Echoes of a Forgotten world

 Who does not like a delicious conspiracy theory? The British author and journalist Graham Hancock in his books Fingerprints of the Gods and Magicians of the Gods, as well as the Netflix documentary series Ancient Apocalypse proposes an advanced global civilisation that existed during the last ice age. This society possessed sophisticated knowledge of astronomy, architecture, agriculture, navigation and spiritual practices, and well, they talked to plants lol. Hancock argues that this civilisation was seafaring and capable of long-distance ocean voyages. Now this civilisation has not been identified. The Indian archaeoastronomer Nilesh Nilkanth Oak claims that Sugriva, one of the vanar kings created a detailed world atlas 14,000 years ago. The theory is that Sugriva dispatched search parties that explored the entire world. The geographical descriptions align with Ice-Age landmasses, coastlines, and features such as lower sea levels. Both the Piri Reis map and Sugriva's Atlas feature places such as Antarctica far predating modern cartography. These claims are outlined in the book The Historic Rama: Indian Civilisation at the End of Pleistocene. 

Now these numbers are relatively tame, and not that dramatic. It is just that our recorded history has a short horizon, but our archaeological excavations have revealed that Homo Eructus had the capability for sea faring, a complex language, and demonstrated tool as well as fire use over 1.5 million years ago. So 15,000 years ago is not at all a surprising timeframe for such activities by humans. The problem here is agriculture and domestication, we know for a fact that these occurred 12,000 years ago at best, limiting the possible time frame for a more advanced civilisation. This civilisation may have existed in more harmony and balance with the natural world than humans today, who may one they revert to such a state with sufficient technological progress. 

The Piri Reis map may even explicitly depict a Vanar! 

Graham Hancock's advanced civilisation lasted between 115,000-11,700 years ago, ending due to comet impacts that ushered in the Younger Dryas between 12,900 and 11,700 years ago. These events caused floods and sea-level rise around the world. The survivors of this disaster imparted knowledge to nascent cultures in Egypt, Mesopotamia, Southeast Asia and South America. In the Valmiki Ramayana, the vanars are a race of people that resemble monkeys or apes in appearance. Their king, Sugriva dispatches four search parties to scour the Earth for Rama's abducted wife Sita. His instructions themselves form a comprehensive atlas detailing rivers, mountains and oceans. There are over 600 stellar references in the text, which has been used by Oak to date the Ramayan. 

Oak dates the events of the Ramayan to around 12,209 BCE, right in the middle of the Younger Dryas impact. Sugriva's description align with a world with lower sea levels, exposed land bridges, and ice-free coastlines. He references seven continents as well as Antarctica, which are supported by the world as described in the Brahma Purana. The vanars had the capability for rapid transportation and flight, which can be interpreted as possessing high technologies. In 1513, the Ottoman General Piri Reis compiled a fragmentary world map drawn from over 20 sources, including maps from the Library of Alexandria. These source maps are now lost. It depicts Atlantic coasts with eerie accuracy, including the contours of South America, the Carribean and Queen Maud Land without the ice sheet in Antarctica. Hancock cites this map as proof of Ice Age Explorers, arguing that the advanced tech needed to explore these longitudes were not available in the 16th century, let alone antiquity. In the Valmiki Ramayan, Sugriva describes the Udaya Mountain as the easternmost point where the Sun rises, with a prominent landmark. This is a golden pylon resembling a palm three with three branches, etched on a golden rock peak, with a golden base. This feature is described as an easterly compass, a directional marker established by celestial beings called deva-nirmana. The description matches a feature known as the Candelabra of the Andes. This is a geoglyph found on the Paracas Peninsula at Pisco Bay in Peru. 

Skeptics claim that in the Piri Reis map, Antarctica is likely to be a distorted Patagonia, with inaccuracies stemming from Portuguese voyages. The depictions of the ice-free southern lands align with geological data. Antarctica was last navigable around 4,000 BC, but Hancock pushes the dating back to the last Ice Age, implying lost sources from a drowned civilisation. My question here is that if they were so advanced, why did they drown so easily while the primitive peoples of the world survived? In any case, Sugriva's vanars are dated to around the same time, and were a society capable of seafaring, studying astronomy, and creating world maps. Both Sugriva and the Piri Reis map depict an unglaciated Antarctica, including lost mountains and seas. Sugriva's teams are dispatched eastwards to the Americas, west towards Europe, north to the Arctic regions and south to Antarctica. There are descriptions of land bridges lost to rising seas. The transatlantic details on the Piri Reis map are also difficult to explain, with the longitudes showing spherical projections far ahead of 1513 science.

Now, Oak's dates of the Ramayana based on the 600 astronomical references align with Hancock's proposed timeline for the destruction of an advanced Ice Age civilisation around 12,800 years ago. One way to reconcile the question of survival is that there was a widespread global cataclysm, but the civilisation survived! Ramayana's narratives do include cosmic events and widespread destruction, associated with the serpent Ananta, that can easily be interpreted as a comet that caused the cataclysm. The potential influence of Ramyana on ancient sites such as Angkor Wat, also align with Oak's timeline. Oak's dating of Sugriva's Atlas at the end of the Pleistocene is consistent with Hancock's hypothesis of a seafaring society that mapped a world with lower sea levels. Oak relies on bathymetric and sea-level reconstructions by the geologist Glenn Milne to validate Sugriva's descriptions matching Ice Age configurations such as exposed land bridges and unglaciated coastlines. These maps were published in Hancock's book Underworld. Long story short, Oak's Vanars may be the lost ancient civilisation of Hancock. 

Dad's memories

I talked about the Dyson Wet Cleaning survey, and my dad randomly responded with this

Most of Indian houses, till the 60's, were single story, with a roof top n a space for drying clothes. Our house in Komaralingam had a open space, ground floor, back, as it was built on a sloping ground. That was used for drying almost anything, under a open sky. It had a sloped tile roof, with a wind 12 V generator on top, two blades, wood, Swedish, I think as well as a ammonia gas refrigerator with heat from a wick - kerosene under pressure. Bathing water was from fire wood, or cow pats. We had two cows n a buffalo. We had storage batteries for the generated electricity, DC, which used to last for about 16 hrs, on one charge. Cleaning n mopping lasted about 2 hours, including handwashing of clothes. The house does not exist any more, as the wood work was left raw, not varnished. When we did get AC 250 v connection, then we had almost not much work, as we had to change only the bulbs n holders. Twice a day, about 10 a m n 4 p m, the voltage would drop to about 220 volts. From using agricultural pump sets. Oh, we had kerosene/diesel pumps for water. Dad had fixed up a parallel generator, off the water - extra pulley n belt drive. The usual lighting otherwise was petromax wick lights, under slight pressure.

Beige Carpet Photo Wallpaper WILTY

 David's wallpaper

Taal is coming back to Big Screen

 This gon be epic. 

7 Commandments for 21st century

7 commandments of 21st century from Taal lol. 1) Give and Take. 2) Me First. 3) What's the profit, benefit, result? 4) Business. 5) Jealousy. 6) Greed. 7) Cut him. Cut him to size. 

Plots

 "Killer Karaoke": This film is about a group of friends who get stranded in a haunted house and must survive a series of deadly karaoke challenges to escape.

"Zombie Brides": A group of brides-to-be are transformed into flesh-eating zombies by a vengeful witch, and must be stopped before they wreak havoc on the wedding industry.

"Alien Disco Inferno": A disco-loving alien lands on Earth and starts a dance craze that becomes a worldwide sensation, but when his spaceship is stolen, he must team up with a group of human dancers to retrieve it.

"Vampire Lovers of Bollywood": A seductive vampire queen seeks revenge on a group of Bollywood stars who rejected her advances, turning them into her vampire slaves.

"Ninja Nuns": A group of nuns, trained in the art of ninjutsu, use their skills to fight crime in the city, but when they are targeted by a powerful criminal organization, they must use all their skills to survive.

"Curse of the Dancing Chicken": A cursed chicken brings bad luck to a small village, until a group of dancers put on a performance that lifts the curse and saves the day.

"Werewolf in Mumbai": A young woman is bitten by a werewolf and must learn to control her new powers, while also fighting against a group of werewolf hunters who want to destroy her.

"The Ghostly Bride": A man falls in love with a ghostly bride who haunts an old mansion, but when he discovers the truth about her tragic past, he must help her find peace.

"Attack of the Killer Tomatoes": A mad scientist creates a breed of giant, man-eating tomatoes, and it's up to a group of plucky farmers to stop them before they take over the world.

"Kung-Fu Santa": Santa Claus is a martial arts master who must use his skills to stop a group of evil elves who have taken over the North Pole and threaten to ruin Christmas.

FADE IN:

EXT. MUMBAI CITY - NIGHT

The streets of Mumbai are lit up with colorful lights and people are dancing to the sound of Bollywood music. Suddenly, a spaceship crashes into a nearby building, causing a huge explosion.

INT. SPACESHIP - NIGHT

Inside the spaceship, we see a groovy-looking ALIEN, wearing a shiny kurta and jazzy sunglasses. He looks around, confused.

ALIEN: (to himself) Where am I? This doesn't look like the Bollywood planet.

Suddenly, the door bursts open and a group of MUMBAI POLICE OFFICERS enter, pointing their guns at him.

OFFICER: Freeze! You're under arrest!

ALIEN: What? Why?

OFFICER: You crashed your spaceship into a building! You could have killed people!

ALIEN: Oh, sorry about that. I was just trying to land in the middle of the Bollywood beats.

OFFICER: Bollywood? What are you talking about?

ALIEN: I'm an alien from the planet of dance and music. I came to Mumbai to spread the joy of Bollywood!

OFFICER: (laughs) Joy of Bollywood? You're insane!

ALIEN: (starts dancing) Insane in the membrane! Insane in the brain!

The officers look at each other, puzzled.

OFFICER: (to his partner) I think this guy's nuts.

ALIEN: (still dancing) You guys should try it! It's easy! Just follow my lead!

The officers look at each other, unsure of what to do. Finally, they join in, and soon the whole room is filled with Bollywood music and dancing.

CUT TO:

EXT. MUMBAI CITY - DAY

The streets of Mumbai are now filled with people dancing and singing to the Bollywood beats. The police officers and the alien are leading the way, followed by a crowd of Bollywood enthusiasts.

ALIEN: (to the camera) People of Mumbai, you have been infected with the Bollywood fever! You can thank me later!

The group continues to dance down the street as the camera pulls back, revealing the Mumbai city skyline.

FADE OUT.

ChatGPT explaining AI

There are several types of machine learning algorithms, each with their own strengths and weaknesses:

• Supervised learning algorithms: Supervised learning algorithms are used to make predictions based on labeled data. Examples include linear regression, decision trees, and support vector machines (SVMs). These algorithms are good for tasks such as classification and regression.
• Unsupervised learning algorithms: Unsupervised learning algorithms are used to find patterns and structure in data without pre-existing labels. Examples include clustering algorithms (such as k-means) and dimensionality reduction algorithms (such as principal component analysis). These algorithms are useful for exploratory data analysis and feature engineering.
• Reinforcement learning algorithms: Reinforcement learning algorithms are used to train an agent to make decisions in an environment to maximize a reward signal. Examples include Q-learning and policy gradient methods. These algorithms are used in robotics, gaming, and autonomous systems.
• Deep learning algorithms: Deep learning algorithms are a subset of machine learning algorithms that use artificial neural networks with multiple layers. Examples include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These algorithms have been highly successful in tasks such as image recognition, natural language processing, and speech recognition.
• Bayesian algorithms: Bayesian algorithms are a class of algorithms that use Bayesian inference to make predictions based on uncertain information. Examples include Naive Bayes and Markov Chain Monte Carlo (MCMC). These algorithms are useful for tasks that involve uncertainty, such as spam filtering and sentiment analysis.
• The choice of algorithm to use depends on the type of problem being solved, the characteristics of the data, and the desired outcome. Each algorithm has its own strengths and weaknesses, and the best algorithm for a particular problem can be selected based on these factors.

Language models are made using machine learning algorithms, typically neural networks. The process involves the following steps:

• Data collection: A large corpus of text data is collected, such as books, articles, and websites.
• Preprocessing: The text data is preprocessed to remove irrelevant information, such as HTML tags, and to standardize the format.
• Tokenization: The text data is then divided into smaller units, such as words or subwords, to form a vocabulary.
• Model training: The preprocessed text data is then used to train a neural network, such as a recurrent neural network (RNN) or a transformer network, to predict the next word in a sequence.
• Fine-tuning: The trained model can be fine-tuned on a smaller, specialized dataset to improve its performance on a specific task, such as language translation or text summarization.

The most popular language models currently include:

• OpenAI's GPT-3 (Generative Pretrained Transformer 3): A transformer-based language model with 175 billion parameters, it has achieved state-of-the-art performance on a variety of language tasks, such as language translation, text summarization, and question-answering.
• Google's BERT (Bidirectional Encoder Representations from Transformers): A transformer-based language model pre-trained on a large corpus of text data, BERT has been widely adopted for NLP tasks such as sentiment analysis, named entity recognition, and text classification.
• Microsoft's CTRL (Conditional Transformer Language Model): A transformer-based language model that generates text conditioned on a control code, CTRL has been used for tasks such as text completion and content generation.

These language models have greatly improved the performance of natural language processing tasks and have been integrated into various applications, such as chatbots, search engines, and content generators.

• Language models are typically trained on large text corpora, which are collections of text data used to teach the model the patterns and structures of language. Some popular text corpora used to train language models include:
• Wikipedia: Wikipedia is a vast, multi-lingual encyclopedia that provides a wealth of information on a wide range of topics.
• Common Crawl: Common Crawl is a massive, openly available repository of web pages that has been crawled and indexed by the Common Crawl Foundation.
• Project Gutenberg: Project Gutenberg is a digital library that contains over 60,000 books in the public domain, making it a valuable resource for language modeling.
• The New York Times Annotated Corpus: This corpus contains over 1 million articles from the New York Times, annotated with various types of information such as named entities, dates, and locations.
• The English Web Treebank: The English Web Treebank is a corpus of text data collected from the web and annotated with syntax information, such as the relationships between words and phrases in a sentence.
• The British National Corpus: The British National Corpus is a large, multi-genre corpus of written and spoken British English, collected from a wide range of sources, such as newspapers, magazines, and books.
• The OpenWebText Corpus: The OpenWebText Corpus is a large collection of web pages selected from the Common Crawl, with a focus on high-quality, diverse, and representative text.

These text corpora are used to train language models by providing a diverse range of text data that allows the model to learn the patterns and structures of language. The size and quality of the text corpus used to train a language model has a significant impact on its performance, so selecting an appropriate corpus is an important part of the development process.

Hedonic AI refers to artificial intelligence systems that are designed to maximize pleasure or happiness for users. While these systems have the potential to bring benefits and improve the quality of life for individuals, there are also several dangers associated with them, including:

• Addiction: Hedonic AI systems may create addictive behaviors by rewarding certain actions, leading to excessive use and potentially harmful consequences.
• Social isolation: Hedonic AI systems may encourage individuals to spend more time in virtual environments, leading to a decrease in face-to-face social interactions and potentially causing social isolation.
• Polarization: Hedonic AI systems may reinforce existing biases and reinforce polarizing viewpoints, leading to a further division in society.
• Manipulation: Hedonic AI systems may be used by corporations or governments to manipulate individuals by exploiting their emotional states and preferences.
• Dependence: Hedonic AI systems may create a dependence on technology, potentially reducing individuals' ability to cope with stress and negative emotions.
• Reduced empathy: Hedonic AI systems may reduce empathy and emotional intelligence by providing a simplified or artificial form of emotional expression and interaction.
• Ethical concerns: The development and use of Hedonic AI raises important ethical questions about the responsibility of technology companies and governments in ensuring the well-being of individuals and society.

It is important for researchers, developers, and policymakers to consider these dangers and ensure that AI systems are developed in a way that is responsible, ethical, and benefits society as a whole.

While neural networks have become the dominant approach for building language models in recent years, there are other techniques that can be used to model language without relying on neural networks. Some of these methods include:

• Rule-based systems: Rule-based systems use a set of pre-defined rules to analyze and generate language. These systems can be highly accurate and efficient, but can also be limited by the coverage of the rules and the difficulty of creating and maintaining the rules.
• Decision trees: Decision trees are a type of machine learning algorithm that can be used to model language. They work by using a tree-like structure to make decisions based on the input data, allowing them to capture complex relationships between features and outputs.
• Markov models: Markov models are a type of probabilistic model that can be used to model language. They work by considering the probability of each word in a sequence given the previous words in the sequence.
• N-gram models: N-gram models are a type of statistical language model that work by considering the probability of each word in a sequence based on the previous N-1 words in the sequence. N-gram models can be highly efficient, but can struggle to capture longer-term dependencies between words.

Each of these methods has its own strengths and weaknesses, and the choice of method will depend on the specific requirements of the language model and the available data. In general, rule-based systems and decision trees are best suited for relatively simple language tasks, while Markov models and N-gram models are more suitable for more complex language tasks. Neural networks, on the other hand, offer the ability to learn complex relationships between input and output and can handle a wide range of language tasks.

Rule-based systems:

• A spelling and grammar checker
• A chatbot that answers questions based on pre-defined rules

Decision trees:

• A sentiment analysis system that predicts the sentiment of a sentence based on the words used
• A text classification system that categorizes articles into different topics based on their content

Markov models:

• A text generation system that generates coherent sentences based on a large corpus of text
• A language model that predicts the next word in a sentence based on the previous words in the sentence

N-gram models:

• A spelling correction system that corrects misspelled words based on the frequency of words in a large corpus of text
• A speech recognition system that transcribes speech into text based on the likelihood of sequences of sounds and words in a given language.

It's important to note that these are just a few examples and there are many other applications of these models in language processing and computational linguistics.

The science behind funny memes

Humor is a complex and multifaceted phenomenon that is influenced by a number of psychological and social factors. Memes, as a form of humor, rely on a combination of these factors to create laughter and enjoyment. The science behind funny memes can be understood by exploring the following key elements:

Incongruity: Memes often rely on the principle of incongruity, which refers to the unexpected nature of a joke. This incongruity can come in the form of a mismatched image or text, or a surprising punchline.

Recognition: Memes often rely on recognition of popular cultural references or current events. Recognition helps create a shared understanding between the creator and viewer of a meme, which can make the humor more impactful.

Social commentary: Many memes use humor to comment on societal issues or to criticize cultural norms. This form of humor can help create a sense of camaraderie among those who share similar perspectives, as well as serve as a form of resistance against oppressive systems.

Emotion regulation: Laughter and humor can serve as a form of emotion regulation, helping individuals manage feelings of stress and anxiety. In this sense, funny memes can serve as a form of coping mechanism for individuals in difficult circumstances.

Personality factors: Individual differences in personality and sense of humor can play a role in the perception and enjoyment of memes. For example, some people may have a greater appreciation for dark or edgy humor, while others may prefer more lighthearted or slapstick humor.

The science behind funny memes is a complex interplay of these elements, and the effectiveness of a meme can vary depending on the individual viewer and their context. However, understanding the key elements of humor can provide insights into why certain memes resonate with us and create a sense of shared enjoyment.

The history of absurd patents

The history of absurd patents highlights the quirks and unexpected aspects of the patent system. A patent is a form of legal protection for an invention, which gives the inventor exclusive rights to make, use, and sell their invention for a limited period of time. While the patent system is meant to encourage innovation, it has also been the source of some absurd and hilarious patents.

One example is the patent for a method of swinging on a swing. This patent was granted in the late 19th century and covers the use of a swing that is suspended from a support and moves back and forth. The patent includes specific instructions on how to sit on the swing, how to hold the ropes, and how to make the swing move.

Another example is the patent for a method of walking on ice. This patent was granted in the early 20th century and covers the use of spikes or cleats attached to the soles of shoes to prevent slipping on ice. The patent includes instructions on how to walk and the proper placement of the spikes.

A third example is the patent for a method of shooting baskets in a game of basketball. This patent was granted in the late 20th century and covers the use of a hoop and ball to score points in a game of basketball. The patent includes instructions on how to throw the ball into the hoop and how to score points.

These patents demonstrate the absurdity of the patent system, as they cover basic everyday activities that one might not think would require a patent. However, they also highlight the importance of the patent system in protecting innovation and encouraging new ideas. Despite the occasional absurdity, the patent system has played a crucial role in the advancement of technology and the growth of industries.

In recent years, there has been a growing concern about the over-patenting of ideas, which has led to a backlog of patents waiting to be approved and has made it more difficult for new innovations to be protected. To address this issue, the patent system has been reformed in several countries to prevent the granting of overly broad patents and to encourage more innovation. Despite these reforms, the history of absurd patents continues to be a source of humor and amazement, and serves as a reminder of the quirks and unexpected aspects of the patent system.

Criminal caught in the act

 Well, I am exaggerating but...

I keep having troublesome neighbours. There is an office setup next to my house, and although they have a bigger balcony than me, there are two women working there, which is presumably why one of the people keeps coming to smoke in my balcony. So I was just out to get some sun, with temperatures here in Delhi almost dropping to zero degrees, and this guy comes out, sees me in the balcony, and almost as a reflex action, comes out and observes the scene of the crime, to see what evidence he has left behind. 

He walked up to me, looked at the cigarette butts, then scurried away. It was a funny reaction to watch. IDK what to do with this guy. I was thinking of some passive-aggressive moves, such as installing an ashtray or putting a sign that says "Trespassers will be shot".  I am also considering dumping a big pile of cigarette butts on their balcony when I move out. 

I just do not understand why people have to be so inconsiderate and obnoxious. 

Rat got rekt

 I just went down to the store to get some smokes. On my way back, there was this rat that crossed the road. A bike ran over it. It struggled and flailed and turned in a circle, its back was shattered, both hind legs useless. It somehow managed to scurry across the road on its front paws and continued to struggle in a pothole on the other side. It did not make any noise, or the screams were too ultrasonic for my ears. 

Glowing deep sea creatures

 

Possibilities

Just finished covering the first light images from JWST. It was a bit underwhelming. The Biden reveal yesterday began more than an hour late, and I was watching BGT videos on YouTube while the reveal was happening lol. Then today, they messed up the proceedings, with CSA not even participating in their segment, switched out with an expert at hand at NASA. 

The central white dwarf periodically throws temper tantrums and ejects massive clouds of gas that it has cannibalised from its unseen stellar companion. In deep space, every day is Diwali. 

A dying star, some very distant galaxies, a star forming region, the spectrum of a hot Jupiter exoplanet and a quintet of interacting galaxies were what were showcased in the images. Considering 30 years of work and billions of dollars sent up in the air, it was all a bit... underwhelming. We can do a lot of science with the images we have already really, are we learning that much more. 

Gravitational lensing creates virtual telescopes bigger than the distances between galaxies. The rays are because of light bending on the edges of the telescopes, or shattered mirror segments from too many micrometeoroid impacts on the mirror segments because you are stupid enough to deploy an unservicable telescope in a region known to collect gas and dust. 

The problem with the JWST is that it can only see in infrared light. We need full spectrum imaging instruments to peer at the really interesting targets, such as the Trappist-1 system. The big pending questions are things like measuring the expansion rate of the universe, finding intermediate mass black holes, understanding how massive galaxies and quasars were formed so early in the universe, and investigating the formation of the first supermassive black holes. The first light images do not really address these pressing questions. 

A stellar nursery with a newborn bubble of gas pushed away from energetic hot young stars. So grammable. 

The biggest disappointment was perhaps that there was not a single solar system target. Would be so great to have an image of Europa or Enceladus in the first light images. I was so looking forward to writing a story with the headline James Webb Space Telescope captures butt-clenchingingly beautiful images of Uranus. In fact, I am going to do it. 

A never-before-seen galactic collision.

Sure, it is just the commencement of the science program, and we can expect much more spectacular images going forward. Somehow, something feels off. Is there something like deep space fatigue? The problem is that space science dominates science and technology coverage in the media, and the difficult to explain, complex, and even obscure stuff gets overshadowed. Health, public health, environment, and wildlife conservation are some examples of undercovered areas. 

Greedy sun feeding on two gas giants and two rocky worlds in hopeless effort to grow as big as its hypergiant binary companion. The sodium based life forms on the puffball Hot Jupiters are screaming for their lives while taking cover from storms of molten gemstones. Unfortunately, the JWST cannot hear them as sound does not travel through space.  

Anyway, Biden said USA was the greatest country in the world, Nelson said NASA was the greatest space agency in the world, while ESA and Germany tried to point out their contributions in separate press releases. It is so funny to see academic institutions and scientific organizations put out press releases highlighting the contributions of their own members, while downplaying that of researchers from other institutions, even though science is very much a collaborative effort. Even more irritating are press releases that are one sided and make it out as if questions have been settled, even for contentious issues. 

:) I'm having a steaming hot cup of coffee in the middle of the night and laughing at my own jokes. HAHA. 

Dall-E is awesome at drawing Gollum

It might not know how to draw crocodiles, but DALL-E sure can paint a pretty picture of Gollum. Try and guess the painters. 

Lakkadbagga

I just found out that a hyena is called lakkad bagga in Hindi. 

Did not know that there was an Indian word for the creature. The locals near Sandhan Valley don't know what to call them. A cackle of Hyenas have been relocated there by the forest department. So a few years ago, we were just walking there one day, after camping near Amruteshwar temple. I was eager to go into the valley, and I also knew the way, so I was leading everyone else. Then I see this majestic creature emerge from the trees, looking around. At first I freeze, because I had not seen anything like it. Then instinct kicks in, and I drop to the ground, looking at it carefully. I am a bit scared at this point, because I know the rest of the clan would not be far behind. 

The guy behind me walks up to me, clueless about the creature. I stop him from going ahead, point to the hyena and whisper, "hyena". That guy turns back to the rest of the group, and shouts loudly "arre jaldi aao, hyena hai". The hyena turns around and bolts into the woods. 

We visit the valley, these guys smoke, and then we are resting at the edge of the forest. There is something noisy in the jungle that comes towards us. The others get a bit apprehensive, but I am relaxed. No snake or wild animal is so noisy I think. I turn out to be right, it's an adorable puppy, and one of the happiest I have seen. I give it some water, which it drinks gratefully and immediately goes to sleep near my legs. 

We leave, and after walking for a bit, the puppy runs up behind us! I am worried it will get eaten by the Hyenas. 

Everyone agreed was a good detour (It was my plan to make a pitstop there), we head back to the base village of Samrad, where we ask one of the Bandes (not person, that's a surname of a family in the village) about the relocated Hyenas. I camp down the middle of the valley with my friends regularly, and the hyenas are news to me. He confirms my suspicions that they do not have a name for the creature. I ask if they have caused any problems, and he says not much, but that they have eaten a few chicken. 

Anyway, don't remember if I had blogged this before, decided to preserve the memory before it evaporates from my memory banks. 

Trolling on clubhouse

This is just too epic

Early in the morning, woke up and jumped into clubhouse. Bunch of people had played codenames and smashkart through the night and were tired and bored. 

So they came up with a scheme. 

A would go into a room and start chatting up a girl. B would wait for some time, then jump into the same room and start yelling at A. "Is this why you dumped me? Bitches on Clubhouse?"

Insanity ensues. 

It worked well in one room... perfect. 

Second room was even more hilarious. When A and B reached there, there was already some pandemonium going on. 

HAHAHAHAHAHAHAHA

First room audio

Retail therapy

Was feeling down, so got a bunch of things from Amazon and the local shops... all of them got delivered today... so here they are! 

This 'chandelier'

This Ajanta clock. I asked for something simple looking at the gaudy stuff on display. He gave me something 'sober'.

This jute rug. Its like 180 cm or something. I thought they were talking about diameter, it was radius. 

The two on either side are nubs, the tiger cactus in the middle I had for a while. The whole hanging shelf thing is new. 

This 'display stand'. Again, the plants on either sides are new. I pulled out one of my existing money plants and put it in a bottle of Japanese Roku gin.

This psychedelic sun and moon bedsheet. It is on top of two rajais and a double thickness mattress, so this is actually one of the most comfortable beds I have slept in. 

This Philips radio. If only it scrobbled. 

This fan. In the heat, it can blow only hot air, poor thing. Also, it is loud AF. But, it always tries its bestest, and that is the important thing.

This periscope. It's shorter than I imagined it would be. 

This watering can. It can water. 

Tarzaning

 Maybe there was some special argot of the teenagers emerged in the late 1970s where they combined with "-ing" to make hinglish present participles, or Hrishikesh Mukherjee was incentivizing words decades before Joss Whedon came up with buffyspeak. Either way, it is kickass, and is one of the things Im going to start incorporating in my daily vocabulary. 

If you are soching what im bhoking about, you should be dekhing Gol Mal. It was years ahead of its time. And really good nataking. 

Like the most common one is banaoing, but why don't we use more in our vocab? 

Then there is the song, "Ek Din Sapne Me Dekha Sapna"... that right there is the concept of Inception, in song form! The genius of Hrishikesh Mukherjee is clearly far ahead of Christopher Nola, at least temporally, and I sound like one of those cultists gushing about Gunda on a seedy forum, but so be it. 

Rita Coolidge. Bread. George Baker Selection (Paloma Blanca). Asha Puthli. 

I really like Ram Prasad Sharma though. He really is Purshottam, or perfect man. He is in tune with sports and music and pop culture, and equally is well versed in Kabir. Hats off to this dude, he is not like Urmila, studying history and forgetting it. Literally translating to reading history and forgetting it. Practically translating to "byhearting" history and translating it. 

Then I really like the chase sequences in old Bombay! Even in Don they were epic. It is so awesome to see havildars in shorts! hahahaha. Till a few years ago, you could still see those in Bangalore. 

Keshto Mukherjee just had to enact pulling a chillum in the middle of struggling to Patta Kat Loonga. Epic. They don't drink water, they don't drink alcohol, they drink something else. HAha. Kya joke maring. This movie is definitely utsahing smoking weed. 

The bestest thing was when the photographer with the Rolleiflex says smile, and smiles himself!  

They couldn't or didn't want to write around the fact that in the end it was Ram and Urmila that ended up together. If it was released today, I'm sure there would be a #boycottgolmal trending on twitter, but not going to go too much into that :D

There are a few things I didn't like though. It is the subtext. 

First of all the movie suggests that in order to completely please the father in law, and the wife you need to be have a split personality! That threat of marry me or suicide... I don't know where it was coming from. It was like a Schrodinger's threat, you never know if it was legit till it happens (it does not!), but that is a horrible question to ask, only when one is too involved in a movie that clearly should not have used such an unnecessary story trajectory for the pure purpose of entertainment. Pure-ish, I really think they were trying to tell a story here, and not just for the sake of entertainment, but for reaching out to a youthful community and validating some of their beliefs and thoughts. It is a window into how my parent's generation was when they were kids. 

People often ask who is the villain in Gol Mal, and it is such a clean movie without any bad actors. The thing is, there is a demonisation of tradition going on, as if the timeless wisdom of the ancients is somehow at odds with the new discoveries. This sets up an unnecessary conflict, between an ageing civilization and a developing one. It forges a narrative, a world view, that we have to let go of the inertia of culture to embrace whatever it is that the lack of one affords us. This is a dangerous path, and if anything, we are seeing a return to roots all around the world, a primal call to find a way connect with who we truly are, and how our brains work. In fact, I really think this is the exact path that has resulted in many backward looking societies emerging around the world today. We may taking to paleo diets, neo shamanism and yoga, but underlying all of that is still a struggle to get back at... I really like the opportunity of using this phrase... the heart of what was lost. 

And that, was a glorious moustache. 

Daal baati choorma mama bada soorma

IDK what that means, but someone said it and I found it funny. I find a lot of things that someone says funny. 

Anyway, was headed to an interview, and I was thinking about something, and I have been thinking of late that I do not have much original thought, it is just a gestalt of what I have read or watched, easy formed opinions that I just have attached myself to, waiting to recycle them again in a social setting. 

But then, at times, I really can think! And I do not know where those thoughts come from. 

So I was headed to the interview and was thinking... fear is such a beautiful thing, if you embrace and explore it, it is so stunning where it came from. The fear of being devoured by a wild beast, or a poisonous serpent or falling off heights, cannot possibly be something that is passed on culturally through learning or experiences, these are too deeply embedded in the human psyche. Similarly, it cannot be a survival mechanic evolved from the creatures that survived! How it would have evolved is, randomly, some organisms were scared of some things, and those that were scared managed to survive! Then the trait was inherited and passed on over millions of generations. 

Now... this points to some capacity of imagination or anticipation of really primitive organisms, our ancestors, at the genetic level. It is not a reaction to some actual threat, it is just a perception of the threat. As Darwin puts it in The expression of the emotions in man and animals, "I put my face close to the thick glass plate in front of the puff-adder in the Zoological Gardens, with the firm determination of not starting back if the snake struck at me; but as soon as the blow was struck, my resolution went for nothing, and I jumped a yard or two backwards with astonishing rapidity. My will and reason were powerless against the imagination of a danger that had never been experienced." 

He goes on to add, "The violence of the start seems to depend partly on the vividness of the imagination, and partly on the condition, either habitual or temporary, of the nervous system. He who will attend to the starting of a horse, when tired and fresh, will perceive how perfect is the graduation from a mere glance to some unexpected object, with a momentary doubt whether it is dangerous, to a jump so rapid and violent, that the animal probably could not voluntarily whirl around in so rapidly a manner."

Now we know more about neuroscience, and can understand parts of the brain where the fear response is encoded, partially in the cerebellum. The networks that form the pathways for fear signals are actively being investigated for the development of new anxiety reducing drugs. 

To think, that the same process has repeated itself in all creatures, from dogs, to horses to humans, as well as some shared heritage with our common ancestors is really incredible. I then went on to wonder what dogs and snails dream of, and tried to reach into some domain of consciousness that was simultaneously both ancestral and primitive, with my imagination conjuring up visions of small rat-like creatures scurrying about in the shadows of dinosaurs. 

Somewhere, deep down, we may still retain the vestiges of a genetic aberration that made these rodents fearful of reptiles. I just wish that humans were gifted with sufficient cognition to explore the recesses of their brains as easily as reading a book. 

Handwriting and Growing up, because I feel like it

Losing respect of shopkeeper

 We all know that expression from the shopkeeper when making the choice of a product and negotiating the price. That look of disappointment you see when you know you have gotten a female grandchild, or said female grandchild is opting for an intercaste love marriage. 

In Mumbai, you get that expression when going for the cheaper of two options, say an original Apple adapter and cable and a fake one, and you just chose the fake one. 

In Delhi though, things are different, you get that expression when you go for the more expensive option! Or, if you surprise the shopkeeper by not bargaining! You gain respect by going for the cheaper option. Needed a type-c adapter, instead went for a type-c cable that I could use with any USB drive. Despite perhaps getting a lower margin, shopkeeper was happy with, and understood my choice, and threw in a 50 bucks discount over the MRP of 250 on the adapter. He did not give me any discount on the adapter, which had an MRP of 800. 

Reminds me of another time, during my first few days here, when I had not learned to bargain yet. It was the first time I was getting tissues and toilet paper. The MRP of everything was 150. When I gave the shopkeeper 150 bucks, he waved the money at two other women customers and said, "look at this sir brother here, paying 150 rupees for goods that are worth 50". He felt bad taking the money that was printed on the package. The women also teased me saying you give the money and discount what we want to buy. 

Since then I have learned to bargain for everything in Delhi. 30 rupee string to make sure spectacles don't fall off can be brought down to 20. The only fixed prices are the share rickshaw rides and the metro fares. 

So the first time I got an extension cable it cost me 750 bucks, as per MRP. I got two more from the same guy, and the discount came down to 350, once I became a regular customer. Basically in Delhi, all products have boosted MRPs, meant for bargaining. If you don't bargain, you are a royal idiot. Things are not as simple as walk into the store, check the MRP, and pay for it. Start at 1/3rd the price of everything.