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sub 300ms response time cnvs can receive a voice command and take action in less than 1/3 of a second. but I wanted more. so i added an option for gpt realtime which provides a fully conversational experience like jarvis. my mac os app built entirely in swift from...

49,738 просмотров • 2 месяцев назад •via X (Twitter)

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voice prompting is 4x faster than typing. but i NEEEDED more. Nvidia parakeet allows me to fully voice control an agentic development environment with commands firing in under 300ms. and it runs 100% local. I added gpt realtime 2.1 mini, its 20% faster, 7 to 20x cheaper, and lets you have full jarvis style control of your vibe coding agents. but what about orchestration? agents can spawn each other, prompt each other, and read each others output with the CNVS mcp and cli. Fable 5 can create a plan, spawn 10 grok agents to execute, and a kimi k3 agent to review. parallel agents code at 1,000s of TPS anthropic's own research shows improvements ACROSS the board for multi agent workflows over single agent but only CNVS lets you choose exactly which orchestration, worker, and reviewer agent you would like to use. grok, kimi, qwen, claude, codex... the cross agent memory system is based on real 2026 research so all agents share the same brain, its on demand so it never bloats context. what about remote agents?? You can create remote canvasses that run agents your virtual private servers, they keep working even if your mac shuts off, and you can even vibe code straight to production. CNVS is built from the ground up ENTIRELY in swift for RAW performance on apple hardware. PS - its a LIFE TIME LICENSE because you don't need another subscription. PPS - I ship updates every week based off user feedback and livestream myself building it everyday. PPPS - it uses all your existing ai subs, so no api pricing here.

Max Blade

60,658 просмотров • 1 месяц назад

This is how you get 15x fable 5 usage. Fable -> Composer 2.5 reading Fable -> GPT 5.5 execution I built CNVS to make agent orchestration dead simple and insanely visual. You can watch fable 5 delegate work to cursor, codex, open code, all your agents. using your exsisting ai subs no api pricing. The canvas is fully voice controlled locally with nvidia parakeet for fast and free, or gpt realtime 2 for a fully conversational jarvis experience. The built in mcp and cli means agentic control is bidirectional they can prompt and spawn each other and read agent states. but there is more I built it from the ground up in swift for native performance on mac os. My old 16gb m1 mbp can easily spin up and control eight agents across multiple canvasses. The hermes integration + remote canvases allow you to run agents fully in the cloud on your VPS think google docs of vibe coding. You can literally turn off your mac and they keep working, open cnvs back up and pick up where you left off. I built a cross agent memory system based on 2026 research so all your agents feel like one brain. its SIMPLE and on demand. This eliminates context bloat, and the research supports a massive uptick in cross agent performance. There is soo much more. PS - I am a father building this project 100% solo in my basement on live stream everyday, my goal is to push vibe coding to the next level PPS - CNVS is a lifetime license becuase you DO NOT need another subscription in your life.

Max Blade

29,711 просмотров • 2 месяцев назад

Now that I am home and have access to my files I decided to do a quick test with one of my Vtubing avatars. It works way better with my larger iPad Pro (less crashing/more memory and more screen space) compared to the mini so I did want to bring that up. Importing the file over as GLB was more stable than just OBJ plus it kept the color info from the previous texture. I texture all my avatars on substance painter but have been looking at an alternative when I am out and about that is not on the computer and this actually hits all the right "wants". Adding a quick video of the way the app works for me. I like that it has layers, I can alter my brushes and even add stabilization when I am drawing (which I use that extensively on procreate so that was a huge plus). The stamp tools will come in handy as well but did not show it on this video. The file itself is imported as "one mesh" but each part is treated as their own separate piece which I show in the video. I do the same thing on substance painter making it really easy for me to texture. The app then exports the texture out for each object which is the same way I do so on substance. Overall I am extremely impressed with the work Sparseal has done on this application and see how useful this will be for my current work flow of texturing. I for one love the ability to be "portable" which is a big reason I sculpt so much with Nomad Sculpt on the iPad. Having the freedom to do your work away from a desk and if you feel like doing it outside is probable why I yearn for iPad apps so much like so. Give this app a look, it is free to try on the App Store (It is $19 to be able to export, but a one time buy). It is called "Wafer" for the iPad. I already use CozyBlanket from the same creator to do re topology so I expected this level of fantastic with this current app.

Val / LatinVixen ➡️ Megaplex

17,469 просмотров • 6 месяцев назад

A little look behind the scenes: Many of you asked how I handle switching from SwiftData & CloudKit to Firebase and a different way of sharing entries with your friends. Previously, there was no need to create an account and the app just used the user's iCloud ID in the background to "sign up". Why did I do this? SwiftData is really nice to use but after working with it for over a year now, it feels still quite unstable, buggy and errors are super hard to track. Same goes for CloudKit and the CloudKit sharing that I implemented. For a growing app, in my opinion, it's just not feasible to use because errors happen regularly, you don't really know if the data is stored (and shared) correctly and the documentation on CK is just bad. I have big plans for this app (reacting to your friends' entries, Apple Health integration, tagging multiple friends in entries etc.) and all of it is now much simpler - and faster to build. The Migration part When an existing user starts the app, a little migration sheet with a couple of steps opens which can't be dismissed. The goal is that the user maintains all entries and can use the app like before :) Steps: 1) Create an Account using Apple Sign In and Firebase Auth 2) Migrate your user profile (profile picture etc.) and all your friends from SwiftData to Firestore. Here I basically iterate over all the data that is there, run a custom conversion for all the fields (some things need to be stored differently) and save them. Profile Images are now saved in Firebase Storage 3) Migrate all entries to Firestore the same way. Images and Voice Notes are now saved in Firebase Storage (that might take a bit of time, video is sped up) 4) After that, migration is basically done and the user can use the app like before. In the settings, the user can now log out or delete the account, things that weren't possible before. If for any reason the migration has to be done again, this can be triggered from the settings as well. How do I decide if a user needs to migrate? Previously, I saved the user's profile in SwiftData under a specific key (simplified). If the app finds something under this key, user is prompted for a migration. New users don't have to run through the migration. They simply start fresh with the Apple Sign in in the onboarding like seen in the referenced post. How does sharing entries now work? For sharing, I create deeplinks using branch . io now. These links basically work like every other Invite Link you've received or created. The friend opens the link, can accept your invitation and now both of you are connected as buddies. Previously using CloudKit Sharing, this process took two steps - each user had to share a link to give access to their entries. The process now is much simpler and better understand for the user. Developing the migration Tbh I thought this would be way quicker. In the end it was > 100 commits since I had to basically change every screen in the app. Working with SwiftData is quite different to how things work with Firebase (and most other frameworks), so every interaction where data was involved had to be reworked. Now the app runs on a combination of Firestore, Cloud Functions and Firebase Storage. In terms of memory, the app feels notably faster and only needs around 1/4 of the memory bandwidth it needed before. Let's see if it will be worth it! All in all super happy with the new flexibility for the app and not being limited by the technology anymore 👀

Fabian Gruß

26,781 просмотров • 1 год назад

ELON MUSK: We believe the AI5 chip will be roughly comparable performance to an NVIDIA Blackwell, and at much less than 10% of the cost Transcription: I'm super hardcore on chips right now as you may be able to tell. I have chips on the brain. I dream about chips, Literally! Because in order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient So we think we believe the AI5 chip will be probably about a third of the power of say something like a Blackwell, an NVIDIA Blackwell, which is a great chip, for roughly comparable performance. And much less than 10% of the cost. This is a chip that is very much optimized for the Tesla AI software stack. So it's not meant to be a general purpose chip, it's meant to be an amazing chip for the Tesla AI software And I mean a couple of things that I think make... like how is Tesla able to achieve such an improvement? I think it is because we are specialized. We're not trying to... you know, NVIDIA has to serve the superset of all past and future customers. So all of their requirements, all of the software that they've written has to work, which is a very difficult problem. Whereas we just need to make it work for our software. And so we're able to simplify the chip dramatically And then we also, I think we're unique in this, but like we have an integer-based system. And integer operations are fundamentally more efficient than floating point operations. So we can do floating point, but the vast majority of our inference is done in integer. Which is, if you're familiar with sort of logic gates, the simplicity of integer... it's integer is much more power efficient, much more silicon efficient, but you have to, you actually have to train for integer inference, which everyone else is training for floating point. That's kind of like a niche technical detail, but it's actually very important. So, yeah, this is going to be a great chip So this chip will be made in basically in four places: TSMC Taiwan, Samsung Korea, TSMC Arizona, and TSMC Texas. And we already know what improvements to make for AI6. So I'm hopeful that we can within less than a year of AI5 starting production, we can actually transition in the same fab to AI6 and double all of the performance metrics

X Freeze

305,109 просмотров • 10 месяцев назад

$VET, #VeFam. In this video, I demonstrate in less than 4:30 minutes how to create an AI agent on veworld(.)ai. Watch me build a Mr. Robot Monologue Writer agent. If you haven't seen Mr. Robot, I suggest you watch it! This is just early bird access. The options for tools and integrations and such are limited, but what exists is already working quite well. The process is easy peasy. The UI is simple, but effective. It asks you for... 1. Role & Purpose 2. Voice & Style 3. Behavior 4. Rules 5. Tags 6. Avatar image 7. Welcome text. 8. Test drive before publication. ... and that's about it. This free version lets you have at most 3 agents, I am told. This implies that there is also a paid version. I'm all for it, because it sounds to me like VeChain is ready to do real business! I am providing feedback to Jérôme Grillères in order to help improve VeChain's AI agent marketplace. I didn't have to set up anything. The web UI is all I needed! The agent is running on Claude Sonnet 3.7. I did not have to provide a Claude API key. We seem to be riding along on VeChain's. I hope there'll be a choice for more models, including ChatGPT, in the future. This is so user friendly, that I can easily imagine that this would take off in a big, big way. I'm definitely building on this, when it goes into production with full features. Even if my own AI agents aren't successful, then I'm sure others' will be. And that means the $VET / $VTHO / $B3TR flywheel is going to take off in a big, big way. I, for one, am here for it. (See the reply below for the listing of the AI agent I just created.)

₿lackthorne AI

16,706 просмотров • 2 месяцев назад