#2025 Collection dates

[jantanow]

भारतीय नवप्रवर्तकों के लिए हंड्रेड का निमंत्रण, शिक्षा में नवाचार को मिलेगी वैश्विक मान्यता

हंड्रेड ग्लोबल कलेक्शन 2026 ने भारतीय नवप्रवर्तकों के लिए एक अनोखा अवसर प्रस्तुत किया है। इस पहल के तहत, हंड्रेड उन नवाचारों को पहचानने और उनका प्रचार करने की कोशिश कर रहा है जिन्होंने शिक्षा के क्षेत्र में महत्वपूर्ण सुधार किए हैं। यह भारतीय शिक्षकों, ग्रामीण समाजसेवियों, और एनजीओ के लिए एक विशेष मौका है, जो अपने नवाचारों को वैश्विक मंच पर प्रस्तुत करना चाहते हैं। भारतीय नवप्रवर्तक, जो सीमित…

[scriv3lloirl]

ARTFIGHT 2025

Day 13 -

I attcked @aphidclan-clangen n drew their wonderful character, Paradiseskies!!

Attacked @the-depressed-comedic-relief n drew his Date Everything oc, Cody!

(threw in my guy, Brushley, cus they're both 90s boys n.. also wanna crack that shower. /silly)

Day 14 -

Attacked big_snazy's oc, Halfsun n m' girl, Gingerflake.

[freshthoughts2020]

[strwbrrybnny]

Nendoroid Deer (Cocoa/Strawberry Milk/Mint)

[pherryt]

The Great Crossover Collection

So I finally did it.

I made a series for all my crossovers, both related to each other and not.

I'll admit, mostly the series is for my own satisfaction, helping me keep track of how many I actually have, and so I can find them easier if someone asks me what ones I have, etc, etc. But I thought I might share it if any of you were interested.

Various Fandoms Found:

Witcher (11)

Supernatural (16)

Star Trek (8)

Doctor Who (9)

One Piece (8)

ElfQuest (3)

Marvel (MCU, Venom, Power Pack) (17 - some of these are minor, most are not)

My Hero Academia (3)

Scooby Doo (1)

Old Guard (6)

Zelda (3)

Tiger & Bunny (1)

Various minor others

The criteria for this collection are that they are all one TYPE of Crossover (My favorite type to write): where two or more fandoms *meet* rather than fuse or are inspired by.

That means the current series count is at 41 fics, 600k words.

However, Fics that did NOT make the cut into the collection included 8 fics (linked in the about section. To keep the character count down, one of those linked is a 3 part series instead of the individual fics) that were more like these people exist in THAT universe and/or take the place of people in that universe.

BUT WAIT! There's MORE!

There are 5 more fics that I could NOT link into the about section (because of the character limit) that were heavily inspired by *songs*, which were mostly of the fannish variety (except for 1, so I'm still not sure if it fits or not, but it got named anyway)

So that brings the count up from 41 Crossovers to 54 if you count the oddballs.

And that's out of a total of (at the moment) 444 works. Keeping in mind that:

10 of those works are Podfics that are on my Psued account but still get counted towards my total and in the list

27 of those works are art for bangs and other events

1 is just art for one of the series

for a total of 38 non writing works

...I *believe* most or all of my bingo square fills that were only art are simply LINKED IN to the correct bingo series, but they were done a long time ago, and I decided I already spent too long working on this to go searching every work again...

[imliterallyinsanern]

Y’all know the game telephone? Yeah, the fic is that

[brandnewwarehouse]

artist: cesela [bigcartel]

[thesomethingguy]

Don’t Miss Anton Goosen Live – Featuring Illimar Neitz and Mari Bosman at Silverstar

Anton Goosen is certainly one of the best-known names in Afrikaans music and is considered the father of modern Afrikaans music as a songwriter for other artists as well as for himself/ 19 of his hits are included in the latest F.A.K. collection and his career spans almost five decades. Join the Liedjieboer on a journey through all these years of hits and hear some of the stories surrounding the…

[jcmarchi]

Using AI to Predict a Blockbuster Movie

New Post has been published on https://thedigitalinsider.com/using-ai-to-predict-a-blockbuster-movie/

Using AI to Predict a Blockbuster Movie

Although film and television are often seen as creative and open-ended industries, they have long been risk-averse. High production costs (which may soon lose the offsetting advantage of cheaper overseas locations, at least for US projects) and a fragmented production landscape make it difficult for independent companies to absorb a significant loss.

Therefore, over the past decade, the industry has taken a growing interest in whether machine learning can detect trends or patterns in how audiences respond to proposed film and television projects.

The main data sources remain the Nielsen system (which offers scale, though its roots lie in TV and advertising) and sample-based methods such as focus groups, which trade scale for curated demographics. This latter category also includes scorecard feedback from free movie previews – however, by that point, most of a production’s budget is already spent.

The ‘Big Hit’ Theory/Theories

Initially, ML systems leveraged traditional analysis methods such as linear regression, K-Nearest Neighbors, Stochastic Gradient Descent, Decision Tree and Forests, and Neural Networks, usually in various combinations nearer in style to pre-AI statistical analysis, such as a 2019 University of Central Florida initiative to forecast successful TV shows based on combinations of actors and writers (among other factors):

A 2018 study rated the performance of episodes based on combinations of characters and/or writer (most episodes were written by more than one person). Source: https://arxiv.org/pdf/1910.12589

The most relevant related work, at least that which is deployed in the wild (though often criticized) is in the field of recommender systems:

A typical video recommendation pipeline. Videos in the catalog are indexed using features that may be manually annotated or automatically extracted. Recommendations are generated in two stages by first selecting candidate videos and then ranking them according to a user profile inferred from viewing preferences. Source: https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2023.1281614/full

However, these kinds of approaches analyze projects that are already successful. In the case of prospective new shows or movies, it is not clear what kind of ground truth would be most applicable – not least because changes in public taste, combined with improvements and augmentations of data sources, mean that decades of consistent data is usually not available.

This is an instance of the cold start problem, where recommendation systems must evaluate candidates without any prior interaction data. In such cases, traditional collaborative filtering breaks down, because it relies on patterns in user behavior (such as viewing, rating, or sharing) to generate predictions. The problem is that in the case of most new movies or shows, there is not yet enough audience feedback to support these methods.

Comcast Predicts

A new paper from Comcast Technology AI, in association with George Washington University, proposes a solution to this problem by prompting a language model with structured metadata about unreleased movies.

The inputs include cast, genre, synopsis, content rating, mood, and awards, with the model returning a ranked list of likely future hits.

The authors use the model’s output as a stand-in for audience interest when no engagement data is available, hoping to avoid early bias toward titles that are already well known.

The very short (three-page) paper, titled Predicting Movie Hits Before They Happen with LLMs, comes from six researchers at Comcast Technology AI, and one from GWU, and states:

‘Our results show that LLMs, when using movie metadata, can significantly outperform the baselines. This approach could serve as an assisted system for multiple use cases, enabling the automatic scoring of large volumes of new content released daily and weekly.

‘By providing early insights before editorial teams or algorithms have accumulated sufficient interaction data, LLMs can streamline the content review process.

‘With continuous improvements in LLM efficiency and the rise of recommendation agents, the insights from this work are valuable and adaptable to a wide range of domains.’

If the approach proves robust, it could reduce the industry’s reliance on retrospective metrics and heavily-promoted titles by introducing a scalable way to flag promising content prior to release. Thus, rather than waiting for user behavior to signal demand, editorial teams could receive early, metadata-driven forecasts of audience interest, potentially redistributing exposure across a wider range of new releases.

Method and Data

The authors outline a four-stage workflow: construction of a dedicated dataset from unreleased movie metadata; the establishment of a baseline model for comparison; the evaluation of apposite LLMs using both natural language reasoning and embedding-based prediction; and the optimization of outputs through prompt engineering in generative mode, using Meta’s Llama 3.1 and 3.3 language models.

Since, the authors state, no publicly available dataset offered a direct way to test their hypothesis (because most existing collections predate LLMs, and lack detailed metadata), they built a benchmark dataset from the Comcast entertainment platform, which serves tens of millions of users across direct and third-party interfaces.

The dataset tracks newly-released movies, and whether they later became popular, with popularity defined through user interactions.

The collection focuses on movies rather than series, and the authors state:

‘We focused on movies because they are less influenced by external knowledge than TV series, improving the reliability of experiments.’

Labels were assigned by analyzing the time it took for a title to become popular across different time windows and list sizes. The LLM was prompted with metadata fields such as genre, synopsis, rating, era, cast, crew, mood, awards, and character types.

For comparison, the authors used two baselines: a random ordering; and a Popular Embedding (PE) model (which we will come to shortly).

The project used large language models as the primary ranking method, generating ordered lists of movies with predicted popularity scores and accompanying justifications – and these outputs were shaped by prompt engineering strategies designed to guide the model’s predictions using structured metadata.

The prompting strategy framed the model as an ‘editorial assistant’ assigned with identifying which upcoming movies were most likely to become popular, based solely on structured metadata, and then tasked with reordering a fixed list of titles without introducing new items, and to return the output in JSON format.

Each response consisted of a ranked list, assigned popularity scores, justifications for the rankings, and references to any prior examples that influenced the outcome. These multiple levels of metadata were intended to improve the model’s contextual grasp, and its ability to anticipate future audience trends.

Tests

The experiment followed two main stages: initially, the authors tested several model variants to establish a baseline, involving the identification of the version which performed better than a random-ordering approach.

Second, they tested large language models in generative mode, by comparing their output to a stronger baseline, rather than a random ranking, raising the difficulty of the task.

This meant the models had to do better than a system that already showed some ability to predict which movies would become popular. As a result, the authors assert, the evaluation better reflected real-world conditions, where editorial teams and recommender systems are rarely choosing between a model and chance, but between competing systems with varying levels of predictive ability.

The Advantage of Ignorance

A key constraint in this setup was the time gap between the models’ knowledge cutoff and the actual release dates of the movies. Because the language models were trained on data that ended six to twelve months before the movies became available, they had no access to post-release information, ensuring that the predictions were based entirely on metadata, and not on any learned audience response.

Baseline Evaluation

To construct a baseline, the authors generated semantic representations of movie metadata using three embedding models: BERT V4; Linq-Embed-Mistral 7B; and Llama 3.3 70B, quantized to 8-bit precision to meet the constraints of the experimental environment.

Linq-Embed-Mistral was selected for inclusion due to its top position on the MTEB (Massive Text Embedding Benchmark) leaderboard.

Each model produced vector embeddings of candidate movies, which were then compared to the average embedding of the top one hundred most popular titles from the weeks preceding each movie’s release.

Popularity was inferred using cosine similarity between these embeddings, with higher similarity scores indicating higher predicted appeal. The ranking accuracy of each model was evaluated by measuring performance against a random ordering baseline.

Performance improvement of Popular Embedding models compared to a random baseline. Each model was tested using four metadata configurations: V1 includes only genre; V2 includes only synopsis; V3 combines genre, synopsis, content rating, character types, mood, and release era; V4 adds cast, crew, and awards to the V3 configuration. Results show how richer metadata inputs affect ranking accuracy. Source: https://arxiv.org/pdf/2505.02693

The results (shown above), demonstrate that BERT V4 and Linq-Embed-Mistral 7B delivered the strongest improvements in identifying the top three most popular titles, although both fell slightly short in predicting the single most popular item.

BERT was ultimately selected as the baseline model for comparison with the LLMs, as its efficiency and overall gains outweighed its limitations.

LLM Evaluation

The researchers assessed performance using two ranking approaches: pairwise and listwise. Pairwise ranking evaluates whether the model correctly orders one item relative to another; and listwise ranking considers the accuracy of the entire ordered list of candidates.

This combination made it possible to evaluate not only whether individual movie pairs were ranked correctly (local accuracy), but also how well the full list of candidates reflected the true popularity order (global accuracy).

Full, non-quantized models were employed to prevent performance loss, ensuring a consistent and reproducible comparison between LLM-based predictions and embedding-based baselines.

Metrics

To assess how effectively the language models predicted movie popularity, both ranking-based and classification-based metrics were used, with particular attention to identifying the top three most popular titles.

Four metrics were applied: Accuracy@1 measured how often the most popular item appeared in the first position; Reciprocal Rank captured how high the top actual item ranked in the predicted list by taking the inverse of its position; Normalized Discounted Cumulative Gain (NDCG@k) evaluated how well the entire ranking matched actual popularity, with higher scores indicating better alignment; and Recall@3 measured the proportion of truly popular titles that appeared in the model’s top three predictions.

Since most user engagement happens near the top of ranked menus, the evaluation focused on lower values of k, to reflect practical use cases.

Performance improvement of large language models over BERT V4, measured as percentage gains across ranking metrics. Results were averaged over ten runs per model-prompt combination, with the top two values highlighted. Reported figures reflect the average percentage improvement across all metrics.

The performance of Llama model 3.1 (8B), 3.1 (405B), and 3.3 (70B) was evaluated by measuring metric improvements relative to the earlier-established BERT V4 baseline. Each model was tested using a series of prompts, ranging from minimal to information-rich, to examine the effect of input detail on prediction quality.

The authors state:

‘The best performance is achieved when using Llama 3.1 (405B) with the most informative prompt, followed by Llama 3.3 (70B). Based on the observed trend, when using a complex and lengthy prompt (MD V4), a more complex language model generally leads to improved performance across various metrics. However, it is sensitive to the type of information added.’

Performance improved when cast awards were included as part of the prompt – in this case, the number of major awards received by the top five billed actors in each film. This richer metadata was part of the most detailed prompt configuration, outperforming a simpler version that excluded cast recognition. The benefit was most evident in the larger models, Llama 3.1 (405B) and 3.3 (70B), both of which showed stronger predictive accuracy when given this additional signal of prestige and audience familiarity.

By contrast, the smallest model, Llama 3.1 (8B), showed improved performance as prompts became slightly more detailed, progressing from genre to synopsis, but declined when more fields were added, suggesting that the model lacked the capacity to integrate complex prompts effectively, leading to weaker generalization.

When prompts were restricted to genre alone, all models under-performed against the baseline, demonstrating that limited metadata was insufficient to support meaningful predictions.

Conclusion

LLMs have become the poster child for generative AI, which might explain why they’re being put to work in areas where other methods could be a better fit. Even so, there’s still a lot we don’t know about what they can do across different industries, so it makes sense to give them a shot.

In this particular case, as with stock markets and weather forecasting, there is only a limited extent to which historical data can serve as the foundation of future predictions. In the case of movies and TV shows, the very delivery method is now a moving target, in contrast to the period between 1978-2011, when cable, satellite and portable media (VHS, DVD, et al.) represented a series of transitory or evolving historical disruptions.

Neither can any prediction method account for the extent to which the success or failure of other productions may influence the viability of a proposed property – and yet this is frequently the case in the movie and TV industry, which loves to ride a trend.

Nonetheless, when used thoughtfully, LLMs could help strengthen recommendation systems during the cold-start phase, offering useful support across a range of predictive methods.

First published Tuesday, May 6, 2025

[scriv3lloirl]

Artfight 2025 — Day 1

(feel free attack me!)

Attacked @spicyl3m0n's eeveesona, Jay!

I had a silly pose saved n knew I had t' use it fer @grape-jucie-dog's Welcome Home oc, Calypso :3

OC x Canon attack on carrot_kiraa s Date Everything OC, Denny Brusque! (No Tumblr t' tag, so I wrote in their Artfight user.)

[freshthoughts2020]

[successfulblackparenting]

Here Comes Kenbassador LeBron James—The First-Ever Barbie® Kenbassadors™ Doll

By Janice Robinson-Celeste, Publisher of Successful Black Parenting Magazine Start clearing some shelf space and set those reminders, because something iconic is coming your way! The Barbie® Signature Kenbassadors™ LeBron James Doll is dropping the second week of April 2025, and trust us—you’ll want to be first in line. Retailing at $75, this isn’t just any doll. This is LeBron. This is history.…

[strwbrrybnny]

Mo Dao Zu Shi - Feng He Ju Ver. Plush Mascot

Lan Wangji Wei Wuxian

[balu88r-blog]

Chhaava - ಛಾವಾ ಸಿನಿಮಾ ಒಟಿಟಿಗೆ ಯಾವಾಗ? ಏಪ್ರಿಲ್ 11 ರಂದು ನೆಟ್ ಫ್ಲಿಕ್ಸ್ ನಲ್ಲಿ ಸ್ಟ್ರೀಮಿಂಗ್…!

Chhaava – ವಿಕ್ಕಿ ಕೌಶಲ್ ಮತ್ತು ರಶ್ಮಿಕಾ ಮಂದಣ್ಣ ಅಭಿನಯದ ‘ಛಾವಾ’ ಸಿನಿಮಾ ಬಾಕ್ಸ್ ಆಫೀಸ್‌ನಲ್ಲಿ ಭಾರೀ ಹಿಟ್ ಆಗಿದ್ದು, ಹಳೆಯ ದಾಖಲೆಗಳನ್ನು ಮುರಿಯುತ್ತಾ ಹೊಸ ದಾಖಲೆಗಳು ಬರೆಯುತ್ತಿದೆ. ಫೆಬ್ರವರಿ 14, 2025 ರಂದು ತೆರೆಕಂಡ ಈ ಇತಿಹಾಸಾಧಾರಿತ ಸಿನಿಮಾ ಪ್ರೇಕ್ಷಕರಿಂದ ಉತ್ತಮ ಪ್ರತಿಕ್ರಿಯೆ ಪಡೆದಿದೆ. ಪ್ರಖ್ಯಾತ ನಿರ್ಮಾಪಕ ದಿನೇಶ್ ವಿಜಾನ್ ಅವರು ನಿರ್ಮಿಸಿರುವ ಈ ಚಿತ್ರ ಭಾರತದಲ್ಲಿ ಮಾತ್ರವೇ 633 ಕೋಟಿ ರೂ. ಮತ್ತು ವಿಶ್ವಾದ್ಯಂತ 718.50 ಕೋಟಿ ರೂ. ಗಳಿಕೆ ಮಾಡಿದ್ದು, ಒಟ್ಟು 800…

[reasonsforhope]

When self-described “ocean custodian” Boyan Slat took the stage at TED 2025 in Vancouver this week, he showed viewers a reality many of us are already heartbreakingly familiar with: There is a lot of trash in the ocean.

“If we allow current trends to continue, the amount of plastic that’s entering the ocean is actually set to double by 2060,” Slat said in his TED Talk, which will be published online at a later date. 

Plus, once plastic is in the ocean, it accumulates in “giant circular currents” called gyres, which Slat said operate a lot like the drain of the bathtub, meaning that plastic can enter these currents but cannot leave.

That’s how we get enormous build-ups like the Great Pacific Garbage Patch, a giant collection of plastic pollution in the ocean that is roughly twice the size of Texas.

As the founder and CEO of The Ocean Cleanup, Slat’s goal is to return our oceans to their original, clean state before 2040. To accomplish this, two things must be done.

First: Stop more plastic from entering the ocean. Second: Clean up the “legacy” pollution that is already out there and doesn’t go away by itself.

And Slat is well on his way.

Pictured: Kingston Harbour in Jamaica. Photo courtesy of The Ocean Cleanup Project

When Slat’s first TEDx Talk went viral in 2012, he was able to organize research teams to create the first-ever map of the Great Pacific Garbage Patch. From there, they created a technology to collect plastic from the most garbage-heavy areas in the ocean.

“We imagined a very long, u-shaped barrier … that would be pushed by wind and waves,” Slat explained in his Talk. 

This barrier would act as a funnel to collect garbage and be emptied out for recycling. 

But there was a problem.

“We took it out in the ocean, and deployed it, and it didn’t collect plastic,” Slat said, “which is a pretty important requirement for an ocean cleanup system.”

Soon after, this first system broke into two. But a few days later, his team was already back to the drawing board. 

From here, they added vessels that would tow the system forward, allowing it to sweep a larger area and move more methodically through the water. Mesh attached to the barrier would gather plastic and guide it to a retention area, where it would be extracted and loaded onto a ship for sorting, processing, and recycling. 

It worked. 

“For 60 years, humanity had been putting plastic into the ocean, but from that day onwards, we were also taking it back out again,” Slat said, with a video of the technology in action playing on screen behind him.

To applause, he said: “It’s the most beautiful thing I’ve ever seen, honestly.”

Over the years, Ocean Cleanup has scaled up this cleanup barrier, now measuring almost 2.5 kilometers — or about 1.5 miles — in length. And it cleans up an area of the ocean the size of a football field every five seconds.

Pictured: The Ocean Cleanup's System 002 deployed in the Great Pacific Garbage Patch. Photo courtesy of The Ocean Cleanup

The system is designed to be safe for marine life, and once plastic is brought to land, it is recycled into new products, like sunglasses, accessories for electric vehicles, and even Coldplay’s latest vinyl record, according to Slat. 

These products fund the continuation of the cleanup. The next step of the project is to use drones to target areas of the ocean that have the highest plastic concentration. 

In September 2024, Ocean Cleanup predicted the Patch would be cleaned up within 10 years. 

However, on April 8, Slat estimated “that this fleet of systems can clean up the Great Pacific Garbage Patch in as little as five years’ time.”

With ongoing support from MCS, a Netherlands-based Nokia company, Ocean Cleanup can quickly scale its reliable, real-time data and video communication to best target the problem. 

It’s the largest ocean cleanup in history.

But what about the plastic pollution coming into the ocean through rivers across the world? Ocean Cleanup is working on that, too. 

To study plastic pollution in other waterways, Ocean Cleanup attached AI cameras to bridges, measuring the flow of trash in dozens of rivers around the world, creating the first global model to predict where plastic is entering oceans.

“We discovered: Just 1% of the world’s rivers are responsible for about 80% of the plastic entering our oceans,” Slat said.

His team found that coastal cities in middle-income countries were primarily responsible, as people living in these areas have enough wealth to buy things packaged in plastic, but governments can’t afford robust waste management infrastructure. 

Ocean Cleanup now tackles those 1% of rivers to capture the plastic before it reaches oceans.

Pictured: Interceptor 007 in Los Angeles. Photo courtesy of The Ocean Cleanup

“It’s not a replacement for the slow but important work that’s being done to fix a broken system upstream,” Slat said. “But we believe that tackling this 1% of rivers provides us with the only way to rapidly close the gap.”

To clean up plastic waste in rivers, Ocean Cleanup has implemented technology called “interceptors,” which include solar-powered trash collectors and mobile systems in eight countries worldwide.

In Guatemala, an interceptor captured 1.4 million kilograms (or over 3 million pounds) of trash in under two hours. Now, this kind of collection happens up to three times a week.

“All of that would have ended up in the sea,” Slat said.

Now, interceptors are being brought to 30 cities around the world, targeting waterways that bring the most trash into our oceans. GPS trackers also mimic the flow of the plastic to help strategically deploy the systems for the most impact.

“We can already stop up to one-third of all the plastic entering our oceans once these are deployed,” Slat said.

And as soon as he finished his Talk on the TED stage, Slat was told that TED’s Audacious Project would be funding the deployment of Ocean Cleanup’s efforts in those 30 cities as part of the organization’s next cohort of grantees. 

While it is unclear how much support Ocean Cleanup will receive from the Audacious Project, Head of TED Chris Anderson told Slat: “We’re inspired. We’re determined in this community to raise the money you need to make that 30-city project happen.”

And Slat himself is determined to clean the oceans for good.

“For humanity to thrive, we need to be optimistic about the future,” Slat said, closing out his Talk.

“Once the oceans are clean again, it can be this example of how, through hard work and ingenuity, we can solve the big problems of our time.”

-via GoodGoodGood, April 9, 2025

[sameergaza]

Very important

Hello guys, as you know, there are some monthly costs that are continuous. Last month we collected the rent costs of the house where my family of 7 members live. Fortunately, I was able to collect that amount. And keep my family safe and away from all risks,

I am now on the verge of the beginning of a new month, and I need to collect the costs required for the new month, so that we can continue to be safe..

I don’t want to repeat the same words every time, but I will share the link to the old post, I think everyone has seen it before, but also to explain the purpose of the publication and clarify it if there are new people, they can understand the subject as fully as possible.

The link to the old post that explains everything .

To clarify, guys, the payment date is 03/07/2025

That is, we have about 6 or 7 days of time .

0/500$ .

Verification link from my campaign:

If there is any inquiry, I will be available most of the time, I love you guys 🫂🫶🏻