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This article introduces the open-source list awesome-jev-projects, which decouples System 1 fast decision models from System 2 reasoning LLMs to optimize AI agent workflows, reduce latency and costs, and provide code-level references.
Guillermo Rauch discusses the three key components of successful AI agents: brain (model and logic), hands (tools and computer), and files (memories and repos), suggesting an easy way to build them using stateful computers like Mac Mini.
The article explains the four architectural layers of an AI agent system—Loop, Graph, Harness, and Meta-harness—emphasizing that reliable agents depend on system architecture rather than just model strength or prompting.
A user is asking for a better AI model than Qwen3.8-27B for coding tasks, noting its limitations in higher-level reasoning, system architecture, separation of concerns, and abstractions.
The article outlines a multi-gate architecture for a high-accuracy semantic evidence and RAG system for financial documents, emphasizing traceability, reconciliation, and hybrid retrieval, and seeks feedback on its design.
The article argues that AI agent architectures should separate layers like identity, memory, tools, and authority to avoid unintended dependencies and improve system robustness.
The article discusses the importance of system design for AI agents, covering concepts like Agent Harness, LLMOps, and Evals, and provides a proof-of-concept implementation with plans for future parts.
The article explains how to force an ARM64X executable to run as a specific architecture on Windows, using the PROC_THREAD_ATTRIBUTE_MACHINE_TYPE attribute to relaunch the process if needed for plug-in compatibility.
This article argues that LLM hallucinations in production are typically a system architecture problem rather than a model problem, and outlines four key guardrails: RAG grounding, live tools/function calling, selective human oversight, and red teaming/adversarial testing.
OpenAI describes how they built GPT-Live, a full-duplex realtime voice AI system that eliminates the turn detector, enabling natural continuous conversation. The article details architecture improvements in inference, context management, and media transport over six months.
This article discusses the challenges of scaling voice agents, noting that failures occur at different layers, and identifies the most common bottleneck that limits performance first.
This article provides an in-depth analysis of WorkBuddy's six-layer technical architecture, pointing out that it is essentially an Agent operating system rather than a single model, and demonstrates real failures and fixes in Agent engineering through update logs.
This paper proposes the concept of the world wide AI-Model Network (AI-ModelNet), a novel paradigm for interconnecting, sharing capabilities, and enabling collaborative reasoning among diverse large models. The authors review current single- and multi-model research, present a hierarchical architecture, and validate feasibility through a prototype system and application cases.
This paper proposes a model-native computing architecture, envisioning future system design through the lens of computer architecture.
Sculpt OS 26.04, based on Genode, introduces a live, interactive system architecture allowing users to customize and restructure components on the fly, updates Linux-based drivers to kernel 6.18, and adds Qt6, Falkon browser, and native Goa SDK for development without a Linux VM.
This article details the production architecture behind the open-source browser-use library, explaining how to scale browser agents using AWS Lambda, SQS, and S3 for state management and retries.
A practitioner shares ten critical lessons for deploying AI agents in production, emphasizing code-based constraints, context management, and security over relying solely on prompts.