{ "@context": "https://schema.org", "@type": "Person", "@id": "https://tommymagee.com/#person", "name": "Tommy Magee", "url": "https://tommymagee.com/about", "description": "Tommy Magee leads enterprise AI enablement and implementation, combining hands-on building with systems thinking and organizational change. He leads Tech & Applied AI for Capital Projects & CRE at Rivian, has a background in technology for large capital programs, and is the independent founder of VibeLaunch.", "email": "tcmagee@gmail.com", "image": "https://tommymagee.com/images/tommy-studio-2026.png", "sameAs": [ "https://www.linkedin.com/in/tcmagee/", "https://github.com/Tcmagee" ], "jobTitle": "Sr. Manager, Tech & Applied AI, Capital Projects & CRE", "worksFor": { "@type": "Organization", "name": "Rivian" }, "knowsAbout": [ "AI enablement", "Enterprise AI implementation", "Systems thinking", "Change management", "Product strategy", "Capital program technology", "AI agent evaluation", "AI ROI assessment", "Agentic operating models", "AI team leadership", "Applied AI", "AI agents", "Construction technology", "Real estate", "Enterprise AI adoption", "Workflow design", "Capital projects", "Corporate real estate technology", "Product development", "Model Context Protocol" ], "contactUrl": "https://tommymagee.com/contact", "speaking": { "url": "https://tommymagee.com/speaking", "bio": "Tommy Magee leads Tech & Applied AI for Capital Projects & CRE at Rivian. His work combines hands-on AI implementation with helping teams learn, build, and adopt new ways of working. His background spans capital program technology, product development, and systems and strategy consulting at Turner & Townsend. He also founded VibeLaunch, an independent publishing platform for people creating with AI. Tommy speaks about enterprise AI adoption, agents at work, and the technology behind construction and real estate.", "topics": [ { "title": "What happens after the AI workshop?", "description": "Helping people get started, giving them useful problems to work on, and building the support that keeps the effort going." }, { "title": "Where does AI actually earn its keep?", "description": "Choosing the work, testing agents, and looking at the return after review, integration, and maintenance." }, { "title": "AI for organizations that build things", "description": "What capital programs and real estate teach us about connected data, handoffs, and decisions that have consequences in the physical world." } ] }, "independentWork": { "name": "VibeLaunch", "role": "Founder", "url": "https://tommymagee.com/library/vibelaunch" }, "experience": [ { "role": "Sr. Manager, Tech & Applied AI", "organization": "Rivian", "period": "2025 – NOW", "description": "Capital Projects & CRE" }, { "role": "Manager, Tech & Strategy", "organization": "Rivian", "period": "2024 – 2025", "description": "Capital Projects & CRE" }, { "role": "Product Manager", "organization": "Rivian", "period": "2020 – 2024", "description": "Capital Projects & CRE" }, { "role": "Consultant to Senior Consultant", "organization": "Turner & Townsend", "period": "2017 – 2020", "description": "Systems & Strategy" }, { "role": "Solutions Consultant & Implementation Manager", "organization": "Novatech Systems", "period": "2016 – 2017", "description": "IT strategy & delivery" } ], "library": [ { "title": "Is this agent making the work better?", "type": "Resource", "description": "A field-test worksheet for quality, human effort, operating cost, and the decision to scale.", "url": "https://tommymagee.com/library/agent-workflow-field-test", "markdownUrl": "https://tommymagee.com/library/agent-workflow-field-test.md", "downloadUrl": "https://tommymagee.com/templates/agent-workflow-field-test.md", "tags": [ "AI ROI", "Agent evaluation", "Enterprise AI" ], "body": [ "Use this worksheet to evaluate one real workflow before expanding an agent’s responsibility. It is a template to adapt, not a report of measured outcomes.", "## Measure the whole job", "Compare the human effort required for an acceptable result before and after the change. Include checking, correction, failures, and ongoing support. Keep elapsed time separate from active work.", "## Distinguish capacity from cash", "Time released gives a team capacity. It becomes cash savings only when spending is actually avoided or reduced. The worksheet keeps those claims separate and makes operating and rollout costs explicit.", "## Make a decision", "Define the quality standard, delegated actions, and conditions for escalation before the pilot. Use the evidence to stop, revise, continue testing, or expand. Savings alone do not settle whether the system is ready.", "Download the editable Markdown worksheet below. It includes the baseline, evaluation questions, capacity calculation, costs, and a decision record." ] }, { "title": "When everyone starts building agents", "type": "Writing", "description": "The work shifts from helping people get started to deciding what deserves to scale.", "url": "https://tommymagee.com/library/when-everyone-starts-building-agents", "markdownUrl": "https://tommymagee.com/library/when-everyone-starts-building-agents.md", "tags": [ "Enterprise AI", "Agent operations", "AI ROI", "Team leadership" ], "body": [ "Getting people to try AI is one kind of work. Managing what happens once they start building is another. My experience spans both: teaching people to build, building agents myself, and helping put an operating model around the work that follows.", "## Start with the work people already do", "I’ve led process-mapping work with the people doing the job: the handoffs, the systems, the time spent, and the places where work gets stuck. That gives a team something much more useful to build against than a list of AI ideas.", "Sometimes the best result is removing a step. If a team can stop producing a report nobody uses, it doesn’t need an agent to produce that report faster. I want that option on the table before anyone starts building.", "## Participation creates a different problem", "Hands-on build sessions, training, and recurring time to share work helped turn interest into practice. I’ve taught the tools while building with them myself. That matters when someone needs help getting through a specific problem.", "As more people build, the questions change. Are two teams solving the same problem? Who supports a useful tool when its creator moves on? Which ideas deserve engineering time? Enthusiasm creates a pipeline of possibilities. It doesn’t decide where the investment should go.", "## A decision I had to make", "After recurring build sessions got teams working in parallel, participation was no longer the main problem. Teams were duplicating effort, and useful ideas were competing for support. Running more build sessions would not resolve who should own the work or which problems deserved investment.", "I helped put an intake and ownership model around that activity: personal tools could stay with their makers, team solutions needed a team owner, and broader initiatives needed a path to an execution team. The decision was to keep room for experimentation while giving shared dependencies a different level of support.", "That changed the question from ‘who has an AI idea?’ to ‘what kind of responsibility are we taking on if other people start depending on this?’ It is one of the operating decisions behind scaling AI that rarely appears in a demo.", "## Give different kinds of work different support", "The transition matters. Before a team tool becomes a shared service, someone needs to own its data connections, access, evaluation, maintenance, and response when it fails. A successful demo is a reason to investigate those responsibilities.", "## Make the return a testable question", "I look at the work the business gets back: time, capacity, cost, and speed of decisions. Then I look at the work we’ve added: integration, checking outputs, handling exceptions, and keeping the system running.", "An estimate helps decide what to test. It isn’t a measured result. I want a baseline, a bounded workflow, and a way to compare the result after people actually use it. If human review eats the time saved, that changes the business case. If the bottleneck simply moves to another team, we need to see that too.", "## A worked example: when I would hold off", "The following numbers are hypothetical. They illustrate how I would test a business case; they are not results from Rivian or any client.", "Suppose a team handles 100 tasks a month at 20 minutes of human effort per task. A pilot brings the average down to 8 minutes, including review, corrections, and failed tasks that fall back to a person. That releases 20 hours a month. Six hours of recurring support leaves 14 hours of net capacity.", "At an illustrative labor value of $50 an hour, that capacity is worth $700. Subtract $300 of incremental monthly software and model costs and the estimated monthly net value is $400. With $2,400 in one-time implementation costs, simple payback would be six months, if those assumptions hold. This is a capacity-value estimate, not cash saved.", "Now suppose real use needs 14 minutes of human effort per task instead of 8. The same calculation leaves four hours of capacity after support, worth $200 against $300 in recurring costs. The labor-based case is now negative before recovering the implementation cost.", "I would hold off on expanding it on that basis. First I’d investigate the review burden and test whether a narrower task helps. Better quality or faster decisions might justify the cost, but those benefits need their own evidence. We shouldn’t keep claiming a time-saving business case after the test stops supporting it.", "## Keep learning after the first rollout", "My operating principle is to expand responsibility as the evidence improves. Decide what a good result looks like, make failures visible, and keep a person accountable for the decisions that matter. The agent finishing its task is only one signal.", "The work I find most interesting is connecting all of this: what to build, how to test it, who owns it, and whether it makes the organization work better. That is what managing an organization with agents starts to involve." ] }, { "title": "The best interface might be no interface", "type": "Writing", "description": "If AI is supposed to remove work, why are we creating another place for humans to work?", "url": "https://tommymagee.com/library/less-interface", "markdownUrl": "https://tommymagee.com/library/less-interface.md", "sourceUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7502513748235096064/", "tags": [ "AI & work", "Applied AI" ], "body": [ "SaaS founders, a request: build products my team and I don’t have to log into.", "I actively solicit demos and get pitched new tools. Lately, I keep coming back to the same question. Do we really need another interface? A product can solve a narrow problem and still give the organization more work overall.", "Another tool means another workflow to learn. Someone has to train the team, manage access, keep the information current, and support it when something changes. Eventually, someone has to replace it. Those costs rarely fit into the demo.", "## Start with what needs to happen", "When I see a promising solution, I increasingly ask whether it can do the work for us as a service, or make its capabilities available to our agents. I’m interested in the outcome. The screen is one possible way to get there.", "Take a hypothetical project update. If the information already exists across the systems a team uses, asking everyone to enter it into one more dashboard adds a coordination job. A more useful product might assemble the current picture, show its sources, and flag the few things that need a person’s attention.", "That still requires an interface somewhere. People need to check the answer, understand what changed, and correct mistakes. But there’s a difference between giving someone control and making them operate every step.", "## There’s no maintenance-free option", "Internally built agents have maintenance costs too. So does vibe coding. Someone needs to own the connections, permissions, quality, and behavior when the system fails. Building it yourself doesn’t make that responsibility disappear.", "The comparison I care about is the total work involved. What does this remove? What does it add? Who ends up looking after it? A smaller interface is useful only if the complexity hasn’t just moved somewhere harder to see.", "For teams buying software, I’d put those questions into the evaluation early. For founders, I think there’s an opportunity to make the product useful wherever the customer already works, including through an agent.", "If AI is supposed to remove work, a new place for people to manage it is a pretty incomplete proposition. Show me the work that goes away. That’s where it gets interesting." ], "originalPostDate": "2026-09-06" }, { "title": "What lean manufacturing gets right about AI", "type": "Writing", "description": "Some of my best AI lessons came from moms on YouTube.", "url": "https://tommymagee.com/library/information-factory", "markdownUrl": "https://tommymagee.com/library/information-factory.md", "sourceUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7490994059873001472/", "tags": [ "Systems thinking", "Applied AI" ], "body": [ "A surprising amount of my AI philosophy comes from moms on YouTube and Japanese lean manufacturing.", "The first one is legit. People running households are constantly coordinating information, juggling constraints, delegating, and dealing with exceptions. Some of the clearest AI tutorials I’ve seen come from people trying to make real life a little easier. They know what the job is. The tool has to earn its place.", "That’s a useful starting point for a company too. Before asking which agent platform to buy, understand the work people are already trying to get through.", "## Watch the work", "One lean principle I keep coming back to is Gemba: understand the work where it happens. A process diagram tells you what’s supposed to happen. Watching someone do the job tells you where they wait, re-enter information, chase a decision, or work around the system.", "For a construction or real estate team, that could mean following a single decision from the person who spots a problem to the person who can resolve it. Where does the context get lost? Why does it need another meeting? Is there actually a decision to make, or is somebody just looking for information?", "Those are useful questions whether the answer involves AI or not.", "## Keep responsibility visible", "I also think about Jidoka, which I translate into my AI work as automating while keeping people responsible for quality. An agent finishing a task doesn’t tell us whether it did the right thing.", "We need to decide what a good result looks like, who can check it, and when the system should stop and ask. That doesn’t mean reviewing every small action forever. It means being deliberate about which decisions we’re comfortable delegating and what evidence would change that judgment.", "Then there’s Yokoten: spread what works. Once a team finds a useful approach, share the reasoning and the conditions that made it work. Copying a prompt without the surrounding context is a pretty thin form of knowledge transfer.", "## Think about the whole system", "Japanese manufacturers didn’t simply add technology to an unchanged factory. They rethought how the work moved. I see a similar opportunity for organizations that move information and decisions all day.", "The phrase I use is information factories, with both human and digital workers. The interesting part is how they work together: who has the context, who owns the decision, and how the next person knows it’s their turn.", "I’d start with one real workflow, learn from the people doing it, and make the smallest change that improves it. The tools will keep changing. Understanding the work is still the part that matters." ], "originalPostDate": "2026-08-05" }, { "title": "Your AI is only as good as its context", "type": "Writing", "description": "Clean, connected, trusted data is the work behind the impressive demo.", "url": "https://tommymagee.com/library/data-first", "markdownUrl": "https://tommymagee.com/library/data-first.md", "sourceUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7480368579847106561/", "tags": [ "Applied AI", "Applied AI" ], "body": [ "Show me a company’s data quality and structure, and I’ll have a much better idea of what its AI can actually do.", "My rough rule: enterprise AI is probably 80% data and 20% AI. We spend a lot of attention on models and agents. The less visible constraint is whether they have useful context.", "An organization can have years of documents and still struggle to answer a basic question reliably. Having information somewhere is different from knowing which information to trust.", "## Context has a job to do", "Consider a hypothetical capital project. There’s a schedule in one system, a cost forecast in another, and a decision recorded in an email. An agent can retrieve all three and still get the answer wrong if the schedule is stale, the forecast uses a different project identifier, or the decision has been superseded.", "A bigger model doesn’t settle those disagreements for us. Someone has to establish the relationships and decide which source owns which fact.", "Before adding another AI tool, I want to understand a few practical things. Where does the information come from? How current is it? What does it refer to? Who owns it? And does the person asking the question have permission to see it?", "Those questions sound ordinary because they are. They’re also the difference between a convincing answer and an answer a team can safely use.", "## Start with a bounded problem", "I wouldn’t wait for every piece of company data to be perfect. That can turn into a program with no useful stopping point. Pick a workflow and identify the context it actually needs. Clean up that slice, connect it, and see whether the result holds up in real use.", "For example, an assistant answering questions about a project should be able to point back to the record behind its answer. If the record is missing or outdated, that should be visible. The gap is useful information, even when it means the agent can’t finish the task.", "As the workflow grows, the data responsibilities need to grow with it. Access, ownership, and updates are ongoing work. They aren’t a one-time cleanup before the interesting AI part starts.", "I think there’s a lot of value in the companies and teams that make this foundation easier to build and maintain. The impressive demo gets attention. Clean, connected, trusted context is what gives it a chance of becoming useful every day." ], "originalPostDate": "2026-07-07" }, { "title": "Do we need to heat the whole house for this?", "type": "Writing", "description": "Choosing an AI model is starting to feel a lot like becoming your dad with the thermostat.", "url": "https://tommymagee.com/library/thermostat", "markdownUrl": "https://tommymagee.com/library/thermostat.md", "sourceUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7495932739339046912/", "tags": [ "In practice", "Applied AI" ], "body": [ "Choosing a model is starting to feel like adjusting the thermostat. Does this task need the most powerful setting, or is a smaller one enough?", "Matching the model to the work can reduce cost and resource use. The goal is to use the capability the task actually needs, with enough attention to quality to know the difference.", "I’m interested in keeping quality high without using more capability than the work needs." ], "originalPostDate": "August 2026" }, { "title": "Building AI capability at Rivian", "type": "Project", "description": "Joined to help design the capital projects platform; now leading AI implementation, training, and decisions about what deserves to scale.", "url": "https://tommymagee.com/library/rivian", "markdownUrl": "https://tommymagee.com/library/rivian.md", "sourceUrl": "https://www.linkedin.com/in/tcmagee/", "tags": [ "Operating design", "Applied AI", "Team leadership" ], "body": [ "My work brings together hands-on AI building, team leadership, and the operating decisions that turn individual experiments into a capability an organization can use.", "Start with the actual work\n\nI’ve led efforts to map workflows with the people doing them, looking at handoffs, systems, time, and friction. Those maps helped give teams real problems to work on. Removing unnecessary work was an acceptable outcome before introducing any new tool.", "Build the capability, not just the first tool\n\nI’ve combined hands-on build sessions, recurring training, and opportunities to share work with building agents myself. The aim was to help people develop the judgment and confidence to apply the tools to their own work.", "Decide what deserves to scale\n\nAs participation grew, the operating problem changed: duplicated effort, competing ideas, and tools that needed support beyond their creators. I helped establish a model separating personal productivity, team solutions, and enterprise initiatives, with a path for deciding what needed engineering ownership.", "Something you can inspect\n\nI’ve published [Chief of Staff agent instructions](https://github.com/Tcmagee/My-Chief-of-Staff) for meeting preparation, decisions, and work coordination. They’re a public example of how I translate a job into context and instructions someone else can adapt.", "Connect investment to the work it improves\n\nMy approach considers time, cost, capacity, and delivery speed alongside the effort required to integrate, review, and maintain a solution. Expected benefits guide prioritization; measured outcomes require a baseline and evidence from use.", "The foundation behind it\n\nI joined Rivian in December 2020 as a Product Manager, helping design its capital projects platform and initial portfolio analytics. I became Manager of Tech & Strategy in February 2024, then Sr. Manager of Tech & Applied AI in February 2025. That systems and operating experience informs how I approach AI today." ] }, { "title": "VibeLaunch", "type": "Project", "description": "My independent publishing product for people creating with AI, with a public Claude Code connector.", "url": "https://tommymagee.com/library/vibelaunch", "markdownUrl": "https://tommymagee.com/library/vibelaunch.md", "sourceUrl": "https://www.vibelaunch.live", "tags": [ "Product strategy", "AI tools", "Built independently" ], "body": [ "AI made it easier to build something useful. Sharing it was still harder than it needed to be.", "The problem\n\nA person builds a useful app, dashboard, or website with AI, then runs into hosting, configuration, and unfamiliar technical choices. The creation is done. The journey is not.", "What I built\n\nVibeLaunch is an independent publishing platform for people who create with AI. It helps turn those creations into websites they can share.", "The product judgment\n\nMake publishing feel like the next natural step in creating. The user should be thinking about the thing they made and who it helps.", "A piece of the build\n\nI also published a [Claude Code connector](https://github.com/Tcmagee/vibelaunch-claude-code-plugin) for VibeLaunch. The public repository includes the configuration and setup instructions. It’s one way I’m exploring how publishing fits into the tools people already use to build.", "What I’m learning\n\nThis is an independent product I’m building alongside my day job. It makes the tradeoffs personal: what to build, what to leave out, and how much setup someone should have to understand before they can get value from it." ] }, { "title": "Capital program technology at Turner & Townsend", "type": "Project", "description": "Advising capital program owners on systems, portfolio information, and how to make a change work beyond the software.", "url": "https://tommymagee.com/library/capital-systems", "markdownUrl": "https://tommymagee.com/library/capital-systems.md", "sourceUrl": "https://www.linkedin.com/in/tcmagee/", "tags": [ "Capital programs", "Digital strategy", "Change management" ], "body": [ "Before applied AI, there was a more basic challenge: helping organizations understand how their work actually flowed.", "The setting\n\nAt Turner & Townsend, I advised enterprise real estate and capital program clients on systems, strategy, and portfolio performance.", "My contribution\n\nI analyzed portfolio data, mapped workflows, assessed reporting gaps, and translated the findings into operating frameworks and implementation plans.", "The lesson\n\nA new system only creates value when people can use it to work differently. Executive alignment and change management are part of the product.", "The arc\n\nConsultant, Systems & Strategy: August 2017 to May 2019. Senior Consultant, Systems and Strategy: May 2019 to December 2020." ] }, { "title": "VibeLaunch for Claude Code", "type": "Code", "description": "An MCP connector that brings VibeLaunch into Claude Code.", "url": "https://tommymagee.com/library/vibelaunch-claude-code", "markdownUrl": "https://tommymagee.com/library/vibelaunch-claude-code.md", "sourceUrl": "https://github.com/Tcmagee/vibelaunch-claude-code-plugin", "tags": [ "MCP", "AI agents", "Claude Code" ], "body": [ "A small, public integration connecting Claude Code to VibeLaunch through the Model Context Protocol (MCP).", "The repository contains the connector configuration and setup instructions. It is MIT licensed. " ] }, { "title": "Chief of Staff agent instructions", "type": "Resource", "description": "Reusable agent instructions for meeting prep, decisions, and keeping work moving.", "url": "https://tommymagee.com/library/my-chief-of-staff", "markdownUrl": "https://tommymagee.com/library/my-chief-of-staff.md", "sourceUrl": "https://github.com/Tcmagee/My-Chief-of-Staff", "tags": [ "AI agents", "Glean", "Agent instructions" ], "body": [ "A public set of Glean agent instructions and conversation starters for a chief-of-staff style assistant.", "The README covers meeting preparation, communication, decisions, and work coordination. These are instructions to adapt to your own context, rather than a standalone app." ] }, { "title": "Find the work worth removing", "type": "Resource", "description": "Six questions to ask before reaching for another AI tool.", "url": "https://tommymagee.com/library/workflow-prompt", "markdownUrl": "https://tommymagee.com/library/workflow-prompt.md", "tags": [ "Applied AI", "Workflow design", "Prompt" ], "body": [ "Start with a real workflow. Look for waiting, repetition, and lost context before deciding what to automate.", "Before choosing an AI tool, map the work.\n\n1. What outcome does someone actually need?\n2. Where does the work wait, repeat, or lose information?\n3. Can we remove that step entirely?\n4. What reliable context would AI need?\n5. Who owns the decision and checks the result?\n6. What would prove this made someone's day easier?\n\nDescribe one real workflow. Ask me one question at a time to uncover the bottleneck, then propose the smallest experiment and a way to measure it. Do not start by recommending software." ] }, { "title": "The AI Evolution of Retail", "type": "Talk", "description": "A conversation about AI, adoption, and the work behind retail real estate.", "url": "https://tommymagee.com/library/ai-evolution-retail", "markdownUrl": "https://tommymagee.com/library/ai-evolution-retail.md", "sourceUrl": "https://www.linkedin.com/posts/auderbekov_sftechweek-ugcPost-7514054384595496961-x-2n/", "tags": [ "Speaking", "Applied AI", "Retail real estate", "AI adoption" ], "body": [ "I joined Laura Tinetti and Joe V. for The AI Evolution of Retail, hosted by Alim Uderbekov with JLL during SF Tech Week in October 2026.", "The conversation explored the repeatable work behind retail real estate and what gets in the way of adopting AI. The host’s recap brings together the panel’s perspectives on change management, the decisions before a store opens, and the relationships that still matter.", "One point I raised: as people experience more capable AI in their personal lives, the friction at work becomes harder to ignore. If a personal agent can coordinate a trip, a long email chain to resolve a routine question starts to feel less inevitable." ], "eventMonth": "October 2026" }, { "title": "Fireside chat with Google & Rivian", "type": "Talk", "description": "An Autodesk fireside conversation with Google and Rivian on capital delivery and construction technology.", "url": "https://tommymagee.com/library/autodesk-owners-forum", "markdownUrl": "https://tommymagee.com/library/autodesk-owners-forum.md", "sourceUrl": "https://acsprograms.autodesk.com/owntheoutcomeownersforumsf", "tags": [ "Speaking", "Construction technology", "Capital projects", "Real estate" ], "body": [ "I spoke at Autodesk’s Own the Outcome: Owners Forum in San Francisco in July 2026.", "The agenda lists an Industry Leaders Fireside Chat with Google and Rivian, hosted by Eric Thomas of Autodesk’s Digital Builder podcast, with Phil Williams, Director of Design Build Construction at Google, and me representing Rivian.", "The wider forum brought owners and capital project leaders together to discuss connected construction workflows, project visibility, and outcomes across the asset lifecycle. It was held at the LUMA Hotel in San Francisco." ], "eventMonth": "July 2026" }, { "title": "How emerging tech is disrupting real estate", "type": "Talk", "description": "A fireside conversation with RICOH Tours about VR/AR, IoT, and AI.", "url": "https://tommymagee.com/library/emerging-tech-real-estate", "markdownUrl": "https://tommymagee.com/library/emerging-tech-real-estate.md", "sourceUrl": "https://www.linkedin.com/in/tcmagee/", "tags": [ "Speaking", "Real estate", "Emerging technology" ], "body": [ "I joined RICOH Tours as a fireside guest speaker on July 26, 2018, for a conversation about emerging technology and the real estate industry.", "The discussion covered VR/AR, the Internet of Things, and artificial intelligence. This speaking engagement is listed on my LinkedIn profile." ] } ] }