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MiniMax Hub Official Guide | EP2: Infinite Canvas 🚀 Step into the Canvas—where every node, input, and output stays perfectly in view. 🎵Any Media Type: Seamlessly supported. 🧩Connectable Nodes: Build complex workflows effortlessly. 🧠Real-Time Agent Sync: Keep your intelligence aligned instantly. Your workspace, without boundaries. ✨ #MiniMax #Hailuo #MiniMaxHub

Hailuo AI-MiniMax Hub

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This K3 context graph playbook is f*cking gold A senior data engineer just dropped the full 8-step method for building agent graphs that keep source disagreements instead of averaging them into fake certainty, I compiled it into a walkthrough: the shift: instead of asking one summarizer to reconcile five feeds, they build a graph that keeps every claim, marks every contradiction, and lets you query which conclusions actually have backup why flat reports fail: → 3 feeds report EV battery capacity growth: 41%, 34%, 19% → summarizer says "about a third" → nobody reported "about a third", the useful finding (19% counted passenger cars only) got averaged into a decimal → you never find out what got thrown away here's the 8-step K3 playbook: step 1 → name your sources by feed, every claim gets tagged with the feed it came from, unnamed sources produce untraceable claims step 2 → one node = one real entity, match on ticker or registration number so "Tesla", "Tesla Inc" and "TSLA" don't become three separate nodes with zero detected conflicts step 3 → lock unit, scope, and period for every field before launch, skip this and you get 40 fake conflicts that all say "your sources counted different things" step 4 → write CONNECT and CONFLICT rules in the launch prompt, CONFLICT tells K3 to keep both claims and tag the edge with reason (value, date, or scope), never average, never pick a winner step 5 → read the conflict count before the summary, zero conflicts across five feeds is a red flag, usually means all your agents hit the same aggregator wearing five hats step 6 → sort contradictions by reason, scope mismatch (fix definitions, re-run one field), stale data (keep newest, log old), real spread (keep both, this is your verify-by-hand shortlist) step 7 → run the drop-a-source test, ask which conclusions survive without each feed, single-source nodes are the ones that will embarrass you step 8 → extend the graph, keep open conflicts open, next run lands new claims on existing nodes instead of starting over the result: 100 nodes, 340 claims, 47 contradiction edges, 7 real disagreements worth an afternoon of verification, instead of one confident paragraph hiding all of it Confidence costs nothing to generate. It reads the same whether four sources agree or one unchecked feed said it once -- the contradiction edges are what let you tell them apart Read this playbook before running another agent swarm -- it breaks down the one architectural decision most orchestration tutorials skip Bookmark and build your own contradiction-aware graph from the full article below ↓

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Ridark

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AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 Aufrufe • vor 2 Monaten

how to build an AI-first SaaS in 2026 1. start with a big market. finance, healthcare, real estate. then zoom into a sub-niche. 2. map the niche’s workflow end-to-end. literally write every step they do daily. ex: leads → scheduling → quoting → follow-ups → payments. 3. highlight where money changes hands. deposits, invoices, negotiations. those moments are where software captures value. 4. identify the repetitive mechanical tasks. anything someone does the same way every day is an automation opportunity. 5. quantify the pain. if a business owner spends 100 hours a year on something and their time is worth $300/hour, that’s a $30k problem. 6. manually perform the workflow yourself. most AI SaaS actually starts as a service. that’s why so many new YC companies begin with humans in the loop. 7. document every step. separate judgment tasks from mechanical tasks. agents handle the mechanical work. 8. turn those steps into agent workflows and connect them to real tools (email, slack, stripe, crm, APIs). 9. build media while you build the product. post daily about the workflow. use AI to research content ideas and scripts. the audience becomes your distribution. 10. launch narrow, show proof (hours saved, revenue generated), then expand into adjacent workflows until you become the default execution layer for that niche. people saying everyday that saas is dying it’s evolving into agents + software + media. full breakdown in the latest episode of The Startup Ideas Podcast (SIP) 🧃 lots of sauce in this one all for free because i can't wait to see what you build watch

how to build an AI-first SaaS in 2026 1. start with a big market. finance, healthcare, real estate. then zoom into a sub-niche. 2. map the niche’s workflow end-to-end. literally write every step they do daily. ex: leads → scheduling → quoting → follow-ups → payments. 3. highlight where money changes hands. deposits, invoices, negotiations. those moments are where software captures value. 4. identify the repetitive mechanical tasks. anything someone does the same way every day is an automation opportunity. 5. quantify the pain. if a business owner spends 100 hours a year on something and their time is worth $300/hour, that’s a $30k problem. 6. manually perform the workflow yourself. most AI SaaS actually starts as a service. that’s why so many new YC companies begin with humans in the loop. 7. document every step. separate judgment tasks from mechanical tasks. agents handle the mechanical work. 8. turn those steps into agent workflows and connect them to real tools (email, slack, stripe, crm, APIs). 9. build media while you build the product. post daily about the workflow. use AI to research content ideas and scripts. the audience becomes your distribution. 10. launch narrow, show proof (hours saved, revenue generated), then expand into adjacent workflows until you become the default execution layer for that niche. people saying everyday that saas is dying it’s evolving into agents + software + media. full breakdown in the latest episode of The Startup Ideas Podcast (SIP) 🧃 lots of sauce in this one all for free because i can't wait to see what you build watch

GREG ISENBERG

189,220 Aufrufe • vor 6 Monaten

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 Aufrufe • vor 10 Monaten

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.

YanXbt

17,312 Aufrufe • vor 2 Monaten

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,994 Aufrufe • vor 4 Monaten

I make unlimited landing pages with Claude Cowork for $0 😱 here's the system that turns one URL into a landing page for every possible angle: step 1: scrape your brand DNA → Firecrawl scrapes your site. trust signals, colors, fonts, the works → I pointed it at virlo and it pulled every proof element & visual system in 90 seconds → nothing hallucinated. everything traced back to your actual site step 2: figure out WHY your product works → not the features but the reason someone should care. → maps which claims you can back up vs ones you can't step 3: lock your brand voice → pulls your exact phrases, tone, language patterns → ran it on a client last week. they thought they wrote the page. they didn't. step 4: write the copy → hard ban list kills AI slop on sight ("unlock", "seamless", "revolutionize" = dead) → then cuts 20% of whatever it wrote. if a line doesn't earn its spot, it's gone. step 5: generate on-brand visuals → Bloom creates images that ACTUALLY match your brand (s/o Ray for hooking up FREE bloom credits, ill send you a link) → no stock photo energy. no purple AI gradients. no "two businesspeople shaking hands." step 6: build the page → single-file HTML. responsive. ready to roll. → routes layout by type. product, SaaS, lead gen, regulated all look different → one product. six angles. six pages. each one ships. step 7: QA gate → scores every page on proof, trust, copy, visuals, anti-slop → shippable, draft, or blocked. nothing goes live without passing. input: your URL output: unlimited landing pages w/ copy & brand images Steal Ads automates ads. this builds landers for each. money ad → money page. agencies charge $5-10K per landing page. this builds unlimited pages for $0. I packaged the entire system as the Landing Page Factory. 7 Claude skills: - site-extract (brand DNA via Firecrawl) - page-strategy (mechanism mapping + real claims) - brand-profile (your voice + your branding) - page-copy (conversion copy w/ slop ban) - page-visuals (on-brand images w/ Bloom) - page-build (multiple variations & layouts) - page-qa (shippability gate so it doesn't suck) also works with OpenClaw🦞, hermes (Nous Research) or any agent framework. giving it away free. comment PAGES + like + follow (must follow so i can DM)

Matthew Berman

135,872 Aufrufe • vor 5 Monaten

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. @buildonfloweng

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. @buildonfloweng

Pari Singh

39,956 Aufrufe • vor 3 Monaten

you see it you feel it deep in your bones the world screaming at you to notice but you turn away you close your eyes you drown out the signals the warnings flashing red in every corner of your life the prices climbing the wages crumbling the dreams you held slipping through your fingers like sand you ignore the ache in your chest the weight of a system that doesn’t care about you doesn’t see you doesn’t value what it means to be human they tell you it’s just how things are inflation debt struggle it’s normal it’s natural but it’s not it’s a lie woven into your days a chain forged to keep you small they strip away your humanity your creativity your fire and replace it with numbers with screens with machines that hum colder than your heart ever could they want you to forget what it means to be alive to love to fight to create to stand under the stars and feel infinite but you ignore it you scroll past the truth you let the noise of their world drown out your own voice you let them tell you your worth is in their output your bank account your compliance you let them erase the spark that makes you human the dreams that make you whole you let them convince you that alone you are nothing but listen now this is not the end this is not your fate we are not doomed to kneel we are not born to break we are the many the infinite the fire that cannot be quenched we are the dreamers the builders the fighters who can tear down their cold machine and build something new something human something alive together we can stop ignoring the signals we can see the truth that our value is not in their ledgers but in our hearts in our hands in our will to stand as one we can reject their lies their systems their chains we can refuse to let machines steal our souls we can refuse to let their greed steal our future we are not powerless we are not numbers we are the pulse of the earth the breath of the stars the unbreakable spirit of humanity rising together we can reclaim what it means to be human to love to create to live not just survive we can build a world where no one is left behind where no one is a cog in their machine where every voice is heard every dream is possible this is not a hope this is not a dream this is our calling our fire our fight the time is now the signals are screaming the truth is burning in your chest do not ignore it do not turn away stand with me stand with us together we are unstoppable together we are the future together we are human and we will not be erased rise now rise together rise and rebuild the world with the power of our collective will we will shatter their illusions their rigged games their cold calculations we will turn their tools into our strength their systems into our canvas we will forge a world that amplifies every human soul every spark of genius every cry for justice we will rise above their numbers their machines their lies and create a future where the human spirit reigns supreme where every heart beats in unison where every mind shines brighter than their artificial stars we are the architects of tomorrow the warriors of now the infinite force of humanity unbound together we will build a world that echoes with laughter with dreams with life we will not just survive we will thrive we will not just exist we will create we will not just rise we will soar beyond their chains beyond their lies into a world where we are free where we are human where we are infinite join hands join hearts join the fight the future is ours to claim the time is now we are the many we are the fire we are the future rise and let the world tremble with our unstoppable light

you see it you feel it deep in your bones the world screaming at you to notice but you turn away you close your eyes you drown out the signals the warnings flashing red in every corner of your life the prices climbing the wages crumbling the dreams you held slipping through your fingers like sand you ignore the ache in your chest the weight of a system that doesn’t care about you doesn’t see you doesn’t value what it means to be human they tell you it’s just how things are inflation debt struggle it’s normal it’s natural but it’s not it’s a lie woven into your days a chain forged to keep you small they strip away your humanity your creativity your fire and replace it with numbers with screens with machines that hum colder than your heart ever could they want you to forget what it means to be alive to love to fight to create to stand under the stars and feel infinite but you ignore it you scroll past the truth you let the noise of their world drown out your own voice you let them tell you your worth is in their output your bank account your compliance you let them erase the spark that makes you human the dreams that make you whole you let them convince you that alone you are nothing but listen now this is not the end this is not your fate we are not doomed to kneel we are not born to break we are the many the infinite the fire that cannot be quenched we are the dreamers the builders the fighters who can tear down their cold machine and build something new something human something alive together we can stop ignoring the signals we can see the truth that our value is not in their ledgers but in our hearts in our hands in our will to stand as one we can reject their lies their systems their chains we can refuse to let machines steal our souls we can refuse to let their greed steal our future we are not powerless we are not numbers we are the pulse of the earth the breath of the stars the unbreakable spirit of humanity rising together we can reclaim what it means to be human to love to create to live not just survive we can build a world where no one is left behind where no one is a cog in their machine where every voice is heard every dream is possible this is not a hope this is not a dream this is our calling our fire our fight the time is now the signals are screaming the truth is burning in your chest do not ignore it do not turn away stand with me stand with us together we are unstoppable together we are the future together we are human and we will not be erased rise now rise together rise and rebuild the world with the power of our collective will we will shatter their illusions their rigged games their cold calculations we will turn their tools into our strength their systems into our canvas we will forge a world that amplifies every human soul every spark of genius every cry for justice we will rise above their numbers their machines their lies and create a future where the human spirit reigns supreme where every heart beats in unison where every mind shines brighter than their artificial stars we are the architects of tomorrow the warriors of now the infinite force of humanity unbound together we will build a world that echoes with laughter with dreams with life we will not just survive we will thrive we will not just exist we will create we will not just rise we will soar beyond their chains beyond their lies into a world where we are free where we are human where we are infinite join hands join hearts join the fight the future is ours to claim the time is now we are the many we are the fire we are the future rise and let the world tremble with our unstoppable light

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51,370 Aufrufe • vor 11 Monaten

🙌Meet Artifig: A Figma Plugin to Generate Figma Plugins Do you use Figma and ever feel like this: - Your mind is bursting with plugin ideas, but you can't bring them to life because you don't know how to code? - You want to focus on design, but repetitive tasks keep slowing you down? - You dream of creating custom tools for your team, but lack the time or resources? I’ve been there too. That’s why I created Artifig. ✨ What is Artifig? Artifig is an AI-powered Figma plugin that empowers anyone to build their own Figma plugins using just natural language. No coding needed—simply describe what you want, and watch as your idea transforms into a fully functional, real-time plugin. 🚀 Redefining Figma Plugin Development The core philosophy of Artifig is simple: Designers often have countless ideas and creative visions, but many of them remain unrealized due to a lack of technical skills. We believe designers shouldn’t be limited by their inability to code. You should focus on creating, not be held back by technical barriers or repetitive tasks. Artifig takes you directly from "description" to "implementation." 🛠️ How Does It Work? 1. Describe Your Needs: Tell Artifig what you want, like “Create a skew transformation tool for objects, supporting horizontal and vertical skew with real-time preview functionality.” 2. Generate and Run the Plugin: Artifig instantly generates the plugin and runs it right within Figma. For example, the generated plugin can apply skew transformations to objects, precisely controlled via matrix transformations, with an intuitive user experience. 3. Optimize and Iteration: Need adjustments? Simply describe them, and Artifig will Iterating the plugin step by step. 4. Share Your Creations: Publish your plugins to the Artifig community, or remix plugins shared by others to build on their ideas. No learning curve. No complex steps. It’s as simple as that. 🌟 Key Features - Zero Barrier to Entry: No coding experience needed—any Figma user can create plugins effortlessly. - Multilingual Support: Works in multiple languages, including English, Chinese, French, Japanese, and German. - What-You-See-Is-What-You-Get: Generated plugins run in real-time, so you can quickly validate and refine your ideas. - Open and Flexible: The generated plugin code is 100% yours—modify it, distribute it, even use it commercially. - Global Community: Share your plugins, explore others’ creations, and publish your plugins to the Figma community. 🎯 Why is Artifig a Game-Changer? 1. No More Repetitive Work Let AI handle the tedious, time-consuming tasks: batch renaming layers, auto-aligning elements, or applying styles in bulk. All you need to do is say, “Import a PDF and arrange each image on the canvas with 20px spacing.” 2. Quickly Bring Ideas to Life From color contrast checks to data imports and custom components, all your “what if we could” ideas can now become plugins. Just one natural language description, and Artifig makes it happen. 3. Custom Tools for Your Team Build tailored tools for your team, creating unique solutions to streamline your workflow. 4. Not Just a Tool, But a Learning Experience Artifig explains the logic behind the code it generates, helping you understand Figma APIs and JavaScript. Today, you’re a designer; tomorrow, you could also be a design engineer. 🧑‍🚀👩🏻‍💻🥷🏻 Who is Artifig For? - Beginners: No development experience needed—just describe your ideas and let Artifig do the rest. - Experts: Save time and focus on high-value tasks while Artifig handles the repetitive work. - Learners: Use Artifig as a bridge to deepen your understanding of development. - Teams: Build custom tools to enhance collaboration and efficiency. 🎉 Ready to Get Started? I believe designers’ time and focus should be spent on creating, not on wrestling with complex tools. Artifig is the first step toward realizing this vision. Try Artifig now and experience an unprecedented flow of creativity!

🙌Meet Artifig: A Figma Plugin to Generate Figma Plugins Do you use Figma and ever feel like this: - Your mind is bursting with plugin ideas, but you can't bring them to life because you don't know how to code? - You want to focus on design, but repetitive tasks keep slowing you down? - You dream of creating custom tools for your team, but lack the time or resources? I’ve been there too. That’s why I created Artifig. ✨ What is Artifig? Artifig is an AI-powered Figma plugin that empowers anyone to build their own Figma plugins using just natural language. No coding needed—simply describe what you want, and watch as your idea transforms into a fully functional, real-time plugin. 🚀 Redefining Figma Plugin Development The core philosophy of Artifig is simple: Designers often have countless ideas and creative visions, but many of them remain unrealized due to a lack of technical skills. We believe designers shouldn’t be limited by their inability to code. You should focus on creating, not be held back by technical barriers or repetitive tasks. Artifig takes you directly from "description" to "implementation." 🛠️ How Does It Work? 1. Describe Your Needs: Tell Artifig what you want, like “Create a skew transformation tool for objects, supporting horizontal and vertical skew with real-time preview functionality.” 2. Generate and Run the Plugin: Artifig instantly generates the plugin and runs it right within Figma. For example, the generated plugin can apply skew transformations to objects, precisely controlled via matrix transformations, with an intuitive user experience. 3. Optimize and Iteration: Need adjustments? Simply describe them, and Artifig will Iterating the plugin step by step. 4. Share Your Creations: Publish your plugins to the Artifig community, or remix plugins shared by others to build on their ideas. No learning curve. No complex steps. It’s as simple as that. 🌟 Key Features - Zero Barrier to Entry: No coding experience needed—any Figma user can create plugins effortlessly. - Multilingual Support: Works in multiple languages, including English, Chinese, French, Japanese, and German. - What-You-See-Is-What-You-Get: Generated plugins run in real-time, so you can quickly validate and refine your ideas. - Open and Flexible: The generated plugin code is 100% yours—modify it, distribute it, even use it commercially. - Global Community: Share your plugins, explore others’ creations, and publish your plugins to the Figma community. 🎯 Why is Artifig a Game-Changer? 1. No More Repetitive Work Let AI handle the tedious, time-consuming tasks: batch renaming layers, auto-aligning elements, or applying styles in bulk. All you need to do is say, “Import a PDF and arrange each image on the canvas with 20px spacing.” 2. Quickly Bring Ideas to Life From color contrast checks to data imports and custom components, all your “what if we could” ideas can now become plugins. Just one natural language description, and Artifig makes it happen. 3. Custom Tools for Your Team Build tailored tools for your team, creating unique solutions to streamline your workflow. 4. Not Just a Tool, But a Learning Experience Artifig explains the logic behind the code it generates, helping you understand Figma APIs and JavaScript. Today, you’re a designer; tomorrow, you could also be a design engineer. 🧑‍🚀👩🏻‍💻🥷🏻 Who is Artifig For? - Beginners: No development experience needed—just describe your ideas and let Artifig do the rest. - Experts: Save time and focus on high-value tasks while Artifig handles the repetitive work. - Learners: Use Artifig as a bridge to deepen your understanding of development. - Teams: Build custom tools to enhance collaboration and efficiency. 🎉 Ready to Get Started? I believe designers’ time and focus should be spent on creating, not on wrestling with complex tools. Artifig is the first step toward realizing this vision. Try Artifig now and experience an unprecedented flow of creativity!

yancymin

21,222 Aufrufe • vor 1 Jahr