AI+AEC in News · AEC Magazine
With AI, making AEC software is easy, but making it safe, supportable and accountable is another matter entirely. AEC Magazine spoke with Igor Tsinman, president and CEO of AMC Bridge, about the opportunities, risks and responsibilities of AI-driven software development Inside most large AEC firms there is now a layer of software nobody commissioned. AI coding tools have let architects, engineers, and estimators build working applications in days. The result is a growing estate of vibe-coded utilities with no documentation, no named owner, and in some cases write access to live project models. This follow a familiar patten. A decade ago, Dynamo and Grasshopper bred the same culture of personal scripts, but now the volume and speed are new, and so is the proximity to production data, client information, and contractual output.
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CloudBrain helps architecture, engineering and construction firms in India turn developments like this into working tools. Related service:
Workflow automationAutomate BOQs, compliance checks, RFIs, specs, tenders and reporting.The headline and excerpt are from AEC Magazine, which owns them; CloudBrain is not affiliated with AEC Magazine. Read the original article (opens in a new tab) · All AI+AEC news
Dezeen ·
Robotics startup OKA has released a lamp-shaped artificial intelligence robot designed to offer companionship by greeting people, dancing to music and playing nature sounds. Mori by OKA (One Kind Artificat) is 26.4 centimetres tall, with a cloth-covered base and an illuminated, dome-shaped head that lifts to reveal cartoonish eyes that blink and swivel. According to OKA, Mori "resembles a contemporary table lamp" and was designed to "help people feel calmer, more at ease, and more connected to the world around them". "We wanted Mori to feel like a familiar object in the home rather than an unfamiliar machine," OKA told Dezeen. "Many robots look technical, mechanical, or overly human, while a lamp is something people already live with comfortably." Gallery Previous Next Gallery Close The robot exists in two modes: a lamp setting, when it's compact and at rest, and a responsive setting, when the head is lifted and tilted.
AEC Magazine ·
Trimble Connect platform introduces AI agents and automation to improve project coordination, manage risk and streamline workflows Trimble has embedded AI within Trimble Connect , expanding the platform beyond its traditional role as a common data environment (CDE) to connect site information with design, cost and operational workflows. The new capabilities are intended to help teams identify and mitigate risks across the project lifecycle, while keeping human judgement at the centre of decision-making. Unveiled at Trimble’s Dimensions User Conference, the AI capabilities span design, construction and project operations. In design, they include generating architectural options using live as-built point-cloud data, converting natural-language prompts into constructible structural models, and automatically classifying scanned point clouds into structural elements. For construction teams, applications include AI-powered diagnostics for machine-control systems, converting 360-degree site walk videos into 3D point clouds, and voice-driven site inspections that automatically transcribe observations and link them to project drawings.
AEC Magazine ·
UK-manufactured 2U Q-Power offers firms an on-premise alternative to premium cloud AI services G2 Digital, a UK specialist in custom-built rack-mount computers, has launched the 2U Q-Power, a high-performance GPU server designed for complex AI workloads and rendering. The system can accommodate up to four dual slot Nvidia RTX GPUs, offering twice the capacity of most conventional 2U servers and greater computing density within a rack. This is made possible by a custom chassis design that mounts the GPUs horizontally, parallel to the motherboard. When configured with four Nvidia RTX Pro 6000 Blackwell Max-Q GPUs, the Q-Power delivers 384 GB of combined GPU memory, enabling organisations to run demanding AI workloads entirely on-site. This includes large language models (LLMs) with 70 billion parameters at full precision, as well as significantly larger models using lower-precision formats and quantisation.
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