Technology on Sam's Blog

Google kills off Tenor Gif integrations

Tue, 30 Jun 2026 00:00:00 +0000

Google killed the Tenor gif API today, and I wish I could say that I was surprised.

If you’ve built anything even remotely social (chat apps, dashboards, internal tools) you’ve probably leaned on Tenor at some point. It was simple, reliable, and did exactly what it needed to do: give you a clean, searchable gif experience without forcing you into some bloated ecosystem. That’s rare. Even if you’ve never touched an IDE, chances are you used Tenor when picking out the perfect gif to send to someone. It was Twitter’s gif selector, it was the main gif selector in Discord, and it was even built into the keyboard app on many phones.

And as of this morning, June 30th, 2026, it’s gone. Not deprecated with a thoughtful migration path. Not evolved into something better. Just… gone as a viable option.

And the frustrating part isn’t just Tenor being unplugged; it’s the pattern once again rearing its head.

Google has a long, well-documented habit of killing products that people actually use. Not experimental side projects, but real tools embedded in workflows. I’m sure many people will remember how “Gmail” was in an open Beta test for an absurdly long time, but there have been many other projects (both Google-created and projects acquired along the way) that Google has sunset. Two that strike partiucularly close to my heart:

Google Domains: one of the cleanest, least annoying domain registrars out there. No upsell circus, no dark patterns—just buy a domain and move on with your life. Gone.

Google Podcasts: easily the most straightforward podcast app. No algorithmic nonsense, no clutter, just a chronological feed of what you subscribed to. Also gone.

And now Tenor: a foundational piece of internet culture quietly pulled out from under developers.

As someone deep in open source development and maintainability, this kind of thing hits differently. You build systems assuming some level of stability from major providers, especially when the product is widely adopted and integrated. But with Google, there’s always this background risk that anything not directly tied to ad revenue can disappear overnight.

At the risk of sounding like someone who has spent WAY too much on a homelab (I have, but that’s not the point), I have to point out that if you don’t host it, you don’t control it. The public option will usually be fine—until it isn’t. And when it isn’t, you don’t want to be scrambling to replace a critical dependency overnight.

There’s a saying when it comes to VPNs: You shouldn’t have a setup where you can’t easily swap your VPN provider. I think that this same mentality need to be applied to any hosted service.

Because at some point, one way or another, the thing you depend on might end up joining Google Domains, Google Podcasts, and now Tenor.

AI isn't an option; it's a requirement

Sat, 06 Dec 2025 00:00:00 +0000

The Turning Point

You know what? I’ve been thinking about this whole AI thing lately, and I keep coming back to the same conclusion: it’s not really a choice anymore, is it? Like, we’ve passed that point where you could shrug and say “eh, maybe later” when someone mentions AI integration or automation or whatever buzzword is hot this week.

The Pattern Recognition

I remember when I first started in tech—and Jesus, that feels like a lifetime ago—there were these waves of “revolutionary” technologies that would come and go. Remember when everything had to be “cloud-native”? Or when blockchain was going to solve world hunger? Half the time, you could safely ignore the hype for six months and watch it fizzle out. But AI… AI is different.

The Idealist vs. The Cynic

The thing is, I’m an idealist at heart. I really am. I got into technology because I believed—still believe, actually—that we can build things that make people’s lives better. That code can be poetry, that algorithms can create beauty, that somewhere in all this silicon and electricity, we’re crafting tools that extend human capability in genuinely meaningful ways.

But I’m also exhausted by the industry’s tendency to oversell and under-deliver. How many “game-changing” platforms have I watched get hyped to the moon only to quietly disappear two years later? How many times have I sat in meetings where executives throw around terms they don’t understand, demanding impossible timelines for implementing technologies they’ve never actually used?

So when AI started gaining traction, my initial reaction was… skepticism. Here we go again, right? Another shiny object for leadership to chase while engineering teams scramble to figure out what any of it actually means. Another round of venture capital money flooding into solutions looking for problems.

The “Oh Shit” Moment

Except this time, something was different.

I started playing with ChatGPT early on—not because I was particularly excited about it, but because I’m cursed with curiosity and an ADHD brain that won’t let me ignore interesting patterns. And within about twenty minutes of that first conversation, I had this sinking realization: this isn’t hype. This is the real deal.

Not because it’s perfect—it’s absolutely not perfect. The hallucinations, the biases, the way it confidently tells you completely wrong information with the authority of a tenured professor… all of that is still there. But the underlying capability? The fundamental shift in what’s suddenly possible? That’s legitimate.

The Smartphone Moment

I’ve watched entire industries get disrupted by technologies that seemed impossible just a few years prior. The smartphone didn’t just replace flip phones; it fundamentally changed how humans interact with information, with each other, with commerce, with reality itself. And most of us saw it coming, even if we didn’t quite grasp the full implications.

AI feels like that, except the timeline is compressed and the scope is… well, potentially everything.

Why It’s No Longer Optional

Here’s what I mean by “it’s not an option anymore”: your competitors are already using it. Maybe not well, maybe not intelligently, but they’re using it. The companies that figure out how to integrate AI thoughtfully, strategically, into their workflows and products—they’re going to have advantages that compound over time.

And I don’t just mean the obvious stuff like customer service chatbots or automated email responses. I mean the deeper integrations. Code generation that doesn’t just spit out boilerplate but actually understands context and architecture. Data analysis that can spot patterns human analysts would miss. Content creation that can maintain voice and tone across thousands of pieces.

The ADHD Brain’s Dream Assistant

The ADHD part of my brain loves this, honestly. AI is like having a really patient assistant who never gets annoyed when I interrupt them mid-sentence to chase down a random tangent. Need help brainstorming? AI’s got infinite patience for my scattered thoughts. Need to context-switch between six different projects? AI remembers where I left off on each one.

The Coming Wave of Bad Implementations

But the jaded part of me—the part that’s been through enough tech cycles to recognize the patterns—knows that we’re about to see a lot of really bad AI implementations. Companies rushing to slap “AI-powered” on everything without thinking through the implications. Startups burning through millions of dollars building “AI solutions” that are just glorified search engines. Enterprise software vendors retrofitting their legacy products with half-baked AI features that make everything slower and more confusing.

The thing is, though, that doesn’t change the fundamental equation. Just because there will be a lot of bad implementations doesn’t mean the technology itself isn’t transformative. The internet spawned a million terrible websites, but it still changed everything. Mobile apps gave us countless pieces of digital garbage, but smartphones are still revolutionary.

So here’s where I land on this: AI isn’t optional because the world is already changing around it. Your customers are starting to expect AI-level responsiveness and personalization. Your employees are already using AI tools to be more productive, whether you’ve officially approved them or not. Your competitors are building AI advantages that will be harder to catch up to six months from now than they are today.

The Real Question

The question isn’t whether to adopt AI—it’s how to adopt it intelligently.

And that’s where the idealist in me gets excited again, because I think there’s an opportunity here to do this right. To build AI integrations that actually solve problems instead of creating new ones. To use these tools to augment human creativity and capability rather than replace human judgment and empathy.

But it requires being intentional. It requires understanding what AI is actually good at versus what we’re pretending it’s good at. It requires building systems that degrade gracefully when AI makes mistakes—and it will make mistakes. It requires thinking through the ethical implications, the bias implications, the long-term consequences of the shortcuts we’re tempted to take.

Mostly, it requires accepting that this train has already left the station, and our choice is whether to get on board thoughtfully or get run over by it.

Choosing Thoughtfully

I choose thoughtfully. Even if my ADHD brain wants to jump straight to the fun implementation details, and even though my industry-jaded side is already cringing at all the ways this could go wrong.

Because here’s the thing: the future is being written right now, by people who are building with AI today. And I’d rather be part of shaping that future than reacting to it five years from now.

Enhancing Customer Engagement through Digital Transformation

Tue, 04 Nov 2025 00:00:00 +0000

In today’s fast-paced digital world, businesses face the ongoing challenge of meeting and exceeding customer expectations. Digital transformation has become a game-changing strategy that empowers companies to improve customer engagement and strengthen their relationships with clients. This blog post explores the significance of digital transformation, the pivotal role of cloud technologies, and examples of brands successfully implementing these strategies.

What’s This Digital Transformation Buzz?

Digital transformation means integrating digital technology into every aspect of a business, fundamentally changing how organizations operate and deliver value to customers. It’s not merely a matter of adopting new software; it encompasses a cultural shift where businesses rethink their processes and how they connect with customers in our increasingly tech-savvy age.

The Importance of Customer Engagement

Customer engagement is crucial for any business aiming to thrive in today’s competitive landscape. Engaged customers are not only satisfied customers; they are also enthusiastic advocates who promote the brand. Research indicates that companies excelling in customer engagement can achieve up to 24% higher revenue growth compared to their competitors. This underscores the need for organizations to prioritize engagement strategies.

Digital transformation enhances customer engagement by equipping businesses with the tools to understand and connect with their customers more effectively. Here’s how:

1. Personalization Like Never Before

One of the key benefits of digital transformation is the ability to deliver highly personalized experiences. With the help of cloud technologies and advanced data analytics, businesses can collect and analyze customer data, allowing them to tailor offerings to individual preferences.

For instance, Netflix employs sophisticated algorithms to recommend shows based on viewing history and user ratings. This level of personalization fosters customer loyalty, as individuals feel that the brand understands their preferences.

2. Multi-Channel Communication: Connecting the Dots

Today’s customers expect to engage with brands across multiple channels—be it social media, email, or chatbots. Digital transformation facilitates a continuous multi-platform experience, ensuring that every interaction is seamless and consistent.

Imagine a customer starting a conversation on Instagram, continuing it via email, and completing a purchase through a mobile app. This fluidity is what customers desire, and cloud technologies play a significant role in synchronizing customer data across various platforms. Brands that master this approach can build stronger relationships with their customers.

3. Boosting Customer Support with Tech

Long wait times for customer support are a common frustration. Digital transformation addresses this issue by leveraging cloud-based solutions, such as chatbots, to provide instant responses to frequently asked questions.

The rapid growth of AI has turned what used to be a challenging tool to build into a simply drag-and-drop solution, with options like Azure Copilot offering low- or zero-code tools to build your own chatbot in minutes.

Companies like Zappos have embraced this approach, offering 24/7 support across multiple platforms to ensure customers can access assistance whenever they need it. This commitment to accessibility enhances customer satisfaction and fosters loyalty.

4. Gathering Feedback Like a Pro

Digital transformation opens the door to real-time feedback collection. Tools like surveys, social media monitoring, and online reviews enable businesses to gauge customer sentiment and make informed improvements.

Platforms such as Google Forms and SurveyMonkey simplify the process of collecting feedback and analyzing it in the cloud. When businesses act on this feedback—addressing concerns or amplifying positive experiences—they demonstrate that customer opinions matter, leading to deeper engagement.

5. Engaging Content and Community Building

To capture customer attention, creating engaging content is essential. Digital transformation equips brands with the tools to produce high-quality content efficiently, whether through blogs, videos, or social media posts.

Moreover, fostering online communities enables brands to build stronger relationships with customers. By creating spaces for customers to connect—such as Facebook Groups or dedicated forums—companies can cultivate a loyal following. When customers feel part of a community, they are more likely to remain engaged and advocate for the brand.

Real-World Examples of Success

1. Starbucks

Starbucks has effectively leveraged digital transformation through its mobile app. The app allows customers to order and pay ahead, earn rewards, and receive personalized offers, creating a seamless experience that encourages repeat business. Their cloud-based infrastructure ensures efficient operations and data management.

2. Nike

Nike has embraced digital transformation by integrating advanced technology into its products and customer interactions. The Nike app offers personalized training plans and product recommendations while connecting users to a community of fellow athletes. By blending technology with a focus on athletic culture, Nike has strengthened its bond with customers, enhancing loyalty and engagement.

3. Sephora

Sephora has harnessed digital transformation to create personalized shopping experiences both online and in-store. Their app allows customers to try virtual makeup looks, receive tailored product suggestions, and access exclusive content. By leveraging technology to elevate the beauty shopping experience, Sephora keeps customers engaged and excited.

Wrap-up

Digital transformation is more than just a trend; it is a vital strategy for businesses seeking to enhance customer engagement in today’s digital landscape. By leveraging cloud technologies to create personalized experiences, streamline communication, and gather valuable insights, companies can forge deeper connections with their customers.

As demonstrated by leading brands such as Starbucks, Nike, and Sephora, successful digital transformation leads to increased customer loyalty and satisfaction. Therefore, embracing innovative digital solutions is not merely an option; it is a necessity for organizations that wish to remain relevant and competitive.

In an era where customer expectations are continuously evolving, adopting digital transformation strategies is essential for building lasting relationships with customers and achieving sustained business success.

Unfiltered thoughts from an ADHD Engineer

Sun, 09 Mar 2025 00:00:00 +0000

The Silent Struggle of ADHD

So here’s the thing about having ADHD in tech: nobody talks about it. Not really. We’ll talk about imposter syndrome and burnout and work-life balance, but we won’t talk about what it’s like to have a brain that works differently in an industry that expects you to think in straight lines.

As someone who’s been aware of their ADHD diagnosis for the last 20+ years, this is nothing new. And yet despite all of that time, and the many years spent trying to better understand how my brain works, I still only somewhat have a grasp on what is going on inside my mind. We don’t fully understand ADHD (nor the treatment options) from a scientific perspective, so how can I be expected to understand it as a regular person?

And now I’m trying to figure out how to talk about it professionally without sounding like I’m making excuses or asking for special treatment. Because the truth is, ADHD is both my biggest professional asset and my biggest professional liability, sometimes within the same hour.

It’s Not a Deficit: It’s Dopamine Disregulation

But here’s what I wish more people understood: ADHD isn’t really a deficit disorder, despite what the name suggests. It’s fundamentally about dopamine regulation—or more accurately, the inability to regulate dopamine distribution in your brain.

Dopamine is your brain’s reward and motivation system. It’s what makes tasks feel rewarding, what helps you maintain attention on things that aren’t immediately interesting, what drives executive function. In neurotypical brains, dopamine gets released in predictable ways that help maintain steady attention and motivation.

In ADHD brains? It’s chaos. We don’t produce dopamine consistently, and we don’t distribute it where it needs to go when it needs to be there. This explains so much about why we function the way we do.

The Dopamine Feast or Famine

Sometimes we get massive dopamine floods from interesting, novel, or challenging tasks, which is what creates hyperfocus. Your brain finally gets the chemical reward it’s been craving, so it latches onto that activity with laser intensity. This is why I can spend fourteen hours straight debugging a fascinating race condition but can’t force myself to spend twenty minutes updating documentation.

Other times, we’re running on dopamine fumes. Boring but necessary tasks don’t trigger any reward response, so our brains literally can’t engage. It’s not laziness or lack of discipline: it’s neurochemistry. The executive function circuits that control planning, working memory, and task switching all depend on adequate dopamine, and when it’s not there, they just… don’t work.

This feast-or-famine cycle also explains why ADHD brains are at higher risk for addiction. We’re constantly seeking that dopamine hit, whether it’s from stimulating work, caffeine, social media, video games, or unfortunately sometimes more dangerous substances. Our brains are literally wired to chase rewards because we’re not getting consistent baseline dopamine like neurotypical people do.

The Superpowers (Yes, Really)

Let me start with the good stuff, because there is good stuff, even though most of the literature focuses on deficits and disorders. The ADHD brain is really, really good at pattern recognition. I can look at a codebase and immediately spot the places where abstractions are breaking down, where complexity is accumulating, where the architecture is starting to buckle under its own weight.

I can context-switch between projects faster than most people can remember what they were working on. I can hold multiple complex mental models in my head simultaneously. I can see connections between seemingly unrelated problems and come up with solutions that nobody else was considering.

When I’m in hyperfocus mode (which is basically the ADHD equivalent of flow state, except more intense and less controllable) I can solve problems that have been stumping teams for weeks. I’ll disappear into code for eight hours straight and emerge with some elegant solution that addresses not just the immediate problem but three other issues that nobody even realized were related.

These are genuine strengths. They’re not consolation prizes or silver linings. They’re legitimate advantages that have made me better at my job in measurable ways.

The Hidden Costs

But here’s the thing: they come with costs that are really hard to explain to people whose brains work more predictably.

When Hyperfocus Goes Wrong

Let me talk about hyperfocus for a moment, because everyone seems to think it’s a superpower without understanding the downsides. Yes, hyperfocus can be incredibly productive. But it’s also completely involuntary and often inappropriate.

Hyperfocus happens when something finally triggers enough dopamine to flood your attention circuits. The problem is, you can’t control what triggers it or when it stops. I can hyperfocus on optimizing a function that saves three milliseconds while completely ignoring the critical bug that’s affecting thousands of users. I can spend six hours researching the perfect keyboard instead of working on the deadline that’s tomorrow.

And when you’re in hyperfocus, everything else ceases to exist. I’ll forget to eat, forget to go to the bathroom, forget that I have a family. I’ve missed meetings, ignored urgent messages, and worked through severe dehydration because my brain was locked onto something and wouldn’t let go. This isn’t my experience only; this is a pattern that most people will ADHD will recognize immediately. “Have you had food/water today” is a question that is honestly not asked of me enough.

Executive Function: When the CEO of Your Brain Goes Missing

Then there’s executive functioning (or in this case, disfunctioning): basically the CEO of your brain. This is the element of your mind that is supposed to prioritize tasks, manage working memory, control impulses, and coordinate between different brain regions. In ADHD brains, that CEO is either completely absent or drunk on the job.

This is why I can architect systems that handle millions of users but need seventeen different productivity apps to remember basic tasks. The part of my brain that can solve complex technical problems is completely separate from the part that’s supposed to manage day-to-day executive tasks. And when dopamine isn’t flowing to the executive function circuits, that CEO just… doesn’t show up to work.

It’s not that I don’t care about responding to emails or submitting timesheets. It’s that the neurochemical infrastructure required to prioritize and remember those tasks literally isn’t functioning properly.

The Dopamine-Seeking Trap

Here’s something nobody talks about: ADHD brains are basically addiction machines. Not because we have addictive personalities, but because we’re constantly seeking the dopamine hits that neurotypical brains get naturally.

This shows up in tech work in ways that can look productive on the surface. The endless scroll through Hacker News or Reddit “for research.” The compulsive checking of notifications, email, Slack. The inability to step away from a problem even when you’re exhausted because your brain finally found something that’s giving it the chemical reward it craves.

I’ve seen ADHD engineers burn out not because they were working too hard, but because they couldn’t stop working once they found something stimulating. They’d code for eighteen hours straight, forget to eat, ignore their families, and crash completely when the dopamine finally ran out.

The addiction risk is real and higher than most people realize. Stimulants (including caffeine), alcohol, gaming, social media, even workaholism: they’re all ways our brains try to self-medicate the dopamine disregulation. The line between “passionate about technology” and “addicted to the dopamine hit of solving problems” can be frighteningly thin.

The Invisible Struggle

And the worst part is that these struggles are invisible. When someone sees me solve a complex technical problem quickly, they assume I’m just naturally organized and productive. They don’t see the internal chaos, the constant background noise of half-formed thoughts and mental tangents, the exhaustion that comes from fighting your own brain all day just to appear normal.

They don’t see the imposter syndrome that kicks in when hyperfocus wears off and suddenly I can’t remember how I solved that problem or whether I actually understand it as well as I thought I did.

They definitely don’t see the shame spiral that happens when I miss deadlines not because the work is hard, but because I couldn’t make myself do the boring parts. Or when I have to ask someone to repeat instructions for the third time because my mind wandered during the explanation.

The Diversity We Don’t Talk About

The tech industry loves to talk about diversity and inclusion, but we mostly mean visible diversity. We’re getting better at recognizing that teams work better when they include different backgrounds and perspectives and experiences. We’re slowly learning to accommodate different working styles and communication preferences.

But we’re still not great at accommodating different cognitive styles. We still design processes and practices around neurotypical assumptions about attention and motivation and memory.

When Agile Isn’t So Agile

Take agile ceremonies, for example. Daily standups are supposed to be quick check-ins, but they often turn into rambling status updates that go on for thirty minutes. For someone with ADHD, that’s torture. My attention starts wandering after about five minutes, and by the end I can’t remember what anyone said, including what I said about my own work.

Retros are supposed to be collaborative reflection sessions, but they’re often dominated by whoever talks the loudest or thinks the fastest. The ADHD brain has great insights, but they often come in bursts, or tangents, or connections that seem random until you explain the underlying logic. By the time I’ve figured out how to articulate a thought, the conversation has moved on.

Project planning sessions assume that you can estimate how long tasks will take based on their apparent complexity. But ADHD brains don’t work that way. Sometimes the “easy” task takes forever because it’s boring and my brain won’t engage with it. Sometimes the “hard” task gets done in two hours because it triggered hyperfocus.

I’ve learned to work around most of these issues—I take detailed notes during meetings, I break large tasks into smaller pieces, I use timers and reminders and accountability systems. But it’s exhausting to constantly translate between how my brain works and how the workplace expects it to work.

The Performance of Normal

I’m one of the lucky ones. I’m in a field where being weird and obsessive about details is often seen as a positive trait. I work for companies that value results over process, where being able to solve hard problems quickly matters more than being able to sit still during long meetings.

But I still feel like I’m performing “normal” most of the time. Still worrying that if people knew how much effort it takes me to do things that seem effortless for others, they’d question whether I actually belong here.

The truth is, I don’t know if I’m a good engineer despite having ADHD or because of it. Probably both. The pattern recognition and systems thinking and ability to see connections—those come from the same brain that can’t remember where I put my keys or what I had for lunch.

We’re Not Alone

What I do know is that there are a lot of us out there, more than most people realize. Engineers and designers and product managers and CTOs who are quietly struggling with executive function and working memory and attention regulation while trying to build the future.

And maybe we’d all benefit from talking about it more. From acknowledging that diversity includes cognitive diversity, that different brains solve problems differently, that the weird kid who can’t sit still during meetings might be the same person who sees the solution that nobody else was looking for.

Worth Celebrating

Ultimately, the best technology comes from people who think differently. Who see patterns others miss, who ask questions others don’t think to ask, who approach problems from angles that seem random until they reveal insights that change everything.

That’s worth protecting. That’s worth accommodating. That’s worth celebrating.

Even if it means figuring out how to make meetings shorter.

What it takes to get hired

Sun, 19 Jan 2025 00:00:00 +0000

I’ve been on both sides of the hiring table enough times now to have some opinions about this whole process, and honestly, most of them are pretty depressing. The way we hire engineers is fundamentally broken, but it’s broken in predictable ways that you can game if you understand the system.

Which is itself part of the problem.

Let me start with the obvious stuff that everyone knows but nobody wants to admit: technical interviews don’t really test your ability to do the job. They test your ability to solve algorithmic puzzles on a whiteboard under pressure while someone watches you and judges your every move.

I’ve hired people who aced the leetcode-style questions but couldn’t debug a production issue to save their lives. I’ve also passed on people who struggled with the toy problems but had the kind of deep systems knowledge that comes from years of actually building and maintaining real software.

The disconnect is obvious once you think about it. When was the last time you had to implement a binary search tree at work? When was the last time you had to reverse a linked list? When was the last time you solved any problem without access to Google, Stack Overflow, and the ability to run your code to see if it works?

But we keep doing these interviews because they’re easy to standardize, easy to score, and they feel objective. They give hiring managers the illusion that they’re making rational, data-driven decisions rather than subjective judgments about whether they like a person or not.

The reality is much messier.

What actually matters for doing engineering work well? Let me think about the best engineers I’ve worked with and what made them effective:

They could break down complex problems into manageable pieces. They could debug issues systematically rather than randomly trying different things until something worked. They could read and understand existing code quickly. They could communicate technical concepts clearly to both technical and non-technical audiences. They could collaborate effectively with teammates who had different working styles and communication preferences.

They had good instincts about when to optimize and when to ship. They understood the business context around their technical decisions. They could balance perfectionism with pragmatism. They could learn new technologies quickly without getting overwhelmed by choice.

Most importantly, they gave a shit. They cared about the quality of their work, about the experience of people using their software, about the maintainability of the systems they built.

None of these qualities are tested by traditional technical interviews. Some of them can’t be tested in an interview at all—they only emerge over time, through actually working with someone.

So what does get you hired? Unfortunately, it’s mostly signal rather than substance.

You need to be able to perform competence rather than just demonstrate it. You need to tell a compelling story about your background and your interests. You need to ask thoughtful questions that show you’ve researched the company and understand their challenges. You need to seem enthusiastic about their specific problems rather than just looking for any job.

You need to practice the artificial interview questions until you can solve them smoothly and confidently, even though they have nothing to do with the work you’ll actually be doing. You need to be able to explain your thought process clearly while solving these problems, which is a completely different skill from actually solving them.

And increasingly, you need to have the right keywords on your resume and the right projects in your GitHub. The screening process has become so automated that humans often never see candidates who don’t match the exact technology stack the company thinks they need.

This is particularly challenging for people whose careers don’t fit the standard narrative. If you’re changing careers, if you took time off, if you learned to code through non-traditional paths, if your experience is broad rather than deep in trendy technologies—you’re at a disadvantage regardless of your actual capabilities.

The ADHD angle makes this even more complicated. Many of us are really good at the actual work but terrible at the performance aspects of interviewing. We might nail the technical questions but struggle with the behavioral questions that require constructing coherent narratives about past experiences.

Or we might do great in casual technical conversations but freeze up when asked to solve problems on a whiteboard with someone watching. The artificial pressure and lack of normal tools can trigger anxiety that has nothing to do with technical ability.

I’ve bombed interviews for jobs I could have done in my sleep, and I’ve gotten offers for roles that were probably above my level at the time, all based on how well I performed in the interview rather than how well I could actually do the work.

The optimist in me wants to believe this is changing. Some companies are experimenting with take-home projects, pair programming sessions, code review demonstrations, and other formats that better simulate actual work. The rise of remote work and AI tools have made it easier for companies to hire based on demonstrated ability rather than cultural fit.

But the pessimist in me knows that most companies are still using the same broken process because it’s what everyone else does, and changing hiring practices is risky and expensive.

So what’s the practical advice for people trying to navigate this system?

First, accept that the game is partly artificial and prepare accordingly. Practice the leetcode problems, not because they matter, but because hiring managers think they matter. Get good at telling your story in a compelling way. Learn to project confidence even when you’re not feeling it.

Second, focus on companies and roles where you can demonstrate your actual abilities. Look for places that value take-home projects, open source contributions, or other evidence of real work. Seek out startups and smaller companies that might be more flexible about their requirements.

Third, build genuine relationships in the industry. The best opportunities often come through referrals, where someone who knows your work can vouch for your abilities rather than relying on interviews alone.

Fourth, don’t take rejection personally. The hiring process is noisy and often arbitrary. I know excellent engineers who got rejected by companies they would have thrived at, simply because the interview process didn’t capture their strengths.

Finally, (this is by far the most important step:) once you’re in a position to influence hiring-whether as a senior engineer, tech lead, or manager-try to make the process better. Push for interview formats that actually test relevant skills. Advocate for candidates whose experience doesn’t fit the standard template. Question whether you’re optimizing for interview performance or job performance.

Because ultimately, the goal should be building great teams that can solve interesting problems effectively. And that requires people with diverse backgrounds, experiences, and ways of thinking.

The current hiring process doesn’t optimize for that. But maybe we can slowly make it better.