NVIDIA GTC CFP Speaking Session Analysis: Emphasizing Solutions Over “Things”

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NVIDIA’s GPU Technology Conference (GTC) just wrapped up in Paris, and GTC is often dubbed the “Super Bowl of AI”. I was fortunate enough to speak virtually on the topic of Explainable AI and attend the North American version in San Jose this year. I was impressed by the quality of the sessions and the breadth of subject matter covered. You can geek out on the cutting edge of AI techniques to discover some of the amazing things happening today, such as AI-assisted healthcare.

NVIDIA GTC Logo

Part of my day-to-day job is thinking about new interesting ideas and topics to present at AI/ML conferences like GTC. Because of this, I thought it would be worthwhile to examine what the NVIDIA GTC selection committee considers talks worthy of being presented in-person on stage. GTC might be hosted by the world’s leading GPU maker, but you’d be mistaken to think that it’s just about the hardware they build. In reality, the overwhelming majority of sessions focus on software, AI solutions, and what you can do with NVIDIA’s technology… not the chips themselves. This isn’t just my opinion; it’s supported by the numbers and the way GTC is organized. That’s the focus of this blog post here… let’s dive right in.

GTC Is About What You Can Do, Not Just What You Have

NVIDIA is no longer just a chipmaker cranking out GPUs. The company has been transforming into a full-stack AI platform provider. As one observer put it, NVIDIA went “from being a hardware company” to also wanting to be an AI software company. This shift is evident at GTC. The conference features hundreds of sessions across AI domains from computer vision and generative AI to robotics and healthcare… showcasing real-world applications and breakthroughs. In fact, GTC 2025 boasted over 1,000 sessions with 2,000+ speakers (in-person and virtual), covering everything from large language models to cloud computing and scientific discovery. The focus is clear: it’s on how NVIDIA’s platform is used to solve tough problems and transform industries, rather than on plain product pitches.

Problem Solving

This approach makes sense. People attend GTC to learn and be inspired by what’s possible with AI. They want to hear how a researcher used GPUs to decode the human genomes faster, or how a startup is deploying AI at the edge in hospitals… not a sales presentation about specs or cloud capabilities. NVIDIA’s own messaging around GTC highlights “breakthroughs happening now” and how AI is “powering the everyday brands that shape people’s lives” across various industries. The underlying hardware is crucial, but it’s the enabler, not the star of the show.

By the Numbers: Software and Solutions Dominate GTC Sessions

I took a deep dive into the latest GTC session catalog in Paris (this was also true of San Jose) and categorized the typical speaking sessions (excluding workshops, trainings, casual chats, etc). There were 485 in-person talks at the GTC event in this European event last month, spanning multiple topic tracks. The breakdown by topic highlights that software, AI applications, and data science topics dominate the agenda, whereas discussions on pure hardware are relatively few. Here’s a snapshot:

  • Generative AI: 81 talks (only 9 were sponsored slots, ~11% “paid”)

  • Simulation / Modeling / Design: 67 talks (2 sponsored, ~3%)

  • Data Science: 25 talks (0 sponsored, 0%)

  • Computer Vision: 21 talks (all content, 0% sponsored)

  • Edge Computing: 16 talks (0 sponsored, 0%)

  • MLOps (AI Deployment): 10 talks (all content, 0% sponsored)

  • Natural Language Processing: 6 talks (all content, 0% sponsored)

  • Tooling (Dev Tools, Frameworks): 33 talks (1 sponsored, ~3%)

  • Cloud Services and Infrastructure: 55 talks (27 sponsored, ~49%)

  • Hardware (GPUs & chips): 25 talks (16 sponsored, ~64%)

In plain terms, the “Generative AI” track alone had over 80 sessions, reflecting the huge interest in what people are building with large language models and AI creativity. “Simulation/Modeling/Design” was another big track with dozens of sessions. Traditional AI application areas, such as vision, NLP, and data science, collectively accounted for many talks as well. These are the kinds of sessions where speakers share research results, developer tips, and success stories using NVIDIA’s software stacks and GPUs.

Pay-to-Play

Notably, none of the talks in Data Science, Vision, NLP, Edge, or MLOps were paid talks… they were all merit-based content. The same was true for the vast majority of Generative AI and Simulation track talks (over 90% of those were non-sponsored content). This means the speakers earned their spot by having something interesting to say, not by buying a slot.

Contrast that with the Cloud and Hardware categories. The Cloud track (covering topics like cloud GPU services, data centers, and infrastructure) had about half of its talks come from sponsored sessions. The Hardware track, sessions about new chips, systems, and so on, was even more skewed: nearly two-thirds were “paid talks.” In other words, many of the sessions about cloud or hardware were more or less vendor presentations that likely came as part of a sponsorship package. This wasn’t a fluke or an oversight by NVIDIA; it appears to be by design.

Why Some Tracks Have So Many Sponsored Talks

If a conference track has a high percentage of sponsored talks, it’s a sign that the organizers expect those proposals could be salesy or promotional in nature. NVIDIA knows that a talk titled “Buy Our Cloud GPU Service!” isn’t what most attendees are eager to hear in any kind of session. The non-tech equivalent of this would be walking into a room only to find out you are trapped listening to a pitch to buy a timeshare in the Bahamas or something.

Not a Timeshare

By comparison, a talk titled “How We Optimized Healthcare NLP Models on GPUs in the Cloud” is much more appealing… if it focuses on the solution and not just a particular product. (Big if… if you ask me) GTC organizers seem to allow the more promotional talks a space at the table only if the presenters pay for the privilege (through sponsorship). This keeps the main content tracks filled with talks that are interesting to the audience on technical or innovative merits, and pushes anything overtly commercial into clearly marked slots.

Think about it: Hardware and Cloud are inherently areas where NVIDIA’s partners (or NVIDIA itself) might be tempted to pitch their latest and greatest. It’s not that hardware advances aren’t important… after all, NVIDIA is one of these hardware chip makers. GTC always has some exciting new GPU or system announcements, which are typically covered in keynotes or select sessions. But an hour-long breakout session that is essentially a product advertisement is not what GTC is curated for (by design).

Therefore, if Cloud providers or hardware vendors wish to present their offerings, they often appear as sponsors (the data indicates that 49-64% of those sessions were paid). This is a strong hint of what NVIDIA is looking for (and not looking for) in the session selection process.

What GTC Wants: Inspiring Use-Cases, Open Sharing, and Real Techniques

NVIDIA GTC sessions are meant to educate, inspire, and enable the audience of developers, researchers, and tech leaders. The best sessions tell a story of solving a real problem using AI and GPUs. They often involve sharing code, frameworks, or lessons learned that others can apply. In fact, open-source tools and projects are frequently front and center. From NVIDIA’s own perspective, many of their recent announcements have incorporated open-source elements; for instance, open-source foundation models for robotics were highlighted at GTC 2025. This open theme extends to the sessions, where speakers frequently discuss using open-source libraries (such as PyTorch and CUDA libraries), contributing to them, or building solutions based on open standards.

This alignment with open source is no coincidence. The open source community thrives on knowledge-sharing and collaboration, not on sales pitches. GTC, in spirit, tries to cultivate that same vibe. Attendees should walk away with new ideas, sample code, or a GitHub repo to check out, and a sense of possibility.

Knowledge Sharing

NVIDIA’s own conference description emphasizes how AI is transforming everything from healthcare to automotive to finance… again focusing on applications and impact. As a GTC attendee myself, I’ve noticed that the energy is much higher in sessions where the speaker is teaching something or demoing a breakthrough. To be fair, even the sponsored talks can contain useful info… but only if the speaker remembers to focus on how to solve problems using their tech, rather than just what their tech does.

Another trend… GTC content has become increasingly practical and concrete over the years. One conference veteran noted that the 2024 GTC was about “what if” future possibilities, while 2025 shifted to “what is”, thereby focusing on how to advance current AI projects to the next level. That tells us NVIDIA is curating sessions that speak to today’s challenges and solutions, not just wild future speculation. If you’re proposing a talk, you’re more likely to be selected if you can demonstrate tangible results or techniques that attendees can use now (or very soon), as opposed to just theoretical ideas.

How to Get Your Talk Selected for AI/ML Conferences

From the data and observations above, we can distill a few clear guidelines for anyone hoping to speak at NVIDIA GTC (or really any top-tier AI/ML conference):

  • Focus on Solving a Problem: Frame your talk around a compelling problem or use case you tackled with AI. For example, instead of a generic “About Our GPU Product,” present “How We Reduced Training Time for Medical Imaging Models by 80%”. Show the audience how you did it, and share numbers, insights, or code repo. GTC selectors appreciate real-world applications that have a tangible impact.

  • Keep It Technical and Educational: Contrary to popular belief, GTC is a developer-centric conference. Attendees appreciate code snippets, demos, benchmarks, and concrete tips. Don’t shy away from the technical aspect. Explain how you achieved your results (e.g., which SDKs, which algorithms, which optimization tricks). Make it a learning experience for the audience.

  • Avoid Marketing Hype: Steer clear of marketing fluff or self-promotion in your proposal and content, but above all, talk about something people actually want to hear. If you wouldn’t sit in on a session, odds are others won’t want to either. Phrases like “revolutionary platform” or slides of your product catalog are red flags, NVIDIA’s organizers can sniff out a sales pitch a mile away… and they’ll route those to sponsored slots (or reject them). Be honest and straightforward about what the audience will get from your talk.

  • Highlight Open-Source and Community Angle: If your solution utilizes open-source tools or you’re open-sourcing your work, mention that. Talks that share code or frameworks with the community inherently feel more like knowledge-sharing (which is exactly the spirit GTC wants).

  • Showcase NVIDIA Tech in Context: Since this is NVIDIA’s conference, it doesn’t hurt (as in you should at least mention) that your solution leverages their technology, but do it in a way that feels natural. It’s fine (and even expected) to use CUDA, RTX, or NVIDIA SDKs in your project; just don’t make the discussion about those tools themselves a product. Instead, make it about what they enabled.

  • Keep the Audience in Mind: Ask yourself, “What will someone watching my talk learn or be inspired to do?” GTC is meant to spark new ideas. If your talk proposal answers that question with something concrete, you’re aligning well with what GTC wants.

Wrapping It All Up!

To sum it up, NVIDIA is seeking talks that ignite curiosity and demonstrate to attendees how to achieve great things with AI, utilizing the hardware and software as tools. The hardware announcements will always have their place on the big stage (keynotes and a few deep-dive sessions), but the heart of GTC is all the amazing stuff people are doing with that hardware/platform. By structuring the conference this way, NVIDIA keeps GTC valuable and authentic for its audience. As an AI/ML developer and data scientist myself, it’s the only way to run a conference or get me to sit through a session at one.

The Full Stop Thought… if you want to speak at GTC (or any AI/ML conference for that matter), bring real substance. Tell a story of innovation or problem-solving, backed by data and live demos. Align with the interests of the community (open science, open source, and cutting-edge applications). NVIDIA’s selection trends show they favor the inspiring engineer or researcher over the slick salesperson; I have definitely noticed this myself while entering this very data science driven world. The GPUs and cloud instances are just means to an end… what matters is the awesome things you accomplished with them. Keep that focus, and you’ll not only increase your chances of getting a CFP accepted but also deliver a talk that resonates with one of the largest AI audiences in the world. And that’s a win-win for everyone.

Rethinking Jobs When AI Does the Thinking: The Human Jobs AI Can’t Touch

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AI isn’t just coming… It’s already here, and it’s reshaping how we think about jobs. But the big question isn’t about which jobs AI will take; it’s about what new types of jobs humans will step into. We are transitioning from a knowledge-based economy to one driven by critical thinking and creative problem-solving. Soon, accessing PhD-level knowledge will be as simple as prompting an AI agent. The real skill will lie in knowing what questions to ask, how to ask them via a prompt, and how to guide AI to produce meaningful results.

PhD-level Expert on Phone

BUT here’s the issue: If AI handles most knowledge tasks, how do we ensure people, especially younger generations, learn enough foundational knowledge to ask smart questions with the context that AI prompts need? There is a tricky balance between relying on AI’s intelligence and cultivating our own critical thinking and problem-solving abilities. If we don’t get this right, we’ll have a generation comfortable giving commands without truly understanding how the systems they’re commanding actually work.

Balancing Knowledge Acquisition and Critical Thinking

Today, deep expertise and hands-on learning seem less profitable because AI provides rapid, high-level insights. However, true critical thinking isn’t something you can instantly download from an AI model. It requires humans to spend real time mastering foundational concepts, learning from failures, and experiencing the physical and mental process of problem-solving firsthand.

Foundational Knowledge

Adults already established in the workforce face a unique challenge here. Many have built careers on knowledge-based tasks that AI increasingly automates. For them, embracing continuous learning via 1) understanding how AI systems work, 2) how to direct them effectively, or 3) even pivoting toward entirely new roles will become crucial. The workforce will need robust retraining programs to smoothly guide mid-career professionals into roles enhanced, rather than diminished, by AI. The cynic in me knows this will largely depend on individuals with zero financial support (unless you count unemployment benefits) and formal training (such as extension courses) made available en masse. China, on the other hand, knows this is important and is going all in.

To effectively direct AI agents, humans must understand the basics behind the tools they use. Imagine supervising a robotics system without knowing basic mechanics, or troubleshooting code generated by AI without understanding the fundamentals of software engineering. We have already seen “vibe coding” landing in production and it isn’t pretty. As I have been saying a LOT recently, if you are in the technology field, your true job is that you are a lifelong learner who MUST pivot to the next big thing happening in tech, even if it means short-term disruption to your career.

What Future Jobs Might Look Like: Rooted in the Physical World

As AI and automation increasingly dominate knowledge-based roles, future jobs will shift towards:

  • Tangible, hands-on tasks that AI struggles to replicate,
  • Tasks requiring rapport, empathy, and that human touch,
  • Where the cost-to-benefit ratio of replacing humans with AI is costly or there isn’t a financial benefit for doing so.

The future of human employment will likely be grounded in the physical world, focusing on tasks that require nuanced physical interaction, spatial reasoning, empathy, or personalized service.

Future of Human Employment

For tech professionals, this could mean adapting from pure software roles to tech roles grounded in physical reality, such as IoT integration, robotics, hardware troubleshooting, and hands-on cybersecurity assessments. It also means shifting toward roles where AI is complementary, such as AI integration specialists or human-AI interaction designers. There has been explosive growth in AI, but honestly, like everything in tech… it’s only a matter of time (perhaps around 5ish years) before we reach peak AI/ML and the natural contraction begins to occur. It has happened in EVERY wave I have caught in my career: virtualization, containerization, and it will happen to AI/ML in some form. This doesn’t mean you shouldn’t get on the AI bandwagon… Get on NOW!

Humans naturally excel at adapting quickly to unpredictable situations and understanding context intuitively… areas where AI often falters. Jobs that require agility, physical dexterity, intuition, and empathy will thrive. Tech workers who embrace this shift will find new opportunities where their understanding of both technology and human interaction becomes indispensable.

Example Job 1: Repairing Robots, IoT, and Edge Devices

Take, for example, robot repair technicians. As factories, homes, and public spaces become increasingly populated with robots and IoT devices, the job of maintaining, repairing, and optimizing these devices will be crucial. AI can diagnose many issues, but can’t perform nuanced physical repairs, deal with unexpected hardware malfunctions, or adapt flexibly to unique real-world problems.

IoT/Edge Robots

Software engineers and IT professionals could naturally transition into roles that blend coding skills with physical troubleshooting. AI can help quickly identify software or hardware faults, but human technicians will perform the intricate, hands-on adjustments that ensure devices run smoothly. I am from Long Beach, CA which happens to have the first and second large container ports in the United State and robotic has been been transforming the port for years, but when these automated cargo container movers need to change a tire, they are going to rely on humans to do this task.

Example Job 2: Physical and In-Person Trades

Jobs like plumbing, electrical work, construction, and HVAC installation will remain essential… and human-driven. These jobs require fine motor skills, judgment based on real-world conditions, and improvisation in unpredictable environments. Even sophisticated robotics will struggle to navigate cramped crawl spaces, creatively reroute pipes in unexpected wall cavities, or intuitively understand the quirks of an aging home.

Where Robots Lack Agility

Interestingly, professionals from technology sectors might find unexpected opportunities in these traditionally non-tech trades. The skills developed in software or IT roles, such as problem-solving, troubleshooting, and logic-based thinking, can be easily applied to these physical trades. Bringing tech skills into traditional industries can lead to lucrative niches, such as smart home installations, intelligent building automation, or specialized technical consultancy within physical trades.

Example Job 3: Service-Related Jobs

Human-focused service jobs, such as those in healthcare, hospitality, personal training, and caregiving, will continue to see demand. These roles require empathy, sound judgment, and effective interpersonal connections. AI can complement these roles by providing better diagnostics, improved customer experience tools, or intelligent scheduling, but it cannot replace the human element of genuine care and interaction.

Human Related Jobs

Tech professionals accustomed to remote, purely digital interactions might also find fulfilling and secure employment by pivoting toward more human-connected tech roles. Consider roles in telemedicine support, where understanding both human psychology and technology enables an engaging patient experience. Or perhaps educational technology support, blending AI tools with human interaction to create personalized learning environments. Embracing roles that bridge technology and human connection will become an increasingly rewarding career strategy.

Example Job 4: AI Security Specialists

AI systems themselves create new security demands both in protecting and disrupting these technologies. Roles in AI security will become highly specialized, including professionals who intentionally probe AI systems to discover vulnerabilities, making AI more secure and robust against misuse. AI penetration testers will simulate attacks on machine learning systems, uncovering weaknesses that could otherwise go unnoticed until exploited maliciously.

AI Hacker

Other professionals might specialize in disrupting foreign AI systems, whether for national defense or corporate security. This requires a deep understanding of sophisticated machine-learning architectures to infiltrate, confuse, or disable these systems, thereby ensuring our own technological and geopolitical safety.

Privacy specialists will also become increasingly essential, helping individuals and organizations protect personal data and maintain anonymity in an increasingly AI-monitored environment. The growth of AI surveillance and profiling makes safeguarding privacy and anonymity more critical and complex than ever, demanding highly skilled and ethical professionals committed to navigating these nuanced challenges.

The Full Stop Thought: The World Will Be a Different Place

The AI-driven economy will undoubtedly transform jobs, shifting value away from pure knowledge roles toward hands-on, problem-solving, and critical-thinking tasks. Rather than displacing humans entirely, AI will reshape our work, emphasizing roles that leverage uniquely human strengths, such as physical skill, creativity, adaptability, and emotional intelligence. Those pure knowledge based roles will disappear, if it hasn’t already.

Humans Helping Other Humans

Both young professionals and adults already in the workforce must adopt a mindset of continuous learning and adaptability. Tech professionals, in particular, need to explore roles beyond traditional coding; embracing jobs that connect technology with human experiences or physical environments. Through proactive preparation and adaptation, we can ensure a balanced, human-centered future in which AI enhances, rather than replaces, our roles and responsibilities.

Will AI replace jobs? There is no doubt about that. A lot of jobs will be replaced, but it will also create even more jobs, just like when the automobile replaced the horse-drawn carriage. Entire industries were created which included everything from auto repair shops to air fresheners hanging from the rearview mirror. Everyone (regardless of industry) should learn about AI and see how it can help you out. It’s always been my belief that humans will be an integral part of directing AI to do work for some time. As the world starts to enter the next phase of AI, you aren’t likely to be replaced by AI, but you will likely be replaced by someone who’s using AI.

Beyond the Resume: Speculative Hiring Trends in an AI World

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I was recently at a conference where I started chatting with a computer science graduate about job hunting in the world today. We discussed the job market landscape as it exists today and all of the economic influences, disruptive technology (cough, AI, cough), and competition out there.

Old School Cell Phone

When I first jumped into my first professional job out of college, the world was entirely a different place. Social media was still an infant, we were rapidly approaching the dot-com bubble, and we were a few years away from (real) smartphones becoming available. In this conversation, I reflected on what challenges I faced then and wondered how I would react to the demands and problems faced in the current technology climate.

Having been on both sides of the aisle when it comes to the interview process as an interviewee and building one of the best teams as an interviewer, I thought it might be good to share the conversation I had and also expand on it a little further having had a little more time to think about it. Let’s dive into it…

How LinkedIn Connects Candidates to Employers

LinkedIn has become the go-to platform for recruiters seeking top talent, but it’s evolving beyond a simple job board. The platform has adjusted its algorithm to prioritize actively engaged candidates – those who post updates, comment on other people’s posts, and interact with their network. Simply having a profile and being logged in isn’t enough anymore. Recruiters want to connect with individuals who use the platform daily in the event an opportunity finds its way into your inbox, and, second, that you demonstrate interest and expertise in their field through their activity.

Look at this from the recruiter’s and LinkedIn’s point of view. If you are paying buckets of cash for LinkedIn Hire to find a candidate for an open position, you, as a recruiter, ideally want a response to each message sent. Also, LinkedIn doesn’t want to connect individuals to a recruiter who might not respond. This is the summary of the interaction right here… full stop. This shift means job seekers need to rethink their approach.

Be Active On Social Media

Staying visible requires active participation, from sharing industry insights to engaging with thought leaders. Those who embrace this shift can significantly increase their chances of being noticed and approached for opportunities. In contrast, passive candidates who only update their profiles when job hunting may find themselves overlooked. Being “active” on any job platform (especially LinkedIn) usually means you will reply to an inquiry for an open job.

AI Will Kill the Resume

The rise of AI tools has transformed the job application process, making it easier than ever for candidates to create tailored resumes that align perfectly with job descriptions. Tools like ChatGPT can generate highly customized resumes that match job listings with striking accuracy. I haven’t done this myself because I am very selective of the positions that I am seeking. Still, as someone more open to different types of work, using a prompt that mashes your resume and the job description together, I am guessing it might put you at the top of the list.

I have a set of skills

However, this has created a significant challenge for recruiters… many candidates look great on paper but lack the actual skills needed for the job. This trend has led to an increase in candidates getting through the initial screening, only to falter during technical interviews or practical assessments. I see a lot of chatter on subreddits where it’s been very difficult to land a job, let alone get a call back after the first interview. As AI-driven resume generation becomes more common, companies will need to adopt new strategies to verify a candidate’s true abilities before moving forward in the hiring process.

As someone who has helped build teams, it’s VERY time consuming hiring people. The time spent on the interview process is time spent away from doing my actual day-to-day tasks; unfortunately, that work doesn’t stop just because I am interviewing candidates. Even back then, I was very selective about the individuals who got an email for an interview.

How Do You Prove Competence?

With AI making it easier to “embellish” resumes, the challenge for employers is determining whether a candidate truly possesses the skills they claim. Just as students can use AI to complete homework without fully understanding the material, job seekers can list expertise they don’t genuinely have or may just have passing knowledge in. This presents a costly dilemma for businesses… how do they identify qualified individuals without wasting resources on lengthy interview processes?

Be Active On Social Media

Organizations are adopting different screening mechanisms, such as skill assessments, project-based evaluations, and real-world problem-solving tests. Conducting multiple rounds of interviews can be expensive and inefficient, so refining the process to quickly filter out candidates that may not be a good match is crucial to maintaining productivity and hiring success. I think we are in the middle of this shift right now.

Having said that, I hope this isn’t a new era of “Interview 2.0” questions because you know… all software engineers need to be able to tell you how to get 4 gallons of water using only a 3 and 5-gallon jug or to estimate the number of trees in Central Park. Although, I would rather do that than have a week-long programming assignment to prove I know how to program. Trust me, I have declined many of those because it’s like I have an infinite amount of time in my day and love doing work for free.

Public Speaking and Open Source May Hold the Answer

So what do we do about this particular problem?

To address the challenge of validating skills without extensive in-person interviews, companies/interviewers may want to turn to alternative proof of competencies, such as public speaking engagements and, for the tech world, their open-source contributions. Reviewing a candidate’s GitHub activity, technical blog posts, or recorded presentations can provide valuable insights into their expertise and problem-solving abilities.

GitHub Contributions

Although I never really looked at user content 7-10 years ago, I did look at GitHub and open source contributions on other platforms. With AI being able to generate code in any language these days, there is something to be said about supporting a product or an open source project. When a user/customer reports an issue, the project maintainer must triage, root cause the problem, and interact with another human being. This speaks volumes.

GitHub Contributions

Similarly, public speaking appearances or videos posted on social media platforms like LinkedIn, YouTube, etc, at industry events or webinars allow recruiters to see how well candidates can articulate complex concepts. At the end of presentations, there is inevitably a Q&A session where they aren’t going to be able to use ChatGPT to answer a question live in-person. These in-person examples or recorded sessions provide a more authentic measure of skill and commitment than a polished resume ever could.

The Full Stop Thought

So, where do we go from here? We are seeing some of these changes happen in recruiting today. I have heard of interviews where a link kicks off a recorded session, and you, as the interviewer, are presented with questions to answer on video for review later. I don’t know how effective this is, but I have heard of this happening. Is this a good solution? It sounds horrible if you ask me, but change is happening.

As someone who has been on both sides of the fence, the challenges in hiring today are interesting and unique, to say the least. However, there is something to be said for verifiable contributions, like GitHub or posted videos. As someone who thinks social media has done a number on society and who only has socials for work-related purposes only, I came to one possible answer… this content can provide a window into someone’s vested interest in topics they chose and how they demonstrate understanding of that topic.

Until next time!