 Image generated with ChatGPT. Artificial Intelligence (AI) is one of those topics where the conversation can quickly become either utopian or apocalyptic. Depending on who you ask, AI is either going to solve every problem or take every job (and sometimes both). The truth, as usual, is more interesting and more complicated. That is why I found this video useful and wanted to share it. It takes a calm, practical look at artificial intelligence without pretending the risks are imaginary. The central message is a good one: AI is real, powerful, and disruptive, but it is still a tool.Today’s AI does not think, scheme, or secretly plan a robot uprising. It predicts patterns, generates plausible responses, analyzes data, and helps people navigate complex information. That makes it useful. It does not make it trustworthy by default. One of the strongest parts of the video is that it separates artificial intelligence from chatbots. Tools like ChatGPT, Claude, Gemini, and image generators are the most visible examples of AI right now, but they are only one part of a much larger field. AI is already used in medicine, logistics, fraud detection, recommendation systems, spam filtering, scientific research, manufacturing, and infrastructure. In many cases, AI is not dramatic at all. It is invisible machinery helping complicated systems function better. That point matters for digital literacy. If we only think of AI as “the chatbot that writes essays or makes funny images”, we misunderstand both its usefulness and its risks. AI is better understood as a broad set of tools for recognizing patterns, generating drafts, sorting information, and supporting decisions. Sometimes that is extremely helpful. Sometimes it is dangerously misleading.The video also explains one of the most important habits for using AI well: Verification. Large language models are not databases of truth. They are pattern engines. They can summarize, draft, explain, translate, code, and brainstorm, but they can also produce confident nonsense. The practical lesson is not to reject AI or trust it blindly. The lesson is to guide it, question it, check it, and understand what kind of task it is suited for. I also appreciated the discussion of jobs. The video does not pretend disruption will be painless. Some work will be automated. Some career paths will change. Some people will be hurt by bad transitions. But it also avoids the simplistic conclusion that AI automatically makes human beings obsolete. New tools often amplify human capability.They change what skills matter, what work is valuable, and how people enter professions. That is one of the real questions for schools, libraries, workplaces, and public institutions: How do we help people adapt when the tools change this quickly?For anyone looking for a quick rundown of artificial intelligence, this video is a useful place to start. It is not a short five-minute explainer, but it is clear, accessible, and grounded. It covers the promise, the risks, the economic disruption, the limits, and the need for human judgment. My main takeaway is practical: Learn how AI works, use it carefully, do not surrender your judgment to it, and do not assume tomorrow’s machines are already here today. Don’t panic. Pay attention. Stay curious. Recommended Viewing: ‘Don’t Panic: A Guide to Artificial Intelligence’ by Science & Futurism with Isaac Arthur.
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I recently finished reading ‘Program or Be Programmed: Eleven Commands for the AI Future’ by Douglas Rushkoff. Its central claim is simple. The technologies we use are not neutral tools. They carry assumptions about time, identity, truth, relationships, and value. When we accept defaults without awareness, we end up living according to those assumptions. Most modern systems are optimized for efficiency, scale, engagement, and prediction. Those priorities are not inherently wrong, but they are not synonymous with human flourishing. If left unexamined, they quietly reshape our habits, our expectations, and even our sense of what it means to be present with one another. Rushkoff’s eleven commands function less as rules and more as calibration points. They help us recognize the built-in biases of digital systems and reclaim agency in how we use them. I recommend reading the book, but I also wanted to share the eleven commands here. For each one, I’ve included the bias it addresses, the liability it creates, the opportunity it enables, and a tiny practice you can use to practically incorporate the command into your daily life.  Image generated with ChatGPT. 1) Time — Do Not Be Always OnTech Bias: Platforms are engineered for continuous engagement. “Now” is the only time that matters. Notifications are gravity wells for attention. Liability: You live in reactive mode and confuse urgency with importance. Sleep, focus, and deep work erode. Opportunity: Treat your attention like a telescope. A telescope is powerful because it’s aimed. Constant scanning doesn’t reveal faint galaxies. Stillness does. Tiny Practice:
2) Place — Live In PersonTech Bias: Remote, scalable interaction is rewarded. Embodied local life is treated like inefficiency. Liability: You get lots of contact and less connection. Context collapses. Everything becomes a comment thread. Opportunity: In-person life is high-bandwidth. Libraries understand this instinctively. A room full of humans is a different internet. An internet that is slower, warmer, and more accountable. Tiny Practice:
3) Choice — You May Always Choose None of the AboveTech Bias: Interfaces push binary choices: Like/dislike, accept/decline, upvote/downvote, subscribe/leave, buy now/miss out. Liability: You get shepherded into options that serve the platform’s goals, not yours. Opportunity: “None of the above” is a superpower. It’s how you reclaim agency. Tiny Practice: Before clicking anything important, ask:
4) Complexity — You Are Never Completely RightTech Bias: Algorithms reward certainty and confidence. Nuance performs poorly. Outrage and anger performs extremely well. Liability: You get pulled toward overconfidence. You start arguing to win, not to learn. Opportunity: Complexity is not a weakness. Reality is layered, contingent, and rarely just black and white. Tiny Practice: Add one sentence to your hot takes:
5) Scale — One Size Does Not Fit AllTech Bias: Digital systems love scale: Uniform rules, one interface, one policy, one feed, one “community standard”. Liability: Local needs get steamrolled. People become “users”. Edge cases become invisible. Opportunity: Build small, adaptable systems where feedback can actually change the shape of the tool. Libraries are anti-scale by design. Even in a large system, each branch community adapts its own way of doing things. Tiny practice:
6) Identity — Be YourselfTech Bias: Platforms encourage performative identity: Branding, engagement metrics, persona maintenance. You become a product with a posting schedule. Liability: You drift from authenticity into optimization. You start “being” for the algorithm. Opportunity: Identity is not a static profile; it’s a living process. AI makes this tricky because it can mirror you back a cleaner, more marketable version of yourself. Don’t confuse that with your actual self. Tiny Practice:
7) Social — Do Not Sell Your FriendsTech Bias: Social networks are monetized. Relationships become data. Sharing becomes extraction. Even the language shifts as friends become “connections”. Liability: Social life becomes transactional, trackable, and subtly performative. Opportunity: Rebuild a commons mentality. Relationships are not inventory. Communities should not be strip-mined for engagement. Tiny Practice:
8) Fact — Tell The TruthTech Bias: Virality outruns verification. AI can generate plausible nonsense at industrial scale. Incentives reward the compelling, not the correct. Liability: Epistemic collapse: You stop trying to know what’s real, or you pick a tribe (a “truth team”). Opportunity: Truth-telling becomes a cultural skill again: Cite sources, verify claims, contextualize, revise, and employ nuance. Tiny Practice: Before sharing, pause and verify one key claim.
9) Openness — Share, Don’t StealTech Bias: Copy is effortless. Ownership is muddy. AI training and scraping amplify this by treating creation as raw material. Liability: Creators get hollowed out. People stop making original work because it feels pointless. Opportunity: Practice ethical sharing: Credit sources, ask permission when needed, and build reciprocity. Tiny Practice:
10) Purpose — Program Or Be ProgrammedTech Bias: Tools shape behaviour. If you use default settings, you accept default goals. Many systems are optimized for revenue, engagement, surveillance, and lock-in. Liability: You become a passenger in your own life—nudged, directed, puppeted. Opportunity: Purpose is writing the requirements document for your tech. What is this tool for? What is it not for? Tiny Practice: For any new app or workflow, complete the following sentences:
11) AI — Value The HumanTech Bias: AI reduces the world into what can be measured, predicted, categorized, and optimized. It’s a powerful pattern engine. Liability: You outsource judgment. Machine confidence replaces human wisdom. People get treated like inputs and outputs. Opportunity: Use AI as a tool, not an authority. Tiny Practice:
Stay CalibratedEvery tool has a bias: Toward speed, scale, extraction, certainty. Mindfulness means noticing that bias. Curiosity means questioning and asking whether it aligns with your values. Agency means adjusting accordingly. Remain attentive to the technologies you use and the biases they carry. With curiosity and mindfulness, you can ensure your tools serve your purposes rather than quietly programming your life. Technology should serve you. Not the reverse.  Image generated with ChatGPT.
I started with a simple goal: Cut the clutter and minimize my screens. It had been a while since I last organized my apps. The number of apps had increased, my categories had drifted, and while I could still find what I wanted, the less than optimal organization was slowing me down. After reviewing all my apps, I decided on the target of organizing them all into three screens.
Once I saw how many “daily” apps I wanted, I split the first page into two: One for general utilities (camera, calendar, messages, notes, photos, clock, settings) and one for social/health/media (LinkedIn, WhatsApp, Hoopla, Libby, Health, Fitness, ChatGPT, etc.). That separation lessened the visual noise and made room for some widgets. For widgets I added ones for Weather, Fitness, ChatGPT, Notes, and Night Sky. My final four screens:
While I chose the core apps and widgets myself, ChatGPT helped immensely with the rest. I fed it my complete “Everything Else” app list as screenshots and asked for short, clear, memorable folder names and sensible groupings. It spotted overlaps I’d missed, suggested intuitive labels, and turned a procrastination project into a one-session cleanup.  Image generated with ChatGPT. If your home screens are due for a reset (and especially if you’re stuck or short on time) use ChatGPT (or your preferred generative AI) as your sorting partner. It won’t choose what matters to you, but it will speed up decisions, sharpen your categories, and help you complete your reorganization today instead of “someday”.
We might be at the precipice of a fundamental transformation in our relationship with technology. Familiar computing paradigms—desktop metaphors, point-and-click interfaces, and even voice assistants—are evolving into something profoundly more personal, intuitive, and interconnected. At the core of this shift is the concept of an AI Operating System (AI OS): A context-aware, intelligent companion that learns, adapts, teaches, and collaborates in real-time. This emerging reality is driven by rapid advancements in multimodal large language models (LLMs), embedded sensors, and distributed AI ecosystems. An AI OS represents a paradigm shift in AI assistance. A shift from commanding machines to a more symbiotic relationship. A Personalized, Adaptive RelationshipImagine an AI OS that leverages contextual data through direct access to cameras, microphones, biometric sensors, and user data. By doing so it could become capable of interpreting your emotional state, recognizing subtle gestures, body language, and vocal nuances. It wouldn’t simply respond to commands but to how you feel, move, and engage. The result would be a deeply personalized user experience that transforms your devices from static tools into responsive collaborators. Whether you're composing documents, debugging code, preparing presentations, or experiencing creative blocks, an AI OS would attune itself uniquely to you. It would recognize your patterns, preferences, and goals, proactively adapting its support. For instance, an AI OS might gently suggest a break if it detects rising stress, offer visual aids if it knows you're a visual learner, or autonomously generate helpful resources when sensing your intention or struggle. Over time, this nuanced understanding would craft an interaction that feels profoundly intimate. Your technology would grow with you, enhancing efficiency and emotional connection in tandem. From Local to Global IntelligenceThe true potential of an AI OS arises when we consider that AI will become ubiquitous, integrated into everything from smartphones and smart homes to vehicles and public spaces. These intelligent systems will communicate and collaborate, creating a dynamic ecosystem of networked intelligence. Imagine your smart glasses recognizing objects and synchronizing silently with your AI OS to present relevant information instantaneously. Your home AI might sense elevated stress after work, prompting your AI OS to suggest relaxation exercises, playing video games, reading, or watching your favourite video show, all while rescheduling less critical tasks. In professional settings, interconnected AI agents could streamline collaboration, anticipate challenges, and transparently mediate conflicts, fostering more productive interactions. This interconnected intelligence surpasses mere productivity. It reshapes our collaborative processes, education systems, healthcare approaches, and governance models, amplifying critical thinking, creativity, and informed decision-making throughout society. A New Cognitive InfrastructureThe convergence of AI capabilities into an operating system would not only be a technological leap but a socio-cultural transformation. An AI OS blurs digital and cognitive boundaries, enabling users to accomplish complex tasks through intuitive dialogue rather than technical mastery alone. The societal implications are profound:
This shift redefines human-computer interactions at a societal scale, bringing us closer to a reality that was previously only imagined in science fiction. Cautious, Grounded OptimismYet, this promising future demands careful consideration. The depth of personal and contextual data required by an AI OS raises significant ethical questions around privacy, transparency, consent, and security. Risks of misinterpretation, manipulation, or over-dependence highlight the necessity of responsible, human-centric development. However, with thoughtful design prioritizing human flourishing, an AI OS holds extraordinary promise—not to replace humanity but to amplify it. It can foster creativity, expand knowledge, increase productivity, and enhance emotional and cognitive well-being. The Future: Not Just Smarter Devices, but Smarter LivesUltimately an AI OS signifies a shift from operating systems managing files and applications to operating selves. Merging tools, intelligence, and emotional understanding into a unified experience for living, learning, and creating. As AI becomes more embedded, empathetic, and socially integrated, our relationships with technology will become more meaningful. We are no longer simply designing interfaces; we are creating and guiding relationships with intelligent machines that listen, adapt, and evolve with us. This marks not only a technological breakthrough but a cultural renaissance, heralding a future of genuine human-AI symbiosis: A future we must build mindfully, courageously, and optimistically. I’m Excited. Are You? Image generated with ChatGPT.
Two recent stories serve as a powerful reminder: Generative AI must always be fact-checked. Human oversight isn’t optional. It’s essential. In one story, major newspapers including the Chicago Sun-Times and The Philadelphia Inquirer published a summer reading list with books that didn’t exist. Ten of the fifteen titles were completely fabricated by AI but falsely attributed to real authors like Isabel Allende and Percival Everett. The list, syndicated by King Features, slipped through editorial review and misled readers, damaging trust in both AI-assisted writing and journalism. In the other story, covered by the CBC, lawyers are facing disciplinary action for citing AI-generated legal cases that never existed. These “hallucinations” might have appeared convincing on the surface, but were entirely fiction. This highlights how insufficient human oversight over generative AI outputs can put clients, court outcomes, and careers at risk. As the CBC article notes, “AI tools, such as ChatGPT, are not information retrieval devices but tools that match patterns in language. The result can be inaccurate information that looks ‘quite real’ but is in fact fabricated.” These incidents highlight a key truth: Generative AI is a supercharged autocomplete, not a database or search engine. It predicts what should come next based on patterns, not understanding. It doesn’t know facts. It guesses. That kind of predictive power can be useful, but without proper review, it can just as easily produce elegant and convincing nonsense. If we use AI in our work, we must treat its output as a starting point—something to refine, verify, and build upon—not as a finished product or reliable source. Verification is non-negotiable. Every citation, name, date, and fact needs to be reviewed. The AI might not know better. We must.  Image generated with ChatGPT.
On May 6th and 7th, I attended the Manitoba Libraries Conference hosted by the Manitoba Library Association (MLA). As an MLA member, I deeply appreciate this gathering. It's an invaluable opportunity to reconnect with colleagues, discover innovative practices, and reflect on my own professional growth. This year was especially exciting as I co-presented a session titled "Demystifying ChatGPT: AI Innovations for Libraries & Digital Repositories" alongside Mike Ellis.  Day 1: Insights, Ideas, and AI InnovationsThe conference began with a powerful keynote by Niigaan Sinclair. Niigaan, an Anishinaabe professor from Peguis First Nation, immediately captured my attention with his compelling storytelling and incisive commentary. He contextualized Manitoba’s past and present, thoughtfully reflecting on the Legislative building and the statues toppled in recent years. His point about the absence of Indigenous representation being akin to starting a story at chapter two was particularly impactful. Niigan’s discussion on generational change, highlighted by Manitoba electing Canada’s first Indigenous premier, Wag Kinew, provided insight and perspective. His masterful balance between serious topics, such as residential schools and red dress day, and his use of humour underscored the value of open and straightforward conversations. The first session I attended, “Not Just for Kids: Engaging Adults and Building Community Using Storytime and Music Programs”, led by Austin Matheson and Brittany Lagasse from Winnipeg Public Library, was delightful. It expanded my perspective on adult programming and reminded me of the potential for community-building through creative initiatives like ukulele jams. Given that my previous assistant branch head occasionally serenaded us with her ukulele, this session triggered some memories. After preparing the laptop for my presentation, I quickly assembled a delicious lunch plate, though I had to temporarily stash it behind the projector screen. Despite starting slightly late due to the lunchtime rush, Mike and I had an impressive turnout, with attendees overflowing onto the floor! Mike’s engaging case study on PastFORWARD, Winnipeg Public Library’s digital repository, showcased an innovative AI application in archiving and elicited both laughter and lively participation from the audience. Although time for questions was limited, attendees raised insightful queries about generative AI trained on creative commons materials and the environmental implications of AI. Post-session, I enjoyed meaningful one-on-one discussions about generative AI and potential applications, including possibilities for interlibrary loan systems. Afterward, I enjoyed my lunch in the main hall and had an engaging conversation with Trevor, a new connection who shared interests in generative AI, libraries, astronomy, camping, and world travel. The afternoon continued with enlightening lightning talks on diverse library initiatives, from updating furniture (“Hold on to Your Seat - Or Don't!”) to enhancing bilingual collections and supporting male caregivers in early literacy programs. These brief yet impactful presentations sparked numerous programming ideas for my own library. The day concluded wonderfully with finger foods and mingling, leaving me eager for day two. Day 2: Exploring Library Practice and PhilosophyDay two started with the MLA Annual General Meeting, providing a relaxed and productive beginning to the day. It was wonderful connecting with colleagues over coffee, meeting new faces, and exchanging insights. The first session of the day, “Nature Programs in a Rural Public Library: Hatching Chicks and Growing Vegetables”, inspired fresh ideas for nature-focused programming. Learning about initiatives like donating produce grown in library gardens reinforced the innovative ways libraries serve their communities. “Staff Picks: A Fun, Online Readers’ Advisory Program Model for Your Library” provided practical inspiration for an upcoming autumn ‘Staff Picks’ display. A valuable takeaway from this session was the reminder that “tech should be a tool that supports what you do, not dictate it.” The session “In Search of the Lost Library”, presented by librarians from the University of Winnipeg, demonstrated creative solutions for addressing discrepancies in catalogue entries. While their final solution didn’t utilize generative AI, their recognition of it as a potential solution brought a smile to my face, aligning with my interest in integrating AI into library workflows. Lunch and the awards ceremony, featuring speaker Chimwemwe Undi, were enjoyable and celebratory. Congratulations to all award winners! In the afternoon, Sam Popowich’s session, “The Cultural Politics of Libraries”, was particularly thought-provoking. Sam compellingly argued for recognizing libraries as politically active institutions, examining the 'enlightenment' versus 'social control' perspectives on library history. After the session, Sam generously gifted me his book, "Solving Names: Worldliness and Metaphysics in Librarianship", a thoughtful gesture and a read I’ve already begun to enjoy. The final session I attended, “The Burnt-Out Librarian: Moving on From Vocational Awe”, tackled an important yet often overlooked issue. Carolyn and Monique shared personal experiences and offered practical strategies to address burnout, reinforcing the importance of maintaining healthy engagement with our profession. If you’re interested in exploring the content from my session, I’ve included two versions of the presentation slides in PDF format: a short presentation version (as delivered at the conference) and a more detailed version for deeper context and explanation. I hope these resources offer insight into our session and inspire new ways to explore the role of generative AI in libraries. Reflecting on these two enriching days, I felt a great sense of community and connection. The Manitoba Libraries Conference reaffirmed my passion for librarianship, highlighted extraordinary work happening throughout Manitoba, and reinforced my belief that librarians and library workers truly do rule. Until next time!  The digital world is brimming with information—but not all of it is accurate. With AI-generated content flooding our feeds and misinformation becoming more sophisticated, verifying facts has never been more crucial. Whether you're researching for work, keeping up with the news, or simply scrolling through social media, sharpening your fact-checking skills can help you separate truth from deception. Here’s how to sharpen your perception, enhance your awareness, and become a more informed consumer of information.  Generated with DALL·E. Five Key Strategies for Verifying Information1. Research the Author or Organization A source’s credibility matters. Before trusting information, investigate who is behind it:
2. Use Smart Search Techniques Finding reliable sources quickly depends on how you search. Here are a few techniques to refine your results:
3. Verify the Original Source Many articles cite secondhand sources—but are they trustworthy?
4. Consult Fact-Checking Websites Independent fact-checkers help cut through the noise. Some recommended resources include:
5. Pause and Reflect If a claim sparks an emotional reaction, that’s a red flag. Misinformation thrives on outrage and urgency. Before sharing or believing a story, take a step back and ask:
Beyond the Basics: Fact-Checking FrameworksThe SIFT Method: A Fast, Effective Approach Mike Caulfield’s SIFT method offers a quick way to assess information:
The P.R.O.V.E.N. Method: A Deeper Dive For more thorough evaluation, use the P.R.O.V.E.N. method:
Lateral Reading: Thinking Like a Fact-Checker Instead of staying on one page, open new tabs and check:
Final Thoughts: Build Your Information ResilienceAI-generated content isn’t going anywhere, and misinformation continues to evolve. Developing strong fact-checking habits keeps you informed and protects you from misleading claims. Next time you come across a viral story, a surprising statistic, or a claim that seems off, take a moment to verify before you share. The more we question, the better we can navigate today’s information landscape.  Generated with DALL·E. What’s Your Go-To Fact-Checking Method?Have a favourite strategy or a trusted source you rely on? Let’s discuss in the comments!
The internet is a vast, ever-expanding landscape of information, social connection, and convenience. But just like any city with bustling streets and hidden alleyways, navigating the digital world requires caution. Online safety isn’t just about avoiding obvious scams—it’s about developing a mindset that keeps your personal information secure, your devices protected, and your digital footprint under control.  Generated with DALL·E. What is Online Safety?Online safety refers to the practices and precautions individuals take to protect themselves, their personal data, and their digital identities from cyber threats. These threats range from phishing scams and malware to identity theft and privacy breaches. Staying safe online means understanding these risks and actively taking steps to reduce them. Main Facets of Online Safety
Tips, Best Practices, and Rules of Thumb1. Strengthen Your Passwords
2. Recognize and Avoid Phishing Scams
3. Protect Your Devices with Security Software
4. Be Mindful of Your Digital Footprint
5. Browse Safely and Avoid Suspicious Websites
6. Stay Vigilant on Social Media
7. Verify Before Trusting Online Information
8. Shop and Bank Securely Online
Final Thoughts: Stay Informed, Stay SecureOnline safety isn’t a one-time action—it’s an ongoing practice. Cyber threats are constantly evolving, but by staying informed, adopting secure habits, and using common sense, you can navigate the digital world with confidence. Whether you’re shopping, socializing, or simply browsing, a little caution goes a long way in protecting yourself from potential threats. By treating your online presence like you would your home—locking doors (passwords), checking visitors (verifying links), and securing valuables (personal data)—you can enjoy the internet’s benefits while minimizing its risks. Stay safe, stay smart, and stay cyber-aware! Generated with DALL·E.
I've always been curious about how well ChatGPT can generate functional code. To test its capabilities, I decided to start with something relatively simple but still interactive: coding a basic Space Invaders game. I wanted to see how well ChatGPT could generate a working program, how adaptable it would be to my requests, and whether I could refine and improve the code through iterative prompts. This experience turned into an engaging coding experiment, showing me just how powerful AI-assisted development can be.  Generated with DALL·E. Defining the ProjectSpace Invaders is a classic arcade game where the player controls a spaceship that moves left and right, shooting enemies descending from the top of the screen. The game involves essential programming concepts like:
Iterating on the CodeI began by asking ChatGPT to generate a basic Space Invaders game in CodeSkulptor. The initial version included:
The Final ResultBy the end of this experiment, I had a functional and customizable Space Invaders game running in CodeSkulptor. The iterative process demonstrated how well ChatGPT can understand and implement coding requests, allowing for quick modifications and enhancements. For those interested, I highly recommend trying out the code in CodeSkulptor and playing around with your own modifications. The experience is a great way to learn how game logic works while also exploring AI-assisted development. Download the Code Below
 Generated with DALL·E. Looking Ahead: The Power of AI Coding AssistanceThis experience left me excited to continue exploring coding with ChatGPT. Now that o3-mini-high is available—a model that is supposed to be even more proficient at coding—I’m even more eager to see how it improves code generation, debugging, and refactoring. With each iteration, AI models are becoming more adept at understanding context, implementing changes effectively, and even suggesting improvements I might not have thought of myself. If this simple Space Invaders game was just the beginning, I can only imagine how far AI-powered coding assistance can take us. Imagine being able to code entirely in natural language! Whether you're a beginner learning the basics or an experienced developer looking to prototype ideas quickly, ChatGPT is proving to be an invaluable tool in the coding process.
I've been playing around with ChatGPT for a while now, experimenting with its ability to generate and refine stories, especially those rooted in science. One of my latest projects was crafting a science fiction short story that balances scientific accuracy with a sense of curiosity and wonder—something in the flavour of Carl Sagan.  Generated with DALL·E. The process? A mix of notes, structured planning, AI-assisted brainstorming, research, and a lot of tweaking:
Final Touches: Bringing the Story to Life with Video & MusicTo enhance the experience, I experimented with Sora to create short videos for each chapter, the title screen, and ending. I compiled these into videos for the story, trying two different approaches:
On top of that, I wanted an atmospheric soundtrack, so I used ChatGPT to craft a dungeon synth instrumental prompt for Suno. I’ve been really into dungeon synth lately, and this story felt like the perfect inspiration for something melancholic, immersive, and cosmic. Suno generated two versions: After all that experimenting, refining, and assembling, here’s the final outcome: ‘Echoes of a Dying Star’—a story that explores the cosmic scale of a supernova through the perspectives of a doomed autonomous research probe, a distant spaceship, Earth-based observers, and even the Andromeda Galaxy. Check out the story, and watch the accompanying videos below! Echoes of a Dying StarEchoes of a Dying Star - Video 1Echoes of a Dying Star - Video 2 |
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