Data gravity is compounding integration debt in API-first AI stacks, making vendor lock-in a gradual process that most teams drift into rather than choose, according to a technical analysis by Glukhov…
Anthropic's Claude Code subagents provide isolated context windows and restricted tool allowlists to handle noisy, parallelizable tasks, keeping main conversation context clean. The feature is distinc…
Ollama's simplicity can mask when a local experiment becomes a shared inference service, and vLLM offers better scheduling and observability for production workloads. Migration is warranted when multi…
AI coding agents cause specs, tests, and code to drift apart, creating false confidence and circular test coverage. A traceability model using six identifiers—requirement ID, design decision ID, task …
An LLM Wiki fails when old facts remain plausible, contradictions become polished, and generated summaries drift from their sources, according to a technical guide on LLM Wiki maintenance. The guide a…
A circuit breaker pattern stops Go services from hammering failing dependencies, preventing cascading failures that consume goroutines, sockets, and memory until the entire system collapses. The patte…
NVIDIA, AMD, and Intel compete in the 2026 AI GPU market, with NVIDIA's Blackwell RTX 50-series, AMD's Radeon AI Pro R9700, and Intel's Arc Pro B70 targeting local LLM inference. VRAM capacity and mem…
Hermes Agent now supports a headless server setup with remote desktop access, requiring two server processes (serve backend and gateway) and a single client connection. The architecture separates the …
GitHub Spec Kit, AWS Kiro, and Claude Code workflows implement Spec-Driven Development with different trade-offs in portability, integration depth, and process enforcement. GitHub Spec Kit offers port…
Spec-Driven Development (SDD) is a workflow that moves through five phases—requirements, design, tasks, implementation, and validation—each producing reviewable artifacts to reduce ambiguity before co…
A new guide on agent protocol security for A2A and MCP systems emphasizes that identity, authorization, delegation limits, and audit trails are essential when agents cross trust boundaries. The guide …
The A2A protocol introduces an async task model for long-running agent workflows, moving beyond synchronous request/response patterns to handle tasks that may run for minutes or hours, require human i…
Speculative decoding accelerates large language model inference by 20-50% without quality loss, using a draft-verify mechanism that generates multiple tokens per forward pass. The technique amortizes …
A new guide on multi-agent orchestration patterns reveals that 40% of multi-agent pilots fail within six months of production deployment, primarily due to selecting the wrong coordination model. The g…
Spec-Driven Development (SDD) is emerging as a workflow where a versioned specification serves as the source of truth for AI coding agents, addressing the ephemeral nature of prompts. The approach inv…
Spec-Driven Development (SDD) has emerged as a disciplined alternative to vibe coding for AI-assisted software development, with tools like GitHub Spec Kit and Kiro implementing the approach. Proponen…
Decision records are emerging as a critical memory layer in AI-assisted software development, capturing not just what was built but why, as AI tools make code cheaper to generate and easier to refacto…
Google's Agent2Agent (A2A) protocol, announced in April 2025, has seen mixed adoption in its first year, with skepticism due to overlap with MCP and premature market timing. However, by 2026, A2A is p…
A new guide outlines 11 implementation patterns for polling agents in AI assistants, enabling proactive background monitoring of sources like inboxes, task lists, and GitHub issues. The patterns empha…
The AI industry is developing two distinct protocols for agent architecture: MCP (Model Context Protocol) for agent-to-tool integration and A2A (Agent2Agent Protocol) for agent-to-agent communication.…