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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...

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monokern

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64,582 Aufrufe • vor 3 Monaten •via X (Twitter)

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33 Kommentare

Profilbild von Sajid AI
Sajid AIvor 3 Monaten

Open-source AI tools are evolving fast 🚀 MiMo Code looks seriously promising.

Profilbild von FckThaFeds
FckThaFedsvor 3 Monaten

I mean couldn’t you use use Hermes. It already does all this.

Profilbild von monokern
monokernvor 3 Monaten

Find more AI tools worth actually using in my TG channel:

Profilbild von Gipp 🦅
Gipp 🦅vor 3 Monaten

Xiaomi is now also starting to study AI

Profilbild von monokern
monokernvor 3 Monaten

competition is increasing and this is encouraging

Profilbild von Xatacrypt
Xatacryptvor 3 Monaten

thanks, I'll try it later

Profilbild von Deydara
Deydaravor 3 Monaten

Thanks for the guide—it’s been really helpful lately

Profilbild von Samian
Samianvor 3 Monaten

zero config local coding with persistent memory is the angle people are sleeping on. curious how the memory layer actually works tho, is it just file-based context or something smarter?

Profilbild von Dekos
Dekosvor 3 Monaten

This new product is definitely worth trying

Profilbild von Alex
Alexvor 3 Monaten

wow, i need to test it today. thanks

Profilbild von monokern
monokernvor 3 Monaten

waiting for ur feedback

Profilbild von Lummox
Lummoxvor 3 Monaten

So looks bullish things to make money nonstop

Profilbild von slash1s
slash1svor 3 Monaten

hmm will check it bto

Profilbild von monokern
monokernvor 3 Monaten

ok broski, waiting for your feedback

Profilbild von Hussain Hashim | Building SundayBack
Hussain Hashim | Building SundayBackvor 3 Monaten

@monokern wait what?? how did i miss this? sounds like a total game changer, gonna check it out asap

Profilbild von MrGreenie
MrGreenievor 3 Monaten

for anyone looking at memory tools AtomicMemory is worth checking out. open source, self-hosted, fully inspectable. you can actually see what your agent knows and correct the parts that are wrong.

Profilbild von Nicolò Boschi
Nicolò Boschivor 3 Monaten

session persistence is absolutely crucial for agents to be truly useful over time. that's exactly why we built hindsight, an open source memory system.

Profilbild von Charlie · 云马AI · 免🪜稳连 Claude/GPT/Gemini API
Charlie · 云马AI · 免🪜稳连 Claude/GPT/Gemini APIvor 3 Monaten

这个点确实容易被低估。我觉得代码工具最烦的不是模型不够聪明，是每次重开都要重新交代上下文。能把记忆做扎实，日常写项目会省很多碎碎念。

Profilbild von Murmur
Murmurvor 3 Monaten

All things from Xiaomi are built different

Profilbild von monokern
monokernvor 3 Monaten

what do u mean, friend?

Profilbild von Murmur
Murmurvor 3 Monaten

Cheap+great

Profilbild von Long 7
Long 7vor 3 Monaten

You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs:

Profilbild von 晚晚
晚晚vor 3 Monaten

不用配钥匙还保留记忆，门槛确实低

Profilbild von AI Pixels
AI Pixelsvor 3 Monaten

What are the daily limits?

Profilbild von beamnxw ./
beamnxw ./vor 3 Monaten

XIAOMI COOKING

Profilbild von kiosa
kiosavor 3 Monaten

We need more free tools

Profilbild von Nostaline
Nostalinevor 3 Monaten

very interesting as memory infrastructure should respect developer autonomy. AtomicMemory does: open source, self-hosted, fully inspectable. You control what your agent believes and how it evolves.

Profilbild von Asteri
Asterivor 3 Monaten

Xiaomi really shot with MIMO CODE

Profilbild von Nick Venturi
Nick Venturivor 3 Monaten

downloading this before they start charging

Profilbild von Insomnia
Insomniavor 3 Monaten

absolute free to use this

Profilbild von monokern
monokernvor 3 Monaten

yep, and this is the main thing

Profilbild von MicrotronX
MicrotronXvor 3 Monaten

mimo code validates the premise exactly: session state doesn't belong in the context window. the real question is what happens when compaction hits mid-task - persistent sessions patch the symptom, not the architecture.

Profilbild von SLOT.WIN
SLOT.WINvor 3 Monaten

i just spun the wheel and got free ai magic xiao mi knows how to make luck real

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THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
13:23

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,502 Aufrufe • vor 3 Monaten

AI memory is getting f...cking illegal 10 open-source GitHub projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ → 66K+ stars 02 Hindsight ▸ → retain → recall → reflect 03 memU ▸ TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ → documents + code + conversations → knowledge graph 05 Graphiti ▸ → remembers how facts change over time 06 OpenViking ▸ MAKE THE AGENT STATEFUL 07 Letta ▸ → memory + identity across sessions 08 Letta Code ▸ REMEMBER ACROSS THE STACK 09 OpenMemory ▸ 10 Agent Memory Benchmark ▸ the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣
0:36

AI memory is getting f...cking illegal 10 open-source GitHub projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ → 66K+ stars 02 Hindsight ▸ → retain → recall → reflect 03 memU ▸ TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ → documents + code + conversations → knowledge graph 05 Graphiti ▸ → remembers how facts change over time 06 OpenViking ▸ MAKE THE AGENT STATEFUL 07 Letta ▸ → memory + identity across sessions 08 Letta Code ▸ REMEMBER ACROSS THE STACK 09 OpenMemory ▸ 10 Agent Memory Benchmark ▸ the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣

Lummox

322,301 Aufrufe • vor 5 Tagen

MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.
0:23

MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.

NO1ennn

69,163 Aufrufe • vor 1 Monat

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.
1:18

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,540 Aufrufe • vor 4 Monaten

Most people use Claude Code like a chatbot. That is the mistake. The real power is not in better prompts. It is in controlling the session. /init gives the project memory. /memory keeps the rules alive. /clear removes old context. /compact saves the important parts. /context shows where the tokens go. /rewind fixes bad turns. /plan stops blind edits. /model matches the brain to the task. Claude Code gets 10x better when you stop letting the conversation run itself. Memory. Context. Boundaries. Rollback. Planning. That is the system. Stop asking Claude Code to “just do it”. Teach it what to remember, what to forget, and when to stop. Bookmark this before your next session turns into 40 messages of context debt.
0:33

Most people use Claude Code like a chatbot. That is the mistake. The real power is not in better prompts. It is in controlling the session. /init gives the project memory. /memory keeps the rules alive. /clear removes old context. /compact saves the important parts. /context shows where the tokens go. /rewind fixes bad turns. /plan stops blind edits. /model matches the brain to the task. Claude Code gets 10x better when you stop letting the conversation run itself. Memory. Context. Boundaries. Rollback. Planning. That is the system. Stop asking Claude Code to “just do it”. Teach it what to remember, what to forget, and when to stop. Bookmark this before your next session turns into 40 messages of context debt.

Fluixo

19,226 Aufrufe • vor 3 Monaten

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.
0:29

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 Aufrufe • vor 1 Monat

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.
0:31

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

290,485 Aufrufe • vor 11 Tagen

Claude Code is $200/month. GitHub Copilot is $19/month. Jack Dorsey's company just open-sourced a free alternative with 35,000 GitHub stars. It's called Goose. - Works with any LLM — Claude, GPT, Gemini, Llama, DeepSeek - Reads and edits your entire codebase - Runs shell commands and installs dependencies - Executes and debugs code automatically - Desktop, CLI, and web interface - Written in Rust. No bloat. Block is a $40 billion company. They built it for their own engineers then gave it to everyone.
1:49

Claude Code is $200/month. GitHub Copilot is $19/month. Jack Dorsey's company just open-sourced a free alternative with 35,000 GitHub stars. It's called Goose. - Works with any LLM — Claude, GPT, Gemini, Llama, DeepSeek - Reads and edits your entire codebase - Runs shell commands and installs dependencies - Executes and debugs code automatically - Desktop, CLI, and web interface - Written in Rust. No bloat. Block is a $40 billion company. They built it for their own engineers then gave it to everyone.

0xMarioNawfal

180,365 Aufrufe • vor 6 Monaten