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Unpopular opinion: VS Code is holding back AI coding. I’ve been spinning up multiple Cline instances across tmux panes and terminal tabs. Each agent has its own isolated state and can run a different task, branch, or idea in parallel. They just keep going while I focus on something... show more

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🚨 This tool just made every AI coding plugin look outdated. Cline CLI 2.0 is a completely rebuilt, open-source AI coding agent that runs entirely in the terminal. Model-agnostic, editor-agnostic, Apache 2.0 licensed, and trusted by over 5 million developers. Same core engine as the VS Code extension rebuilt for how serious development actually happens. The new interactive UI feels genuinely native. Redesigned navigation, real-time agent reasoning, model switching mid-session, all without leaving the terminal. Parallel agents let multiple Cline instances run across tmux panes simultaneously, each with fully isolated state, working different tasks on different branches without conflicts. Headless mode with the `-y` flag plugs Cline directly into CI/CD pipelines, pipe a git diff in, get a code review out. And the `--acp` flag connects to Zed, Neovim, Emacs, and any ACP-compatible editor, so there's no forced editor choice. At launch, Minimax M2.5 and Kimi K2.5 are completely free. No API key. No credit card. Zero barrier to try it. `npm install -g cline` Docs → GitHub → The future of AI coding isn't a plugin. It's infrastructure.

🚨 This tool just made every AI coding plugin look outdated. Cline CLI 2.0 is a completely rebuilt, open-source AI coding agent that runs entirely in the terminal. Model-agnostic, editor-agnostic, Apache 2.0 licensed, and trusted by over 5 million developers. Same core engine as the VS Code extension rebuilt for how serious development actually happens. The new interactive UI feels genuinely native. Redesigned navigation, real-time agent reasoning, model switching mid-session, all without leaving the terminal. Parallel agents let multiple Cline instances run across tmux panes simultaneously, each with fully isolated state, working different tasks on different branches without conflicts. Headless mode with the `-y` flag plugs Cline directly into CI/CD pipelines, pipe a git diff in, get a code review out. And the `--acp` flag connects to Zed, Neovim, Emacs, and any ACP-compatible editor, so there's no forced editor choice. At launch, Minimax M2.5 and Kimi K2.5 are completely free. No API key. No credit card. Zero barrier to try it. `npm install -g cline` Docs → GitHub → The future of AI coding isn't a plugin. It's infrastructure.

AI Frontliner

60,126 görüntüleme • 5 ay önce

Greg Brockman says the long-term future is clear: AI will run on its own computer, manage fleets of agents, and solve tasks in parallel. Brockman imagines a workflow where you review your agent’s work in the morning, guiding it like a partner, but models are not yet smart enough for that. He explains that today’s reality is AI helping directly in the terminal or editor, making coding workflows familiar but more efficient.

Greg Brockman says the long-term future is clear: AI will run on its own computer, manage fleets of agents, and solve tasks in parallel. Brockman imagines a workflow where you review your agent’s work in the morning, guiding it like a partner, but models are not yet smart enough for that. He explains that today’s reality is AI helping directly in the terminal or editor, making coding workflows familiar but more efficient.

Wes Roth

39,230 görüntüleme • 10 ay önce

Google Opal is vastly underrated but can replace n8n or Make in many situations You can create powerful AI workflows for free and with a single prompt: - Ask the user for any input - Run multiple deep research simultaneously - Add any context you want (YT video, text, docs...) - Use tools like web, Maps, code execution - Synthesize with Gemini 2.5 Pro - Generate results in any format I created this agent to prepare meetings in literally 10 minutes. Tip: make sure to use the microphone feature to give as much context as possible to Opal... So it can generate the workflow almost in one-shot!

Google Opal is vastly underrated but can replace n8n or Make in many situations You can create powerful AI workflows for free and with a single prompt: - Ask the user for any input - Run multiple deep research simultaneously - Add any context you want (YT video, text, docs...) - Use tools like web, Maps, code execution - Synthesize with Gemini 2.5 Pro - Generate results in any format I created this agent to prepare meetings in literally 10 minutes. Tip: make sure to use the microphone feature to give as much context as possible to Opal... So it can generate the workflow almost in one-shot!

Paul Couvert

68,011 görüntüleme • 9 ay önce

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,425 görüntüleme • 8 ay önce

i vibecoded a fully working terminal from scratch using parallel remote agents, w/o editing a single line of code. took the best agent just ~6 hrs would take me ~6-12 mo to do the same by hand w/o ai the terminal def has bugs (i posted the gh link in the article so you can test it) but can run opencode, claude code, etc! i've been able to replace iterm with it. wrote up a guide for Extreme Vibecoding with Remote Agents👇

i vibecoded a fully working terminal from scratch using parallel remote agents, w/o editing a single line of code. took the best agent just ~6 hrs would take me ~6-12 mo to do the same by hand w/o ai the terminal def has bugs (i posted the gh link in the article so you can test it) but can run opencode, claude code, etc! i've been able to replace iterm with it. wrote up a guide for Extreme Vibecoding with Remote Agents👇

andrew gao

57,629 görüntüleme • 6 ay önce

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Jerry Liu

30,015 görüntüleme • 6 ay önce

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

11,234 görüntüleme • 1 ay önce

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.

Akshay 🚀

239,507 görüntüleme • 7 gün önce

Grok 3 was just released. You won't believe it, I've already created a game. (I got early access THIS MORNING). This game was 100% created by GROK, I just told it what I wanted, and put the code in the right place. I just keep asking for adjustments, and it keeps spitting the game out in a single file that I can put on my desktop and run. The game is changed forever. I've been developing a lot with AI's from every other major AI builder lately, trying to decide what I like best, and grok is a PLAYER. I don't have official benchmarks, and I don't have API setup yet so it's not my normal workflow, but it felt every bit as capable as Sonet, 4o, or anything else. In the next day or so I'll get it set up as part of my workflow in NVIM and put it to real work. This is incredible. We live in the future. Everyone is a developer now.

Grok 3 was just released. You won't believe it, I've already created a game. (I got early access THIS MORNING). This game was 100% created by GROK, I just told it what I wanted, and put the code in the right place. I just keep asking for adjustments, and it keeps spitting the game out in a single file that I can put on my desktop and run. The game is changed forever. I've been developing a lot with AI's from every other major AI builder lately, trying to decide what I like best, and grok is a PLAYER. I don't have official benchmarks, and I don't have API setup yet so it's not my normal workflow, but it felt every bit as capable as Sonet, 4o, or anything else. In the next day or so I'll get it set up as part of my workflow in NVIM and put it to real work. This is incredible. We live in the future. Everyone is a developer now.

Penny2x

1,606,034 görüntüleme • 1 yıl önce

I built something I believe is useful for agentic coding today. I’ve been too worried it’s not the best it can be, but my wife encouraged me to fail fast if I was worried about failure, so here we are. Introducing Kontinuo by Pull Base, the continuity layer for your AI coding agents. You’re mid-task, you hit a rate limit on Claude, and you want to keep going on Codex or Cursor. But the next agent has no idea what just happened. Kontinuo fixes that, it leaves a verifiable context log the next agent can actually use and it’s really token efficient. Check it out: Use it, break it. I hope it’s useful to at least one person.

I built something I believe is useful for agentic coding today. I’ve been too worried it’s not the best it can be, but my wife encouraged me to fail fast if I was worried about failure, so here we are. Introducing Kontinuo by Pull Base, the continuity layer for your AI coding agents. You’re mid-task, you hit a rate limit on Claude, and you want to keep going on Codex or Cursor. But the next agent has no idea what just happened. Kontinuo fixes that, it leaves a verifiable context log the next agent can actually use and it’s really token efficient. Check it out: Use it, break it. I hope it’s useful to at least one person.

Big Sheddy 🦅

52,501 görüntüleme • 1 ay önce

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

nich

285,544 görüntüleme • 3 ay önce

SOMEONE MADE IT SO YOU CAN CODE WITH 174 AI MODELS FROM 23 PROVIDERS FOR FREE one npm package and one install: "npm i -g free-coding-models" switch between any model instantly. benchmark them against each other in real time to see which one actually codes best for your use case. no subscriptions. no API keys. no cost. 174 models. 23 providers, all free, all from your terminal. if you're tired of paying for 3 different AI subscriptions just to compare which model codes better, this is it

SOMEONE MADE IT SO YOU CAN CODE WITH 174 AI MODELS FROM 23 PROVIDERS FOR FREE one npm package and one install: "npm i -g free-coding-models" switch between any model instantly. benchmark them against each other in real time to see which one actually codes best for your use case. no subscriptions. no API keys. no cost. 174 models. 23 providers, all free, all from your terminal. if you're tired of paying for 3 different AI subscriptions just to compare which model codes better, this is it

Om Patel

86,969 görüntüleme • 4 ay önce

Most AI video tools still feel like traditional editors with a few AI features added in. You still end up adjusting timelines, fixing frames, and spending time editing. But Hailuo AI felt different to me. I gave it a simple prompt and one image, and it generated a full video on its own. No complicated workflow, no manual editing just idea to video in minutes. The text-to-video results are surprisingly good, image-to-video transitions look smooth, and the overall output feels cinematic. What I liked most is the speed. You can quickly test ideas without getting stuck in editing. Try it here: #Hailuo

Most AI video tools still feel like traditional editors with a few AI features added in. You still end up adjusting timelines, fixing frames, and spending time editing. But Hailuo AI felt different to me. I gave it a simple prompt and one image, and it generated a full video on its own. No complicated workflow, no manual editing just idea to video in minutes. The text-to-video results are surprisingly good, image-to-video transitions look smooth, and the overall output feels cinematic. What I liked most is the speed. You can quickly test ideas without getting stuck in editing. Try it here: #Hailuo

Markandey Sharma

32,326 görüntüleme • 3 ay önce

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,475 görüntüleme • 4 ay önce

Today we are taking Vy to another dimension 🚀 and we can’t be more excited! You can now run multiple agents in parallel, all on your own device 🧠💻 That means: • You can kickoff a bunch of tasks in parallel • Vy will run them in the background • You can keep using your computer like nothing’s happening — no weird cloud browser, no giving your authentication to a third party, no purchasing a new license, nothing blocking your access or your IP Think of it like a team of AI coworkers 🧑‍💻 You give them jobs, they get to work, and come back when they’re done. You can check in any time, and they’ll update you when they finish. It’s smooth. It’s fast. It feels natural.

Today we are taking Vy to another dimension 🚀 and we can’t be more excited! You can now run multiple agents in parallel, all on your own device 🧠💻 That means: • You can kickoff a bunch of tasks in parallel • Vy will run them in the background • You can keep using your computer like nothing’s happening — no weird cloud browser, no giving your authentication to a third party, no purchasing a new license, nothing blocking your access or your IP Think of it like a team of AI coworkers 🧑‍💻 You give them jobs, they get to work, and come back when they’re done. You can check in any time, and they’ll update you when they finish. It’s smooth. It’s fast. It feels natural.

Vercept

10,668 görüntüleme • 1 yıl önce

I'm open-sourcing tws, the tool I've been using for the last few months to organize my engineering work. I've always been a terminal-first engineer; tmux + nvim has been my default. Then AI coding agents arrived and shifted what I needed from that setup. Running several Claude Code and Codex sessions in parallel, the flat tmux session list stopped being enough. So I built tws to fit how I actually work now. It's built on top of tmux, no reinvented wheel. You keep all of tmux's power and flexibility underneath. On top of that, it gives you: - Threads: an abstract organizational layer above tmux sessions, so related work groups together instead of sitting in a flat list - Notes: quick markdown notes attached to each thread or session, kept right next to your work - Fast switching: fuzzy finding, recent-session jumps, and keyboard-driven navigation between everything - Agents view: every Claude Code / Codex session running across your projects in one place, with the ability to pin the ones you keep coming back to Fully configurable — themes, palette, keybindings — via a TOML file. If tmux + terminal isn't your setup, this probably isn't for you. But if you like working from terminal, you might like it. It's been the core piece of how I work for a while now. Github:

I'm open-sourcing tws, the tool I've been using for the last few months to organize my engineering work. I've always been a terminal-first engineer; tmux + nvim has been my default. Then AI coding agents arrived and shifted what I needed from that setup. Running several Claude Code and Codex sessions in parallel, the flat tmux session list stopped being enough. So I built tws to fit how I actually work now. It's built on top of tmux, no reinvented wheel. You keep all of tmux's power and flexibility underneath. On top of that, it gives you: - Threads: an abstract organizational layer above tmux sessions, so related work groups together instead of sitting in a flat list - Notes: quick markdown notes attached to each thread or session, kept right next to your work - Fast switching: fuzzy finding, recent-session jumps, and keyboard-driven navigation between everything - Agents view: every Claude Code / Codex session running across your projects in one place, with the ability to pin the ones you keep coming back to Fully configurable — themes, palette, keybindings — via a TOML file. If tmux + terminal isn't your setup, this probably isn't for you. But if you like working from terminal, you might like it. It's been the core piece of how I work for a while now. Github:

Yusuf

220,215 görüntüleme • 2 ay önce

An AI agent can now register its on-chain identity, pay for API calls, and deploy an ERC-20 token from the terminal. No browser. No API key. No human approval loop. Today we launch the Brickken CLI: a command-line interface for agentic transaction workflows. The developer journey is direct: CLI → x402 → ERC-8004 → Token Deploy What this means in practice: • A developer or the AI agent installs the CLI. • The agent pays for Brickken API calls through x402. • The agent registers its identity through ERC-8004. • The agent deploys and manages an ERC-20 token. Together, the stack gives agents three things they usually lack: a way to pay, a way to prove who they are, and a way to execute token operations without leaving the terminal. • x402 handles payment. • ERC-8004 handles agent identity. • Token Deploy handles ERC-20 execution. This is live today. Developers can now move from agent setup to payment, identity registration, and token deployment in one terminal workflow. Start here: Watch the demo. Run the commands. Deploy from the terminal.

An AI agent can now register its on-chain identity, pay for API calls, and deploy an ERC-20 token from the terminal. No browser. No API key. No human approval loop. Today we launch the Brickken CLI: a command-line interface for agentic transaction workflows. The developer journey is direct: CLI → x402 → ERC-8004 → Token Deploy What this means in practice: • A developer or the AI agent installs the CLI. • The agent pays for Brickken API calls through x402. • The agent registers its identity through ERC-8004. • The agent deploys and manages an ERC-20 token. Together, the stack gives agents three things they usually lack: a way to pay, a way to prove who they are, and a way to execute token operations without leaving the terminal. • x402 handles payment. • ERC-8004 handles agent identity. • Token Deploy handles ERC-20 execution. This is live today. Developers can now move from agent setup to payment, identity registration, and token deployment in one terminal workflow. Start here: Watch the demo. Run the commands. Deploy from the terminal.

Brickken

119,221 görüntüleme • 1 ay önce

We tried OWL with 𝐆𝐞𝐦𝐢𝐧𝐢 𝟐.𝟓 𝐏𝐫𝐨 (the results were seriously impressive) We gave the agent a task: "Research Gemini 2.5 Pro, get its benchmark scores, write Python code to plot them, and run it." It did everything on its own: ✔️ Collected benchmark data ✔️ Wrote clean python code ✔️ Saved both the chart + code locally No manual steps. Just full-on autonomous execution. This is how agent workflows should feel in 2025. You can try it, check the code, or run your own workflows:

We tried OWL with 𝐆𝐞𝐦𝐢𝐧𝐢 𝟐.𝟓 𝐏𝐫𝐨 (the results were seriously impressive) We gave the agent a task: "Research Gemini 2.5 Pro, get its benchmark scores, write Python code to plot them, and run it." It did everything on its own: ✔️ Collected benchmark data ✔️ Wrote clean python code ✔️ Saved both the chart + code locally No manual steps. Just full-on autonomous execution. This is how agent workflows should feel in 2025. You can try it, check the code, or run your own workflows:

CAMEL-AI.org

20,256 görüntüleme • 1 yıl önce

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 görüntüleme • 25 gün önce

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

ℏεsam

62,794 görüntüleme • 1 ay önce