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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...

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Anuj

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337,509 Aufrufe • vor 15 Tagen •via X (Twitter)

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4 Kommentare

Profilbild von 0xKachm
0xKachmvor 14 Tagen

The real friction is that most teams don't actually know what context they'd want shared - and Switch can't solve that missing clarity. What happens when two agents in the same channel reach contradictory conclusions from the same history? That's the part nobody's stress-tested yet.

Profilbild von Anuj
Anujvor 15 Tagen

⭐ Star on GitHub →

Profilbild von Jyoti
Jyotivor 13 Tagen

This sounds like a great solution for teams. Making AI agents work together should really boost productivity.

Profilbild von Divyam
Divyamvor 12 Tagen

shared context solves isolation but not ownership. once every agent sees the same thread, someone still has to own the final result and call it done, or they all keep answering.

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Defileo🔮

17,576 Aufrufe • vor 1 Monat

anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇
16:19

anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇

MIKE

26,662 Aufrufe • vor 2 Monaten

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.
0:22

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 Aufrufe • vor 3 Monaten

Block just open sourced Berd, the desktop app its teams use internally to work with AI agents across projects, skills, tools, and models. "We had access to increasingly capable agents through tools such as Goose, Claude Code, and Codex, but working with them meant navigating different interfaces, configuration systems, and ways of managing context. The technology was powerful, but the experience around it was fragmented." Berd brings conversations, files, instructions, agents, and skills together in one place with persistent projects. Built on top of Goose, the open source agent framework Block contributed to the Agentic AI Foundation under the Linux Foundation. Berd connects to Goose through the Agent Client Protocol, handling the desktop experience while Goose runs the underlying agent loop. Block also recently released Buzz, a separate open source workspace for collaborative agent work. Berd is where you start alone. Buzz is where you go multiplayer.
1:40

Block just open sourced Berd, the desktop app its teams use internally to work with AI agents across projects, skills, tools, and models. "We had access to increasingly capable agents through tools such as Goose, Claude Code, and Codex, but working with them meant navigating different interfaces, configuration systems, and ways of managing context. The technology was powerful, but the experience around it was fragmented." Berd brings conversations, files, instructions, agents, and skills together in one place with persistent projects. Built on top of Goose, the open source agent framework Block contributed to the Agentic AI Foundation under the Linux Foundation. Berd connects to Goose through the Agent Client Protocol, handling the desktop experience while Goose runs the underlying agent loop. Block also recently released Buzz, a separate open source workspace for collaborative agent work. Berd is where you start alone. Buzz is where you go multiplayer.

TFTC

54,922 Aufrufe • vor 1 Monat

Introducing the External Agents API: bring any agent into Notion, even the ones you build yourself. We’ve also partnered with Claude, OpenAI Codex, Decagon, Cursor, Warp, Cognition, FLORA ©, Amplitude, Console, and Serval so they work out of the box. With all your agents in Notion, their work is visible, reviewable, and actionable alongside the rest of the team, without copy-pasting context between tools. Join the External Agents waitlist →
2:33

Introducing the External Agents API: bring any agent into Notion, even the ones you build yourself. We’ve also partnered with Claude, OpenAI Codex, Decagon, Cursor, Warp, Cognition, FLORA ©, Amplitude, Console, and Serval so they work out of the box. With all your agents in Notion, their work is visible, reviewable, and actionable alongside the rest of the team, without copy-pasting context between tools. Join the External Agents waitlist →

Notion Developers

42,308 Aufrufe • vor 4 Monaten

Buzz is Slack where the agents are teammates instead of integrations. Here's what that unlocks: Agents: - The ones you start with are just defaults - Edit them, add your own, write their instructions like a system prompt Swappable Harness: - This is the killer feature - Swap Claude Code, Codex, Goose under the same agent Context: - Every chat you've had with that agent carries over to the new harness - Global skills and skill files come too Think: Slack = the shape Agents = the teammates Harness = the engine you can change without losing anything "I get model fatigue" is a real thing. Buzz sits one layer above it.
3:58

Buzz is Slack where the agents are teammates instead of integrations. Here's what that unlocks: Agents: - The ones you start with are just defaults - Edit them, add your own, write their instructions like a system prompt Swappable Harness: - This is the killer feature - Swap Claude Code, Codex, Goose under the same agent Context: - Every chat you've had with that agent carries over to the new harness - Global skills and skill files come too Think: Slack = the shape Agents = the teammates Harness = the engine you can change without losing anything "I get model fatigue" is a real thing. Buzz sits one layer above it.

The Startup Ideas Podcast (SIP) 🧃

43,523 Aufrufe • vor 1 Monat