与交互应用开发引擎，适合把生成素材组装成可运行作品。

  • DaVinci Resolve

    集剪辑、调色、音频与合成为一体的视频后期工具。

  • Qwen / 通义千问

    以 Qwen 系列模型为基础的助手，支持聊天以及语音和图片相关交互。

  • 豆包 / 火山引擎

    豆包模型的开发者入口，提供通用多模态理解和应用开发能力。

  • GLM / 智谱开放平台

    智谱的模型服务平台，提供模型推理、精调、评测及应用构建入口。

  • Cocos Creator

    具备场景编辑器与组件工作流的跨平台游戏引擎，适合2D、3D和小游戏项目。

  • LayaAir

    提供引擎、IDE和发布流程，并有官方AI协同开发与MCP说明。

  • PixiJS

    专注网页2D图形、精灵和交互渲染，适合轻量互动页面与自定义游戏画面。

  • Three.js

    用JavaScript构建网页3D场景，适合模型查看器、互动展示与定制3D画面。

  • Phaser

    面向浏览器2D游戏的JavaScript / TypeScript框架，带场景、资源和物理等游戏功能。

  • Babylon.js

    面向Web的3D引擎及工具生态，可构建交互场景、模型展示和游戏原型。

  • PlayCanvas

    开源Web游戏引擎配合在线编辑器，适合协作搭建3D互动项目。

浏览全部大模型 · 实用工具