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Teamwork makes the dream work. Now running locally. Watch Gemma 4 26B orchestrate 10 parallel sub-agents to code an SVG art gallery in seconds. Hitting 100+ tokens/sec, imagine how you can scale this for complex tasks or local chatbots for entire teams!!

Google Gemma

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158,027 просмотров • 3 месяцев назад •via X (Twitter)

Наука и технологии

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Gemma 4 is now available in Android Studio! By running Gemma 4 locally on your machine you get access to AI code-assistance that doesn't require an internet connection or an API key for its core operations — so you’ll never run out of token quota →

Gemma 4 is now available in Android Studio! By running Gemma 4 locally on your machine you get access to AI code-assistance that doesn't require an internet connection or an API key for its core operations — so you’ll never run out of token quota →

Android Studio

124,342 просмотров • 5 месяцев назад

Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work Open Source Harness: Open Weights: We’re excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work. 🧠 Frontier-level intelligence for complex work Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search. 🤝 Asynchronous Agent Team Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time. 🔬 Open-source research workbench We’re open-sourcing FrontierAgent—a locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team. Available now: 🔹 Apodex 1.1 — our most capable frontier model, available through the Apodex online workbench 🔹 Apodex 1.1 mini — open-weight model for running complex work locally 🔹 FrontierAgent — open-source, locally deployable research workbench 🌐 Live workbench: 🦾 API platform: 📃 Paper:

Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work Open Source Harness: Open Weights: We’re excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work. 🧠 Frontier-level intelligence for complex work Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search. 🤝 Asynchronous Agent Team Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time. 🔬 Open-source research workbench We’re open-sourcing FrontierAgent—a locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team. Available now: 🔹 Apodex 1.1 — our most capable frontier model, available through the Apodex online workbench 🔹 Apodex 1.1 mini — open-weight model for running complex work locally 🔹 FrontierAgent — open-source, locally deployable research workbench 🌐 Live workbench: 🦾 API platform: 📃 Paper:

Apodex

927,682 просмотров • 26 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

Andrew Ng just dropped 1-hour course on full agent engineering with Claude Code: 1 prompt → agents team → graphs from 0% to 100%: 10% → 10:45 - build your first agent in Claude Code 20% → 16:24 - Graphs: Claude Code for subagents 55% → 33:15 - agent for grunt work on a cheaper model 80% → 43:58 - your first model, running locally 100% → 49:57 - fully local: zero cloud, zero bill the only course that takes you from Claude Code prompt to fully local agent - by Andrew Ng watch it today - then read how to build full graph system in the article below ↓

Andrew Ng just dropped 1-hour course on full agent engineering with Claude Code: 1 prompt → agents team → graphs from 0% to 100%: 10% → 10:45 - build your first agent in Claude Code 20% → 16:24 - Graphs: Claude Code for subagents 55% → 33:15 - agent for grunt work on a cheaper model 80% → 43:58 - your first model, running locally 100% → 49:57 - fully local: zero cloud, zero bill the only course that takes you from Claude Code prompt to fully local agent - by Andrew Ng watch it today - then read how to build full graph system in the article below ↓

codila

26,302 просмотров • 1 месяц 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

77,382 просмотров • 2 месяцев назад

Claude Code v2.1.16 adds new task management system with full dependency tracking I demoed this through CC Mirror a few weeks ago and it was extremely powerful Watch the full video - this is going to change the way you work forever Each task can be assigned to sub agents in parallel and they can easily pick up tasks as they become unblocked

Claude Code v2.1.16 adds new task management system with full dependency tracking I demoed this through CC Mirror a few weeks ago and it was extremely powerful Watch the full video - this is going to change the way you work forever Each task can be assigned to sub agents in parallel and they can easily pick up tasks as they become unblocked

Numman Ali

51,261 просмотров • 8 месяцев назад

You can now orchestrate subagents running in Claude Code, Codex, or the Warp Agent. Let the agent create a delegation plan, and watch subagents coordinate with message passing. Each agent gets its own worktree for isolation. Here's how it works with eng Matthew Albright:

You can now orchestrate subagents running in Claude Code, Codex, or the Warp Agent. Let the agent create a delegation plan, and watch subagents coordinate with message passing. Each agent gets its own worktree for isolation. Here's how it works with eng Matthew Albright:

Warp

20,810 просмотров • 4 месяцев назад

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,880 просмотров • 1 год назад

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

Julian Goldie SEO

11,363 просмотров • 2 месяцев назад

I just built an entire app in 7 minutes using Codex Spark. EveryMCP: a universal MCP server installer that configures 15+ coding agents from a single CLI command. 12 Codex Spark subagents. All running in parallel. At the same time. The project itself isn't complicated, but just imagine what this looks like when you scale the complexity. My setup: • Codex 5.3 High to plan • Codex 5.3 Medium to orchestrate • 12 Codex Spark agents (xhigh) to implement in one shot Spark isn't competing with the big models. It's a different tool entirely. You orchestrate it. You run it in swarms. And it moves at a speed that wasn't possible before, even for the top 1% of AI coders. More complex workflows coming soon. Comment below if you want me to tell you how I did it.

I just built an entire app in 7 minutes using Codex Spark. EveryMCP: a universal MCP server installer that configures 15+ coding agents from a single CLI command. 12 Codex Spark subagents. All running in parallel. At the same time. The project itself isn't complicated, but just imagine what this looks like when you scale the complexity. My setup: • Codex 5.3 High to plan • Codex 5.3 Medium to orchestrate • 12 Codex Spark agents (xhigh) to implement in one shot Spark isn't competing with the big models. It's a different tool entirely. You orchestrate it. You run it in swarms. And it moves at a speed that wasn't possible before, even for the top 1% of AI coders. More complex workflows coming soon. Comment below if you want me to tell you how I did it.

am.will

16,916 просмотров • 7 месяцев назад

There's an insane arbitrage opportunity right now with OpenClaw. You can have it spawn sub-agents to go out and apply to proposals on Upwork with the fully finished, complete project already built out for you. This can happen all at once in parallel if you give OpenClaw the ability to spawn sub-agents inside of their own computers on Orgo.

There's an insane arbitrage opportunity right now with OpenClaw. You can have it spawn sub-agents to go out and apply to proposals on Upwork with the fully finished, complete project already built out for you. This can happen all at once in parallel if you give OpenClaw the ability to spawn sub-agents inside of their own computers on Orgo.

Nick Vasilescu

361,019 просмотров • 7 месяцев назад

MLX + TurboQuant = Local Super Power Take a local (private) document(s) or codebase, pre-fill the 256k KV cache (the context) with the document(s) and system prompt, quantise and run on Apple's MLX and you have almost instantaneous, lossless document queries with total privacy. For a 75-page PDF (some 30k tokens), I was seeing sub-150ms TTFT (Time To First Token), that's "instant" for a human. We run this on an office server (one of our Mac Minis) for staff to work with, instant answers on critical, confidential company/client documents on phone, iPads or laptops. Imagine the "What were the 3Q25 profits?", "When did Joe start working for us?", "Please list the project milestones and team count" answered within seconds of hitting the return key. It works with voice too. This example is 17k lines of code from 177 source files plus an index of the entire 1,902 files, the entire Claude Code codebase (oops Anthropic!). Zero loss (unlike Anthropic's security). What this means is the ability to work better on local (private) code bases, off-the-shelf private document analysis with better response than the best public models - all locally.

MLX + TurboQuant = Local Super Power Take a local (private) document(s) or codebase, pre-fill the 256k KV cache (the context) with the document(s) and system prompt, quantise and run on Apple's MLX and you have almost instantaneous, lossless document queries with total privacy. For a 75-page PDF (some 30k tokens), I was seeing sub-150ms TTFT (Time To First Token), that's "instant" for a human. We run this on an office server (one of our Mac Minis) for staff to work with, instant answers on critical, confidential company/client documents on phone, iPads or laptops. Imagine the "What were the 3Q25 profits?", "When did Joe start working for us?", "Please list the project milestones and team count" answered within seconds of hitting the return key. It works with voice too. This example is 17k lines of code from 177 source files plus an index of the entire 1,902 files, the entire Claude Code codebase (oops Anthropic!). Zero loss (unlike Anthropic's security). What this means is the ability to work better on local (private) code bases, off-the-shelf private document analysis with better response than the best public models - all locally.

John T Davies 🇪🇺

85,778 просмотров • 5 месяцев назад

Today's Hermes Agent Masterclass is the finale! Module 10 covers security, an important topic for running agents to ensure each agent can perform its tasks while minimizing exposure. Hermes has a ton of built-in security features. In this clip, I show how you can set approvals for each profile depending on your needs.

tonbi

383,918 просмотров • 2 месяцев назад

Stop paying for Claude Code. You can run it for FREE. No API tokens. No monthly plan. No cloud costs. Here’s the simple setup: 1. Download Ollama (free). 2. Install a local model like GLM 4.7 Flash. 3. Launch Claude Code using that local model. 4. Done. You’re now running Claude Code fully powered by a local model. Same interface. No API bill. You can even run OpenClaw the same way. Local AI agents. Free. 24/7. Most people are overpaying because they don’t know this exists.

Stop paying for Claude Code. You can run it for FREE. No API tokens. No monthly plan. No cloud costs. Here’s the simple setup: 1. Download Ollama (free). 2. Install a local model like GLM 4.7 Flash. 3. Launch Claude Code using that local model. 4. Done. You’re now running Claude Code fully powered by a local model. Same interface. No API bill. You can even run OpenClaw the same way. Local AI agents. Free. 24/7. Most people are overpaying because they don’t know this exists.

Julian Goldie SEO

104,024 просмотров • 6 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 • 3 месяцев назад

GOOGLE JUST MADE EVERY CHATBOT FEEL SLOW Diffusion Gemma 26b doesn’t predict word by word, it generates 256 tokens in parallel using bi-directional attention, like stable diffusion but for language it’s MoE so only 3.8B params activate during inference, fits on a single RTX 4090 with 18GB VRAM and you can run it right now via llama.cpp, the same engine powering every tool in my local llm playbook the whole ecosystem exists for moments like this ↓

GOOGLE JUST MADE EVERY CHATBOT FEEL SLOW Diffusion Gemma 26b doesn’t predict word by word, it generates 256 tokens in parallel using bi-directional attention, like stable diffusion but for language it’s MoE so only 3.8B params activate during inference, fits on a single RTX 4090 with 18GB VRAM and you can run it right now via llama.cpp, the same engine powering every tool in my local llm playbook the whole ecosystem exists for moments like this ↓

leopardracer

27,385 просмотров • 3 месяцев назад

SpaceXAI just dropped full guide on how to run multiple agents "I stopped running Grok Bot as separate chats. now every project gets its own AI team, manager and task board" one Projects Manager agent creates the project, opens the channel and assigns the right agents: • coder • researcher • writer every team gets its own context, tasks and workspace → instead of managing prompts, you manage teams → instead of running one agent, you run an organization SpaceXAI shared the full setup, roles and workflow for running multiple GrokBot teams at once worth more than 100 multi-agent posts on X save now, then read how to build a parallel agent system in the article below

SpaceXAI just dropped full guide on how to run multiple agents "I stopped running Grok Bot as separate chats. now every project gets its own AI team, manager and task board" one Projects Manager agent creates the project, opens the channel and assigns the right agents: • coder • researcher • writer every team gets its own context, tasks and workspace → instead of managing prompts, you manage teams → instead of running one agent, you run an organization SpaceXAI shared the full setup, roles and workflow for running multiple GrokBot teams at once worth more than 100 multi-agent posts on X save now, then read how to build a parallel agent system in the article below

0xRafy

12,230 просмотров • 7 дней назад

Fuc*k your paid courses, Kimi.ai AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything. The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS. This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world. - Dynamic orchestration, PARL rewards, real scaling beyond context windows. - Trained with smart RL rewards (instantiation, finish & outcome) - 100-300+ specialized spawns agents , sub-agents on the fly working in parallel. - Thousands of tool calls. - 4.5x faster execution. This isn't theory it's how they're scaling Kimi to handle real complexity. Better than most local paid courses. Swap your Netflix tonight watch this instead.

Fuc*k your paid courses, Kimi.ai AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything. The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS. This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world. - Dynamic orchestration, PARL rewards, real scaling beyond context windows. - Trained with smart RL rewards (instantiation, finish & outcome) - 100-300+ specialized spawns agents , sub-agents on the fly working in parallel. - Thousands of tool calls. - 4.5x faster execution. This isn't theory it's how they're scaling Kimi to handle real complexity. Better than most local paid courses. Swap your Netflix tonight watch this instead.

Md Ismail Šojal 🕷️

60,757 просмотров • 2 месяцев назад

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

Vinny

1,357,789 просмотров • 1 месяц назад

Sculptor: the missing UI for Claude Code 🎨 Imagine running 5 Claudes in parallel, safely in containers, while you stay in flow. Then bring their work straight into your IDE to test/edit together. This is how one developer ships like a team. Try it with Sonnet 4.5!

Sculptor: the missing UI for Claude Code 🎨 Imagine running 5 Claudes in parallel, safely in containers, while you stay in flow. Then bring their work straight into your IDE to test/edit together. This is how one developer ships like a team. Try it with Sonnet 4.5!

Kanjun 🐙

518,950 просмотров • 11 месяцев назад