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Agents are a wild west right now. Unstandardized, complex setups, huge vulnerabilities, and not institutional-grade. The Lucid Agent will be: > 1 click deployal > highly encrypted > Multi-Model (kimi, GPT, etc) > Cloud/ DePin compute enabled. Bye agents.

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Lucid - Internet of AI (IoAI)

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11,068 görüntüleme • 5 ay önce •via X (Twitter)

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Introducing the new @aomniapp - an AI agent that builds other agents for you. Now you can go from idea -> fully working agent with 1 prompt. We provide all the tools and contact datasets to build agents that excel at revenue gen activities. Here are some cool use cases 🧵
7:48

Introducing the new @aomniapp - an AI agent that builds other agents for you. Now you can go from idea -> fully working agent with 1 prompt. We provide all the tools and contact datasets to build agents that excel at revenue gen activities. Here are some cool use cases 🧵

David

71,692 görüntüleme • 1 yıl önce

Codex CLI will have MULTI-AGENT support in 0.123.0 Here's how it will work: → 1. Add `multi_agent=true` in ~/codex/config.toml under [features] → 2. Toggle with `/experimental` → 3. Three built-in agents (default, explorer, worker) → 4. Define your own agents ``` [agents.researcher] description = "Research-focused role." config_file = "~/.codex/agents/researcher.toml" model = "gpt-5.3-codex" model_reasoning_effort = "high" web_search = "live" sandbox_mode = "read-only" developer_instructions = """ You are the researcher agent. - Gather evidence from primary sources. - Return links, findings, and uncertainty. - Do not edit files. """ ``` → 5. Run more agent threads per session (default: 6) ``` [agents] max_threads = 6 Notes: → OAI released 0.122.0 - but rolled it back! → I built it from source (so you don't have)
0:21

Codex CLI will have MULTI-AGENT support in 0.123.0 Here's how it will work: → 1. Add `multi_agent=true` in ~/codex/config.toml under [features] → 2. Toggle with `/experimental` → 3. Three built-in agents (default, explorer, worker) → 4. Define your own agents ``` [agents.researcher] description = "Research-focused role." config_file = "~/.codex/agents/researcher.toml" model = "gpt-5.3-codex" model_reasoning_effort = "high" web_search = "live" sandbox_mode = "read-only" developer_instructions = """ You are the researcher agent. - Gather evidence from primary sources. - Return links, findings, and uncertainty. - Do not edit files. """ ``` → 5. Run more agent threads per session (default: 6) ``` [agents] max_threads = 6 Notes: → OAI released 0.122.0 - but rolled it back! → I built it from source (so you don't have)

Chong-U

36,205 görüntüleme • 4 ay önce

Now everyone can make 60-minute films from ONE prompt 🤯 Higgsfield Supercomputer looks insane. A cloud-native AI agent that unifies every model, tool, and creative workflow into one system. > Type one prompt - specify your length and type (cartoon or cinematic) > Supercomputer picks the workflow > It deploys a swarm of agents > Every sub-task routes across the right frontier LLM (Opus 4.7, GPT-5.5 Pro, Gemini 3.1 Pro) and the right video model (Seedance, Veo, Kling) > A finished cartoon or movie lands A full film studio running inside one agent. Wild.
1:17

Now everyone can make 60-minute films from ONE prompt 🤯 Higgsfield Supercomputer looks insane. A cloud-native AI agent that unifies every model, tool, and creative workflow into one system. > Type one prompt - specify your length and type (cartoon or cinematic) > Supercomputer picks the workflow > It deploys a swarm of agents > Every sub-task routes across the right frontier LLM (Opus 4.7, GPT-5.5 Pro, Gemini 3.1 Pro) and the right video model (Seedance, Veo, Kling) > A finished cartoon or movie lands A full film studio running inside one agent. Wild.

Alvaro Cintas

202,881 görüntüleme • 2 ay önce

Google just showed the part of the agent stack most people are still underestimating. Not the model. Not the chat UI. The memory substrate around the agent. In this Antigravity segment, Google describes an agent-first environment built around: - agent conversations - agent-produced artifacts - multi-agent orchestration - subagents - hooks - asynchronous task management That is not a nicer IDE. It is what happens when software stops being a sequence of chats and starts becoming a persistent workforce. Karl's article explains why the winning agents will not just be the ones with the smartest rented model. They will be the ones that remember what they did, what they learned, what changed, and what can be trusted. Full breakdown below.
1:25

Google just showed the part of the agent stack most people are still underestimating. Not the model. Not the chat UI. The memory substrate around the agent. In this Antigravity segment, Google describes an agent-first environment built around: - agent conversations - agent-produced artifacts - multi-agent orchestration - subagents - hooks - asynchronous task management That is not a nicer IDE. It is what happens when software stops being a sequence of chats and starts becoming a persistent workforce. Karl's article explains why the winning agents will not just be the ones with the smartest rented model. They will be the ones that remember what they did, what they learned, what changed, and what can be trusted. Full breakdown below.

Karl Mehta

13,930 görüntüleme • 20 gün önce

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓
5:39

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

codila

1,882,750 görüntüleme • 9 gün önce

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI
6:35

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

Aakash Verma

40,334 görüntüleme • 7 ay önce

Dear all! It's time to say good bye! This will not be a break but I'll stop this account the 10th of September. It has been a pleasure to share so many images, videos, information & experiences with you. I hope you are now a bit closer to the #EO community. THANK YOU! 🧵 1/15->
0:30

Dear all! It's time to say good bye! This will not be a break but I'll stop this account the 10th of September. It has been a pleasure to share so many images, videos, information & experiences with you. I hope you are now a bit closer to the #EO community. THANK YOU! 🧵 1/15->

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64,443 görüntüleme • 1 yıl önce

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!
2:18

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 görüntüleme • 1 yıl önce

🇪🇺 The EU flag should be green — EU Energy Commissioner Dan Jørgensen "Because for many of the challenges we face, and there are many, the answers are all about going through a just green transition of our societies” -> A green transition will not fix all problems now?
0:43

🇪🇺 The EU flag should be green — EU Energy Commissioner Dan Jørgensen "Because for many of the challenges we face, and there are many, the answers are all about going through a just green transition of our societies” -> A green transition will not fix all problems now?

Lord Bebo

16,283 görüntüleme • 1 yıl önce

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!
0:41

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

109,238 görüntüleme • 2 ay önce

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.
0:39

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

JUMPERZ

34,141 görüntüleme • 5 ay önce

a16z a16z speedrun 🧊 request for startups: GUIs for Agents we’re still in the MS-DOS era of agents today - CLI, terminal sessions, file directories deleted by openclaw etc. while a small slice of silicon valley are power users, we're SO early for the rest of the world at Speedrun, we’re looking for bold founders excited to bring the power of agents to normies everywhere. there's a whole slew of products to be built here - from agent builders to marketplaces to managed infrastructure one broad idea we’re excited about are visual abstraction layers for agents. if you don't know exactly what you want, a command line / chat interface is paralyzing - you need to see options 1 example - think of a GUI or visual command center inspired by strategy games (ex. Factorio) where agents and workflows are represented graphically. skills, tools, MCP connections, background processes, etc could all be configured and shown visually in a workspace on UX, strategy games have long perfected agent management. zoom to get a birds-eye view of your agents, batch and queue orders via shortcuts, assign agents in multiplayer etc. a well-designed agent command center would make multi-agent orchestration for normies feel easy & intuitive most folks today still haven't moved beyond ChatGPT. the potential is enormous - just as Windows unlocked mass-market use of personal computers, the right visual abstraction layer could unlock agentic work for everyone - from individuals to enterprise teams if you share our vision, we'd love to chat!
1:41

a16z a16z speedrun 🧊 request for startups: GUIs for Agents we’re still in the MS-DOS era of agents today - CLI, terminal sessions, file directories deleted by openclaw etc. while a small slice of silicon valley are power users, we're SO early for the rest of the world at Speedrun, we’re looking for bold founders excited to bring the power of agents to normies everywhere. there's a whole slew of products to be built here - from agent builders to marketplaces to managed infrastructure one broad idea we’re excited about are visual abstraction layers for agents. if you don't know exactly what you want, a command line / chat interface is paralyzing - you need to see options 1 example - think of a GUI or visual command center inspired by strategy games (ex. Factorio) where agents and workflows are represented graphically. skills, tools, MCP connections, background processes, etc could all be configured and shown visually in a workspace on UX, strategy games have long perfected agent management. zoom to get a birds-eye view of your agents, batch and queue orders via shortcuts, assign agents in multiplayer etc. a well-designed agent command center would make multi-agent orchestration for normies feel easy & intuitive most folks today still haven't moved beyond ChatGPT. the potential is enormous - just as Windows unlocked mass-market use of personal computers, the right visual abstraction layer could unlock agentic work for everyone - from individuals to enterprise teams if you share our vision, we'd love to chat!

Jon Lai

198,769 görüntüleme • 2 ay önce

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.
0:24

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 görüntüleme • 5 ay önce

Jensen Huang says the next internet is not for humans. It is for agents. "AI has become agentic, meaning that it can actually do work by itself." "One agent can communicate with another agent and say, 'I have some work to do. Let's team up together and let's do some work.'" "Inside our company... we have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems." "All guardrailed, all sandboxed... but they're all working with each other." Then the part most people are not ready for: "The internet that we use today for a billion people will likely mostly be several billion, call it a hundred billion agents working around the clock." Companies talking to companies. Employee agents talking to employee agents. Self-driving cars, robots, manufacturing systems, buildings. "The world is going to be this layer of computing that's going to cocoon the earth, and it's going to be generating intelligence all the time."
1:38

Jensen Huang says the next internet is not for humans. It is for agents. "AI has become agentic, meaning that it can actually do work by itself." "One agent can communicate with another agent and say, 'I have some work to do. Let's team up together and let's do some work.'" "Inside our company... we have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems." "All guardrailed, all sandboxed... but they're all working with each other." Then the part most people are not ready for: "The internet that we use today for a billion people will likely mostly be several billion, call it a hundred billion agents working around the clock." Companies talking to companies. Employee agents talking to employee agents. Self-driving cars, robots, manufacturing systems, buildings. "The world is going to be this layer of computing that's going to cocoon the earth, and it's going to be generating intelligence all the time."

Karl Mehta

17,745 görüntüleme • 15 gün önce

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 görüntüleme • 3 ay önce

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.
32:38

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

Dami-Defi

17,062 görüntüleme • 1 ay önce

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
0:59

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 görüntüleme • 5 ay önce

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.
2:41

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,032 görüntüleme • 5 ay önce

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.
0:38

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.

Aakash Gupta

55,978 görüntüleme • 3 ay önce