build
build on Beyond Market Intelligence: a running collection of 11 stories we have gathered and hand-picked because they are worth your time. Every post here touches on build in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around build, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
The Builders Stage brings practical strategies for scaling startups to TechCrunch Disrupt 2026
Scaling a startup to TechCrunch Disrupt 2026 demands practical strategies, and The Builders Stage delivers. Returning to Disrupt, this stage unites founders, operators, and investors for actionable conversations on building and scaling impactful companies. Expect focused discussions on the critical steps needed to navigate growth, from early-stage challenges to securing investment. Learn from those who’ve successfully navigated the journey—a roadmap to accelerated success. For deeper insights into the evolving tech landscape, explore our recent profile of AfterQuery, Y Combinator's fastest-ever unicorn.
a16z creates a $1.1B ‘Machine Age’ fund to ‘accelerate the physical buildout of AI’
a16z is accelerating the physical infrastructure underpinning AI with a new $1.1 billion “Machine Age” fund. This marks a significant shift for the firm, traditionally focused on software, toward investing in the hardware essential for AI’s continued advancement. The fund will support companies building the foundational components of the AI ecosystem. For a deeper dive into optimizing AI models for efficiency, explore our article, "Quantization and Pruning Methods to Make Your LLM Leaner," which details practical techniques for reducing latency and cost.
What is a Forward Deployed Engineer? Role, Skills & Salary
A Forward Deployed Engineer (FDE) represents a pivotal shift in software engineering, moving beyond recommendations to deliver actively running code within a customer’s production environment. Unlike traditional consulting roles, the FDE embeds directly within a client’s team, building and integrating systems firsthand. This role demands a willingness to embrace complexity and deliver tangible results. For a deeper dive into related platform engineering considerations, explore "Article: Rightsizing Platform Engineering." Expect competitive salaries reflecting this specialized, impactful skillset.
Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File
Traditional Retrieval-Augmented Generation (RAG) often focuses on parsing individual PDFs, but a more effective approach prioritizes understanding the relational structure *within* a case file folder. Our latest Enterprise Document Intelligence report, Vol. 1 #14D, reveals that the most valuable data for RAG isn't found in retrieval questions, but in identifying and leveraging the core relational tables. This allows for a future-focused approach, empowering users to anticipate case demands *before* even opening a file.
React Router v8: A Deliberately Boring Release with ESM-Only Builds and Default Middleware
React Router v8 arrived on June 17, 2026, prioritizing stability and a streamlined developer experience. This deliberately “boring” release focuses on foundational improvements, most notably an ESM-only build for modern JavaScript tooling and sensible default middleware configurations. React Router v6 and Remix v2 have reached End of Life, prompting developers to migrate or explore alternatives like TanStack Router. For those tracking broader tech shifts, Apple's recent adjustments to EU App Store fees represent a significant change in distribution models.
Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong
For six decades, the data warehousing industry has prioritized storage and structure. However, simply granting an AI agent access to this data doesn't equate to readiness. The core challenge lies in equipping the agent with the contextual understanding to interpret data meaning and assess its reliability. Traditional architectures fall short here. Explore how to bridge this gap and unlock the true potential of agentic data access—discover a future-focused approach to building truly agent-ready data warehouses.
The Minimal AI Engineer Toolkit for 2026
The future of AI engineering demands a streamlined toolkit. Introducing the Minimal AI Engineer Toolkit for 2026 – a curated selection of six essential tools for building and deploying production-grade autonomous systems. These tools represent the foundation for success, empowering engineers to navigate increasingly complex challenges. For deeper insights into data acquisition, explore our article, "7 Best Web Crawling Tools and APIs in 2026," and discover how to efficiently gather the data that fuels intelligent systems.
Presentation: Microservices Platforms: When Team Topologies Meets Microservices Patterns
Accelerate your microservices delivery with a strategic blend of Team Topologies and proven patterns. Chris Richardson’s presentation explores how internal platforms, built around six key areas—security, observability, build, and deployment—can minimize cognitive load for development teams. Richardson shares practical strategies to avoid common platform engineering challenges and maximize efficiency. Discover how to empower stream-aligned teams and unlock faster innovation. For a deeper dive into the broader context, see our related article, "Platform Engineering Maturity Emerges as a Key Differentiator for Enterprise AI Success."
I want to use AI coding agents for machine learning projects [D]
As a software engineer transitioning to machine learning, you’re seeking a streamlined workflow that combines AI coding agents with cloud GPU power. Many engineers face this challenge. Platforms enabling local development with AI agents like Codex, Claude Code, or OpenCode, while executing code on remote GPUs, are emerging. These solutions bridge the gap between your existing editor and the computational resources needed for ML. Explore options that offer seamless integration, remote debugging, and iterative development—approaches detailed further in our article, "Understanding GPU Inference Workloads."
Build and Run an Intelligent Document Processing (IDP) System in the Cloud
Unlock streamlined data management with an Intelligent Document Processing (IDP) system, now accessible in the cloud. This guide details building and running a solution on AWS to automate the classification and extraction of Personally Identifiable Information (PII) from emails – a critical step for compliance and efficiency. Discover how to transform unstructured data into actionable insights, empowering your workflows. For a deeper dive into the foundation models underpinning such systems, explore "Tabular LLMs: An Introduction" on our site.
7 Python Frameworks for Orchestrating Local AI Agents
As local AI agent development accelerates, engineers require robust orchestration frameworks. This article details seven Python tools actively employed in 2026 to build, coordinate, and run these agents on local infrastructure, providing a practical guide for implementation. These tools empower developers to manage complex agent interactions and resource utilization efficiently. For broader context on the evolving landscape, explore "Vint Cerf is working on a plan to unleash AI agents on the open internet," offering insights into the standardization efforts shaping the future of AI agency.