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MINIMAX OPEN SOURCED A CODING AGENT THAT LIVES IN YOUR TERMINAL its called MiniMax Code CLI. i gave it one prompt, 19 minutes later i had a working interactive 3d globe. → 249 countries → 14 animated flight arcs across 16 hubs → click a country and the camera... flies to it → fresnel atmosphere, bloom, the full pass it did it in 9 phases at about 96 tok/s, committing after every one. Mcode is MIT and it is not locked to MiniMax models, you can bring your own key and point it at claude, gpt, or a local model. setup in your terminal: 1.⁠ ⁠curl -fsSL | bash 2.⁠ ⁠cd into the project and run mcode 3.⁠ ⁠run mcode init . first, that is the step that writes the AGENTS.md the AGENTS.md is doing more work here than the model. build commands, what not to touch, and a rule that it verifies before it calls anything done i will drop the repo belowshow more

Israfil

21,288 subscribers

25,153 просмотров • 7 дней назад •via X (Twitter)

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Комментарии: 23

Фото профиля neamtu

neamtu7 дней назад

Garbage

Фото профиля Israfil

Israfil7 дней назад

have you tried mcode bro?

Фото профиля Alan

Alan7 дней назад

wait open source lemme run in my pc wait sir

Фото профиля Israfil

Israfil7 дней назад

just install it in your terminal its one click process

Фото профиля Sakata

Sakata7 дней назад

I’ve been using their CLI for a while now and it’s super cool

Фото профиля Israfil

Israfil7 дней назад

Good to hear it holds up beyond a one off demo. The AGENTS.md and phased-commit flow look like the parts that make it actually usable.

Фото профиля Avid

Avid7 дней назад

Amazing! I love MiniMaX.The harness is amazing

Фото профиля Israfil

Israfil7 дней назад

yes my man its harness is so good at coding stuffs

Фото профиля K2S

K2S7 дней назад

19 mins for all that is actually insane ngl

Фото профиля Rakib

Rakib7 дней назад

Dammnnn Let me try

Фото профиля Fajar M Reza

Fajar M Reza7 дней назад

Terminal-native coding agents could turn single prompts into reproducible software workflows.

Фото профиля Peng

Peng7 дней назад

thanks for dropping the details

Фото профиля Ayyaz

Ayyaz7 дней назад

Open-sourcing MiniMax Code CLI is the useful part. A 19-minute globe from one prompt is greenfield; I'd want the same loop on a dirty worktree with broken tests.

Фото профиля D. Michel Morelli

D. Michel Morelli7 дней назад

I had tried Minimax M3 for programming, but the results weren't great.

Фото профиля Manna Code

Manna Code7 дней назад

AGENTS.md carrying the rules is the right instinct. I’d still want a live spend meter on that 19-minute globe run—phases are cute; knowing what each phase cost is how you decide to keep looping.

Фото профиля Ruuj

Ruuj7 дней назад

nice results israfil, good one

Фото профиля Farea

Farea7 дней назад

i tried mcode earlier its harness is kinda good tbh

Фото профиля Rivon

Rivon7 дней назад

Will check it out

Фото профиля painn

painn7 дней назад

would try this

Фото профиля Isoldegwow

Isoldegwow7 дней назад

the AGENTS.md doing more work than the model is the most relatable part of AI coding agents

Фото профиля J.𝙳𝚛𝚊𝚟𝚎𝚗

J.𝙳𝚛𝚊𝚟𝚎𝚗7 дней назад

Wanna try it

Фото профиля Roan

Roan7 дней назад

coding agent living in terminal sounds interesting bro

Фото профиля Isilin

Isilin7 дней назад

What was your machine config to reach 96tk/s ?

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Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

397,093 просмотров • 1 месяц назад

I ASKED GPT-6 ASTRA TO BUILD A PLAYABLE 3D GAME WITHOUT OPENING BLENDER ONCE It wrote the code and it got the model. It just didn't make the model itself. It called the Hyper3D Rodin MCP for that. Full run below:

I ASKED GPT-6 ASTRA TO BUILD A PLAYABLE 3D GAME WITHOUT OPENING BLENDER ONCE It wrote the code and it got the model. It just didn't make the model itself. It called the Hyper3D Rodin MCP for that. Full run below:

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104,651 просмотров • 15 дней назад

There’s a free model that beats paid coding AIs. And it runs locally on your own machine. Here’s the exact “MiniMax M2.1 + NotebookLM + Gemini + Antigravity” workflow. → NotebookLM: paste your project goal + constraints + examples → Gemini: turn that into a clean spec and prompt → Antigravity: run the build loop with agent testing and terminal execution → MiniMax M2.1: generate clean code, tests, fixes, and refactors fast → Ship: deploy the landing page or automation and iterate weekly Save this video, you’ll build apps in minutes. Want the SOP? DM me. 💬

There’s a free model that beats paid coding AIs. And it runs locally on your own machine. Here’s the exact “MiniMax M2.1 + NotebookLM + Gemini + Antigravity” workflow. → NotebookLM: paste your project goal + constraints + examples → Gemini: turn that into a clean spec and prompt → Antigravity: run the build loop with agent testing and terminal execution → MiniMax M2.1: generate clean code, tests, fixes, and refactors fast → Ship: deploy the landing page or automation and iterate weekly Save this video, you’ll build apps in minutes. Want the SOP? DM me. 💬

Julian Goldie SEO

57,200 просмотров • 9 месяцев назад

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

Sudo su

23,816 просмотров • 2 месяцев назад

I turned Grok Bot into one giant AI brain and gave it life and death over every agent under it. Its only incentive: make money or die. 290% overnight. The thing really did not want to be switched off. $1,000 went in. $3,900 came out. 15 hours, and I slept through most of it. One of the agents it hired is not allowed to trade at all. That one is the reason the rest are still alive. Every prompt and the whole build is in the post below ↓ Save it. The wiring is the whole trick, and it reads in four minutes.

I turned Grok Bot into one giant AI brain and gave it life and death over every agent under it. Its only incentive: make money or die. 290% overnight. The thing really did not want to be switched off. $1,000 went in. $3,900 came out. 15 hours, and I slept through most of it. One of the agents it hired is not allowed to trade at all. That one is the reason the rest are still alive. Every prompt and the whole build is in the post below ↓ Save it. The wiring is the whole trick, and it reads in four minutes.

cvxv666

20,970 просмотров • 28 дней назад

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

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

Yarchi

58,591 просмотров • 3 месяцев назад

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.

shmidt

16,085 просмотров • 3 месяцев назад

grok-bot just got open-sourced and it run locally completely FREE. 3.8k stars in 2 weeks. MIT licensed. it gives every AI agent its own computer. own browser, own files, own container. every action is approved against a policy before it runs, and written to an audit log after. here's how to set it up: 1️⃣prerequisites Docker + Bun 1.3+ 2️⃣clone and create your env git clone cd OpenBot cp .env.example .env 3️⃣get intelligence credentials (free plan works) npx --yes copilotkit@latest login npx --yes copilotkit@latest project select npx --yes copilotkit@latest license --write drop the cpk-... key into .env as INTELLIGENCE_API_KEY 4️⃣ add your model key "OPENAI_API_KEY=sk-..." 5️⃣ run it bun install bash scripts/start.sh open localhost:3010. that's it. the underrated part: the repo ships a prompt.txt you paste into claude code or cursor and it walks the whole setup for you. every claim in it is verified against the actual repo. what it actually costs: the code is MIT, free forever. you pay your own model tokens. intelligence has a free tier and can be fully self-hosted if you want zero external dependency. works with any AG-UI agent. langgraph, mastra, crewai, pydantic ai. repo in in the CS.

grok-bot just got open-sourced and it run locally completely FREE. 3.8k stars in 2 weeks. MIT licensed. it gives every AI agent its own computer. own browser, own files, own container. every action is approved against a policy before it runs, and written to an audit log after. here's how to set it up: 1️⃣prerequisites Docker + Bun 1.3+ 2️⃣clone and create your env git clone cd OpenBot cp .env.example .env 3️⃣get intelligence credentials (free plan works) npx --yes copilotkit@latest login npx --yes copilotkit@latest project select npx --yes copilotkit@latest license --write drop the cpk-... key into .env as INTELLIGENCE_API_KEY 4️⃣ add your model key "OPENAI_API_KEY=sk-..." 5️⃣ run it bun install bash scripts/start.sh open localhost:3010. that's it. the underrated part: the repo ships a prompt.txt you paste into claude code or cursor and it walks the whole setup for you. every claim in it is verified against the actual repo. what it actually costs: the code is MIT, free forever. you pay your own model tokens. intelligence has a free tier and can be fully self-hosted if you want zero external dependency. works with any AG-UI agent. langgraph, mastra, crewai, pydantic ai. repo in in the CS.

m0h

59,144 просмотров • 27 дней назад

Comfy MCP is now local and open-source! The #1 ask after Comfy Cloud MCP shipped in June. Your agent sees your actual install. So every node, custom nodes included, and every model on disk. It fetches files, starts your instance, gets a workflow to the point where it runs. It is now the easiest way to help with your local Minimax H3 workflows! Cloud MCP still does everything it did. Tell your agent where a job goes, or let it decide.

Comfy MCP is now local and open-source! The #1 ask after Comfy Cloud MCP shipped in June. Your agent sees your actual install. So every node, custom nodes included, and every model on disk. It fetches files, starts your instance, gets a workflow to the point where it runs. It is now the easiest way to help with your local Minimax H3 workflows! Cloud MCP still does everything it did. Tell your agent where a job goes, or let it decide.

ComfyUI

184,611 просмотров • 1 месяц назад

Xiaomi just dropped a FREE CLAUDE CODE with memory that actually persists between sessions and nobody is talking about it MiMo Code dropped 4 days ago. 8k GitHub stars. Built-in free model called MiMo Auto - you just install it and it works, zero configuration, zero API keys What's inside: > persistent memory across sessions - agent picks up exactly where it stopped > subagents that spawn automatically and run in parallel > /dream extracts knowledge from your session into project memory > /distill turns your repeated workflows into reusable skills > /goal with a judge model that decides if the work is actually done - not just if the agent thinks it is How to install: > Mac / Linux: curl -fsSL | bash > Windows npm install -g MIMO-ai/cli Then run: > mimo video of me installing and running it is attached github(.)com/XiaomiMiMo/MiMo-Code

Xiaomi just dropped a FREE CLAUDE CODE with memory that actually persists between sessions and nobody is talking about it MiMo Code dropped 4 days ago. 8k GitHub stars. Built-in free model called MiMo Auto - you just install it and it works, zero configuration, zero API keys What's inside: > persistent memory across sessions - agent picks up exactly where it stopped > subagents that spawn automatically and run in parallel > /dream extracts knowledge from your session into project memory > /distill turns your repeated workflows into reusable skills > /goal with a judge model that decides if the work is actually done - not just if the agent thinks it is How to install: > Mac / Linux: curl -fsSL | bash > Windows npm install -g MIMO-ai/cli Then run: > mimo video of me installing and running it is attached github(.)com/XiaomiMiMo/MiMo-Code

monokern

64,312 просмотров • 3 месяцев назад