When life gives you lemons...
LemonSlice Review: Why Cloud AI Video Generation Is Your Only Sane Option
My first AI assisted Podcast: Tips on creating cinematic videos.
In the last blog post, I was very excited to discover Notebook LLM as a study tool for various topics. And I love the fact that it can even generate 20-minute podcasts for you to listen to on your commute. It also produces short podcasts around five minutes long. So a logical thought was, why not try to make a talking head video?
After a 17-hour render on my RTX 4090 died during a brief power outage, I learned a brutal lesson: local AI-generated talking-head video is a fool's errand. My attempt using ComfyUI Infinite Flow still required 20 minutes of manual audio separation in CapCut to isolate the male and female speakers in my Notebook LLM podcast. All that work evaporated in a three-second power outage.
LemonSlice's cloud-based solution isn't just better—it's the only viable path forward. At $40/month for the pro plan, you generate roughly ten 5-minute videos. That's $4 per video, and after my catastrophic loss, it feels like insurance. The platform even automatically detects and assigns male and female voices from Notebook LLM podcasts to appropriate speakers—eliminating my manual CapCut nightmare.
Version 2.5 produces stable but slightly stiff talking heads with minimal body movement. Version 2.7 offers more natural motion but burns through credits at several times the rate.
For content creators serious about converting AI podcasts into engaging video, Lemon Slice isn't just convenient—it's essential. The local GPU alternative combines technical headaches, hardware limitations, and risk. Sometimes the cloud isn't just easier; it's the only rational choice. My 17 hours wasted were the expensive education I needed to finally understand that.
Brain-Ups Over Push-Ups: The Real Anti-Aging Revolution
Our culture operates a brilliant grift: sell pre-teens products to look older, then sell adults anti-aging potions to look younger. We're trapped in a dizzying cycle of chasing youth instead of building substance. But clinging to physical glories—reliving your undefeated wrestling season or performing in your "dancing prime"—is just living backward. It's accepting stagnation under the illusion of preservation. Physiology prevents perpetual physical growth. You cannot push-up your way out of aging.
The only escape from becoming ossified is to grow what biology cannot constrain: your mind. Do brain-ups, not push-ups. Make it a daily practice to learn something that genuinely challenges your neural architecture. Not trivia, but skills that force you to grapple with uncertainty and sit with intellectual discomfort. This is how you become vastly wiser.
Not through knowledge accumulation, but by achieving true insight: seeing what everyone else has seen and thinking what no one else has thought. The awareness that you're one day closer to death isn't depressing—it's energizing. It creates urgency, giving your remaining days profound meaning.
True wisdom isn't knowing everything. It's knowing exactly what you don't know, then having the courage to explore that void. Let your body age. Your mind can expand forever.
Neil deGrassi Tyson on lifelong learning and growing older
How LLMs Are Quietly Destroying Computer Science Education
Large Language Models haven't just changed computer science education—they've gutted it. The golden age of CS required wrestling with complex problems until 3 AM, sleeping in libraries for two consecutive nights, and debugging data structure assignments through sheer intellectual stubbornness. That struggle built the critical thinking muscles that defined exceptional engineers.
Today? Students pay $19.99 a month for diploma insurance. Feed a three-sentence prompt, get 1,500 lines of code. No chewing on problems. No internalizing knowledge. Just instant, effortless output that bypasses the learning process entirely. This is creating a generation of developers who treat LLMs like junior engineers—tools that do the work without possessing underlying technical competency.
The result is a fragile house of cards: complex systems built on black boxes that the creator cannot debug or understand. If we don't change course, engineering faces its own 2008 financial crisis moment. Students perched atop the high-level language iceberg, oblivious to the foundational code beneath, will eventually encounter catastrophic problems their magic Vibe Coding tools cannot solve. The solution isn't banishing LLMs. It's using them as resources, not oracles.
Struggle remains the only path to mastery.
The code that comes easily won't keep our infrastructure running when it truly matters. That's when you'll need the muscles you never bothered to build.
Critical thinking is critical in the age of LLMs.
To aid me in writing this week's blog post, I tried Kimi.com, a new LLM that is supposed to be very good at both thinking and writing.
My dictation is done, as I've previously mentioned, using Wispr Flow. If you haven't checked out this amazing AI software, which runs on iOS or PC, you can use my referral code to unlock 1 month. https://wisprflow.ai/r?SEBASTIAN339,