Slack | Techlist.io

spotifyMar 12, 20263 min readCurated summary

Let’s Talk Agentic Development: Spotify x Anthropic Live | Spotify Engineering

Agentic development is reshaping software engineering at Spotify and Anthropic, from how developers write code to how organizations manage delivery. The discussion highlights Claude-powered agents, enterprise-scale context management, and the need to rethink testing, review, and accountability. The speakers conclude that agents will soon handle more of the full software lifecycle, including maintenance and deletion. ## The Opus 4.5 Inflection Point - Spotify observed a sharp increase in agent-driven development after Opus 4.5 went online on November 25, 2025. - Engineers increasingly shifted from working primarily in IDEs to using terminals and agent-based workflows. - The change was presented as a practical transformation in daily engineering work, not merely an experimental trend. ## Honk: Spotify’s Background Coding Agent - Spotify employees can invoke Honk by mentioning it in Slack. - Honk evolved from deterministic code migrations into a Slack-native agent capable of complex migrations across thousands of repositories. - Teams can discuss a problem in Slack and ask Honk to investigate or implement a solution directly. - Spotify is continuing to explore how background coding agents can operate at larger scale. ## Context Engineering and Control - Scaling agents across many repositories requires consistent, reproducible configuration. - Anthropic recommends well-structured `CLAUDE.md` files and reusable skills that describe engineering roles, domains, and expected workflows. - The emphasis is on simple, standardized context rather than overly complex orchestration. - Both companies are still identifying gaps in how agents receive context and how their actions are coordinated across enterprise systems. ## Testing, Reviews, and Accountability - Agent-generated code can be produced faster than humans can review it, creating new bottlenecks. - Organizations must reconsider testing, governance, and approval processes as output volume increases. - Accountability should remain tied to the outcome, regardless of whether code was produced by a human or an agent. - The discussion frames agent adoption as an organizational change, not just a tooling upgrade. ## The Next Stage of Agentic Development - The current phase has focused largely on code creation; the next phase will expand into maintenance, deletion, and other less popular but essential engineering work. - Spotify is evolving Backstage from a human-oriented developer portal into an agent-first platform. - MCP connections are expected to replace more manual developer workflows. - Anthropic’s internal “ant-fooding” practice continues to generate product ideas from employees using its own tools, including Claude Code and Cowork. Organizations adopting agentic development should start with reliable feedback loops, standardized context, and clear human accountability. The most significant gains will come when agents are integrated across the entire software lifecycle rather than used only for writing new code.

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Slack Claude Context Engineering Ai Infrastructure+4

naverNov 25, 2025Original article

Naver TV (opens in new tab)

This session from NAVER Engineering Day 2025 explores how developers can transition AI from a simple assistant into a functional project collaborator through local automation. By leveraging local Large Language Models (LLMs) and the Model Context Protocol (MCP), development teams can automate high-friction tasks such as build failure diagnostics and crash log analysis. The presentation demonstrates that integrating these tools directly into the development pipeline significantly reduces the manual overhead required for routine troubleshooting and reporting. ### Integrating LLMs with Local Environments * Utilizing **Ollama** allows teams to run LLMs locally, ensuring data privacy and reducing latency compared to cloud-based alternatives. * The **mcp-agent** (Model Context Protocol) serves as the critical bridge, connecting the LLM to local file systems, tools, and project-specific data. * This infrastructure enables the AI to act as an "agent" that can autonomously navigate the codebase rather than just processing static text prompts. ### Build Failure and Crash Monitoring Automation * When a build fails, the AI agent automatically parses the logs to identify the root cause, providing a concise summary instead of requiring a developer to sift through thousands of lines of terminal output. * For crash monitoring, the system goes beyond simple summarization by analyzing stack traces and identifying the specific developer or team responsible for the affected code segment. * By automating the initial diagnostic phase, the time between an error occurring and a developer beginning the fix is dramatically shortened. ### Intelligent Reporting via Slack * The system integrates with **Slack** to deliver automated, context-aware reports that categorize issues by severity and impact. * These reports include actionable insights, such as suggested fixes or links to relevant documentation, directly within the communication channel used by the team. * This ensures that project stakeholders remain informed of the system's health without requiring manual status updates from engineers. ### Considerations for LLM and MCP Implementation * While powerful, the combination of LLMs and MCP agents is not a "silver bullet"; it requires careful prompt engineering and boundary setting to prevent hallucination in technical diagnostics. * Effective automation depends on the quality of the local context provided to the agent; the more structured the logs and metadata, the more accurate the AI's conclusions. * Organizations should evaluate the balance between the computational cost of running local models and the productivity gains achieved through automation. To successfully implement AI-driven automation, developers should start by targeting specific, repetitive bottlenecks—such as triaging build errors—before expanding the agent's scope to more complex architectural tasks. Focusing on the integration between Ollama and mcp-agent provides a secure, extensible foundation for building a truly "smart" development workflow.

Slack AI Large Language Models Automation+4

figmaJul 31, 20252 min readCurated summary

The Duolingo Method: Collaboration As A Core Practice | Figma Blog

The Duolingo Math team treats collaboration as a continuous practice rather than a one-time handoff from design to engineering. Designers, engineers, and product managers co-create ideas, validate them through rapid prototypes, and refine features together. This approach helps the team make better decisions quickly while building entirely new learning experiences. ## Ideating Together - The Math team develops new lessons and games without established templates or patterns to follow. - Designers, engineers, and product managers begin projects together in a shared FigJam file. - After agreeing on a direction, designers create detailed Figma mockups, including interactions and motion. - Engineers participate early by evaluating technical complexity and commenting on implementation challenges. - Jira is connected directly to Figma, reducing context switching during development. ## Experimenting Through Rapid Prototyping - Engineers quickly build initial prototypes using components from Duolingo’s design system. - Designers and engineers share Figma files, working prototypes, questions, and feedback in Slack. - The team repeats a “prototype, test, tweak” cycle for each feature. - Duolingo’s “show don’t tell” principle encourages building and experiencing ideas instead of debating them abstractly. - Prototypes reveal how an interaction actually feels, helping the team test assumptions and make decisions before investing in a polished implementation. ## Polishing and Shipping Together - Continuous collaboration allows the team to make decisions and polish features during development. - The team may simplify animations or remove nonessential mechanics to ship faster. - When exploring educational games, Duolingo first created a batch of simple prototypes instead of building two fully developed games. - The team tested seven different prototypes to learn what resonated with users and prioritized features based on those findings. The Duolingo method replaces rigid handoffs with shared ownership, frequent communication, and working software. For exploratory products, teams should involve engineers early, prototype quickly, and use real user or team feedback to guide what gets polished and shipped.

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Slack Figma Design Systems Figjam+3

datadogSep 23, 20243 min readCurated summary

How we optimized LLM use for cost, quality, and safety to facilitate writing postmortems

Bits AI uses large language models to generate first drafts of incident postmortems from Datadog Incident Management metadata and Slack discussions. The goal is to reduce documentation effort without replacing the human-led reflection that makes postmortems valuable. Datadog found that production-quality LLM features require extensive prompt experimentation, safeguards against hallucinations, careful model selection, and strong privacy and feedback mechanisms. ## Why LLMs Are Difficult to Use for Postmortems - Postmortems must remain factual, consistent, and useful, unlike more creative LLM applications. - LLMs can ignore instructions, produce inconsistent formatting, repeat information, or hallucinate plausible but false details. - More than 100 hours were spent refining section structures and instructions so they worked across varied incidents. - Building these systems requires a combination of software engineering, product management, data science, and technical writing. ## Balancing Structured and Unstructured Incident Data - Structured Datadog data includes incident metadata, alerts, graphs, and manually entered fields such as customer impact. - Slack discussions provide current, detailed, and unstructured context from the incident response process. - Manually maintained fields are often more reliable but can become outdated, while LLMs can continually reassess live discussions. - Combining both sources helped the model understand the full incident and prioritize information appropriately. ## Cost, Speed, and Quality Trade-offs - Datadog compared models including GPT-3.5 and GPT-4. - Results varied by as much as 50 times in cost and speed. - GPT-4 generally produced more accurate output but was substantially slower and more expensive. - Model choice therefore depended on the required balance between quality, latency, and operating cost. ## Preserving Human Control and Privacy - AI-generated drafts were intended to support authors, not become final documents automatically. - Keeping engineers involved preserves the discovery and learning process that postmortems are meant to encourage. - Because incidents may contain sensitive information or secrets, Datadog scrubbed data before sending it to an LLM. - The system was designed to ensure authors remained responsible for reviewing and improving the draft. ## Experimentation and Reliability Safeguards - A custom API extracted and structured incident data from Datadog, Slack, and other sources. - The framework enabled rapid testing of different datasets, models, input configurations, and output token limits. - Datadog refined prompts and instructions to guide the model toward useful recommendations while retaining author agency. - Lowering model temperature and tuning other parameters reduced irrelevant or fabricated content. - A related feature generated concise incident summaries for responders joining Slack channels, providing a faster testing environment for LLM-based incident analysis. ## Evaluating Generated Drafts - Datadog used both qualitative and quantitative evaluation methods. - Authors of earlier postmortems compared their human-written documents with AI-generated drafts for the same incidents. - Evaluation included criteria such as factual accuracy and conciseness, with the provided article ending before the full evaluation approach was described. - Continuous feedback was used to refine prompts, models, and the overall drafting workflow. Overall, reliable AI-assisted postmortems require more than selecting a capable model. The strongest approach combines carefully prepared incident data, privacy controls, model and prompt experimentation, systematic evaluation, and mandatory human review.

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Slack Large Language Models Datadog Data Science+4

figmaApr 9, 20243 min readCurated summary

The Anatomy of a Component Sprint | Figma Blog

The Washington Post’s design system team developed a roughly 10-day “component sprint” to replace siloed, linear workflows with continuous designer–developer collaboration. Each component is jointly planned, designed, implemented, refined, and documented, with input from the wider team throughout. The process aims to reduce miscommunication, uncover technical constraints early, and create components that serve real product needs. ## Why the Component Sprint Was Needed - The Washington Post launched its design system in 2019 to help teams deliver news experiences quickly and consistently. - Earlier components followed either: - A design-led process for inputs such as selects, radios, and checkboxes. - A developer-led process for technically complex components such as carousels and search inputs. - Both approaches caused delays, unexpected compromises, and gaps in communication. - Designers sometimes overlooked existing components or pursued custom solutions, creating overrides and last-minute requests for new variants. - Developers could be forced to focus on visual details instead of solving technical and product problems. - The new process brings the relevant people together early and gives designers and developers shared ownership. ## Sprint Structure and Shared Ownership - A designer and developer from the core design system team lead each component from beginning to end. - Every planned component receives its own sprint, although the timeline can vary. - The approximately 10-day process is intentionally open and inclusive rather than closed and sequential. - The wider team contributes feedback and expertise at multiple stages. ## Kickoff: Balancing Impact and Effort - The team holds a weekly 30-minute meeting to evaluate candidate component tickets in Jira. - Ideas are prioritized according to potential impact and required effort. - Jira tickets serve as shared, evolving spaces where stakeholders can: - Leave feedback asynchronously. - Record insights from Slack discussions. - Group related ideas. - Connect proposals to business goals. - An impact-versus-effort matrix visualizes priorities: - Larger circles represent ideas with more votes. - Numbers identify clusters of related ideas. - Colors indicate associated business goals. - Once a ticket is prioritized, a designer and developer are assigned to lead delivery. ## Concept: Defining Scope and Goals - The sprint begins with a two-hour meeting involving the wider team. - In a FigJam file, participants: - Define the component’s goals. - Agree on requirements. - Assess the scope of work. - Clarify assumptions and technical needs. - The meeting reserves the final 15 minutes for review. - Using FigJam enables both technical and non-technical contributors to participate. - The team focuses first on shared expectations and requirements, avoiding premature debate over detailed visual design. The component sprint provides a practical framework for building design-system components collaboratively: prioritize openly, pair design and development from the start, and establish scope with broad input before implementation begins.

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Slack Figma Design Systems Figjam+3

figmaDec 6, 20232 min readCurated summary

From Slack-lash to Toggle Tax: The New Vernacular of Work | Figma Blog

The article presents a playful glossary for the changing realities of digital and hybrid work. These terms capture common frustrations, habits, and moments in modern workplaces—from endless notifications and multitasking to awkward meetings and collaborative design. Together, they suggest that humor and shared language help people navigate an increasingly virtual work environment. ## Balancing Effort and Iteration - **Fidelity Fluency:** Knowing how polished a project needs to be, and avoiding unnecessary pixel-perfect work when a rough concept is sufficient. - **WIP Waltz:** The continual cycle of revising, tweaking, and rethinking work as it develops. - **Style Guide Safari:** Exploring a design system’s typography, colors, and other visual elements to find the right creative direction. - **UI Lock Ness Monster:** A frequently discussed interface feature that is always expected in a future update but never appears. ## Digital Overload and Distraction - **Keyboard Cardio:** The frantic effort of sending emails and Slack messages, likened to an intense keyboard workout. - **Slack-lash:** The shock of being hit by a sudden flood of Slack notifications and messages. - **Tab Tsunami:** Having so many browser tabs open that the screen—and one’s attention—feels overwhelmed. - **Toggle Tax:** The mental cost of constantly switching between tasks and contexts. - **Workplace Whack-a-Mole:** Repeatedly responding to incoming tasks, alerts, and emails as soon as they appear. ## Virtual Meeting Experiences - **Icebroken:** Oversharing during an icebreaker and turning a friendly introduction into an uncomfortable moment. - **Screenshare Scramble:** Quickly hiding embarrassing or irrelevant browser tabs before sharing a screen. - **Zoombie:** Someone who appears present on a video call but is mentally disengaged. - **Zoom Zen:** The rare, highly effective meeting where the agenda is clear, participation runs smoothly, and the call ends on time. ## Collaboration and Social Validation - **Sudden Heavy Stamping:** Receiving an unexpected rush of positive reactions—such as hearts or “+1” votes—on a collaborative idea. - **Fidelity Fluency** also reflects collaborative judgment: teams must match the level of detail to the project’s actual needs rather than overinvesting in polish. The glossary turns the habits and tensions of modern work into memorable expressions. Recognizing these patterns can make digital collaboration more manageable—and encourage teams to protect focus, communicate intentionally, and find humor in workplace chaos.

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Slack Figma Design Systems User Interface+3

figmaDec 6, 20233 min readCurated summary

The Art of the Professional Pivot | Figma Blog

The article presents professional pivots as normal career adjustments rather than dramatic reinventions. Through six creatives’ stories, it shows how people can redirect their careers by reframing their approach, returning to a familiar company in a new role, or making larger changes. The central message is that pivots can help professionals align their skills and interests more effectively with their work. ## The Reframe: Small Changes with Big Effects A reframe changes how someone approaches their existing role without requiring a complete career change. - Ry Reid moved from customer support into UX writing, later working at Pinterest, Spotify, and Uber Eats. - At Uber Eats, Ry struggled to persuade colleagues through written proposals and was passed over for promotion. - A design manager suggested visualizing ideas instead of explaining them in documents. - Ry began creating rough concepts with pen and paper and Google Slides, then sharing them with colleagues. - This shift helped Ry communicate more effectively and contributed to their proposed user experience being adopted. - The experience showed that presenting an idea and polishing it into a finished design are different skills. ### Lessons from the Reframe - Accept difficult feedback and investigate why opportunities were missed. - Seek advice from mentors in adjacent disciplines. - Test a new approach in low-risk situations. - Expect some discomfort when developing unfamiliar skills. ## The Boomerang: Changing Roles in a Familiar Environment A boomerang involves making a substantial career change while returning to—or remaining within—a company whose culture and operations are already familiar. - Erica Simunovic first worked at Tatari in people operations. - After five years and completing a product design bootcamp, she returned to the same company as a product designer. - Her story illustrates how an existing relationship with an organization can reduce some of the risks of starting over in a new discipline. - The article frames this path as a way to pursue significant change while retaining familiarity with the workplace. ## The Article’s Broader Career-Pivot Framework The six profiles represent different forms of professional change: - **The reframe:** Adjusting perspective or working methods. - **The boomerang:** Taking on a new role at a familiar company. - **The unfolding:** Making a deeper, more personal leap. - **The stretch:** Exploring unfamiliar opportunities. - **The hard left:** Changing direction completely. - **The blend:** Combining experiences, skills, and professional networks. A practical approach is to begin with the smallest change that addresses the problem: seek candid feedback, experiment with adjacent skills, and use existing relationships where possible.

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Slack Figma User Experience Product Design+2

figmaOct 24, 20232 min readCurated summary

How Figma’s data science and user research teams weave together insights that count | Figma Blog

Figma’s data science and user research teams combined quantitative and qualitative methods to understand why notifications were not driving enough collaboration. Data revealed where users dropped out of the notification funnel, while user research explored the reasons behind those behaviors. Their synthesis showed that the biggest issue was not interaction with notifications, but that many users were never receiving them, leading to new alert types and changes to notification recipients and timing. ## Building a Cross-Functional Process - Data Scientist Caitlin Hudon and Researcher Jennifer Sanders began with a teamwide FigJam brainstorm. - They reviewed notification funnel metrics, including the percentages of users who: - Were eligible for notifications - Received them - Viewed them - Interacted with them - The team identified strategic questions, potential notification types, and data gaps. - Questions were divided according to whether data science or user research was best suited to answer them. - Caitlin and Jennifer maintained close communication while conducting their separate investigations, treating quantitative and qualitative work as complementary rather than independent. ## Understanding the Notification Funnel - Figma notifications can arrive through email, Slack, mobile devices, the file browser, the system tray, or the desktop app. - Notification events include comments, replies, reactions, invitations, editing access, and @mentions. - Users must move through several stages before engaging: - All Figma users - Users eligible for notifications - Users who receive notifications - Users who view them - Users who interact with them - Quantitative analysis showed what users were doing at scale. - User research was needed to understand why users behaved that way, since people may not always accurately explain their own motivations. ## Finding the Biggest Opportunity - The activity team initially needed to determine which stage of the funnel deserved attention. - Cross-functional analysis revealed that most users were not receiving notifications at all. - This shifted the focus away from simply improving notification engagement. - The main opportunities were to create new alert types and reconsider who should receive notifications and when. ## From Insights to Impact - Figma’s activity team began running notification experiments based on the combined findings. - A new notification type was released to address major user pain points. - The broader goal is to help teams stay connected and collaborate effectively across Figma and FigJam. The case demonstrates that product teams get stronger, more actionable conclusions when behavioral data is paired with direct user research.

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Slack Figjam User Research Data Science+2

datadogJun 1, 2023Original article

2023-03-08 incident: A deep dive into our incident response | Datadog (opens in new tab)

Datadog’s first global outage on March 8, 2023, served as a rigorous stress test for their established incident response framework and "you build it, you own it" philosophy. While the outage was triggered by a systemic failure during a routine systemd upgrade, the company's commitment to blameless culture and decentralized engineering autonomy allowed hundreds of responders to coordinate a complex recovery across multiple regions. Ultimately, the event validated their investment in out-of-band monitoring and rigorous, bi-annual incident training as essential components for managing high-scale system disasters. ## Incident Response Structure and Philosophy * Datadog employs a decentralized "you build it, you own it" model where individual engineering teams are responsible for the 24/7 health and monitoring of the services they build. * For high-severity incidents, a specialized rotation is paged, consisting of an Incident Commander to lead the response, a communications lead, and a customer liaison to manage external messaging. * The organization prioritizes "people over process," empowering engineers to use their judgment to find creative solutions rather than following rigid, pre-written playbooks that may not apply to unprecedented failures. * A blameless culture is strictly maintained across all levels of the company, ensuring that post-incident investigations focus on systemic improvements rather than assigning fault to individuals. ## Multi-Layered Monitoring Strategy * Standard telemetry provides internal visibility, but Datadog also maintains "out-of-band" monitoring that operates completely outside its own infrastructure. * This out-of-band system interacts with Datadog APIs exactly like a customer would, ensuring that engineers are alerted even if the internal monitoring platform itself becomes unavailable. * Communication is streamlined through a dedicated Slack incident app that automatically generates coordination channels, providing situational awareness to any engineer who joins the effort. ## Anatomy of the March 8 Outage * The outage began at 06:00 UTC, triggered by a systemd upgrade that caused widespread Kubernetes failures and prevented pods from restarting correctly. * The global nature of the outage was diagnosed within 32 minutes of the initial monitoring alerts, leading to the activation of executive on-calls and the customer support management team. * Responders identified "unattended upgrades" as the incident trigger approximately five and a half hours after the initial failure. * Recovery was executed in stages: compute capacity was restored first in the EU1 region, followed by the US1 region, with full infrastructure restoration completed by 19:00 UTC. Organizations should treat incident response as a perishable skill that requires constant practice through a low threshold for declaring incidents and regular training. By combining out-of-band monitoring with a culture that empowers individual engineers to act autonomously during a crisis, teams can more effectively navigate the "not if, but when" reality of large-scale system failures.

Slack Kubernetes Datadog Automation+4

figmaMar 14, 20232 min readCurated summary

How to get the most out of teams | Figma Blog

Effective people management is about creating conditions where teams can act with autonomy, focus, and purpose. The article recommends a few practical systems: limit goals, protect uninterrupted work, make participation equitable, encourage experimentation, and actively support career development. These practices help employees do better work while making managers accountable for the team’s success. ## Set Fewer, More Meaningful Goals - Ask team members to list their priorities at the start of a quarter or half. - Reduce an initial list of 10–15 goals to just three, forcing clear choices about what can realistically be completed. - Connect individual goals to the company’s most urgent strategic initiatives. - Make goals concrete and measurable by defining the impact they should create. - Share goals with other leaders to improve alignment and accountability. ## Make Meetings Worthwhile - Work & Co. limits teams to one daily check-in, leaving the rest of the day for focused individual work and collaboration. - Managers should distinguish between issues that require meetings and those that can be handled asynchronously in Slack or FigJam. - For unavoidable meetings, use structures that prevent outspoken participants from dominating. - Twitch adapted Amazon’s written-memo format for hybrid work: - Participants read a shared six-page document. - They spend 10–15 minutes adding questions and comments. - The group discusses the annotations afterward. - Written participation gives quieter employees a meaningful voice, especially in large meetings. ## Make Ideas More Valuable Than Turf - Ambiguous responsibilities can cause conflict, while overly rigid job descriptions can prevent innovation. - Oura Ring treats campaigns and projects as experiments built around strong hypotheses. - Teams measure results, then decide whether to scale, repeat, or abandon an idea. - Making experimentation everyone’s responsibility reduces confusion about ownership. - Employees are encouraged to pursue opportunities outside their formal roles, even when that means occasionally crossing boundaries. ## Support Nonlinear Career Conversations - Careers rarely follow a straightforward upward path. - Managers should help employees imagine multiple possible directions rather than presenting advancement as a single ladder. - This guidance is particularly important in remote work, where informal networking and spontaneous conversations happen less often. Managers can unlock their teams’ potential by replacing excessive control with clear priorities, focused collaboration, room for experimentation, and deliberate career support.

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Slack Figma Figjam Product Management+2

figmaMar 29, 20222 min readCurated summary

What’s new in Figma: March 2022 | Figma Blog

Figma’s March 2022 updates focus on making Figma and FigJam more accessible, flexible, and useful across devices and global teams. Major additions include FigJam for iPad, right-to-left text support, font-independent superscript and subscript characters, and improved Slack notifications. Together, these features help users ideate anywhere, design for more languages, and keep distributed teams informed. ## FigJam for iPad - FigJam is now available on the iPad through the App Store. - Users can sketch, brainstorm, and annotate away from desktop distractions. - Work can be resumed across iPad and desktop, supporting more fluid workflows. ## Right-to-left text support - Figma and FigJam now support right-to-left writing systems. - Arabic, Hebrew, Urdu, and other languages can be designed and edited more naturally. - The update improves Figma’s suitability for global audiences and international products. ## Superscript and subscript in any font - Superscript and subscript characters can now be used even when the selected font lacks those glyphs. - Figma automatically creates a synthetic, or “faux,” glyph. - The glyph is resized and repositioned to match the surrounding font style. ## Slack notifications for teams - Figma’s updated Slack app helps teams track activity in shared files, teams, and projects. - Notifications can arrive in real time or as hourly and daily digests. - Teams can subscribe project files to dedicated Slack channels. - Comment and collaboration updates can be shared where teammates already coordinate work. Overall, the release recommends using Figma’s new cross-device, localization, typography, and collaboration features to make design work more accessible and connected.

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Slack Figma Figjam Typography+3

figmaFeb 7, 20223 min readCurated summary

Jamming on FigJam: a journey from beta to GA | Figma Blog

FigJam emerged from Figma’s shift to remote work and the growing need for a shared digital space for collaboration and connection. Figma launched it in beta to avoid perfectionism, learn quickly from users, and balance immediate needs with a longer-term product vision. Its path from inception to general availability was driven by cross-functional teamwork, rapid decisions, and continuous user feedback. ## A Collaborative Product Culture - Product Manager Emily Lin describes her role as a facilitator rather than a sole decision-maker. - Figma’s culture encourages ideas and decisions from anyone, with designers, engineers, product, support, sales, and marketing working together. - This collaborative model was especially important for building Figma’s second product. ## Why FigJam Was Created - As Figma began working remotely, the company saw users using its design tool to connect, brainstorm, socialize, and collaborate beyond traditional design work. - Figma employees experienced the same need for a digital gathering space during a difficult transition to remote work. - Because the need was urgent and FigJam became a company-wide priority, teams across Figma contributed to its development. ## Using Beta to Avoid Perfectionism - Building a second product created pressure to match the quality and care associated with Figma. - The team recognized that trying to perfect every detail could slow development and prevent them from responding to real user needs. - A beta launch allowed Figma to ship sooner, gather feedback, and iterate while continuing to pursue a broader product direction. ## Prioritizing Decisions and Feedback - The team focused first on questions that could block future decisions, such as how FigJam should relate to Figma and whether it needed to be a separate application. - Rather than extensively deliberating over every detail, they made decisions, consulted stakeholders, and adjusted based on feedback. - Hundreds of alpha testers participated through a shared Slack workspace and supplied ongoing insights. - Requests were divided into launch-critical fixes and fast-follow improvements: - Author names on sticky notes were required for the initial launch. - A timer could be released after launch as a follow-up feature. ## Iterating After the Beta Launch - After FigJam’s April 2021 beta introduction, product decisions continued to center on solving user problems. - A panel of users opted into new features and provided proactive feedback. - Ideas also came from support conversations, Twitter, and internal teams across the company. - The process emphasized giving active FigJam users the functionality they actually needed rather than following a rigid predetermined roadmap. The FigJam launch demonstrates the value of shipping an adaptable beta, resolving foundational questions early, and involving users and cross-functional teams throughout development. For products facing uncertainty or time pressure, iterative learning can be more effective than waiting for a perfectly defined first release.

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Slack Figma User Experience Figjam+3

figmaOct 27, 20212 min readCurated summary

Behind the build: a Q&A with developer Tru Narla | Figma Blog

The post profiles Tru Narla, a software engineer and creator of FigJam’s Soundboard widget. Her project adds playful audio interactions to FigJam boards and demonstrates how accessible the widget platform can be for developers. Despite API limitations, documentation, experimentation, community support, and livestreaming helped her turn a side-project idea into a published product. ## Soundboard for FigJam - Soundboard lets users add fun sounds to FigJam boards. - Tru initially wanted to support user-recorded audio and preset sounds, but widget limitations prevented audio recording. - The idea came from wanting to create an interactive experience focused specifically on sound. ## Building the Widget - Tru began by reading the documentation and studying demo projects. - Much of the development involved trial and error and reducing the project’s scope to fit the available APIs. - A private Slack community for widget developers helped her share code, troubleshoot bugs, and stay motivated. - She found widget development easier and more enjoyable than expected. ## Inspiration and Motivation - Tru draws ideas from Twitter and everyday experiences. - She records potential project ideas and prefers building tools she would personally use. - Publishing a finished project was a major motivation after repeatedly starting—but not completing—side projects. - Streaming the development process on Twitch provided additional accountability and encouragement. ## Future Projects and Platform Potential - Her next project is an interactive piano with clickable keys and keyboard shortcuts mapped to different notes. - She is also considering multiplayer games, though they require more planning and development time. - Tru is excited by FigJam’s growing ecosystem of plugins and widgets and the possibilities still open to developers. - She particularly likes the new code embed feature and imagines using it for tutorials, system design, and code explanations. - She would also like to see running code displayed in a sandbox within FigJam. Tru’s experience suggests that developers can begin with a small, playful idea, use the available documentation and community resources, and gradually expand their ambitions as the FigJam platform evolves.

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Slack Figma Figjam API+4

figmaJun 15, 20212 min readCurated summary

The five stages of an effective brainstorm | Figma Blog

Effective brainstorming requires more than generating creative ideas: it depends on preparation, inclusive facilitation, focused ideation, and a clear path toward action. Figma recommends creating psychological safety while also giving participants structure, context, and a shared goal. The process should ultimately turn divergent ideas into tangible outcomes. ## Prepare and Plan Ahead - Explain why each participant was invited and how they can contribute. - Share pre-reading, briefs, or collaborative files before the session. - Use short pre-meetings with key stakeholders to gather feedback and address concerns early. - Treat the work before and after the meeting as just as important as the brainstorm itself. ## Make Introductions and Manage Expectations - Begin with an icebreaker, such as drawing an animal or creating collaborator cards, to encourage a creative mindset. - Establish shared rules for participation, including expectations about attention and multitasking. - Clearly explain the brainstorm’s purpose and acknowledge that not every idea will be used. - Facilitators should create an open, inclusive environment where people feel comfortable contributing. ## Generate Ideas - Use adaptable templates as “recipes” rather than starting from a blank canvas each time. - Lower the barrier to participation with prompts such as “How might we…?” or simple warm-up exercises. - Define a “north star” that states what problem the group is solving and what it should produce. - Encourage ambitious ideas by making the session playful, including small rewards for especially creative suggestions. ## Share Artifacts and Outcomes - Move deliberately from divergent idea generation toward convergence and prioritization. - Ask questions and refine suggestions rather than accepting ideas at face value. - Evaluate ideas against the session’s shared goal or “north star.” - Ensure the brainstorm produces useful artifacts and clear tactical direction, rather than ending with a collection of disconnected ideas. The provided text ends partway through the fourth stage, so details of the fifth stage are not included.

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Slack Figma Figjam Design Thinking+1

figmaMay 12, 20212 min readCurated summary

Shifting team culture at Config 2021 | Figma Blog

The Config 2021 sessions argued that inclusive, collaborative design depends on building cultures of transparency, trust, and shared participation. Speakers emphasized two complementary practices: acknowledging people’s real emotions and circumstances, and creating accessible spaces where newcomers and diverse perspectives can contribute. The overall conclusion was that teams cannot create inclusive experiences alone; they need intentional routines, open resources, and community support. ## Embracing Vulnerability - Figma researcher Nannearl Brown described how openness about personal struggles can create a more supportive team culture. - Team members were encouraged to share: - How they were feeling - What support they needed - What they were focusing on during the day - Daily Slack stand-ups included non-work activities—such as exercise, hobbies, or family time—to encourage work-life balance and help teammates understand one another. - Support requires more than listening; teammates should make room for each other’s emotions, experiences, and difficult moments. - The central message was that people do not need to appear fine all the time. ## Creating Inclusive Spaces - Bitcoin designers Johns Beharry and Christoph Ono noticed that Bitcoin design resources often excluded people without technical expertise or geographic access. - They responded by building shared spaces where experienced designers could support newcomers, including: - A Slack community - GitHub resources - The Bitcoin Design resource hub - Weekly community calls - The open-source Bitcoin Design Guide - The resources were designed to work across cultures, regions, languages, and levels of technical knowledge. - Their goal was to create a friendly environment that encouraged broader participation and more diverse perspectives. - They argued that inclusive and accessible design requires collaboration with people who bring different experiences. ## Building Collaborative Culture - Design is inherently collaborative and benefits from varied experiences and perspectives. - Inclusive culture helps more team members and collaborators participate in the design process. - Trust and transparency are developed through both interpersonal habits—such as honest check-ins—and structural tools that make knowledge widely accessible. Teams seeking a more inclusive culture should normalize vulnerability, establish supportive communication routines, and create shared resources that invite participation from people with different backgrounds and levels of expertise.

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