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David Soria Parra & Demetrios Brinkmann · Jul 28th, 2026
In this episode, we're joined by David Soria Parra, Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP), to explore the biggest evolution of the protocol since its launch and why MCP is becoming the foundation for the next generation of AI agents.
# MCP
# AI Agents
# Anthropic
Demetrios Brinkmann · Jul 28th, 2026
Rosie is iFood's AI support agent, serving millions of customers across Brazil. Rather than trying to eliminate latency, the team designed the system around it. In this conversation, César Gomes explains how iFood uses asynchronous workflows, specialized agents, rollback strategies, delayed memory consolidation, and a reviser agent to keep conversations responsive while reducing costly hallucinations. The result is a production-ready architecture focused on one outcome: shortening the path from customer problem to resolution.
# AI Agents
# Agentic AI
# Multi-Agent System
# IFood Rosie
Manish Dasaur & Demetrios Brinkmann · Jul 24th, 2026
Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.
# Tokenomics
# FinOps
# PwC
+2
David DeStefano, Valdimar Eggertsson, Sparsh Jain & 2 more speakers · Jul 22nd, 2026
Loop engineering is the shift from manually prompting AI agents turn-by-turn to designing autonomous systems that do the prompting for you. Instead of writing a prompt, reading output, then writing the next prompt, you build a small control system that discovers work, delegates tasks to agents (and sub-agents), verifies results, persists state, and decides what to do next on a schedule or until a goal is met. It sits one level above agent harness engineering: where a harness equips a single agent run, a loop keeps firing agents on a cadence, spawning helpers, and feeding itself. The framing is increasingly tool-agnostic, as both Claude Code and OpenAI Codex have converged on similar primitives. The people building at the frontier are already living it: Boris Cherny, head of Claude Code at Anthropic, has said his job is no longer writing prompts but writing the loops that do.
# Loop Engineering
# AI Agents
# Prompt Engineering
Médéric Hurier · Jul 21st, 2026
Letting coding agents search the web for documentation wastes tokens, increases latency, and risks importing outdated or incorrect API patterns. To resolve this, this article presents `agent-docs`, a system of local references stored directly within the repository. By instructing agents to search local Markdown documentation maps before attempting web queries, developers can ensure that agents always build on correct, version-aligned instructions.
# Artificial Intelligence
# Machine Learning
# AI Agents
# Coding
# Programming
Jeremiah Lowin & Demetrios Brinkmann · Jul 20th, 2026
Jeremiah Lowin — founder and CEO of Prefect and creator of FastMCP — traces how a two-hour struggle to build a basic MCP server turned into FastMCP, a side project that Anthropic folded into the official SDK and that now underpins roughly 70% of all MCP servers. He argues the biggest real-world use of MCP isn't reaching external customers but serving data and business logic to teams inside a company (especially data, platform, and ML teams building self-serve data platforms), where the client is known and trusted. He pushes back on the "MCP is dead / it should just be a CLI" narrative, blaming bad clients that dump every tool into an LLM's context rather than the protocol itself. The centerpiece is Prefab, a newly launched Python DSL of 100+ prefabricated, fully interactive React components (ShadCN-based) that let you return a chart, table, or form straight from a tool as an MCP App — token-efficient, streamable, and routed to the user's UI without polluting the LLM's context window. He closes with candid stories about his OpenClaw agent deleting his conference slides (forcing him to rebuild with Claude Code), how he uses a memory-driven home agent for family logistics, and the intimate, builders-only PyAI conference Prefect co-hosts with Pydantic.
# MCP
# FastMCP
# Prefect
Stephen O'Grady & Demetrios Brinkmann · Jul 19th, 2026
Stephen O'Grady has spent 20+ years at RedMonk tracking what developers actually adopt — not what the hype cycle says they should. In this conversation from the MCP Dev Summit, he breaks down why MCP became the fastest de facto standard he's ever measured, why "SaaS is dead" is laughable, and what really happens to your infrastructure when one developer turns into a swarm of twenty.
# MCP
# Ai agents
# RedMonk
Matt DeBergalis, Alex Salkever & Demetrios Brinkmann · Jul 17th, 2026
Matt DeBergalis, CTO and co-founder of Apollo GraphQL, makes the case that AI agents should be treated as untrusted — maybe even adversarial — code running inside your firewall.
In this conversation with Alex Salkever, Matt breaks down why the rush to wire agents into every enterprise system through MCP is creating a brand-new security surface, and how GraphQL's typed, governed "supergraph" model gives teams a safer way to connect agents to their APIs. It's a sharp, practical look at the collision of MCP, GraphQL, and enterprise AI from one of the people building the plumbing.
# MCP
# AI Agents
# GraphQL
Shaun Smith & Demetrios Brinkmann · Jul 14th, 2026
Shaun Smith is a software engineer, open source advocate, and MCP server maintainer at Hugging Face — creator of Fast Agent, the first agent framework built from the ground up around the Model Context Protocol.
In this conversation from the MCP Dev Summit in New York, Shaun and Demetrios dig into why powerful models are "liquifying" open source libraries, how MCP Apps are reshaping AI interfaces, and what it means when the distribution of ideas matters more than the distribution of code.
# Hugging Face
# MCP
# Open Source AI
Axel Mendoza · Jul 14th, 2026
This post compares the leading model serving runtimes for deploying production ML APIs, including TensorFlow Serving, TorchServe, BentoML, and NVIDIA Triton Inference Server. It breaks down the strengths, trade-offs, and key selection criteria—such as framework support, inference performance, infrastructure integration, and ease of use—to help teams choose the best runtime for their machine learning workloads.
# MLOps
# Model Serving
# Tool Comparison
# Portfolio