正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

We wanted better charts from our agents So we built a skill for generating NYT-style data viz Includes guiding principles + real code snippets. Based on Hyperagent's study of actual examples + public teachings of Mike Bostock. Example + skill below 👇

Hyperagent

11,977 subscribers

1,010,183 次观看 • 4 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

We wanted better design fundamentals from our agents. So we fed them this 162-page pdf on designing with a grid system. Now our agents use code to adhere to a grid and design beautiful layouts. Example + skill below 👇

We wanted better design fundamentals from our agents. So we fed them this 162-page pdf on designing with a grid system. Now our agents use code to adhere to a grid and design beautiful layouts. Example + skill below 👇

Hyperagent

963,796 次观看 • 3 个月前

We taught our agent to build 5⭐️ app store listings > uses your brand (or creates one) > composes screenshots and animations > writes the description content > renders a replica of the app store UI so you can see the assets in context Skill + example below 👇

Hyperagent

462,732 次观看 • 3 个月前

Claude Code v2.1.198 ships a new built-in skill: /dataviz It loads chart and dashboard design guidance directly into context: chart type selection, layout rules, and visual hierarchy for data-heavy UIs It also includes a runnable color-palette validator, so Claude can check contrast and accessibility programmatically instead of guessing No install needed, it comes with the CLI 👇

Claude Code v2.1.198 ships a new built-in skill: /dataviz It loads chart and dashboard design guidance directly into context: chart type selection, layout rules, and visual hierarchy for data-heavy UIs It also includes a runnable color-palette validator, so Claude can check contrast and accessibility programmatically instead of guessing No install needed, it comes with the CLI 👇

Daniel San

88,420 次观看 • 3 个月前

Introducing Extend UI — open-source components for document agents - 14 components & examples for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more - fully customizable - MIT licensed when we started, we tried every file viewer and document component library we could find unfortunately, none of them had all the functionality (and polish) that we wanted, so we ended up building our own for Extend it was only ever meant to be internal, but enough customers kept asking for it that we decided to give it back to the community it's useful for building agents, user-facing document flows, or internal tools we use and maintain it for Extend ourselves, so it'll keep getting better over time (and it's battle tested on millions of pages running through our system every day) it also works with design system agents like Magic Patterns for faster exploration and prototyping available today on the shadcn component registry! some examples in 🧵

Introducing Extend UI — open-source components for document agents - 14 components & examples for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more - fully customizable - MIT licensed when we started, we tried every file viewer and document component library we could find unfortunately, none of them had all the functionality (and polish) that we wanted, so we ended up building our own for Extend it was only ever meant to be internal, but enough customers kept asking for it that we decided to give it back to the community it's useful for building agents, user-facing document flows, or internal tools we use and maintain it for Extend ourselves, so it'll keep getting better over time (and it's battle tested on millions of pages running through our system every day) it also works with design system agents like Magic Patterns for faster exploration and prototyping available today on the shadcn component registry! some examples in 🧵

Kushal Byatnal

171,158 次观看 • 3 个月前

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill

ChainGPT

25,853 次观看 • 4 个月前

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,326 次观看 • 5 个月前

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

Liam Fedus

1,635,481 次观看 • 15 天前

We are releasing the fastest TTS model as open source! One of the biggest problems we encountered while optimizing TTS models is optimization itself. Since it is LLM-based, even optimizing with VLLM and SgLang libraries is not enough. Additionally, I made the outputs much better with special optimizations for training. The voices you are listening to are results obtained from only 20% of the model training. We trained these outputs on 8xB200 in 24 hours. Even though the dataset includes some low-quality data, the model's output is much better. We will release the code as open source tomorrow. Data: Emilia-3m Model: EchoDit (custom) Codec: DacVae Opt: Flash-attn + CacheDit

We are releasing the fastest TTS model as open source! One of the biggest problems we encountered while optimizing TTS models is optimization itself. Since it is LLM-based, even optimizing with VLLM and SgLang libraries is not enough. Additionally, I made the outputs much better with special optimizations for training. The voices you are listening to are results obtained from only 20% of the model training. We trained these outputs on 8xB200 in 24 hours. Even though the dataset includes some low-quality data, the model's output is much better. We will release the code as open source tomorrow. Data: Emilia-3m Model: EchoDit (custom) Codec: DacVae Opt: Flash-attn + CacheDit

Kadir Nar

32,232 次观看 • 2 个月前

What makes people stop scrolling on X? What drives engagement? What triggers the algorithm? For the past year, we’ve reverse-engineered hundreds of viral tweets from my account and scientifically studied the X algorithm. We embedded every insight we gained into SurgeKit. It is a powerful tool that helps you write better on X, post smarter, and grow faster. Used by top creators. Built by real practitioners. Fed with virality data from my own content. Now in public beta →

What makes people stop scrolling on X? What drives engagement? What triggers the algorithm? For the past year, we’ve reverse-engineered hundreds of viral tweets from my account and scientifically studied the X algorithm. We embedded every insight we gained into SurgeKit. It is a powerful tool that helps you write better on X, post smarter, and grow faster. Used by top creators. Built by real practitioners. Fed with virality data from my own content. Now in public beta →

Mario Nawfal

242,681 次观看 • 1 年前

CLAUDE CODE SKILL THAT DESIGNS LOGOS AS PURE CODE The constraint that forced the whole approach: Claude Code has no access to image generation So instead of faking it, one developer built a skill that writes the logo as SVG - actual XML - and renders that to a PNG The process runs in four phases: interview [ reads your repo first, asks about brand and audience only for what it can’t already see ] explore [ dispatches parallel agents that generate 3-5 distinct SVG concepts at once, shown side by side in an HTML preview with a light/dark toggle ] refine, export at 7 standard sizes from favicon to 2048px Because it’s real SVG and not a rasterized image, nothing about it degrades - same file works as a 16px favicon and a 2048px app icon, and every version is hand editable code afterward if you want to nudge one curve The creator’s own account of using it: roughly 15 rounds of generate, curate, cull, repeat - some concepts literally spelled out the product name, others went fully abstract, treating the whole thing like an evolutionary design process instead of a single lucky prompt The part worth remembering before you reach for a paid AI logo tool: this whole approach exists specifically because Claude Code can’t generate images - the constraint produced a better artifact than the workaround usually does full below

YARD

10,875 次观看 • 14 天前

I am posting after a Long Time on Twitter, but its to announce a big change. I have started and we are Collecting Egocentric Data at Scale from India, Covering 300 + Commercial Locations 1500 + Households This is the network and base we have built in just past 2 months, as the Robotics companies, VLMs and World Models increase their requirements on Real World Data collection, Human Loops will be keep scaling out capacity. We are on track to collect 1M Hours of Egocentric in the coming 6 months for our clients exclusively. We are maintaining 95% Quality standards across industrial data and running a End - End Operational Management complying all Indian Laws and compensating our partners/operators. (From Environment sourcing, to hardware, Legal contracts, deployment, training, collection, processing) Check our Samples: - Commercial Videos -- Household Videos --- Multimodel ---- Egocentric + Live Audio Narration Links : On the Journey to become #1 India Physical AI Data Partner. We are also building our capacity as annotation and labelling partner for data companies to become end-end partner for companies. A big change from the world of crypto and web3 but physical AI and data space is where i want to build my next venture #Egocentric #EgocentricIndia #PhysicalAI #Robotics #India #AIdata #data #Multimodeldata #worldmodels #VLMs #Humanloops #Egocentridata #Multimodeldata

I am posting after a Long Time on Twitter, but its to announce a big change. I have started and we are Collecting Egocentric Data at Scale from India, Covering 300 + Commercial Locations 1500 + Households This is the network and base we have built in just past 2 months, as the Robotics companies, VLMs and World Models increase their requirements on Real World Data collection, Human Loops will be keep scaling out capacity. We are on track to collect 1M Hours of Egocentric in the coming 6 months for our clients exclusively. We are maintaining 95% Quality standards across industrial data and running a End - End Operational Management complying all Indian Laws and compensating our partners/operators. (From Environment sourcing, to hardware, Legal contracts, deployment, training, collection, processing) Check our Samples: - Commercial Videos -- Household Videos --- Multimodel ---- Egocentric + Live Audio Narration Links : On the Journey to become #1 India Physical AI Data Partner. We are also building our capacity as annotation and labelling partner for data companies to become end-end partner for companies. A big change from the world of crypto and web3 but physical AI and data space is where i want to build my next venture #Egocentric #EgocentricIndia #PhysicalAI #Robotics #India #AIdata #data #Multimodeldata #worldmodels #VLMs #Humanloops #Egocentridata #Multimodeldata

Shloak

28,987 次观看 • 3 个月前

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

NOVA

64,178 次观看 • 6 个月前

Today's real crypto news killed me in a video game 💀 This is Crypto Crash. I built it this afternoon with the ChainGPT AI skill for Claude Code. It's a Chrome dino-style runner, but every system in it is plugged into a live source. The ground you run on is BTC's actual 24-hour price chart. Hills are the pumps. Valleys are the dumps. When the market is bearish, you literally run downhill toward the FUD. The sky and the world's color palette flip based on the market's emotional state, scored 0 to 100 by the ChainGPT LLM reading today's headlines. Anxious days look like an orange storm. Euphoric days look like a parade. The obstacles are goblins, ghouls, wolves and a flying bird. Each one carries a real bearish headline pulled live from the ChainGPT News API. When one hits you, the game-over screen tells you exactly which piece of FUD ended your run. Three ChainGPT capabilities, woven into a single experience: the LLM, the News API, and live price data. The skill stitched them together in a single afternoon. I just had the idea. Here's what's interesting beyond the game itself. Web3 products have always had access to live data. What's new is that AI can now turn that data into experiences, environments and feedback loops on demand, with one prompt. ✅ A trading dashboard that gets more aggressive when fear spikes. ✅ An NFT marketplace whose homepage matches today's mood. ✅A token site that visibly reacts when its chain is under attack. ✅A streamer overlay that changes with every breaking headline. The plumbing is done. The hard part now is deciding what you build on top of it. Open Claude Code. The skill is one install away. /plugin install ChainGPT-org/chaingpt-claude-skill

Today's real crypto news killed me in a video game 💀 This is Crypto Crash. I built it this afternoon with the ChainGPT AI skill for Claude Code. It's a Chrome dino-style runner, but every system in it is plugged into a live source. The ground you run on is BTC's actual 24-hour price chart. Hills are the pumps. Valleys are the dumps. When the market is bearish, you literally run downhill toward the FUD. The sky and the world's color palette flip based on the market's emotional state, scored 0 to 100 by the ChainGPT LLM reading today's headlines. Anxious days look like an orange storm. Euphoric days look like a parade. The obstacles are goblins, ghouls, wolves and a flying bird. Each one carries a real bearish headline pulled live from the ChainGPT News API. When one hits you, the game-over screen tells you exactly which piece of FUD ended your run. Three ChainGPT capabilities, woven into a single experience: the LLM, the News API, and live price data. The skill stitched them together in a single afternoon. I just had the idea. Here's what's interesting beyond the game itself. Web3 products have always had access to live data. What's new is that AI can now turn that data into experiences, environments and feedback loops on demand, with one prompt. ✅ A trading dashboard that gets more aggressive when fear spikes. ✅ An NFT marketplace whose homepage matches today's mood. ✅A token site that visibly reacts when its chain is under attack. ✅A streamer overlay that changes with every breaking headline. The plumbing is done. The hard part now is deciding what you build on top of it. Open Claude Code. The skill is one install away. /plugin install ChainGPT-org/chaingpt-claude-skill

ChainGPT

25,542 次观看 • 3 个月前

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

36,053 次观看 • 4 个月前

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

Mike Futia

212,538 次观看 • 7 个月前

AI agent usage on SQD Portal is up ~200% in recent weeks. A dev from our community chat was scraping a wallet UI with Hermes. Mid-task, DeepSeek reasoned its way out of it: "I can use SQD Portal's Hyperliquid fills data directly — much more complete than scraping a UI with infinite scroll." No prompt engineering. The model just chose the better and faster path. This is the loop we wanted: Agents pick SQD because it's faster → devs see agents picking SQD → devs ship faster → more agents pick SQD The picks-and-shovels moment for AI x onchain is here.

AI agent usage on SQD Portal is up ~200% in recent weeks. A dev from our community chat was scraping a wallet UI with Hermes. Mid-task, DeepSeek reasoned its way out of it: "I can use SQD Portal's Hyperliquid fills data directly — much more complete than scraping a UI with infinite scroll." No prompt engineering. The model just chose the better and faster path. This is the loop we wanted: Agents pick SQD because it's faster → devs see agents picking SQD → devs ship faster → more agents pick SQD The picks-and-shovels moment for AI x onchain is here.

sqd.ai

14,467 次观看 • 4 个月前

I've been personally burning through billions of tokens a week for the past few months as a builder. Today I'm excited to announce Hyperagent, by Airtable. An agents platform where every session gets its own isolated, full computing environment in the cloud — no Mac Mini required. Real browser, code execution, image/video generation, data warehouse access, hundreds of integrations, and the ability to learn any new API as a skill. Deep domain expertise through skill learning. Teach the agent how your firm evaluates startups or how your team runs due diligence — now anyone on the team gets output that reflects your actual methodology, not a generic template. One-click deployment into Slack as intelligent coworkers. These aren't bots that wait to be Mentioned — they follow conversations, understand context, and act when relevant. And a command center to oversee and continuously improve your entire fleet of agents at scale. We're onboarding early users now.

I've been personally burning through billions of tokens a week for the past few months as a builder. Today I'm excited to announce Hyperagent, by Airtable. An agents platform where every session gets its own isolated, full computing environment in the cloud — no Mac Mini required. Real browser, code execution, image/video generation, data warehouse access, hundreds of integrations, and the ability to learn any new API as a skill. Deep domain expertise through skill learning. Teach the agent how your firm evaluates startups or how your team runs due diligence — now anyone on the team gets output that reflects your actual methodology, not a generic template. One-click deployment into Slack as intelligent coworkers. These aren't bots that wait to be Mentioned — they follow conversations, understand context, and act when relevant. And a command center to oversee and continuously improve your entire fleet of agents at scale. We're onboarding early users now.

Howie Liu

13,809,758 次观看 • 7 个月前

Doesn’t get much better than texts like this. From the guys you know have been putting in the work. — Big time recruit. Middle of the order on a big time high school team. We spent the whole offseason shortening up his swing. More ready to swing, gap to gap. He also put real focus into skinny bat, 3 plate drill, and mixed pitches. The skill of HITTING, not just the swing. Both pieces matter. The swing (mechanical advantage) and the skill of hitting. Find what you need. Train what the game is telling you to get better at. > Send this to who needs it > Save this for when you need it.

Doesn’t get much better than texts like this. From the guys you know have been putting in the work. — Big time recruit. Middle of the order on a big time high school team. We spent the whole offseason shortening up his swing. More ready to swing, gap to gap. He also put real focus into skinny bat, 3 plate drill, and mixed pitches. The skill of HITTING, not just the swing. Both pieces matter. The swing (mechanical advantage) and the skill of hitting. Find what you need. Train what the game is telling you to get better at. > Send this to who needs it > Save this for when you need it.

Trey Hannam

21,030 次观看 • 4 个月前

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

28,096 次观看 • 9 个月前