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It's here! Just open-sourced a Certificate Generator that automates personalised certificate creation!✨ Features: • Upload CSV → Generate custom PDFs • Drag-and-drop text positioning with live preview • Multiple cloud storage providers (Cloudinary, Catbox, etc.) • Custom templates & fonts Perfect for courses, events, & training programs. Built with...

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15,079 views • 6 months ago •via X (Twitter)

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i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below
0:52

i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below

Cody Schneider

47,255 views • 1 year ago

Heya #ICPFamily, this is a big one. I’d like to introduce you guys to uBin, a cloud storage experience built on #ICP that allows you to create and own a personal file storage space on a blockchain network! This project is about 3 years old and offers a wide range of features: • Self-paid, autonomous smart contract creation and ownership. • In browser file manager interface with drag and drop upload. • In-app music/video player, document viewer (docx, pdf, etc), and a 3D model viewer. • Sharing via QR code and direct link. • User menu with saved settings customization. • In-app bug report/support ticket information. • Choice between public and encrypted canisters. • Support for mobile with NFID and Stoic. uBin has a 3+ year roadmap and lots to come. We have a few hundred users on and off chain, and we’re super excited to finally show what we’ve been cooking. We are in beta and have a whitelist, but you can login and submit the request right in the app if you’d like to try it! We love ICP and plan to stay forever, and in the process I’d like to sincerely thank everyone for their support in building this. Without you guys I wouldn’t be here. Love you all! ❤️
2:21

Heya #ICPFamily, this is a big one. I’d like to introduce you guys to uBin, a cloud storage experience built on #ICP that allows you to create and own a personal file storage space on a blockchain network! This project is about 3 years old and offers a wide range of features: • Self-paid, autonomous smart contract creation and ownership. • In browser file manager interface with drag and drop upload. • In-app music/video player, document viewer (docx, pdf, etc), and a 3D model viewer. • Sharing via QR code and direct link. • User menu with saved settings customization. • In-app bug report/support ticket information. • Choice between public and encrypted canisters. • Support for mobile with NFID and Stoic. uBin has a 3+ year roadmap and lots to come. We have a few hundred users on and off chain, and we’re super excited to finally show what we’ve been cooking. We are in beta and have a whitelist, but you can login and submit the request right in the app if you’d like to try it! We love ICP and plan to stay forever, and in the process I’d like to sincerely thank everyone for their support in building this. Without you guys I wouldn’t be here. Love you all! ❤️

Daniel

45,133 views • 1 year ago

If you’re anything like me, you have some videos in your library that can use a little TLC. A little zoom, crop and straighten can go a long ways when kids or pets (or, let’s be honest, even adult friends) are involved 📷 I’m really excited to share some tools we’ve been building that have been so helpful in our internal Google Photos dogfood! 🤩 -- Our redesigned video editor (on Android & iOS) is faster and supports multi-clip editing with an adaptive canvas. Less time figuring out edits, more time creating! ✨ Open a video, hit edit, and you’ll see several new tools. -- Add the perfect soundtrack from our music library! 🎶 -- Tell your story with custom text (on Android) using new fonts, colors, and backgrounds. But sometimes, we need a bit more help to make a really special multi asset clip. I’m so excited to announce Video Templates on Android! Quick, shareable videos automatically cut to the beat with built-in music and text. Just pick a template & your clips! 🎬 Available in the Create Tab. All these updates are rolling out now — try them out and turn those memories into masterpieces! 👇 #GooglePhotos #VideoEditing #GenAI
0:24

If you’re anything like me, you have some videos in your library that can use a little TLC. A little zoom, crop and straighten can go a long ways when kids or pets (or, let’s be honest, even adult friends) are involved 📷 I’m really excited to share some tools we’ve been building that have been so helpful in our internal Google Photos dogfood! 🤩 -- Our redesigned video editor (on Android & iOS) is faster and supports multi-clip editing with an adaptive canvas. Less time figuring out edits, more time creating! ✨ Open a video, hit edit, and you’ll see several new tools. -- Add the perfect soundtrack from our music library! 🎶 -- Tell your story with custom text (on Android) using new fonts, colors, and backgrounds. But sometimes, we need a bit more help to make a really special multi asset clip. I’m so excited to announce Video Templates on Android! Quick, shareable videos automatically cut to the beat with built-in music and text. Just pick a template & your clips! 🎬 Available in the Create Tab. All these updates are rolling out now — try them out and turn those memories into masterpieces! 👇 #GooglePhotos #VideoEditing #GenAI

shimrit ben-yair

24,361 views • 7 months ago

Turn Any Song Into a Fresh Instrumental in Just a Few Clicks If you're a content creator, editor, or music enthusiast, you know how hard it is to find background tracks that truly match your style. That’s where Mureka AI comes in. Upload a track you like from your device or a YouTube link, and Mureka will generate a brand new instrumental that captures a similar vibe. No plugins. No editing skills. Just fast, high-quality output ready to use. Here’s how it works: 1. Upload any track with vocals from your device or YouTube 2. Pick a music scenario and mood 3. Click Done — Mureka handles the rest 4. Preview and download your custom instrumental The result is a clean, demo-level track that feels polished and personal. ✅ Mureka uses your reference to match the style ✅ Great for beat demos, background music, or creative projects ✅ Built for speed and made to sound good Perfect for monetization, music creation, or powering your content channel. Whether you’re editing a reel, building a beat pack, or just need fresh sound quickly, Mureka makes it simple. Make your content sound original: #MurekaAI #InstrumentalMusic #MusicForCreators #AIMusic
0:26

Turn Any Song Into a Fresh Instrumental in Just a Few Clicks If you're a content creator, editor, or music enthusiast, you know how hard it is to find background tracks that truly match your style. That’s where Mureka AI comes in. Upload a track you like from your device or a YouTube link, and Mureka will generate a brand new instrumental that captures a similar vibe. No plugins. No editing skills. Just fast, high-quality output ready to use. Here’s how it works: 1. Upload any track with vocals from your device or YouTube 2. Pick a music scenario and mood 3. Click Done — Mureka handles the rest 4. Preview and download your custom instrumental The result is a clean, demo-level track that feels polished and personal. ✅ Mureka uses your reference to match the style ✅ Great for beat demos, background music, or creative projects ✅ Built for speed and made to sound good Perfect for monetization, music creation, or powering your content channel. Whether you’re editing a reel, building a beat pack, or just need fresh sound quickly, Mureka makes it simple. Make your content sound original: #MurekaAI #InstrumentalMusic #MusicForCreators #AIMusic

Markandey Sharma

94,526 views • 1 year ago

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.
0:34

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Akshay 🚀

33,358 views • 8 months ago

Been using Nous Research Hermes Agent for media editing and it completely replaced my workflow. You know the drill : - Trim a video - open CapCut. - Convert to mp3 - another site. - Remove audio - another one. - Make a gif - yet another one. - more tasks, more different tools minutes of uploading and downloading.. Now you can do all of that just by chatting like asking a designer friend to edit your media and getting it back instantly :) I built a media toolset for Hermes that handles all of this natively. It registers as a skill inside the agent - Hermes sees your video, understands what you're asking, takes the right operations and use the best skills and give you what you really wanted perfectly ▪️ Trim any segment down to the second ▪️ Speed up or slow down with pitch corrected audio ▪️ Compress without destroying quality ▪️ Create a gif on defined times range ▪️ Cut the video and rotate it and apply instagram ▪️ Convert between any format - mp4, mp3, ogg, webm ▪️ Chain edits: "trim 0:30 1:00 and speed up 1.5x and compress" ▪️ Burn .srt subtitles directly into the video ▪️ Add or remove text watermarks with custom position ▪️ Generate thumbnails from any timestamp ▪️ Fade in/out with custom duration ▪️ ASCII video art with multiple character styles and many more with customizable skills Works the same on Telegram, Discord, WhatsApp etc. This is what agents should be doing and Hermes does.
3:01

Been using Nous Research Hermes Agent for media editing and it completely replaced my workflow. You know the drill : - Trim a video - open CapCut. - Convert to mp3 - another site. - Remove audio - another one. - Make a gif - yet another one. - more tasks, more different tools minutes of uploading and downloading.. Now you can do all of that just by chatting like asking a designer friend to edit your media and getting it back instantly :) I built a media toolset for Hermes that handles all of this natively. It registers as a skill inside the agent - Hermes sees your video, understands what you're asking, takes the right operations and use the best skills and give you what you really wanted perfectly ▪️ Trim any segment down to the second ▪️ Speed up or slow down with pitch corrected audio ▪️ Compress without destroying quality ▪️ Create a gif on defined times range ▪️ Cut the video and rotate it and apply instagram ▪️ Convert between any format - mp4, mp3, ogg, webm ▪️ Chain edits: "trim 0:30 1:00 and speed up 1.5x and compress" ▪️ Burn .srt subtitles directly into the video ▪️ Add or remove text watermarks with custom position ▪️ Generate thumbnails from any timestamp ▪️ Fade in/out with custom duration ▪️ ASCII video art with multiple character styles and many more with customizable skills Works the same on Telegram, Discord, WhatsApp etc. This is what agents should be doing and Hermes does.

ogichain

11,291 views • 4 months ago

Cloud GPU training is a scam. A single M4 MacBook does 2.9 TFLOPS. Seven friends with MacBooks match an NVIDIA A100. Alexander Hayes just open-sourced a tool that makes this work over Wi-Fi. It's called AirTrain. Here's how it works: Traditional distributed training (DDP) syncs gradients after every single step. For a 124M parameter model, that's ~500MB exchanged per step. You need 50 GB/s of sustained bandwidth. Impossible over Wi-Fi. AirTrain uses the DiLoCo algorithm. Each Mac trains independently for 500 steps, then syncs only the difference. One sync per 500 steps instead of one per step. 500x less network communication. Wi-Fi actually works. The entire sync takes ~2 seconds. Here's what makes it wild: → Zero-config discovery. Devices find each other automatically via mDNS/Bonjour. Same protocol as AirDrop. → Fault tolerant. Nodes can join and leave mid-training without killing the run. → Checkpoint relay. Train for a few hours, export a checkpoint, hand it off to someone else to continue. Like a relay race for ML training. → Built on Apple's MLX framework. Native to M1/M2/M3/M4/M5 unified memory. No host-to-device copy overhead. → Local dashboard. Real-time loss curves, peer monitoring, throughput metrics in your browser. Here's the wildest part: An M4 Max with 128GB unified memory can train a 70B parameter model without offloading. An NVIDIA RTX 4090 has 24GB VRAM. Apple Silicon gets ~245-460 GFLOPS per watt. Training on MacBooks costs almost nothing in electricity compared to cloud GPUs. And there are hundreds of millions of Apple Silicon Macs in the world. The math: Traditional DDP: 1 sync per step = 50 GB/s required AirTrain (DiLoCo): 1 sync per 500 steps = 0.1 GB/s required Wi-Fi handles 0.1 GB/s. That's it. That's the breakthrough. They even built a community platform at with live session browsing, checkpoint sharing, and a contributor leaderboard. Training a 124M parameter GPT-2? Instead of renting cloud GPUs at $3/hr, pool three MacBooks in a coffee shop and train for free. MIT licensed. Built in Python. 1 contributor. Early stage but the idea is insane. 100% Open Source. (Link in the comments)
0:44

Cloud GPU training is a scam. A single M4 MacBook does 2.9 TFLOPS. Seven friends with MacBooks match an NVIDIA A100. Alexander Hayes just open-sourced a tool that makes this work over Wi-Fi. It's called AirTrain. Here's how it works: Traditional distributed training (DDP) syncs gradients after every single step. For a 124M parameter model, that's ~500MB exchanged per step. You need 50 GB/s of sustained bandwidth. Impossible over Wi-Fi. AirTrain uses the DiLoCo algorithm. Each Mac trains independently for 500 steps, then syncs only the difference. One sync per 500 steps instead of one per step. 500x less network communication. Wi-Fi actually works. The entire sync takes ~2 seconds. Here's what makes it wild: → Zero-config discovery. Devices find each other automatically via mDNS/Bonjour. Same protocol as AirDrop. → Fault tolerant. Nodes can join and leave mid-training without killing the run. → Checkpoint relay. Train for a few hours, export a checkpoint, hand it off to someone else to continue. Like a relay race for ML training. → Built on Apple's MLX framework. Native to M1/M2/M3/M4/M5 unified memory. No host-to-device copy overhead. → Local dashboard. Real-time loss curves, peer monitoring, throughput metrics in your browser. Here's the wildest part: An M4 Max with 128GB unified memory can train a 70B parameter model without offloading. An NVIDIA RTX 4090 has 24GB VRAM. Apple Silicon gets ~245-460 GFLOPS per watt. Training on MacBooks costs almost nothing in electricity compared to cloud GPUs. And there are hundreds of millions of Apple Silicon Macs in the world. The math: Traditional DDP: 1 sync per step = 50 GB/s required AirTrain (DiLoCo): 1 sync per 500 steps = 0.1 GB/s required Wi-Fi handles 0.1 GB/s. That's it. That's the breakthrough. They even built a community platform at with live session browsing, checkpoint sharing, and a contributor leaderboard. Training a 124M parameter GPT-2? Instead of renting cloud GPUs at $3/hr, pool three MacBooks in a coffee shop and train for free. MIT licensed. Built in Python. 1 contributor. Early stage but the idea is insane. 100% Open Source. (Link in the comments)

Guri Singh

160,201 views • 3 months ago

The bottleneck in AI has quietly shifted. - It's not the models. They are capable. - It's not the frameworks. They are mature. - It's not even the data, in many cases. When you want to train a model today, the first question isn't "what architecture should I use?" Instead, it's: "Where am I going to get infrastructure that actually works?" Not just GPUs but the entire stack: compute, deployment, scaling, storage. The traditional path is major cloud providers or specialized GPU clouds. Both have the same problem: they're built for enterprises with committed workloads, minimum spend requirements, contract negotiations, and involve quota approvals that take days. Even the "on-demand" options require you to piece together training, deployment, and scaling across different services. By the time you're actually training, hours, if not days, have passed. And there's a subtler cost: part of your brain is always managing infrastructure instead of thinking about the actual problem. I've been using Runpod for a while now, and it's the closest I've found to infrastructure that just disappears. I pay for the serverless solution by the second, and stop when I'm done. This sounds like it should be the default across all providers, but it isn't. For instance, when I'm prototyping, I don't need an H100. Instead, I need the flexibility to use cheaper GPUs that are actually available, where I can iterate fast and not worry about cost. An A40 at a few cents per hour is perfect for this. Then, when the approach is validated, I scale up. This matches how good engineering actually works. Running distributed training across multiple nodes for multi-GPU training usually requires significant infra work. RunPod abstracts most of this away. A lot of the advantage in AI comes from iteration speed. Infra that adds days of latency to that loop is a real cost, even if it's hard to measure. But good infra gets out of your way. It's available when you need it, invisible when you don't. In the video below, I have shown a simple model training workflow trained using PyTorch in Jupyter Lab. It runs in a dedicated PyTorch Pod hosted on Runpod, and I worked with the team to put this together for you. Find a link to start using Runpod in the replies!
0:23

The bottleneck in AI has quietly shifted. - It's not the models. They are capable. - It's not the frameworks. They are mature. - It's not even the data, in many cases. When you want to train a model today, the first question isn't "what architecture should I use?" Instead, it's: "Where am I going to get infrastructure that actually works?" Not just GPUs but the entire stack: compute, deployment, scaling, storage. The traditional path is major cloud providers or specialized GPU clouds. Both have the same problem: they're built for enterprises with committed workloads, minimum spend requirements, contract negotiations, and involve quota approvals that take days. Even the "on-demand" options require you to piece together training, deployment, and scaling across different services. By the time you're actually training, hours, if not days, have passed. And there's a subtler cost: part of your brain is always managing infrastructure instead of thinking about the actual problem. I've been using Runpod for a while now, and it's the closest I've found to infrastructure that just disappears. I pay for the serverless solution by the second, and stop when I'm done. This sounds like it should be the default across all providers, but it isn't. For instance, when I'm prototyping, I don't need an H100. Instead, I need the flexibility to use cheaper GPUs that are actually available, where I can iterate fast and not worry about cost. An A40 at a few cents per hour is perfect for this. Then, when the approach is validated, I scale up. This matches how good engineering actually works. Running distributed training across multiple nodes for multi-GPU training usually requires significant infra work. RunPod abstracts most of this away. A lot of the advantage in AI comes from iteration speed. Infra that adds days of latency to that loop is a real cost, even if it's hard to measure. But good infra gets out of your way. It's available when you need it, invisible when you don't. In the video below, I have shown a simple model training workflow trained using PyTorch in Jupyter Lab. It runs in a dedicated PyTorch Pod hosted on Runpod, and I worked with the team to put this together for you. Find a link to start using Runpod in the replies!

Avi Chawla

13,696 views • 6 months ago

I just vibe coded a static ad generator in Claude Code that creates 100+ Meta ads in minutes. All using the new, insane ChatGPT Images 2.0 model. One competitor ad + your product photo + your brand kit = dozens of on-brand variations, each targeting a different customer persona. Built 100% in Claude Code on the new ChatGPT Images 2.0. Perfect for DTC brands and agencies who need more statics at scale. Here's how it works: → Upload any competitor ad as your reference template → Add your product photos and brand kit (colors, fonts, logos) → AI generates 10 customer profiles from your brand research → Pick how many variations you want (10, 20, 30) → Tool fires every prompt to ChatGPT Images 2.0 with persona-specific copy for each one No designer back-and-forth. No Canva templates. No generic "Shop Now" on everything. What you get: → Ads that mirror winning concepts in your brand's voice → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Copy targeted to specific customer pain points and personas → Multi-brand/client support with saved brand kits → Reusable customer profiles you build once and generate from forever I recorded a full walkthrough showing exactly how this works, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "STATICS" And I'll send it over (must be following so I can DM)
0:31

I just vibe coded a static ad generator in Claude Code that creates 100+ Meta ads in minutes. All using the new, insane ChatGPT Images 2.0 model. One competitor ad + your product photo + your brand kit = dozens of on-brand variations, each targeting a different customer persona. Built 100% in Claude Code on the new ChatGPT Images 2.0. Perfect for DTC brands and agencies who need more statics at scale. Here's how it works: → Upload any competitor ad as your reference template → Add your product photos and brand kit (colors, fonts, logos) → AI generates 10 customer profiles from your brand research → Pick how many variations you want (10, 20, 30) → Tool fires every prompt to ChatGPT Images 2.0 with persona-specific copy for each one No designer back-and-forth. No Canva templates. No generic "Shop Now" on everything. What you get: → Ads that mirror winning concepts in your brand's voice → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Copy targeted to specific customer pain points and personas → Multi-brand/client support with saved brand kits → Reusable customer profiles you build once and generate from forever I recorded a full walkthrough showing exactly how this works, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "STATICS" And I'll send it over (must be following so I can DM)

Mike Futia

52,386 views • 3 months ago

I just watched AI agents map Nazi escape routes across two continents. This is the coolest fork of our agentic RAG framework that I've seen so far 🔽 𝗩𝗲𝗿𝗼 𝗗𝗮𝗹𝗹'𝗔𝗴𝗹𝗶𝗼 built a complete 𝗢𝗦𝗜𝗡𝗧 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 on top of Elysia, and it's fully open source. 𝗜𝗻𝘁𝗲𝗹𝗹𝘆𝗪𝗲𝗮𝘃𝗲 takes Elysia's decision tree architecture and extends it for intelligence analysis. Upload documents, ask questions in natural language, and get comprehensive intelligence assessments with entity extraction, geospatial mapping, and network analysis. Main features: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗘𝗻𝘁𝗶𝘁𝘆 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Uses GLiNER for zero-shot recognition of 7 entity types (persons, organizations, locations, dates, events, laws, cryptonyms). No training required. 𝗧𝘄𝗼-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘃𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: The "Quartermaster" agent maps the information landscape (discovers archives, classifies access levels), while the "Case Officer" conducts hypothesis-driven investigations with confidence scoring and evidence citations. 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 + 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Interactive 3D maps (Mapbox) and force-directed network graphs (vis-network) to reveal hidden connections between entities using Elysia's build in customizable display types. 𝟲-𝗣𝗵𝗮𝘀𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: Automated pipeline that goes from extraction → relationship mapping → geospatial analysis → network analysis → pattern detection → synthesis, with automatic task generation for follow-up investigation. They even built a demo analyzing 17 historical documents about Nazi escape networks to South America (1945-1962). The system automatically extracted entities, mapped three distinct escape routes, and generated hypotheses with confidence scores. This is exactly what we hoped people would build with Elysia 💚 so so cool to see. Elysia's decision tree architecture makes it straightforward to add domain-specific tools (like GLiNER entity extraction or archive discovery or custom map displays) while keeping all the core functionality (error handling, streaming, self-healing, transparency) that comes built-in. Check out the repo: Interactive demo: Elysia blog post: Huge shoutout to the contributors for building this and sharing it with the community! 🫶
1:01

I just watched AI agents map Nazi escape routes across two continents. This is the coolest fork of our agentic RAG framework that I've seen so far 🔽 𝗩𝗲𝗿𝗼 𝗗𝗮𝗹𝗹'𝗔𝗴𝗹𝗶𝗼 built a complete 𝗢𝗦𝗜𝗡𝗧 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 on top of Elysia, and it's fully open source. 𝗜𝗻𝘁𝗲𝗹𝗹𝘆𝗪𝗲𝗮𝘃𝗲 takes Elysia's decision tree architecture and extends it for intelligence analysis. Upload documents, ask questions in natural language, and get comprehensive intelligence assessments with entity extraction, geospatial mapping, and network analysis. Main features: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗘𝗻𝘁𝗶𝘁𝘆 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Uses GLiNER for zero-shot recognition of 7 entity types (persons, organizations, locations, dates, events, laws, cryptonyms). No training required. 𝗧𝘄𝗼-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘃𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: The "Quartermaster" agent maps the information landscape (discovers archives, classifies access levels), while the "Case Officer" conducts hypothesis-driven investigations with confidence scoring and evidence citations. 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 + 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Interactive 3D maps (Mapbox) and force-directed network graphs (vis-network) to reveal hidden connections between entities using Elysia's build in customizable display types. 𝟲-𝗣𝗵𝗮𝘀𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: Automated pipeline that goes from extraction → relationship mapping → geospatial analysis → network analysis → pattern detection → synthesis, with automatic task generation for follow-up investigation. They even built a demo analyzing 17 historical documents about Nazi escape networks to South America (1945-1962). The system automatically extracted entities, mapped three distinct escape routes, and generated hypotheses with confidence scores. This is exactly what we hoped people would build with Elysia 💚 so so cool to see. Elysia's decision tree architecture makes it straightforward to add domain-specific tools (like GLiNER entity extraction or archive discovery or custom map displays) while keeping all the core functionality (error handling, streaming, self-healing, transparency) that comes built-in. Check out the repo: Interactive demo: Elysia blog post: Huge shoutout to the contributors for building this and sharing it with the community! 🫶

Victoria Slocum

19,562 views • 6 months ago

This custom GPT writes perfect Sora 2 prompts every time 🤯 Most people are wasting hours trying to get Sora 2 to generate decent UGC videos. The problem isn't Sora 2. It's you (and your prompts). Here's what I learned after generating 100+ UGC videos with Sora 2: Sora 2 needs hyper-specific prompts with shot composition, timing, camera movement, and scene details. Most people write: "Create a UGC video for my protein bar" What you get? Terrible results. What actually works: "9:16 vertical handheld shot, natural lighting, female creator in kitchen holding [brand] protein bar at eye level, excited testimonial energy, 0-3s hook showing bite and reaction, shallow depth of field, warm color grade" That's the difference between amateur & professional output. So I built a custom GPT that does this automatically: → Tell it your product (protein bar, skincare, SaaS tool, etc.) → It generates a detailed timeline-based Sora 2 prompt → Includes shot composition, camera angles, timing breakdowns → Optimized for 9:16 UGC TikTok/Reels format → Copy/paste directly into Sora 2 No more guessing. No more mediocre results. Just professional UGC prompts in 10 seconds. Want free access to the Sora 2 Prompt Generator GPT? > Comment "GPT" > Like this post And I'll send it over (must be following so I can DM)
0:23

This custom GPT writes perfect Sora 2 prompts every time 🤯 Most people are wasting hours trying to get Sora 2 to generate decent UGC videos. The problem isn't Sora 2. It's you (and your prompts). Here's what I learned after generating 100+ UGC videos with Sora 2: Sora 2 needs hyper-specific prompts with shot composition, timing, camera movement, and scene details. Most people write: "Create a UGC video for my protein bar" What you get? Terrible results. What actually works: "9:16 vertical handheld shot, natural lighting, female creator in kitchen holding [brand] protein bar at eye level, excited testimonial energy, 0-3s hook showing bite and reaction, shallow depth of field, warm color grade" That's the difference between amateur & professional output. So I built a custom GPT that does this automatically: → Tell it your product (protein bar, skincare, SaaS tool, etc.) → It generates a detailed timeline-based Sora 2 prompt → Includes shot composition, camera angles, timing breakdowns → Optimized for 9:16 UGC TikTok/Reels format → Copy/paste directly into Sora 2 No more guessing. No more mediocre results. Just professional UGC prompts in 10 seconds. Want free access to the Sora 2 Prompt Generator GPT? > Comment "GPT" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

49,783 views • 9 months ago

I just vibe-coded a Meta Ads Bulk Uploader in Claude Code that uploads dozens of Facebook and Instagram ads in minutes 🤯 Drag and drop your creatives, connect your live Meta account, publish directly through the Marketing API. Built 100% in Claude Code. Perfect for media buyers and DTC brands who are still uploading ads one at a time in Ads Manager. If you're a media buyer spending 10+ hours a week uploading ads one at a time in Ads Manager — attaching videos, picking thumbnails, naming ads, toggling enhancements, repeating 50 times ... This eliminates the entire workflow: → Drag and drop dozens of images/videos at once → Add multiple primary texts, headlines, and descriptions per batch → Pick your campaign and ad set directly from your live Meta account → Clone existing ad sets with one click → Upload custom thumbnails, pick your CTA, launch paused or active No uploading ads one by one. No 2-3 hours of clicking. No $99/month SaaS subscription. What this tool does: → A bulk uploader that publishes directly through Meta's Marketing API → Full creative control — thumbnails, CTAs, ad names, enhancements — all in one shot → Ad set cloning so you can scale winning setups instantly → A free alternative to the $99/month tools that do the exact same thing I'm giving away every prompt I used to build it so you can vibe-code the whole thing yourself. Want all the prompts? > Like this post > Comment "BULK" And I'll send it over (must be following so I can DM)
0:20

I just vibe-coded a Meta Ads Bulk Uploader in Claude Code that uploads dozens of Facebook and Instagram ads in minutes 🤯 Drag and drop your creatives, connect your live Meta account, publish directly through the Marketing API. Built 100% in Claude Code. Perfect for media buyers and DTC brands who are still uploading ads one at a time in Ads Manager. If you're a media buyer spending 10+ hours a week uploading ads one at a time in Ads Manager — attaching videos, picking thumbnails, naming ads, toggling enhancements, repeating 50 times ... This eliminates the entire workflow: → Drag and drop dozens of images/videos at once → Add multiple primary texts, headlines, and descriptions per batch → Pick your campaign and ad set directly from your live Meta account → Clone existing ad sets with one click → Upload custom thumbnails, pick your CTA, launch paused or active No uploading ads one by one. No 2-3 hours of clicking. No $99/month SaaS subscription. What this tool does: → A bulk uploader that publishes directly through Meta's Marketing API → Full creative control — thumbnails, CTAs, ad names, enhancements — all in one shot → Ad set cloning so you can scale winning setups instantly → A free alternative to the $99/month tools that do the exact same thing I'm giving away every prompt I used to build it so you can vibe-code the whole thing yourself. Want all the prompts? > Like this post > Comment "BULK" And I'll send it over (must be following so I can DM)

Mike Futia

42,670 views • 4 months ago

This will retire 90% of RAG systems with dignity (and a sad song playlist). Powered by DSPy: If you're still building "text in, text out" chatbots that only perform blind vector and text searches, you're not gonna make it! My team just dropped Elysia, and it's not just an incremental successor to Verba… It's a whole rethink of how we interact with our data using AI. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗹𝘆𝗶𝘀𝗮? An open-source platform for building agentic RAG architectures. It learns from your preferences, intelligently categorizes, labels, and searches through your data, and provides complete transparency into its decision-making process. The long & exciting feature list: • 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗧𝗿𝗲𝗲 𝗔𝗴𝗲𝗻𝘁𝘀: Elysia’s core is a customizable decision tree, and it visualizes its entire reasoning process, showing you why it chooses a specific tool or path. It enables advanced error handling, self-healing from failed queries, and prevents infinite loops. You can also add custom tools and branches to build complex, state-aware workflows. • 𝗗𝗮𝘁𝗮 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀: Before it even attempts a query, Elysia performs a full analysis of your data collections. This eliminates the blind search problem plaguing most RAG systems and allows for far more complex and accurate query generation. • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Your RAG pipeline shouldn't be limited to text, right? That’s why Elysia analyzes each query's results and chooses the best way to display them, from tables and charts to product cards and GitHub tickets. It also features a comprehensive data explorer with search, sorting, and filtering capabilities. • 𝗛𝘆𝗽𝗲𝗿-𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘃𝗶𝗮 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: It uses your positively-rated queries as few-shot examples to improve future responses. This allows you to use smaller, faster models that perform like larger ones over time, cutting costs without sacrificing quality for most use cases. • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Elysia chunks documents at query time. It performs initial searches on document-level vectors and only chunks relevant documents on the fly, storing them in a parallel quantized collection with cross references for future use. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗰𝗸 Elysia is built from scratch on Weaviate, using its native features like named vectors, a variety of search types, filters, cross references, quantization, etc. It uses DSPy for LLM interactions and is delivered as a production-ready application via FastAPI, serving a NextJS frontend as static HTML. Also available as a Python package via pip: 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗲𝗹𝘆𝘀𝗶𝗮-𝗮𝗶 Type: 𝗲𝗹𝘆𝘀𝗶𝗮 𝘀𝘁𝗮𝗿𝘁 Connect your Weaviate cluster and go explore what’s possible.
0:35

This will retire 90% of RAG systems with dignity (and a sad song playlist). Powered by DSPy: If you're still building "text in, text out" chatbots that only perform blind vector and text searches, you're not gonna make it! My team just dropped Elysia, and it's not just an incremental successor to Verba… It's a whole rethink of how we interact with our data using AI. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗹𝘆𝗶𝘀𝗮? An open-source platform for building agentic RAG architectures. It learns from your preferences, intelligently categorizes, labels, and searches through your data, and provides complete transparency into its decision-making process. The long & exciting feature list: • 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗧𝗿𝗲𝗲 𝗔𝗴𝗲𝗻𝘁𝘀: Elysia’s core is a customizable decision tree, and it visualizes its entire reasoning process, showing you why it chooses a specific tool or path. It enables advanced error handling, self-healing from failed queries, and prevents infinite loops. You can also add custom tools and branches to build complex, state-aware workflows. • 𝗗𝗮𝘁𝗮 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀: Before it even attempts a query, Elysia performs a full analysis of your data collections. This eliminates the blind search problem plaguing most RAG systems and allows for far more complex and accurate query generation. • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Your RAG pipeline shouldn't be limited to text, right? That’s why Elysia analyzes each query's results and chooses the best way to display them, from tables and charts to product cards and GitHub tickets. It also features a comprehensive data explorer with search, sorting, and filtering capabilities. • 𝗛𝘆𝗽𝗲𝗿-𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘃𝗶𝗮 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: It uses your positively-rated queries as few-shot examples to improve future responses. This allows you to use smaller, faster models that perform like larger ones over time, cutting costs without sacrificing quality for most use cases. • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Elysia chunks documents at query time. It performs initial searches on document-level vectors and only chunks relevant documents on the fly, storing them in a parallel quantized collection with cross references for future use. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗰𝗸 Elysia is built from scratch on Weaviate, using its native features like named vectors, a variety of search types, filters, cross references, quantization, etc. It uses DSPy for LLM interactions and is delivered as a production-ready application via FastAPI, serving a NextJS frontend as static HTML. Also available as a Python package via pip: 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗲𝗹𝘆𝘀𝗶𝗮-𝗮𝗶 Type: 𝗲𝗹𝘆𝘀𝗶𝗮 𝘀𝘁𝗮𝗿𝘁 Connect your Weaviate cluster and go explore what’s possible.

Philip Vollet

93,598 views • 11 months ago

Claude Code can now make full videos from your terminal.. Not slideshows. Not text on screen.. Actual motion graphics with animations, transitions, custom photos, and background music. [ SHARED A TUTORIAL BELOW EDITED WITH THIS SETUP IN JUST 5mins ] ▫️Here's the setup: Claude Code + Remotion Remotion is a React based framework that renders video programmatically. You describe what you want in plain English, Claude writes the React components and Remotion renders it into a real MP4. What you can actually do with this: > Generate 9:16 vertical videos for TikTok / Reels / Shorts > Add animated text with viral hooks and safe zones > Pull live web screenshots directly into your scenes using Chrome MCP > Fact-check your content in real time with Perplexity MCP > Drop in your own photos and background music > Edit existing talking-head footage cut bloopers, add captions > Schedule posts to your socials straight from the terminal ▫️How to set it up (takes 5 minutes) : > Make sure you have Node.js installed ( node -v to check ) > Create a new Remotion project: npx create-video@latest Pick the Blank template, enable TailwindCSS, and install the Skills package when prompted. > Install dependencies: cd my-video npm install > Start the preview server: npm run dev > Open Claude Code in the same project folder: cd my-video claude That's it. You can now prompt videos in plain English. If you already have a Remotion project, just add the skill directly: npx skills add remotion-dev/skills This drops a SKILL.md into your project that gives Claude expert knowledge of Remotion.. animations, compositions, captions, assets, 3D content everything. Example prompt you can steal: "Create a 30-second 9:16 vertical video about the top 3 AI tools this week. Use animated text with a hook in the first 2 seconds. Add smooth transitions between scenes. Keep text in the safe zone for TikTok. Use a dark tech aesthetic with blue accent colors." Claude writes all the React code, renders a preview, you tweak with natural language, and export when ready. The crazy part is this whole pipeline is local, free (minus your Claude sub), and you never open a video editor. imo this kills CapCut for anyone making info-style content. You describe the video in English and get back a rendered MP4. try it now.
2:01

Claude Code can now make full videos from your terminal.. Not slideshows. Not text on screen.. Actual motion graphics with animations, transitions, custom photos, and background music. [ SHARED A TUTORIAL BELOW EDITED WITH THIS SETUP IN JUST 5mins ] ▫️Here's the setup: Claude Code + Remotion Remotion is a React based framework that renders video programmatically. You describe what you want in plain English, Claude writes the React components and Remotion renders it into a real MP4. What you can actually do with this: > Generate 9:16 vertical videos for TikTok / Reels / Shorts > Add animated text with viral hooks and safe zones > Pull live web screenshots directly into your scenes using Chrome MCP > Fact-check your content in real time with Perplexity MCP > Drop in your own photos and background music > Edit existing talking-head footage cut bloopers, add captions > Schedule posts to your socials straight from the terminal ▫️How to set it up (takes 5 minutes) : > Make sure you have Node.js installed ( node -v to check ) > Create a new Remotion project: npx create-video@latest Pick the Blank template, enable TailwindCSS, and install the Skills package when prompted. > Install dependencies: cd my-video npm install > Start the preview server: npm run dev > Open Claude Code in the same project folder: cd my-video claude That's it. You can now prompt videos in plain English. If you already have a Remotion project, just add the skill directly: npx skills add remotion-dev/skills This drops a SKILL.md into your project that gives Claude expert knowledge of Remotion.. animations, compositions, captions, assets, 3D content everything. Example prompt you can steal: "Create a 30-second 9:16 vertical video about the top 3 AI tools this week. Use animated text with a hook in the first 2 seconds. Add smooth transitions between scenes. Keep text in the safe zone for TikTok. Use a dark tech aesthetic with blue accent colors." Claude writes all the React code, renders a preview, you tweak with natural language, and export when ready. The crazy part is this whole pipeline is local, free (minus your Claude sub), and you never open a video editor. imo this kills CapCut for anyone making info-style content. You describe the video in English and get back a rendered MP4. try it now.

Axel Bitblaze 🪓

31,250 views • 3 months ago

-> someone cloned claude -> design interface and -> made it completely free -> it's work on YouTube -> and also suitable for kids -> it’s called open design -> and it’s live on github -> same clean split-screen ui -> you get in claude artifacts -> prompt on the left, live -> design/code preview on -> the right, type what you -> want to build and it -> generates the ui in real -> time, but here’s the twist -> you pick the ai model -> not locked into one -> company, want to use -> gemini, mistral, llama, -> deepseek any model -> with an api work -> if you’re running local -> models with ollama -> that works too -> no subscription walls -> the big difference -> vs claude artifacts -> works with any free -> ai model you’re not -> paying $20/mo just to -> design, use free tiers -> local models, or whatever -> you already have access to -> fully local, your prompts -> and code never leave -> your machine unless -> you want them to -> no data training -> no cloud storage -> privacy by default -> no usage limits -> claude cuts you off -> after a few designs -> here you can generate, -> iterate, break things -> and rebuild all day -> the only limit is your -> don’t like how a button -> works, change it -> want to add your own -> components, go ahead -> you own the tool -> so if you’ve been gatekept -> by paywalls or worried -> about sensitive prompts -> going to some company’s -> servers, this fixes that. -> same workflow, more -> control, zero monthly fee
0:20

-> someone cloned claude -> design interface and -> made it completely free -> it's work on YouTube -> and also suitable for kids -> it’s called open design -> and it’s live on github -> same clean split-screen ui -> you get in claude artifacts -> prompt on the left, live -> design/code preview on -> the right, type what you -> want to build and it -> generates the ui in real -> time, but here’s the twist -> you pick the ai model -> not locked into one -> company, want to use -> gemini, mistral, llama, -> deepseek any model -> with an api work -> if you’re running local -> models with ollama -> that works too -> no subscription walls -> the big difference -> vs claude artifacts -> works with any free -> ai model you’re not -> paying $20/mo just to -> design, use free tiers -> local models, or whatever -> you already have access to -> fully local, your prompts -> and code never leave -> your machine unless -> you want them to -> no data training -> no cloud storage -> privacy by default -> no usage limits -> claude cuts you off -> after a few designs -> here you can generate, -> iterate, break things -> and rebuild all day -> the only limit is your -> don’t like how a button -> works, change it -> want to add your own -> components, go ahead -> you own the tool -> so if you’ve been gatekept -> by paywalls or worried -> about sensitive prompts -> going to some company’s -> servers, this fixes that. -> same workflow, more -> control, zero monthly fee

BeingInvested

12,134 views • 1 month ago

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.
0:47

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

75,901 views • 2 days ago

JUST ONE DAY after the tragic National Guardsmen shooting, RADICAL GROUPS in Dearborn, Michigan, is already calling for a REVOLUTION in AMERICA. If you live in America and are NOT here to contribute in a positive way and live in harmony with Western civilization, you need to GTFO. We have enough problems here as it is and do NOT need a whole new group of people, with a whole new group of problems who want to change America into where they just fled from. You must understand that this entire situation, to flood the U.S. and other countries were entirely engineered to destroy and take over multiple countries. Look at the U.K... Completely occupied and no 2A. The U.S. will fall if we don't end thIs NOW. There are also multiple players, groups, agencies, organizations, etc., who are all involved, and George Soros is one of them who has helped millions of refugees enter countries to collapse their societies, even mastercard teamed up with Soros who has spent BILLIONS aiding migrants into America and Europe. George Soros is linked through the World Relief organization and the Evangelical Churches through USAID and other NGOs like his Open Society Foundation. The National Guardsmen shooter was brought in by the Biden administration and settled from Afghanistan to Washington State by an NGO linked through Soros. Since he did have intelligence ties to the CIA, there is no confirmation yet that this was an inside job or could have been connected to radicalism. Either way, it doesn't matter. Because if he wasn't here, this would have never happened. The terror attacks on the guardsmen yesterday are a perfect example of the outcome of what aiding these foreigners who do NOT mix with Western values, ideas, and ways of life. They want to take over and destroy us because their religion says so. There is nothing wrong with good people from foreign countries going through the proper procedures to become a U.S. citizen and contribute in a positive manner and aren't here to murder, rape, steal, or start revolutions. We have enough to worry about within our own country and our own people, and we don't need others who don't want to adapt to Western civilization. They want to destroy it. Ladies and gentlemen, it is time to call your representatives and demand that they send ALL of them back before it's too late.
1:13

JUST ONE DAY after the tragic National Guardsmen shooting, RADICAL GROUPS in Dearborn, Michigan, is already calling for a REVOLUTION in AMERICA. If you live in America and are NOT here to contribute in a positive way and live in harmony with Western civilization, you need to GTFO. We have enough problems here as it is and do NOT need a whole new group of people, with a whole new group of problems who want to change America into where they just fled from. You must understand that this entire situation, to flood the U.S. and other countries were entirely engineered to destroy and take over multiple countries. Look at the U.K... Completely occupied and no 2A. The U.S. will fall if we don't end thIs NOW. There are also multiple players, groups, agencies, organizations, etc., who are all involved, and George Soros is one of them who has helped millions of refugees enter countries to collapse their societies, even mastercard teamed up with Soros who has spent BILLIONS aiding migrants into America and Europe. George Soros is linked through the World Relief organization and the Evangelical Churches through USAID and other NGOs like his Open Society Foundation. The National Guardsmen shooter was brought in by the Biden administration and settled from Afghanistan to Washington State by an NGO linked through Soros. Since he did have intelligence ties to the CIA, there is no confirmation yet that this was an inside job or could have been connected to radicalism. Either way, it doesn't matter. Because if he wasn't here, this would have never happened. The terror attacks on the guardsmen yesterday are a perfect example of the outcome of what aiding these foreigners who do NOT mix with Western values, ideas, and ways of life. They want to take over and destroy us because their religion says so. There is nothing wrong with good people from foreign countries going through the proper procedures to become a U.S. citizen and contribute in a positive manner and aren't here to murder, rape, steal, or start revolutions. We have enough to worry about within our own country and our own people, and we don't need others who don't want to adapt to Western civilization. They want to destroy it. Ladies and gentlemen, it is time to call your representatives and demand that they send ALL of them back before it's too late.

The SCIF

11,015 views • 8 months ago

is our AI project to make computing feel more human L A N D E R Here are the 4 best demo videos of the magic of DATA in action. DATA is a personalized assistant who knows and remembers every conversation you have with it accross your iPhone, Mac, iPad, Watch, Texts, Emails, and HomePods. You can talk to DATA right in your AirPods or text it just like a person. DATA can read, write, understand, speak any language, and translate between them. It can help with real work and home life tasks like research, writing, scheduling, reminders, and triage. And it's easily customizable so you can have DATA automatically do whatever you want whenever you want with just a few taps and natural language instructions - no code required. DATA can do just about anything you can do on your phone on your behalf automatically including very advanced things Siri can't, like summarizing, analyzing, and drafting replies or writing documents. It can read web pages, texts or emails you show it, or PDFs of any kind. It can do other real world tasks that require complex analysis and common sense too, like: - figure out where the nearest beach is (even when you're in Colorado) and instantly fetch the current surf report up to the current minute. - summarize and drafting replies to entire email chains - plan out entire work projects or multi-day vacations on your calendar - sketch out ideas for you in picture form or drafting Notion pages with charts and graphs. DATA can also use its own judgement to determine when to run an action or not, even if you've scheduled it, allowing you to make VERY complex automations that require many different inputs to make a decision, like for example: - only opening the blinds on your lunch break if it's sunny out and you're working from home. DATA works natively and easily with Apple HomeKit & other shortcuts. DATA can also take initiative and check in with you throughout the day by voice or text and proactively send messages to you and others on your behalf based on your personal and professional goals, current tasks, and calendar. DATA can integrate with many apps on your phone, and is compatible with multiple large AI language models. I've gotten to make a few demo videos that I think really capture how powerful DATA can be for every day life. Here they are all in one tweet. Make sure your sound is on as you watch them. 1. This is the first demo video I ever made from April 19th, 2023. It walks through all the ways you can interact with and use the DATA shortcuts. Everything from saying "Hey Siri" to tapping on custom apps on your home-screen. 2. The second demo video was made May 5 and is an example use case I made of how commands work - commands allow DATA to actually run actions on your phone like taking pictures and sending messages. This demo shows me taking a picture of an email template, and data drafting an email based on that template. It's gotten much better at realizing when it has just run a command and incorporating that information naturally into the conversation now, especially on GPT-4. 3. This third Commands video, May 12 is a walkthrough of ALL the phone functions that commands allow DATA to do: sending texts and emails, making pictures, seeing pictures, reading things, and scheduling events. Since this video we've added auto-replies to texts and emails, summarizing documents, writing documents, health app data retrieval, web surfing, scheduling alarms, making playlists, and more. 4. This last demo I made today, June 15, shows everything DATA does working in concert to generate a crazy detailed morning briefing with background music - including making a unique playlist and giving a detailed analysis of current events complete with Ski & Surf conditions near me other live information from the internet. So now that you've seen everything DATA can do, what's the coolest feature? What features should we add? What would you use DATA for first?
12:54

is our AI project to make computing feel more human L A N D E R Here are the 4 best demo videos of the magic of DATA in action. DATA is a personalized assistant who knows and remembers every conversation you have with it accross your iPhone, Mac, iPad, Watch, Texts, Emails, and HomePods. You can talk to DATA right in your AirPods or text it just like a person. DATA can read, write, understand, speak any language, and translate between them. It can help with real work and home life tasks like research, writing, scheduling, reminders, and triage. And it's easily customizable so you can have DATA automatically do whatever you want whenever you want with just a few taps and natural language instructions - no code required. DATA can do just about anything you can do on your phone on your behalf automatically including very advanced things Siri can't, like summarizing, analyzing, and drafting replies or writing documents. It can read web pages, texts or emails you show it, or PDFs of any kind. It can do other real world tasks that require complex analysis and common sense too, like: - figure out where the nearest beach is (even when you're in Colorado) and instantly fetch the current surf report up to the current minute. - summarize and drafting replies to entire email chains - plan out entire work projects or multi-day vacations on your calendar - sketch out ideas for you in picture form or drafting Notion pages with charts and graphs. DATA can also use its own judgement to determine when to run an action or not, even if you've scheduled it, allowing you to make VERY complex automations that require many different inputs to make a decision, like for example: - only opening the blinds on your lunch break if it's sunny out and you're working from home. DATA works natively and easily with Apple HomeKit & other shortcuts. DATA can also take initiative and check in with you throughout the day by voice or text and proactively send messages to you and others on your behalf based on your personal and professional goals, current tasks, and calendar. DATA can integrate with many apps on your phone, and is compatible with multiple large AI language models. I've gotten to make a few demo videos that I think really capture how powerful DATA can be for every day life. Here they are all in one tweet. Make sure your sound is on as you watch them. 1. This is the first demo video I ever made from April 19th, 2023. It walks through all the ways you can interact with and use the DATA shortcuts. Everything from saying "Hey Siri" to tapping on custom apps on your home-screen. 2. The second demo video was made May 5 and is an example use case I made of how commands work - commands allow DATA to actually run actions on your phone like taking pictures and sending messages. This demo shows me taking a picture of an email template, and data drafting an email based on that template. It's gotten much better at realizing when it has just run a command and incorporating that information naturally into the conversation now, especially on GPT-4. 3. This third Commands video, May 12 is a walkthrough of ALL the phone functions that commands allow DATA to do: sending texts and emails, making pictures, seeing pictures, reading things, and scheduling events. Since this video we've added auto-replies to texts and emails, summarizing documents, writing documents, health app data retrieval, web surfing, scheduling alarms, making playlists, and more. 4. This last demo I made today, June 15, shows everything DATA does working in concert to generate a crazy detailed morning briefing with background music - including making a unique playlist and giving a detailed analysis of current events complete with Ski & Surf conditions near me other live information from the internet. So now that you've seen everything DATA can do, what's the coolest feature? What features should we add? What would you use DATA for first?

steve

640,114 views • 3 years ago

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

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