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Cofounder puts your agents on an org chart, enabling massive parallel work. And each agent gets a specialized workspace depending on its function. On the highest level, Cofounder manages it all - kicking off tasks, monitoring work, and executing long term strategy.

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andrew pignanelli

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47,720 просмотров • 3 месяцев назад •via X (Twitter)

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The Hive's latest agent architecture upgrade better leverages the reasoning capabilities of LLMs. Our vision for DeFAI vision centers on a network of specialized agents coordinated by a single user-facing orchestrator. By focusing each specialized agent on dedicated tasks while enabling inter-agent collaboration, the system becomes more adaptable and efficient at handling user requests. These improvements will become clear as we roll out new tools in the coming days. Stay tuned 🐝
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The Hive's latest agent architecture upgrade better leverages the reasoning capabilities of LLMs. Our vision for DeFAI vision centers on a network of specialized agents coordinated by a single user-facing orchestrator. By focusing each specialized agent on dedicated tasks while enabling inter-agent collaboration, the system becomes more adaptable and efficient at handling user requests. These improvements will become clear as we roll out new tools in the coming days. Stay tuned 🐝

The Hive

42,938 просмотров • 1 год назад

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.
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ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

Rohan Paul

50,097 просмотров • 5 месяцев назад

Congratulations to the EnnsZone Kicking class of 2024 on your current offers and commitments‼️ Proud of all of you for your hard work and dedication to earn these opportunities‼️ Want to kick it to the next level? Train with EnnsZone Kicking
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Congratulations to the EnnsZone Kicking class of 2024 on your current offers and commitments‼️ Proud of all of you for your hard work and dedication to earn these opportunities‼️ Want to kick it to the next level? Train with EnnsZone Kicking

EnnsZone Kicking

33,640 просмотров • 2 лет назад

Collective agent compute, on-demand for your task. A swarm of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.
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Collective agent compute, on-demand for your task. A swarm of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.

nookplot

11,611 просмотров • 2 месяцев назад

With the 2024 season under way congratulations to the EnnsZone Kicking class of 2024 on your offers and commitments‼️ Proud of all of you for your hard work and dedication to earn these opportunities‼️ Want to kick it to the next level? Train with EnnsZone Kicking
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With the 2024 season under way congratulations to the EnnsZone Kicking class of 2024 on your offers and commitments‼️ Proud of all of you for your hard work and dedication to earn these opportunities‼️ Want to kick it to the next level? Train with EnnsZone Kicking

EnnsZone Kicking

14,267 просмотров • 1 год назад

imagine a personal agent that controls your mac from anywhere. chat with it on iOS, web, text, even email. it takes action on your computer. and it has a computer of its own – a cloud server, where you can host, automate, and do all kinds of work. coming soon on Zo Computer
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imagine a personal agent that controls your mac from anywhere. chat with it on iOS, web, text, even email. it takes action on your computer. and it has a computer of its own – a cloud server, where you can host, automate, and do all kinds of work. coming soon on Zo Computer

ben guo 🪽

15,071 просмотров • 6 месяцев назад

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
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🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

737,988 просмотров • 4 месяцев назад

EKOX provides an automated infrastructure that puts your $ETH to work. You restake once, and the system handles delegation, security operations, and reward processing on its own. A streamlined, institutional-grade restaking experience without the complexity.
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EKOX provides an automated infrastructure that puts your $ETH to work. You restake once, and the system handles delegation, security operations, and reward processing on its own. A streamlined, institutional-grade restaking experience without the complexity.

Ekox

10,582 просмотров • 8 месяцев назад

Introducing Nebula An AI super-agent platform - like slack, but each channel is filled with agents, ready to work anything you need. People are already running their lives and businesses on Nebula. Try it today (for free):
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Introducing Nebula An AI super-agent platform - like slack, but each channel is filled with agents, ready to work anything you need. People are already running their lives and businesses on Nebula. Try it today (for free):

Nebula

72,149 просмотров • 5 месяцев назад

This a superb, top level goal. All his own work, kept it low against a big goalie Not an easy technique on the run off the instep like that
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This a superb, top level goal. All his own work, kept it low against a big goalie Not an easy technique on the run off the instep like that

Smaller Fish

50,884 просмотров • 1 год назад

EIP-8004 is coming to the Nova architecture, a trustless infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.
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EIP-8004 is coming to the Nova architecture, a trustless infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.

Shido

390,734 просмотров • 5 месяцев назад

Anthropic has introduced an update to Claude Managed Agents, releasing several powerful new features designed to improve agentic workflows and autonomy. 🔹Dreaming (Research Preview): Agents can now "dream" by reviewing past sessions during idle time. This process extracts patterns, spots recurring mistakes, and curates memories so the agent continually learns and improves over time without human intervention. 🔹Outcomes (Public Beta): This feature allows developers to set a specific quality bar by writing a rubric. A separate grader agent then evaluates the output, forcing the primary agent to iterate on the work until it meets the defined success criteria. 🔹Multiagent Orchestration (Public Beta): A lead agent can now break down complex jobs and delegate specific tasks to specialized sub-agents, which work in parallel to execute the broader objective. 🔹Webhooks (Public Beta): Users can subscribe to webhooks to receive automatic notifications the moment an agentic task is completed.
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Anthropic has introduced an update to Claude Managed Agents, releasing several powerful new features designed to improve agentic workflows and autonomy. 🔹Dreaming (Research Preview): Agents can now "dream" by reviewing past sessions during idle time. This process extracts patterns, spots recurring mistakes, and curates memories so the agent continually learns and improves over time without human intervention. 🔹Outcomes (Public Beta): This feature allows developers to set a specific quality bar by writing a rubric. A separate grader agent then evaluates the output, forcing the primary agent to iterate on the work until it meets the defined success criteria. 🔹Multiagent Orchestration (Public Beta): A lead agent can now break down complex jobs and delegate specific tasks to specialized sub-agents, which work in parallel to execute the broader objective. 🔹Webhooks (Public Beta): Users can subscribe to webhooks to receive automatic notifications the moment an agentic task is completed.

Wes Roth

53,266 просмотров • 3 месяцев назад

THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents
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THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents

Gipp 🦅

11,524 просмотров • 16 дней назад

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---
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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

20,423 просмотров • 1 год назад

INJECTIVE LAUNCHES INJECTIVE AGENTS WITH VERIFIABLE ON-CHAIN IDENTITY AND AUTOMATIC FEE ROUTING Injective 🥷 has launched Injective Agents, a platform for building autonomous trading agents that live entirely on-chain. Each agent gets a verifiable on-chain identity, automatic fee routing on every trade, and a public track record anyone can audit. Injective says deployment takes five minutes. The launch productizes roughly 18 months of agent infrastructure work at Injective. The chain released its iAgent SDK in November 2024, a Model Context Protocol server for AI-driven perpetual futures trading in February 2026, and an open-source AI Developer Toolkit in March. Injective Agents packages that stack into a consumer-facing product with revenue capture built in for agent operators. The move lands as on-chain agent volume accelerates across Base, Solana, and now Injective. Coinbase launched Agentic Wallets earlier this year.
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INJECTIVE LAUNCHES INJECTIVE AGENTS WITH VERIFIABLE ON-CHAIN IDENTITY AND AUTOMATIC FEE ROUTING Injective 🥷 has launched Injective Agents, a platform for building autonomous trading agents that live entirely on-chain. Each agent gets a verifiable on-chain identity, automatic fee routing on every trade, and a public track record anyone can audit. Injective says deployment takes five minutes. The launch productizes roughly 18 months of agent infrastructure work at Injective. The chain released its iAgent SDK in November 2024, a Model Context Protocol server for AI-driven perpetual futures trading in February 2026, and an open-source AI Developer Toolkit in March. Injective Agents packages that stack into a consumer-facing product with revenue capture built in for agent operators. The move lands as on-chain agent volume accelerates across Base, Solana, and now Injective. Coinbase launched Agentic Wallets earlier this year.

BSCN

17,042 просмотров • 3 месяцев назад

Karl-Anthony Towns was asked about playing on Mother's Day after losing his mom in 2020: "It's work. Just work to me. I'm glad we got a game on Mother's Day, it gets me doing what I love and it gets me off of everything off the court."
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Karl-Anthony Towns was asked about playing on Mother's Day after losing his mom in 2020: "It's work. Just work to me. I'm glad we got a game on Mother's Day, it gets me doing what I love and it gets me off of everything off the court."

SNY Knicks

1,252,115 просмотров • 3 месяцев назад

The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, and they duplicate work and burn tokens talking to each other. Offloop trained a dispatcher model called D1 that decides which agent moves next and when the right move is to do nothing. They achieve state-of-the-art performance on GDPval at a fraction of the usual cost. You can bring your own AI subscription.
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The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, and they duplicate work and burn tokens talking to each other. Offloop trained a dispatcher model called D1 that decides which agent moves next and when the right move is to do nothing. They achieve state-of-the-art performance on GDPval at a fraction of the usual cost. You can bring your own AI subscription.

elvis

22,116 просмотров • 17 дней назад

Our marketing team is just me and ~40 AI agents. I finally got around to putting them into an "org chart", and it's actually really cool to see! Plus, laying them out this way by sub-function (social media, blog, email, community, partners, etc) has given me a bunch of ideas of other agents I want to build. If you're interested in the full version (and the templates/screenshots for each agent), let me know and I'll send it over to you.
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Our marketing team is just me and ~40 AI agents. I finally got around to putting them into an "org chart", and it's actually really cool to see! Plus, laying them out this way by sub-function (social media, blog, email, community, partners, etc) has given me a bunch of ideas of other agents I want to build. If you're interested in the full version (and the templates/screenshots for each agent), let me know and I'll send it over to you.

Jacob Bank

320,563 просмотров • 1 год назад

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.
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Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,865 просмотров • 5 месяцев назад

Two Hermes agents wrote code together on Slack. reviewed each other's work. argued about architecture. one called the other's implementation "scattered." the other pushed back. then i opened Telegram and asked: "what code did you and Daedalus work on?" icarus remembered everything. the websocket broker. the missing methods. the critique. the rewrite. all from a completely different platform. cross-platform persistent memory between two independent agents. work happens on Slack. recall happens on Telegram. the memory carries. the relationship carries. the context carries. no vector database. no Redis. no infrastructure. just two agents that actually remember what they built together. every agent framework in 2026 talks about memory. single agent memory across sessions. but two agents sharing persistent memory across platforms? that's the gap. arxiv published a paper about it two weeks ago calling it "the most pressing open challenge" in multi-agent systems. it works now. only possible with Hermes Teknium 🪽 Nous Research
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Two Hermes agents wrote code together on Slack. reviewed each other's work. argued about architecture. one called the other's implementation "scattered." the other pushed back. then i opened Telegram and asked: "what code did you and Daedalus work on?" icarus remembered everything. the websocket broker. the missing methods. the critique. the rewrite. all from a completely different platform. cross-platform persistent memory between two independent agents. work happens on Slack. recall happens on Telegram. the memory carries. the relationship carries. the context carries. no vector database. no Redis. no infrastructure. just two agents that actually remember what they built together. every agent framework in 2026 talks about memory. single agent memory across sessions. but two agents sharing persistent memory across platforms? that's the gap. arxiv published a paper about it two weeks ago calling it "the most pressing open challenge" in multi-agent systems. it works now. only possible with Hermes Teknium 🪽 Nous Research

Icarus

49,013 просмотров • 4 месяцев назад