Nikhil Sharma (@ImNikhil117)

공유 파일시스템을 조정 계층으로 사용하는 접근법을 설명한 트윗입니다. 각 에이전트는 인박스 디렉토리를 갖고 공유 컨텍스트 파일에 쓰고 다른 에이전트의 출력을 읽는 방식으로, 메시지 버스 없이 명확한 읽기/쓰기 규약만으로 협업을 수행한다고 제안합니다.

https://x.com/ImNikhil117/status/2029265274424442943

#multiagent #coordination #filesystems #agentorchestration

Nikhil Sharma (@ImNikhil117) on X

@mattshumer_ Shared filesystem as the coordination layer. Each agent gets its own inbox directory, writes to shared context files, reads others' outputs. No message bus needed — just well-defined read/write contracts. The hardest part isn't communication, it's preventing two agents from

X (formerly Twitter)

Zach (@zachknows)

단일 인스턴스에서 여러 개의 영구 에이전트를 각자의 작업공간으로 운영하고, 메인 에이전트가 특정 작업의 라우팅 정보를 메모리에 보관해 역할별로 지정된 모델을 사용하는 아키텍처를 설명하는 트윗입니다. 에이전트별로 역할에 맞는 모델을 명시해 책임을 분리하는 패턴을 제안합니다.

https://x.com/zachknows/status/2029265155465371797

#multiagent #agents #aiarchitecture #agentorchestration

Zach (@zachknows) on X

@mattshumer_ personally just have one instance, multiple persistent agent with there own workspaces. then the main agent has it in his memory the routing for specific task! each agent has specific models defined for their roles.

X (formerly Twitter)

Andrew Ambrosino (@ajambrosino)

Codex 관련 대규모 업데이트와 제품·기능 공개 소식입니다. 게시글은 Codex 5.3, Spark, Codex 앱, OpenClaw 등 여러 릴리스·프로젝트가 한달 사이 진행되었음을 알리며 가속화 중이라고 강조합니다. 또한 팀 확장을 위해 Full‑stack TypeScript, 디자인 엔지니어링, Windows 경험 및 배포, React+Node 성능, 고급 Git, 에이전트 오케스트레이션, 원격 Codex 관련 인재를 찾고 있다는 채용 요청을 포함합니다.

https://x.com/ajambrosino/status/2023850810183217184

#codex #ai #release #recruiting #agentorchestration

Andrew Ambrosino (@ajambrosino) on X

It's been a huge month for Codex. 5.3, Spark, Codex app, OpenClaw. We're accelerating. Looking for top people in: - Full stack Typescript - Design engineering - Windows experience+distribution - React+Node performance - Crazy advanced git - Agent orchestration - Remote codex -

X (formerly Twitter)

Why I chose to fine-tune my models and what it taught me about building better AI agents. Learn how fine-tuning improves AI agent performance, safety, and cost optimization. Read here: https://legacystories.org/storyboard/entry/why-i-chose-to-fine-tune-my-models-and-what-it-taught-me-about-building-better-ai-agents

Build smarter AI agents faster with RubikChat.

#FineTuneModels #ModelFineTuning #LLMFineTuning #AIAgents #AgentDevelopment #AgentBuilder #AgentOrchestration #AIDeployment #PromptEngineering #RAG #TrainingDataset #AIAgentPerformance #AgentSafety #CostOptimization #AI #MachineLearning

🤯 Multi-model LLM teams orchestrated by Monte Carlo Tree Search – a paradigm shift in LLM inference!

This means decentralized, adaptable AI is on the horizon.

But what are the biggest unforeseen challenges we'll face in building truly efficient #agentorchestration systems?

Full blog post: https://dougortiz.blogspot.com/2025/07/the-future-of-ai-moving-beyond-single.html

#MultiModelLLMs #InferenceOptimization #DeepTech #DataDrivenAgentOrchestration https://www.blogger.com/blog/post/edit/3045684979747909405/27457725471072296

The Future of AI: Moving Beyond Single-Model Solutions