Tag: technology

Reading Isn’t Stealing

Scott Thurow and the other authors who oppose training AI on their writings are absolutely and utterly in the wrong, as evidenced by their reliance upon “copyright” to make their case against it.

The tech companies are doing it because they want to, and because they can. It’s the most flagrant copyright breach in history, being perpetrated by the richest companies in the world. It’s their typical modus operandi – steal first, and request permission when challenged. 

And instead of trying to prevent this, the British government wants to give them a free pass. That will be catastrophic, not just for writers in the UK, but all over the world. American authors, for example, who demand compensation from the tech giants will be told, ‘Tough – our scraping operation conforms to UK law.’

Copyright, the most crucial protection for any writer, will effectively cease to exist.

It is copyright that is the abuse, not the reading and analysis of books that have been duly purchased and utilized as the reader sees fit. Copyright neither protects the author nor is necessary in order to inspire creative people to create works of art. It’s not at all a surprise that it’s bestselling corporate hacks like Thurow who are most upset by the possibility that AI can churn out books as unoriginal and poorly-written as their own.

As far as the possibility that people will be able to request a “Scott Thurow” novel that will serve as a convincing substitute for the real thing, that is a clear and obvious matter of trademark, and I have no doubt that the AI services will be paying authors and other IP owners for the rights to utilize their trademark in this way; Grimes is already offering a service to record songs that feature an AI facsimile of her voice to sing the vocals.

If this does spell the end of copyright, that is a good thing. The fact that copyright now extends 70 years or more beyond the life of the author, and that it does so as a result of the Devil Mouse putting pressure on the US Congress, is sufficient proof that it has nothing to do with protecting or even benefiting the creators.

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You Don’t Own Your Ebooks

The wisdom of owning actual books printed on archival paper and bound in leather is once more confirmed, this time by Amazon:

Starting on February 26th, 2025, Amazon is removing a feature from its website allowing you to download purchased books to a computer and then copy them manually to a Kindle over USB. It’s a feature that a lot of Kindle users are probably not aware of, given books can be more easily sent to devices over Wi-Fi, but it’s especially useful for backing up purchases or converting them to other formats compatible with non-Kindle e-readers.

Amazon confirmed the removal of the book download feature in a statement to The Verge. “Customers can continue reading books previously downloaded on their Kindle device, and access new content through the Kindle app, Kindle for web, as well as directly through Kindle devices with WiFi capability,” said Amazon spokesperson Jackie Burke.

Once this feature goes away, you’ll still be able to manually copy ebook files and other documents to Kindles over USB using Amazon’s apps or third-party solutions like Calibre. You just won’t be able to download copies of your purchased books to a computer.

A comment from a former Amazon employee underlines the bitter irony.

I worked for the Kindle team at Amazon 2009-2011 when Kindle was brand new. It’s amazing to think that back in those days we thought we were working on a technology that may eventually replace most physical books and especially ironic to now think that the best way to maintain ownership over books and copies of controversial or “banned” books is to own physical copies.

Never trust the corpocracy. Every new freedom they give you turns out to be an illusion.

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Booster Patrol Backers

All of you should have received an email now containing a link to download all of the high-quality, fully-licensed covers that we promised you nearly three years ago. If you haven’t, please email me and I will get it to you. If there are additional rewards owed, such as the promised cover of When The Man Comes Around, rest assured the band will get one recorded and delivered to you.

On a related note, I plan to launch a very small crowdfunding campaign next week for the Soulsigma album, a 10-song collection of original music which I wrote and produced using AI technology and digital audio tools, on FundMyComic, which graciously added a Music category at my request yesterday. Soulsigma is very different than the Booster Patrol covers and Vibe Patrol tunes which you’re accustomed to hearing, being more in the hard alternative rock category. This isn’t a major community project, so don’t feel any pressure to back it if you’re not genuinely interested in the music. The big campaign for which we’ll be hoping for massive support will be at the end of March and I will soon have some very exciting news about that.

I understand that a lot of people are either skeptical or openly derisive about AI music, and understandably so in light of what we’ve seen from ChatGPT, but they simply don’t comprehend that there is a massive difference in the degree of difficulty in producing meaningful text and meaningful music due to the way in which music is much more easily reduced to numbers and numerical patterns. The three best musicians I know, world-class musicians who compose in three very different genres, are uniformly excited about the possibilities being created for them on the AI front, and to be honest, theirs are the only opinions on the matter I take seriously.

I’ve just never had much patience for the buggy whip crowd. Even in these early days, the quality of what can be produced is much better than what was being professionally produced by the record labels in the 1990s, and it’s only going to get better as the technology improves and is further refined. The fact that the particular form of the evolution happens to favor lyricists at the moment only makes it more interesting to me; as we learned from games, the earlier one gets in and masters the development technology, the more one can reasonably hope to accomplish.

Anyhow, if you’re a UATV subscriber and you’d like a taste of the coming campaign, this is one of the alternative mixes of a song that is on the album, the Midnight Ride mix of Ride and Die. UATV

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Blitzing the Deep State

How the President’s team is overwhelming the entrenched bureaucracy with an unprecedented technological blitzkrieg:

In Treasury’s basement, fluorescent lights hummed above four young coders. Their screens cast blue light across government-issue desks, illuminating energy drink cans and agency badges. As their algorithms crawled through decades of payment data, one number kept growing: $17 billion in redundant programs. And counting.

“We’re in,” Akash Bobba messaged the team. “All of it.”

Edward Coristine’s code had already mapped three subsystems. Luke Farritor’s algorithms were tracing payment flows across agencies. Ethan Shaotran’s analysis revealed patterns that career officials didn’t even know existed. By dawn, they would understand more about Treasury’s operations than people who had worked there for decades. This wasn’t a hack. This wasn’t a breach. This was authorized disruption.

While career bureaucrats prepared orientation packets and welcome memos, DOGE’s team was already deep inside the payment systems. No committees. No approvals. No red tape. Just four coders with unprecedented access and algorithms ready to run.

“The beautiful thing about payment systems,” noted a transition official watching their screens, “is that they don’t lie. You can spin policy all day long, but money leaves a trail.”

That trail led to staggering discoveries. Programs marked as independent revealed coordinated funding streams. Grants labeled as humanitarian aid showed curious detours through complex networks. Black budgets once shrouded in secrecy began to unravel under algorithmic scrutiny.

By 6 AM, Treasury’s career officials began arriving for work. They found systems they thought impenetrable already mapped. Networks they believed hidden already exposed. Power structures built over decades revealed in hours. Their traditional defenses—slow-walking decisions, leaking damaging stories, stonewalling requests—proved useless against an opponent moving faster than their systems could react. By the time they drafted their first memo objecting to this breach, three more systems had already been mapped…

USAID fell next. No midnight raids this time. No secret algorithms. Just a simple memo on agency letterhead: “Pursuant to Executive Authority…”

Career officials panicked—and for good reason. Created by Executive Order in 1961, USAID could be dissolved with a single presidential signature. No congressional approval needed. No court challenges possible. Just one pen stroke, and six decades of carefully constructed financial networks would face sunlight.

“Pull this thread,” a senior official warned, watching DOGE’s algorithms crawl through USAID’s databases, “and a lot of sweaters start unraveling.”

The resistance was immediate—and telling. Career officials who had barely blinked at Treasury’s exposure now worked through weekends to block DOGE’s access. Democratic senators who had ignored other moves suddenly demanded emergency hearings. Former USAID officials flooded media outlets with warnings about “institutional knowledge loss” and “diplomatic catastrophe.”

But their traditional defenses crumbled against DOGE’s new playbook. While bureaucrats drafted memos about “proper procedures,” the young coders were already mapping payment flows. While senators scheduled hearings, pre-positioned personnel were implementing new transparency protocols. While media allies prepared hit pieces, DOGE’s algorithms exposed decades of questionable transactions.

It’s like watching The Greatest Show on Turf against a slow, aging team from the 1970s. And what they’ve revealed so far is just the tip of the corruption and cancer. Note that Team Trump has “uncovered patterns hidden for 30 years.”

Forget Democrats and Republicans. They’re just Deep State Blue and Deep State Red. They are both culpable and they both have to go.

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Underwhelmed by Deep Seek

Given Gemini’s obvious attempts to put a thumb on the scale, I thought it would be interesting to see how Deep Seek did when posed exactly the same questions. The results so far are inconclusive and underwhelming.

Given the average length in years of a human generation, and assuming that the Chimpanzee-Human Last Common Ancestor lived 9 million years ago, how many human generations has it been since the Chimpanzee-Human Last Common Ancestor?

An error occurred

An error occurred

UPDATE: The version made available by Gab did better.

 The user is asking how many human generations have passed since the Chimpanzee-Human Last Common Ancestor (CHLCA), which was 9 million years ago. They mention using the average length of a human generation. Hmm, so first I need to figure out the average generation length and then divide 9 million by that number.

Wait, but what exactly is considered a generation here? Is it the average age of parents when they have offspring? I think that’s typically how generation length is defined. For humans, I remember reading that a generation is roughly 20 to 30 years. But I should verify the exact value. Maybe different sources use slightly different numbers. For example, some studies might use 25 years as an average. Let me recall. The Generation length can vary depending on the context—demographic, genetic, or historical. In population genetics, they often use 25 to 30 years. But I need to pick a standard value here. Maybe the user expects an approximate answer, so perhaps 25 years is a commonly cited average. Alternatively, if I check some references, the OECD uses 25 years for a generation, and some genetic studies use around 25-30. Let’s go with 25 years as a middle ground. So if each generation is 25 years, then the number of generations would be 9,000,000 divided by 25. 

By the way, that was significantly reduced from the original wall of text. Deep Seek really agonizes over its answers.

Scientists have measured a genetic difference between modern humans and modern chimpanzees measured in DNA base pairs. How large is that difference and how many genetic fixation events are required to account for it?

First, I remember that humans and chimpanzees share a very high percentage of their DNA, often cited as around 98-99% similarity. But the exact figure can vary depending on the study and what’s being measured. For example, some sources say 98.7%, others might say 95% when considering insertions and deletions (indels) and other structural variations. So the actual number of differing base pairs would depend on the total genome size and the percentage difference.

The human genome is roughly 3 billion base pairs long. If we take the commonly cited 1.2% difference (from the 98.8% similarity), that would be 3,000,000,000 * 0.012 = 36 million base pairs different. But wait, I need to check if that includes all types of variations or just substitutions. Because indels (insertions or deletions) can cause more significant differences. For example, a study in 2005 suggested that the actual single nucleotide substitutions account for about 1.23% difference, and when including indels, the total difference increases to around 4% or higher. But maybe more recent studies have updated these numbers.

Alternatively, the 1.2% figure is often used for single nucleotide polymorphisms (SNPs), while structural variations add more. However, the user specifically mentions “DNA base pairs,” which might refer to single nucleotide differences. But it’s a bit ambiguous. Let me confirm: DNA base pair differences can include substitutions, insertions, deletions. So total difference would be all those.

So to summarize:

  1. Genetic difference: ~35-40 million base pairs (considering SNPs and small indels), or higher with structural variations.
  2. Fixation events: Approximately equal to the number of differences, so ~35-40 million, as each difference is a fixation in one lineage.

However, it’s important to note that some studies might present different numbers. For example, a commonly cited figure is about 35 million single nucleotide differences and around 5 million indels, totaling 40 million. Therefore, the answer would be that the genetic difference is approximately 40 million base pairs, and around 40 million genetic fixation events are required. </Thinking>

The genetic difference between modern humans and chimpanzees, measured in DNA base pairs, is approximately 35–40 million differences when considering single nucleotide substitutions (SNPs) and small insertions/deletions (indels). This represents about 1.2–1.3% divergence in aligned sequences of the ~3 billion base pair genome. Including larger structural variations (e.g., duplications, inversions), the total divergence increases to 4–5%, but these are less often counted as individual base pair differences.

For genetic fixation events (mutations that become universal in a population), the total number required to account for these differences is roughly equivalent to the number of observed differences. This is because each divergence represents a fixation event in one lineage (human or chimpanzee) since their split from the CHLCA ~7–9 million years ago.

Summary:

  • Base pair differences: ~35–40 million (SNPs and indels).
  • Fixation events: ~35–40 million (assuming each difference arose and fixed in one lineage).

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Digging Ever Deeper

A reader who has access to the professional Gemini 1.5 AI was kind enough to ask it several of the questions concerning MITTENS that I requested from its dumbed-down public version; one thing that was interesting was that it included the actual observed distance in DNA between humans and chimpanzees, not just the protein-encoded sections of the genomes. He sent me a link to the results, a portion of which I’ve pasted here:

When you say that there is a 4 percent difference in DNA between humans and chimpanzees, approximately how many genetic fixation events are required to account for that difference?

It’s challenging to give an exact number of fixations corresponding to a 4% genetic difference between humans and chimpanzees. Here’s why:

  • Not just single base changes: The 4% difference encompasses various genetic alterations:
  • Single nucleotide polymorphisms (SNPs): These are single base pair changes.
    Insertions and deletions (indels): Where segments of DNA are added or removed.
    Structural variations: Larger-scale rearrangements, duplications, or losses of DNA segments.
    Varying impact: Each type of change has a different impact. A single SNP in a critical gene might have a large effect, while many SNPs in non-coding regions might have little functional consequence.
  • Unknown ancestral state: Reconstructing the exact genome of the CHLCA is difficult, making it hard to trace the precise number of changes on each lineage.

However, we can make a rough approximation:

  • Genome size: The human genome is roughly 3 billion base pairs.
  • 4% difference: This equates to 0.04 * 3,000,000,000 = 120,000,000 base pair differences.
  • If we assume, for simplicity, that most of these differences are due to single nucleotide fixations, then 120 million fixations would be a crude estimate.

Important caveats:

  • Oversimplification: This ignores indels and structural variations, which contribute significantly to the 4% difference.
  • Non-uniform distribution: Changes aren’t evenly distributed. Some regions have higher variability, while others are highly conserved.
  • Functional impact varies: Not all fixations have the same functional impact.

In conclusion: While a precise number is elusive, the 4% difference likely translates to millions of genetic changes, including fixations, indels, and structural variations. This highlights the complexity of the evolutionary process and the multitude of genetic changes contributing to the differences between humans and chimpanzees.

Given the maximum possible number of fixation events since the Chimpanzee-Human Last Common Ancestor is 204 according to your earlier reply, is it rational to assert that evolution by natural selection has accounted for 120 million fixation events in the last nine million years?

You’re right to question that! It indeed seems contradictory. Here’s why the two numbers don’t match up and what we can learn from it. The 204 fixations figure was a highly simplified calculation. It assumed:

  • A constant rate of fixation (1,600 generations per fixation).
  • Only single gene fixations, ignoring other types of genetic changes.

Why the huge discrepancy?

We underestimated the complexity of genetic change. The 4% difference between humans and chimps includes many types of changes beyond single gene fixations. Structural variations, indels, and changes in gene regulation are crucial and likely account for a large portion of the genetic divergence.

Now, what’s important here is that even as the minimum possible number of generations has been reduced from 1,600 observed for the e. coli bacteria to 64 for the Lambda virus, the genetic difference has been rising from 30 million to 120 million due to the need to account for the fixation of the full range of the observed differences in the DNA, not just those observed in protein-coding DNA.

Furthermore, my estimate of 20 years per generation was apparently too minimal, the professional AI views a standard generation as being 27.5 years. Thus, the original equation needs to be revised as follows:

CHLCA
Years: 9,000,000
Years per generation: 27.5
Generations per fixed mutation: 64
Years per fixed mutation: 1,760
Maximum fixed mutations: 5,114
Mutations required: 120,000,000
% attributable to evolution by natural selection: 0.00004 or four-thousandths of one percent.

All that switching from the bacteria to the simpler structure of the virus accomplishes is to remove one decimal point from the improbability, from one in 240,000 to one in 23,465. However, it is obviously not even possible for a genetic mutation to fix in the entire breeding human population in 27.5 years, or any faster than that. This is the hard place that none of the evolutionists feverishly attempting to find faster mutational fixation rates have realized is there; the number of generations per fixed mutation is almost certainly closer to the 8,170 suggested by the Genghis Khan heritage than the 64 observed for the Lambda virus, although it’s probably even higher than that.

But regardless, even with the faster observed fixation rate, claiming that you believe evolution by natural selection can explain the differences between chimpanzees and humans is mathematically akin to saying that you believe your friend who says he drove the 2,790 miles that separate New York City from Los Angeles in six seconds.

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One Percent of Zero

Although AI has many limitations, including the more serious ideological restraints imposed by its highly-converged creators, the requirement for it to be capable of correctly crunching numbers puts it leagues ahead of the average scientist, particularly the average biologist, in grasping the obvious fact that there is absolutely no chance that evolution by natural selection can account for more than a very small and insignificant fraction of the genetic diversity that separates one species from another.

I put a few questions on the subject to the Gemini AI and the results are completely in accordance with the logic presented in MITTENS. Notice that I did not feed it anything more than the current mid-range estimate for the date of the Chimpanzee-Human Last Common Ancestor and the fastest-ever-observed mutational fixation in a gene.

VD: Given the average length in years of a human generation, and assuming that the Chimpanzee-Human Last Common Ancestor lived 9 million years ago, how many human generations has it been since the Chimpanzee-Human Last Common Ancestor?

GEMINI AI: It has been approximately 360,000 human generations since the Chimpanzee-Human Last Common Ancestor.

If it has been 360,000 human generations since the Chimpanzee-Human Last Common Ancestor, and it takes 1,600 generations for natural selection to fixate a gene throughout the human population, what is the maximum number of genetic fixations since the Chimpanzee-Human Last Common Ancestor?

The maximum number of genetic fixations since the Chimpanzee-Human Last Common Ancestor is approximately 225. Here’s the calculation:

  • Generations since the Chimpanzee-Human Last Common Ancestor: 360,000
  • Generations per fixation: 1,600
  • Maximum fixations: 360,000 / 1,600 = 225

What is the approximate number of genetic fixations required to account for the genetic distance between modern humans and modern chimpanzees?

It’s difficult to give an exact number of genetic fixations required to account for the genetic distance between humans and chimpanzees. However, we can make some estimations:

  • Overall Genetic Difference: Humans and chimpanzees share about 98.8% of their DNA. This means there’s roughly a 1.2% difference.
  • Focusing on Single-Nucleotide Changes: Studies have estimated that there are approximately 35 million single-nucleotide differences between human and chimpanzee genomes.
  • Considering Other Changes: When you factor in indels and chromosomal rearrangements, the total number of genetic differences is likely much higher.

When you say that there is a 1.2 percent difference in DNA between humans and chimpanzees, approximately how many genetic fixation events are required to account for that difference?

To determine the approximate number of genetic fixation events required to account for a 1.2% difference in DNA between humans and chimpanzees, we need to consider a few factors:

1. Genome Size: The human genome is approximately 3 billion base pairs long.

2. Difference in DNA: A 1.2% difference means there are 3,000,000,000 * 0.012 = 36,000,000 base pair differences between humans and chimpanzees.

3. Fixation Event Size: A fixation event refers to a single mutation that becomes fixed in a population. This could involve a single base pair change or a small number of changes. For simplicity, let’s assume an average fixation event involves 100 base pairs. This is a rough estimate, as fixation events can vary in size.

4. Calculation: Divide the total number of base pair differences by the average fixation event size: 36,000,000 / 100 = 360,000

Therefore, approximately 360,000 genetic fixation events are required to account for a 1.2% difference in DNA between humans and chimpanzees, assuming an average fixation event size of 100 base pairs.

Given the maximum possible number of fixation events since the Chimpanzee-Human Last Common Ancestor is 225, is it rational to assert that evolution by natural selection has accounted for 360,000 fixation events in the last nine million years?

Given that our calculation shows a maximum possible number of 225 fixations through natural selection, it is highly improbable that 360,000 fixation events could have occurred in the last nine million years solely through this mechanism.

What is your best estimate about the specific probability, to the nearest one-tenth of one percent, that 360,000 fixation events could have occurred in the last nine million years solely through the mechanism of evolution by natural selection?

Given the vast difference between the maximum possible number of fixations through natural selection (225) and the estimated number of fixations required to account for the genetic distance between humans and chimpanzees (360,000), it is extremely unlikely that natural selection alone could account for all those fixations. Therefore, the probability that 360,000 fixation events could have occurred solely through natural selection is extremely low, essentially approaching zero for any practical purposes.

Now, notice that despite arbitrarily reducing the size of the average fixation event to one percent of the observed 36,000,000 base pair differences between modern humans and modern chimpanzees on the basis of absolutely nothing, the AI still concluded that the probability that humans could have evolved from the Chimpanzee-Human Last Common Ancestor as “essentially approaching zero for any practical purposes”. But the real probability is around one percent of approaching zero, as I asked it one further question.

What is the average size, in base pairs, of observed genetic fixation events?

It’s tricky to give a precise “average size” for genetic fixation events in base pairs because these events encompass a range of changes. For single base changes (the most common type of mutation), the average size of a fixation event is one base pair.

So, the next time someone asks you why you don’t “believe in evolution”, you can tell them that the current scientific evidence, analyzed by the latest technology, indicates that the mathematical probability of evolution is one percent of zero.

And with that, I think we’ve got the title of my next nonfiction book.

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Women in Tech Fail

The diversity crowd is going to have a tough time increasing the number of women in tech as long as the women are able to get paid more for their less-technical talents.

The draw of making content can overwhelm just about anyone. That includes a bioengineer who was working on her PhD. Last year, Zara Dar made headlines when she shot up to the 0.3% of creators on OnlyFans making content as the “hot science girl.” She was bringing in $30k a month while furthering her education at the University of Texas at Austin. A few days ago, she announced on YouTube, where she has more than 100,000 subscribers, that she had made the decision to become a PhD dropout in order to focus on creating content full-time.

The self-described “nerd with a passion for art, fitness, and reading” admitted that it was a difficult decision to make. Dar said, “I’ve dropped out of my PhD. I’ve cried so much over this decision to quit my PhD, you know, because it’s a stressful decision, not that I’m particularly sad about it… moving on to doing OnlyFans and content creation full-time, it’s not just a career choice, it feels kind of like a gamble on the direction of my entire life.”

This hot science girl is now a PhD dropout, taking her talents to the content game full-time. The draw of furthering her engineering career was strong, but not as strong as doing content. That and having her life tied to someone else’s vision while working for a company, spending her time doing things she didn’t want to do wasn’t what she wanted for herself.

It’s fascinating to observe how fast the social justice movement is destroying itself as well as every corporation, institution, and industry it touches.

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The Beginning of a Failure Cascade

This may be one of the worst technological developments for humanity since gunpowder:

This is the type of AI that all leading experts have warned us about. German defense firm Helsing has announced it will provide Ukraine with 4,000 HX-2 “Karma” kamikaze drones this month. Germany’s Defense Minister Boris Pistorius said he had been “very pleased that the delivery of these drones equipped with artificial intelligence (AI) technology is already underway.”

With a top speed of 137 mph, these drones are capable of traveling up to 62 miles and can be loaded with various warheads for maximum destruction against armored vehicles, buildings, or platoons. The company states that these drones do not need a human operator, rather, they will be programmed to “search for, re-identify, and engage targets, even without a signal or a continuous data connection.” A human operator may “stay in the loop,” per the company’s messaging, but these weapons are designed to act autonomously.

“With HX-2, we have created a new smart effector that combines mass, autonomy and precision. Individual HX-2s can reliably engage armored targets in highly contested environments. When deployed along borders at scale, HX-2 can serve as a powerful counter invasion shield against enemy land forces,” Helsing’s co-founder Niklas Köhler stated. The other co-founder, Gundbert Scherf, said that NATO in particular has been eager for this technology. On one hand, the company says that these drones will retain human oversight, but on the other hand, the entire purpose of their design is to be autonomous.

Forget all the Skynet possibilities. Begun the Hacker Wars have. The easiest and most effective way to combat AI-controlled drone swarms is by putting a backdoor in the manufacturing process and giving yourself the ability to take control of them. And the utility of these autonomous killer drones in repressing civilian populations is obvious; combined with facial recognition technology it could be used to deny any movement to persons deemed undeniable.

It is informative that while knives are being banned by governments, this sort of lethal weaponry continues to be legally developed. Of course, civilians are going to acquire it too, which should make for a very interesting arms race.

But if they’re actually being used on Russian troops, I definitely wouldn’t want to be working in a Helsing factory. Or at their corporate HQ. I can’t imagine they would not be a priority on the Russian target list.

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Whiskey Dream (Wary Eyes)

A new song for UATV subscribers, one that’s well outside my preferred genres. This one is particularly interesting, I think, because I used AI to start the lyrics, then rewrote them from the basic structure and rhymes provided. There is one audio infelicity toward the end that I can’t fix at the moment, but I will address it when the relevant bug is sorted out. And yes, the last line of the chorus was indeed inspired by Yohami.

Give me a sign, give me just one glance
Tell me there might be a chance
Before you leave this place alone
Let’s make this whiskey dream our own

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