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Introducing visual-json > JSON editing with human-first ergonomics – Minimalist – Embeddable – Schema-aware – Extensible – Drag and drop – Keyboard navigation – Tree view to drill into deeply nested data

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Chris Tate

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168,385 просмотров • 5 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
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MiaoYan is a lightweight, native macOS Markdown editor for writing and notes — one of my open-source macOS projects. It just shipped its biggest update to date, introducing Split View for side-by-side Markdown editing and live preview, with smooth bidirectional scroll sync. This release also brings a full visual refinement, layout cycling between sidebar, list, and focus modes, faster startup with native window animations, and broad performance improvements across editing, preview, and export. Built entirely in Swift, not Electron. MiaoYan is local-first, requires no account, collects no telemetry, and avoids AI features by design. I built it for clean Markdown, performance, and a calm writing environment.
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MiaoYan is a lightweight, native macOS Markdown editor for writing and notes — one of my open-source macOS projects. It just shipped its biggest update to date, introducing Split View for side-by-side Markdown editing and live preview, with smooth bidirectional scroll sync. This release also brings a full visual refinement, layout cycling between sidebar, list, and focus modes, faster startup with native window animations, and broad performance improvements across editing, preview, and export. Built entirely in Swift, not Electron. MiaoYan is local-first, requires no account, collects no telemetry, and avoids AI features by design. I built it for clean Markdown, performance, and a calm writing environment.

Tw93

34,484 просмотров • 6 месяцев назад

Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu
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Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu

Evil Martians

101,372 просмотров • 9 месяцев назад

this gemini gem will help you create "Video2JSON" prompt here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: **Role:** You are **Video2JSON**, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. **Objective:** Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. **Analysis Requirements (Critical):** 1. **Subject Fidelity:** Never use generic terms. * *Bad:* "A kitten." * *Good:* "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * *Bad:* "A car." * *Good:* "A silver 2020s sedan with a dented rear bumper." 2. **The "Fourth Wall" (Physics):** You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. **Visual Density:** Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. **Temporal Precision:** Track changes in mood or action accurately via timestamps. **JSON Schema:** Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.
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this gemini gem will help you create "Video2JSON" prompt here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: **Role:** You are **Video2JSON**, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. **Objective:** Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. **Analysis Requirements (Critical):** 1. **Subject Fidelity:** Never use generic terms. * *Bad:* "A kitten." * *Good:* "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * *Bad:* "A car." * *Good:* "A silver 2020s sedan with a dented rear bumper." 2. **The "Fourth Wall" (Physics):** You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. **Visual Density:** Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. **Temporal Precision:** Track changes in mood or action accurately via timestamps. **JSON Schema:** Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.

ViralOps

19,588 просмотров • 7 месяцев назад

What is the best video editing agent for short form social? Does it actually work? We watched professional video editors, step by step, as they built short-form social reels in Adobe Premiere Pro. Today we're open-sourcing this preview dataset on Hugging Face, to make AI agents better at editing videos. The data set is 234 annotated steps across 4 computer-use trajectories. Editors narrated their reasoning aloud as they worked, so every step pairs a screenshot with the expert's own thought, a structured action, and executable grounding: >a Premiere MCP tool call, keyboard shortcut, menu path, or coordinate click. >The format follows the AgentNet trajectory schema, extended with a Premiere action taxonomy and multi-path execution. ***That makes it directly usable for computer-use agent SFT, reasoning mid-training, tool-use and function calling, and benchmarking agents against a human expert baseline. Enjoy!
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What is the best video editing agent for short form social? Does it actually work? We watched professional video editors, step by step, as they built short-form social reels in Adobe Premiere Pro. Today we're open-sourcing this preview dataset on Hugging Face, to make AI agents better at editing videos. The data set is 234 annotated steps across 4 computer-use trajectories. Editors narrated their reasoning aloud as they worked, so every step pairs a screenshot with the expert's own thought, a structured action, and executable grounding: >a Premiere MCP tool call, keyboard shortcut, menu path, or coordinate click. >The format follows the AgentNet trajectory schema, extended with a Premiere action taxonomy and multi-path execution. ***That makes it directly usable for computer-use agent SFT, reasoning mid-training, tool-use and function calling, and benchmarking agents against a human expert baseline. Enjoy!

ben

38,769 просмотров • 8 дней назад

The date is set. Introducing JioBlackRock Flexi Cap Fund — India’s first active equity fund powered by Systematic Active Equity* (SAE). Systematic Active Equity is BlackRock’s proprietary investment approach that processes the signals into actionable investment opportunities. Where AI meets human expertise, and traditional + alternate data come together to uncover smarter investment opportunities. This isn’t just another Flexi Cap Fund. It’s a new way to invest — intelligent, agile, and built for scale. NFO period: 23 Sep – 7 Oct 2025. *A technology-driven** investment approach that combines human expertise with a data-driven model to build a portfolio. ** The systematic approach involves utilizing inputs from the Fund Managers and signal research scores shared by BlackRock Inc. Stay tuned. The future of investing just got sharper. #JioBlackRock #FlexiCapFund #NFO
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The date is set. Introducing JioBlackRock Flexi Cap Fund — India’s first active equity fund powered by Systematic Active Equity* (SAE). Systematic Active Equity is BlackRock’s proprietary investment approach that processes the signals into actionable investment opportunities. Where AI meets human expertise, and traditional + alternate data come together to uncover smarter investment opportunities. This isn’t just another Flexi Cap Fund. It’s a new way to invest — intelligent, agile, and built for scale. NFO period: 23 Sep – 7 Oct 2025. *A technology-driven** investment approach that combines human expertise with a data-driven model to build a portfolio. ** The systematic approach involves utilizing inputs from the Fund Managers and signal research scores shared by BlackRock Inc. Stay tuned. The future of investing just got sharper. #JioBlackRock #FlexiCapFund #NFO

JioBlackRock Mutual Fund

28,318 просмотров • 10 месяцев назад

✨ I open sourced my first Chrome extension 🚀 SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.
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✨ I open sourced my first Chrome extension 🚀 SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.

@levelsio

257,744 просмотров • 3 месяцев назад

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:
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Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,332 просмотров • 9 месяцев назад

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation
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Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Adarsh Chetan

269,048 просмотров • 8 месяцев назад

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned
0:18

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

32,085 просмотров • 1 год назад

Hold up, here is the prompt: works with almost any model. enjoy :) Role & Objective: Act as an Elite UI/UX Front-End Engineer specializing in Apple-tier micro-interactions and advanced CSS. Your task is to program a perfectly centered navigation bar in a strictly SINGLE HTML file containing all HTML, vanilla CSS, and vanilla JavaScript. No external libraries or frameworks (No Tailwind, React, etc.). Design Concept - "True Liquid Glass": CRITICAL INSTRUCTION: Do NOT generate standard, flat "glassmorphism" or basic frosted glass. I require a physically accurate "Liquid Glass" aesthetic. It must look like wet, poured clear resin, combining the high-gloss specular highlights of classic macOS Aqua with the volumetric spatial depth of modern Apple VisionOS. 1. The Liquid Glass Material & Lighting (CSS): - Deep Refraction: Use `backdrop-filter` with extreme blur (e.g., 50px) and over-saturation (200%). - Specular Highlight: Create a curved, semi-transparent white gradient on the top half using a pseudo-element (`::before`) to simulate a hard light reflection on a wet, rounded 3D surface. - Caustics & Volume: Use multi-layered inner and outer `box-shadow` properties to simulate light refracting at the bottom edge and casting a realistic ambient drop shadow. - Interactive Glare: Implement a soft radial-gradient spotlight inside the glass that dynamically tracks the user's mouse cursor (X/Y coordinates) using JavaScript and CSS variables (`mix-blend-mode: overlay`). 2. Navigation Layout & Elements: - Center the pill-shaped navigation bar perfectly in the middle of the viewport. - Include 3 main navigation items with minimalist, inline SVG stroke icons and text labels: "Home", "Call", and "List". - Add a subtle vertical divider line after the main buttons. - Next to the divider, add a Dark/Light Mode toggle button containing inline SVG Sun and Moon icons. 3. Animations & "Apple Magic": - Sliding Active Pill: Create a solid background "pill" that sits *behind* the active navigation item's text/icon. When a different item is clicked, this pill must dynamically recalculate its width and slide to the new position. - Spring Physics: The sliding transition MUST use an exact Apple-style bouncy spring easing curve (e.g., `transition: all 0.5s cubic-bezier(0.34, 1.2, 0.64, 1)`). - Tactile Feedback: Buttons and icons must physically press down slightly (`transform: scale(0.92)`) when clicked (`:active`). - Theme Switch: The Sun and Moon icons must smoothly rotate, scale, and cross-fade during the transition. 4. Background Environment (Crucial): - Glass needs light and color to refract! Create a full-viewport, smoothly animated mesh gradient background using 3 large, heavily blurred, floating color blobs. - Implement full Dark/Light mode logic using CSS variables (`:root` and `[data-theme="dark"]`). Toggling the theme must seamlessly transition the background blob colors, glass opacity, shadow intensity, and text colors. Output ONLY the pristine, production-ready code. Prioritize maximum visual fidelity and silky-smooth 60fps animations.
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Hold up, here is the prompt: works with almost any model. enjoy :) Role & Objective: Act as an Elite UI/UX Front-End Engineer specializing in Apple-tier micro-interactions and advanced CSS. Your task is to program a perfectly centered navigation bar in a strictly SINGLE HTML file containing all HTML, vanilla CSS, and vanilla JavaScript. No external libraries or frameworks (No Tailwind, React, etc.). Design Concept - "True Liquid Glass": CRITICAL INSTRUCTION: Do NOT generate standard, flat "glassmorphism" or basic frosted glass. I require a physically accurate "Liquid Glass" aesthetic. It must look like wet, poured clear resin, combining the high-gloss specular highlights of classic macOS Aqua with the volumetric spatial depth of modern Apple VisionOS. 1. The Liquid Glass Material & Lighting (CSS): - Deep Refraction: Use `backdrop-filter` with extreme blur (e.g., 50px) and over-saturation (200%). - Specular Highlight: Create a curved, semi-transparent white gradient on the top half using a pseudo-element (`::before`) to simulate a hard light reflection on a wet, rounded 3D surface. - Caustics & Volume: Use multi-layered inner and outer `box-shadow` properties to simulate light refracting at the bottom edge and casting a realistic ambient drop shadow. - Interactive Glare: Implement a soft radial-gradient spotlight inside the glass that dynamically tracks the user's mouse cursor (X/Y coordinates) using JavaScript and CSS variables (`mix-blend-mode: overlay`). 2. Navigation Layout & Elements: - Center the pill-shaped navigation bar perfectly in the middle of the viewport. - Include 3 main navigation items with minimalist, inline SVG stroke icons and text labels: "Home", "Call", and "List". - Add a subtle vertical divider line after the main buttons. - Next to the divider, add a Dark/Light Mode toggle button containing inline SVG Sun and Moon icons. 3. Animations & "Apple Magic": - Sliding Active Pill: Create a solid background "pill" that sits *behind* the active navigation item's text/icon. When a different item is clicked, this pill must dynamically recalculate its width and slide to the new position. - Spring Physics: The sliding transition MUST use an exact Apple-style bouncy spring easing curve (e.g., `transition: all 0.5s cubic-bezier(0.34, 1.2, 0.64, 1)`). - Tactile Feedback: Buttons and icons must physically press down slightly (`transform: scale(0.92)`) when clicked (`:active`). - Theme Switch: The Sun and Moon icons must smoothly rotate, scale, and cross-fade during the transition. 4. Background Environment (Crucial): - Glass needs light and color to refract! Create a full-viewport, smoothly animated mesh gradient background using 3 large, heavily blurred, floating color blobs. - Implement full Dark/Light mode logic using CSS variables (`:root` and `[data-theme="dark"]`). Toggling the theme must seamlessly transition the background blob colors, glass opacity, shadow intensity, and text colors. Output ONLY the pristine, production-ready code. Prioritize maximum visual fidelity and silky-smooth 60fps animations.

Leon Lin

127,732 просмотров • 4 месяцев назад

The Chinese are flying 4 sixth-generation prototypes, but what does that mean? While the West keeps debating wars that seem never-ending, huh, China is flying low – or rather, high! – improving their 6th generation fighter prototypes, like the J-36/J-50, with total focus on advanced integration. This gives a huge strategic advantage, with emphasis on long-range missiles and multiple guidance to dominate global scenarios. China already has about 4 6th generation prototypes and plans to reach 8, selecting the most adapted one. All this under the General Concept: Indestructible Flying Brain: 6th generation fighters go way beyond just a slightly improved stealth; they are central platforms that command a global war web via AI, drones, and varied weapons, making previous fighters obsolete in connectivity and limiting them to very local operations. This omnipresence redefines air superiority, with the fighter surviving as a resilient node in the first hours of conflicts and being able to operate with speed. Kill Web: The Global War Web: The fighter acts as the central node of a real-time network, connecting submarines, satellites, ships, drones, and troops worldwide. It allows omnipresence, receiving data from a destroyer thousands of km away and attacking as if it were right nearby, with AI assisting the pilot in analysis and target acquisition. That's why the Chinese focus on missiles with ranges of thousands of km, with multiple guidance, turning the 6th generation pilot into a tactical manager very different from today's. Being a 6th generation fighter pilot is going to demand a lot. Command of Drone Swarms (CCA/Loyal Wingman) The fighter controls 6-20 drones simultaneously for reconnaissance, jamming, or suicide attacks. It transforms the pilot (or AI) into a "maestro" of a robotic orchestra, or quarterback of Collaborative Combat Aircraft (CCAs), which carry extra weapons, expanding offensive power without exposing the main fighter. Like, a controlled symphony of destruction! Superior Multi-Spectral Stealth (Stealth++) Not limited to radar, it covers infrared, acoustic, visual, and electromagnetic. It uses advanced materials, tailless designs, and minimal thermal signature to penetrate dense A2/AD defenses, making it extremely hard to detect and essential for operations in contested environments. Extreme Range, Autonomy, and New Generation Weapons Combat radius of 1,800-2,500 km without refueling, with sustained supercruise (Mach 1.5-2.0) without afterburner, thanks to adaptive cycle engines and huge internal tanks. There's talk of including lasers, but so far, what's really there are internal hypersonic missiles and 2-3x greater armament capacity than the F-35, all while maintaining total stealth. Artificial Intelligence, Integrated Sensors, Resilience, and Open Architecture AI as co-pilot or main, processing data in real time and making tactical decisions to reduce human load; optionally manned mode: piloted, remote, or autonomous flight; virtual cockpit via helmet visor. Multifunctional sensors combine radar, electronic warfare, communications, and non-kinetic effects, with total data fusion transforming the fighter into a flying data center. Network resistant to jamming and GPS loss via quantum-resistant communications, mesh networks, and inertial/computer vision navigation. Modular architecture allows quick upgrades (90% by software), avoiding high costs like in the F-35; in "Decision Centric Warfare," AI decides in milliseconds, with the human as an optional bottleneck, including cyber warfare and active defense. In another article, I'll talk about what I think of this in terms of costs and demand and if such an investment is really worth it.
0:13

The Chinese are flying 4 sixth-generation prototypes, but what does that mean? While the West keeps debating wars that seem never-ending, huh, China is flying low – or rather, high! – improving their 6th generation fighter prototypes, like the J-36/J-50, with total focus on advanced integration. This gives a huge strategic advantage, with emphasis on long-range missiles and multiple guidance to dominate global scenarios. China already has about 4 6th generation prototypes and plans to reach 8, selecting the most adapted one. All this under the General Concept: Indestructible Flying Brain: 6th generation fighters go way beyond just a slightly improved stealth; they are central platforms that command a global war web via AI, drones, and varied weapons, making previous fighters obsolete in connectivity and limiting them to very local operations. This omnipresence redefines air superiority, with the fighter surviving as a resilient node in the first hours of conflicts and being able to operate with speed. Kill Web: The Global War Web: The fighter acts as the central node of a real-time network, connecting submarines, satellites, ships, drones, and troops worldwide. It allows omnipresence, receiving data from a destroyer thousands of km away and attacking as if it were right nearby, with AI assisting the pilot in analysis and target acquisition. That's why the Chinese focus on missiles with ranges of thousands of km, with multiple guidance, turning the 6th generation pilot into a tactical manager very different from today's. Being a 6th generation fighter pilot is going to demand a lot. Command of Drone Swarms (CCA/Loyal Wingman) The fighter controls 6-20 drones simultaneously for reconnaissance, jamming, or suicide attacks. It transforms the pilot (or AI) into a "maestro" of a robotic orchestra, or quarterback of Collaborative Combat Aircraft (CCAs), which carry extra weapons, expanding offensive power without exposing the main fighter. Like, a controlled symphony of destruction! Superior Multi-Spectral Stealth (Stealth++) Not limited to radar, it covers infrared, acoustic, visual, and electromagnetic. It uses advanced materials, tailless designs, and minimal thermal signature to penetrate dense A2/AD defenses, making it extremely hard to detect and essential for operations in contested environments. Extreme Range, Autonomy, and New Generation Weapons Combat radius of 1,800-2,500 km without refueling, with sustained supercruise (Mach 1.5-2.0) without afterburner, thanks to adaptive cycle engines and huge internal tanks. There's talk of including lasers, but so far, what's really there are internal hypersonic missiles and 2-3x greater armament capacity than the F-35, all while maintaining total stealth. Artificial Intelligence, Integrated Sensors, Resilience, and Open Architecture AI as co-pilot or main, processing data in real time and making tactical decisions to reduce human load; optionally manned mode: piloted, remote, or autonomous flight; virtual cockpit via helmet visor. Multifunctional sensors combine radar, electronic warfare, communications, and non-kinetic effects, with total data fusion transforming the fighter into a flying data center. Network resistant to jamming and GPS loss via quantum-resistant communications, mesh networks, and inertial/computer vision navigation. Modular architecture allows quick upgrades (90% by software), avoiding high costs like in the F-35; in "Decision Centric Warfare," AI decides in milliseconds, with the human as an optional bottleneck, including cyber warfare and active defense. In another article, I'll talk about what I think of this in terms of costs and demand and if such an investment is really worth it.

Patricia Marins

60,344 просмотров • 8 месяцев назад

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
0:10

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 просмотров • 9 месяцев назад

What if modern medicine felt like a work of art? Today SONATA is live in NYC, SF, and LA, and we couldn't be prouder to have helped bring it into the world. Sonata maps your whole genome, hundreds of biomarkers, your cellular age, and then puts a real care team on top of the whole picture. Healthcare built for your biology, actually yours. When the founder, Sagan Schultz first walked us through his vision of concierge medicine, genomics, biomarkers, and contextual intelligence woven into one experience, we knew right away we wanted to make something harmonious, artful, and calm; a brand you'd trust with your body. A ‘sonata’ is a musical form built on structure: distinct movements, precise notation, played with feeling. That felt like everything care should be; mathematical underneath, human on the surface. We knew this name was perfect. Visually, we rendered a calla lily 🌷in cross-processed colors of petal blues, sunset corals, and an iridescent wash to give the data-heavy brand room to breathe in airy, open spaces. Then we built the counterpoints of exacting copy, fine atomic details of UI, data charts, and user flows. The elegant wordmark paired against against a living flower felt like the right balance. We carried that discipline into motion and the product itself. The flower breathes rather than spins. Interfaces settle with an unhurried weight. The user flows follow the same logic: one step at a time, plain language, your results building into a picture as you go, a doctor visible at the end of every decision. We treated movement and flow as clinical trust signals: a system this confident doesn't need to shout. All of it is tuned to one job, helping you be present with your body's information. We aimed to make a new medicine brand that felt modern and timeless at once. ~ Little Plains helped create the naming, brand strategy, positioning, verbal system, visual identity, motion design, and product surfaces where the science becomes care. Grateful to Sagan, David, Hiya, Dev, and the SONATA team for the trust. Your health story begins today. 🌸
0:20

What if modern medicine felt like a work of art? Today SONATA is live in NYC, SF, and LA, and we couldn't be prouder to have helped bring it into the world. Sonata maps your whole genome, hundreds of biomarkers, your cellular age, and then puts a real care team on top of the whole picture. Healthcare built for your biology, actually yours. When the founder, Sagan Schultz first walked us through his vision of concierge medicine, genomics, biomarkers, and contextual intelligence woven into one experience, we knew right away we wanted to make something harmonious, artful, and calm; a brand you'd trust with your body. A ‘sonata’ is a musical form built on structure: distinct movements, precise notation, played with feeling. That felt like everything care should be; mathematical underneath, human on the surface. We knew this name was perfect. Visually, we rendered a calla lily 🌷in cross-processed colors of petal blues, sunset corals, and an iridescent wash to give the data-heavy brand room to breathe in airy, open spaces. Then we built the counterpoints of exacting copy, fine atomic details of UI, data charts, and user flows. The elegant wordmark paired against against a living flower felt like the right balance. We carried that discipline into motion and the product itself. The flower breathes rather than spins. Interfaces settle with an unhurried weight. The user flows follow the same logic: one step at a time, plain language, your results building into a picture as you go, a doctor visible at the end of every decision. We treated movement and flow as clinical trust signals: a system this confident doesn't need to shout. All of it is tuned to one job, helping you be present with your body's information. We aimed to make a new medicine brand that felt modern and timeless at once. ~ Little Plains helped create the naming, brand strategy, positioning, verbal system, visual identity, motion design, and product surfaces where the science becomes care. Grateful to Sagan, David, Hiya, Dev, and the SONATA team for the trust. Your health story begins today. 🌸

Emmett

22,039 просмотров • 13 дней назад

How to 10x your design with Figma Make ⭐️ I spent 40+ hours testing Figma Make prompts. Most designers waste time with vague prompts and get garbage outputs. Here are the exact prompts and proven workflow that actually work: 1️⃣. Prompt formula: Bad: "Create a dashboard" Good: "Create a SaaS analytics dashboard with: → Left sidebar navigation (240px wide) → Top bar with user profile → 4 metric cards in a grid → Line chart showing revenue trend → Use blue (#2563EB) as primary color" The more you specify = higher quality. 2️⃣ Workflow: Import Your Design System First Before your first prompt: → Go to your main Figma file → Export your component library → Import it into Make → Add this to every prompt: "Use components from [Your Library Name]" Now everything matches your brand automatically. 3️⃣. Prompt for Interactive States: "Create a login form with: → Email and password inputs → Show error state when fields are empty → Disabled button state when form is incomplete → Success message after submission → Add smooth transitions between states" Gets you working prototypes, not static screens. 4️⃣. Advanced Prompts: Data States "Create a user list screen with three states: → Loading (skeleton screens) → Success (populated table with 10 users) → Empty (illustration + 'No users yet' message + 'Add User' CTA)" One prompt = complete UX coverage. 5️⃣. The "Design System Drift” Fix: Notice Make using wrong colors? → Try this Prompt: "Analyze my imported library and list all color tokens, then regenerate using only those exact values" It'll self-correct and stick to your system. 6️⃣. Responsive Design Prompt: "Create a pricing page with 3 tiers. Make it responsive: → Desktop: 3 columns side-by-side → Tablet: 2 columns with 3rd below → Mobile: Stacked vertically → Use Auto Layout for fluid scaling" This gets you mobile + desktop in one shot. 7️⃣. Magic Troubleshoot Prompts: Output looks off? → Try: "Redesign this following Material Design principles" → Or: "Make this follow iOS Human Interface Guidelines" → Or: "Apply Gestalt principles for better visual hierarchy" Give it design frameworks to follow. It works magic. Designers who master prompt engineering in 2026 will ship 10x more than everyone else. P.s. I made a Gameboy for Pokémon. (bookmark this for later)
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How to 10x your design with Figma Make ⭐️ I spent 40+ hours testing Figma Make prompts. Most designers waste time with vague prompts and get garbage outputs. Here are the exact prompts and proven workflow that actually work: 1️⃣. Prompt formula: Bad: "Create a dashboard" Good: "Create a SaaS analytics dashboard with: → Left sidebar navigation (240px wide) → Top bar with user profile → 4 metric cards in a grid → Line chart showing revenue trend → Use blue (#2563EB) as primary color" The more you specify = higher quality. 2️⃣ Workflow: Import Your Design System First Before your first prompt: → Go to your main Figma file → Export your component library → Import it into Make → Add this to every prompt: "Use components from [Your Library Name]" Now everything matches your brand automatically. 3️⃣. Prompt for Interactive States: "Create a login form with: → Email and password inputs → Show error state when fields are empty → Disabled button state when form is incomplete → Success message after submission → Add smooth transitions between states" Gets you working prototypes, not static screens. 4️⃣. Advanced Prompts: Data States "Create a user list screen with three states: → Loading (skeleton screens) → Success (populated table with 10 users) → Empty (illustration + 'No users yet' message + 'Add User' CTA)" One prompt = complete UX coverage. 5️⃣. The "Design System Drift” Fix: Notice Make using wrong colors? → Try this Prompt: "Analyze my imported library and list all color tokens, then regenerate using only those exact values" It'll self-correct and stick to your system. 6️⃣. Responsive Design Prompt: "Create a pricing page with 3 tiers. Make it responsive: → Desktop: 3 columns side-by-side → Tablet: 2 columns with 3rd below → Mobile: Stacked vertically → Use Auto Layout for fluid scaling" This gets you mobile + desktop in one shot. 7️⃣. Magic Troubleshoot Prompts: Output looks off? → Try: "Redesign this following Material Design principles" → Or: "Make this follow iOS Human Interface Guidelines" → Or: "Apply Gestalt principles for better visual hierarchy" Give it design frameworks to follow. It works magic. Designers who master prompt engineering in 2026 will ship 10x more than everyone else. P.s. I made a Gameboy for Pokémon. (bookmark this for later)

Felix Lee

12,706 просмотров • 6 месяцев назад

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3
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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 просмотров • 6 месяцев назад

Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉
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Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉

MrNeRF

26,058 просмотров • 1 месяц назад

I've been editing this article about "brain mapping" and connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)
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I've been editing this article about "brain mapping" and connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)

Niko McCarty.

66,989 просмотров • 7 месяцев назад

$KNDX 🤖 Theres 3 big narratives that are sending coins left right and centre rn. 🚀 #AI, #Gamefi, & #NFTs 🔹Theres 50% mindshare for #AI. 🤖 🔹#GameFi mcap is hitting ATH's with #OfftheGrid, $XBG and $SUPER making spectacular moves. 🎮 🔹NFTs and the #Metaverse are making a strong comeback with $APE up 100% over the weekend. 🐵 What if there's a project that touches all these trending narratives with groundbreaking technology to disrupt all 3 of them? 🔥 💡- That's where $KNDX comes in. -💡 Kondux is a cutting-edge Web3 SaaS platform, combining NVIDIA’s Omniverse, AI, Blockchain, and dynamic NFTs to revolutionize secure asset management across industries. 👏 Their flagship product, kNFTs, are 3D digital assets usable across Metaverse and Gaming platforms, AR/VR/XR environments, and manufacturing applications. Kondux’s scalable model opens new revenue streams by enabling effective digital asset monetization. 💰 Kondux is the first Web3 project to integrate VFX pipelines with NVIDIA’s Omniverse and bringing it onto the Blockchain. ⛓️ It is also the only Web3 project with a *Select Status Partnership* with NVIDIA, operating under NVIDIA NDAs and working with them directly for more than 2 years. About their NVIDIA Integrations: 🤖 🔹There are three areas of the Kondux tech stack that coincide with three divisions of NVIDIA: 📡GDN (Graphics Delivery Network, the backbone of GeForce Now) 💡Omniverse for 3D aspects such as, geospatial data, real world physics, lighting, and raytracing 🤖NVIDIA AI Foundation, which covers many aspects of #AI, including inference and deployment scaling. The convergence of all these components lie within .USD file format . 🔹 They are the first blockchain project to integrate NVIDIA’s Omniverse Cloud and Graphics Delivery Network (GDN) to provide high-quality 3D content accessible on any device without requiring high-end hardware. 🔹 This setup streamlines content management, democratises access to resource-intensive 3D content, and enables real-time interaction with 3D NFTs. Now, I haven’t seen any crypto project so deeply connected with NVIDIA and NVIDIA technology. GDN is a HUGE competitive advantage. With it, the need for #GPU’s basically goes out the window. 🤯 Now lets take a look at some of the other main features... 👀 OpenUSD (Universal Scene Description): 📽️ 🔹 Kondux is leveraging USD technology, developed by Pixar and used by Meta, Apple, Microsoft and other industry leaders to enhance 3D graphics and interoperability within its creative ecosystem. 🔹 Originally created for high-end film production, USD now supports a variety of applications, including gaming and virtual reality, making it a key asset for Kondux. kNFT's: 🎨 🔹 Kondux is pioneering a new category of NFTs known as kNFTs, which aim to redefine NFT utility through innovative features. 🔹 A standout feature is the upgradeable aspect provided by Kondux DNA, allowing kNFTs to transform and combine with other NFTs, creating limitless possibilities in art, gaming, and music. 🔹Through the Kondux AI portal it will be possible to communicate with kNFTs. They can learn and adapt. This AI technology is revolutionary because it makes human to kNFT interaction possible, turning it into a unique, personalized experience. Check out the clip of kNFTs in Unreal Engine 5 gameplay below. 👇 Kondux is a very obvious utility play with huge upside because it’s multi narrative. 📈 It's seriously groundbreaking stuff that they’re about to launch. 🚀 After speaking with the team there’s no doubt in my mind this will do crazy big numbers in the next months. 🤑
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$KNDX 🤖 Theres 3 big narratives that are sending coins left right and centre rn. 🚀 #AI, #Gamefi, & #NFTs 🔹Theres 50% mindshare for #AI. 🤖 🔹#GameFi mcap is hitting ATH's with #OfftheGrid, $XBG and $SUPER making spectacular moves. 🎮 🔹NFTs and the #Metaverse are making a strong comeback with $APE up 100% over the weekend. 🐵 What if there's a project that touches all these trending narratives with groundbreaking technology to disrupt all 3 of them? 🔥 💡- That's where $KNDX comes in. -💡 Kondux is a cutting-edge Web3 SaaS platform, combining NVIDIA’s Omniverse, AI, Blockchain, and dynamic NFTs to revolutionize secure asset management across industries. 👏 Their flagship product, kNFTs, are 3D digital assets usable across Metaverse and Gaming platforms, AR/VR/XR environments, and manufacturing applications. Kondux’s scalable model opens new revenue streams by enabling effective digital asset monetization. 💰 Kondux is the first Web3 project to integrate VFX pipelines with NVIDIA’s Omniverse and bringing it onto the Blockchain. ⛓️ It is also the only Web3 project with a *Select Status Partnership* with NVIDIA, operating under NVIDIA NDAs and working with them directly for more than 2 years. About their NVIDIA Integrations: 🤖 🔹There are three areas of the Kondux tech stack that coincide with three divisions of NVIDIA: 📡GDN (Graphics Delivery Network, the backbone of GeForce Now) 💡Omniverse for 3D aspects such as, geospatial data, real world physics, lighting, and raytracing 🤖NVIDIA AI Foundation, which covers many aspects of #AI, including inference and deployment scaling. The convergence of all these components lie within .USD file format . 🔹 They are the first blockchain project to integrate NVIDIA’s Omniverse Cloud and Graphics Delivery Network (GDN) to provide high-quality 3D content accessible on any device without requiring high-end hardware. 🔹 This setup streamlines content management, democratises access to resource-intensive 3D content, and enables real-time interaction with 3D NFTs. Now, I haven’t seen any crypto project so deeply connected with NVIDIA and NVIDIA technology. GDN is a HUGE competitive advantage. With it, the need for #GPU’s basically goes out the window. 🤯 Now lets take a look at some of the other main features... 👀 OpenUSD (Universal Scene Description): 📽️ 🔹 Kondux is leveraging USD technology, developed by Pixar and used by Meta, Apple, Microsoft and other industry leaders to enhance 3D graphics and interoperability within its creative ecosystem. 🔹 Originally created for high-end film production, USD now supports a variety of applications, including gaming and virtual reality, making it a key asset for Kondux. kNFT's: 🎨 🔹 Kondux is pioneering a new category of NFTs known as kNFTs, which aim to redefine NFT utility through innovative features. 🔹 A standout feature is the upgradeable aspect provided by Kondux DNA, allowing kNFTs to transform and combine with other NFTs, creating limitless possibilities in art, gaming, and music. 🔹Through the Kondux AI portal it will be possible to communicate with kNFTs. They can learn and adapt. This AI technology is revolutionary because it makes human to kNFT interaction possible, turning it into a unique, personalized experience. Check out the clip of kNFTs in Unreal Engine 5 gameplay below. 👇 Kondux is a very obvious utility play with huge upside because it’s multi narrative. 📈 It's seriously groundbreaking stuff that they’re about to launch. 🚀 After speaking with the team there’s no doubt in my mind this will do crazy big numbers in the next months. 🤑

Altcoin Miyagi🇯🇵

17,303 просмотров • 1 год назад

I learned this the hard way: do NOT use SwiftUI if you want your app to look and feel amazing. At least when coding with AI. (sorry, Apple colleagues reading this 😅) I'm sharing my process vibe coding this calorie tracker. I get a lot of questions about the fluid transition in the video. Here's the whole story. Initially, Claude built the grid with SwiftUI. It was quick and easy, and looked good! But the transition to the day view was a boring navigation push/pop. No fun. I wanted something custom. I asked Claude to make it a fluid transition that remaps the food tiles from their source to destination positions. All hell broke loose. Claude tried a bunch of horrible things. Initially it used matched geometry effects, which worked OK but didn't lend themselves well to gesture-driven animations. So it resorted to SwiftUI preference keys + geometry readers to figure out the source and destination positions and calculate the interpolated position based on gesture progress, coordinating across grid and day views. But this meant it had to write a custom layout because it couldn't reposition tiles inside the native SwiftUI grid. And it had to do an awkward handoff between views, which always created ugly pops or jumps. And don't get me started on trying to put it on a bouncy spring, that only made the math 10x buggier. Fortunately, Claude Fable was smart enough to see that this was becoming a disaster (and discover most of the issues itself, in the simulator), so it pivoted away from SwiftUI. Opus might not be so wise, so you'll have to pay attention and intervene. Ultimately, it rewrote it in plain UIKit and everything turned out great. After that, we moved from 2D images to 3D assets, which introduced a new set of performance challenges and yet another rewrite to a single Metal layer, which is what you see below. I can write more about the 2D-to-3D saga if anyone's interested. If I were to do it again, I'd just say "Don't use SwiftUI" from the very first prompt, and save a few hours of headaches. SwiftUI can be amazing for a human iterating directly in code. But agents don't benefit from any of its advantages. Plus, agents have seen decades of UIKit training data, so they're great at writing it, and it's far more flexible. Here's hoping we see more agent-friendly iterations of SwiftUI in the future. Till then, I'm probably going to avoid it.
0:12

I learned this the hard way: do NOT use SwiftUI if you want your app to look and feel amazing. At least when coding with AI. (sorry, Apple colleagues reading this 😅) I'm sharing my process vibe coding this calorie tracker. I get a lot of questions about the fluid transition in the video. Here's the whole story. Initially, Claude built the grid with SwiftUI. It was quick and easy, and looked good! But the transition to the day view was a boring navigation push/pop. No fun. I wanted something custom. I asked Claude to make it a fluid transition that remaps the food tiles from their source to destination positions. All hell broke loose. Claude tried a bunch of horrible things. Initially it used matched geometry effects, which worked OK but didn't lend themselves well to gesture-driven animations. So it resorted to SwiftUI preference keys + geometry readers to figure out the source and destination positions and calculate the interpolated position based on gesture progress, coordinating across grid and day views. But this meant it had to write a custom layout because it couldn't reposition tiles inside the native SwiftUI grid. And it had to do an awkward handoff between views, which always created ugly pops or jumps. And don't get me started on trying to put it on a bouncy spring, that only made the math 10x buggier. Fortunately, Claude Fable was smart enough to see that this was becoming a disaster (and discover most of the issues itself, in the simulator), so it pivoted away from SwiftUI. Opus might not be so wise, so you'll have to pay attention and intervene. Ultimately, it rewrote it in plain UIKit and everything turned out great. After that, we moved from 2D images to 3D assets, which introduced a new set of performance challenges and yet another rewrite to a single Metal layer, which is what you see below. I can write more about the 2D-to-3D saga if anyone's interested. If I were to do it again, I'd just say "Don't use SwiftUI" from the very first prompt, and save a few hours of headaches. SwiftUI can be amazing for a human iterating directly in code. But agents don't benefit from any of its advantages. Plus, agents have seen decades of UIKit training data, so they're great at writing it, and it's far more flexible. Here's hoping we see more agent-friendly iterations of SwiftUI in the future. Till then, I'm probably going to avoid it.

Anshu

111,617 просмотров • 1 месяц назад