Low Code | Techlist.io

figmaNov 5, 20252 min readCurated summary

ServiceNow and Figma Launch Strategic Collaboration to Turn Design Vision Into Enterprise Transformation | Figma Blog

ServiceNow and Figma have launched an MCP-powered integration that turns Figma designs directly into enterprise applications. By using a Figma design link as a prompt for ServiceNow’s Build Agent, teams can automate the transition from visual concept to secure, scalable software. The collaboration aims to combine Figma’s design context with ServiceNow’s AI workflows, governance, and platform intelligence. ## From Design to Enterprise Application - Developers can provide a Figma design link directly to the ServiceNow integrated development environment. - ServiceNow’s Build Agent interprets layouts, components, styles, and other design details. - The agent generates a functional enterprise application rather than merely reproducing an image. - The process is intended to reduce manual coding, improve consistency, and accelerate development from minutes-long design-to-build workflows. ## Powered by Figma’s MCP Server - Figma’s Model Context Protocol (MCP) server gives ServiceNow structured design context. - This deeper understanding supports higher-fidelity translations of designs into working applications. - The integration connects design intent with production code, helping designers, product builders, and professional developers collaborate more effectively. ## Security and Governance - The integration uses OAuth 2.0 authentication and secure server-to-server communication. - Access tokens are stored within the customer’s ServiceNow instance to support privacy and compliance. - Applications created through Build Agent inherit ServiceNow capabilities such as permissions, audit trails, version control, and enterprise governance. ## Availability and Broader Impact - The integration is available in the latest ServiceNow Build Agent release through the ServiceNow Store. - Customers must request access after installation. - ServiceNow and Figma position the collaboration as a way to deliver AI-powered experiences faster while preserving human-centered design and enterprise-scale reliability. - Figma’s CTO emphasizes that design quality will remain a key differentiator as AI-generated software becomes more common. Organizations using both platforms can now shorten the path from prototype to production while maintaining security, governance, and design fidelity. The integration is especially suited to teams that want to accelerate enterprise application development without losing the original design intent.

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Low Code Figma Generative AI Model Context Protocol+4

googleApr 17, 2025Original article

InstructPipe: Generating Visual Blocks pipelines with human instructions and LLMs (opens in new tab)

InstructPipe is a research prototype designed to simplify machine learning prototyping by generating visual programming pipelines directly from natural language instructions. By leveraging a multi-stage large language model (LLM) framework, the system automates the selection and connection of nodes to lower the barrier for novice users. The result is a streamlined workflow that transforms abstract text commands into functional, editable node-graph diagrams within the Visual Blocks for ML environment. ### Pipeline Representation and Efficiency - Visual Blocks pipelines are structured as Directed Acyclic Graphs (DAGs) and are typically stored in a verbose JSON format. - To improve LLM performance, InstructPipe utilizes a "pseudocode" intermediate representation that is highly token-efficient, compressing pipeline data from 2.8k tokens down to approximately 123 tokens. - This pseudocode defines output variables, unique node IDs, and node types while specifying arguments such as input images or text prompts (e.g., `pali_1_out:pali(image=input_image_1, prompt=input_text_1)`). ### Two-Stage LLM Refinement - The **Node Selector** module acts as a high-level filter, using brief node descriptions to identify a relevant subset of tools from the library based on the user's intent. - The **Code Writer** module receives the filtered list and uses detailed node configurations—including specific input/output data types and usage examples—to draft the actual pipeline logic. - This dual-prompting strategy mimics human developer behavior by first scanning documentation categories and then focusing on specific function requirements to ensure accurate node connections. ### Interpretation and Execution - A dedicated **Code Interpreter** parses the generated pseudocode to reconstruct the final JSON-formatted pipeline required by the visual editor. - The system renders the resulting graph in an interactive workspace, allowing users to immediately execute, modify, or extend the machine learning workflow. - Technical evaluations indicate that this approach effectively supports multimodal pipelines, such as those involving the PaLI model for vision-language tasks, while significantly reducing the learning curve for new users. InstructPipe demonstrates how LLMs can bridge the gap between high-level human intent and low-code visual programming environments. For developers and researchers, this approach mitigates the "blank canvas" problem, allowing for faster experimentation and the rapid prototyping of complex machine learning architectures through simple text-based collaboration.

Low Code AI Large Language Models Machine Learning+5

coupangNov 15, 2024Original article

Coupang SCM Workflow: Developing (opens in new tab)

Coupang has developed an internal SCM Workflow platform to streamline the complex data and operational needs of its Supply Chain Management team. By implementing low-code and no-code functionalities, the platform enables developers, data scientists, and business analysts to build data pipelines and launch services without the traditional bottlenecks of manual development. ### Addressing Inefficiencies in SCM Data Management * The SCM team manages a massive network of suppliers and fulfillment centers (FCs) where demand forecasting and inventory distribution require constant data feedback. * Traditionally, non-technical stakeholders like business analysts (BAs) relied heavily on developers to build or modify data pipelines, leading to high communication costs and slower response times to changing business requirements. * The new platform aims to simplify the complexity found in traditional tools like Jenkins, Airflow, and Jupyter Notebooks, providing a unified interface for data creation and visualization. ### Democratizing Access with the No-code Data Builder * The "Data Builder" allows users to perform data queries, extraction, and system integration through a visual interface rather than writing backend code. * It provides seamless access to a wide array of data sources used across Coupang, including Redshift, Hive, Presto, Aurora, MySQL, Elasticsearch, and S3. * Users can construct workflows by creating "nodes" for specific tasks—such as extracting inventory data from Hive or calculating transfer quantities—and linking them together to automate complex decisions like inter-center product transfers. ### Expanding Capabilities through Low-code Service Building * The platform functions as a "Service Builder," allowing users to expand domains and launch simple services without building entirely new infrastructure from scratch. * This approach enables developers to focus on high-level algorithm development while allowing data scientists to apply and test new models directly within the production environment. * By reducing the need for code changes to reflect new requirements, the platform significantly increases the agility of the SCM pipeline. Organizations managing complex, data-driven ecosystems can significantly reduce operational friction by adopting low-code/no-code platforms. Empowering non-technical stakeholders to handle data processing and service integration not only accelerates innovation but also allows engineering resources to be redirected toward core architectural challenges.

Low Code Automation Microservices Data Pipeline+3

figmaNov 15, 20233 min readCurated summary

The Figma + Adobe Deal, Explained | Figma Blog

Figma’s post explains why it believed its proposed acquisition by Adobe would benefit users and withstand regulatory scrutiny. It presents Figma as a collaborative tool for building digital products, distinct from Adobe’s traditional creative software, and emphasizes a broad, fast-changing competitive landscape. However, the deal was ultimately abandoned on December 18, 2023, after the companies concluded that regulatory approval was unlikely. ## Figma’s Role in Digital Product Development - Figma describes itself as a web-based platform for teams building apps and websites. - Its tools support multiple stages of development: - **FigJam** for brainstorming and concepting - **Figma** for interface design and prototyping - **Dev Mode** for helping developers translate designs into code - The company says it spent thousands of hours explaining its products and market to competition regulators. ## Product Design vs. Traditional Graphic Design - Figma focuses on creating interactive digital products rather than static advertisements, illustrations, or posters. - Building an app or website requires collaboration among designers, developers, product managers, and other specialists. - This broader, team-oriented workflow has contributed to the growth of the product development software market. ## A Broad and Competitive Market - Figma portrays its market as highly dynamic, with both comprehensive platforms and specialized tools. - Competitors and adjacent products mentioned include: - Sketch, Penpot, and Figma - Miro, Flinto, Anima, ProtoPie, and Zeplin - Salesforce’s low-code development tools - The company argues that new startups and products enter the market frequently, while AI is accelerating innovation. - Figma says its competitive landscape slide became outdated only three weeks after it was created. ## Adobe’s Position - Adobe XD had previously competed with Figma but was placed into maintenance mode after Adobe stopped developing new features. - Photoshop and Illustrator serve different purposes, such as photo editing and advanced illustration. - Figma argues that those tools are not designed for collaborative website and application development. ## The Proposed Benefits of Combining Figma and Adobe - Figma contributes collaborative product design and development expertise. - Adobe contributes widely used creative tools and access to hundreds of millions of users. - Figma argued that the companies’ complementary strengths could create new consumer benefits, including closer connections between design, creativity, and product development. - The proposed acquisition was announced on September 15, 2022, as a major collaboration between the companies. ## Regulatory Outcome - Despite Figma’s efforts to demonstrate that the deal would benefit users and occur in a competitive market, regulatory concerns continued for fifteen months. - On December 18, 2023, Figma and Adobe abandoned the proposed acquisition because they no longer saw a path to regulatory approval.

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Low Code Figma AI Prototyping+4