Developer Plans AI Platform to Automate CAD/CAE Engineering Workflows

·3 views

A developer is building an AI platform specifically designed to support CAD and CAE engineering workflows. The project aims to go beyond traditional embedding-based retrieval, which the developer argues struggles with complex CAD models, feature trees, and procedural design logic. The proposed system would use AI agents capable of engineering reasoning to help mechanical engineers automate tasks, set up simulations, and optimize designs. The long-term goal is an AI assistant that understands how engineering models are constructed, not just answers questions about them. The developer is seeking collaborators including engineers, AI researchers, and CAD/CAE specialists to help build the platform.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Git Worktrees Offer a Practical Fix for Parallel Claude Code Agent Conflicts

Running multiple Claude Code AI agents simultaneously on the same codebase often leads to file conflicts, where one agent's changes overwrite another's. Git worktrees address this by giving each agent its own isolated working directory linked to the same repository but on a separate branch, preventing mid-run file collisions. The setup is straightforward: a new branch and worktree are created for each task before an agent is launched, so changes remain isolated until an explicit merge. However, worktrees defer rather than eliminate merge conflicts, meaning overlapping code changes still require manual review once agents finish. A further challenge is that each agent operates within its own context window, meaning independent agents may make inconsistent design decisions without any awareness of each other.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Building AI code generation into a no-code platform: why longevity beats generation

A no-code platform has integrated AI-powered code generation to fill gaps when its pre-built component catalog falls short of a user's needs. The team found that producing code with an AI model in 2026 is straightforward, but making that code reliably fit and persist within a live app proved far more challenging. To address this, they built a narrow, server-side-checked API surface so generated sections can only access explicitly declared platform capabilities, preventing scope creep that tends to break over time. The system runs a generate-validate-correct loop, feeding errors back to the model automatically rather than shipping broken output. Together, these guardrails prioritise long-term maintainability and stability over raw generation speed, bridging the gap between a working demo and production-ready code.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How AgentGateway Uses RFC 8693 Token Exchange to Secure AI Agent Identity

In agentic AI systems, passing a user's broad-scoped identity token across multiple downstream services creates serious security risks, including over-privileged access and potential impersonation. RFC 8693, the OAuth 2.0 Token Exchange standard, addresses this by allowing a gateway to swap the original user token for a fresh, scoped, short-lived credential tailored to each downstream service. AgentGateway, a Rust-based AI proxy from the AI Agent Infrastructure Foundation, implements this pattern via its built-in backendAuth.oauthTokenExchange policy. The setup involves authenticating users through Keycloak, after which AgentGateway handles all token exchanges so upstream services never receive the original credential. The tutorial also covers token caching for performance, delegation with actor claims, and support for additional grant types such as JWT Bearer and Microsoft Entra On-Behalf-Of flows.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Reverse Proxy, Load Balancer, and API Gateway: Key Structural Differences Explained

A technical article published on DEV Community on July 13 by Folasayo Samuel Olayemi breaks down the structural differences between three commonly confused system design components. The piece focuses on Reverse Proxies, Load Balancers, and API Gateways, clarifying that despite frequent overlap in usage, they are not the same thing. The article is categorized under system design, architecture, and API topics and is estimated to be a seven-minute read. It aims to help developers better understand when and why to use each component in software architecture.

0 comments Read more at DEV Community