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mad weekend experiment 🧪 agentOS sandboxes on browserbase chromium provides the sandboxing agentOS provides the linux-compatible virtual OS: - powered by wasm - native vim + bash + more - controlled over cdp - runs pi/claude/codex/opencode (needs proxy)

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Nathan Flurry 🔩

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11,532 次观看 • 1 个月前 •via X (Twitter)

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Say hello to agentOS (beta) A portable open-source OS built just for agents. Powered by WASM & V8 isolates. 🔗 Embedded in your backend ⚡ ~6ms coldstarts, 32x cheaper than sbxs 📁 Mount anything as a file system (S3, SQLite, …) 🥧 Use Pi, Claude Code/Codex/Amp/OpenCode soon
0:28

Say hello to agentOS (beta) A portable open-source OS built just for agents. Powered by WASM & V8 isolates. 🔗 Embedded in your backend ⚡ ~6ms coldstarts, 32x cheaper than sbxs 📁 Mount anything as a file system (S3, SQLite, …) 🥧 Use Pi, Claude Code/Codex/Amp/OpenCode soon

Rivet

268,332 次观看 • 5 个月前

How do you deploy AI agents for financial institutions without compromising on security, isolation, or scale? Rogo powers complex research, analysis, and deal workflows for leading financial firms, supporting complex tasks across tens of thousands of concurrent users. To meet the diverse needs of financial teams, Rogo relies on both specialist and general-purpose agents that can adapt to a wide range of workflows. With Claude Managed Agents and self-hosted sandboxes, Rogo gets the orchestration and post-training capabilities needed to design these agent systems, while E2B provides the execution layer underneath. Every piece of code generated by an agent runs inside its own E2B sandbox with full auditability and traceability. Each sandbox is powered by an isolated microVM, providing the kernel-level isolation critical for Rogo's customers. The result is a seamless agent experience backed by the security, reliability, and compliance requirements that financial institutions demand.
2:29

How do you deploy AI agents for financial institutions without compromising on security, isolation, or scale? Rogo powers complex research, analysis, and deal workflows for leading financial firms, supporting complex tasks across tens of thousands of concurrent users. To meet the diverse needs of financial teams, Rogo relies on both specialist and general-purpose agents that can adapt to a wide range of workflows. With Claude Managed Agents and self-hosted sandboxes, Rogo gets the orchestration and post-training capabilities needed to design these agent systems, while E2B provides the execution layer underneath. Every piece of code generated by an agent runs inside its own E2B sandbox with full auditability and traceability. Each sandbox is powered by an isolated microVM, providing the kernel-level isolation critical for Rogo's customers. The result is a seamless agent experience backed by the security, reliability, and compliance requirements that financial institutions demand.

Vasek Mlejnsky

16,666 次观看 • 2 个月前

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
2:24

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

472,126 次观看 • 1 年前

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.
1:39

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC

20,932,195 次观看 • 2 个月前

THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this
9:32

THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this

Yarchi

816,449 次观看 • 2 个月前

Elon Musk just put a 30-month timeline on data centers in space. Not a concept. A deadline. Gerstner: “You’re merging the two biggest TAMs in the world. All of artificial intelligence and all of space.” Every ceiling AI has ever hit comes down to one thing. Power. Land is finite. Cooling is expensive. Energy grids were never built for what this technology demands. Every major AI company on Earth is fighting over the same stressed infrastructure, competing for power that simply does not exist in sufficient supply. Space does not have that problem. Gerstner: “Power is the proxy, power is the primitive to AI.” Orbital data centers drawing unlimited solar energy do not improve the cost curve. They collapse it. No atmosphere. No weather. No darkness. Just continuous unfiltered energy. Forever. Starlink scales to 20 million people connected directly to their phones. xAI provides the intelligence. SpaceX provides the infrastructure. One person building both. Gerstner: “Elon is kind of an N of one. His ability to dream this and execute at this scale.” No comparison exists. Most companies spend lifetimes executing inside one industry. Musk is merging the two largest markets in human history into a single vertically integrated system powered by orbital infrastructure nobody else can build. Gerstner: “I’m glad he’s on Team America.” Whoever controls orbital infrastructure for AI and communications does not just win a market. They own the foundation everything runs on for generations. China sees this. Cannot replicate it. Reusable rockets plus frontier AI plus capital plus execution velocity do not exist together anywhere else on Earth. In 30 months the game changes permanently. The person who solved Earth’s energy constraint by leaving Earth just made every competitor’s roadmap irrelevant.
1:31

Elon Musk just put a 30-month timeline on data centers in space. Not a concept. A deadline. Gerstner: “You’re merging the two biggest TAMs in the world. All of artificial intelligence and all of space.” Every ceiling AI has ever hit comes down to one thing. Power. Land is finite. Cooling is expensive. Energy grids were never built for what this technology demands. Every major AI company on Earth is fighting over the same stressed infrastructure, competing for power that simply does not exist in sufficient supply. Space does not have that problem. Gerstner: “Power is the proxy, power is the primitive to AI.” Orbital data centers drawing unlimited solar energy do not improve the cost curve. They collapse it. No atmosphere. No weather. No darkness. Just continuous unfiltered energy. Forever. Starlink scales to 20 million people connected directly to their phones. xAI provides the intelligence. SpaceX provides the infrastructure. One person building both. Gerstner: “Elon is kind of an N of one. His ability to dream this and execute at this scale.” No comparison exists. Most companies spend lifetimes executing inside one industry. Musk is merging the two largest markets in human history into a single vertically integrated system powered by orbital infrastructure nobody else can build. Gerstner: “I’m glad he’s on Team America.” Whoever controls orbital infrastructure for AI and communications does not just win a market. They own the foundation everything runs on for generations. China sees this. Cannot replicate it. Reusable rockets plus frontier AI plus capital plus execution velocity do not exist together anywhere else on Earth. In 30 months the game changes permanently. The person who solved Earth’s energy constraint by leaving Earth just made every competitor’s roadmap irrelevant.

Dustin

102,993 次观看 • 6 个月前

A 27-year-old marketer built a second brain in Obsidian powered by Claude and now clears $18,400 a month from UGC campaigns. He used to juggle 47 content ideas, 19 creator briefs, and endless revisions across Notion tabs and Google Docs. One missed trend cost him a $9,200 client. Now the system runs itself. He drops raw UGC footage into Obsidian. Claude instantly tags hooks, scripts 7 variations, matches brand voice from his vault, and flags winning patterns from past 214 campaigns. Links surface automatically. No more searching. The second brain stores every high-performer: 43 proven hooks, 12 lighting setups, 9 CTAs that convert above 4.8%. Claude cross-references new footage against this library in seconds and spits out ready-to-shoot briefs. One creator gets the brief at 9 AM. Shoots by noon. Edited clips land by 3 PM. He posts 4-6 pieces daily across TikTok, Instagram Reels, and Douyin without lifting a finger. Retention jumped from 41% to 67%. Average campaign now hits $42K in 11 days instead of $19K over 30. He scaled to 9 active UGC models running on autopilot. The Obsidian vault grew to 2,800 linked notes. Claude handles 80% of the thinking. The setup took him one weekend: vault structure, custom prompts in Claude Projects, and daily sync rules. His couch hasn't moved since. The second brain prints money while he sleeps.
0:48

A 27-year-old marketer built a second brain in Obsidian powered by Claude and now clears $18,400 a month from UGC campaigns. He used to juggle 47 content ideas, 19 creator briefs, and endless revisions across Notion tabs and Google Docs. One missed trend cost him a $9,200 client. Now the system runs itself. He drops raw UGC footage into Obsidian. Claude instantly tags hooks, scripts 7 variations, matches brand voice from his vault, and flags winning patterns from past 214 campaigns. Links surface automatically. No more searching. The second brain stores every high-performer: 43 proven hooks, 12 lighting setups, 9 CTAs that convert above 4.8%. Claude cross-references new footage against this library in seconds and spits out ready-to-shoot briefs. One creator gets the brief at 9 AM. Shoots by noon. Edited clips land by 3 PM. He posts 4-6 pieces daily across TikTok, Instagram Reels, and Douyin without lifting a finger. Retention jumped from 41% to 67%. Average campaign now hits $42K in 11 days instead of $19K over 30. He scaled to 9 active UGC models running on autopilot. The Obsidian vault grew to 2,800 linked notes. Claude handles 80% of the thinking. The setup took him one weekend: vault structure, custom prompts in Claude Projects, and daily sync rules. His couch hasn't moved since. The second brain prints money while he sleeps.

Rugikk

54,355 次观看 • 1 个月前

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!
0:25

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Avi Chawla

187,076 次观看 • 8 个月前

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)
0:39

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

Akshay 🚀

169,957 次观看 • 1 个月前

**Doris Yin 2025 New Year's Greetings to Pi Network Community** Dear Global Pioneers, GCV Ambassadors, and all GCV merchants, Happy New Year to everyone! Today marks the first day of 2025! I hope you all feel confident and energized as we work towards a successful OM for the Pi Network in Q1.🙏🙏🙏✊✊✊ In 2024, we made significant progress within the Pi Network. This progress serves as both motivation and a foundation for the current mass KYC and migration processes, which means we can finally anticipate a deadline for the OM—within a maximum of 89 days. Most likely, we will have the OM on Pi Day, which is just 72 days away. What has driven this success? GCV $314,159 has been confirmed since Pi2 Day 2024. Why am I so confident? The reasoning is straightforward, and most analysts can understand it. If GCV cannot meet the expectations set by Core Team for the long-term success of the Pi Network, they would not provide us with a deadline for OM by Pi2Day of 2025 on Pi2Day 2024 nor would they issue the recent deadline of Q1 2025. It’s important to remember that every project has its vision, mission, and goals. Considerable time and investment are put in to ensure that the project moves in the right direction. If the direction GCV does not align with their requirements, they cannot and will not give us the time frame for OM until we meet the necessary standards. Understand? Therefore, those who argue against GCV clearly lack understanding of Pi Network, and we can totally ignore them. There’s no need to explain or respond to their questions. Our Pioneers Handbook provides a strong theoretical foundation and guidelines for GCV helping Pi Network 100% succeed without any business risk. We already have more than 5 million GCV data on the blockchain, so there’s no need to debate what is Pi value. The only GCV value is $314,159. Our Pioneer’s Handbook has been updated to topic #73 and translated into 30 major languages. Pioneers who wish to live happily should obtain a copy from the Telegram International Group. It can not only boost your confidence but also bring you peace of mind. During the next 72 or 89 days, what should pioneers do? It’s simple: study the Pioneers Handbook. I recommend printing it out and reading it five times. This will contribute to the long-term success of the Pi Network, building this mindset is critical. Additionally, please share this Handbook with all your groups. It will help other pioneers develop a correct outlook on life and values. If you are a merchant or know any merchants, please also share the Pioneers Handbook and my articles about partial Pi payments at GCV. This will help both your business and your friends' businesses by leveraging the GCV community to increase exposure and attract more customers. After the OM, they can join the Pi Network Web3 blockchain, which will give them an edge over competitors. Make sure they register on MapofPi and remember to include GCV $314,159. CT needs to see how many merchants support GCV to determine how many pioneers can undergo KYC and migration. The logic here is straightforward: more GCV merchants signify lower failure risks and low policy violations. As demand for Pi increases in this region to support GCV ecosystem, this will lead to more KYC and migration opportunities in your area, including for high amounts of Pi wallets. We have witnessed already in GCV active countries which more than 10,000 or 20,000 above wallets have been migrated a lot. We achieved significant success in 2024, and I am confident that we will achieve even greater success in 2025, culminating in our OM celebration in about 2-3 months. Isn’t that exciting? Please block any accounts trying to convince you that your Pi value is $10, $100, $1,000, or $100,000. Ignore the noise; If you believe them, it will only slow down your region. GCV is unshakeable, and regions that support GCV and have more merchants in the ecosystem will gain more KYC and migration opportunities. For all pioneers who are facing financial difficulties, this is your only one opportunity in life. Don’t expect to still hold Pi if you choose to sell some for quick cash. Selling 10, 100, or even 1,000 Pi at this moment may entirely derail your chance for a better life because all your Pi account will be frozen. If you transfer your bought Pi from eco wallet to your own wallet, your policy violation will be tracked and all your accounts will be frozen. Any money spent to purchase Pi will be wasted. All pioneers should learn to be good citizens who respect government regulations and act honestly. Never try to take advantage of others. Greed in the Pi Network can cost you the opportunity to change your life and lose all your investment in buying Pi. I believe that most of our pioneers are honest, and you will be smart enough not to engage in harmful actions against others, the Pi Network, or yourselves. A person who knows how to navigate challenges is a hero. I wish for all pioneers to be heroes. Once again, Happy New Year! I wish all of you health, happiness, and prosperity in 2025.🌹🌹🌹 Doris Yin 🪷🪷🪷 Founder, Global GCV Movement Jan.1st, 2025
9:01

**Doris Yin 2025 New Year's Greetings to Pi Network Community** Dear Global Pioneers, GCV Ambassadors, and all GCV merchants, Happy New Year to everyone! Today marks the first day of 2025! I hope you all feel confident and energized as we work towards a successful OM for the Pi Network in Q1.🙏🙏🙏✊✊✊ In 2024, we made significant progress within the Pi Network. This progress serves as both motivation and a foundation for the current mass KYC and migration processes, which means we can finally anticipate a deadline for the OM—within a maximum of 89 days. Most likely, we will have the OM on Pi Day, which is just 72 days away. What has driven this success? GCV $314,159 has been confirmed since Pi2 Day 2024. Why am I so confident? The reasoning is straightforward, and most analysts can understand it. If GCV cannot meet the expectations set by Core Team for the long-term success of the Pi Network, they would not provide us with a deadline for OM by Pi2Day of 2025 on Pi2Day 2024 nor would they issue the recent deadline of Q1 2025. It’s important to remember that every project has its vision, mission, and goals. Considerable time and investment are put in to ensure that the project moves in the right direction. If the direction GCV does not align with their requirements, they cannot and will not give us the time frame for OM until we meet the necessary standards. Understand? Therefore, those who argue against GCV clearly lack understanding of Pi Network, and we can totally ignore them. There’s no need to explain or respond to their questions. Our Pioneers Handbook provides a strong theoretical foundation and guidelines for GCV helping Pi Network 100% succeed without any business risk. We already have more than 5 million GCV data on the blockchain, so there’s no need to debate what is Pi value. The only GCV value is $314,159. Our Pioneer’s Handbook has been updated to topic #73 and translated into 30 major languages. Pioneers who wish to live happily should obtain a copy from the Telegram International Group. It can not only boost your confidence but also bring you peace of mind. During the next 72 or 89 days, what should pioneers do? It’s simple: study the Pioneers Handbook. I recommend printing it out and reading it five times. This will contribute to the long-term success of the Pi Network, building this mindset is critical. Additionally, please share this Handbook with all your groups. It will help other pioneers develop a correct outlook on life and values. If you are a merchant or know any merchants, please also share the Pioneers Handbook and my articles about partial Pi payments at GCV. This will help both your business and your friends' businesses by leveraging the GCV community to increase exposure and attract more customers. After the OM, they can join the Pi Network Web3 blockchain, which will give them an edge over competitors. Make sure they register on MapofPi and remember to include GCV $314,159. CT needs to see how many merchants support GCV to determine how many pioneers can undergo KYC and migration. The logic here is straightforward: more GCV merchants signify lower failure risks and low policy violations. As demand for Pi increases in this region to support GCV ecosystem, this will lead to more KYC and migration opportunities in your area, including for high amounts of Pi wallets. We have witnessed already in GCV active countries which more than 10,000 or 20,000 above wallets have been migrated a lot. We achieved significant success in 2024, and I am confident that we will achieve even greater success in 2025, culminating in our OM celebration in about 2-3 months. Isn’t that exciting? Please block any accounts trying to convince you that your Pi value is $10, $100, $1,000, or $100,000. Ignore the noise; If you believe them, it will only slow down your region. GCV is unshakeable, and regions that support GCV and have more merchants in the ecosystem will gain more KYC and migration opportunities. For all pioneers who are facing financial difficulties, this is your only one opportunity in life. Don’t expect to still hold Pi if you choose to sell some for quick cash. Selling 10, 100, or even 1,000 Pi at this moment may entirely derail your chance for a better life because all your Pi account will be frozen. If you transfer your bought Pi from eco wallet to your own wallet, your policy violation will be tracked and all your accounts will be frozen. Any money spent to purchase Pi will be wasted. All pioneers should learn to be good citizens who respect government regulations and act honestly. Never try to take advantage of others. Greed in the Pi Network can cost you the opportunity to change your life and lose all your investment in buying Pi. I believe that most of our pioneers are honest, and you will be smart enough not to engage in harmful actions against others, the Pi Network, or yourselves. A person who knows how to navigate challenges is a hero. I wish for all pioneers to be heroes. Once again, Happy New Year! I wish all of you health, happiness, and prosperity in 2025.🌹🌹🌹 Doris Yin 🪷🪷🪷 Founder, Global GCV Movement Jan.1st, 2025

Doris Yin 东方紫莲🪷

44,760 次观看 • 1 年前

Introducing QuickSwap Bonds, now live on the DEX 🔥 Discover an exciting new way to LP and earn rewards on #Polygon! Users provide liquidity and receive tokens at a discount that vest over time, represented by a Bond NFT. Projects get protocol-owned liquidity in return. Powered by ApeBond, the dragon and ape communities have teamed up to bring this awesome product to Polygon DeFi 🤝 Are you a yield farmer looking to tap into some cool Polygon ecosystem projects? Then QuickSwap Bonds may just be the right fit for you! Partner Projects Offering QuickSwap Bonds Dogelon Mars Dogelon Mars is a doge-themed cryptocurrency and comic series inspired by Dogecoin. Dogelon is a Shiba Inu on a mission to bring humanity to Mars. 50% of the token supply was locked forever in a decentralised liquidity pool and the other 50% was donated to Vitalik Buterin, the founder of Ethereum. $ELON is the main token that powers the ecosystem. GENSO Meta GensoKishi Online Metaworld is the Web3 incarnation of the award-winning Nintendo Switch/PS4 game “Elemental Knights," where players explore a fantasy 3D virtual world. It merges a MMORPG game style with in-game ownership through NFTs. Users can create, buy, and sell skins, maps, weapons, and more as NFTs through the $MV token. PLANET IX Planet IX is an online NFT-strategy game where users embark on an exciting adventure within a digital earth. The aim is to restore this planet to what it once was: a more thriving and lush environment. $IX is the game's native utility token that lets users farm, earn, and trade. BlockWallet BlockWallet is a self-custodial Web3 wallet that highly focuses on user security. It supports all EVM chains, is open source, has built-in swaps and bridges, supports multiple hardware wallets, and much more. BlockWallet has its own $BLANK token, which allows users to invest in the project's future, receive rewards, special offers, and engage in unique events like the upcoming STAKING campaign. Forest Knight Forest Knight is a free-to-play, play-to-own turned-based RPG game on mobile devices that lets players battle against one another through PVP and PVE game styles in a virtual fantasy world while also collecting NFTs and other items. Its in-game economy is powered by the $KNIGHT token, where holders can receive special benefits and participate in governance. Yellow Yellow Duckies is a NFT trading card game that lets players collect, trade and evolve Ducklings NFTs! The game uses the DUCKIES token for utility but also for the upcoming Duckies Canary Network 👀🎁🪂 Orbs Orbs is an open, decentralised, and public blockchain infrastructure that's made possible through a secure network of permissionless validators using PoS consensus. They've already integrated several powerful products on the QuickSwap DEX, including Liquidity Hub and dTWAP/limit orders. $ORBS is the token that powers their ecosystem. Veloce Veloce is a large, decentralised gaming and sports media organisation that is made up of 6 brands, taking Web3 esports and racing to the next level. VEXT (or $VEXT) is the ecosystem's native currency that provides holders with perks and benefits in the ecosystem. Borderless.Money | BorderlessMoney.lens (🌸, 🌿) Borderless Money is a DeFi protocol that lets users contribute capital to social investments (UN sustainable goals) to participate in various social causes. The platform's native token is $BOM, which users can stake, participate in decentralised governance, and earn rewards. BitCone (CONE) BitCone is a decentralised community powered by the $CONE token, best known for its "Conemunity" on subreddits. Users can earn the token through airdrops and BitCone mining. yusril mahendra FireBot enhances returns with DeFi strategies and trading algorithms, offering tokenized investments on Polygon. Their $FBX token is backed by quantitative strategies to mitigate crypto market volatility.
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Introducing QuickSwap Bonds, now live on the DEX 🔥 Discover an exciting new way to LP and earn rewards on #Polygon! Users provide liquidity and receive tokens at a discount that vest over time, represented by a Bond NFT. Projects get protocol-owned liquidity in return. Powered by ApeBond, the dragon and ape communities have teamed up to bring this awesome product to Polygon DeFi 🤝 Are you a yield farmer looking to tap into some cool Polygon ecosystem projects? Then QuickSwap Bonds may just be the right fit for you! Partner Projects Offering QuickSwap Bonds Dogelon Mars Dogelon Mars is a doge-themed cryptocurrency and comic series inspired by Dogecoin. Dogelon is a Shiba Inu on a mission to bring humanity to Mars. 50% of the token supply was locked forever in a decentralised liquidity pool and the other 50% was donated to Vitalik Buterin, the founder of Ethereum. $ELON is the main token that powers the ecosystem. GENSO Meta GensoKishi Online Metaworld is the Web3 incarnation of the award-winning Nintendo Switch/PS4 game “Elemental Knights," where players explore a fantasy 3D virtual world. It merges a MMORPG game style with in-game ownership through NFTs. Users can create, buy, and sell skins, maps, weapons, and more as NFTs through the $MV token. PLANET IX Planet IX is an online NFT-strategy game where users embark on an exciting adventure within a digital earth. The aim is to restore this planet to what it once was: a more thriving and lush environment. $IX is the game's native utility token that lets users farm, earn, and trade. BlockWallet BlockWallet is a self-custodial Web3 wallet that highly focuses on user security. It supports all EVM chains, is open source, has built-in swaps and bridges, supports multiple hardware wallets, and much more. BlockWallet has its own $BLANK token, which allows users to invest in the project's future, receive rewards, special offers, and engage in unique events like the upcoming STAKING campaign. Forest Knight Forest Knight is a free-to-play, play-to-own turned-based RPG game on mobile devices that lets players battle against one another through PVP and PVE game styles in a virtual fantasy world while also collecting NFTs and other items. Its in-game economy is powered by the $KNIGHT token, where holders can receive special benefits and participate in governance. Yellow Yellow Duckies is a NFT trading card game that lets players collect, trade and evolve Ducklings NFTs! The game uses the DUCKIES token for utility but also for the upcoming Duckies Canary Network 👀🎁🪂 Orbs Orbs is an open, decentralised, and public blockchain infrastructure that's made possible through a secure network of permissionless validators using PoS consensus. They've already integrated several powerful products on the QuickSwap DEX, including Liquidity Hub and dTWAP/limit orders. $ORBS is the token that powers their ecosystem. Veloce Veloce is a large, decentralised gaming and sports media organisation that is made up of 6 brands, taking Web3 esports and racing to the next level. VEXT (or $VEXT) is the ecosystem's native currency that provides holders with perks and benefits in the ecosystem. Borderless.Money | BorderlessMoney.lens (🌸, 🌿) Borderless Money is a DeFi protocol that lets users contribute capital to social investments (UN sustainable goals) to participate in various social causes. The platform's native token is $BOM, which users can stake, participate in decentralised governance, and earn rewards. BitCone (CONE) BitCone is a decentralised community powered by the $CONE token, best known for its "Conemunity" on subreddits. Users can earn the token through airdrops and BitCone mining. yusril mahendra FireBot enhances returns with DeFi strategies and trading algorithms, offering tokenized investments on Polygon. Their $FBX token is backed by quantitative strategies to mitigate crypto market volatility.

QuickSwap 🐲 DragonFi 2.0

42,808 次观看 • 2 年前

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)
0:30

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)

Varun

30,689 次观看 • 5 个月前

Risk oracles, automated systems that monitor and manage protocol risk onchain, are set to become a cornerstone of onchain finance. However, their reliability and design philosophy determine whether they actually reduce or introduce risk. When risk oracles fail to operate throughout volatile conditions, depend on poor-quality data, or lack transparency, they become a point of failure. Onchain finance needs infrastructure that’s neutral, transparent, and battle-tested under extreme conditions. That’s where the Chainlink Runtime Environment (CRE) comes in. CRE’s modular orchestration framework lets developers build highly-customizable and resilient risk oracle systems leveraging the industry’s most reliable infrastructure and highest-quality data. With CRE, protocol developers, DeFi risk managers, and risk curators can create risk oracle solutions that include: • Custom pricing logic to reflect complex instruments • Dynamic risk parameters to maximize efficiency and safety • Automated freeze or circuit-breaker functionality All built on verifiable, decentralized infrastructure. That’s why LlamaRisk chose CRE to power LlamaGuard NAV, a next-gen oracle integrated by Aave Horizon that prices tokenized RWAs using dynamic price bounds and advanced risk controls. In combining Chainlink’s proven reliability and neutrality with LlamaRisk’s expertise in protocol risk, LlamaGuard provides Aave Horizon with a transparent, automated risk management system designed for institutional-grade tokenized assets. This design separates responsibilities: • Chainlink provides the trusted infrastructure for secure data validation and workflow orchestration. • LlamaRisk configures the transparent, dynamic risk logic that runs on top of CRE. The result: a highly-reliable and transparent risk oracle solution that ensures neutrality at the system level and adheres to DeFi’s decentralization ethos. As Sergey Nazarov and Stani noted at SmartCon 2025, CRE-powered risk oracles on Aave Horizon bring together specialized experts to deliver transparent, verifiable, and robust systems for the next era of DeFi. Protocols that implement properly designed risk oracles have an inherent advantage by offering users stronger safety, more efficient markets, and better institutional alignment. Safer oracles = Safer DeFi.
1:14

Risk oracles, automated systems that monitor and manage protocol risk onchain, are set to become a cornerstone of onchain finance. However, their reliability and design philosophy determine whether they actually reduce or introduce risk. When risk oracles fail to operate throughout volatile conditions, depend on poor-quality data, or lack transparency, they become a point of failure. Onchain finance needs infrastructure that’s neutral, transparent, and battle-tested under extreme conditions. That’s where the Chainlink Runtime Environment (CRE) comes in. CRE’s modular orchestration framework lets developers build highly-customizable and resilient risk oracle systems leveraging the industry’s most reliable infrastructure and highest-quality data. With CRE, protocol developers, DeFi risk managers, and risk curators can create risk oracle solutions that include: • Custom pricing logic to reflect complex instruments • Dynamic risk parameters to maximize efficiency and safety • Automated freeze or circuit-breaker functionality All built on verifiable, decentralized infrastructure. That’s why LlamaRisk chose CRE to power LlamaGuard NAV, a next-gen oracle integrated by Aave Horizon that prices tokenized RWAs using dynamic price bounds and advanced risk controls. In combining Chainlink’s proven reliability and neutrality with LlamaRisk’s expertise in protocol risk, LlamaGuard provides Aave Horizon with a transparent, automated risk management system designed for institutional-grade tokenized assets. This design separates responsibilities: • Chainlink provides the trusted infrastructure for secure data validation and workflow orchestration. • LlamaRisk configures the transparent, dynamic risk logic that runs on top of CRE. The result: a highly-reliable and transparent risk oracle solution that ensures neutrality at the system level and adheres to DeFi’s decentralization ethos. As Sergey Nazarov and Stani noted at SmartCon 2025, CRE-powered risk oracles on Aave Horizon bring together specialized experts to deliver transparent, verifiable, and robust systems for the next era of DeFi. Protocols that implement properly designed risk oracles have an inherent advantage by offering users stronger safety, more efficient markets, and better institutional alignment. Safer oracles = Safer DeFi.

Chainlink

32,099 次观看 • 9 个月前

ChatGPT 5.5 is cooked. Claude Opus 4.7 is cooked. Every $420/mo SaaS AI just got an open-source assassin. Mind blown: an open-source desktop AI just hit #7 trending overnight, runs 100% on your laptop, ships with 100+ native integrations, and is quietly killing the entire ChatGPT-subscription era. Introducing OpenHuman by tinyhumansai -> your Personal AI super intelligence. Private. Simple. Powerful. Two weeks ago they quietly dropped it on GitHub. Today: 300+ stars, 100+ daily paying users, 1,129 commits, zero marketing budget. > What is OpenHuman? A native desktop agent (macOS, Windows, Linux) that lives on YOUR machine instead of feeding your data back to OpenAI. Download the app, sign in once, and the agent harness gives you 100+ native connectors out of the box: Gmail, Slack, Notion, GitHub, Reddit, Instagram, Calendar, Drive, Telegram, Discord, and dozens more. One click each. From that moment it builds an encrypted, on-device knowledge base of your entire digital life. No terminal. No Python envs. No API keys. No CLI. > What the agent actually does: Steven, the creator, just dropped a Loom showing real prompts: - "Send Mark a joke" -> drafts in your voice and ships it. - "List my top 5 emails today" -> surfaces what matters from a flooded inbox. - "Summarize that thread and email it to the team" -> done in 3 seconds. One prompt --> multiple connected tools --> end-to-end execution. No tab-switching. > What's actually inside: - Screen intelligence -> the agent SEES what's on your screen and feeds it into your local context. - Memory-aware keyboard autocomplete -> system-wide, in YOUR voice, trained on YOUR past replies. Gmail Smart Compose for your entire OS. - Local knowledge base -> every email, message, and note parsed, embedded, encrypted, on YOUR device. Day 30 it knows you better than your therapist. - 75% Rust core -> memory-safe, brutally fast, runs local AI directly on your machine. > The "but wait" moment: OpenClaw and Hermes Agent are excellent. But they live in the terminal. Virtualenvs. SKILL.md files. Shell debugging at 2am. OpenHuman doesn't ask any of that. Their README compares itself to "The Tet" from Oblivion -- that alien superintelligence Morgan Freeman calls "a brilliant machine". And tomorrow they're dropping the official OpenHuman mascot. Sneak peek already in Steven's Loom. The cloud-first AI decade is ending. OpenHuman is GPL-3, fully auditable, shipping a release every few days. Save this -- you just got the link to the thing replacing every SaaS AI on the market. -> Repo:
0:31

ChatGPT 5.5 is cooked. Claude Opus 4.7 is cooked. Every $420/mo SaaS AI just got an open-source assassin. Mind blown: an open-source desktop AI just hit #7 trending overnight, runs 100% on your laptop, ships with 100+ native integrations, and is quietly killing the entire ChatGPT-subscription era. Introducing OpenHuman by tinyhumansai -> your Personal AI super intelligence. Private. Simple. Powerful. Two weeks ago they quietly dropped it on GitHub. Today: 300+ stars, 100+ daily paying users, 1,129 commits, zero marketing budget. > What is OpenHuman? A native desktop agent (macOS, Windows, Linux) that lives on YOUR machine instead of feeding your data back to OpenAI. Download the app, sign in once, and the agent harness gives you 100+ native connectors out of the box: Gmail, Slack, Notion, GitHub, Reddit, Instagram, Calendar, Drive, Telegram, Discord, and dozens more. One click each. From that moment it builds an encrypted, on-device knowledge base of your entire digital life. No terminal. No Python envs. No API keys. No CLI. > What the agent actually does: Steven, the creator, just dropped a Loom showing real prompts: - "Send Mark a joke" -> drafts in your voice and ships it. - "List my top 5 emails today" -> surfaces what matters from a flooded inbox. - "Summarize that thread and email it to the team" -> done in 3 seconds. One prompt --> multiple connected tools --> end-to-end execution. No tab-switching. > What's actually inside: - Screen intelligence -> the agent SEES what's on your screen and feeds it into your local context. - Memory-aware keyboard autocomplete -> system-wide, in YOUR voice, trained on YOUR past replies. Gmail Smart Compose for your entire OS. - Local knowledge base -> every email, message, and note parsed, embedded, encrypted, on YOUR device. Day 30 it knows you better than your therapist. - 75% Rust core -> memory-safe, brutally fast, runs local AI directly on your machine. > The "but wait" moment: OpenClaw and Hermes Agent are excellent. But they live in the terminal. Virtualenvs. SKILL.md files. Shell debugging at 2am. OpenHuman doesn't ask any of that. Their README compares itself to "The Tet" from Oblivion -- that alien superintelligence Morgan Freeman calls "a brilliant machine". And tomorrow they're dropping the official OpenHuman mascot. Sneak peek already in Steven's Loom. The cloud-first AI decade is ending. OpenHuman is GPL-3, fully auditable, shipping a release every few days. Save this -- you just got the link to the thing replacing every SaaS AI on the market. -> Repo:

slash1s

70,940 次观看 • 3 个月前

10 free github repos that can replace major SaaS with subscriptions. all free. open-sourced. some are MIT licensed. — 1️⃣ openscreen — replaces screen studio ($29/mo) - a clean macOS/windows/linux screen recorder for polished demos. - blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio. - doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X. — 2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo) - local-first AI voice studio. - clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages, - dictate into any text field with a global hotkey. - nothing leaves your machine. - runs on apple silicon, cuda, rocm. — 3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo) - free AI video platform. - clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers). - also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host. — 4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each) - stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint. ~800M tokens/month. - smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard. — 5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo) - microsoft's official MCP server that gives any AI agent full browser control. - uses accessibility trees, not screenshots — deterministic and token-efficient. - works with claude code, cursor, windsurf, codex out of the box. — 6️⃣ vibe-trading — replaces tradingview premium ($60/mo) - natural-language finance research agent. - 7 backtest engines across stocks, crypto, futures, forex. - 75 specialist skills (factor analysis, options strategy, ML strategy). - 29 multi-agent swarm presets. - 21 of 22 MCP tools work with zero API keys. — 7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo) - the open-source scheduling infrastructure. - one-on-ones, group events, round-robin, team booking, - payment collection (stripe), routing forms, workflows. - integrates with google/outlook/apple calendar, zoom, meet, teams. - self-host in 10 minutes with docker. 40k stars. — 8️⃣ whisper — replaces otter ($17/mo) - openAI's open-source speech-to-text model. - transcribe audio in 99 languages, translate to english, generate timestamps. - runs locally on cpu or gpu. - the actual model behind most "AI transcription" SaaS tools you're paying for. — 9️⃣ postiz — replaces buffer ($15/mo) - AI-powered social media scheduler. - cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest. - AI captions and hashtags. - analytics dashboard. team workspaces. 31k stars and rising. — 🔟 vaultwarden — replaces 1password ($8/mo) - unofficial bitwarden-compatible server written in rust. - works with every official bitwarden client (mobile, desktop, browser). - unlimited users, unlimited vaults, full enterprise feature set. - runs on a $5 VPS or your home server. — disclaimer: open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version. for builders, prototypers, and indie hackers — that's the whole point. for everyone else, the paid tools still have their place. bookmark this. share with one friend bleeding subscription fees. ~m0h
0:31

10 free github repos that can replace major SaaS with subscriptions. all free. open-sourced. some are MIT licensed. — 1️⃣ openscreen — replaces screen studio ($29/mo) - a clean macOS/windows/linux screen recorder for polished demos. - blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio. - doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X. — 2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo) - local-first AI voice studio. - clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages, - dictate into any text field with a global hotkey. - nothing leaves your machine. - runs on apple silicon, cuda, rocm. — 3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo) - free AI video platform. - clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers). - also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host. — 4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each) - stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint. ~800M tokens/month. - smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard. — 5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo) - microsoft's official MCP server that gives any AI agent full browser control. - uses accessibility trees, not screenshots — deterministic and token-efficient. - works with claude code, cursor, windsurf, codex out of the box. — 6️⃣ vibe-trading — replaces tradingview premium ($60/mo) - natural-language finance research agent. - 7 backtest engines across stocks, crypto, futures, forex. - 75 specialist skills (factor analysis, options strategy, ML strategy). - 29 multi-agent swarm presets. - 21 of 22 MCP tools work with zero API keys. — 7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo) - the open-source scheduling infrastructure. - one-on-ones, group events, round-robin, team booking, - payment collection (stripe), routing forms, workflows. - integrates with google/outlook/apple calendar, zoom, meet, teams. - self-host in 10 minutes with docker. 40k stars. — 8️⃣ whisper — replaces otter ($17/mo) - openAI's open-source speech-to-text model. - transcribe audio in 99 languages, translate to english, generate timestamps. - runs locally on cpu or gpu. - the actual model behind most "AI transcription" SaaS tools you're paying for. — 9️⃣ postiz — replaces buffer ($15/mo) - AI-powered social media scheduler. - cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest. - AI captions and hashtags. - analytics dashboard. team workspaces. 31k stars and rising. — 🔟 vaultwarden — replaces 1password ($8/mo) - unofficial bitwarden-compatible server written in rust. - works with every official bitwarden client (mobile, desktop, browser). - unlimited users, unlimited vaults, full enterprise feature set. - runs on a $5 VPS or your home server. — disclaimer: open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version. for builders, prototypers, and indie hackers — that's the whole point. for everyone else, the paid tools still have their place. bookmark this. share with one friend bleeding subscription fees. ~m0h

m0h

253,313 次观看 • 3 个月前

I finally finished my Rust version of Mario Zechner's (Mario Zechner) excellent Pi Agent, which I made with his blessing and which is called pi_agent_rust. You can get it here: If you're not familiar with Pi, it's a minimalist and extensible agent harness (similar to Claude Code and Codex) and, among other uses, serves as the core agent harness inside the OpenClaw project. I say my Rust "version" instead of "port" because it's really quite different in how it's implemented for it to be called a port. Arguably, the incremental functionality in the implementation was more complex than the rest of the project combined. Still, it provides the same features and functionality as the original, and is proven to be compatible with hundreds of popular extensions to Pi (the conformance harness shows 224 out of 224 extensions working perfectly). But the way it's architected has some major changes. Pi Agent relies on node or bun to provide access to the filesystem and for various other tasks, and that is also how Pi's extension system works. I decided early on that I didn't want to do things that way. Instead, I wanted to integrate that functionality directly into the binary itself; that is, to provide equivalent functionality for everything that would normally be provided by node/bun in the original. I did this for several reasons: one, it's a lot more performant in terms of footprint and latency. On realistic end-to-end large-session workloads (not toy microbenchmarks), pi_agent_rust is now: - 4.95x faster than legacy Node and 2.80x faster than legacy Bun at 1mm-token session scale - 4.32x faster than legacy Node and 2.14x faster than legacy Bun at 5mm-token session scale - ~8x to ~13x lower RSS memory footprint in those same scenarios But the other reason is security and control: by handling everything internally in an end-to-end way, we can do all sorts of clever things to harden the system against insecure or malicious extensions. Those extensions no longer have direct access to the ambient filesystem: they now need to go through pi_agent_rust, and we can analyze extensions carefully before ever running them and also block things that look suspicious at runtime. In practice that means explicit capability-gated hostcalls, with policy/risk/quota enforcement and runtime telemetry/auditability. In order to do all this, I had to effectively build the missing runtime substrate from scratch in Rust, not just translate TypeScript syntax: - define and implement a typed hostcall ABI for extension->host interactions - build native Rust connectors for tool/exec/http/session/ui/events instead of ambient Node/Bun access - implement a compatibility/shim layer so real-world Pi extensions still behave correctly - add capability policy evaluation, runtime risk scoring, per-extension quotas, and audit telemetry on the execution path - wire the whole thing through structured concurrency (asupersync) so cancellation/lifetimes are deterministic and failure handling is explicit - build a conformance + benchmark harness large enough to validate behavior/perf across hundreds of extensions and realistic long-session workloads This was a full re-architecture of the execution model while preserving the Pi workflow and extension ecosystem. And indeed, this aspect of it dwarfs the entire rest of the project in size and complexity. To put hard numbers on that: the extension/runtime/security subsystem alone is now about 86.5k lines of Rust across src/extensions.rs (~48.1k), src/extensions_js.rs (~23.4k), src/extension_dispatcher.rs (~13.4k), and src/extension_index.rs (~1.7k), with roughly 2.5k callable units in just those files. For context, the original Pi coding-agent production code is about 27.4k lines total. So this one subsystem by itself is roughly 3.2x the size of the original harness, which is why calling this a “port” would seriously undersell what had to be built. And on top of that, pi_agent_rust introduces a bunch of genuinely new capabilities beyond the legacy harness, not just a faster core: - Security and enforcement are materially stronger at runtime: capability-gated hostcalls with explicit policy profiles (safe/balanced/permissive), per-extension trust lifecycle (pending -> acknowledged -> trusted -> killed), explicit kill-switch operations, and audited state transitions. - Shell execution mediation is deterministic and argument-aware: rule/feature-based risk scoring plus heredoc AST inspection (dcg_rule_hit, dcg_heredoc_hit) before spawn, instead of relying on coarse deny patterns. - Containment and forensics are first-class: tamper-evident runtime risk ledger tooling (verify/replay/calibrate), unified incident evidence bundles, and forced-compat controls that let you contain issues without disabling the whole extension system. - The extension runtime architecture is native: JS extensions run in embedded QuickJS with typed hostcall boundaries and Rust-native connectors for tool/exec/http/session/ui/events, plus compatibility shims for real-world legacy extensions. - Runtime behavior under load is explicitly engineered: deterministic hostcall reactor mesh, fast-lane vs compat-lane routing, and warm-isolate prewarm handoff for more predictable throughput and latency under contention. - Long-session reliability is upgraded: JSONL v3 sessions with indexed sidecar acceleration and optional SQLite-backed sessions, plus operational controls via --session-durability, --no-migrations, and migrate. - Provider and auth coverage are broader and more operationally explicit: native Anthropic/OpenAI (Chat + Responses)/Gemini/Cohere/Azure/Bedrock/Vertex/Copilot/GitLab plus large OpenAI-compatible routing; pi --list-providers currently shows 90 providers with aliases and required auth env keys. - Auth is not just API keys: OAuth (Anthropic/OpenAI Codex/Gemini CLI/Antigravity/Kimi/Copilot/GitLab plus extension-defined OAuth), AWS credential chains (Bedrock), service-key exchange (SAP AI Core), and bearer-token flows. - Operator tooling is stronger: pi doctor supports scoped checks (config, dirs, auth, shell, sessions, extensions), machine-readable output (--format json|markdown), and safe auto-remediation (--fix). - Extension/package lifecycle workflows are built in: install, remove, update, update-index, search, info, and list. I want to thank Mario for making a great harness and for not telling me to get lost when I asked him if he was OK with me porting it to Rust. I may give him a hard time in jest about not going "full clanker," but that doesn't mean that I don't respect his work a huge amount. PS: There could still be bugs. If you find some, please let me know in GitHub Issues and I'll fix them same day. There's always a tradeoff between perfect and getting stuff out the door and I felt like it was time to release this.
0:38

I finally finished my Rust version of Mario Zechner's (Mario Zechner) excellent Pi Agent, which I made with his blessing and which is called pi_agent_rust. You can get it here: If you're not familiar with Pi, it's a minimalist and extensible agent harness (similar to Claude Code and Codex) and, among other uses, serves as the core agent harness inside the OpenClaw project. I say my Rust "version" instead of "port" because it's really quite different in how it's implemented for it to be called a port. Arguably, the incremental functionality in the implementation was more complex than the rest of the project combined. Still, it provides the same features and functionality as the original, and is proven to be compatible with hundreds of popular extensions to Pi (the conformance harness shows 224 out of 224 extensions working perfectly). But the way it's architected has some major changes. Pi Agent relies on node or bun to provide access to the filesystem and for various other tasks, and that is also how Pi's extension system works. I decided early on that I didn't want to do things that way. Instead, I wanted to integrate that functionality directly into the binary itself; that is, to provide equivalent functionality for everything that would normally be provided by node/bun in the original. I did this for several reasons: one, it's a lot more performant in terms of footprint and latency. On realistic end-to-end large-session workloads (not toy microbenchmarks), pi_agent_rust is now: - 4.95x faster than legacy Node and 2.80x faster than legacy Bun at 1mm-token session scale - 4.32x faster than legacy Node and 2.14x faster than legacy Bun at 5mm-token session scale - ~8x to ~13x lower RSS memory footprint in those same scenarios But the other reason is security and control: by handling everything internally in an end-to-end way, we can do all sorts of clever things to harden the system against insecure or malicious extensions. Those extensions no longer have direct access to the ambient filesystem: they now need to go through pi_agent_rust, and we can analyze extensions carefully before ever running them and also block things that look suspicious at runtime. In practice that means explicit capability-gated hostcalls, with policy/risk/quota enforcement and runtime telemetry/auditability. In order to do all this, I had to effectively build the missing runtime substrate from scratch in Rust, not just translate TypeScript syntax: - define and implement a typed hostcall ABI for extension->host interactions - build native Rust connectors for tool/exec/http/session/ui/events instead of ambient Node/Bun access - implement a compatibility/shim layer so real-world Pi extensions still behave correctly - add capability policy evaluation, runtime risk scoring, per-extension quotas, and audit telemetry on the execution path - wire the whole thing through structured concurrency (asupersync) so cancellation/lifetimes are deterministic and failure handling is explicit - build a conformance + benchmark harness large enough to validate behavior/perf across hundreds of extensions and realistic long-session workloads This was a full re-architecture of the execution model while preserving the Pi workflow and extension ecosystem. And indeed, this aspect of it dwarfs the entire rest of the project in size and complexity. To put hard numbers on that: the extension/runtime/security subsystem alone is now about 86.5k lines of Rust across src/extensions.rs (~48.1k), src/extensions_js.rs (~23.4k), src/extension_dispatcher.rs (~13.4k), and src/extension_index.rs (~1.7k), with roughly 2.5k callable units in just those files. For context, the original Pi coding-agent production code is about 27.4k lines total. So this one subsystem by itself is roughly 3.2x the size of the original harness, which is why calling this a “port” would seriously undersell what had to be built. And on top of that, pi_agent_rust introduces a bunch of genuinely new capabilities beyond the legacy harness, not just a faster core: - Security and enforcement are materially stronger at runtime: capability-gated hostcalls with explicit policy profiles (safe/balanced/permissive), per-extension trust lifecycle (pending -> acknowledged -> trusted -> killed), explicit kill-switch operations, and audited state transitions. - Shell execution mediation is deterministic and argument-aware: rule/feature-based risk scoring plus heredoc AST inspection (dcg_rule_hit, dcg_heredoc_hit) before spawn, instead of relying on coarse deny patterns. - Containment and forensics are first-class: tamper-evident runtime risk ledger tooling (verify/replay/calibrate), unified incident evidence bundles, and forced-compat controls that let you contain issues without disabling the whole extension system. - The extension runtime architecture is native: JS extensions run in embedded QuickJS with typed hostcall boundaries and Rust-native connectors for tool/exec/http/session/ui/events, plus compatibility shims for real-world legacy extensions. - Runtime behavior under load is explicitly engineered: deterministic hostcall reactor mesh, fast-lane vs compat-lane routing, and warm-isolate prewarm handoff for more predictable throughput and latency under contention. - Long-session reliability is upgraded: JSONL v3 sessions with indexed sidecar acceleration and optional SQLite-backed sessions, plus operational controls via --session-durability, --no-migrations, and migrate. - Provider and auth coverage are broader and more operationally explicit: native Anthropic/OpenAI (Chat + Responses)/Gemini/Cohere/Azure/Bedrock/Vertex/Copilot/GitLab plus large OpenAI-compatible routing; pi --list-providers currently shows 90 providers with aliases and required auth env keys. - Auth is not just API keys: OAuth (Anthropic/OpenAI Codex/Gemini CLI/Antigravity/Kimi/Copilot/GitLab plus extension-defined OAuth), AWS credential chains (Bedrock), service-key exchange (SAP AI Core), and bearer-token flows. - Operator tooling is stronger: pi doctor supports scoped checks (config, dirs, auth, shell, sessions, extensions), machine-readable output (--format json|markdown), and safe auto-remediation (--fix). - Extension/package lifecycle workflows are built in: install, remove, update, update-index, search, info, and list. I want to thank Mario for making a great harness and for not telling me to get lost when I asked him if he was OK with me porting it to Rust. I may give him a hard time in jest about not going "full clanker," but that doesn't mean that I don't respect his work a huge amount. PS: There could still be bugs. If you find some, please let me know in GitHub Issues and I'll fix them same day. There's always a tradeoff between perfect and getting stuff out the door and I felt like it was time to release this.

Jeffrey Emanuel

135,770 次观看 • 6 个月前

Given the current bullish market sentiment and the evident shift of users towards more volatile assets, there's a steadily increasing demand for stablecoins within the ecosystem. This shift is underscored by the growing use of leverage, where users borrow stablecoins to amplify their exposure to preferred volatile assets or to implement various strategies in DeFi. As the #MultiversX ecosystem currently lacks a native stablecoin, it faces challenges in achieving mature stable liquidity. Recognizing this gap, Hatom has significantly advanced in developing $USH, the first native stablecoin for #MultiversX. This stablecoin is akin to $DAI, the pioneering decentralized and over-collateralized stablecoin known for its resilience through numerous stress tests over the years, but will also feature some unique characteristics and design implementation. Within the #MultiversX ecosystem, the currently limited liquidity of stablecoins has led to notable metrics in the Hatom Lending Protocol. Here, the yields users can generate on their $USDC or $USDT have escalated to impressive middle double-digit percentages. This situation offers a golden opportunity for individuals with idle stable assets in their portfolios. The Lending Protocol is an appealing option to leverage these assets, offering remarkable flexibility—there are no lock-up periods, and it carries no risks of impermanent loss. This makes it an excellent choice to generate additional revenue while waiting for those assets to be deployed. Breaking down the current yields through the Lending Protocol as follows: • A 36.83% yield on $USDC, with 32.88% APY derived from the natural supply and demand within the lending protocol—where borrowers are paying the lenders. Additionally, the yield can be increased by 3.95% through the Booster. • A 40.24% yield on $USDT, with a 33.68% APY from providing liquidity to the Lending Protocol, which can be further boosted by 6.56% by staking $HTM into the Booster. All rewards generated through the Booster can be further amplified by 5% with the Accumulator if claimed in $HTM. *For a comprehensive understanding of how the Booster and Accumulator work, please read Hatom's official documentation. Clarification on the yields is crucial, as there is considerable interest in understanding the mechanics behind these attractive rates. Essentially, the yields on both $USDC and $USDT within the Lending Protocol are derived from the dynamics of supply and demand. Suppliers contribute funds to a pool from which other users borrow. As borrowing increases, so does the pool's utilization rate, leading to higher interest rates in both the supply and borrow markets. To achieve an optimal balance, borrowers are incentivized to repay their loans due to the higher cost of loan, which, in turn, provides lenders with more attractive returns on their deposits. This self-regulating mechanism ensures the Lending Protocol maintains a healthy equilibrium between supply and demand, optimizing yields for all participants. Rewards are paid out in the same assets that users deposit. For instance, if a user deposits $USDT into the money market, the yield generated will also be paid in $USDT. The sole exception to this rule applies to Booster rewards, which are paid out in $USDC or $HTM, with the latter offering a 5% premium. **Please note that the yields presented in this post represent current values at the time of posting and may differ by the time you read this. The most efficient way to take advantage of the high yields on the stablecoins is to bridge liquidity into the ecosystem through the official bridge developed by the #MultiversX team. The process is simple and efficient, allowing users to bridge from both #Ethereum and #BSC. You can access the bridge through the following link: To participate in the #MultiversX ecosystem, you will require a compatible wallet, which can be found here: Once your assets are ready, you can supply on the Hatom Lending Protocol by accessing this link: To facilitate your journey, please follow this step-by-step video tutorial, which covers all the basics, from the creation of a #MultiversX wallet to bridging and depositing in the Lending Protocol, to take full advantage.
2:52

Given the current bullish market sentiment and the evident shift of users towards more volatile assets, there's a steadily increasing demand for stablecoins within the ecosystem. This shift is underscored by the growing use of leverage, where users borrow stablecoins to amplify their exposure to preferred volatile assets or to implement various strategies in DeFi. As the #MultiversX ecosystem currently lacks a native stablecoin, it faces challenges in achieving mature stable liquidity. Recognizing this gap, Hatom has significantly advanced in developing $USH, the first native stablecoin for #MultiversX. This stablecoin is akin to $DAI, the pioneering decentralized and over-collateralized stablecoin known for its resilience through numerous stress tests over the years, but will also feature some unique characteristics and design implementation. Within the #MultiversX ecosystem, the currently limited liquidity of stablecoins has led to notable metrics in the Hatom Lending Protocol. Here, the yields users can generate on their $USDC or $USDT have escalated to impressive middle double-digit percentages. This situation offers a golden opportunity for individuals with idle stable assets in their portfolios. The Lending Protocol is an appealing option to leverage these assets, offering remarkable flexibility—there are no lock-up periods, and it carries no risks of impermanent loss. This makes it an excellent choice to generate additional revenue while waiting for those assets to be deployed. Breaking down the current yields through the Lending Protocol as follows: • A 36.83% yield on $USDC, with 32.88% APY derived from the natural supply and demand within the lending protocol—where borrowers are paying the lenders. Additionally, the yield can be increased by 3.95% through the Booster. • A 40.24% yield on $USDT, with a 33.68% APY from providing liquidity to the Lending Protocol, which can be further boosted by 6.56% by staking $HTM into the Booster. All rewards generated through the Booster can be further amplified by 5% with the Accumulator if claimed in $HTM. *For a comprehensive understanding of how the Booster and Accumulator work, please read Hatom's official documentation. Clarification on the yields is crucial, as there is considerable interest in understanding the mechanics behind these attractive rates. Essentially, the yields on both $USDC and $USDT within the Lending Protocol are derived from the dynamics of supply and demand. Suppliers contribute funds to a pool from which other users borrow. As borrowing increases, so does the pool's utilization rate, leading to higher interest rates in both the supply and borrow markets. To achieve an optimal balance, borrowers are incentivized to repay their loans due to the higher cost of loan, which, in turn, provides lenders with more attractive returns on their deposits. This self-regulating mechanism ensures the Lending Protocol maintains a healthy equilibrium between supply and demand, optimizing yields for all participants. Rewards are paid out in the same assets that users deposit. For instance, if a user deposits $USDT into the money market, the yield generated will also be paid in $USDT. The sole exception to this rule applies to Booster rewards, which are paid out in $USDC or $HTM, with the latter offering a 5% premium. **Please note that the yields presented in this post represent current values at the time of posting and may differ by the time you read this. The most efficient way to take advantage of the high yields on the stablecoins is to bridge liquidity into the ecosystem through the official bridge developed by the #MultiversX team. The process is simple and efficient, allowing users to bridge from both #Ethereum and #BSC. You can access the bridge through the following link: To participate in the #MultiversX ecosystem, you will require a compatible wallet, which can be found here: Once your assets are ready, you can supply on the Hatom Lending Protocol by accessing this link: To facilitate your journey, please follow this step-by-step video tutorial, which covers all the basics, from the creation of a #MultiversX wallet to bridging and depositing in the Lending Protocol, to take full advantage.

Hatom Labs

159,969 次观看 • 2 年前

10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export • lightweight and perfect for product walkthroughs without extra editing — 2️⃣ VoiceBox — an alternative to ElevenLabs ($22/mo) + Wispr Flow ($15/mo) • privacy-first AI voice toolkit that runs locally • clone voices with a few seconds of audio • supports 7 TTS engines, 23 languages, and system-wide voice dictation • works with Apple Silicon, CUDA, and ROCm — 3️⃣ OpenShorts — an alternative to Opus Clip ($19/mo) + Submagic ($16/mo) • convert long videos into viral vertical clips • auto captions, face tracking, and AI clip selection • includes AI UGC video generation • easy Docker deployment for self-hosting — 4️⃣ FreeLLMAPI — an alternative to ChatGPT Pro + Claude Pro ($20/mo each) • combine 14 free AI providers behind one API • OpenAI-compatible endpoint • roughly 800M free tokens/month • built-in routing, failover, encrypted key storage, and dashboard — 5️⃣ Playwright MCP — an alternative to Browserbase ($39/mo) + Browser Use ($25/mo) • Microsoft's official browser automation MCP • AI agents interact using accessibility trees instead of screenshots • faster, cheaper, and more reliable automation • works with Claude Code, Cursor, Windsurf, and Codex — 6️⃣ Vibe Trading — an alternative to TradingView Premium ($60/mo) • AI-powered investing and strategy research platform • supports stocks, crypto, forex, futures, and options • dozens of built-in research skills • backtesting included without requiring paid APIs — 7️⃣ — an alternative to Calendly ($12/mo) + SavvyCal ($12/mo) • open-source scheduling platform • round robin, team scheduling, routing forms, payments • integrates with Google Calendar, Outlook, Apple Calendar, Zoom, Meet, and Teams • deploy yourself in minutes — 8️⃣ Whisper — an alternative to ($17/mo) • OpenAI's speech recognition model • transcribes and translates audio in nearly 100 languages • timestamp support included • runs locally on CPU or GPU — 9️⃣ Postiz — an alternative to Buffer ($15/mo) • schedule content across all major social platforms • AI-generated captions and hashtags • built-in analytics and collaborative workspaces • growing rapidly with a large open-source community — 🔟 Vaultwarden — an alternative to 1Password ($8/mo) • lightweight Bitwarden-compatible server written in Rust • works with official Bitwarden apps • unlimited users and vaults • self-host on almost any VPS or home server — Worth knowing: Open-source isn't always a perfect replacement. You may spend a little more time setting things up. In return, you get: • no monthly subscription • full ownership of your data • complete control over your workflow That's a trade many builders happily make. Save this for later. Someone on your timeline is probably paying for at least three of these. — Kshitij Mishra
0:31

10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export • lightweight and perfect for product walkthroughs without extra editing — 2️⃣ VoiceBox — an alternative to ElevenLabs ($22/mo) + Wispr Flow ($15/mo) • privacy-first AI voice toolkit that runs locally • clone voices with a few seconds of audio • supports 7 TTS engines, 23 languages, and system-wide voice dictation • works with Apple Silicon, CUDA, and ROCm — 3️⃣ OpenShorts — an alternative to Opus Clip ($19/mo) + Submagic ($16/mo) • convert long videos into viral vertical clips • auto captions, face tracking, and AI clip selection • includes AI UGC video generation • easy Docker deployment for self-hosting — 4️⃣ FreeLLMAPI — an alternative to ChatGPT Pro + Claude Pro ($20/mo each) • combine 14 free AI providers behind one API • OpenAI-compatible endpoint • roughly 800M free tokens/month • built-in routing, failover, encrypted key storage, and dashboard — 5️⃣ Playwright MCP — an alternative to Browserbase ($39/mo) + Browser Use ($25/mo) • Microsoft's official browser automation MCP • AI agents interact using accessibility trees instead of screenshots • faster, cheaper, and more reliable automation • works with Claude Code, Cursor, Windsurf, and Codex — 6️⃣ Vibe Trading — an alternative to TradingView Premium ($60/mo) • AI-powered investing and strategy research platform • supports stocks, crypto, forex, futures, and options • dozens of built-in research skills • backtesting included without requiring paid APIs — 7️⃣ — an alternative to Calendly ($12/mo) + SavvyCal ($12/mo) • open-source scheduling platform • round robin, team scheduling, routing forms, payments • integrates with Google Calendar, Outlook, Apple Calendar, Zoom, Meet, and Teams • deploy yourself in minutes — 8️⃣ Whisper — an alternative to ($17/mo) • OpenAI's speech recognition model • transcribes and translates audio in nearly 100 languages • timestamp support included • runs locally on CPU or GPU — 9️⃣ Postiz — an alternative to Buffer ($15/mo) • schedule content across all major social platforms • AI-generated captions and hashtags • built-in analytics and collaborative workspaces • growing rapidly with a large open-source community — 🔟 Vaultwarden — an alternative to 1Password ($8/mo) • lightweight Bitwarden-compatible server written in Rust • works with official Bitwarden apps • unlimited users and vaults • self-host on almost any VPS or home server — Worth knowing: Open-source isn't always a perfect replacement. You may spend a little more time setting things up. In return, you get: • no monthly subscription • full ownership of your data • complete control over your workflow That's a trade many builders happily make. Save this for later. Someone on your timeline is probably paying for at least three of these. — Kshitij Mishra

Kshitij Mishra | AI & Tech

16,698 次观看 • 1 个月前

KIMI K2.6 JUST CRUSHED GPT-5 AND A SINGLE PERSON CAN NOW POTENTIALLY BUILD AN $80K/MONTH BUSINESS WITH 300 AI AGENTS AND JUST $500 IN OVERHEAD The video attached is proof that almost everyone missed Kimi K2 Thinking didn’t just score 44.9% on Humanity’s Last Exam, it outperformed GPT-5 (41.7%), Claude, and every other major model across multiple benchmarks It’s open source Over a trillion parameters, trained for just $4.6M Runs locally on a Mac Studio and in the demo, it turns a 100-page PDF into a fully designed PowerPoint presentation in under two minutes while other models are still thinking In the article below, the author lays out a clear blueprint for turning this into a real business: > 300 parallel sub-agents running up to 4000 steps per execution - research, coding, analysis and visual creation all happen simultaneously > 65.8% on SWE-Bench solving real GitHub engineering tasks end-to-end with little to no human intervention > Skill injection through simple .md files - instant vertical specialization (HIPAA compliance, financial regulations, Shopify workflows and more) > Automated client acquisition: monitor job listings for “Data Analyst” or “Automation Engineer” roles and pitch an AI solution before companies even start hiring The math is simple: A $10k project Traditional agency → salaries, office costs, QA, project management and overhead eat most of the profit AI agency powered by Kimi → roughly $500 in operating costs plus one operator managing client relationships = the potential for 72k$+ monthly profit at scale Read the article Save this post Start building AI-native agencies while everyone else is still doing things the old way
0:43

KIMI K2.6 JUST CRUSHED GPT-5 AND A SINGLE PERSON CAN NOW POTENTIALLY BUILD AN $80K/MONTH BUSINESS WITH 300 AI AGENTS AND JUST $500 IN OVERHEAD The video attached is proof that almost everyone missed Kimi K2 Thinking didn’t just score 44.9% on Humanity’s Last Exam, it outperformed GPT-5 (41.7%), Claude, and every other major model across multiple benchmarks It’s open source Over a trillion parameters, trained for just $4.6M Runs locally on a Mac Studio and in the demo, it turns a 100-page PDF into a fully designed PowerPoint presentation in under two minutes while other models are still thinking In the article below, the author lays out a clear blueprint for turning this into a real business: > 300 parallel sub-agents running up to 4000 steps per execution - research, coding, analysis and visual creation all happen simultaneously > 65.8% on SWE-Bench solving real GitHub engineering tasks end-to-end with little to no human intervention > Skill injection through simple .md files - instant vertical specialization (HIPAA compliance, financial regulations, Shopify workflows and more) > Automated client acquisition: monitor job listings for “Data Analyst” or “Automation Engineer” roles and pitch an AI solution before companies even start hiring The math is simple: A $10k project Traditional agency → salaries, office costs, QA, project management and overhead eat most of the profit AI agency powered by Kimi → roughly $500 in operating costs plus one operator managing client relationships = the potential for 72k$+ monthly profit at scale Read the article Save this post Start building AI-native agencies while everyone else is still doing things the old way

Bonsai 🌳

21,487 次观看 • 3 个月前