Project Management | Techlist.io

googleApr 13, 20263 min readCurated summary

Towards developing future-ready skills with generative AI

Vantage is a Google Research experiment that uses generative AI to assess durable “future-ready” skills such as critical thinking, collaboration, conflict resolution, and creativity. It places students in realistic conversations with AI teammates, dynamically introduces challenges, and evaluates performance against educational rubrics. A study with New York University found that AI-generated scores agreed with human expert ratings at a comparable level to agreement between human raters. ## Why Future-Ready Skills Are Difficult to Measure - Skills such as collaboration, creative thinking, and conflict resolution are increasingly important as technology changes work and education. - Traditional tests are too rigid to capture how people think, communicate, and respond in realistic situations. - Human-based assessments can be resource-intensive, difficult to standardize, and dependent on whether challenging situations arise naturally. - Vantage aims to make these skills measurable, scalable, and useful for guiding instruction and student growth. ## AI-Simulated Team Assessments - Students participate in open-ended tasks, such as preparing a debate or pitching a creative idea, alongside AI avatars. - An “Executive LLM” uses an assessment rubric to manage the conversation and introduce targeted challenges, such as disagreement or conflict. - This adaptive process is designed to elicit enough evidence to assess a particular skill while keeping the interaction natural. - An “AI Evaluator” reviews the conversation transcript using the same rubric. - Students receive a visual skill map and qualitative feedback describing their demonstrated strengths and areas for improvement. ## Validation with New York University - Google Research partnered with NYU to align Vantage’s tasks and scoring criteria with established educational rubrics. - The joint study involved 188 U.S. participants aged 18–25 and focused on conflict resolution and project management. - Researchers tested whether the Executive LLM could steer conversations toward specific skills. - Steered conversations produced significantly more skill-relevant information than conversations involving independent, uncoordinated AI avatars. - The AI Evaluator’s scores showed agreement with human expert ratings comparable to the agreement between two human raters. - The results suggest that LLM-based assessment can provide a scalable alternative for evaluating complex interpersonal skills. ## Additional Research - Google also collaborated with OpenMic to study creativity and English language arts. - The collaboration analyzed work from 180 students completing creative multimedia assignments, including character interviews and literature-related media articles. - These studies tested whether the evaluation approach could extend beyond collaboration-focused tasks. Vantage is available in English through Google Labs as a research experiment. Its approach could help educators provide more consistent practice, evidence-based feedback, and scalable assessment for skills that conventional tests struggle to capture.

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Project Management Generative AI Conflict Resolution Executive Llm+4

githubMar 2, 20262 min readCurated summary

GitHub for Beginners: Getting started with GitHub Issues and Projects

GitHub Issues and Projects work together to organize tasks, bugs, ideas, and broader project goals. Issues capture individual pieces of work, while Projects provide visual planning and tracking through boards, views, workflows, and charts. The post introduces beginners to creating an issue, building a Kanban project, and managing progress collaboratively. ## Why Issues and Projects Matter - Issues provide a shared space for tracking bugs, tasks, and ideas. - Projects organize issues into a larger workflow for planning and prioritization. - Using both tools helps teams coordinate work, communicate progress, and avoid missed tasks. ## Creating a GitHub Issue - Open a repository and select the **Issues** tab, then click **New issue**. - Add: - A clear title. - A detailed description explaining the expected behavior or required change. - An assignee. - Labels for classification. - An issue type, such as a bug or task. - A related project or milestone. - After creation, team members can comment and collaborate. - Typing `#` followed by an issue number creates a clickable link to another issue. - Completed work can be marked by closing the issue. ## Creating a GitHub Project - Open the repository’s **Projects** tab and click **New project**. - Choose the **Kanban** template, name the project, disable bulk import, and create it. - GitHub automatically provides columns, which can be customized. - Multiple project tabs offer different ways to view and manage the same work. ## Managing and Customizing Projects Project settings allow users to: - Control access permissions. - Create or modify custom fields. - Rename the project and edit its description. - Add a README. - Copy the project board. - Change project visibility. ### Insights and Charts - The **Insights** section provides charts for analyzing project data. - Charts can be customized through the **Configure** menu, including their layout and displayed information. ### Automated Workflows - Built-in workflows can update item statuses automatically. - Examples include: - Setting new items to `todo`. - Closing issues when their project status changes. - Marking items as `Done` when an issue is closed. ### Project Status Updates - The **Add status update** feature lets teams report project health and progress directly from the project view. GitHub beginners can start with well-written issues and then connect them to a Kanban Project board. Custom fields, automated workflows, charts, and status updates make it easier to turn individual tasks into a transparent, trackable team workflow.

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Project Management Github Automation Github Issues+2

grammarlyJan 29, 2026Original article

How to Use AI Agents: A Simple Guide to Getting Started (opens in new tab)

AI agents represent a shift from reactive, prompt-based AI to proactive, goal-oriented systems capable of planning and executing multi-step tasks with minimal oversight. By operating in a continuous loop of gathering context, selecting tools, and evaluating results, these agents can manage complex workflows that previously required manual follow-up. The most effective implementation strategy involves starting with small, repeatable processes and gradually increasing agent autonomy as reliability is proven through feedback and testing. ### The Mechanism of Agentic AI * Unlike traditional generative AI that responds to isolated instructions, agents possess "agency," allowing them to decide the next best action to reach a defined objective. * Agents function through an iterative operational cycle: they analyze relevant context, select an action, utilize available tools, and evaluate the outcome to determine if the goal is met. * Advanced writing agents, such as those integrated into workplace tools, can proactively suggest revisions for tone, logical progression, and specificity by maintaining contextual awareness across a document's lifecycle. ### Deploying Agents via Repeatable Workflows * Initial use cases should focus on contained, well-understood tasks rather than end-to-end process overhauls to ensure the agent’s logic can be easily monitored. * In research and organization, agents can be tasked with continuously gathering and categorizing sources, updating citations as new data becomes available. * Communication workflows benefit from agents that can reference historical conversation threads to draft follow-ups, summarize long discussions, and adjust meeting agendas dynamically. * Content creation agents can manage the transition from rough notes to structured outlines, applying specific tone and clarity feedback across multiple versions of a draft. ### Integration and Tool Selection * Effective deployment often requires no coding experience, as agentic capabilities are increasingly built into existing word processors, email clients, and project management platforms. * Using familiar software ecosystems reduces the technical barrier to entry and allows for easier scaling of the agent’s behavior over time. * Project management agents can be utilized to monitor task progress, adjust timelines based on changing conditions, and surface high-priority items automatically. ### Establishing Goals and Ownership * Success depends on defining specific end states rather than vague instructions; for example, asking an agent to "flag logical gaps and suggest supporting evidence" is more effective than asking it to "improve writing." * Defining clear ownership ensures the agent knows which parameters to prioritize, such as maintaining a consistent brand voice while revising for conciseness. * Testing should begin with small-scale scenarios, like a single recurring email update, to allow for the refinement of instructions and priorities based on real-world performance. ### Scaling Autonomy and Oversight * Once an agent demonstrates consistent accuracy in a narrow task, its scope can be broadened to include related steps, such as tracking data throughout the week to prepare a draft before being prompted. * Increased autonomy does not mean a lack of control; humans should remain in the loop to provide feedback, which the agent uses to refine its future decision-making logic. * The transition from prompts to progress is achieved by allowing agents to work across different tools and contexts as they prove their ability to handle more complex judgment calls. To get the most out of AI agents, treat them as collaborative partners by starting with a narrow focus and providing specific, goal-oriented feedback. Rather than handing off entire processes immediately, focus on delegating repeatable tasks where the agent’s ability to plan and adapt can yield the highest immediate value.

Project Management AI Agents Automation Communication Tools+2

figmaSep 9, 20253 min readCurated summary

Are Roles and Responsibilities a Thing of the Past? | Figma Blog

Product teams are moving away from rigid job boundaries toward more fluid, overlapping responsibilities. Figma’s research, based on 51 interviews and a survey of 1,199 product professionals, found that 64% of respondents identify with at least two roles, while more than a third span three or more. This shift can improve collaboration and speed, but it also creates friction through tool overload and requires teams to coordinate more deliberately. ## Roles Are Becoming More Fluid - Product development responsibilities increasingly overlap across design, product management, engineering, research, data, and marketing. - 56% of non-designers say they participate heavily in at least one design-related task. - PMs are prototyping ideas, engineers are contributing to early design decisions, and marketers and content specialists are commenting directly in design files. - Figma conducted the research with Factworks and Fusion Hill through 51 qualitative interviews and a survey of 1,199 participants. ## Design Isn’t Just for Designers - Non-designer participation in design tasks such as mockups and brand exploration rose by 10% in the past year. - 70% of product managers create low-fidelity mockups or wireframes, and 59% create interactive prototypes. - One in four product builders has recently adopted a new design tool; 42% of those who have not yet adopted one plan to do so within a year. - Cross-functional design work can: - Help teams align on ideas earlier. - Expose technical feasibility problems before implementation. - Reduce back-and-forth and save time. - Designers can use the time saved for research, strategy, design-system maintenance, and craft. - The goal is not to eliminate design expertise, but to encourage shared visual communication and earlier collaboration. - Teams can support this transition by building stronger relationships, establishing a shared vision early, and welcoming contributions from colleagues who are still learning design tools. ## Tool Overload Is Causing Friction - 72% of respondents identify AI tools as the main force changing their roles. - Tools such as Figma Make, Claude Code, and GitHub Copilot make prototyping and code generation more accessible. - Notion, Figma Buzz, and Airtable similarly support rapid marketing production and project management. - Role expansion has led 71% of respondents to use more tools and software. - Participants use tools across an average of 7.6 out of 16 categories, including spreadsheets, project management, graphic design, and coding assistants. - Although new tools enable more people to contribute, the growing number of platforms can make workflows harder to manage and reduce the effectiveness of individual tools. Teams should treat overlapping roles as an opportunity for earlier alignment and broader participation, while actively simplifying their tool ecosystems. Clear collaboration practices—not rigid ownership boundaries—will be essential as product work continues to evolve.

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Project Management Figma AI Design Systems+3

figmaMay 28, 20243 min readCurated summary

The Linear Method: Opinionated Software | Figma Blog

Linear argues that software should be intentionally designed around a clear use case rather than offering unlimited flexibility. Its defaults reduce process debates and help teams start working quickly, while the company remains willing to evolve its opinions through customer feedback and experimentation. The approach balances strong product principles with deliberate, manageable compromises. ## Opinionated Software as a Default Way of Working - Linear was created by founders with experience at companies such as Airbnb and Coinbase, who wanted an issue-tracking and project-management tool suited to modern product teams. - Unlike general-purpose software, opinionated software guides users toward one effective workflow. - The goal is to prevent the chaos that can emerge when every team or individual invents a different process as an organization scales. - Linear does not insist that its default is the only possible approach, but it makes the recommended path clear. ## Designing at the Atomic Level - Linear favors familiar concepts such as projects, teams, labels, and due dates instead of introducing specialized jargon. - Users should be able to start without reading a handbook or learning an elaborate methodology. - The company is most opinionated about small, foundational decisions—such as treating labels and due dates as issue properties. - For broader structures, such as how projects should work, Linear responds more heavily to customer feedback because organizations differ in how they operate. - The intended result is less time spent configuring processes and more time spent building products. ## Managing Product Debt Deliberately - Linear distinguishes product debt from technical debt: - Technical debt is generally associated with poor or costly code. - Product debt comes from narrowing scope, postponing polish, or optimizing for short-term delivery. - The team treats product debt as borrowing against the future, with “interest” paid through later customer feedback, redesign work, or additional resources. - Settings are an example: Linear has continued adding features and preferences without redesigning the overall experience. - Because settings are not central to the primary workflow, the team considers the accumulated debt relatively low-interest and acceptable to repay gradually. - These shortcuts are framed less as reducing quality and more as intentionally limiting scope. ## Strong Opinions That Can Change - Linear does not treat its principles as a rigid recipe; it expects the product and its processes to evolve. - The team supports experimentation and iteration instead of preserving every decision indefinitely out of fear of user resistance. - Changes that disrupt established workflows require care, but the company believes software must retain the ability to remove or rethink features. - Strong opinions are therefore held firmly enough to guide decisions, but flexibly enough to change when evidence shows a better direction. Linear’s method is most useful when teams want to reduce workflow complexity without eliminating adaptability. Establish clear defaults for common tasks, be deliberate about where flexibility matters, and treat shortcuts as temporary product debt that should be tracked and repaid when its cost becomes significant.

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Project Management Product Development Linear Issue Tracking+2

figmaMay 31, 20212 min readCurated summary

Embracing the mess at Config 2021 | Figma Blog

Config 2021 emphasized that product development is inherently messy, and setbacks, conflicts, and failures can ultimately improve teams and products. The central recommendation is to treat difficult moments as learning opportunities: step back from failed work, communicate openly, and intentionally preserve what can be learned. ## Learning from Setbacks Shanique Shields of Shopify described discovering that a feature she had worked on for 18 months would be rolled back. Initially, she felt personally responsible, but taking a break helped her recognize the experience’s value. - **Don’t personalize feedback:** Stakeholders may simply have different perspectives or priorities; criticism is not necessarily a judgment of the designer or the work’s quality. - **Document the process:** Preserve design decisions, Figma comments, and personal notes, even when work does not ship. These records help future projects and allow others to learn. - **Be intentional about growth:** Identify the skills, business knowledge, or cross-functional experience gained from each project. - **Start the next project positively:** Use a new workstream as a fresh start and rally the team around a shared goal. ## Addressing Conflict Through Openness Frank Bach of Headspace argued that even strong teams experience communication breakdowns. The best response is to address friction directly rather than allowing problems to grow. - Be transparent about concerns and listen carefully to the other person’s perspective. - Approach difficult conversations candidly and objectively. - Ignoring conflict prevents teams from making progress. - Practice gratitude by recognizing the value of design work, teammates, and collaborative opportunities. The article’s practical message is to embrace the difficult parts of product work. Teams become more resilient when they reflect on failure, communicate courageously, and carry lessons from messy experiences into future projects.

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Project Management Figma Design Systems Product Development+2

figmaAug 12, 20203 min readCurated summary

How we use Figma templates to facilitate collaboration at Shopify | Figma Blog

Shopify developed a standardized Figma template to make project files easier for designers, product managers, engineers, and other collaborators to navigate. The template applies Shopify’s lightweight “Get Sh*t Done” framework—Think, Explore, and Build—while presenting build-ready work first. Consistent file structure, clear status indicators, dates, links, and annotations reduce confusion about a project’s current state and next steps. ## The Problem: Collaboration Creates File Complexity - Figma makes collaboration easy, but projects accumulate designs, prototypes, feedback, and multiple versions over time. - Teams were using different file-organization methods, making it difficult to find: - The latest designs - Approved layouts - Prototypes intended for user testing - Relevant project context and contacts - Shopify’s template provides a predictable structure across files and helps teams start projects faster. ## The Template’s Project Structure - The template is based on Shopify’s “Get Sh*t Done” framework: - **Think:** Understand the problem space. - **Explore:** Investigate and test possible solutions. - **Build:** Finalize the chosen direction for implementation. - Pages appear in reverse process order, with build-ready work placed first so collaborators see the most important information immediately. - Teams should create the cover and project overview first, then begin documenting the Think phase before moving into exploration. ## Cover and Project Overview - The cover communicates: - Project name - Owning team - Current project status - A cover also makes files easier to identify in Figma’s grid view. - The status indicator should be updated as the project progresses. - The project overview provides: - Additional context - Links to briefs, research, and related documents - Contact information for questions and feedback - Links to documents or Slack profiles can be added directly to the Figma file for quick access. ## Think: Establish the Foundation - The Think section collects the information needed to understand the project and begin design work. - Useful content includes: - User flows and journey maps - Job stories - Design sprint explorations - Screenshots and previous designs - Inspiration and links to external documents - Its purpose is to preserve the project’s background and problem space before solutions are explored. ## Explore: Investigate Possible Solutions - Explore is the flexible collaboration area where teams diverge, test ideas, and converge on promising approaches. - Recommended practices include: - Create separate pages for different devices or form factors. - Add titles to explorations and flows. - Use badges to show the state of each exploration. - Add a date to every new page to indicate freshness. - Keep user-testing prototypes on separate pages. - Use sticky notes for commentary, ideas, and feedback. - This section is expected to contain substantial collaboration, comments, and alternative layouts. ## Build: Prepare the Chosen Direction - Once a solution is selected, move the relevant pages or layouts from Explore into Build. - This keeps finalized work separate from experimentation and makes implementation-ready designs easier to find. - The structure supports a clear transition from exploration to delivery while preserving earlier thinking and feedback. Using a shared template like Shopify’s gives every collaborator the same mental model for navigating Figma files. Teams should adapt the sections as needed, while consistently maintaining project status, dates, ownership, context, and a clear separation between exploratory and implementation-ready work.

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Project Management Figma Design Systems Prototyping+2

figmaJun 23, 20202 min readCurated summary

How to evaluate design tools | Figma Blog

Choosing a design tool should be treated as a structured team project, not a simple feature comparison. The right choice depends on factors such as team size, remote distribution, workflow, collaboration needs, and existing pain points. Figma recommends a phased process that typically takes about a month and combines stakeholder input, defined criteria, hands-on testing, and documented feedback. ## Phase 0: Lay the Groundwork - Treat the evaluation like a product launch or design sprint and include it in the team’s quarterly planning. - Create a workback schedule, allowing roughly: - Two weeks for preparation - Two weeks to test tools on short-term projects - Set a firm decision deadline so the process can end early if one tool clearly stands out. - Identify stakeholders across design, product, development, marketing, and project approval teams. - Consider forming a cross-functional working group with regular check-ins and a shared communication channel. - Document milestones, formal feedback, and informal observations in a shared workspace. - Set expectations that adopting a new tool may require several weeks of ramp-up before productivity returns to normal. ## Phase 1: Define the Problem - Clarify what the team actually needs before comparing specific products. - Consider team characteristics such as: - Size and expected growth - Whether members work in one office or remotely - Onboarding and scalability requirements - Identify concrete pain points in the current workflow, including moments where collaboration breaks down. - Gather feedback from people outside the immediate design team, especially developers, product managers, and marketers. - Focus on specific scenarios such as developer handoff rather than vague dissatisfaction. ## Phase 2: Establish Evaluation Criteria - Convert the team’s needs and pain points into a small number of priority themes. - Productivity is one important theme, covering activities such as: - Individual design work - Sprint planning - Design critiques - Developer handoff - Match criteria to the team’s circumstances: - Large teams may prioritize consistency, shared libraries, and design-system support. - Fast-growing teams may emphasize onboarding and scalability. - Distributed teams may place greater weight on collaboration and communication. The practical recommendation is to invest in preparation before testing tools. A documented, stakeholder-informed evaluation helps teams choose software that fits their real workflows and reduces the disruption and cost of a poorly planned migration.

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Project Management Figma Design Systems Collaboration+2