Carlin is a GitHub Product Manager focused on GitHub Advanced Security and Dependabot. Her experience in software engineering and data science supports a data-driven approach to product management.
### Professional Background
- Works on GitHub Advanced Security.
- Focuses specifically on Dependabot.
- Brings experience in software engineering and data science.
### Personal Interests
- Lives in Washington with her partner and dog, Cookie.
- Enjoys cycling and competitive board games.
No technical blog post content was provided beyond this author biography.
Dalia is a software engineer on GitHub’s Copilot Agent Control Plane team. Her work focuses on building the subagent governance layer for Copilot customers.
### Role and Focus
- Works on GitHub Copilot’s Agent Control Plane.
- Builds governance capabilities for subagents.
- Supports Copilot customers through controls and management features.
The provided content contains only a brief professional description, not a full blog post, so there are no additional technical sections or conclusions to summarize.
Alexander is a senior software engineer on GitHub’s Issues team, where he focuses on making developer workflows feel fast and seamless. His background spans computer graphics, machine learning, and geospatial software, giving him a broad technical perspective.
### Professional Role
- Works on the GitHub Issues team.
- Focuses on improving everyday developer workflows.
- Enjoys finding creative ways to make interactions feel instant.
### Technical Background
- Computer graphics
- Machine learning
- Geospatial software
The provided content is a short professional biography rather than a technical blog post, so it does not include a specific argument, technical explanation, or conclusion.
Friend Bubbles may look like a simple Reels feature, but building it required substantial engineering work. The feature surfaces Reels that friends have watched or reacted to, relying on an evolving machine-learning model and platform-specific behavior. Meta engineers explain that a key, unexpected discovery ultimately helped make the experience work.
### What Friend Bubbles Does
- Highlights Reels that a user’s friends have watched or reacted to.
- Connects social activity with Reels recommendations in a more visible way.
### Engineering Challenges
- The team had to evolve the machine-learning model powering the feature.
- iOS and Android users exhibited different behaviors, requiring the team to account for platform-specific usage patterns.
- The feature’s apparent simplicity concealed complex recommendation and product-engineering challenges.
### Podcast Discussion
- Meta Tech Podcast host Pascal Hartig speaks with Facebook Reels engineers Subasree and Joseph.
- They discuss the model’s development, differences between mobile platforms, and the surprising insight that helped the feature succeed.
- The episode is available through Meta’s podcast channels and services including Spotify, Apple Podcasts, and Pocket Casts.
The episode illustrates why seemingly straightforward social features can demand deep experimentation, modeling, and cross-platform engineering.
The current AI hype cycle is a significant economic bubble where massive infrastructure investments of $560 billion far outweigh the modest $35 billion in generated revenue. However, drawing parallels to the 1995 dot-com era, the author argues that while short-term expectations are overblown, the long-term transformation of the developer role is inevitable. The conclusion is that developers won't be replaced but will instead evolve into "Code Creative Directors" who manage AI through the lens of technical abstraction and delegation.
### The Economic Bubble and Amara’s Law
* The industry is experiencing a 16:1 imbalance between AI investment and revenue, with 95% of generative AI implementations reportedly failing to deliver clear efficiency improvements.
* Amara’s Law suggests that we are overestimating AI's short-term impact while potentially underestimating its long-term necessity.
* Much of the current "AI-driven" job market contraction is actually a result of companies cutting personnel costs to fund expensive GPU infrastructure and AI research.
### Jevons Paradox and the Evolution of Roles
* Jevons Paradox indicates that as the "cost" of producing code drops due to AI efficiency, the total demand for software and the complexity of systems will paradoxically increase.
* The developer’s identity is shifting from "code producer" to "system architect," focusing on agent orchestration, result verification, and high-level design.
* AI functions as a "power tool" similar to game engines, allowing small teams to achieve professional-grade output while amplifying the capabilities of senior engineers.
### Delegation as a Form of Abstraction
* Delegating a task to AI is an act of "work abstraction," which involves choosing which low-level details a developer can afford to ignore.
* The technical boundary of what is "hard to delegate" is constantly shifting; for example, a complex RAG (Retrieval-Augmented Generation) pipeline built for GPT-4 might become obsolete with the release of a more capable model like GPT-5.
* The focus for developers must shift from "what is easy to delegate" to "what *should* be delegated," distinguishing between routine boilerplate and critical human judgment.
### The Risks of Premature Abstraction
* Abstraction does not eliminate complexity; it simply moves it into the future. If the underlying assumptions of an AI-generated system change, the abstraction "leaks" or breaks.
* Sudden shifts in scaling (traffic surges), regulation (GDPR updates), or security (zero-day vulnerabilities) expose the limitations of AI-delegated work, requiring senior intervention.
* Poorly managed AI delegation can lead to "abstraction debt," where the cost of fixing a broken AI-generated system exceeds the cost of having written it manually from the start.
To thrive in this environment, developers should embrace AI not as a replacement, but as a layer of abstraction. Success requires mastering the ability to define clear boundaries for AI—delegating routine CRUD operations and boilerplate while retaining human control over architecture, security, and complex business logic.
AI is blurring the boundaries between design, development, and product management, making traditional job titles less stable. Figma argues that titles still matter, however: they communicate expertise, shape expectations, support career development, and contribute to professional identity. As AI changes the tasks within jobs, people may increasingly identify as generalists or combinations of roles rather than occupying a single fixed profession.
## The rise of hybrid roles
- Figma reports that **64% of product builders identify with two or more roles**.
- AI is taking on increasingly specialized tasks, increasing the value of people who can connect ideas across disciplines.
- Designers, developers, and product managers are increasingly working across traditional boundaries.
- Jobs can be understood as “bundles of tasks” whose importance changes as technology and industry needs evolve.
## Why titles still matter
- Titles provide shorthand for understanding someone’s expertise, responsibilities, and status.
- They help establish expectations when people meet or collaborate for the first time.
- Professional titles can support career ladders and communicate alignment with the values of an industry.
- Research cited from Adam Grant found that allowing employees to choose their own titles improved psychological safety and reduced emotional exhaustion by up to 10% over five weeks.
- Professional organizations and certifications reinforce the importance of titles in fields such as architecture, engineering, and medicine.
## Titles evolve with technology
- The meaning of “designer” has changed from a focus on physical objects and print to digital products and software.
- “Software engineer” emerged in the 1960s as the industry confronted the complexity of building software for increasingly powerful computers.
- New technology continually creates, reshapes, and sometimes eliminates roles; “prompt engineer” is presented as a recent example.
- During the dot-com era, some professionals combined responsibilities spanning product management, program management, development, and art.
## Roles shape expectations and identity
- Titles influence how others perceive a person’s expertise and what work they are expected to perform.
- Nikolas Klein describes himself as “a product designer in a PM trenchcoat,” showing how people may retain one identity while operating in another role.
- Moving into product management made Klein’s strategic and service-design skills more visible and reduced assumptions that his work centered mainly on visual design.
- Developer advocate Jake Albaugh views roles as potentially limiting because their definitions change as expertise grows.
- At the same time, adopting the title “software engineer” after working as a web designer gave Albaugh a sense of confidence and recognition.
Organizations and individuals will likely need to treat titles as flexible signals rather than rigid boundaries. The most durable professional identity may come from the value someone creates and the connections they make—not from a single fixed job label.
Design has become a strategic driver of software success, not merely a visual layer added at the end of development. An IDC study of more than 500 development leaders found that teams prioritizing design report stronger business outcomes, faster delivery, and better collaboration. The research also shows that human design expertise is increasingly important as AI accelerates product creation.
## The Business Case for Design
- 75% of development leaders consider design “very” or “extremely important” to modern software development.
- Leaders who rated design as “extremely important” were five times more likely to say their last project far exceeded expectations.
- The main business benefits associated with design investment were:
- Improved customer retention
- Higher customer engagement
- Increased product innovation
- Design now influences customer satisfaction, employee productivity, accessibility, and data-driven decision-making.
## Designer–Developer Collaboration
- 52% of leaders said their organizations have increased software output over the past two years.
- Collaboration between design and development from the beginning of a project helps teams:
- Align on scope and trade-offs earlier
- Reduce rework and friction
- Iterate in real time
- Improve morale and time to market
- Among respondents, stronger design collaboration led to:
- More innovation for 54%
- Better customer experiences for 47%
- Faster time to market for 43%
- Effective collaboration was described as synchronized, structured, and mutually supportive rather than divided into isolated handoffs.
## Design Expertise in AI-Driven Development
- AI makes it easier to create initial concepts, but generated outputs still require substantial human refinement.
- Designers provide essential oversight in:
- Quality control
- User experience
- Brand consistency
- Cross-functional review
- 80% of development leaders said design has become more important to the success of AI-powered products than it was two years ago.
- The strongest teams treat AI output as a starting point, not a finished product. Human judgment is needed to turn fast-generated concepts into usable, user-centered experiences.
Organizations seeking an advantage should treat design as a business priority, integrate designers and developers throughout the development process, establish scalable design practices, and preserve human oversight of AI-generated work.
Figma’s sixth issue of *The Prompt* examines how AI is changing design, engineering, product development, and human curiosity. Its central argument is that adopting AI requires more than learning new tools: it requires understanding which human skills—judgment, creativity, problem selection, and curiosity—remain essential. The issue presents AI as a collaborator that can expand creative and technical work without replacing the craft behind it.
## AI and the Future of Design
- Figma’s design leaders ask what “good design” means as AI makes product development more accessible.
- As execution becomes easier to automate, design judgment and strong craft principles become more important differentiators.
- The focus is on building practical, thoughtful products with AI rather than pursuing novelty for its own sake.
## What Engineers Contribute Beyond Code
- Figma CTO Kris Rasmussen argues that engineering is not merely the production of code.
- Engineers provide value by identifying which problems matter and determining effective ways to solve them.
- AI may commoditize portions of coding, but it also creates space for engineers to focus on architecture, judgment, problem framing, and higher-level innovation.
## Curiosity and Judgment-Free Questions
- Perplexity CEO Aravind Srinivas describes AI-powered search as a continuation of encyclopedias and wikis.
- The goal is to give people a source of answers without the social pressure or embarrassment that can inhibit curiosity.
- AI can act as a “copilot” for exploration, though improving the reliability and usefulness of its answers remains an ongoing challenge.
## Humanoid Robots and Embodied AI
- The issue considers whether humanoid robots are finally moving from science fiction into everyday reality.
- Androids reflect humanity’s longstanding fascination with reproducing intelligence and ourselves in technological form.
- Conversations with robotics builders explore both the promise and the risks of giving AI a physical body.
## The Role of Print and Material Experience
- *The Prompt* is also an 80-page print magazine produced with Figma’s Brand Studio and designer Chloe Scheffe.
- Vellum paper, illustration, color, layout, and physical materiality interpret the issue’s themes.
- The print edition emphasizes experiences that digital tools and AI cannot fully reproduce, reinforcing the value of tangible, intentional creative work.
AI’s greatest potential lies in extending human abilities rather than eliminating them. Designers and engineers should use it to increase experimentation and efficiency while preserving the judgment, creativity, and curiosity that give their work meaning.
FigJam introduced native tables to make roadmapping, planning, and organizing information clearer and easier to edit collaboratively. The team deliberately focused on visual presentation rather than complex data manipulation, prioritizing simplicity for both table creators and viewers. Building the feature required extensive work on multiplayer conflict resolution, responsive editing controls, and visual clarity at different zoom levels.
## Why FigJam Needed Tables
- Internal teams were already creating tables with stickies and shapes for:
- Feature requirements and prioritization
- Project tracking
- Product marketing messaging
- Design critique feedback
- Brainstorming and vote counting
- Native tables improve performance and reduce the effort of assembling makeshift grids.
- The core requirements were to make tables:
- Clear
- Easy to create
- Editable by everyone
- FigJam intentionally avoided becoming a complex spreadsheet or data-manipulation tool.
## Designing for Creators and Users
- The team treated table creation and table editing as equally important experiences.
- A single toolbar click creates a pre-styled table, similar to other FigJam elements.
- Users can choose from a limited style palette, while the entire table remains visually consistent.
- Adding rows or columns derives styling from the neighboring row or column.
- Changing a table’s color automatically adjusts its text for readability.
- Programmatically drawn borders help separate cells without creating excessive visual noise.
## Multiplayer Editing Challenges
- Tables may contain many cells edited simultaneously by users at different zoom levels.
- Unlike ordinary elements, simultaneous table edits cannot simply use “last update wins” behavior.
- Changes often need to be merged so multiple users’ contributions are preserved.
- Multiplayer support consumed at least half of the engineering effort.
- The team spent months addressing concurrency bugs and edge cases, including collaborative typing in the same cell.
- Testing included simulated second users, such as a script that repeatedly typed “FigJam.”
## Interaction Design for Collaborative Tables
- Standard FigJam selection-based editing became visually overwhelming when multiple people edited different cells.
- The team changed table behavior in two important ways:
- Editing controls appear on hover and follow the user’s cursor.
- A prominent button lets users add a complete row or column.
- Table functionality adapts to zoom level:
- At distant zoom levels, users can move and arrange the table.
- At closer zoom levels, they can resize it and add rows or columns.
- This approach keeps the interface uncluttered while exposing more controls when they are useful.
The resulting feature keeps tables intentionally simple and presentation-focused while making them native to FigJam’s collaborative, visual environment. For teams that need lightweight planning, tracking, or structured brainstorming, native tables are preferable to building grids manually from shapes or sticky notes.
Jaelyn Brown describes her 2022 internship at Figma, from discovering the opportunity through a platform supporting minority students to leading an HBCU recruiting strategy. She found Figma’s interview process welcoming, its hybrid workplace flexible, and its culture strongly focused on intern growth and inclusion. Her experience showed how an internship can combine professional development with meaningful work aimed at improving access to technology careers.
## Finding the Figma Opportunity
- Brown discovered Figma’s Early Career Program Manager internship through Student Career Studio, which connects minority students with career opportunities.
- As a Howard University student, she understood the experiences and priorities of HBCU applicants.
- Conversations with hiring manager Kristen Dauler emphasized Figma’s investment in employees and inclusive company culture.
- The interview process focused on Brown’s interests, passions, and growth rather than formal presentation, allowing her to be authentic.
## Starting as a Figtern
- Brown moved from North Carolina to San Francisco and began her internship on May 23, 2022.
- Her onboarding included virtual sessions and meeting the intern cohort.
- Hybrid work allowed her to split time between home and Figma’s San Francisco office.
- Interns were treated as important members of the company, including through an ask-me-anything session with co-founder Dylan Field.
## Working in Early Career Recruiting
- The Early Career Recruiting team manages hiring and strategy for interns and candidates with up to 12 months of industry experience.
- Brown received regular one-on-ones and guidance on interview processes, recruiting strategy, and external partnerships.
- The team collaborated with Engineering, Design, Marketing, and other departments to improve the applicant experience.
- She worked on Maker Week projects and Fignite, a program offering interview preparation to underserved people pursuing software engineering roles.
## Developing an HBCU Recruiting Strategy
- Brown’s experience with Fignite inspired her main internship project: creating a strategy for recruiting HBCU students.
- She conducted cross-functional meetings, held one-on-ones, and led brainstorming sessions to develop campus and virtual engagement plans.
- The project allowed her to take ownership of work with significant potential impact.
- Because both Brown and her grandmother attended HBCUs, the initiative had personal importance and addressed what she viewed as a broader lack of lasting relationships between technology companies and HBCU communities.
Figma’s internship program gave Brown practical recruiting experience, meaningful ownership, and the opportunity to use her personal background to advance more inclusive hiring connections.
The article shares career advice from four 2021 Figma interns and new graduates: Emily Jia, Langston Dziko, Daniela Velez, and Jago Pang. Their experiences show that early-career candidates should evaluate company culture, mentorship, ownership, growth opportunities, and alignment with a product’s mission—not just the role itself. Figma attracted them through its collaborative culture, enthusiasm for design, and opportunities to make meaningful contributions.
## Choosing an Early-Career Role
- Emily looked for:
- Data science roles matching her technical background and goals.
- Fast-paced, product-focused work at growing startups.
- An empathetic culture centered on communication and connection.
- Daniela prioritized authenticity, community, and coworkers who cared deeply about Figma’s mission of making design more accessible.
- Langston focused on career growth, asking:
- Whether young engineers receive meaningful ownership.
- Whether their work contributes to broader company goals.
- Whether employees see opportunities to develop and stay long term.
## Using Internships and Conversations to Explore Options
- Emily explored different companies through summer and off-cycle internships.
- Conversations with alumni and interviewers helped her understand less obvious tradeoffs between roles.
- She considered not only people’s day-to-day responsibilities, but also how they had grown in their positions—and whether she wanted a similar trajectory.
- Langston used internships to identify the types of work he enjoyed and those he did not.
- He also spoke with many engineers, repeatedly hearing that a supportive manager and team can matter as much as the specific project.
## Why They Chose Figma
- Emily was influenced by a Figma blog post written by a software engineering new graduate. She valued that Figma gave early-career employees a platform to share their experiences.
- She was especially interested in joining a growing data team making its first new-grad hires and deliberately developing its culture.
- Langston was impressed by how enthusiastically Figma engineers spoke about the product. Their enthusiasm suggested he would own innovative projects while developing as an engineer.
- Daniela first encountered Figma through a collaborative side project. She enjoyed the product’s collaborative design experience and connected with the company’s emphasis on individuality and community.
- Jago credited Figma with helping launch his design career and wanted to support its mission of democratizing design, particularly through Figma for Education and the Figma Community team.
The interview suggests that students and new graduates should use internships, informational conversations, and employee perspectives to assess both the work and the environment. The strongest early-career choice is likely to be a role offering supportive mentorship, genuine ownership, meaningful growth, and a mission the candidate cares about.
Figma’s engineering values were created to preserve effective collaboration as the team grows without promoting a monoculture. They are intended to describe existing behaviors, guide decisions, and make explicit the tradeoffs behind how the team works. The post focuses on communication, teamwork, feedback, inclusion, and sustainable growth.
## Communicate Early and Often
- Share design documents, product specifications, architecture sketches, and works in progress before implementation is complete.
- Early communication helps teams:
- Identify problems before significant effort is invested.
- Solve problems collaboratively rather than in isolation.
- Encourage people to ask for help and exchange knowledge.
- Feedback must be welcomed as seriously as it is requested; sharing is useful only when people are receptive to changing direction.
- Communication is not a rigid process:
- Code may be the clearest way to discuss an idea.
- Simple bug fixes or obvious changes may not require extensive discussion.
- This value rejects the “solo genius” model in favor of using the team’s collective expertise.
- The tradeoff is slower decision-making: involving more people can require additional discussion and iteration to ensure diverse voices are heard.
## Lift Your Team
- Engineers should help one another grow, prioritize teammates’ success and well-being, and create an inclusive environment.
- The emphasis is on lifting the team—not sacrificing individual sustainability for the company’s interests.
- The value supports:
- Continuous learning and mentorship.
- Weekly technical talks.
- Formal onboarding mentorship.
- Encouragement to develop new skills.
- Feedback should focus on ideas and work rather than attacking individuals.
- Insults, condescension, and belittling are considered ineffective feedback.
- Team members are also expected to receive feedback thoughtfully and remain open to others’ ideas.
Figma’s approach is to make collaboration and mutual growth explicit expectations while acknowledging their costs. Teams adopting similar values should define concrete behaviors, ensure feedback is genuinely welcomed, and state the tradeoffs they are willing to accept.
