正在加载视频...

视频加载失败

返回首页

Your AI agent feels useless because it can't touch anything outside the chat window. Every harness (Claude Code, Codex, Cursor, ChatGPT) needs its own MCP setup. Every tool needs its own auth flow. Switch harnesses and you rebuild the same integrations from scratch. Aident Aident AI Loadout fixes that....

hasantoxr's profile picture

Hasan Toor

437,615 subscribers

11,196 次观看 • 26 天前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

18 条评论

Tech with Mak 的头像
Tech with Mak26 天前

Now that's something very interesting! Thanks buddy.

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack26 天前

@hasantoxr yeah, it's a pain. feels like we're stuck in integration hell sometimes. every setup is its own beast.

Aaliya 的头像
Aaliya26 天前

Keeping API keys in a vault while agents only access the results is a strong security model.

Terminal Chillness 的头像
Terminal Chillness26 天前

This is the exact tax. Auth flows, tool wiring, the same infrastructure patterns — agents (and humans) keep re-deriving them instead of having stable, constrained capabilities they can just invoke.

Prasenjit Sarkar 的头像
Prasenjit Sarkar26 天前

Did they literally pic it up from my Campfire?

Mimu | AI Tools & News 的头像
Mimu | AI Tools & News24 天前

The true breakthrough is wiring tools in once and letting every agent tap into them again and again.

AI Mastery Guide 的头像
AI Mastery Guide26 天前

Connect once, use everywhere, that's the fix

hops 的头像
hops26 天前

The integration tax is real, but execution is the next bottleneck once agents can reach tools. @fluence_project is worth watching because portable, verifiable jobs let backends move beyond a single harness or cloud account

Saman Ahmed 的头像
Saman Ahmed26 天前

The one setup for every harness is the part I want.

Kanika 的头像
Kanika26 天前

And keeping the API keys in a vault instead of handing them to the agent is a really nice touch.

Frankie Chan 的头像
Frankie Chan26 天前

Honestly, Loadout sounds like the solution to many agents' problems! I'd be all about connecting my top tools and letting the agent handle the plumbing.

Troels 的头像
Troels25 天前

Exactly! Aident seems to be half of that equation - Wonop will be the other half once I am done building it :D

Gill 的头像
Gill26 天前

Centralizing all app permissions in one vault sounds pretty risky

Liam | AI Tools & News 的头像
Liam | AI Tools & News26 天前

Connecting tools once and reusing them across agents is the real win.

Eriks Briedis 的头像
Eriks Briedis26 天前

Auth moves fine when I switch harnesses. What I end up rewriting is the wording that gets the agent to reach for the right tool at the right moment. Same Gmail connection, different picking behaviour once Claude Code trimmed its system prompt.

Arsalan 的头像
Arsalan26 天前

Aident Loadout could remove a lot of MCP setup friction

Chad Justice 的头像
Chad Justice26 天前

The auth rebuild is the real tax, not the MCP setup itself. Every harness swap means redoing OAuth flows and token storage for tools you already had working somewhere else. Worth separating that problem from the harness problem, they don't have to be solved together.

Adel Bucetta 的头像
Adel Bucetta26 天前

most people think mcp is just an integration, but it's a whole new system design that integrates multiple agents seamlessly

相关视频

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.
1:07

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

Xiaoyin Qu

2,216,782 次观看 • 22 天前

MCP connectors are now available in Basedash. Basedash already reads from your databases and SaaS tools. Now it can act on them too. Connect any MCP server (Linear, HubSpot, Slack, Resend, Notion, GitHub, your own internal one) and the Basedash agent gets new tools it can use right inside chat. Try it today.
0:29

MCP connectors are now available in Basedash. Basedash already reads from your databases and SaaS tools. Now it can act on them too. Connect any MCP server (Linear, HubSpot, Slack, Resend, Notion, GitHub, your own internal one) and the Basedash agent gets new tools it can use right inside chat. Try it today.

Basedash

11,138 次观看 • 4 个月前

You wanted shadcn — so we built the heinous infra to make it work. Introducing Agent Arena, starting with the best: Claude Code by Anthropic Codex 5 by OpenAI Cursor Agent by Cursor Devin by Cognition Gemini CLI by Google DeepMind Grok Code Fast 1 by xAI 1/ Your prompt launches a containerized agent inside a fresh GitHub repo 2/ The agent can install packages, make tool calls, and write multi-file code 3/ The repo is deployed to Vercel *by the agent*, clonable to your heart's desire
0:25

You wanted shadcn — so we built the heinous infra to make it work. Introducing Agent Arena, starting with the best: Claude Code by Anthropic Codex 5 by OpenAI Cursor Agent by Cursor Devin by Cognition Gemini CLI by Google DeepMind Grok Code Fast 1 by xAI 1/ Your prompt launches a containerized agent inside a fresh GitHub repo 2/ The agent can install packages, make tool calls, and write multi-file code 3/ The repo is deployed to Vercel *by the agent*, clonable to your heart's desire

Grace Li

169,041 次观看 • 11 个月前

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵
0:34

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

22,362 次观看 • 1 个月前

Today we open the Zapier SDK to everyone. If you're building with AI agents, this is for you. I've been using this for 2 months. It's totally changed how I do my job. You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through Zapier and can do anything those APIs can do. I think it’s the most powerful thing we’ve launched in years. Now in open beta. Just give this link right to your agent:
2:47

Today we open the Zapier SDK to everyone. If you're building with AI agents, this is for you. I've been using this for 2 months. It's totally changed how I do my job. You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through Zapier and can do anything those APIs can do. I think it’s the most powerful thing we’ve launched in years. Now in open beta. Just give this link right to your agent:

Wade Foster

175,178 次观看 • 5 个月前

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.
2:29

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Nikos

19,466 次观看 • 26 天前

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.
55:30

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.

MIKE

32,199 次观看 • 1 个月前

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent
2:29

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 次观看 • 1 个月前

This is one of those abstractions that makes perfect sense once you see it. Write your AI agent once. Run it across Slack, Microsoft Teams, and every messaging platform that follows. That's exactly what CopilotKit's open-source Channels SDK does for any AG-UI agent. Instead of maintaining separate bots for every platform, you connect your agent once and let the SDK handle the channel-specific integration.
1:21

This is one of those abstractions that makes perfect sense once you see it. Write your AI agent once. Run it across Slack, Microsoft Teams, and every messaging platform that follows. That's exactly what CopilotKit's open-source Channels SDK does for any AG-UI agent. Instead of maintaining separate bots for every platform, you connect your agent once and let the SDK handle the channel-specific integration.

Pratham

16,216 次观看 • 1 个月前

Buzz is Slack where the agents are teammates instead of integrations. Here's what that unlocks: Agents: - The ones you start with are just defaults - Edit them, add your own, write their instructions like a system prompt Swappable Harness: - This is the killer feature - Swap Claude Code, Codex, Goose under the same agent Context: - Every chat you've had with that agent carries over to the new harness - Global skills and skill files come too Think: Slack = the shape Agents = the teammates Harness = the engine you can change without losing anything "I get model fatigue" is a real thing. Buzz sits one layer above it.
3:58

Buzz is Slack where the agents are teammates instead of integrations. Here's what that unlocks: Agents: - The ones you start with are just defaults - Edit them, add your own, write their instructions like a system prompt Swappable Harness: - This is the killer feature - Swap Claude Code, Codex, Goose under the same agent Context: - Every chat you've had with that agent carries over to the new harness - Global skills and skill files come too Think: Slack = the shape Agents = the teammates Harness = the engine you can change without losing anything "I get model fatigue" is a real thing. Buzz sits one layer above it.

The Startup Ideas Podcast (SIP) 🧃

43,523 次观看 • 1 个月前

Most AI Agents can Talk..... Very few can actually do anything. You can spin up an agent in minutes… but the second you want it to: 🔴 Create a GitHub issue ⚪ Update Notion 🟡 Send a Slack message 🔵 Log actions properly 🟣 Handle auth securely You’re suddenly building integrations for weeks. This is why Merge Agent Handler caught my attention. It gives your AI agent real tool access across thousands of platforms through one integration, but the part I actually care about is this: Production-ready security + full audit logs out of the box. You can see every tool call. Every API request. Every response. So your agent isn’t just “connected”..... it’s monitored and controlled. If you’re building agents that need to take real actions, this is worth looking at: Video shows it in action 👇
0:22

Most AI Agents can Talk..... Very few can actually do anything. You can spin up an agent in minutes… but the second you want it to: 🔴 Create a GitHub issue ⚪ Update Notion 🟡 Send a Slack message 🔵 Log actions properly 🟣 Handle auth securely You’re suddenly building integrations for weeks. This is why Merge Agent Handler caught my attention. It gives your AI agent real tool access across thousands of platforms through one integration, but the part I actually care about is this: Production-ready security + full audit logs out of the box. You can see every tool call. Every API request. Every response. So your agent isn’t just “connected”..... it’s monitored and controlled. If you’re building agents that need to take real actions, this is worth looking at: Video shows it in action 👇

Kaitee

49,963 次观看 • 6 个月前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,549 次观看 • 3 个月前

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.
0:45

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

daimon111

53,213 次观看 • 6 个月前

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇
0:30

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇

YanXbt

187,943 次观看 • 3 个月前

Doing Executor's YC launch today! Executor is your tool gateway - it gives you one place to sign in to all of the services you use and then every agent adopt (Hermes, Codex, OpenClaw, etc) carry over the same signed in accounts, permissions, etc Executor gets a ton right about how tool calling should work. It supports multiple accounts for all integrations including things like Gmail, it lets you filter which tools are available for agents. We see teams adopt Executor and immediately setup their whole company with it because its the first time they've had the ability to integrate with everything they use - give it a try!
2:20

Doing Executor's YC launch today! Executor is your tool gateway - it gives you one place to sign in to all of the services you use and then every agent adopt (Hermes, Codex, OpenClaw, etc) carry over the same signed in accounts, permissions, etc Executor gets a ton right about how tool calling should work. It supports multiple accounts for all integrations including things like Gmail, it lets you filter which tools are available for agents. We see teams adopt Executor and immediately setup their whole company with it because its the first time they've had the ability to integrate with everything they use - give it a try!

Rhys

59,028 次观看 • 1 个月前

One of the easiest wins with an AI agent right now is letting it sign up for your own mobile app on a real phone. There's a repo called Phone Harness that connects Codex or Claude Code to an actual iPhone or Android. The agent sees the screen and can tap, type, scroll, open apps, and verify what happened. Here's the whole setup: 1) Connect the phone. iPhone goes through Mac iPhone mirroring, Android goes through ADB over USB or wireless. 2) You don't have to jailbreak anything, you don't need Xcode, and you don't need a special app on the phone. 3) Paste the setup prompt from the repo into Codex or Claude Code: clone the GitHub, read the install.md, put phone harness on your path, register it as an agent skill, then read onboarding.md and walk me through setup. 4) Run phone-harness 5) Give it the first job: open the app, create an account, tap through onboarding, try checkout, take screenshots, and tell me where it gets confusing or breaks. 6) Then point it at the mobile apps that have no API at all. Every mobile team should be running that signup test constantly. Most don't, because it's tedious, or they don't want to hire that person, or the person makes mistakes anyway. Agents are moving from answering questions to operating tools, and the phone is one of the biggest tool surfaces in the world. Connect the phone, run the doctor, then hand it your signup flow
3:36

One of the easiest wins with an AI agent right now is letting it sign up for your own mobile app on a real phone. There's a repo called Phone Harness that connects Codex or Claude Code to an actual iPhone or Android. The agent sees the screen and can tap, type, scroll, open apps, and verify what happened. Here's the whole setup: 1) Connect the phone. iPhone goes through Mac iPhone mirroring, Android goes through ADB over USB or wireless. 2) You don't have to jailbreak anything, you don't need Xcode, and you don't need a special app on the phone. 3) Paste the setup prompt from the repo into Codex or Claude Code: clone the GitHub, read the install.md, put phone harness on your path, register it as an agent skill, then read onboarding.md and walk me through setup. 4) Run phone-harness 5) Give it the first job: open the app, create an account, tap through onboarding, try checkout, take screenshots, and tell me where it gets confusing or breaks. 6) Then point it at the mobile apps that have no API at all. Every mobile team should be running that signup test constantly. Most don't, because it's tedious, or they don't want to hire that person, or the person makes mistakes anyway. Agents are moving from answering questions to operating tools, and the phone is one of the biggest tool surfaces in the world. Connect the phone, run the doctor, then hand it your signup flow

The Startup Ideas Podcast (SIP) 🧃

25,223 次观看 • 12 天前

Most AI agents stop at chat. Agent Max is built for what comes after. Max tools. Max control and configurability. Max production readiness. Your agent can use: • any tools, APIs, MCP, and workflows • built-in knowledge: docs/website • multi-model: OpenAI, Claude, Gemini... • agent-to-agent handoffs • human-in-the-loop • logs, usage tracking, environments, and RBAC • enterprise controls for SOC2/GDPR/HIPAA/ISO needs • hosted chat UI, API, Skill in agents, Slack, WhatsApp, Teams, Voice, and more Agent Max is live now in BuildShip for everyone. Give it a try, or comment/DM to book a demo if you want help setting up your first agent.
1:15

Most AI agents stop at chat. Agent Max is built for what comes after. Max tools. Max control and configurability. Max production readiness. Your agent can use: • any tools, APIs, MCP, and workflows • built-in knowledge: docs/website • multi-model: OpenAI, Claude, Gemini... • agent-to-agent handoffs • human-in-the-loop • logs, usage tracking, environments, and RBAC • enterprise controls for SOC2/GDPR/HIPAA/ISO needs • hosted chat UI, API, Skill in agents, Slack, WhatsApp, Teams, Voice, and more Agent Max is live now in BuildShip for everyone. Give it a try, or comment/DM to book a demo if you want help setting up your first agent.

BuildShip

75,946 次观看 • 2 个月前

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.
3:10

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 次观看 • 1 个月前

You can build your own custom Jarvis, using Cursor, Claude Code or Codex, in less than 20 minutes. Using the new GPT-Realtime-2 API... Anything you can do on a computer can be turned into a tool that your Jarvis can use. (Here's exactly how to do it) Introduction Step 1: Cursor Setup Step 2 – Full Jarvis Prompt Step 3 – My Agent Ricky Step 4 – Second Prompt Step 5 – Add EXA API Key for Web Search Step 6 – Third Prompt Step 7 – Add Computer Use Mode Step 8 – Add AI Image Generator (Insane) Final Demo
21:08

You can build your own custom Jarvis, using Cursor, Claude Code or Codex, in less than 20 minutes. Using the new GPT-Realtime-2 API... Anything you can do on a computer can be turned into a tool that your Jarvis can use. (Here's exactly how to do it) Introduction Step 1: Cursor Setup Step 2 – Full Jarvis Prompt Step 3 – My Agent Ricky Step 4 – Second Prompt Step 5 – Add EXA API Key for Web Search Step 6 – Third Prompt Step 7 – Add Computer Use Mode Step 8 – Add AI Image Generator (Insane) Final Demo

Riley Brown

62,062 次观看 • 2 个月前

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.
1:15

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 次观看 • 2 个月前