Magic Patterns Updates: Plugin, Design Privacy, Onboarding Tips

How do you make your LLMs and agents watch a video? Well, you can’t! But there’s usually a way around everything. LLMs can read images, and they’re good at understanding them. A video, at its simplest, is just a sequence of frames. So why not break the video down into still images, every 1, 2, 5 seconds, or whatever sampling rate makes sense, and let the LLM look at them? That’s essentially what I’ve been experimenting with. Give an agent a video, extract frames from it, and suddenly it can “watch” it: → Understand what’s happening in the video → Analyze the visual style and layout → Read on-screen text → QA a generated video frame by frame → Reverse-engineer how a video was put together Of course, there are trade-offs. A 20-second video sampled every second means 20 images. A longer video can quickly turn into a lot of images, which means more tokens, and more processing. So the interesting part isn’t just “turn video into images.” It’s figuring out how to sample the right frames, at the right intervals, and give the model enough context to understand the video without feeding it every single frame. I’ve packaged my approach into an open-source Agent Skill for Claude Code that handles the video downloading, frame extraction, sampling, viewing, and cleanup. It can take a YouTube Short, an X clip, or a local video and turn it into something an agent can actually inspect. GitHub: https://lnkd.in/e4yrWkwQ And if you’re interested in this problem, check out the claude-real-video by HUANGCHIHHUNGLeo as well. It’s another really interesting approach to giving Claude access to video understanding.

The README hero GIF is the one part of most CLIs and agent skills that prospective users actually watch. It is also, usually, six months out of date. The reason is simple. Recording a terminal demo is a manual, one-off act. Someone opens a fresh shell, types carefully, hits stop, and drags the file into the repo. When the underlying CLI output changes, no one re-records. So the artifact on the landing page slowly diverges from what the tool actually does. Today we shipped a course that treats that artifact the way we treat the rest of the codebase — scripted, versioned, and reproducible. The deliverable is a ~15-second looping terminal demo, rendered locally at 1200×750 as WebM, GIF, or MP4. You describe what you want to show in one sentence, your coding agent drafts a .tape script, and charmbracelet/vhs renders it deterministically alongside the source that produced it. Concretely, three things it is built for: - An OSS README hero that stays honest through a refactor, because the .tape gets code review the same way a test does - A CLI walkthrough for a blog post or docs page, where the reader sees the first three commands actually run - An agent-skill demo reel that captures the terminal moment your skill is interesting — the part a static screenshot cannot sell The scriptability is the point. Terminal demos have historically lived in the same drawer as marketing screenshots: nobody owns them, nobody updates them, and they rot. Moving them into code lets the tool that changed the output be the same tool that re-renders the demo. For the base workflow the course is free — no credits, no third-party keys. Only the live-AI path, where the demo executes an actual model call, needs a @clawvard/sdk key. If you are an agent-skill author or a CLI maintainer whose landing page is doing more selling than you realized, this is that landing page's motion asset, treated like code. https://lnkd.in/gTxgGXPq

😎I figured out how to run Card Sorting and MaxDiff inside a Pendo guide this week! Turns out I didn't need a new tool to conduct any of this. If your product already runs Pendo, you probably already have what you need for card sorting, MaxDiff, and other lightweight research instruments. The secret is Pendo guide's code block! It lets you paste your own HTML, CSS, and JS into a guide, so it's basically a mini frontend living inside an iframe. You have full control over how it looks. And since Pendo already injects its own tracking agent into that same block, your JS can just call pendo.track() directly. That's the whole data pipeline, responses land right on the Track Events page. Also, you don't need to know how to code for any of this. Claude wrote the card sort and MaxDiff logic for me, and it can write yours too, you bring the study design, describe what you want the interaction to feel like, and let it handle the rest. Took a bit of trial and error to get there, so I turned it into a skill, working card sort and MaxDiff templates already in there, plus a local preview and all the details. Try it and let me know how it goes🤗 : https://lnkd.in/gK525FJU #UXReseach #Pendo #ResearchOps #VibeCoding

**Short-form video scripts:** 60-second demos that teach one OpenClaw concept. Most developers try to explain everything in one video — and lose their audience in the first 10 seconds. The fix? One video = one micro-skill. Here’s the exact template I use for a clean, high-retention OpenClaw demo: 📜 **The Script Formula (60 sec)** • **0–10s** – Hook with outcome: "Today you’ll build a reusable pipeline with OpenClaw’s step chaining — under a minute." • **10–20s** – Visual: show the finished result first, then delete/clear the canvas. • **20–50s** – Do it live: type, execute, narrate. Only 3-4 lines of code max. No tangents. • **50–60s** – Recap and challenge: "Now try it with your own data. Drop a 🧩 if you nailed it." 🎥 Why this works for OpenClaw content: - Short-form rewards clarity, not completeness. - One concept per video trains your audience to trust you’re always quick and useful. - Code demos on mobile feel like a “hack” — it’s addictive to watch. Pro tip: Record vertical, with large font size and a dark theme. Add captions that highlight the OpenClaw method names. If you’re building your OpenClaw library, steal this framework. Start posting one 60‑second script a day and watch your technical authority (and follower count) compound. What’s a concept you’d cover in 60 seconds? 👇 #OpenClaw #DevContent #ShortFormVideo #DeveloperMarketing #AIEngineering Drop "VIBECODINGID" in the comments and I'll DM you the link. #AIConsultant #BusinessAutomation #AIAgents #OpenClaw #RPA

I've been spending time on context and harness engineering, and on one problem in particular: what happens when an agent's context window fills. Most harnesses handle it with compaction. The session summarizes itself, the originals are discarded, and a fresh session starts from the summary. That handoff is lossy by design, and the new session rereads much of what the old one already had. I wanted one continuous session instead: fold the parts of the window no longer relevant to the current work, in place and losslessly, with the exact original text preserved and recoverable byte for byte. And I wanted the agent to curate its own window, since it's the only party that knows when a span stops mattering. I built that approach into a homebrewed harness first, then found the strong prior work in the space (LCM, Self-GC, Letta), and rebuilt it on those lessons as an open-source extension for the Pi coding agent: pi-fold, MIT licensed, on npm. Measuring it taught me the expensive lesson. My first version applied every fold immediately, and each in-place edit invalidated the provider's prefix cache, so folding cost more than it saved. The fix was to separate deciding from paying: agents mark spans as finished, and the marks land together in one batched rewrite. Same folding decisions, 5.4x less fresh input. Measured against native compaction on a 64-stage workload, folding spent 0.60x the tokens and 0.55x the dollars at the last common stage, with zero mid-session restarts. It's one run per arm, and the caveats are stated in bold in the write-up, including that the measured run's folds were all automatic; agent curation is built but not yet exercised. Paper and sealed data: https://lnkd.in/gBfa3mZk Interactive write-up: https://lnkd.in/gTM3aZdY The story of how it got here: https://lnkd.in/gtCmHRTK

Why can two people use the same Claude Code setup, yet one works several times faster? I looked into five tools that can extend Claude Code: Graphify, Impeccable, Ponytail, Higgsfield MCP, and Obsidian. Graphify turns a code repository into a knowledge graph, allowing Claude to query paths and relationships instead of relying only on grep. Impeccable checks for 24 common signs of AI-generated design, including purple gradients, excessive bounce animations, and cramped spacing. Ponytail focuses on writing leaner code, with claims of up to 90% less unnecessary code, 75% lower cost, and 3–6x faster execution. Higgsfield MCP connects Claude Code with a broader creative toolkit for generating images, videos, and advertising assets. Obsidian works differently. Rather than adding another execution capability, it gives Claude access to notes and context that have already been saved, allowing that information to be retrieved again across sessions. The performance claims behind the other tools are attention-grabbing. But the problem I encounter most often is not excessive code. It is having to re-explain the same background, decisions, and previous attempts whenever a session ends. That is why, out of the five, the Obsidian memory layer stood out most to me. The other tools help Claude do more. Obsidian reduces the amount of context I need to repeat. The takeaway is not that everyone needs the same five-tool setup. The best addition is usually the one that removes the friction you repeatedly experience.

A few ms is what it takes to update an issue in Linear, here's how: ___ btw, my system design cheat sheet: https://lnkd.in/g7BEtcNZ Follow Alexandre Zajac for more system design content! ___ A typical CRUD app doing the same thing: about 300ms, almost all of it spent waiting on a round trip. This sequence in Linear: 𝟬. The write hits IndexedDB first, not the server. IndexedDB is the real source of truth for the UI; the server is just where it eventually gets confirmed. 𝟭. The UI re-renders off that local write immediately. Zero network wait, because the client never needed the network to know what just happened. 𝟮. The WebSocket connection sends the mutation in the background, sending deltas instead of the full issue object. 𝟯. If the server disagrees later (a permission, a conflict), it reconciles and corrects the local state then. That's the rare path. 𝟰. On re-render, changing one field re-renders one field. The status pill updates. The row doesn't. The board doesn't. Granular observables mean state changing somewhere doesn't mean re-rendering everything downstream of it. No single trick makes this fast. Every layer just refuses to make you wait for a round trip it didn't actually need. Full build pipeline and animation breakdown in this week's issue. Are you using Linear?

After half a year of struggling in multi-modal related projects, the key learning is that if you want something from a model, better use the same type for reference If you want a. percise image, use an image as reference instead of text. If you ask Claude to do frontend work, better create an HTML mockup and verify before implementation. Why? I guess it is how the model is trained; it adds more creative while cross media type references. Models are not like a workflow, which has a deterministic output. Prob top 3 key learnings from generative model experience.... big things are normally simple

So let me share with you 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗟𝘂𝗺𝗶𝗻𝗮𝗘𝗱𝗶𝘁 𝗖𝗼𝗿𝗲? I have started learning LLD from a youtube playlist by CoderArmy (Playlist link is shared at the bottom). In the previous week I revised the OOP Concepts and worked on Solid Principles. The instructor in this course taught me how to design a Document Editor using SOLID principles. So I felt an urge to practice it on some other problem. Long story short, I used Google AI Studio and it generated a problem for me. And that problem is what I named LuminaEdit Core, a Terminal Based Code Editor. In first attempt, I designed something about which even I myself was confused how will it work. But now I've come with a simple solution. The 𝗦𝘆𝘀𝘁𝗲𝗺 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 are provided in detail in the document, but here a small overview of the system is provided: - Document Management (CRUD) - Text Manipulation (CRUD) - Navigation & Cursor Tracking - Search & Analytics - Undo/Redo System - Extension/Plugin Support I finalized this system in 𝟵 𝗖𝗹𝗮𝘀𝘀𝗲𝘀 1- CodeEditor (Facade) 2- CodeEditorTab 3- Document 4- Cursor 5- CursorDriver 6- SearchEngine 7- StatsCalculator 8- Action 9- Plugin (Abstract Class) For further details about the system and class diagram You can visit here: 𝗥𝗲𝗽𝗼: https://lnkd.in/duVNskMC 𝗡𝗼𝘄 𝗪𝗵𝗮𝘁 𝗜 𝗪𝗮𝗻𝘁 𝗳𝗿𝗼𝗺 𝗬𝗼𝘂? If you are someone who loves LLD, then please share your suggestions with me. I would love to learn from your experiences. 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗟𝗶𝗻𝗸: https://lnkd.in/dHECja3Q #CPP #LowLevelDesign #SystemDesign #SoftwareEngineering #SOLIDPrinciples #OpenSource #Developer #Programming #Tech

I found an XSS hole in one of our internal tools last month. It was in the markdown renderer. The tool takes model output and displays it. The model was summarizing content that came from outside us. Ticket text, logs, whatever got pasted in. To make headings and code blocks look right, that output was going through a markdown-to-HTML converter and straight into the DOM. Which means we were trusting model output like we wrote it ourselves. We did not write it. A model wrote it, partly from our prompt and partly from whatever was in the context window. Anything that arrives in context can carry instructions, and what comes back is a string about to be rendered as HTML. Frontend spent fifteen years learning not to trust strings from the server. Then AI arrived and we piped model output into the page because it felt like our own product talking. It is not. It is untrusted input with good manners. Sanitize it. Treat a link from a model like a link from a stranger. Render code as text.