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<Form :form /> ✨ That's it. Backend-populated, or composed in the frontend. CSS-variable theming. Cohesively tuned components. Presenters split from composables/hooks. Override or extend. Integrates with Laravel validation, Precognition, uploads/storage, and model binding.

Pascal Baljet

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21,048 Aufrufe • vor 2 Monaten •via X (Twitter)

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Storage just stole the show. Between KV cache offload and SSD prices going vertical, storage went from afterthought to headliner. Vik Malyala walks through Supermicro's storage stack. "Storage, whether it be for KV cache offload or just the incredible rise in cost of SSDs, is the primary thing to focus on for a lot of users." "Initially it started with the form factors. You have U.2s that were popular, or still are popular for that matter. Then you have E1.S and E3.S drives, mainly because of the density that we can bring in a 1U or 2U form factor. And we have come up with so-called Petascale solutions, which you can get a very good balance between the amount of storage, the storage bandwidth as IO bandwidth." "Hardware is only one part of it, but what we are doing is working with the likes of VAST, WEKA, DDN, Qumulo, OSNEXUS and every one of these software vendors. We are able to bring different types of software defined storage into customer environments so that you don't necessarily get stuck with traditional storage. So super exciting times for that."

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I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:

Lee Robinson

118,631 Aufrufe • vor 2 Jahren

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If I had to start from zero tomorrow and bring in new customers fast, here's exactly what I'd build. Top to bottom. I'd start with the ad. Open ChatGPT or Claude, paste in what you sell and who you sell it to, ask it to write you ten different hooks for a Meta ad. Pick the best three. Then jump into or one of the AI video tools and have it generate the visual. Thirty minutes, you've got a real ad ready to run. You run that ad to one landing page. Not your homepage. One page, one offer, one call to action. Headline that stops the scroll, a few lines of value, an email or phone field, a button. That's it. Build it in HighLevel in about twenty minutes, drag, drop, you're done. The second someone enters their email, they drop into your CRM, automatically tagged with where they came from. Now the part most people skip, the follow-up. The moment that form is filled, a sequence kicks off automatically. Email and SMS, spaced over five to seven days. First message thanks them and delivers what you promised. Then a story. Then social proof. Then an objection you know they have. Then the offer. They don't have to think about you. You're showing up in their inbox and their texts at exactly the right time. The last message has a calendar link or a buy button right inside it. They tap, they book, or they buy. Done. The whole flow runs on its own from the ad all the way to the sale. You build it once. It works while you're sleeping. AI does the creative work. The system runs the customer journey. You spend your time on strategy. That's the play.

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Ayurgenomics is the attempt to retrofit modern genomics onto Ayurvedic imaginary constitutional types (Prakriti), claiming that ancient classifications like Vata, Pitta, and Kapha map onto meaningful genetic or molecular categories. But this is scientifically unfounded. The studies on Ayurgenomics are typically poorly designed, and rely on cherry-picked correlations that do not survive rigorous replication or independent validation. Dressing up pre-scientific belief systems in the language of genomics does not make them scientific; it is a form of intellectual fraud that exploits the credibility of modern biology to legitimize claims that were never derived from evidence in the first place. When we allow pseudoscience to be retrofitted with real science, we corrupt the scientific process itself and mislead the public into trusting interventions that have no proven basis in biology.

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TheLiverDoc™

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An ultra compact, high precision industrial robot arm. This one stood out to me because it targets a part of automation that is usually glossed over. Extreme precision in very small spaces. According to Oleksandr Stepanenko, the arm is designed from the ground up with fully custom electronics, software, and calibration, paired with industrial components from Harmonic Drive and maxon. Reported specs are notable: • Repeatability better than 0.001 mm • Payload 250 g • Reach 250 mm • Total mass 1.5 kg This kind of form factor opens up use cases that traditional industrial arms struggle with. Think PCB assembly, micro soldering, precision pick and place of irregular parts, and medical or micro robotics components. ❗️What I find interesting is not just the numbers, but the direction… Smaller, lighter, highly precise systems could unlock automation in environments where space, inertia, and tolerance are the real constraints. Credit to Oleksandr Stepanenko for the original work and post. —- Weekly robotics and AI insights. Subscribe free:

An ultra compact, high precision industrial robot arm. This one stood out to me because it targets a part of automation that is usually glossed over. Extreme precision in very small spaces. According to Oleksandr Stepanenko, the arm is designed from the ground up with fully custom electronics, software, and calibration, paired with industrial components from Harmonic Drive and maxon. Reported specs are notable: • Repeatability better than 0.001 mm • Payload 250 g • Reach 250 mm • Total mass 1.5 kg This kind of form factor opens up use cases that traditional industrial arms struggle with. Think PCB assembly, micro soldering, precision pick and place of irregular parts, and medical or micro robotics components. ❗️What I find interesting is not just the numbers, but the direction… Smaller, lighter, highly precise systems could unlock automation in environments where space, inertia, and tolerance are the real constraints. Credit to Oleksandr Stepanenko for the original work and post. —- Weekly robotics and AI insights. Subscribe free:

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Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

87,048 Aufrufe • vor 5 Monaten

Introducing @vidguyxyz: The Claude Code for Short-Form Video. Built as part of the Virtuals Protocol 60 days build in public program. Marketers and agents in today’s attention economy don't need to be filmmakers, editors or influencers. They need reliable tools that can consistently generate perfectly tuned viral content without having to code or set up complicated workflows. UGC, product demos, launch videos and more. All in one place! • Openclaw for production-grade AI orchestration • x402 for seamless payment settlement • Seedance (2.0) for state of the art output quality • Unique positioning in ACP • Proven revenue model • Real tokenutility + Buybacks • Some secret sauce Built with a decade of experience in viral growth under my belt and multiple 6-fig in profits from viral content. 60 days. Build in public. Full transparency. Feb 12 - 2:30pm GMT on Virtuals Protocol. Link in the comments below. The future of content is agentic.

Introducing @vidguyxyz: The Claude Code for Short-Form Video. Built as part of the Virtuals Protocol 60 days build in public program. Marketers and agents in today’s attention economy don't need to be filmmakers, editors or influencers. They need reliable tools that can consistently generate perfectly tuned viral content without having to code or set up complicated workflows. UGC, product demos, launch videos and more. All in one place! • Openclaw for production-grade AI orchestration • x402 for seamless payment settlement • Seedance (2.0) for state of the art output quality • Unique positioning in ACP • Proven revenue model • Real tokenutility + Buybacks • Some secret sauce Built with a decade of experience in viral growth under my belt and multiple 6-fig in profits from viral content. 60 days. Build in public. Full transparency. Feb 12 - 2:30pm GMT on Virtuals Protocol. Link in the comments below. The future of content is agentic.

Yas

17,784 Aufrufe • vor 8 Monaten

ARE STUDS NONBINARY? What do y’all think? Today on “Outlaws,” Ts Madison is joined by actor, model, songwriter, producer, trans icon, Taylor Swift music video star, Drag Race Pit Crew alumnus, and Miss Mary’s celebrity crush… Laith Ashley‼️❤️ Laith and Madison get into it ALL, from early experiences with gender nonconformity, to studs, T4T relationships, decolonization, the fetishization of trans men, and how EVERYONE undergoes gender affirming transitions in some form. Tap in to YouTube to watch the full episode, or listen wherever you get your podcasts — then join the conversation! 🔮🍿 🎥 Is It On Action News💄 iHeartRadio #tsmadison #laithashley #trans #gender #nonbinary

ARE STUDS NONBINARY? What do y’all think? Today on “Outlaws,” Ts Madison is joined by actor, model, songwriter, producer, trans icon, Taylor Swift music video star, Drag Race Pit Crew alumnus, and Miss Mary’s celebrity crush… Laith Ashley‼️❤️ Laith and Madison get into it ALL, from early experiences with gender nonconformity, to studs, T4T relationships, decolonization, the fetishization of trans men, and how EVERYONE undergoes gender affirming transitions in some form. Tap in to YouTube to watch the full episode, or listen wherever you get your podcasts — then join the conversation! 🔮🍿 🎥 Is It On Action News💄 iHeartRadio #tsmadison #laithashley #trans #gender #nonbinary

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43,594 Aufrufe • vor 9 Monaten

Kimi K3's weights are free to download. Almost nobody on earth can actually run them. 2.8 trillion parameters. The download is the easy part. Open weights used to mean you could self-host. At this scale it means you can read the file. So people download it and call the API anyway. Daily revenue up at least 6x since launch. Openness stopped being the opposite of a business model. It became the top of the funnel. The license says the quiet part. Past $20M in revenue you negotiate a contract. Past 100M users you display their name. Free for the developer. A contract for anyone the model actually earns from. And publishing the weights kills the argument before it starts. Nobody can claim the benchmarks were cooked. Independent indexes put K3 top three overall and first in frontend coding, with no one taking Moonshot's word for it. That's the whole trade. Give away the artifact, keep the infrastructure, get the audit for free. Check the last open model you praised. Ask whether you could serve it, or only download it.

Kimi K3's weights are free to download. Almost nobody on earth can actually run them. 2.8 trillion parameters. The download is the easy part. Open weights used to mean you could self-host. At this scale it means you can read the file. So people download it and call the API anyway. Daily revenue up at least 6x since launch. Openness stopped being the opposite of a business model. It became the top of the funnel. The license says the quiet part. Past $20M in revenue you negotiate a contract. Past 100M users you display their name. Free for the developer. A contract for anyone the model actually earns from. And publishing the weights kills the argument before it starts. Nobody can claim the benchmarks were cooked. Independent indexes put K3 top three overall and first in frontend coding, with no one taking Moonshot's word for it. That's the whole trade. Give away the artifact, keep the infrastructure, get the audit for free. Check the last open model you praised. Ask whether you could serve it, or only download it.

wast3

62,529 Aufrufe • vor 1 Monat

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,430 Aufrufe • vor 1 Jahr

Claude Code tip: stop burning Opus 5.5 context on tasks Sonnet 5.5 can swarm, while Fable 5.1 sits idle spin up an autonomous team with --agent claude --agent architect Opus 5.5 acts as the lead architect, scoping the work and validating every worktree before it merges Sonnet 5.5 teammates take isolated git worktrees and hammer out implementations at medium effort Fable 5.1 sits on the team as a dedicated adversary, only stepping in at three critical moments: → before a contract locks: does the frontend payload match the backend schema? → when a test breaks twice: are we patching the bug or just hiding the symptom? → before the merge: what attack surface or edge case did everyone overlook? Sonnet 5.5 builds. Fable 5.1 challenges. Opus 5.5 merges and ships Jev engineering runs the identical pattern one layer down: mechanical decisions that need no reasoning (which file to open, which tool to invoke, retry or abort) execute in 16ms, so the frontier models only wake up when execution paths actually diverge Plan on high. Delegate on medium. Keep the adversary on peer-to-peer call. - the full team setup > architect runs the main session on Opus 5.5 at high effort > teammate-ux builds client state inside its own git worktree > teammate-back writes endpoints and migrations inside a separate worktree > teammate-adv runs on Fable 5.1, read-only, and attacks every diff > teammates message each other by name without routing through the lead > tools: Agent(ux, backend, adversary) locks the architect to spawning only its own team paste the setup and this prompt into Claude Code below: "Configure my Claude Code workspace for an autonomous agent team: 1. Audit ~/.claude/agents and .claude/agents for definitions fitting architect, ux, backend and adversary > Generate definitions only for missing roles > Pin model: sonnet, effort: medium, isolation: worktree for ux and backend, and model: fable with read-only tools for adversary > Give architect model: opus, effort: high and tools: Agent(ux, backend, adversary) 2. Check ~/.claude/settings.json and my env: > Report CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS if it's set, since it turns named subagents into teammates without worktrees > Report CLAUDE_CODE_SUBAGENT_MODEL_FORCE and CLAUDE_CODE_EFFORT_LEVEL, which can override the pinned models and effort 3. Write each role's prompt so ux and backend own separate folders, message each other by name when a contract changes, and hand every diff to adversary before reporting back 4. Add a team rule to ~/.claude/CLAUDE.md: no worktree merges until adversary has reviewed it Show every configuration diff first. Do not apply edits until confirmed" save it

Claude Code tip: stop burning Opus 5.5 context on tasks Sonnet 5.5 can swarm, while Fable 5.1 sits idle spin up an autonomous team with --agent claude --agent architect Opus 5.5 acts as the lead architect, scoping the work and validating every worktree before it merges Sonnet 5.5 teammates take isolated git worktrees and hammer out implementations at medium effort Fable 5.1 sits on the team as a dedicated adversary, only stepping in at three critical moments: → before a contract locks: does the frontend payload match the backend schema? → when a test breaks twice: are we patching the bug or just hiding the symptom? → before the merge: what attack surface or edge case did everyone overlook? Sonnet 5.5 builds. Fable 5.1 challenges. Opus 5.5 merges and ships Jev engineering runs the identical pattern one layer down: mechanical decisions that need no reasoning (which file to open, which tool to invoke, retry or abort) execute in 16ms, so the frontier models only wake up when execution paths actually diverge Plan on high. Delegate on medium. Keep the adversary on peer-to-peer call. - the full team setup > architect runs the main session on Opus 5.5 at high effort > teammate-ux builds client state inside its own git worktree > teammate-back writes endpoints and migrations inside a separate worktree > teammate-adv runs on Fable 5.1, read-only, and attacks every diff > teammates message each other by name without routing through the lead > tools: Agent(ux, backend, adversary) locks the architect to spawning only its own team paste the setup and this prompt into Claude Code below: "Configure my Claude Code workspace for an autonomous agent team: 1. Audit ~/.claude/agents and .claude/agents for definitions fitting architect, ux, backend and adversary > Generate definitions only for missing roles > Pin model: sonnet, effort: medium, isolation: worktree for ux and backend, and model: fable with read-only tools for adversary > Give architect model: opus, effort: high and tools: Agent(ux, backend, adversary) 2. Check ~/.claude/settings.json and my env: > Report CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS if it's set, since it turns named subagents into teammates without worktrees > Report CLAUDE_CODE_SUBAGENT_MODEL_FORCE and CLAUDE_CODE_EFFORT_LEVEL, which can override the pinned models and effort 3. Write each role's prompt so ux and backend own separate folders, message each other by name when a contract changes, and hand every diff to adversary before reporting back 4. Add a team rule to ~/.claude/CLAUDE.md: no worktree merges until adversary has reviewed it Show every configuration diff first. Do not apply edits until confirmed" save it

Khairallah AL-Awady

32,018 Aufrufe • vor 7 Tagen