Generative AI | Techlist.io

figmaOct 25, 20232 min readCurated summary

Storyboarding the Future of Design and Creativity | Figma Blog

The post imagines how a Figma–Adobe partnership could transform design workflows by connecting products, assets, collaboration, AI, and storytelling. It envisions a future where ideas move instantly from user research to interactive storyboards, 3D models, prototypes, and marketing videos. The proposed merger was ultimately abandoned in December 2023 after the companies concluded regulatory approval was unlikely. ## From User Journeys to Storyboards - User journeys could be converted from basic sticky-note diagrams into visual storyboards. - More engaging formats would make it easier for teams to understand, discuss, and improve product experiences. ## Connected Assets Across Tools - Assets created in Adobe Substance 3D could be placed into Figma mockups. - Linked assets would remain synchronized, reducing manual updates across products. - Shared design systems could connect colors, fonts, and other design tokens between Adobe and Figma. - Adobe Fonts could become available directly within Figma. ## Multiplayer Collaboration in 3D - Adobe’s 3D tools could gain Figma-style multiplayer collaboration. - Multiple designers could work simultaneously on different aspects of a model, such as geometry, lighting, and textures. - Real-time collaboration could make learning complex 3D workflows easier. ## Generative AI in Product Design - Adobe Firefly could integrate directly into Figma workflows. - Designers could generate backgrounds that match an interface’s visual style. - Generative Fill could extend images to fit responsive layouts without leaving the design process. ## Prototypes Connected to Marketing - Working app prototypes could be inserted directly into launch videos. - Updates made to the product could automatically appear in the video, helping marketing teams keep pace with last-minute design changes. The post presents these integrations as a vision for a more connected and collaborative creative ecosystem, though the planned Figma–Adobe merger did not proceed.

Read original

Generative AI Figma Design Systems Prototyping+4

figmaAug 22, 20232 min readCurated summary

Give ideas more space with Jambot | Figma Blog

Jambot is a FigJam widget that brings ChatGPT’s generative capabilities into a collaborative, visual canvas. Figma created it to move beyond the limitations of linear chat, allowing people to ideate, branch into related topics, summarize discussions, and explore ideas together. The project reflects Figma’s broader view that AI interfaces should become more spatial, tangible, and multiplayer. ## From Chatbots to Creative Collaboration - Large language models can simulate spontaneous brainstorming and provide a broad base of knowledge. - ChatGPT’s conversational format is useful but can feel one-sided and restrictive during creative work. - Jambot was designed to make AI interaction more collaborative and adaptable to group ideation. ## Limitations of Linear Chat - Chat conversations present ideas in a one-dimensional sequence. - When ChatGPT offers multiple possibilities, exploring one path makes it difficult to return to another without scrolling and repeating questions. - Linear chat makes it unnatural to branch into related topics, compare alternatives, or see how ideas connect. ## A Visual Alternative - Jambot began as an internal Figma AI hackathon project described as “a visual version of ChatGPT.” - Its concept draws on networked-thinking tools such as Roam Research and Logseq, which link and organize ideas across pages. - The team was also inspired by Albus, which gives AI interaction a more visual structure. - LangChain influenced the idea of making sophisticated AI workflows visually tangible rather than requiring users to write code. ## Rethinking AI Interfaces - The team argues that users are currently “stuck in chat boxes,” much as they became dependent on video-call interfaces like Zoom. - Existing AI interfaces can feel primitive and command-line-like, despite decades of progress in graphical user interfaces. - Designers have an opportunity to develop new interaction patterns that provide more context, identity, and flexibility than simple conversational prompts. - A visual canvas can make AI more approachable while supporting branching ideas and shared participation. ## What Jambot Enables - Ideation and brainstorming directly inside FigJam. - Summarizing conversations or collections of ideas. - Riffing on concepts and extending them in multiple directions. - Collaborative exploration of AI-generated output within a multiplayer workspace. Jambot’s central recommendation is to treat AI as something that can inhabit richer environments than a chat window. By placing generative AI on a shared visual canvas, Figma aims to give teams more space to explore, connect, and develop ideas together.

Read original

Generative AI Large Language Models Figjam Chatgpt+2

figmaJul 31, 20232 min readCurated summary

John Maeda on Creativity, AI, and the Human Pursuit of Uphill Thinking | Figma Blog

John Maeda argues that AI will transform creative work without eliminating the need for human creativity. While AI excels at efficiency, repetition, and finding the shortest path to an answer, meaningful creative breakthroughs often require experimentation, difficulty, and unconventional thinking. Designers should use AI to remove tedious work while deliberately preserving the human ability to pursue “uphill” paths. ## AI’s Promise and Threat to Creative Work - AI offers creatives greater efficiency and new capabilities, but may also produce large volumes of repetitive, cookie-cutter design. - Designers are learning to “speak machine” by collaborating with AI, training language models, and refining outputs to match their creative intentions. - Any technique developed with AI can potentially be replicated without its original creator, raising concerns about creative ownership and job security. ## Creativity Through Openness and Code - Maeda compares today’s AI concerns with his own experience in the 1990s, when he created distinctive algorithmic artwork. - Rather than keeping his methods secret, he open-sourced them at MIT so others could build their own creative work. - His involvement with MIT Scratch and Processing reflects his belief that programming can be a creative practice accessible to children, artists, and designers. ## Letting Computers Handle the Mundane - Computers are well suited to repetitive tasks such as: - Producing endless slide variations - Generating mockups - Performing image retouching - Automating this work could give designers more time to address emerging problems and develop more original ideas. - Maeda cites artist Jessie Shefrin’s observation that “by the time you come to the perfect solution, the problem has already changed,” emphasizing the need to keep moving rather than over-optimize a fixed answer. ## The Limits of Efficiency - AI is designed to find the shortest and most efficient route through a problem. - It can evaluate thousands or millions of possible paths rapidly and select the most efficient option. - Efficiency does not necessarily produce the most creative, meaningful, or impactful result. - The article presents “uphill thinking”—choosing difficult, indirect, or exploratory paths—as an important human strength in an increasingly automated world. Designers should embrace AI as a tool for reducing tedious labor, while continuing to protect the slower, less efficient processes that generate originality, insight, and meaningful creative work.

Read original

Generative AI AI Machine Learning Creative Coding+3

figmaJun 20, 20233 min readCurated summary

AI: The Next Chapter in Design | Figma Blog

Figma argues that AI will become a core platform capability reshaping the entire product-development process, not merely another feature. It can accelerate ideation, design, and coding while allowing teams to focus more on problem-solving and creative judgment. Figma announced its acquisition of Diagram as part of this strategy, positioning AI as a force that will change how products are designed, what experiences are created, and who participates in the process. ## Figma’s AI strategy and Diagram acquisition - Figma acquired Diagram, founded by Jordan Singer, whose GPT-3-powered “Designer” plugin generated design concepts from simple prompts. - The acquisition brings Diagram’s team into Figma and builds on Figma’s existing investment in machine learning. - Figma’s open API has already enabled nearly 100 community-built AI plugins. - The company views AI as a platform underlying the entire product-development workflow. ## AI across the product-development process - During discovery, AI could: - Generate and synthesize early ideas from prompts. - Summarize discussions and concepts. - During design, AI could: - Use existing designs and design systems to provide recommendations. - Surface relevant components and patterns. - Help teams produce first drafts faster. - During development, AI could: - Infer design context more effectively. - Generate higher-quality, production-ready code. - The broader goal is to help teams do more work faster while moving their attention toward higher-level problem-solving. ## How design may evolve from pixels to patterns - Design systems already shifted designers away from repetitive details such as border radii and toward composition, direction, and judgment. - Atomic elements such as pixels became reusable components, enabling faster and more consistent workflows. - AI could extend this progression by generating higher-level structures and patterns. - Designers may focus less on assembling basic login components and more on inventing entirely new ways to authenticate. - AI might also recommend color palettes based on a project’s emotional tone or theme. - This could move design beyond familiar interfaces toward smoother, more intuitive, and more human experiences. ## What product teams may design - AI systems such as ChatGPT are shifting interaction away from navigating websites and apps toward asking questions and receiving answers. - AI can reduce the gap between a user’s intention and the actions required to achieve it. - For example, instead of opening a ride-hailing app, entering a destination, comparing options, and requesting a ride, a user could simply say, “Get me to JFK.” - Product builders will need to reconsider whether existing interfaces can deliver the same outcome with fewer steps and decisions. ## The changing role of designers - Technological change has historically transformed design without eliminating the need for thoughtful designers. - Designers have adapted to new platforms, collaborative workflows, and hybrid work. - Figma expects AI to change design roles and collaboration, but frames that shift as an opportunity to spend more time on creative direction, curation, and meaningful problem-solving. The practical recommendation is to treat AI as a foundational design and development capability rather than a standalone feature. Teams should explore how it can remove repetitive work while preserving human judgment, taste, and responsibility for the experiences they create.

Read original

Generative AI AI Design Systems Machine Learning+4

figmaDec 5, 20222 min readCurated summary

How Magician uses Figma’s text review API | Figma Blog

Magician, an AI-powered Figma plugin from Diagram, uses Figma’s text review API to generate copy suggestions directly while designers edit text layers. The API runs in the background and integrates with Figma’s editor rather than requiring a separate plugin window. Diagram argues that this creates a productive intersection between product design and AI, helping users overcome writer’s block and iterate faster. ## Figma’s Text Review API - The API lets developers create default text review plugins that run automatically while users type on the canvas. - Plugins can highlight text ranges and provide replacement suggestions. - Potential applications include: - Spell checking and grammar correction - Improving marketing copy - Enforcing company style guides - Generating alternative wording ## Magician and Its AI “Spells” - Magician is a Figma design tool created by Diagram to support creativity and ideation. - Its initial features are organized as “magic spells”: - **Magic Icon** for generating icons - **Magic Image** for creating imagery - **Magic Copy** for writing assistance - The plugin is designed as an extensible platform so new AI capabilities can be added consistently. ## Magic Copy in Practice - Magic Copy uses the text review API to suggest alternatives as users edit text layers. - It can generate options for: - Headlines - Body text - Calls to action - Suggestions appear directly within the editing workflow, making the feature useful when designers are unsure what to write or want to improve existing copy. ## A New Plugin Interaction Model - Unlike traditional plugins that require users to open and interact with a separate window, the text review API works in the background. - Its results are integrated into Figma’s native editor interface. - Although the API was primarily intended for spell checking, Diagram repurposed it for AI-assisted copywriting. ## Iteration and Experimentation - Diagram began with Magic Copy and other features as separate plugins before combining them into Magician. - The team continuously fine-tuned each spell’s output to make it useful and consistent across different contexts. - Its development was influenced by accessible generative AI tools and models such as Stable Diffusion and OpenAI. - The team’s approach emphasizes starting small, testing ideas quickly, and refining what works. Magician demonstrates how Figma’s text review API can extend beyond correction tools into creative assistance. Developers can use the API’s seamless editor integration to build focused AI experiences that help designers write, explore, and iterate without interrupting their workflow.

Read original

Generative AI AI Deep Learning Openai+3