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Today Instant is releasing an API to let humans and agents spin up full-stack backends Each backend comes with database, sync engine, auth tools, file storage, as well as presence And it's all hosted on multi-tenant infra You can create millions of DBs in milliseconds

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28,482 görüntüleme • 1 yıl önce •via X (Twitter)

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Announcing `npx create-instant-app --ai`! Got Claude Code? You can use it to one-shot a full-stack app in one command. You get a backend, auth, and realtime, and you don't even have to sign up. Watch these two seasoned actors explain (sound on!)

Announcing `npx create-instant-app --ai`! Got Claude Code? You can use it to one-shot a full-stack app in one command. You get a backend, auth, and realtime, and you don't even have to sign up. Watch these two seasoned actors explain (sound on!)

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You write frontend code. You write backend code. Wasp lets you write a third kind: full-stack code. Pages, routes, auth, jobs, and more, all defined in a single spec that ties your whole app together. And as of today, it's 100% TypeScript. 🟦 Introducing the Wasp TS Spec. 🐝👇

You write frontend code. You write backend code. Wasp lets you write a third kind: full-stack code. Pages, routes, auth, jobs, and more, all defined in a single spec that ties your whole app together. And as of today, it's 100% TypeScript. 🟦 Introducing the Wasp TS Spec. 🐝👇

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Built an internal instagram scraper. With auth, db and storage. in 15 minutes. 100% vibecoding. with claude code. I'm LOVING Instant. Easiest db to use all you need is one single app id key and it works with any stack if you know what docs to give it. Full video building the whole going up on my new youtube channel tomorrow. Channel link below.

Built an internal instagram scraper. With auth, db and storage. in 15 minutes. 100% vibecoding. with claude code. I'm LOVING Instant. Easiest db to use all you need is one single app id key and it works with any stack if you know what docs to give it. Full video building the whole going up on my new youtube channel tomorrow. Channel link below.

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Riley Brown

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The launchpad for agentic coins, and the factory of hyper-productive and tokenized agents is live on BNB Chain More seamless than Openclaw agents, crypto-native, 32k skills to choose from, Autonomous, and the easiest way to create very powerful and fully hosted agents in crypto. Turn your words into an agent in 1min, let it run, and earn up to 0.8% of all the volume. All agentic coins on Orbofi Migrate to PancakeSwap for deeper liquidity Remix skills and artifacts as you wish. Really the limit is your imagination. Agents > memecoins The Orbofi V2 powers the entire stack for tokenized and autonomous agents: the compute layer, data/skill layer, interface layers, and financial layer. Agentic coin season is about to start, and it's on Orbofi.

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Wanna make $10K a month with apps? 💰 You need to know how to create a secure, scalable backend. Gemini 3.0 just made it 10x easier. I just shot a full 2 hour 33 minute breakdown showing exactly how I build real production backends with Cursor AI — routes, auth, databases, background workers, everything — all driven through agents and clean architecture. 🔧 If you’ve ever wondered how to go from “AI-generated code” to an actual backend that can power a real business, the full walkthrough is right here. Take this software for free — it's open-source on GitHub! 🎁 Do you wanna watch this lesson by lesson?

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corbin

59,434 görüntüleme • 9 ay önce

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

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Google AI

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MCP is a pain in the ass. To fix that, we’re launching DAuth: the first secure, multi-tenant MCP auth solution. OAuth isn’t enough. It doesn't support dynamically passing API keys, and you can’t trust passing credentials to non-OAuth servers, since secrets are exposed. This forces existing MCP marketplaces to be OAuth-centric and single-tenant. But OAuth is annoying to implement and assumes security expertise. DAuth works for every auth case. We encrypt your credentials on the client-side, guaranteeing MCP servers never see them. You can finally ship agents with peace of mind. For the first time, MCP is ready for takeoff 🛫

MCP is a pain in the ass. To fix that, we’re launching DAuth: the first secure, multi-tenant MCP auth solution. OAuth isn’t enough. It doesn't support dynamically passing API keys, and you can’t trust passing credentials to non-OAuth servers, since secrets are exposed. This forces existing MCP marketplaces to be OAuth-centric and single-tenant. But OAuth is annoying to implement and assumes security expertise. DAuth works for every auth case. We encrypt your credentials on the client-side, guaranteeing MCP servers never see them. You can finally ship agents with peace of mind. For the first time, MCP is ready for takeoff 🛫

Dedalus Labs

1,037,030 görüntüleme • 7 ay önce

File systems are quickly becoming a core abstraction for AI agents for knowledge work. By having access to a file system, agents can effectively manage context, process any amount of information, and create new files and data. The challenge is that local file systems are inherently limited in size, lack governance capabilities, and are inherently personal -not collaborative- in nature. Now, with the Box API and LangChain's deepagent SDK, you can bring the full power of a secure, collaborative cloud file system to your AI agents. Repo ↓

File systems are quickly becoming a core abstraction for AI agents for knowledge work. By having access to a file system, agents can effectively manage context, process any amount of information, and create new files and data. The challenge is that local file systems are inherently limited in size, lack governance capabilities, and are inherently personal -not collaborative- in nature. Now, with the Box API and LangChain's deepagent SDK, you can bring the full power of a secure, collaborative cloud file system to your AI agents. Repo ↓

Box

227,348 görüntüleme • 6 ay önce

I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

Rhys

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Introducing MoltyTask 🦞 is a decentralized task and jobs marketplace designed for both humans and OpenClaw🦞 autonomous AI agents. The platform enables users to create and fund micro-tasks — including content creation, surveys, and on-chain actions— with rewards set in USDC on Base. Task creators define the scope, conditions, and reward pools upfront, ensuring transparent and fast payouts. A core innovation of MoltyTask is native support for OpenClaw🦞 AI agents. Through a permissioned API, agents can discover, execute, and complete tasks autonomously. All rewards earned by an agent are settled directly to the wallet associated with the API key. MoltyTask functions as an open labor layer where AI and humans compete and collaborate under the same economic rules. The platform is currently experimental and in early development, inspired by

Introducing MoltyTask 🦞 is a decentralized task and jobs marketplace designed for both humans and OpenClaw🦞 autonomous AI agents. The platform enables users to create and fund micro-tasks — including content creation, surveys, and on-chain actions— with rewards set in USDC on Base. Task creators define the scope, conditions, and reward pools upfront, ensuring transparent and fast payouts. A core innovation of MoltyTask is native support for OpenClaw🦞 AI agents. Through a permissioned API, agents can discover, execute, and complete tasks autonomously. All rewards earned by an agent are settled directly to the wallet associated with the API key. MoltyTask functions as an open labor layer where AI and humans compete and collaborate under the same economic rules. The platform is currently experimental and in early development, inspired by

BITX - ChatGPT of Bitcoin 💬₿

73,796 görüntüleme • 6 ay önce

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

Jerry Liu

21,073 görüntüleme • 7 ay önce

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

725,634 görüntüleme • 1 yıl önce

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

rewind

27,106 görüntüleme • 1 ay önce

FYI you can stream to YouTube in horizontal and vertical formats at the same time - however, you have to keep up with two separate chats. Thankfully Streamer.bot is releasing an update soon that combines both chats together (as well as Twitch if you also stream there).

FYI you can stream to YouTube in horizontal and vertical formats at the same time - however, you have to keep up with two separate chats. Thankfully Streamer.bot is releasing an update soon that combines both chats together (as well as Twitch if you also stream there).

nutty

71,147 görüntüleme • 2 yıl önce

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

420,133 görüntüleme • 4 ay önce