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10 websites every web designer should bookmark before starting their next project. if you’re tired of staring at a blank screen, these resources can help you find inspiration, study real interfaces, and build better designs. here’s what each one offers: 1. Devault Design 100K+ real product screens, complete UX flows, and examples of how interfaces have evolved. devault.design 2. Mobbin Explore mobile and web app designs, UI patterns, and real product experiences. mobbin.com 3. Land-book A curated gallery of landing pages for layout, typography, and visual inspiration. land-book.com 4. Recent Design Discover creative websites with standout visuals, interactions, and modern design ideas. recent.design 5. Page Flows Watch recorded user journeys to understand how people navigate apps and websites. pageflows.com 6. Really Good Emails A library of email designs for newsletters, campaigns, onboarding, and marketing. reallygoodemails.com 7. Navbar Gallery Explore navigation bar designs and patterns you can learn from and adapt. navbar.gallery 8. Footer Design Find footer layouts, structures, and ideas for different types of websites. footer.design 9. SiteInspire Browse curated websites across different styles, industries, and creative directions. siteinspire.com 10. Minimal Gallery A collection of clean, minimal websites with strong layouts and visual details. minimal.gallery how to use them in your workflow: → start with Mobbin and Devault to study real product interfaces. → use Land-book and SiteInspire to explore visual directions. → check Page Flows to understand user journeys. → explore Navbar Gallery and Footer Design for specific components. → use Really Good Emails when designing email campaigns. don’t just copy what looks good. study the layout, spacing, hierarchy, interactions, and decisions behind each design. save this list before your next design project.

Sonnet builds. Haiku swarms. Opus reviews. this is how to split the work. this Claude Code setup splits the workload across three models here’s how the architecture works: 1. Sonnet 5.5: the lead runs the main coding session, coordinates the work, and makes the key implementation decisions. 2. Haiku 5.5: the swarm handles smaller tasks in parallel through three specialized subagents: → search: maps the codebase and finds relevant files.
 → grep: tracks down functions, references, and call sites.
 → audit: checks the final diff in an isolated worktree. 3. Opus 5.5: the advisor stays on call for difficult decisions and reviews important changes instead of consuming tokens on every small task. how to set it up: step 1: launch Claude Code claude --model sonnet --advisor opus step 2: create your subagents add three agent files inside: .claude/agents/ ├── search.md ├── grep.md └── audit.md step 3: add a rule to CLAUDE.md tell Sonnet to delegate repository searches, call-site discovery, and test runs to the Haiku agents so the main session stays focused. step 4: add JEV routing use JEV for fast, deterministic decisions such as file selection, tool routing, and retry-or-stop decisions. step 5: give Claude Code this prompt Rebuild my Claude Code setup around this architecture: 1. Run Sonnet as the main session with Opus as an on-call advisor. 2. Create three Haiku subagents: - search: maps the repository - grep: finds call sites and references - audit: checks diffs in an isolated worktree 3. Give the audit agent its own worktree so its checks do not modify my working branch. 4. Update CLAUDE.md to delegate repository searches and test runs to the Haiku agents. 5. Route deterministic decisions through JEV. 6. Keep the main context focused and avoid unnecessary context sharing. Inspect the existing setup first. Show every proposed change as a diff. Do not edit anything until I approve. the important distinction: → Sonnet handles the main implementation.
 → Haiku handles parallel research and checks.
 → Opus provides deeper reasoning when needed. don’t waste your most capable model on routine searches. give every model a job that matches its strengths. documentation: code.claude.com/docs/en/sub-… save this architecture before your next Claude Code project.

here is how to coordinate multiple Claude Code agents without constantly passing context around. this setup turns three separate coding sessions into one coordinated AI engineering team. no more manually copying API schemas, passing context between windows, or repeating the same instructions across sessions. here’s the architecture: the command center: Sonnet → runs the main session.
 → builds the shared API layer.
 → keeps schemas aligned across the team. the communication layer: Haiku → relays plain-text messages between sessions.
 → coordinates tasks through SendMessage.
 → passes only the information each agent needs. the reviewer: Opus → acts as the session architect.
 → checks changes across sessions.
 → verifies diffs before approving the work. three agents handle the actual development: @frontend-client: builds the frontend. @backend-api: handles backend logic and endpoints. @db-migration: manages database changes. the proposed transport uses local.sock for local inter-process communication, with a JEV router directing messages between sessions. get started with the main session: claude --model sonnet --settings '{"crossSessionInbound":"accept"}' then paste this into Claude Code: build a coordinated multi-session Claude Code engineering environment. create three peer sessions: @frontend-client @backend-api @db-migration use Sonnet as the main coordinator to manage tasks and synchronize shared API schemas. use Haiku to relay focused plain-text messages between peer sessions. set up local IPC communication and avoid sharing full conversation histories or unnecessary context. use Opus as the session architect to review cross-session changes and verify all diffs. keep the system lightweight and plain-text focused. inspect the existing environment first. show every proposed change as a diff. do not apply changes until I approve them. the goal is simple: one session plans, specialized sessions execute, and a reviewer checks how everything fits together. that’s a much more interesting way to use Claude Code than running one agent at a time. save this architecture for your next multi-agent project.

Grok Bot can now run an entire AI-powered company with 7 specialized bots. 🤯 instead of relying on one chatbot to do everything, you can build a team of agents that plan, design, prototype, code, research competitors, and reverse-engineer software. here are the 7 bots and what each one does: 1. Dr Eggbot by Lauren Tan the bot that helps you create and build your own specialized bots. x.ai/bot/marketplace 2. Alfred by Robin Delta designs the structure of your AI company, then helps audit and govern the bot fleet. x.ai/bot/marketplace 3. PM Bot by Ulysses Ng acts as the product brain. turns ideas into dated specifications, defining what’s included and what stays out. x.ai/bot/marketplace 4. Proto Bot by Shub Gaur turns product ideas into working prototypes you can test. x.ai/bot/marketplace 5. Engineering Lead by Lauren Tan coordinates engineering agents and helps turn specifications into working software. x.ai/bot/marketplace 6. Stalk Bot by Shub Gaur monitors competitors and gathers intelligence to help your team understand what others are building. x.ai/bot/marketplace 7. REA Bot a custom bot built through Dr Eggbot that reverse-engineers applications to understand how they work, down to the binary level. github.com/morluto/rea how the workflow fits together: → Dr Eggbot creates your specialized bots. → Alfred organizes and governs the team. → PM Bot defines what needs to be built. → Proto Bot creates the prototype. → Engineering Lead coordinates implementation. → Stalk Bot tracks competitors. → REA investigates existing applications and their underlying systems. the interesting part is how these agents work together. you’re not just using Grok to answer questions. you’re building a coordinated team that can take a project from research and planning to development. the full list of what REA and Grok Bot can reverse-engineer is worth exploring. save this if you’re experimenting with AI agent teams.

7 GitHub repositories that show what AI agents can actually do in 2027. these aren’t just chatbots. they can search for jobs, research the internet, build websites, edit office documents, find security vulnerabilities, and even run entire AI-powered companies. here’s what you should know: 1. Career-ops an AI job-search agent that scans job boards, scores roles against your CV, and tailors your resume for each application. github.com/career-ops-hq/car… 2. Agent Reach gives your AI agent access to YouTube, Reddit, GitHub, LinkedIn, and the wider web through one command. github.com/Panniantong/Agent… 3. OfficeCLI lets AI agents create, read, and edit Word, Excel, and PowerPoint files without needing Microsoft Office installed. github.com/iOfficeAI/OfficeC… 4. Hypit turns viral video references into editable production workflows, including footage, captions, B-roll, and effects. github.com/hypit-ai/hypit 5. AI Website Cloner takes a website URL and recreates it as an editable Next.js application. github.com/JCodesMore/ai-web… 6. Strix an autonomous AI security agent that investigates code for vulnerabilities and validates findings with working exploits. github.com/usestrix/strix 7. Paperclip lets you organize and manage a company of AI agents with an org chart, budgets, approvals, and a central dashboard. github.com/paperclipai/paper… the bigger picture: AI is moving beyond answering questions. It’s becoming a way to automate entire workflows, connect different tools, and delegate real tasks. start exploring these repositories and learn how to put agents to work. which one would you build with first?

Claude Opus 5.5 generated 100 different motion effects on a single character in just 30 seconds. and here’s the interesting part: every frame shows a different effect. you can pause the animation anywhere and see how the character transforms through different visual styles, effects, and motion treatments. think about what this means for motion designers. creating 100 individual effects manually could take hours of work. this workflow generates them directly from code. here’s how to try it: → download the PDF containing the motion design guide. → drop it into Claude Opus 5.5. → ask it to follow the instructions and generate the effects. → experiment with the prompts and customize the results. the real opportunity isn’t just generating animations faster. it’s using AI to explore visual ideas that would take much longer to create manually. read the full motion design guide in the article below. save this if you’re experimenting with AI-powered motion design.

you can build an entire motion graphics workflow with AI and 20 free GitHub repositories. no expensive software subscriptions. no starting every animation from scratch. the idea is to combine Claude Opus 5.5 with specialized open-source tools that handle each stage of video production. here’s the full stack, organized by how a video gets made: step 1: write motion like code → Remotion: create videos using React components. github.com/remotion-dev/remo… → Remotion Skills: give your coding agent video production workflows. github.com/remotion-dev/skil… → Anime.js: create synchronized animations and complex motion effects. github.com/juliangarnier/ani… → Motion: build smooth UI animations and layout transitions. github.com/motiondivision/mo… → Manim: create mathematical animations and educational explainers. github.com/ManimCommunity/ma… → Lottie: render lightweight vector animations. github.com/airbnb/lottie-web step 2: build the world → Blender MCP: control Blender and create 3D scenes through AI. github.com/ahujasid/blender-… → Three.js: build interactive 3D scenes in the browser. github.com/mrdoob/three.js → GPU Pathtracer: create realistic lighting and path-traced renders. github.com/gkjohnson/three-g… → Postprocessing: add bloom, depth of field, and cinematic effects. github.com/pmndrs/postproces… step 3: make it feel alive → Jolt Physics: simulate realistic collisions, movement, and physics. github.com/jrouwe/JoltPhysic… → three.quarks: create particle effects, sparks, trails, and explosions. github.com/Alchemist0823/thr… → Tweakpane: adjust animation parameters with interactive controls. github.com/cocopon/tweakpane → ComfyUI: generate images and visual assets through node-based workflows. github.com/Comfy-Org/ComfyUI step 4: give it a voice → CosyVoice: generate natural AI voiceovers. github.com/FunAudioLLM/CosyV… → librosa: analyze audio, detect beats, and process sound. github.com/librosa/librosa → WhisperX: generate accurately aligned word-level captions. github.com/m-bain/whisperX step 5: ship the video → OBS Studio: record your screen and production workflow. github.com/obsproject/obs-st… → LosslessCut: trim and cut videos without re-encoding. github.com/mifi/lossless-cut → FFmpeg: handle video conversion, editing, encoding, and final exports. github.com/FFmpeg/FFmpeg how to use the stack: 1. give Claude your video idea and visual references. 2. use Remotion or Manim to build the animation. 3. bring in Blender or Three.js for 3D scenes. 4. add physics, particles, and visual effects. 5. generate voiceovers and synchronize captions. 6. assemble, edit, and export with FFmpeg. the real advantage is that Claude can help coordinate these tools instead of forcing you to do every production task manually. 20 repositories. 5 production stages. countless possibilities. save this stack before your next motion design project.

this is a goldmine for motion designers and anyone creating animations with AI. someone just built a gallery of 200+ motion graphics created with Claude Opus 5.5, and the best part is that you can see exactly how each animation was made. it’s called Prompt Motion. here’s what you’ll find: → 200+ motion examples: explore animations, transitions, visual effects, and creative motion concepts. → prompts behind the results: see the prompts or skills used to generate each animation. → browse by style: find specific motion references that match the look you’re trying to create. → learn by copying: take the techniques, adapt the prompts, and recreate similar effects in your own projects. why this is useful: instead of spending hours trying to reverse-engineer an animation, you can study the result, understand the instructions behind it, and use that as a starting point for your own work. if you create videos, websites, animations, or interactive experiences with AI, this is worth exploring. explore the gallery here: promptmotion.org 🔖 bookmark this one. you’ll definitely want to revisit it.

this blew my mind 🙀 this creator generated $20,783 in estimated revenue from a faceless YouTube channel. look at the numbers: → 123 million views → 1.2 million watch hours → 153.9K subscribers → $20,783 in estimated revenue and he did all this without building a channel around their personal identity or showing their face. this is why faceless YouTube is getting so much attention. with AI tools, creators can simplify major parts of the process: → research profitable niches and content ideas. → analyze successful channels and video formats. → generate scripts and storytelling angles. → create visuals, voiceovers, and video assets. → build a consistent publishing workflow. the opportunity is in combining these tools with good storytelling, smart content strategy, and consistency. you don’t have to be the face of a channel to build an audience. 🔖 save this if you’re exploring faceless YouTube.

the next level of AI productivity is building a team of specialized agents that work together and not using one powerful agent. instead of asking one AI to research, write, review, and make decisions, you give each agent a specific responsibility. here’s a simple AI team you can build in Claude Code: 1. the researcher → gathers information, checks sources, and finds useful insights. → never writes the final content. 2. the writer → turns the research into clear, engaging content. → focuses entirely on writing, not researching. 3. the critic → reviews the output, identifies weak arguments, and catches mistakes. → points out problems but doesn’t rewrite or fix them. 4. the lead agent → runs on Opus 5.5. → plans the task, delegates work, coordinates the team, and makes the final decision. why does this structure matter? each agent has a defined role and clear boundaries. the researcher doesn’t become the writer. the writer doesn’t start researching. the critic doesn’t take over editing. this prevents one agent from carrying every responsibility and gives each stage of the workflow a dedicated focus. what can the team produce? with one command, the agents can work together to deliver a complete launch package: → researched information. → polished written content. → critical review. → a final output approved by the lead agent. the real advantage isn’t simply having multiple agents. it’s giving them separate responsibilities and a clear system for working together. read the article below to learn how to set up your first agent team in Claude Code.

this guy built a faceless YouTube channel that reportedly makes over $1,600 a day by turning TV shows into content. and he used just two AI tools: Gemini + Claude. here’s how he built the system: → studied competitors: analyzed successful faceless YouTube channels to understand their niches, formats, and audiences. → identified winning angles: found topics and storytelling approaches that attract views. → reverse-engineered content: broke down successful titles, thumbnails, and scripts to understand what makes people click and keep watching. → generated 30 video ideas: used AI to turn his research into potential video topics. → built a content strategy: organized the ideas into a structured plan for video production. the results he claims: → 21 videos uploaded. → 28 days of publishing. → over $20,000 reportedly generated. → more than $1,600 in claimed daily earnings. the interesting part is that he didn’t just use AI to write scripts. he used it to research the market, study competitors, find content opportunities, and plan production. watch the full breakdown to see how he built the workflow. 🔖 bookmark this if you’re interested in building a faceless YouTube channel.

Claude Code can have its own second brain, and you can build it with nothing but folders and plain text files. no database. no extra apps. no complicated setup. the idea is simple: give Claude a structured memory system where every fact has a place, every topic has its own file, and every answer can be traced back to its source. here’s the architecture: → notes: stores facts, ideas, and information worth remembering. → people: keeps context about people you work with, including roles, preferences, and past interactions. → projects: stores ongoing work, project details, and decisions. → root files: hold long-term memory, lessons learned, and important decisions. → the map file: acts as the central index, telling Claude where to find each piece of information. how the memory system works: 1. Claude checks the map before retrieving information. 2. It opens the specific file connected to your question. 3. It uses that file as its source and tells you where the information came from. 4. If the information doesn’t exist, it says “not found” instead of inventing an answer. 5. When new information appears, it creates a dedicated file and updates the map. this means you don’t have to keep repeating the same project details, preferences, decisions, and background information. build your own second brain with this prompt: open Claude Code in an empty folder and paste: Build a persistent second-brain memory system in this folder. 1. Create three folders: notes, people, and projects. 2. Create root files for memory, lessons, and decisions. 3. Create a central MAP.md file explaining where all information is stored. 4. Keep important memory and decisions referenced in MAP.md. 5. Store one topic per file using descriptive lowercase-hyphenated names. 6. Before answering a memory-related question, check MAP.md and retrieve the relevant file. 7. Cite the exact filename used. If the information doesn't exist, say "not found" rather than guessing. 8. When new information is worth remembering, create a new file and update MAP.md. 9. Show me the proposed folder structure and plan before creating anything. the goal is to turn Claude Code from an assistant that constantly needs context into one that can work with an organized, growing knowledge base. build the structure once. keep adding knowledge. retrieve it whenever you need it. save this for your next Claude Code setup.

the complete 7-stage blueprint for building, testing, and deploying production-ready AI agents. most people focus on the model and prompt. but a reliable AI agent needs much more than that. here are 7 engineering stages for building a production-ready AI agent: 1. discover: define the problem → identify the task your agent should solve. → define who will use it and what success looks like. → set measurable KPIs and identify potential risks. 2. design: plan the architecture → choose an execution pattern like ReAct or Plan-and-Execute. → map out how the agent makes decisions. → define guardrails and where human approval is required. 3. connect: integrate tools and memory → connect APIs, databases, and external services. → give the agent the right context and memory. → restrict tool permissions so it only accesses what it needs. 4. prompt: build reliable instructions → write structured system instructions. → define tool schemas and expected outputs. → use examples to guide the agent’s decisions and responses. 5. ground: connect it to trusted knowledge → use RAG to retrieve relevant information from your data. → organize and chunk documents for better retrieval. → enforce source permissions to prevent unauthorized data access. 6. test: evaluate reliability → test accuracy, hallucinations, and prompt injection. → simulate tool failures and unexpected inputs. → measure latency, token usage, and operating costs. 7. deploy: monitor and maintain → deploy through a CI/CD pipeline. → use tracing, logs, alerts, and performance monitoring. → version your agent and keep rollback mechanisms ready. what teams often overlook: → no measurable KPIs means you cannot prove the agent is delivering value. → poor grounding leads to unreliable answers. → weak permissions can expose sensitive data or allow unauthorized actions. → missing evaluations allow preventable failures to reach users. → no monitoring makes problems harder to detect and fix. the real difference: LLM + prompt + tools = an AI agent prototype. AI agent + guardrails + evaluations + observability = a system you can move toward production with confidence. building an AI agent is only the beginning. the real engineering challenge is making it reliable, secure, measurable, and maintainable at scale. save this one 🔖

i found 8 websites that can seriously upgrade your AI web design workflow. these resources will help you create better layouts, find premium design references, generate 3D experiences, and add polished interactions. here’s what each website actually helps you do: 1. Studio Motionsites studio.motionsites.ai/ → generate websites with AI from simple instructions, turning ideas into working website designs without building everything manually. 2. MotionSites motionsites.ai/ → explore 800+ website prompts and ready-made sections, including 3D layouts, animated hero sections, and interactive designs. copy a prompt into Claude to kick-start your next project. 3. Land-book land-book.com/ → study real landing pages for ideas on hero sections, pricing layouts, product showcases, navigation, and conversion-focused page structures. 4. Recent Design recent.design/ → discover fresh website designs and creative interfaces to use as visual references for your next project. 5. Landdding landdding.com/ → browse curated landing pages and collect ideas for typography, page layouts, color combinations, and visual hierarchy. 6. Font in Logo fontinlogo.com/ → identify the typefaces used in well-known brand logos and find similar fonts for your own branding projects. 7. Godly godly.website/ → explore visually impressive websites featuring creative animations, interactive elements, experimental layouts, and modern design effects. 8. 60fps 60fps.design/ → study mobile interface animations, transitions, gestures, and micro-interactions to make app experiences feel smoother. how to combine them in one workflow: → find a visual direction on Land-book or Godly. → get ready-made design prompts from MotionSites. → generate your first version with Studio Motionsites. → refine the layout and typography using your references. → use 60fps for mobile interaction ideas. → use Font in Logo to explore branding and typography. you don’t need to start every project with a blank page. use these resources to find better references, create stronger interfaces, and build faster with AI. save this list before your next website project.

give Claude Code 3 reference images and a topic. it can turn them into a storyboarded animated explainer and export an MP4. you provide the visual direction and topic. the AI handles the storyboard, animation, and final video. here’s how to do it: step 1: collect your references → find a few images showing the visual style you want. → use movie stills, magazine ads, illustrations, or design references. → save them in one folder. step 2: open Claude Code → launch Claude Code with Opus 5.5. → set thinking effort to High. → drag your reference images into the conversation. step 3: install the animation skill → add the animation skill to your Claude Code setup. → make sure Claude can access the skill before starting the project. step 4: give it your topic → describe what your explainer should communicate. → specify the format, duration, and visual direction. → let Claude use your reference images to guide the design. step 5: let it build Claude can then: → break your topic into individual scenes. → create a storyboard for the animation. → generate the visual elements and transitions. → review its output and refine scenes. → combine everything into one MP4 video. here’s an example prompt to try: “Create a 60-second animated explainer about how artificial intelligence works. Use my reference images for the visual direction. Build a scene-by-scene storyboard, use smooth transitions, clear typography, engaging motion graphics, and export the finished animation as an MP4.” the interesting part is that you’re not starting with a blank timeline or manually animating every scene. you give Claude the references, the topic, and the direction. it handles much of the production workflow. this could be incredibly useful for creators, educators, marketers, and anyone making explainer videos. save this workflow before your next video project. watch the full breakdown in the video.

11 free GitHub repositories that can handle almost every stage of video editing. this collection of tools can help you cut, stabilize, color-grade, animate, upscale, and export videos without relying on expensive editing software. here’s what each one does: 1. PySceneDetect → automatically detects scenes and splits footage into individual shots. github.com/Breakthrough/PySc… 2. Auto-Editor → automatically cuts silence, dead air, and unwanted pauses. github.com/WyattBlue/auto-ed… 3. vid.stab → stabilizes shaky footage and makes camera movement smoother. github.com/georgmartius/vid.… 4. color-matcher → matches the colors of different shots using a reference image. github.com/hahnec/color-matc… 5. rembg → removes backgrounds from images and video workflows without a green screen. github.com/danielgatis/rembg 6. RIFE → generates intermediate frames for smoother slow-motion footage. github.com/hzwer/ECCV2022-RI… 7. Real-ESRGAN → enhances and upscales low-resolution images and video frames. github.com/xinntao/Real-ESRG… 8. Demucs → separates audio into vocals, drums, bass, and other stems. github.com/facebookresearch/… 9. librosa → analyzes audio and detects beats, tempo, and rhythm for music-based editing. github.com/librosa/librosa 10. HyperFrames → creates animated video titles and motion graphics using HTML. github.com/heygen-com/hyperf… 11. FFmpeg → handles video conversion, compression, filters, and final exports. github.com/FFmpeg/FFmpeg here’s a practical workflow to try: → use PySceneDetect to split your footage into scenes. → run Auto-Editor to remove unnecessary pauses. → stabilize shaky shots with vid.stab. → use color-matcher to create a consistent look. → generate smooth slow motion with RIFE. → separate music and vocals with Demucs. → use librosa to identify beats for your cuts. → create animated titles with HyperFrames. → finish and export everything with FFmpeg. you can even give Claude Code or Opus a clear editing brief and ask it to help you connect these tools into an automated workflow. you don’t need to install all 11 at once. start with the tools that solve your biggest editing problems. 11 repositories. zero software subscriptions. a seriously useful toolkit for video creators. bookmark this before your next edit.

this is one of the most powerful open-source tools for android hacking and reverse engineering. i just found JADX, a powerful Android decompiler that turns app files into readable Java code. it’s used by Android developers, reverse engineers, and security researchers to understand how applications work internally. here’s what you can do with it: → decompile APK, DEX, AAB and other files into readable code. → view Java source code instead of struggling with raw bytecode. → inspect AndroidManifest.xml to understand app permissions, components, and configuration. → decode app resources including layouts and other bundled files. → deobfuscate code to make confusing class and method names easier to understand where possible. → search the codebase and trace where methods are used. → use the GUI or command line depending on your workflow. → inspect Smali code when you need to go deeper into Android bytecode. here’s a practical way to use it: 1. download JADX from its GitHub repository. 2. launch the graphical interface. 3. open an APK file you own or are authorized to analyze. 4. let JADX decompile the application. 5. browse its Java code, manifest, resources, and method references. 6. search for specific classes or functions to understand how the app works. you can use it to audit your own Android apps, investigate suspicious applications, study app architecture, and learn how Android software is structured. why it’s worth bookmarking: it’s free, open source, and released under the Apache 2.0 license. repository: github.com/skylot/jadx if you work with Android development, reverse engineering, or security research, this belongs in your toolkit. save this and follow for more useful open-source tools.

stop paying for multiple AI tools before you try this Notion setup. you can build your own AI agent inside Notion, connect it to your workflow, and choose from the AI models available to your account. here’s how to set it up: STEP 1: create your Notion account → go to app.notion.com → sign up with your Google account → select For Work during onboarding → complete the remaining setup questions STEP 2: check for AI agent access → skip the initial upgrade offer → check whether your account has access to the AI agent trial → if the trial is available, activate it and open the agent workspace STEP 3: create your first agent → select New Agent → name it Morning Brief → open its settings → choose an available AI model → select your preferred effort level → save your configuration STEP 4: give it a real job don’t just create an agent and leave it sitting there. configure Morning Brief to: → summarize your important Notion pages and project updates → turn unfinished tasks into a prioritized daily action list → identify upcoming deadlines and overdue work → prepare a morning briefing with your top priorities → draft follow-up messages and meeting summaries try this prompt: “Review my project notes, tasks, and upcoming deadlines. Give me a morning briefing with my 5 most important priorities, overdue tasks, potential blockers, and the next action I should take for each.” you now have an AI assistant that helps you organize your work instead of simply answering random questions. one workspace. your own AI agent. multiple models to choose from, depending on your account access. save this and experiment with your own setup.

paste this into grok, then drop in your worst prompt. it finds what's broken, rewrites it 3 ways, scores each version, and shows you exactly why the new ones work. tip: when it asks, tell it what's going wrong with the output. that's how it fixes the real problem, not just the wording. you'll learn your most common mistakes too. bookmark this prompt and reply with your worst prompt and i'll tell you the first thing i'd fix.

these 23 are a goldmine for anyone building digital products. i found a collection of useful resources covering almost every stage of product design and development. here’s what’s inside: for website and UI inspiration 1. inspora.design : discover fresh website and product ideas. 2. banani.co : turn prompts into editable UI designs. 3. bestaibuilder.website : find AI tools to build apps. 4. landdding.com : explore impressive landing pages. 5. supahero.io : get creative landing page inspiration. 6. theinternetdesigns.com : find beautifully designed websites. 7. wedoflow.com : browse 1,300+ website page designs. 8. wonderlist.design : discover premium Figma resources. 9. cta.gallery : find better CTA design ideas. 10. footer.design : explore website footer designs. for motion and 3D 11. motionin.design : motion design inspiration. 12. scrolltide.co : explore scroll-based website effects. paperanimation.ai : create paper-style 13. animations. 14. tripo3d.ai : generate 3D models from text and images. 15. launchvideos.gallery : get inspiration for product launch videos. for graphics and branding 16. backgrounds.supply : create custom design backgrounds. 17. grainient.supply : generate gradients and grainy backgrounds. 18. 3dicons.co : download free 3D icons. 19. iconly.design : find icons for apps, games, and websites. 20. logotouse.com : explore logo inspiration. 21. fontinlogo.com : identify fonts used by popular brands. for optimization and discovery 22. scaledown.ai : optimize images and reduce file sizes. 23. on.design : discover and share creative work. the best part? you don’t have to figure everything out from scratch. use these resources to find inspiration, improve your designs, speed up your workflow, and bring your next product to life. save this list. you’ll definitely need it on your next build. follow for more useful AI tools and resources.