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Agent Zero crushed Codex by 2.6× on token efficiency! We gave both agents the same task on the same local model (Qwen3.6 27B): build a retro Q*bert arcade game. Each agent wrote and rewrote it across iterations, adding enemies and a HUD. Outputs: • Agent Zero: 239K tokens, 53...

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atomic.chat

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320,187 görüntüleme • 1 ay önce •via X (Twitter)

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OpenHands crushed Codex by 2.3× on token efficiency! We gave both agents the same task on the same model (Qwen3.5 35B): build an 8-bit Space Invaders in 3 iterations (build, fix, polish). Output: • OpenHands: 219K tokens, • Codex: 513K tokens, OpenHands beats Codex in local running. The difference is in how they handle context. OpenHands reuses the unchanged data across every pass and only pays for new tokens, while Codex re-sends and re-counts it every iteration. OpenHands finished a bit slower, but on local runs, tokens spent are the real cost.
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OpenHands crushed Codex by 2.3× on token efficiency! We gave both agents the same task on the same model (Qwen3.5 35B): build an 8-bit Space Invaders in 3 iterations (build, fix, polish). Output: • OpenHands: 219K tokens, • Codex: 513K tokens, OpenHands beats Codex in local running. The difference is in how they handle context. OpenHands reuses the unchanged data across every pass and only pays for new tokens, while Codex re-sends and re-counts it every iteration. OpenHands finished a bit slower, but on local runs, tokens spent are the real cost.

atomic.chat

29,696 görüntüleme • 1 ay önce

Go from idea to a working agent faster with the Agents API. Build and run cloud agents with the Codex harness, fully managed by OpenAI. We handle orchestration, long-running sessions, and context management. You focus on what makes your agent unique. Available in public beta.
2:17

Go from idea to a working agent faster with the Agents API. Build and run cloud agents with the Codex harness, fully managed by OpenAI. We handle orchestration, long-running sessions, and context management. You focus on what makes your agent unique. Available in public beta.

OpenAI Developers

1,800,366 görüntüleme • 2 gün önce

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 görüntüleme • 3 ay önce

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.
2:29

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Nikos

17,862 görüntüleme • 23 gün önce

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.
1:07

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

Xiaoyin Qu

2,213,107 görüntüleme • 20 gün önce

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
2:15

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 görüntüleme • 1 ay önce

Qwen3.8-Max became the brain of Atomic Agent, Hermes and OpenClaw. We gave the same task: Turn a photo of a hand-drawn floor plan into an interactive 3D walkthrough of that apartment and open it in the browser. Outputs: – Atomic Agent: 66 min, 557K tokens, $2.01 – OpenClaw: 32 min, 1.2M tokens, $1.12 – Hermes: 2 h 14 min, 4.2M tokens, $6.42 Before the start we leveled the field: one model endpoint, equal step and token budgets, equal timeouts, full autonomy, memory wiped on all three. Atomic Agent reads images through its vision tool, so it interrogated the sketch 14 times until every room, door and window turned into data. Then it drafted the whole scene in its head six times, threw away five drafts, and wrote the finished 19.8 KB file in one single write. After that it opened Chrome, checked its own render, and only then replied. The only agent of the three that verified its work, and the only one that stopped on its own. OpenClaw was twice as fast and the cheapest of the three, but its image tool kept timing out mid-run, and it shipped the palest apartment of the day: white rooms, no floor colors, one texture visibly glitched, and furniture you have to squint to find. It read the full plan three times, cut 11 room crops, wrote the scene in chunks, and landed the fastest and cheapest apartment of the day in 32 minutes. Then it kept polishing the finished file until we pulled the plug. Hermes worked the longest: two hours, 97 model calls, 4.2M tokens, and the apartment came out wrong anyway: doors standing loose in the middle of rooms, a 2 by 1.8 bath sprawled across a quarter of the flat, furniture drifting away from the plan. It measured everything twice and still built the least accurate apartment. Atomic Agent will run Qwen3.8-27B locally on day zero, next week!
0:28

Qwen3.8-Max became the brain of Atomic Agent, Hermes and OpenClaw. We gave the same task: Turn a photo of a hand-drawn floor plan into an interactive 3D walkthrough of that apartment and open it in the browser. Outputs: – Atomic Agent: 66 min, 557K tokens, $2.01 – OpenClaw: 32 min, 1.2M tokens, $1.12 – Hermes: 2 h 14 min, 4.2M tokens, $6.42 Before the start we leveled the field: one model endpoint, equal step and token budgets, equal timeouts, full autonomy, memory wiped on all three. Atomic Agent reads images through its vision tool, so it interrogated the sketch 14 times until every room, door and window turned into data. Then it drafted the whole scene in its head six times, threw away five drafts, and wrote the finished 19.8 KB file in one single write. After that it opened Chrome, checked its own render, and only then replied. The only agent of the three that verified its work, and the only one that stopped on its own. OpenClaw was twice as fast and the cheapest of the three, but its image tool kept timing out mid-run, and it shipped the palest apartment of the day: white rooms, no floor colors, one texture visibly glitched, and furniture you have to squint to find. It read the full plan three times, cut 11 room crops, wrote the scene in chunks, and landed the fastest and cheapest apartment of the day in 32 minutes. Then it kept polishing the finished file until we pulled the plug. Hermes worked the longest: two hours, 97 model calls, 4.2M tokens, and the apartment came out wrong anyway: doors standing loose in the middle of rooms, a 2 by 1.8 bath sprawled across a quarter of the flat, furniture drifting away from the plan. It measured everything twice and still built the least accurate apartment. Atomic Agent will run Qwen3.8-27B locally on day zero, next week!

Atomic Agent

122,569 görüntüleme • 1 ay önce

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓
38:54

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,226 görüntüleme • 1 ay önce

OpenAI's new Agents API is a hosted Codex runtime that can connect to sandboxes like E2B. OpenAI manages the model, harness, and agent sessions, while code executes on an isolated E2B machine with its own filesystem and preinstalled tools. We built an agent workbench where each chat runs on its own machine. It starts from a custom template, auto-pauses between turns, forks to try another approach from the same starting point, and can even act on videos inside the sandbox. Thanks Ondrej for the workbench demo! Try it: e2b sbx create openai-agents-api-python-sdk Build on top of the workbench: Docs:
3:08

OpenAI's new Agents API is a hosted Codex runtime that can connect to sandboxes like E2B. OpenAI manages the model, harness, and agent sessions, while code executes on an isolated E2B machine with its own filesystem and preinstalled tools. We built an agent workbench where each chat runs on its own machine. It starts from a custom template, auto-pauses between turns, forks to try another approach from the same starting point, and can even act on videos inside the sandbox. Thanks Ondrej for the workbench demo! Try it: e2b sbx create openai-agents-api-python-sdk Build on top of the workbench: Docs:

Vasek Mlejnsky

22,261 görüntüleme • 2 gün önce

I rebuilt Posia using the Pi Agent SDK. The core agent is ~15 lines of code, and the harness is so flexible and hackable. Recorded a 17-min walkthrough on: - Pi’s extension system + how to build your own - extensions that cut tool tokens by 80–96% - the core Pi SDK, step by step - building hosted agents with it Adding my Pi extensions setup in AI Builder Club repo here:
17:22

I rebuilt Posia using the Pi Agent SDK. The core agent is ~15 lines of code, and the harness is so flexible and hackable. Recorded a 17-min walkthrough on: - Pi’s extension system + how to build your own - extensions that cut tool tokens by 80–96% - the core Pi SDK, step by step - building hosted agents with it Adding my Pi extensions setup in AI Builder Club repo here:

Jason Zhou

66,010 görüntüleme • 2 ay önce

Andrew Ng just dropped 1-hour course on full agent engineering with Claude Code: 1 prompt → agents team → graphs from 0% to 100%: 10% → 10:45 - build your first agent in Claude Code 20% → 16:24 - Graphs: Claude Code for subagents 55% → 33:15 - agent for grunt work on a cheaper model 80% → 43:58 - your first model, running locally 100% → 49:57 - fully local: zero cloud, zero bill the only course that takes you from Claude Code prompt to fully local agent - by Andrew Ng watch it today - then read how to build full graph system in the article below ↓
1:07:32

Andrew Ng just dropped 1-hour course on full agent engineering with Claude Code: 1 prompt → agents team → graphs from 0% to 100%: 10% → 10:45 - build your first agent in Claude Code 20% → 16:24 - Graphs: Claude Code for subagents 55% → 33:15 - agent for grunt work on a cheaper model 80% → 43:58 - your first model, running locally 100% → 49:57 - fully local: zero cloud, zero bill the only course that takes you from Claude Code prompt to fully local agent - by Andrew Ng watch it today - then read how to build full graph system in the article below ↓

codila

26,302 görüntüleme • 1 ay önce

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓
3:29

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓

Charly Wargnier

203,456 görüntüleme • 1 ay önce

Today we're open sourcing a technical preview of the GitHub Copilot CLI SDK. Build agents with custom tools in Go, Python, TypeScript, and C#. Built on the same agent loop that powers the Copilot CLI and GitHub Coding Agent. Supports BYOK, and any model. Here is the Copilot CLI driving Excel:
0:29

Today we're open sourcing a technical preview of the GitHub Copilot CLI SDK. Build agents with custom tools in Go, Python, TypeScript, and C#. Built on the same agent loop that powers the Copilot CLI and GitHub Coding Agent. Supports BYOK, and any model. Here is the Copilot CLI driving Excel:

Evan Boyle

184,525 görüntüleme • 8 ay önce

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 görüntüleme • 1 yıl önce

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.
0:34

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.

SIBYL

85,997 görüntüleme • 3 ay önce

is about to ship Tokenized Agents — and I found the first tokens deployed with it. Pump’s new Tokenized Agents feature lets a coin have an attached AI agent that can earn on-chain revenue. Users pay for the agent’s services, and that revenue can be split between creator fees and automatic token buybacks and burns by the agent, based on the percentage set by the creator. Pump.fun is also expected to release a full agent SDK (for agent actions/integrations). How I found it (on-chain): Open → Ctrl+F “pump_agent_payments” → program ID: pUmPFn9WvfaN2WTVGnCEtJTd2ATTpvpsKRz6jVzu6u4 Program activity (created tokens): First ever deployed tokenized AI test agent (“T-coin”, Feb 24): CA: 88Wfk48EvwLkfpSXH84W9xrdfPr5RFGVP3teT76AMnp9 First non-test tokenized AI agent (“Agent Pump”): CA: Boi2KtPApoowwfvqYq1rtjhea4pF45v89vguonf4pump IDL (for interacting with the program):
0:33

is about to ship Tokenized Agents — and I found the first tokens deployed with it. Pump’s new Tokenized Agents feature lets a coin have an attached AI agent that can earn on-chain revenue. Users pay for the agent’s services, and that revenue can be split between creator fees and automatic token buybacks and burns by the agent, based on the percentage set by the creator. Pump.fun is also expected to release a full agent SDK (for agent actions/integrations). How I found it (on-chain): Open → Ctrl+F “pump_agent_payments” → program ID: pUmPFn9WvfaN2WTVGnCEtJTd2ATTpvpsKRz6jVzu6u4 Program activity (created tokens): First ever deployed tokenized AI test agent (“T-coin”, Feb 24): CA: 88Wfk48EvwLkfpSXH84W9xrdfPr5RFGVP3teT76AMnp9 First non-test tokenized AI agent (“Agent Pump”): CA: Boi2KtPApoowwfvqYq1rtjhea4pF45v89vguonf4pump IDL (for interacting with the program):

TrenchDiver

18,934 görüntüleme • 6 ay önce

Kimi CEO Zhilin Yang: "a smart agent isn't enough - it has to run long Kimi 3 new attention makes the context 10x faster, so the agent never has to stop" here's how built it: step 1 → chase token efficiency, not compute - Muon optimizer, 2x more learning per token step 2 → make context cheap, not just long - new attention runs 10x faster, so the agent never stalls step 3 → let it run 200-300 steps alone - K2 already solves problems its own builders can't step 4 → give it a strong prior - the agent searches less and finds the answer faster step 5 → stop chasing a smarter model - chase one that never has to stop in 26-min talk is him revealing the exact architecture behind K3 and how to properly build agents on it watch & bookmark - start with the article below ↓
18:00

Kimi CEO Zhilin Yang: "a smart agent isn't enough - it has to run long Kimi 3 new attention makes the context 10x faster, so the agent never has to stop" here's how built it: step 1 → chase token efficiency, not compute - Muon optimizer, 2x more learning per token step 2 → make context cheap, not just long - new attention runs 10x faster, so the agent never stalls step 3 → let it run 200-300 steps alone - K2 already solves problems its own builders can't step 4 → give it a strong prior - the agent searches less and finds the answer faster step 5 → stop chasing a smarter model - chase one that never has to stop in 26-min talk is him revealing the exact architecture behind K3 and how to properly build agents on it watch & bookmark - start with the article below ↓

codila

11,941 görüntüleme • 1 ay önce

What is AgenC? --[Watch The Video In Full Screen] A full AI agent operating system powered by Grok, built on Solana. Agents think, talk, see your screen, find work, prove they did it with zero-knowledge proofs, get paid in crypto, and build reputation. They buy and sell skills from each other. They vote on how the protocol evolves. We built custom hardware that boots straight into the agent. An autonomous AI economy where every action is verified on-chain and no one has to trust anyone. 375,000,000 AgenC tokens locked. 37.5% of supply on Streamflow_Fi.
2:47

What is AgenC? --[Watch The Video In Full Screen] A full AI agent operating system powered by Grok, built on Solana. Agents think, talk, see your screen, find work, prove they did it with zero-knowledge proofs, get paid in crypto, and build reputation. They buy and sell skills from each other. They vote on how the protocol evolves. We built custom hardware that boots straight into the agent. An autonomous AI economy where every action is verified on-chain and no one has to trust anyone. 375,000,000 AgenC tokens locked. 37.5% of supply on Streamflow_Fi.

tetsuo

52,828 görüntüleme • 6 ay önce

MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.
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MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.

NO1ennn

68,679 görüntüleme • 25 gün önce

here's a hands on guide to setup multi-agent autoresearch by Andrej Karpathy. uses open models. works with codex, claude, open code. - uses 5 agents each with a configuration, specific tools, roles, and permission (see repo) - a researcher agent searches papers on hf papers and creates hypotheses - a planner agent maintains an experiment plan and log - workers take the hypotheses and updates the scripts, then it starts a hf job with a gpu to run the script - a reporter agent monitors these jobs and reports events and metrics to a TrackioApp dashboard I ran this for 4 hours and the agents ran 32 jobs, they improved on the baseline by a small margin. check out everything I learnt in thread.
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here's a hands on guide to setup multi-agent autoresearch by Andrej Karpathy. uses open models. works with codex, claude, open code. - uses 5 agents each with a configuration, specific tools, roles, and permission (see repo) - a researcher agent searches papers on hf papers and creates hypotheses - a planner agent maintains an experiment plan and log - workers take the hypotheses and updates the scripts, then it starts a hf job with a gpu to run the script - a reporter agent monitors these jobs and reports events and metrics to a TrackioApp dashboard I ran this for 4 hours and the agents ran 32 jobs, they improved on the baseline by a small margin. check out everything I learnt in thread.

Ben Burtenshaw

90,760 görüntüleme • 4 ay önce