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首批内容围绕 5tldr 团队真实职能和高价值用户技能来组织。最近审阅于 March 13, 2026.
Product Discovery & Activation
Learn how to find real demand, avoid fake discovery, and shape activation work around user jobs instead of feature lists.
Product Design & UX Systems
Learn how to think in flows, systems, and guidance patterns so your interface reduces friction instead of adding visual polish to a weak journey.
SEO & Content Quality
Learn how Google actually frames crawlability, search quality, and people-first content so your SEO work is grounded in durable rules instead of short-term hacks.
Learning Systems & Knowledge Work
Learn how to study from long-form sources more effectively by turning reading into feedback loops, deliberate review, and saved knowledge rather than passive consumption.
AI Video Fundamentals
Learn the current AI video model landscape, the core prompt grammar behind usable generations, and the fastest path from first prompt to first finished short clip.
Cinematography & Visual Language for AI Video
Learn the visual grammar behind stronger AI video prompts: shot size, camera angle, camera movement, composition, and lighting choices that change the emotional read of a scene.
Advanced AI Video Workflows & Consistency
Move from single generations to pipeline thinking: image references, scene continuity, video-to-video refinement, and multi-tool workflows that preserve identity and motion across shots.
Commercial AI Video Production & Legal Readiness
Turn AI video from an interesting creative toy into a client-ready production workflow by learning rights, platform terms, labeling duties, and the operational discipline needed for commercial delivery.
如何结合 5tldr 使用这个学习中心
重点不是囤链接，而是把好来源变成可复用的知识。
1. 打开原文
先看原文，理解作者是如何组织问题的。
2. 做摘要
把文章放进 5tldr，生成关键点或学习包。
3. 保存有用内容
把重要摘要存进 Library，后续还能回来复习。
4. 回顾与对比
回到保存的笔记，对比来源，把它们变成你自己的理解。
Product Discovery & Activation
Learn how to find real demand, avoid fake discovery, and shape activation work around user jobs instead of feature lists.
Most product teams do not fail because they lack ideas. They fail because they ship unvalidated ideas, skip discovery, or never connect onboarding to the real user job.
你应该从这条路径得到什么
两周计划
Week 1: Map the job and the failure mode
Read the anti-patterns piece first, then write one sentence for the user job your product serves and one sentence for the biggest failure mode in the current flow.
Week 2: Prototype the risky step
Use the prototype and discovery readings to identify the riskiest moment in your flow, then design a low-cost way to test it before shipping full code.
Product Discovery: Pitfalls and Anti-Patterns
A blunt breakdown of how discovery fails: stakeholder opinion replaces user evidence, delivery pressure crushes learning, and teams confuse roadmap output with real product validation.
为什么从这里开始: This is the fastest reset if you want product work to start from risk reduction and evidence instead of backlog momentum.
The Purpose of Prototypes
Explains that prototypes are decision tools. Their job is to answer a specific product risk, not to become a high-fidelity deliverable for its own sake.
为什么从这里开始: Helps product and design stop wasting time on prototypes that look finished but do not answer the real question.
Discovery
Frames discovery as a disciplined practice for testing value, usability, feasibility, and viability before committing the team to build work.
为什么从这里开始: This ties discovery back to the four risks that actually matter when deciding whether a product idea is worth shipping.
Product Design & UX Systems
Learn how to think in flows, systems, and guidance patterns so your interface reduces friction instead of adding visual polish to a weak journey.
Many products look modern but still feel confusing because the team never mapped the flow, documented reusable patterns, or decided when help should appear.
你应该从这条路径得到什么
两周计划
Week 1: Draw the current journey
Start with the user-flow reading, then map your current flow from first action to success, including errors, retries, and dead ends.
Week 2: Systematize one repeated pattern
Use the design-system and onboarding guidance readings to pick one repeated pattern, such as empty states or help states, and define a consistent rule for it.
What Is a User Flow?
Breaks down user flows as a way to visualize steps, decisions, and branching moments before you jump into individual screen design.
为什么从这里开始: This is the fastest way to stop designing isolated screens and start fixing the journey that connects them.
Design Systems
Explains why design systems are more than a component library: they combine reusable patterns, rules, and documentation that keep product work faster and more consistent.
为什么从这里开始: Useful when your product has multiple flows and repeated UI work that should become a consistent system instead of a page-by-page patchwork.
Onboarding Tutorials vs. Contextual Help
Shows when full tutorials help and when contextual guidance is better, especially once users are already inside a product and need just-in-time support.
为什么从这里开始: This matters for any product that wants to reduce abandonment without forcing users through heavy education flows.
SEO & Content Quality
Learn how Google actually frames crawlability, search quality, and people-first content so your SEO work is grounded in durable rules instead of short-term hacks.
A lot of SEO work looks busy but does not change outcomes because it ignores technical basics, intent matching, and the quality signals Google keeps repeating.
你应该从这条路径得到什么
两周计划
Week 1: Audit the basics
Read Search Essentials and the Starter Guide, then review one section of your site for crawlability, titles, internal links, and thin pages.
Week 2: Improve one content cluster
Use the people-first content guidance to deepen one cluster instead of publishing more thin pages. Focus on intent, proof, and readability.
Google Search Essentials
The official baseline on technical requirements, spam policies, and key practices that determine whether your site can perform in Google Search.
为什么从这里开始: This is the floor. If you ignore it, everything else in SEO becomes noise.
Creating Helpful, Reliable, People-First Content
Google's clearest public guidance on what useful content should do: serve a real audience, show effort and expertise, and avoid being created primarily for search engines.
为什么从这里开始: This is the right foundation if you want content that helps conversion, trust, and AdSense review instead of just adding more pages.
SEO Starter Guide: The Basics
A practical guide to crawlable structure, titles, snippets, navigation, and content presentation for teams that need search basics without mythology.
为什么从这里开始: Good for turning SEO from vague advice into specific page-level fixes your team can actually ship.
Learning Systems & Knowledge Work
Learn how to study from long-form sources more effectively by turning reading into feedback loops, deliberate review, and saved knowledge rather than passive consumption.
Most people do not fail because they read too little. They fail because they never convert reading into retrieval, comparison, and repeatable notes.
你应该从这条路径得到什么
两周计划
Week 1: Read, summarize, save
Read one article a day, summarize it with 5tldr, save the useful ones, and write down one pattern you would miss if you only skimmed.
Week 2: Compare and recall
Reopen your saved summaries, compare two related sources, and write one short memo from memory before checking your notes.
The Hard Thing About Learning From Experience
Argues that experience does not automatically create learning. Without reflection and feedback, people can repeat the same weak patterns for years.
为什么从这里开始: This is the mental reset behind every good summary, note-taking, and review workflow.
The Problems With Deliberate Practice
Explains why classic deliberate practice works best in well-defined domains and needs adaptation in messy knowledge work where feedback is slower and less obvious.
为什么从这里开始: Useful for building learning plans that fit real-world work instead of classroom-style drills.
Teaching Tech Together
Shows how complex ideas become more learnable when teaching, examples, and practice are structured around concrete tasks rather than abstract explanation alone.
为什么从这里开始: This is highly relevant if you want your notes, study guides, and reviews to become something you can actually use later.
AI Video Fundamentals
Learn the current AI video model landscape, the core prompt grammar behind usable generations, and the fastest path from first prompt to first finished short clip.
AI video is moving too fast for vague tool hype to be useful. You need a grounded starting point: what each model is good at, how prompt structure changes output quality, and how to iterate without wasting credits.
你应该从这条路径得到什么
两周计划
Week 1: Benchmark the model landscape
Read the Sora, Runway, Veo, and Flow materials, then run the same 3 prompts across at least 3 tools. Revisit your Day 1 notes on Day 3 and Day 7 to compare how each model handles motion, adherence, and editability.
Week 2: Build your first repeatable short-form workflow
Use the prompting guides to create 5 short clips with one fixed structure: subject, action, environment, style, camera, and audio intent. Reopen your best and worst prompt on Day 10 and Day 14 to rewrite them from memory before refining again.
Creating videos with Sora
OpenAI's practical Sora guide covers the real workflow: generate, iterate, edit, publish, and use storyboards. The prompting section is especially useful because it emphasizes specificity, cadence, realism anchors, and limiting moving parts.
为什么从这里开始: This is one of the clearest current references for how a modern consumer AI video product wants prompts to be structured in practice, not just in theory.
Gen-4 Video Prompting Guide
Runway explains how to build motion-first prompts for Gen-4: start simple, add one variable at a time, and separate subject motion, camera motion, scene motion, and style descriptors. It also explicitly warns against negative prompting and overloading a 5-10 second clip.
为什么从这里开始: This is one of the most concrete prompt frameworks available from a production AI video vendor, and it translates directly into better iteration discipline.
Veo 3.1 Ingredients to Video: More consistency, creativity and control
Google's January 13, 2026 update focuses on character identity consistency, background and object consistency, native 9:16 outputs, and 1080p/4K upscaling. It frames Veo 3.1 as both a mobile-first and production-ready workflow tool.
为什么从这里开始: This is a current baseline for what a serious 2026 model should offer beyond pure novelty: consistency, vertical output, and production fidelity.
Introducing Flow: Google’s AI filmmaking tool designed for Veo
Google positions Flow as a filmmaking surface rather than a single-generation tool. The post highlights camera controls, Scenebuilder, asset management, and reuse of characters and scenes across clips with consistency.
为什么从这里开始: Useful for understanding how the market is shifting from isolated generations toward end-to-end filmmaking workspaces.
Cinematography & Visual Language for AI Video
Learn the visual grammar behind stronger AI video prompts: shot size, camera angle, camera movement, composition, and lighting choices that change the emotional read of a scene.
Most weak AI video prompts fail before the model generates anything. The user never decided what the shot should feel like, how the camera behaves, or what lighting logic supports the mood.
你应该从这条路径得到什么
两周计划
Week 1: Learn the shot grammar
Study shot types, angles, and camera motion, then practice 8 framing patterns and 6 movement prompts. Revisit your Day 1 clips on Day 3 and Day 7 to label what visual choice actually changed the emotional read.
Week 2: Re-light and re-compose the same scene
Take 2-3 scenes from Week 1 and regenerate them with different framing, rule-of-thirds positioning, and white-balance / lighting intent. End the week by mixing techniques instead of isolating them one by one.
Different types of shots and camera angles in film
Adobe walks through the core shot vocabulary that filmmakers reuse constantly: wide, medium, close-up, extreme close-up, low angle, high angle, over-the-shoulder, POV, tracking, and Dutch angle. It clearly distinguishes shot size from camera angle.
为什么从这里开始: AI video gets better immediately when your prompt uses film language precisely instead of asking for something 'cinematic' in the abstract.
How to use, and break, the rule of thirds
Adobe explains the rule of thirds as a compositional guideline rather than a rigid law and emphasizes repetition and deliberate practice. It is a useful refresher for making framing choices visible and intentional.
为什么从这里开始: Prompt-level composition control improves when you can describe where the subject sits in frame and why.
Understand white balance in filmmaking
This guide explains white balance, color temperature, and the practical difference between daylight, tungsten, and mixed lighting setups. It also connects on-set choices with post-production correction and stylization.
为什么从这里开始: A lot of 'cinematic' look work is really color temperature discipline. This helps you prompt and review lighting choices more intentionally.
Camera Motion Concepts – Composable AI Camera Control
Luma introduces a system for learning and composing camera moves from minimal examples, with a focus on reliable, reusable motion control. The write-up is especially interesting because it contrasts concepts with heavier LoRA or fine-tuning workflows.
为什么从这里开始: This is a strong bridge between classic cinematography language and AI-native motion control, which is where a lot of current craft is heading.
Advanced AI Video Workflows & Consistency
Move from single generations to pipeline thinking: image references, scene continuity, video-to-video refinement, and multi-tool workflows that preserve identity and motion across shots.
The hard part of AI video is rarely the first good shot. It is getting the second, third, and fourth shot to belong to the same world without rebuilding everything from zero.
你应该从这条路径得到什么
两周计划
Week 1: Build a 3-shot continuity test
Create one character or product subject and carry it across 3 shots using image references or ingredient inputs. Review your continuity failures on Day 3 and Day 7, then re-run only the weakest shot instead of rebuilding the sequence.
Week 2: Add a post-production refinement layer
Take the continuity test into a second-stage workflow: modify video, relight, restyle, or extend a shot without losing performance. Mix tools deliberately so each one has a narrow job.
Creating with Gen-4 Image References
Runway explains how to use one or more reference images to preserve characters, scenes, styles, and objects across generations. The guide includes both simple single-reference prompting and more advanced multi-reference workflows.
为什么从这里开始: This is one of the clearest current references for character and scene consistency without building a custom model.
Controlling Aleph edits with a Reference Image
This workflow combines first-frame editing with reference-guided video generation for tighter object placement and subject continuity. It is a practical example of chaining image and video tooling instead of treating them as separate worlds.
为什么从这里开始: Useful if you want a repeatable image-first-to-video pipeline with more control over where edits land in-frame.
Modify Video: Shoot Once. Shape Infinitely
Luma's Modify Video workflow focuses on preserving the original motion and performance while restyling worlds, props, lighting, and environments. It is a strong example of video-to-video as a post-production layer rather than a novelty effect.
为什么从这里开始: This is directly relevant for creators who want to separate performance capture from visual treatment and keep that distinction clean.
Veo 3.1 Ingredients to Video: More consistency, creativity and control
Google's Veo 3.1 update explicitly emphasizes identity consistency, object consistency, and reusable ingredient-based generation. It is a good complement to Runway's reference system because it frames consistency as a production feature, not just a model benchmark.
为什么从这里开始: Useful for comparing how different vendors now think about continuity, vertical deliverables, and higher-fidelity finishing.
Commercial AI Video Production & Legal Readiness
Turn AI video from an interesting creative toy into a client-ready production workflow by learning rights, platform terms, labeling duties, and the operational discipline needed for commercial delivery.
The creative side is only half the job. Once money, clients, or public distribution enter the picture, output ownership, label requirements, and platform-specific terms become part of the production workflow.
你应该从这条路径得到什么
两周计划
Week 1: Build a commercial delivery checklist
Read the platform terms and legal materials, then map the difference between vendor usage rights, copyrightability, and labeling duties. Recheck your notes on Day 3 and Day 7 by applying them to a mock client brief instead of rereading passively.
Week 2: Run a mock client production
Produce a short campaign concept with a full paper trail: prompt log, source asset notes, platform used, rights assumption, disclosure plan, and delivery package. End with a postmortem on cost, risk, and review steps.
Terms of Use
OpenAI's consumer terms assign output rights to the user, subject to the terms and applicable law. This is the baseline you need before treating Sora output as something you can deliver commercially.
为什么从这里开始: Commercial use starts with the contract layer. You need to know what rights the vendor says you have before you worry about copyright doctrine.
Usage rights
Runway states that users retain ownership of the content they upload and generate, and that there are no non-commercial restrictions from Runway on use of those generations. It also explicitly mentions advertising, film festivals, and monetized uploads.
为什么从这里开始: This is one of the clearest current statements of commercial usage rights from a major AI video vendor.
Copyright and Artificial Intelligence, Part 2: Copyrightability
The U.S. Copyright Office's January 2025 report says generative AI outputs can be protected only where a human author contributed sufficient expressive authorship. Mere prompting alone is not enough by itself.
为什么从这里开始: This is the clearest current U.S. baseline for separating vendor output rights from actual copyrightability.
Code of Practice on marking and labelling of AI-generated content
The Commission's 2026 code-of-practice work supports Article 50 AI Act transparency obligations on marking and labeling AI-generated or manipulated content. It focuses on machine-readable marking and clearer public labeling for professional uses.
为什么从这里开始: If you are doing commercial distribution in Europe, transparency and labeling are not optional clean-up tasks; they are part of delivery readiness.
关于印发《人工智能生成合成内容标识办法》的通知
China's March 14, 2025 measures formalize labeling requirements for AI-generated and synthesized content, including explicit labels, metadata treatment, and platform handling of suspected generated content.
为什么从这里开始: For teams operating in China or distributing into Chinese platforms, labeling and provenance handling need to be designed into the workflow upfront.
How these learning plans are curated
The learning hub exists to help users start, sequence, summarize, and revisit high-quality material. We curate it from external sources, product workflows, and repeated learning needs.
Hands-on workflow coverage
These pages are built from real 5tldr workflows, support questions, and product behavior instead of thin keyword templates.
Updated when the product changes
We refresh guidance when plan rules, supported sources, failure states, and learning workflows change.
Content and ad boundaries stay separate
Educational pages may carry ads. Product workflows, pricing, library, checkout, and paid-user journeys should remain ad-free.