GPT-5.6 is now available in Figma Make, where Figma says it improves both the speed and quality of AI-generated prototypes. The model is designed to produce stronger first passes, preserve existing designs more faithfully, and recover from errors without stopping. Figma’s examples suggest it can move teams from prompts or static designs to functional, responsive prototypes with less iteration.
## Faster exploration and error recovery
- GPT-5.6 can turn complex prompts into working prototypes quickly, helping teams explore multiple ideas in one session.
- In Figma’s stock-tracking app evaluation, it created:
- An interactive dashboard
- Sample prices and performance data
- Keyboard shortcuts and search
- A dark, gothic visual style
- The model is described as more token-efficient, helping users make better use of Figma Make credits.
- When builds encounter errors, GPT-5.6 can investigate and self-heal instead of stopping. Figma reports that it independently diagnosed and fixed a blank build.
## Faithful design-to-code conversion
- GPT-5.6 can build prototypes from existing design specifications or Figma Design files.
- In a nature sound player test, it preserved:
- Layout and visual hierarchy
- Spacing, proportions, and styling
- A multi-track timeline and sound library
- It also implemented functional interactions, including:
- Play, pause, and skip controls
- Working audio playback
- Multiple playable tracks
- Audio-responsive visual effects
## Higher-quality first passes
- Figma says GPT-5.6 produces polished initial prototypes with functional interactions and responsive layouts.
- A bookshelf e-commerce example included:
- Product descriptions, measurements, and care information
- Populated information dropdowns
- An interactive product photo library
- A clickable navigation menu
- The prototype adapted reliably across different screen sizes without additional prompting.
- Stronger first passes allow teams to spend more time refining ideas collaboratively rather than repairing basic implementation problems.
GPT-5.6 is available through Figma Make’s model selector. Users can select it directly in Make and consult Figma’s help center for guidance on choosing and using AI models.
Toss created **AI Surf Day**, a dedicated weekly time for employees to experiment with AI, share lessons, and redesign their workflows. Running on Fridays from April through June, the initiative aims to reduce the AI gap across technical and nontechnical roles by making experimentation collaborative and accessible. Its broader conclusion is that successful AI transformation depends less on formal programs than on culture, time, and people who actively share what they learn.
## AI Surf Day’s Purpose
- Employees focus on their core work Monday through Thursday and reserve Friday for AI experimentation and practical application.
- The program addresses anxiety and knowledge gaps, especially among nondevelopers who may struggle to identify useful AI information or find time to learn it.
- Its concept comes from Jon Kabat-Zinn’s phrase: “You can’t stop the waves, but you can learn to surf.”
- The goal is to help Toss become a company that works with AI as a foundation, not merely a workplace where individuals use AI tools.
## AI Surf Club
- Employees can create or join informal groups focused on AI topics; roughly 200 clubs were formed at launch.
- An **AI Antipattern Study** focused on failures and mistakes, turning participants’ experiences into a practical guide for avoiding common problems.
- An **LLM Wiki** group explored how to organize scattered organizational knowledge across data engineering, machine learning, and business teams.
- A beginner-focused “Step 0” group helped employees overcome basic technical barriers, such as installing agent tools and asking questions they felt were too fundamental.
- A customer-protection team built an external-complaint monitoring portal in one month, along with automation for complaint-response drafts and classification.
- A marketing team divided AI work into roles such as:
- **Builder:** creates AI-powered tools and workflows
- **Curator:** collects useful examples and resources
- **Operator:** applies AI to repetitive work
- **Scouter:** identifies new opportunities
- The clubs emphasized reusable outputs and shared confidence, rather than isolated individual experimentation.
## AI Surf Weekly
- Weekly sessions share successful internal AI applications, lessons learned, and current industry insights.
- Toss connected employees with similar needs across different departments, enabling them to solve problems quickly by learning from existing internal examples.
- Rather than prescribing specific tools, the program presents ideas and use cases that encourage employees to adapt solutions to their own work.
- Examples included connecting a sales employee with an HR colleague who had built a similar tool, and pairing a marketer with a designer experienced in AI-powered automation.
## AI Surf Evangelists
- Toss selected 142 employees across its affiliated companies and teams to promote AI adoption in their own organizations.
- Evangelists were chosen through peer nominations, recognizing people who already shared useful discoveries and helped colleagues overcome AI-related obstacles.
- Their responsibilities over three months include:
- Reporting effective AI use cases
- Sharing useful insights with colleagues
- Hosting at least one meetup or workshop
- Toss’s Culture team provides workshop templates and facilitation support.
- Many teams have conducted workshops around redesigning their existing workflows with AI.
- The program treats AI adoption as a team-level workflow redesign challenge, rather than simply measuring individual proficiency with AI tools.
## OpenAI Collaboration and Mini-Hackathon
- Toss held a special AI Surf Day with OpenAI on May 15.
- Hands-on sessions covered:
- Codex-based development workflows for developers
- ChatGPT Agent-based automation for nondevelopers
- A 2.5-hour hackathon produced two notable projects:
- An iOS workflow where Codex implements features, operates the simulator, tests the result, iterates on problems, and produces verification footage.
- An agent that classifies thousands of daily Toss Place product records, sends reviewers links, and supports approval or rejection through an admin interface.
- These projects demonstrated how AI can become a reusable agentic workflow rather than a one-time assistant.
## Culture Over Programs
- Toss does not claim to have a fixed answer for managing AI’s rapid evolution.
- The lasting value of AI Surf Day is the protected time for learning and experimentation, along with a culture where employees openly share results and failures.
- Successful examples spread naturally across teams, while evangelist-led workshops translate experimentation into concrete changes in how work is performed.
Organizations pursuing AI transformation can take a similar approach: create dedicated experimentation time, encourage peer-led learning, recognize existing champions, and focus on reusable workflow improvements rather than tool adoption alone.
The AWS weekly roundup highlights a major shift toward agentic AI across Amazon’s products and its partnership with OpenAI. The biggest announcements include expanded Amazon Quick capabilities, four specialized Amazon Connect solutions, and OpenAI models and Codex becoming available through Amazon Bedrock. AWS also introduced new EC2 instances, agent optimization tools, Ruby 4.0 support for Lambda, and a transition plan from Amazon Q Developer to Kiro.
## What’s Next with AWS 2026
- AWS and OpenAI executives presented new ways businesses are using AI agents to automate operations.
- The announcements centered on Amazon Quick, Amazon Connect, and deeper integration with OpenAI through Amazon Bedrock.
## Amazon Quick Expands Beyond Chat
- A new desktop app, currently in preview, connects Quick to local files, calendars, and communications without requiring a browser.
- Users can sign up with a personal email or Google, Apple, GitHub, or Amazon credentials; an AWS account is not required.
- Quick can generate:
- Documents
- Presentations
- Infographics
- Images
- New integrations include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams.
- The preview “Build custom apps with Quick” feature lets users create intelligent applications, dashboards, and web pages using natural-language instructions.
## Amazon Connect Becomes Four Agentic AI Products
- **Amazon Connect Decisions** applies Amazon’s operational expertise and supply-chain tools to help organizations move from reactive crisis management to proactive planning.
- **Amazon Connect Talent** provides AI-led interviews, science-backed assessments, and consistent candidate evaluations for large-scale hiring.
- **Amazon Connect Customer**, the renamed customer-service product, supports personalized voice, chat, and digital experiences, with conversational AI that can be configured in weeks.
- **Amazon Connect Health** supports patient verification, appointment management, patient insights, ambient documentation, and medical coding.
## OpenAI Partnership Expands Through Amazon Bedrock
- OpenAI models, including GPT-5.5 and GPT-5.4, are coming to Bedrock in limited preview.
- Customers can use existing Bedrock APIs with AWS security, governance, and cost controls, without managing new infrastructure.
- **Codex on Amazon Bedrock** brings OpenAI’s coding agent into AWS environments:
- Authentication uses AWS credentials.
- Inference runs through Bedrock.
- Usage can count toward AWS cloud commitments.
- Initial access includes the Codex CLI, desktop app, and Visual Studio Code extension.
- **Bedrock Managed Agents powered by OpenAI** combines OpenAI models with AWS infrastructure and the OpenAI harness for long-running, production-oriented agent workflows.
## New EC2 Instance Families
- **M8in and M8ib** instances are generally available, offering up to 43% higher performance than M6in and M6ib.
- M8in provides up to 600 Gbps of network bandwidth.
- M8ib provides up to 300 Gbps of EBS bandwidth.
- **R8in and R8ib** target memory-intensive workloads such as commercial databases, data lakes, and SAP HANA.
- **C8ine and M8ine** provide up to 2.5 times higher packet performance per vCPU and up to twice the internet-gateway throughput of their predecessors.
- These network-optimized instances are designed for virtual firewalls, load balancers, security appliances, and 5G user-plane workloads.
## AgentCore and Lambda Updates
- Bedrock AgentCore Optimization, in preview, adds:
- Production-trace analysis
- Recommendations for system prompts and tool descriptions
- Batch evaluations
- A/B testing against live traffic
- Recommendations require human approval before deployment.
- AWS Lambda now supports Ruby 4.0 as a managed runtime and container base image.
- Ruby 4.0 support includes advanced logging features such as structured JSON logs, configurable log levels, and custom CloudWatch log groups.
## Amazon Q Developer Moves Toward Kiro
- Amazon Q Developer IDE plugins and paid subscriptions will reach end of support on April 30, 2027.
- New signups will be blocked beginning May 15, 2026.
- Existing subscriptions can continue adding users until then.
- Opus 4.6 will leave Q Developer Pro on May 29, 2026, while newer coding models such as Opus 4.7 will be exclusive to Kiro.
- Q Developer experiences in the AWS Console, documentation, mobile app, Slack, and Microsoft Teams are unaffected.
AWS’s direction is increasingly centered on managed AI agents integrated into everyday business workflows. Organizations adopting these services should evaluate the new Bedrock, Quick, and Connect capabilities while also planning migration from Q Developer to Kiro before the announced support deadlines.
The 2026 “What’s Next with AWS” event focused on how AI agents are reshaping business operations. Major announcements included Amazon Quick, an AI work assistant; four specialized Amazon Connect solutions; and an expanded AWS–OpenAI partnership bringing OpenAI models and Codex to Amazon Bedrock. Together, these offerings emphasize integrated agents that can connect to existing systems, make decisions, and execute tasks within enterprise-controlled infrastructure.
## Amazon Quick Becomes a Broader AI Work Assistant
- Amazon Quick connects to workplace information, learns user preferences, and takes action on users’ behalf.
- A new desktop app, currently in preview, can access local files, calendars, and communications without requiring a browser.
- Free and Plus plans are available without an AWS account. Users can register with a personal email or Google, Apple, GitHub, or Amazon credentials.
- Quick can generate documents, presentations, infographics, and images directly within chat.
- New native integrations include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams.
## Amazon Connect Expands into Four Agentic AI Products
AWS is repositioning Amazon Connect as a portfolio of solutions for specific business workflows:
- **Amazon Connect Decisions:** A supply-chain planning and intelligence platform using AI teammates, Amazon’s operational expertise, and more than 25 specialized tools to support proactive planning.
- **Amazon Connect Talent:** A hiring solution in preview that provides AI-led interviews, science-backed assessments, and standardized evaluations for large-scale recruiting.
- **Amazon Connect Customer:** The renamed customer-experience product, supporting voice, chat, and digital channels. New configuration tools aim to let organizations deploy conversational AI in weeks rather than months.
- **Amazon Connect Health:** Automates patient verification, appointments, patient insights, ambient documentation, and medical coding to improve access to care and reduce administrative workloads.
## AWS and OpenAI Expand Their Partnership
The companies announced several limited-preview offerings that bring OpenAI capabilities into AWS environments:
- **OpenAI models on Amazon Bedrock:** Models including GPT-5.5 and GPT-5.4 will be accessible through existing Bedrock APIs, with AWS security, governance, and cost controls.
- **Codex on Amazon Bedrock:** Organizations can run OpenAI’s coding agent using AWS credentials and infrastructure, with usage counting toward AWS cloud commitments. Initial access includes the Codex CLI, desktop app, and Visual Studio Code extension.
- **Bedrock Managed Agents powered by OpenAI:** This service combines OpenAI models with AWS-managed infrastructure and the OpenAI harness for building production-ready agents capable of reasoning through long-running tasks.
AWS’s announcements point toward a future in which AI agents are embedded directly into workplace tools, operational systems, customer-service platforms, and cloud development environments. Organizations looking to adopt these capabilities should evaluate the available previews, integrations, governance controls, and workflow fit before moving to production.
Dropbox Dash needed a relevance judge that could score query–document pairs accurately, cheaply, and reliably at scale. Its original judge used OpenAI’s o3, but the cost made it impractical for large-scale labeling, while its prompt performed poorly when moved to the cheaper gpt-oss-120b model. Dropbox used DSPy’s GEPA optimizer to turn prompt tuning into a measurable feedback loop, improving alignment with human judgments while preserving production-ready output formatting.
## Measuring Agreement with Human Reviewers
- The judge rates each query–document pair on a 1–5 relevance scale:
- **5** means a perfect match.
- **1** means no meaningful connection to the query or user intent.
- Human annotators provide both:
- A relevance score.
- A short explanation for their judgment.
- Dropbox evaluates the model with normalized mean squared error (NMSE):
- It measures the squared difference between model and human ratings.
- Scores are normalized to a 0–100 scale.
- **0** represents perfect agreement; higher values indicate worse performance.
- Invalid JSON or incorrectly structured responses are treated as fully incorrect because they cannot be consumed reliably by downstream systems.
- The optimization objective is therefore twofold:
- Minimize disagreement with human ratings.
- Ensure consistently parseable, production-ready outputs.
## Moving from o3 to a Lower-Cost Model
- The original judge used OpenAI’s o3 because it delivered strong agreement with human ratings.
- Running o3 across orders of magnitude more query–document pairs was too expensive.
- Dropbox selected **gpt-oss-120b**, an open-weight model offering a better cost-performance balance.
- The carefully tuned o3 prompt did not transfer directly:
- Relevance quality declined under the NMSE metric.
- Manual prompt rewriting would have required extensive iteration and regression testing.
## DSPy and GEPA-Based Prompt Optimization
- Dropbox defined the optimization problem using:
- A fixed relevance-rating task.
- Human-annotated examples.
- NMSE as the evaluation metric.
- DSPy’s **GEPA optimizer** iteratively improves prompts for a specific target model.
- Instead of relying only on an aggregate score, GEPA analyzes individual disagreements and generates structured feedback.
- Feedback combines:
- The difference and direction between predicted and human ratings.
- The human annotator’s explanation.
- The model’s reasoning.
- DSPy then uses a reflection loop:
- Evaluate the current prompt.
- Identify recurring failure modes.
- Revise the prompt with generalizable rules.
- Repeat the process against the human-alignment metric.
- This approach can address systematic errors such as:
- Overvaluing keyword overlap.
- Undervaluing document recency.
- Misinterpreting user intent.
- The feedback explicitly discourages overfitting to individual examples and preserves core task constraints, including the 1–5 rating range.
Dropbox’s experience suggests that relevance judges should be optimized systematically rather than tuned manually. Defining a clear human-alignment metric, including structural validity, allows DSPy to adapt prompts across models while reducing cost and limiting regressions.
AI-driven commerce is emerging quickly, but making it reliable requires much more than adding an AI checkout button. Sellers must manage fragmented catalog integrations, real-time inventory and variant data, evolving protocols, secure payment tokens, fraud detection, fulfillment, and post-purchase operations. The central recommendation is to use adaptable infrastructure and begin with a limited, measurable product selection rather than launching an entire catalog at once.
## Catalog Integration and Data Quality
- Product catalogs are the entry point for AI agents, but each agent may require a different format, such as:
- SFTP file drops
- Custom APIs
- Agent-specific feed specifications
- Reformatting the same catalog for multiple agents creates a costly maintenance burden.
- Reliable “ingestion-ready” data determines whether products appear consistently across AI shopping surfaces.
- A shared commerce layer can syndicate one catalog across supported agents and eliminate duplicate integrations.
## Real-Time Inventory and Product Variants
- Agents need to verify current availability immediately before presenting checkout options.
- Inventory becomes harder to manage when products include combinations of:
- Sizes
- Colors
- Customizations
- Variant-specific availability
- Checkout APIs must support real-time availability checks and alternative recommendations when a particular configuration is unavailable.
- Real-time accuracy is essential for customer trust and brand reputation.
## Protocol Evolution and Compatibility
- Agentic commerce protocols are changing rapidly, with new releases adding payment handlers, scoped tokens, discounts, buyer authentication, and transport methods.
- Sellers risk creating “zombie integrations” that become obsolete when an AI platform changes direction.
- A protocol-agnostic commerce layer can help businesses support standards such as ACP and Google’s UCP without rebuilding their systems for every change.
## Secure Payments Through Shared Payment Tokens
- Shared Payment Tokens allow agents to initiate payments with a buyer’s permission without exposing payment credentials.
- The token layer connects AI agents to existing payment rails while limiting transaction scope.
- Agentic commerce requires more than payment authorization; systems must also support:
- Product discovery
- Checkout state management
- Shipping
- Returns and refunds
- The broader infrastructure must cover the full transaction lifecycle.
## Fraud Detection Without Human Browser Signals
- Traditional fraud tools often depend on signals such as mouse movements, browser fingerprints, device details, and window size.
- Those signals disappear when an AI agent performs the transaction.
- Network-level payment history can provide risk context even when a purchase is new to a particular seller.
- Shared Payment Tokens allow fraud systems such as Radar to evaluate agentic purchases similarly to traditional checkout transactions.
- Early deployments with major retailers reportedly experienced fraud rates near zero.
## Start with a Focused Product Selection
- Sellers should avoid enabling their entire catalog immediately.
- A practical launch strategy is to:
- Select a small group of high-conversion SKUs
- Use simple products with direct-to-home fulfillment
- Monitor conversion, inventory behavior, payment methods, and fulfillment issues
- URBN initially focused on popular categories such as dresses and denim rather than its full range, which also includes complex products like plants and custom furniture.
- Early launches should function as controlled experiments that produce data for broader expansion.
## A Strategic Shift in Retail Discovery
- Agentic commerce moves buying intent from stores, websites, and branded mobile apps onto AI platforms.
- This changes how sellers must approach:
- Product discovery
- Brand control
- Trust
- Dispute resolution
- The relationship between the seller and customer
- Agents increasingly mediate product selection and purchase decisions, requiring sellers to adapt their commerce strategy beyond the traditional storefront.
Sellers should treat agentic commerce as an evolving channel rather than a one-time integration. Start with reliable data, a narrow product scope, secure tokenized payments, and infrastructure that can absorb protocol changes before scaling to more products and complex fulfillment scenarios.
Figma has expanded its ChatGPT app beyond FigJam diagrams, allowing users to generate editable visual assets and presentation decks. ChatGPT provides initial creative directions, while Figma Buzz and Figma Slides support detailed editing, branding, resizing, and collaboration. The workflow is designed to move quickly from an idea or outline to a polished, team-ready result.
## Creating Visual Assets with Figma Buzz
- Users can prompt ChatGPT to generate starting concepts for posters, invitations, digital ads, and video collages.
- The app can produce multiple variations of a visual identity, layout, and copy.
- Assets can then be opened in Figma Buzz for refinement, including:
- Editing text and typography
- Adjusting color palettes with blend modes
- Applying brand-specific changes
- Resizing designs for LinkedIn, X, Instagram, and other platforms
- Figma Buzz is intended to make polished marketing assets accessible even to people without specialized design skills.
## Building Presentations in Figma Slides
- ChatGPT can generate a Figma Slides deck from a user-provided outline or suggest a presentation flow.
- Example decks include “year in review” templates organized around goals, priorities, highlights, and completed projects.
- Users can select a generated option and continue editing collaboratively in Figma Slides.
- Teams can replace generated images, adjust templates, and refine colors and typography to match brand guidelines.
- Interactive features such as live polls, stamps, and alignment scales support feedback and discussion during presentations.
## Availability
- The Figma app is available to ChatGPT users outside the European Union.
- Figma Buzz and Figma Slides features are currently in beta within the app.
The recommended workflow is to use ChatGPT for rapid ideation and first drafts, then move into Figma Buzz or Figma Slides for brand alignment, detailed editing, and collaborative refinement.
To address the operational burden of handling repetitive user inquiries for the AWX automation platform, LY Corporation developed a support bot utilizing Retrieval-Augmented Generation (RAG). By combining internal documentation with historical Slack thread data, the system provides automated, context-aware answers that significantly reduce manual SRE intervention. This approach enhances service reliability by ensuring users receive immediate assistance while allowing engineers to focus on high-priority development tasks.
### Technical Infrastructure and Stack
* **Slack Integration**: The bot is built using the **Bolt for Python** framework to handle real-time interactions within the company’s communication channels.
* **LLM Orchestration**: **LangChain** is used to manage the RAG pipeline; the developers suggest transitioning to LangGraph for teams requiring more complex multi-agent workflows.
* **Embedding Model**: The **paraphrase-multilingual-mpnet-base-v2** (SBERT) model was selected to support multi-language inquiries from LY Corporation’s global workforce.
* **Vector Database**: **OpenSearch** serves as the vector store, chosen for its availability as an internal PaaS and its efficiency in handling high-dimensional data.
* **Large Language Model**: The system utilizes **OpenAI (ChatGPT) Enterprise**, which ensures business data privacy by preventing the model from training on internal inputs.
### Enhancing LLM Accuracy through RAG and Vector Search
* **Overcoming LLM Limits**: Traditional LLMs suffer from "hallucinations," lack of up-to-date info, and opaque sourcing; RAG fixes this by providing the model with specific, trusted context during the prompt phase.
* **Embedding and Vectorization**: Textual data from wikis and chats are converted into high-dimensional vectors, where semantically similar phrases (e.g., "Buy" and "Purchase") are stored in close proximity.
* **k-NN Retrieval**: When a user asks a question, the bot uses **k-Nearest Neighbors (k-NN)** algorithms to retrieve the top *k* most relevant snippets of information from the vector database.
* **Contextual Generation**: Rather than relying on its internal training data, the LLM generates a response based specifically on the retrieved snippets, leading to higher accuracy and domain-specific relevance.
### AWX Support Bot Workflow and Data Sources
* **Multi-Source Indexing**: The bot references two main data streams: the official internal AWX guide wiki and historical Slack inquiry threads where previous solutions were discussed.
* **Automated First Response**: The workflow begins when a user submits a query via a Slack workflow; the bot immediately processes the request and provides an initial AI-generated answer.
* **Human-in-the-Loop Validation**: After receiving an answer, users can click "Issue Resolved" to close the ticket or "Call AWX Admin" if the AI's response was insufficient.
* **Efficiency Gains**: This tiered approach filters out "RTFM" (Read The F***ing Manual) style questions, ensuring that human administrators only spend time on unique or complex technical issues.
Implementing a RAG-based support bot is a highly effective strategy for SRE teams looking to scale their internal support without increasing headcount. For the best results, organizations should focus on maintaining clean internal documentation and selecting embedding models that reflect the linguistic diversity of their specific workforce.
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.
