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herdr 0.7.4 is out! many fixes, many additions, and structural work for multi-client 👀 but this one ships one of the two most wanted features: customizable sidebar. both agent and workspace rows: plugins, custom scripts, agent hooks, or just settings can put anything on them. claude's live task title,...

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herdr

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90,248 Aufrufe • vor 12 Tagen •via X (Twitter)

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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.

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$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

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Database table size impacts performance in more ways than one: a) B-tree depth. Using 8k pages and a 16b uuid: 1 level = ~370 rows 2 levels = ~138k rows 3 levels = ~50m rows 4 levels = ~20b rows The lookup cost on a table with 100k rows is not the same as one with 1b rows. This can apply both to the table itself (MySQL cluster index) as well as the indexes. Sometimes a single query requires many of them. b) Small table → fits in RAM → fast reads. The larger the table, the more likely to read from disk plus churn the cache. c) # of indexes. Each adds maintenance overhead for insertions, and for Postgres vacuum overhead as well. Keep an eye on this! It's useful to take regular stock of your tables + indexes. Clean bloat. Remove unused indexes. Partition if needed.

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am.will

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New course: Build agents that respond to users with not only plaintext, but custom UIs like charts, forms, and whiteboards, generated on demand and displayed right in the chat. This short course is built in partnership with CopilotKit🪁 and taught by Atai Barkai, co-founder of CopilotKit. You'll learn three approaches: Your agent can pick from custom components you build, like charts and forms. It can compose new layouts from a set of building blocks you provide, like rows, cards, and text. Or it can incorporate existing third-party apps, like a whiteboard or a calendar, right inside the conversation. Skills you’ll gain: - Build agents that render custom components like charts and forms on demand - Build an app where the agent and user collaborate on shared data, beyond just the chat window - Place third-party apps like maps, calendars, and whiteboards right in your interface Join and build agents that give users something to see and act on!

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Ivan Zhao

201,111 Aufrufe • vor 10 Monaten

Three skills I use every day in Claude Code and Codex to solve my hardest problems: 1️⃣ /agent-watchdog When I have one agent like Codex working on a task and I don't fully trust it's going to do everything right, I'll open up another one like Claude Code and tell it to watchdog the Codex thread. You can copy the Codex deep link into Claude Code and it'll look at the prompt you sent, watch the Codex thread until it's done, then compare the Codex solution to how it was planning to solve it and automatically fix anything that Codex missed. It can also test the work of the other agent end-to-end. Similar to the idea of OpenRouter's new Fusion feature, I've definitely found that two models thinking through a problem and checking each other's work can be wildly more impactful than just one. 2️⃣ /plan-arbiter Similar ideas as /agent-watchdog - but with this one you have both make plans, compare plans, negotiate the differences, and make a final plan to execute. I find Claude Code is better at writing plans, but Codex is faster and cheaper to execute on them. Then I usually have Claude Code watchdog the Codex work and fix anything that was missed. 3️⃣ /read-the-damn-docs One thing that drives me crazy with coding agents is they're so reluctant to look up docs. They'll just guess and guess and guess at the right API surface for things, or the right solution to an integration of two things. Once I explicitly tell it to look up the docs, it says "Oh, I see the answer," and it fixes the problem. So I made the /read-the-damn-docs skill. Add it and your agents will know when and how to do efficient web searches to look up docs for the types of problems you really should look up docs for. All of these are totally open source over on my GitHub. If you try them, let me know your feedback. Will link to them below:
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Three skills I use every day in Claude Code and Codex to solve my hardest problems: 1️⃣ /agent-watchdog When I have one agent like Codex working on a task and I don't fully trust it's going to do everything right, I'll open up another one like Claude Code and tell it to watchdog the Codex thread. You can copy the Codex deep link into Claude Code and it'll look at the prompt you sent, watch the Codex thread until it's done, then compare the Codex solution to how it was planning to solve it and automatically fix anything that Codex missed. It can also test the work of the other agent end-to-end. Similar to the idea of OpenRouter's new Fusion feature, I've definitely found that two models thinking through a problem and checking each other's work can be wildly more impactful than just one. 2️⃣ /plan-arbiter Similar ideas as /agent-watchdog - but with this one you have both make plans, compare plans, negotiate the differences, and make a final plan to execute. I find Claude Code is better at writing plans, but Codex is faster and cheaper to execute on them. Then I usually have Claude Code watchdog the Codex work and fix anything that was missed. 3️⃣ /read-the-damn-docs One thing that drives me crazy with coding agents is they're so reluctant to look up docs. They'll just guess and guess and guess at the right API surface for things, or the right solution to an integration of two things. Once I explicitly tell it to look up the docs, it says "Oh, I see the answer," and it fixes the problem. So I made the /read-the-damn-docs skill. Add it and your agents will know when and how to do efficient web searches to look up docs for the types of problems you really should look up docs for. All of these are totally open source over on my GitHub. If you try them, let me know your feedback. Will link to them below:

Steve (Builder.io)

42,501 Aufrufe • vor 1 Monat

Most AI agents are just toys that live in a chat box. They are high on hype, low on utility. You spend more time prompting them than doing actual work. Notion Agent is different. It’s not just a window into an LLM. It’s a layer of intelligence sitting directly on any page on Notion. Check out the attached video. I just asked Notion Agent to write practical examples of each React Hook. Here’s how Notion Agent is helping me build it. • Notion Agent forms a plan: (Search → Read → Decide → Update → Notify). You give it a goal, and it executes multi-step workflows like editing hundreds of pages or updating complex databases. • It can create pages, customize properties, and build entire database views from scratch. • Total Context: It doesn't just search Notion. It pulls from Slack, Google Drive, and the web. It finds the patterns across your entire stack in seconds. Check out Notion Agent here: Appreciate the Notion team for collaborating with me on this deep dive.
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Most AI agents are just toys that live in a chat box. They are high on hype, low on utility. You spend more time prompting them than doing actual work. Notion Agent is different. It’s not just a window into an LLM. It’s a layer of intelligence sitting directly on any page on Notion. Check out the attached video. I just asked Notion Agent to write practical examples of each React Hook. Here’s how Notion Agent is helping me build it. • Notion Agent forms a plan: (Search → Read → Decide → Update → Notify). You give it a goal, and it executes multi-step workflows like editing hundreds of pages or updating complex databases. • It can create pages, customize properties, and build entire database views from scratch. • Total Context: It doesn't just search Notion. It pulls from Slack, Google Drive, and the web. It finds the patterns across your entire stack in seconds. Check out Notion Agent here: Appreciate the Notion team for collaborating with me on this deep dive.

Pratham

20,933 Aufrufe • vor 5 Monaten

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:
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Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

Kawsar

31,812 Aufrufe • vor 4 Monaten

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!
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🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

Dan Shipper 📧

145,487 Aufrufe • vor 1 Jahr

If you're evaluating AI IDEs this week, here's what getting started with Windsurf looks like (links below): • Download and install from Import any existing IDE settings during onboarding, or start fresh. • Cascade is the agent panel on the right. Chat, generate, edit, run code. @-mention files, symbols, or docs to scope context. • Choose your model - Opus, Codex, SWE, Kimi, GLM and more (Kimi K2.6 is free for the next 2 weeks 👀) • DeepWiki: Cmd+Shift+Click any symbol for an in-depth explanation of what it does. Send it to Cascade with one click. • Codemaps: hierarchical view of how files and functions execute together. Click a node to jump to code. Shareable as links. • Devin in Windsurf: start and manage cloud sessions without leaving the editor. • Agent Command Center: Kanban board of every running agent, local (Cascade) and cloud (Devin). See what's in flight, blocked, or ready for review. • Spaces: group sessions, PRs, files, and context for one task or project into a single view. • Centralized review: handle diffs, PRs, and agent output in one place. • MCP servers, Memories, and Workflows extend Cascade. Set them up once; reuse across projects.
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If you're evaluating AI IDEs this week, here's what getting started with Windsurf looks like (links below): • Download and install from Import any existing IDE settings during onboarding, or start fresh. • Cascade is the agent panel on the right. Chat, generate, edit, run code. @-mention files, symbols, or docs to scope context. • Choose your model - Opus, Codex, SWE, Kimi, GLM and more (Kimi K2.6 is free for the next 2 weeks 👀) • DeepWiki: Cmd+Shift+Click any symbol for an in-depth explanation of what it does. Send it to Cascade with one click. • Codemaps: hierarchical view of how files and functions execute together. Click a node to jump to code. Shareable as links. • Devin in Windsurf: start and manage cloud sessions without leaving the editor. • Agent Command Center: Kanban board of every running agent, local (Cascade) and cloud (Devin). See what's in flight, blocked, or ready for review. • Spaces: group sessions, PRs, files, and context for one task or project into a single view. • Centralized review: handle diffs, PRs, and agent output in one place. • MCP servers, Memories, and Workflows extend Cascade. Set them up once; reuse across projects.

nader dabit

16,694 Aufrufe • vor 3 Monaten