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🚨 One orchestrator. 10 parallel agents. 100+ tokens a second. All local. The Google Gemma team just dropped a MASSIVE demo for Gemma 4 26B. They built a concurrent workflow that lets the 26B model coordinate an entire team of sub-agents on your machine. Out of the box, the... show more

Charly Wargnier

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31,627 просмотров • 1 месяц назад •via X (Twitter)

Искусство Образование Наука и технологии

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this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

Avid

42,608 просмотров • 5 дней назад

Cursor for Design? This Claude Code UI workflow is insane And many vibe coders not aware of it: 1. Claude Code can assign parallel tasks to sub agents 2. Integrate with git worktree to create sandbox env This means you can get Claude Code generate 10 design at the same time, fork & iterate 🔁 Design is an explorative process, by leveraging sub agent + parallel task capabilities, many interesting product UX can be built; Also turned this workflow into an open-source IDE extension here:

Cursor for Design? This Claude Code UI workflow is insane And many vibe coders not aware of it: 1. Claude Code can assign parallel tasks to sub agents 2. Integrate with git worktree to create sandbox env This means you can get Claude Code generate 10 design at the same time, fork & iterate 🔁 Design is an explorative process, by leveraging sub agent + parallel task capabilities, many interesting product UX can be built; Also turned this workflow into an open-source IDE extension here:

Jason Zhou

40,635 просмотров • 1 год назад

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 просмотров • 23 дней назад

I built an autonomous 3D Python Game Visulizer Agent team with browser use and DeepSeek R1. It has 4 AI agents working as a team to open, code, run, and visualize the game in the browser - all on autopilot. 100% Opensource code with step-by-step tutorial.

I built an autonomous 3D Python Game Visulizer Agent team with browser use and DeepSeek R1. It has 4 AI agents working as a team to open, code, run, and visualize the game in the browser - all on autopilot. 100% Opensource code with step-by-step tutorial.

Shubham Saboo

20,047 просмотров • 1 год назад

9 out of 10 multi-agent projects never leave demo mode Not because the model is bad. Because the structure is missing. Most people who try to build [ a team of AI agents ] end up with one agent talking to itself in five tabs. The agents don't share context → Don't divide work → Don't know what the others are doing. > stage 1: if your agent doesn't have a real loop, observe, act, iterate, you have a long prompt, not an agent > stage 2: subagents need isolated context, the orchestrator never reads their raw transcript, only the summary > stage 3: the orchestrator plans and delegates, the moment it executes, it drowns in details that belong inside subagents > stage 4: without a shared task list it's not a team, it's five agents duplicating each other's work in parallel > stage 5: a permissions file is what lets you sleep, the model cannot bypass it because the rule lives outside the model a team of AI agents is not more model, it is more structure the most ignored stage in every demo that died: - durability... when a 50-step task crashes at step 47 and starts from zero, that's not a model failure. that's a missing write-to-disk call

9 out of 10 multi-agent projects never leave demo mode Not because the model is bad. Because the structure is missing. Most people who try to build [ a team of AI agents ] end up with one agent talking to itself in five tabs. The agents don't share context → Don't divide work → Don't know what the others are doing. > stage 1: if your agent doesn't have a real loop, observe, act, iterate, you have a long prompt, not an agent > stage 2: subagents need isolated context, the orchestrator never reads their raw transcript, only the summary > stage 3: the orchestrator plans and delegates, the moment it executes, it drowns in details that belong inside subagents > stage 4: without a shared task list it's not a team, it's five agents duplicating each other's work in parallel > stage 5: a permissions file is what lets you sleep, the model cannot bypass it because the rule lives outside the model a team of AI agents is not more model, it is more structure the most ignored stage in every demo that died: - durability... when a 50-step task crashes at step 47 and starts from zero, that's not a model failure. that's a missing write-to-disk call

Shadow Nick

14,683 просмотров • 2 месяцев назад

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 просмотров • 1 год назад

Sam Altman, OpenAI CEO: "with an affordable amount of spend on tokens, you can do what a 100-person incredibly great engineering team would do as a startup." in this 40-minute Stanford talk, he breaks down the exact shift that lets a solo founder out-build a 100-person team. free. straight from the Stanford stage. worth more than a $2,000 startup accelerator. so i stopped arguing and built one. local agents. local tool. nothing leaving my laptop. an Obsidian clone, a knowledge graph, 13 agents a full second brain. solo. what a whole team would've shipped. minus the team. minus the cloud. here's the shift he breaks down and what i found building on it: - why tokens replaced headcount - the "one-person frontier lab" model ; your second brain is the lab - the inference layer every founder is sleeping on - why hiring 100 engineers is now the expensive way to build the 100-person team didn't get cheaper. it became one person, a second brain, and a swarm of agents. bookmark this. the full build is in the article below ⬇️

Sam Altman, OpenAI CEO: "with an affordable amount of spend on tokens, you can do what a 100-person incredibly great engineering team would do as a startup." in this 40-minute Stanford talk, he breaks down the exact shift that lets a solo founder out-build a 100-person team. free. straight from the Stanford stage. worth more than a $2,000 startup accelerator. so i stopped arguing and built one. local agents. local tool. nothing leaving my laptop. an Obsidian clone, a knowledge graph, 13 agents a full second brain. solo. what a whole team would've shipped. minus the team. minus the cloud. here's the shift he breaks down and what i found building on it: - why tokens replaced headcount - the "one-person frontier lab" model ; your second brain is the lab - the inference layer every founder is sleeping on - why hiring 100 engineers is now the expensive way to build the 100-person team didn't get cheaper. it became one person, a second brain, and a swarm of agents. bookmark this. the full build is in the article below ⬇️

Avid

65,103 просмотров • 1 месяц назад

the engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work I've seen $300 courses that don't cover what he shows in the first 10 minutes CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns all in one video and completely free after watching this, the next step is agents I wrote a full guide on how to build one yourself with Claude Code article below

the engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work I've seen $300 courses that don't cover what he shows in the first 10 minutes CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns all in one video and completely free after watching this, the next step is agents I wrote a full guide on how to build one yourself with Claude Code article below

Anatoli Kopadze

1,289,265 просмотров • 2 месяцев назад

HERMES AGENT NOW RUNS ON AN 8GB LAPTOP GPU JUST AS EASILY AS IT RUNS ON A 128GB MINI PC Nous Research shipped the official Hermes Agent Desktop App this week. Someone pointed it at a local llama server running on an RTX 4060 with 16GB system RAM. The integration took two minutes The model behind it: Gemma 4 26B MoE, QAT quantized, running on 8GB of VRAM. A 60k token prompt held a stable 20 tokens a second, flat, no slowdown as context grew. The flags were nothing exotic, just -cmoe -c 248000 on llama.cpp What that 8GB setup does out of the box: reads and patches its own code, runs it in a terminal, debugs errors, manages GitHub repos, spawns sub-agents for parallel work. Browses the web with vision to debug a UI. Schedules cron jobs in plain language. Connects to Notion, Google Workspace, Linear, and Obsidian to manage tasks on its own That's the same agent layer running on a Minisforum MS-S1 MAX with 128GB of unified memory, 96GB of it to the GPU, holding a 120B model at 56 tokens a second instead of a 26B model at 20. Same software, same tool execution, same zero API key. The only thing that changes between an $800 laptop and a $2,000 mini PC is how big a model you can afford to run underneath it The barrier to running a real autonomous agent locally didn't just drop. It dropped all the way down to hardware most people already own

HERMES AGENT NOW RUNS ON AN 8GB LAPTOP GPU JUST AS EASILY AS IT RUNS ON A 128GB MINI PC Nous Research shipped the official Hermes Agent Desktop App this week. Someone pointed it at a local llama server running on an RTX 4060 with 16GB system RAM. The integration took two minutes The model behind it: Gemma 4 26B MoE, QAT quantized, running on 8GB of VRAM. A 60k token prompt held a stable 20 tokens a second, flat, no slowdown as context grew. The flags were nothing exotic, just -cmoe -c 248000 on llama.cpp What that 8GB setup does out of the box: reads and patches its own code, runs it in a terminal, debugs errors, manages GitHub repos, spawns sub-agents for parallel work. Browses the web with vision to debug a UI. Schedules cron jobs in plain language. Connects to Notion, Google Workspace, Linear, and Obsidian to manage tasks on its own That's the same agent layer running on a Minisforum MS-S1 MAX with 128GB of unified memory, 96GB of it to the GPU, holding a 120B model at 56 tokens a second instead of a 26B model at 20. Same software, same tool execution, same zero API key. The only thing that changes between an $800 laptop and a $2,000 mini PC is how big a model you can afford to run underneath it The barrier to running a real autonomous agent locally didn't just drop. It dropped all the way down to hardware most people already own

NO1ennn

40,079 просмотров • 1 месяц назад

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Santiago

20,785 просмотров • 2 месяцев назад

When we ask an agent to "fix this bug," we're approving hundreds of actions we'll never see. These agents have our production credentials; and increasingly, they run in the background while we work on something else. Engineers are spinning up multiple Claude Code sessions in parallel, and ClawdBot hit >100k GitHub stars in weeks by letting agents handle email, workflows, even car purchases. The shift to agents without constant supervision is here. Today we're launching MintMCP : governance for AI agents.

When we ask an agent to "fix this bug," we're approving hundreds of actions we'll never see. These agents have our production credentials; and increasingly, they run in the background while we work on something else. Engineers are spinning up multiple Claude Code sessions in parallel, and ClawdBot hit >100k GitHub stars in weeks by letting agents handle email, workflows, even car purchases. The shift to agents without constant supervision is here. Today we're launching MintMCP : governance for AI agents.

Jiquan Ngiam

470,221 просмотров • 6 месяцев назад

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

Avid

636,555 просмотров • 2 месяцев назад

Grok Build is coming. Not autocomplete… a full coding agent. You describe it. Grok plans, codes, and debugs. Up to 8 agents run in parallel. Apps, sites, full repos. Built from plain English. Arena Mode picks the best output. The local CLI keeps your code private. This isn’t “help me code.” It just builds. xAI Grok

Grok Build is coming. Not autocomplete… a full coding agent. You describe it. Grok plans, codes, and debugs. Up to 8 agents run in parallel. Apps, sites, full repos. Built from plain English. Arena Mode picks the best output. The local CLI keeps your code private. This isn’t “help me code.” It just builds. xAI Grok

Mario Nawfal

193,856 просмотров • 3 месяцев назад

Raising An Agent is back! In the first episode of season 2, Quinn Slack and Thorsten Ball discuss why the Amp team went heads-down the last few months to rebuild Amp around remote-controllable, parallel agents, running locally or in orbs. They also talk about how this shift changed our development workflows and what that means for the future of local development. 00:00 Intro: Is local development obsolete? 00:53 What we've been up to since "the coding agent is dead" 03:03 The Amp Neo rewrite and what has changed 07:38 From unconstrained tokens to unconstrained parallel agents 11:25 How orbs change our development workflow 18:25 Cloud agents are underestimated 20:18 Killing obsolete features & choosing agents as a team 23:35 Why developers overestimate local development 27:54 Pushing straight to main & rethinking CI 31:20 How agents will reshape teams, products & companies 36:40 Complex dev environments & standardization 43:20 Amp subscriptions 45:55 What’s shipping next

Raising An Agent is back! In the first episode of season 2, Quinn Slack and Thorsten Ball discuss why the Amp team went heads-down the last few months to rebuild Amp around remote-controllable, parallel agents, running locally or in orbs. They also talk about how this shift changed our development workflows and what that means for the future of local development. 00:00 Intro: Is local development obsolete? 00:53 What we've been up to since "the coding agent is dead" 03:03 The Amp Neo rewrite and what has changed 07:38 From unconstrained tokens to unconstrained parallel agents 11:25 How orbs change our development workflow 18:25 Cloud agents are underestimated 20:18 Killing obsolete features & choosing agents as a team 23:35 Why developers overestimate local development 27:54 Pushing straight to main & rethinking CI 31:20 How agents will reshape teams, products & companies 36:40 Complex dev environments & standardization 43:20 Amp subscriptions 45:55 What’s shipping next

Amp

72,014 просмотров • 26 дней назад

Introducing SuperDesign.Dev The first open-source Design Agent that lives inside Cursor, Windsurf, or any IDE: 👉 Instantly spin up 10 different design in parallel 👉 Explore, fork, and iterate 10x faster 👉 Create new or iterate on your existing UI — all fully local JackJack and I shipped this as a quick weekend experiment to validate a hunch: design is the killer use case for parallel agents. Currently at V0.005 — a simple customization of the Claude Code SDK + static HTML generation. (We might be the first product powered by the Anthropic Claude Code SDK!) More coming soon: web app, mobile app, SVG generation, and more. GitHub: Try it out: ----- Upvote on Hacker News ❤️:

Introducing SuperDesign.Dev The first open-source Design Agent that lives inside Cursor, Windsurf, or any IDE: 👉 Instantly spin up 10 different design in parallel 👉 Explore, fork, and iterate 10x faster 👉 Create new or iterate on your existing UI — all fully local JackJack and I shipped this as a quick weekend experiment to validate a hunch: design is the killer use case for parallel agents. Currently at V0.005 — a simple customization of the Claude Code SDK + static HTML generation. (We might be the first product powered by the Anthropic Claude Code SDK!) More coming soon: web app, mobile app, SVG generation, and more. GitHub: Try it out: ----- Upvote on Hacker News ❤️:

Jason Zhou

49,435 просмотров • 1 год назад

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7

Noisy

525,013 просмотров • 1 месяц назад

Introducing Eigent — the first multi-agent workforce on your desktop. Eigent is a team of AI agents collaborating to complete complex tasks in parallel. It is your long-term working partner with fullly customizable workers and MCPs. Public beta available to download for MacOS, Windows. 100% open-source on Github. Comment for 500 extra credits.

Introducing Eigent — the first multi-agent workforce on your desktop. Eigent is a team of AI agents collaborating to complete complex tasks in parallel. It is your long-term working partner with fullly customizable workers and MCPs. Public beta available to download for MacOS, Windows. 100% open-source on Github. Comment for 500 extra credits.

Guohao Li 🐫

221,074 просмотров • 1 год назад

🚨 Multi-Agent - Lite Agent Swarms - Optimize Cost On Large Agentic Loops After a lot of experimentation we have open-source AI agent swarms live!! - Opus 4.8 and GPT 5.5 do the planning - Deepseek flash and Gemma do the work - Perfect for multiple parallel tasks - 10x cheaper than Opus 4.8 - 2x faster and comparable performance Try prompts like - fix all known bugs in my repo, code review the last 10 PRs etc.

🚨 Multi-Agent - Lite Agent Swarms - Optimize Cost On Large Agentic Loops After a lot of experimentation we have open-source AI agent swarms live!! - Opus 4.8 and GPT 5.5 do the planning - Deepseek flash and Gemma do the work - Perfect for multiple parallel tasks - 10x cheaper than Opus 4.8 - 2x faster and comparable performance Try prompts like - fix all known bugs in my repo, code review the last 10 PRs etc.

Bindu Reddy

20,178,809 просмотров • 2 месяцев назад

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

60,826 просмотров • 25 дней назад

Bring your own agents! Repo Prompt rides on the Claude Code and Codex subscriptions you already have, dispatches them in parallel, and shows you every prompt it sends. I have a feeling a lot of folks are going to be talking about this very very soon. 00:00 Introduction to RepoPrompt App 00:57 RepoPrompt Setup Wizard Walkthrough 03:34 Live App Refactoring Demonstration 05:03 The Scout Agent Workflow 08:38 Concurrent AI Engineer Agents 10:24 Core Benefits and Final Thoughts

Bring your own agents! Repo Prompt rides on the Claude Code and Codex subscriptions you already have, dispatches them in parallel, and shows you every prompt it sends. I have a feeling a lot of folks are going to be talking about this very very soon. 00:00 Introduction to RepoPrompt App 00:57 RepoPrompt Setup Wizard Walkthrough 03:34 Live App Refactoring Demonstration 05:03 The Scout Agent Workflow 08:38 Concurrent AI Engineer Agents 10:24 Core Benefits and Final Thoughts

Ray Fernando

18,248 просмотров • 3 месяцев назад