One thing I often say in my talks is that this version of AI you’re using today is the worst it will ever be.

It’s not a knock—it’s a reminder. The pace of progress in AI is staggering. Features that were laughably bad just a year or two ago have quietly evolved into shockingly capable tools. Nowhere is this more obvious than with image generation.

Designers used to love dunking on AI-generated images. We’d share screenshots of twisted hands, off-kilter eyes, and text that looked like a keyboard sneezed. And for good reason—it was bad. But release by release, the edges have been smoothed. The hands make sense. The faces feel grounded. And the text? It finally looks like, well, text.

Miyazaki’s Legacy Meets AI

This all came to mind again recently when an old clip of Hayao Miyazaki started circulating. If you’re not familiar, Miyazaki is the legendary co-founder of Studio Ghibli, the anime studio behind Spirited Away, My Neighbor Totoro, and Princess Mononoke. His art style is iconic—whimsical, delicate, and instantly recognizable. Ghibli’s work isn’t just beautiful; it’s emotional. It feels human.

So when Miyazaki was shown an early AI-generated video years ago, his response was brutal:

“I strongly feel that this is an insult to life itself.”

Oof. But here we are in 2025, and now people are using ChatGPT’s new image generation feature to recreate scenes in Studio Ghibli’s style with eerie accuracy.

Of course, I had to try it.

And I have to admit—it’s impressive. Not just the style replication, but the fact that the entire composition gets pulled into that world. The lighting, the mood, the characters… the tool doesn’t just apply a filter. It understands the vibe.

Muppets, Comics, and Infographics, Oh My

Inspired by the experiment, I went down the rabbit hole.

First: Muppets. I blame my older brother James for this idea, but I started generating Muppet versions of our family and a few friends. The results were weirdly good—cheery felt faces, button eyes, and backgrounds that still somehow made sense. It even preserved details from the original photos, just muppet-ified.

Then I wondered—could this work for layout-driven design? What about infographics?

Again, it nailed it. The AI could not only generate visuals, but correctly layer and position readable, realistic text onto the images—a feat that was basically impossible in the early days of AI art.

So I pushed further: comics.

Could I recreate the clean simplicity of XKCD or the style of something like the popular The Far side comic strip?

From Toy to Tool

You can’t snap your fingers and expect instant results. But it’s no longer just a toy. It’s a creative partner—and if you’re a designer, marketer, or content creator, it’s something you should be exploring now.

And here’s the big takeaway. Even if the images don’t quite reach your final vision, they’re now good enough to prototype, storyboard, or inspire a full design process. The creative bar keeps rising—and so does the floor.

So if you haven’t played with ChatGPT’s image generation yet, try it out. Generate something weird. Make a comic. Turn yourself into a Muppet. Just remember: This is the worst version of the tool you’ll ever use.

Last week, I sat on a panel at the Maryland Technology Council’s Technology Transformation Conference to discuss Data Governance in the Age of AI alongside an incredible group of experts. During the Q&A, someone asked about DeepSeek and how it changes how we think about data usage—a question that speaks to a fundamental shift happening in AI.

When I give talks on AI, I often compare foundation models—AI models trained on vast datasets—to a high school or college graduate entering the workforce. These models are loaded with general knowledge, and just like a college graduate or a master’s degree holder, they may be specialized for particular industries.

If this analogy holds, models like ChatGPT and Claude are strong generalists, but what makes a company special is its secret sauce—the unique knowledge, processes, and experience that businesses invest heavily in teaching their employees. That’s why large proprietary datasets have been key to training AI, ensuring models understand an organization’s way of doing things.

DeepSeek changes this approach. Unlike traditional AI models trained on massive datasets, DeepSeek was built on a much smaller dataset—partly by distilling knowledge from other AI models (essentially asking OpenAI and others questions). Lacking billions of training examples, it had to adapt—which led to a breakthrough in reasoning. Instead of relying solely on preloaded knowledge, DeepSeek used reinforcement learning—a process of quizzing itself, reasoning through problems, and improving iteratively. The result? It became smarter without needing all the data upfront.

If we go back to that college graduate analogy, we’ve all worked with that one person who gets it. Someone who figures things out quickly, even if they don’t have the same background knowledge as others. That’s what’s happening with AI right now.

Over the last few weeks, every major AI company seems to be launching “reasoning models”—possibly following DeepSeek’s blueprint. These models use a process called Chain of Thought (COT), which allows them to analyze problems step by step, effectively “showing their work” as they reason through complex tasks. Think of it like a math teacher asking students to show their work—except now, AI can do the same, giving transparency into its decision-making process.

Don’t get me wrong—data is still insanely valuable. Now, the question is: Can a highly capable reasoning model using Chain of Thought deliver answers as effectively as a model pre-trained on billions of data points?

My guess? Yes.

This changes how companies may train AI models in the future. Instead of building massive proprietary datasets, businesses may be able to pull pre-built reasoning models off the shelf—just like hiring the best intern—and put them to work with far less effort.

Earlier this week, Meta began a round of job cuts and has signaled that 2025 will be a tough year. But they’re far from alone—Microsoft, Workday, Sonos, Salesforce, and several other tech companies have also announced layoffs, leaving thousands of professionals searching for new roles.

In the DMV (DC-Maryland-Virginia), the federal government is also facing unprecedented headwinds, with DOGE taking the lead on buyout packages and the shutdown of entire agencies, including USAID.

Like many of you, some of my friends and family were impacted, and one thing I hear over and over again? The job application process has become a nightmare.

Why Job Searching Feels Broken

For many, job hunting now means submitting tons of applications per week, navigating AI-powered screening tools, and attempting to “game” Applicant Tracking Systems (ATS) just to get noticed. If you’ve ever optimized a website for search engines (SEO), you already understand the challenge—your resume now needs to be written for AI just as much as for human reviewers.

As someone who has been a hiring manager, I know why these AI-powered filters exist. Companies receive an overwhelming number of applications, making AI screening tools a necessary first layer of evaluation—but they also mean that perfectly qualified candidates might never make it past the system.

To get past these filters, job seekers need to think like SEO strategists, using resume optimization techniques to increase their chances of reaching an actual hiring manager.

AI Resume Optimization Tips

To level the playing field, resume-scoring tools have been developed to help applicants evaluate their resumes against job descriptions and ATS filters. These tools offer insights such as:

• Include the job title in a prominent header.

• Match listed skills exactly as they appear in the job description.

• Avoid image-heavy or complex formats—ATS systems are bots parsing text, not designers.

• Optimize keyword density to align with job descriptions while keeping it readable.

• Ensure your resume meets the minimum qualifications—AI won’t infer missing experience.

Once you’ve optimized your resume with these strategies, AI-powered tools can help you analyze your resume against job descriptions to see how well it matches and provide targeted improvement suggestions.

Testing AI Resume Scoring with JobScan

To put this into practice, I submitted my resume to Jobscan to see how well it aligned with a Chief Technology Officer (CTO) job posting in Baltimore that I found on ZipRecruiter.

I’ll admit, Jobscan was a bit finicky at first and pushed hard for an upgrade, but once I got my resume and job description uploaded, it generated a report analyzing my match score and offering several helpful suggestions to improve my resume for the job description I provided.

The results provided a rating based on my resume’s content and offered useful insights, including:

• Hard and soft skills are mentioned in the job description and I should add.
• Missing sections or details that could improve my resume’s match.
• Formatting adjustments (like date formats) to improve ATS readability.

It also provided a very detailed report with suggestions to improve the readability, and density of keywords for example, the words “collaboration” and “innovation” were both used 3 times in the job description but the resume mentioned collaboration once, and innovation 6 times.

The tool also offers an option to provide a URL to the job listing it will identify the ATS being used and provide additional suggestions specific to what It knows about that tool.

ChatGPT for Resume Optimization

These days many of us have access to a free or paid version of AI tools like ChatGPT or Claude, so I decided to create a prompt and see how well it could help me. I crafted a prompt that spoke to my needs and provided it with the same resume and job description. For reference here is the prompt I used:

I need to optimize my resume for an AI-powered Applicant Tracking System (ATS) to improve my chances of passing the initial screening process. Below is the job description for the role I’m applying for, followed by my current resume.

Please analyze my resume against the job description and provide the following:

1. A match score or summary of how well my resume aligns with the job description.

2. Key skills, keywords, or qualifications from the job posting that are missing or need to be emphasized.

3. Suggestions for improving formatting and structure to ensure compatibility with ATS filters.

4. Any red flags or areas where my resume could be better tailored to the role.

Jobscan rated my resume at 49%, pointing out missing skills, formatting issues, and keyword gaps. On the other hand, ChatGPT, rated it between 80-85%, focusing more on content alignment rather than rigid formatting rules. However, it had great suggestions and naturally picked up on skills missing in my resume that exist in the job description.

While the ranking was different the recommendations and things ChatGPT pointed out are similar to the results of JobScan just not laid out as simply in a dashboard. This final recommendations section gives a pretty good overview of ChatGPT’s recommendations.

Beating the ATS Game

Most resumes now pass through an ATS before reaching a human hiring manager. Understanding how to optimize for these filters is critical in a competitive job market.

In conclusion, AI and resume-scanning tools have the potential to level the playing field for job seekers—provided they know how to leverage them effectively. And if traditional methods fall short, why not turn the tables? Use AI to go on the offensive, automating your job applications and maximizing your opportunities. Tools like Lazy Apply let AI handle the applications for you, so you can focus on landing the right role.

I’m beyond excited to announce AI in A Minor! The Mindgrub team and I have spent the last few months working to generate music and transform it into sheet music the amazing musicians at the BSO can perform. It feels incredible to know that soon you will have a chance to see what we’ve been working on.

I also can’t ask for a better team than the Baltimore Symphony Orchestra, Greater Baltimore Committee, and Amazon Web Services (AWS) to help make this happen.

Join us on August 9th!

Oh, we’re still looking for sponsors and anyone interested in setting up a booth in the BSO hall. If you want to buy tickets get them here!

It appears 9to5Google got it’s hands onto an early APK release of Meta’s Twitter competitor Threads in “Threads, Meta’s Twitter clone, starts launch countdown, plus a few details on how it works“:

Your Threads profile is also strongly connected to your Instagram profile. The two use the same username and display name, and it seems your Threads profile picture may have to be from Instagram. Additionally, anyone you block on one service is also blocked on the other.

I shouldn’t be surprised by the tight coupling to Instagram, but I am. The coupling between Facebook and Instagram has always felt forced and as if they stifle the personalities of the different platforms. Threads (as I expect it) will be heavily text-focused, while Instagram leans into photos and video. How often will cross-posting happen? 

Another unique aspect of Threads that many have been anticipating is the way it can connect to federated social networks like Mastodon (collectively known as the “fediverse”). It seems that Threads may not be ready to launch its fediverse features right away.

Soon, you’ll be able to follow and interact with people on other fediverse platforms, like Mastodon. They can also find you with your full username @username@threads.net.

The only other detail we could uncover about Threads’ integration with the fediverse is that if you choose to restrict replies on a post, it won’t be shared outside of the Threads app.

When you limit replies, your thread will not be shared with your fediverse followers.

Threads’ use of ActivityPub to connect into Mastodon and the collective Fediverse has long been a big question. In my newsletter, I compared Mastodon and the Fediverse to a network of towns, where each city has its form of government and content moderation rules. Threads’ appears to be a gated community that may allow its users to leave the gates and interact with others but still keep exclusive content limited to those within its gates.

This social experiment will be interesting, especially when a metric ton of Meta users who first interact with the larger Fediverse through Threads and branded “@username@threads.net” name. I hope the other cities play nice.

Digging deeper into the code, our team has also found that Threads may indeed have a web app. At the very least, we’ve found that the service’s profile links will look quite similar to Instagram profile links, simply appending your username after the base “threads.net/” URL.

I assumed the animated website for threads hinted at more than just an app.

Gizmodo has a piece on “Google Says It’ll Scrape Everything you Post Online for AI“:

One of the less obvious complications of the post ChatGPT world is the question of where data-hungry chatbots sourced their information. Companies including Google and OpenAI scraped vast portions of the internet to fuel their robot habits. It’s not at all clear that this is legal, and the next few years will see the courts wrestle with copyright questions that would have seemed like science fiction a few years ago. In the meantime, the phenomenon already affects consumers in some unexpected ways.

Twitter’s crazy rate-limiting meltdown and Reddit’s push to charge for API access are about one thing, AI data models. These systems are hungry for data, and access to that data will be vital to building the best AI models. Unsurprisingly, Google is making it known that as it ranks and offers prime search engine placement, all that delicious data is free game to them. When APIs become closed, people result to screen scrapping, and screen scrapping ends with paywalls and Twitter style rate-limiting… Wonder how this all plays out.