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I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one... place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.show more

Aaliya

32,283 subscribers

23,225 views • 1 month ago •via X (Twitter)

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32 Comments

Elizaveta Zabrodskaya1 month ago

@Jarvixdotlive Love how it keeps everything in one spot

Z-Coder1 month ago

@Jarvixdotlive This looks interesting

Eyisha Zyer1 month ago

@Jarvixdotlive Shared context is the real unlock for multi agent workflows

Chidanand Tripathi1 month ago

@Jarvixdotlive Jarvix looks really interesting

Usama Sha1 month ago

@Jarvixdotlive Shared context across agents is what makes the multi agent setup actually practical.

Rohit1 month ago

@Jarvixdotlive Shared context makes AI agents much more useful. This looks really promising!

Robert Smith1 month ago

@Jarvixdotlive Amazing share

Mr Nikola1 month ago

@Jarvixdotlive Great

Artists Voyage 🔶1 month ago

@Jarvixdotlive amazing as I think Jarvix Search + shared context is a practical combo.

James1 month ago

@Jarvixdotlive That agent team mode sounds really useful. I hate having to repeat myself to different bots.

Csaba Kissi1 month ago

@Jarvixdotlive It's like a multi-agent Jarvis

Kevin Parker29 days ago

@Jarvixdotlive Amazing

Fakhr1 month ago

@Jarvixdotlive This one’s new for me

Hussain Hashim | Building SundayBack1 month ago

@Jarvixdotlive @aaliya_va this is actually huge. i've been juggling context issues forever with different AI tools. definitely gonna check out jarvix.

Javeriya Ahsan1 month ago

@Jarvixdotlive Having to explain the same context to every tool is probably the most annoying part of using multiple agents. This fixes a very real headache. XD

Javeria1 month ago

@Jarvixdotlive Keeping the human in control makes this even better.

Muhammad Ayan1 month ago

@Jarvixdotlive Jarvix said no more context déjà vu 🤣

Tarique Sha1 month ago

@Jarvixdotlive great to see as multiple agents working from the same project context removes so much repetitive briefing.

Luqman Ali1 month ago

@Jarvixdotlive Awesome

Aaliya1 month ago

@Jarvixdotlive Thanks

Haider Anis1 month ago

@Jarvixdotlive This is powerful

Charlie Hills1 month ago

@Jarvixdotlive oh woooow shared context across agents 👀

Parul Gautam1 month ago

@Jarvixdotlive this is new for me

Amit1 month ago

@Jarvixdotlive will check it out.

Jack AI1 month ago

@Jarvixdotlive Nice work buddy

Liam | AI Tools & News1 month ago

@Jarvixdotlive Shared context between agents just makes sense.

HarriStack1 month ago

@Jarvixdotlive Finally, an AI that stops making me re-explain my entire life story to every new agent.

Shohan1 month ago

@Jarvixdotlive Amezing share

marium1 month ago

@Jarvixdotlive This sounds like a huge time saver! I'm always looking for ways to streamline my workflow.

Zayan1 month ago

@Jarvixdotlive Shared context, smarter AI workflows.

H A J R A1 month ago

@Jarvixdotlive Shared context across multiple AI agents is a game changer. 🔥

Branding Waves1 month ago

@Jarvixdotlive like that you still approve what happens next. Shared context across agents is useful, but keeping the human in control makes it much more practical.

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🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.

🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.

Jarvix

1,689,930 views • 1 month ago

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

Ronin

15,071 views • 1 month ago

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 views • 1 month ago

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 views • 3 months ago

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

377,138 views • 6 months ago

Introducing Base44 Superagents. AI agents built with managed infrastructure, secured by default, one-click integrations, and 24/7 execution from the start. Everything is taken care of so you can focus on what your agent does, not how to get it running. That means no API keys to juggle, no config files, no security setup, and no maintenance. We handle all of it. Your Superagent connects to all the tools you already use in one click, runs on schedules and triggers, remembers context across sessions, acts proactively on your behalf, and keeps working around the clock. All from wherever you already are, WhatsApp, Telegram, Slack, or your browser. The AI agent everyone's been waiting for, with everything you need already built in. We're excited to get this into your hands, so we're giving free credits to everyone who comments and reposts in the next 24 hours.

Introducing Base44 Superagents. AI agents built with managed infrastructure, secured by default, one-click integrations, and 24/7 execution from the start. Everything is taken care of so you can focus on what your agent does, not how to get it running. That means no API keys to juggle, no config files, no security setup, and no maintenance. We handle all of it. Your Superagent connects to all the tools you already use in one click, runs on schedules and triggers, remembers context across sessions, acts proactively on your behalf, and keeps working around the clock. All from wherever you already are, WhatsApp, Telegram, Slack, or your browser. The AI agent everyone's been waiting for, with everything you need already built in. We're excited to get this into your hands, so we're giving free credits to everyone who comments and reposts in the next 24 hours.

Base44

1,583,044 views • 6 months ago

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt

Anuj

306,574 views • 7 days ago

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

194,171 views • 5 months ago

Your team deserves more from your agent observability platform - a single, connected place for instrumentation, investigation, and measurement, enhanced with intelligence. In Braintrust, you can start with an open-ended prompt about agent behavior and carry the investigation into code, evals, automation, and monitoring. New tools like Patterns and Debugger make it easier to find recurring behavior, get to root causes, and act on what you learn. The context you build in one step automatically follows to the next. Work in the platform with an enhanced Loop experience, or move to your coding agent of choice. Read more →

Your team deserves more from your agent observability platform - a single, connected place for instrumentation, investigation, and measurement, enhanced with intelligence. In Braintrust, you can start with an open-ended prompt about agent behavior and carry the investigation into code, evals, automation, and monitoring. New tools like Patterns and Debugger make it easier to find recurring behavior, get to root causes, and act on what you learn. The context you build in one step automatically follows to the next. Work in the platform with an enhanced Loop experience, or move to your coding agent of choice. Read more →

Braintrust

40,393 views • 12 days ago

I made the worlds best Clipboard for your AI Agents Its called Bluey and its a dumb pointer which you can use to give 10x better context to your agent And you can point at your screen, talk to it, and it will generate a really good annotated screenshot which you can now give to your AI Agent You can use it while designing your website You can use it while explaining something on your screen. And it understands you and you can be really vague about things like "move this" , modify the color "here" and it will understand what you are pointing at. Im loving using it and I built it for myself as I was tired of clicking screenshots all the time and attaching it to my codex or claude code sessions Now I can just point, speak or type and drag and drop it anywhere I am giving free access to it in the comments and I would love for you to try it out. Join the discord and I am sharing the download link there. By the way everything stays 100 percent local. No screenshots are shared anywhere!

I made the worlds best Clipboard for your AI Agents Its called Bluey and its a dumb pointer which you can use to give 10x better context to your agent And you can point at your screen, talk to it, and it will generate a really good annotated screenshot which you can now give to your AI Agent You can use it while designing your website You can use it while explaining something on your screen. And it understands you and you can be really vague about things like "move this" , modify the color "here" and it will understand what you are pointing at. Im loving using it and I built it for myself as I was tired of clicking screenshots all the time and attaching it to my codex or claude code sessions Now I can just point, speak or type and drag and drop it anywhere I am giving free access to it in the comments and I would love for you to try it out. Join the discord and I am sharing the download link there. By the way everything stays 100 percent local. No screenshots are shared anywhere!

Milind S

12,085 views • 3 months ago

Introducing Bloome: a human–AI teammate system built around shared context and multi-agent collaboration. → You bring your task, and AI teammates collaborate with you in real time → Work flows through drafting, challenge, and refinement until it reaches the right answer → Claude, ChatGPT, and Deepseek operate together in a shared context with no information loss Everything stays connected from input to final output. This is how work starts changing.

Introducing Bloome: a human–AI teammate system built around shared context and multi-agent collaboration. → You bring your task, and AI teammates collaborate with you in real time → Work flows through drafting, challenge, and refinement until it reaches the right answer → Claude, ChatGPT, and Deepseek operate together in a shared context with no information loss Everything stays connected from input to final output. This is how work starts changing.

Bloome

345,822 views • 2 months ago

Introducing Nebula Live: The fastest way to work with your agents. You can now talk, text or show them anything. They work in real time, understand what you're saying and autonomously kick off work. Invite your teammates into a live session and everyone can collaborate together. Experience hands free building while just talking and the best part is all your context is saved in the workspace forever. Background mode mutes your agent but they still listen to you, kick off work and let you know when things are done. Share your screen or camera and your agent can see what you are doing. Now in beta across desktop, mobile and web. We're giving away $100 for you to experience Nebula Live. Reply with what you want to talk to your agents about.

Introducing Nebula Live: The fastest way to work with your agents. You can now talk, text or show them anything. They work in real time, understand what you're saying and autonomously kick off work. Invite your teammates into a live session and everyone can collaborate together. Experience hands free building while just talking and the best part is all your context is saved in the workspace forever. Background mode mutes your agent but they still listen to you, kick off work and let you know when things are done. Share your screen or camera and your agent can see what you are doing. Now in beta across desktop, mobile and web. We're giving away $100 for you to experience Nebula Live. Reply with what you want to talk to your agents about.

Nebula

11,584 views • 1 month ago

EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think

EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think

GREG ISENBERG

557,859 views • 1 month ago

Mercury Agent is now available on Atomic Bot! Say it once and every session after already has your context. The longer you work together, the less you have to explain. One click and Mercury is live 24/7 in the cloud!

Mercury Agent is now available on Atomic Bot! Say it once and every session after already has your context. The longer you work together, the less you have to explain. One click and Mercury is live 24/7 in the cloud!

atomicbot.ai

26,745 views • 11 days ago

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

Aaron Levie

87,086 views • 1 year ago

I built the clipboard I always wanted for working with AI agents. It’s called Bluey and It’s 100% local-first. It lives under your mouse cursor. You can draw on your screen, speak to it, or type what you mean, and Bluey turns all of that into rich context for Claude Code or Codex. I’ve been using it to design websites and debug UI because I can finally say things like “move this here” or “make this section feel cleaner” while pointing at the actual screen. No more writing long explanations. No more manually describing screenshots. No more agents getting lost because they don’t know what “this” or "that" means. You can just point to it, speak or type and it will curate the best possible context for your agents Bluey captures the screenshot, annotation, transcript, app/window context, and the details your agent needs, then sends it directly into your coding session. I am giving it out for free for a while! Im building it with my buddy Omkar Satpute and we would love to hear what you think about it on Discord what context do you wish your agent could understand better?

I built the clipboard I always wanted for working with AI agents. It’s called Bluey and It’s 100% local-first. It lives under your mouse cursor. You can draw on your screen, speak to it, or type what you mean, and Bluey turns all of that into rich context for Claude Code or Codex. I’ve been using it to design websites and debug UI because I can finally say things like “move this here” or “make this section feel cleaner” while pointing at the actual screen. No more writing long explanations. No more manually describing screenshots. No more agents getting lost because they don’t know what “this” or "that" means. You can just point to it, speak or type and it will curate the best possible context for your agents Bluey captures the screenshot, annotation, transcript, app/window context, and the details your agent needs, then sends it directly into your coding session. I am giving it out for free for a while! Im building it with my buddy Omkar Satpute and we would love to hear what you think about it on Discord what context do you wish your agent could understand better?

Milind S

13,342 views • 3 months ago

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

66,214 views • 11 days ago

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

22,362 views • 1 month ago

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Daniel San

51,706 views • 4 months ago

Most AI tools wait for you to ask. Revo already knows. It works across your meetings, email, Slack, and tickets, turning what you promised into work that’s already drafted. Invoice follow-ups. Deck updates. Replies based on what your teammates committed to. You just review it and tap “Do it.” Revo is multiplayer by design, with one shared company brain that keeps the whole team in context. Revo Actions is available to everyone today. Try it free at

Most AI tools wait for you to ask. Revo already knows. It works across your meetings, email, Slack, and tickets, turning what you promised into work that’s already drafted. Invoice follow-ups. Deck updates. Replies based on what your teammates committed to. You just review it and tap “Do it.” Revo is multiplayer by design, with one shared company brain that keeps the whole team in context. Revo Actions is available to everyone today. Try it free at

Mehdi Djabri

1,363,311 views • 1 month ago