- Observability Engine
eBPF Observability for AI Microservices: Tracking Token Latency at the Kernel Level
Application-level APM logging adds severe latency overhead to streaming LLM responses. Here is how to use eBPF kernel probes to trace socket buffer bottlenecks and Time-To-First-Token (TTFT) metrics with zero code modification.
AutoStack Team • - Workflow Engineering
The 2026 AI Workflow Stack: Why Make and Zapier Are Still Unbeatable Outer-Loop Routers
Stop writing custom OAuth2 refresh logic and API wrappers for 50 different SaaS tools. Here is how high-performance engineering teams in 2026 pair low-code edge orchestrators like Make and Zapier with custom LLM backends.
AutoStack Team • - Production Engineering
Why Unthrottled AI Agents Crash Databases (And How to Fix It)
A production post-mortem on how autonomous tool-calling loops exhaust PostgreSQL connection pools, and the backpressure architecture that stops connection cascades.
AutoStack Team • - AI Architecture
Building Production-Grade AI Agents with Model Context Protocol (MCP)
A technical guide to implementing Anthropic Model Context Protocol (MCP) servers in TypeScript for secure, standardized tool calling and enterprise context injection.
AutoStack Team • - AI Architecture
Building Sub-100ms RAG Pipelines: Vector Caching, Hybrid Search, and Quantization
Learn how to optimize Retrieval-Augmented Generation latency down to sub-100ms using semantic Redis vector caching, hybrid BM25 search, and quantized embeddings.
AutoStack Team • - AI Workflows
3 Game-Changing AI Workflows Driving Production Engineering
Deep-dive into production-grade AI agentic workflows...
AutoStack Team • - Technical Reviews
Notion AI Under the Hood: Vector Retrieval, RAG Benchmarks, and Enterprise Security
A technical evaluation of Notion AI hybrid retrieval architecture, block-tree context chunking, vector search limits, latency performance, and enterprise ROI.
AutoStack Team • - Comparisons
Make vs. Zapier in 2026: Enterprise Workflow Automation Teardown
A deep-dive technical comparison analyzing execution node limits, JSON data mapping, webhook latency, and total cost of ownership (TCO) for scaling startups.
AutoStack Team • - Tool Roundups
The Best AI Coding Assistants Evaluated: Cursor, GitHub Copilot, and Claude 3.5
Benchmarking AI coding assistants on context window retention, dynamic index mapping, multi-file refactoring accuracy, and latency.
AutoStack Team •
