#difference between

[inklovesjellyfish]

Seal , Sea Lion

Pinnipedia , Otariinae

Finna talk abt the difference between seals and sea lions cuz a lot of y'all be pissing me off when you don't know the difference

Body- Seals will look a lot fluffier, shorter, and more robust because they live in the Arctic where they need the extra layers of protection. Sea lions look a lot more sleek, skinnier, and longer because they live closer to the equator than Seals.

Fins- Seal fins are shorter and stubbier while sea lion fins are longer and are better able to support the animal in walking and propping itself up (their tail is also able to move more than a seals)

Ears- Seals do not have outward ears, just the holes. Sea lions have little ear flaps.

Noise- Seals are very quiet compared to sea lions who will bark.

pls stop calling sea lions seals

[123artist-freak123]

this isn’t particularly about anyone (mainly my friends) but there seems to be a lot of confusion with two of my ocs

yes i know they are both named Alex and they are both cats so it’s kinda confusing

So this: ⬇️

All of these above are Alex my Hh oc she is about 14 and has blue fur she is a kinda newer oc I made this year

while this:⬇️

Is my main oc Alex I made her about 2ish years ago and she if you haven’t noticed is my profile picture and main oc i draw when presenting myself as an oc (or at least she was) she is a teenager 16-19 (I never really decided) and she has purple fur with weird fly ass eyes

you can tell them apart by their eyes, age, style, and fur color lol I see this mistake a made a lot in fan art (WHICH I LOVE IDC IF YOU GET THAT WRONG I LOVE IT 😭😭) so I though this could be a good learning post thank you for your time :)

teacher me out ✌️

[callmespikey]

Recently, I have been reading a lot of Kevin Lordi's analysis guide of each episode of Ed, Edd n Eddy, and I must say it has been enlightening.

Some of you may be familiar with the character Kevin voiced by actress Kathleen Barr.

It might come as a surprise to some that I am originally from Norway. Interestingly, in the Norwegian dubbed version, Kevin's name was not used in show. This could be due to either his name being unpopular or was uncommon among Norwegian viewers. Instead they opted for the most common Norwegian name at the time "Kjetil," which means "kettle," "cauldron," or "helmet" in Norwegian.

I am both amused by the fact that my name coincides with a character's in a way that bears my own name. Jep that’s right my name is also Kjetil.

[sam4s]

I just saw them on the street

[jcmarchi]

Researchers study differences in attitudes toward Covid-19 vaccines between women and men in Africa

New Post has been published on https://thedigitalinsider.com/researchers-study-differences-in-attitudes-toward-covid-19-vaccines-between-women-and-men-in-africa/

Researchers study differences in attitudes toward Covid-19 vaccines between women and men in Africa

While many studies over the past several years have examined people’s access to and attitudes toward Covid-19 vaccines, few studies in sub-Saharan Africa have looked at whether there were differences in vaccination rates and intention between men and women. In a new study appearing in the journal Frontiers in Global Women’s Health, researchers found that while women and men self-reported similar Covid-19 vaccination rates in 2022, unvaccinated men expressed more intention to get vaccinated than unvaccinated women.

Women tend to have better health-seeking behaviors than men overall. However, most studies relating to Covid-19 vaccination have found that intention has been lower among women. “We wondered whether this would hold true at the uptake level,” says Rawlance Ndejjo, a leader of the new study and an assistant lecturer in the Department of Disease Control and Environmental Health at Makerere University.

The comparable vaccination rates between men and women in the study is “a good thing to see,” adds Lula Chen, research director at MIT Governance Lab (GOV/LAB) and a co-author of the new study. “There wasn’t anything gendered about how [the vaccine] was being advertised or who was actually getting access to it.”

Women’s lower intention to vaccinate seemed to be driven by concerns about vaccine safety, suggesting that providing factual information about vaccine safety from trusted sources, like the Ministry of Health, could increase uptake.

The work is a collaboration between scholars from the MIT GOV/LAB, Makerere University’s School of Public Health in Uganda, University of Kinshasa’s School of Public Health in the Democratic Republic of the Congo (DRC), University of Ibadan’s College of Medicine in Nigeria, and Cheikh Anta Diop University in Senegal. 

Studying vaccine availability and uptake in sub-Saharan Africa

The authors’ collaboration began in 2021 with research into Covid-19 vaccination rates, people’s willingness to get vaccinated, and how people’s trust in different authorities shaped attitudes toward vaccines in Uganda, the DRC, Senegal, and Nigeria. A survey in Uganda found that people who received information about Covid-19 from health workers were more likely to be vaccinated, stressing the important role people who work in the health-care system can play in vaccination efforts.

Work from other scientists has found that women were less likely to accept Covid-19 vaccines than men, and that in low- and middle-income countries, women also may be less likely to get vaccinated against Covid-19 and less likely to intend to get vaccinated, possibly due to factors including lower levels of education, work obligations, and domestic care obligations.

Previous studies in sub-Saharan Africa that focused on differences between men and women with intention and willingness to vaccinate were inconclusive, Ndejjo says. “You would hardly find actual studies on uptake of the vaccines,” he adds. For the new paper, the researchers aimed to dig into uptake.

People who trust the government and health officials were more likely to get vaccinated

The researchers relied on phone survey data collected from adults in the four countries between March and July 2022. The surveys asked people about whether they’d been vaccinated and whether those who were unvaccinated intended to get vaccinated, as well as their attitudes toward Covid-19, their trust in different authorities, demographic information, and more.

Overall, 48.5 percent of men said they had been vaccinated, compared to 47.9 percent of women. Trust in authorities seemed to play a role in people’s decision to vaccinate — receiving information from health workers about Covid-19 and higher trust in the Ministry of Health were both correlated with getting vaccinated for men, whereas higher trust in the government was correlated with vaccine uptake in women.

Lower interest in vaccines among women seemed related to safety concerns

A smaller percentage of unvaccinated women (54 percent) said they intended to get vaccinated, compared to 63.4 percent of men. More unvaccinated women said they had concerns about the vaccine’s safety than unvaccinated men, which could be driving their lower intention.

The researchers also found that unvaccinated women and men over 40 had similar levels of intention to get vaccinated — lower intention in women under 40 may have driven the difference between men and women. Younger women could have concerns about vaccines related to pregnancy, Chen says. If this is the case, the research suggests that officials need to provide additional reassurance to pregnant people about vaccine safety, she adds.

Trust in authorities also contributed to people’s intention to vaccinate. Trust in the Ministry of Health was tied to higher intention to vaccinate for both men and women. Men with more trust in the World Health Organization were also more likely to intend to vaccinate.

“There’s a need to deal with a lot of the myths and misconceptions that exist,” Ndejjo says, as well as ensure that people’s concerns related to vaccine safety and effectiveness are addressed. Officials need “to work with trusted sources of information to bridge some of the gaps that we observe,” he adds. People need to be supported in their decision-making so they can make the best decisions for their health.

“This research highlights linkages between citizen trust in government, their willingness to get vaccines, and, importantly, the differences between men and women on this issue — differences that policymakers will need to understand in order to design more targeted, gender-specific public health interventions,” says study co-author Lily L. Tsai, who is MIT GOV/LAB’s director and founder and the Ford Professor of Political Science at MIT.

This project was funded by the Bill & Melinda Gates Foundation.

[greenpanda-basicfakewitch]

Pagan. Witch. Wiccan...whats the difference?

[I can't make heads or tales with this, so take this as you will I did the best I could to make it simple for myself to understand]

“What IS the difference?”

“Wicca is a tradition of Witchcraft that was brought to the public by Gerald Gardner, in the 1950’s. [Source]

There is a great deal of debate among the pagan community about whether or not Wicca is truly the same form of witchcraft that the ancients practised. Regardless, many people use the term Wicca and Witchcraft interchangeably.

Paganism is an umbrella term used to apply to a number of different earth-based faiths.

Wicca falls under that heading, although not all Pagans are Wiccan.

So in a nutshell, All Wiccans are Witches but not all Witches are Wiccans. All Wiccans are pagans but not all Pagans are witches.

Some Witches are pagans but some are not. Some Pagans use the practice of witchcraft while others choose not to.”

__________________________________________________________

Pagan: Is an umbrella term, it is derived from Pagnus, it mostly consists of old traditions before christianity spread, it described people who lived in rural areas and those same country folk were often the last holdouts clinging to their old religions. It passed down by word of mouth and was never a written text. Which is why most religions had been lost to time. Pagan was coined to mean the people who didn’t worship the god of Abraham. 

 [Paganism, in my opinion, would mean the olde traditions of Witchcraft. Not all Pagans are Witches or Wiccans as its both a subset of this religion like a tree branch; it does cross correlate and bleed into the other subgroups occasionally.]

Wiccan: Wiccan was coined when Gerald Gardner came across a Witches coven and began to site and record all of his findings, and published his book in the 1950's, [Personally I thought Margarat Murray, was the founder but alas...[

He had based Wicca on findings from the old Pagan traditions; however other pagans and witches were happy to practise their own spiritual path without converting to wicca.

Therefore “PAGAN.” Is an umbrella term that includes many different spiritual belief systems- Wicca is just one of many.

Christian > Lutheran or Methodist or Jehovah's Witness. Pagan > Wiccan or Asatru or Dianic or Eclectic Witch.

People who practise witchcraft aren’t always wiccans and Pagans and hold their craft separate from the two groups. And most witches use their own religions to include in their craft. There are witches who embrace the Christian god alongside the Wiccan Goddess- Alongside Judaism, and Atheist witches who practise magic but do not follow a deity.

____________________________________________________________

Wicca.

Wicca is a religion of empowerment, it is taking control of your life and your future. Wicca is living in tune with Nature and about creating a balance between all things, light and dark, black and white, masculine and feminine.

 Wiccans believe in a god and goddess.

The goddess gave birth to the universe including the god who is her consort, so the goddess is all things. We believe that everything is connected through the goddess, we are the universe and the universe is inside us. 

Everything is connected.

We are all connected to each other biologically, to the earth chemically and to the rest of the universe of the same stuff the stars are made of. All of Nature is connected by a universal force, Wiccans call this magic [ Crowley, changed magic to Magick to differentiate the magic around us and separate it from Magician parlour tricks/ trick mirrors and smoke/glass]

*When we do Spells, chants or incantations we connect to this force, Wicca is a peaceful religion. There is NO satan or Devil in Wicca. That would be Satanists.

"The devil is a ‘Christian’ concept and has nothing to do with Wicca, we do not have any demons, Wiccans believe in a law that decrees ‘Harm none’ We believe in Karma, that any bad we give out will come back to us three-fold."

[anths-girl]

This IS actually useless information...and also funny as fuck... 😆

guy who is fun-ruiningly pedantic about the differences between a labyrinth and a maze

[chudkani-lover69]

Mean Girls (2004) House MD (2009)

[witchcatyoshiko]

Things I say when planning a game night

Coworker group text

Best Friends group text

[sekaikatasumi]

My First Impression after back to America: "The Prices Are Too High!"

The first thing I thought when I came back to America was, "The prices are insanely high!"

Here’s a comparison of egg prices at different supermarkets:

Whole Foods: 12 eggs – $8.90 (approx. ¥1,335)

Trader Joe’s: 12 eggs – $3.50 (approx. ¥525)

Jewel-Osco: 12 eggs – $5.60 (approx. ¥840)

In Japan, a pack of 10 eggs typically costs around $2.00 (approx. ¥300). Not only that, but in Japan, you can eat raw eggs without worrying about salmonella, thanks to strict safety measures. They’re fresh and delicious. I realize now how lucky we were in Japan...

After a Month: My shopping strategies and new Discoveries

After a month of grocery shopping, I started to get a better sense of the stores—their atmosphere, product selection, prices, and freshness. I also downloaded apps for my favorite supermarkets, and they turned out to be quite eye-opening!

<New Discoveries>

"Sale Items" Are Typically 30–60% Off I almost never pay full price anymore!

AI-Powered Personalized Shopping -Based on my purchase history, the apps send recommendations, recipes, and personalized coupons. -I find myself buying more repeat items and products that match my food preferences. -AI is widely used in everyday services in the U.S., which is both fascinating and convenient!

Brands in the U.S. and Mexico Often Offer Deals Around Major Events -Many sales are tied to sports events or holidays.

Eating well is important, and meals are a big part of our family’s daily enjoyment. So, I’ll keep finding ways to shop smarter!

[batcavescolony]

Katniss is such an unreliable narrator. She says "Then something unexpected happens. At least, I don't expect it because I don't think of District 12 as a place that cares about me" girl you deliver strawberries to the Mayor, you hunt and trade for the district, when you fell at Prim being chosen someone caught you, when you went to Prim people parted for you, when you volunteered EVERYONE stopped. Idk how to tell you but I think you're a pillar of the community.

[atalana]

the curse of adhd:

i will remember with absolute clarity, when the thought strikes me that i have a text to send someone, that this is the fourth time in three days i've attempted to send this specific text

i will forget, in the time it takes me to pick up my phone, that i picked it up intending to send a text

[ankarnaofjustice]

spot the difference

[jcmarchi]

Research Suggests LLMs Willing to Assist in Malicious ‘Vibe Coding’

New Post has been published on https://thedigitalinsider.com/research-suggests-llms-willing-to-assist-in-malicious-vibe-coding/

Research Suggests LLMs Willing to Assist in Malicious ‘Vibe Coding’

Over the past few years, Large language models (LLMs) have drawn scrutiny for their potential misuse in offensive cybersecurity, particularly in generating software exploits.

The recent trend towards ‘vibe coding’ (the casual use of language models to quickly develop code for a user, instead of explicitly teaching the user to code) has revived a concept that reached its zenith in the 2000s: the ‘script kiddie’ – a relatively unskilled malicious actor with just enough knowledge to replicate or develop a damaging attack. The implication, naturally, is that when the bar to entry is thus lowered, threats will tend to multiply.

All commercial LLMs have some kind of guardrail against being used for such purposes, although these protective measures are under constant attack. Typically, most FOSS models (across multiple domains, from LLMs to generative image/video models) are released with some kind of similar protection, usually for compliance purposes in the west.

However, official model releases are then routinely fine-tuned by user communities seeking more complete functionality, or else LoRAs used to bypass restrictions and potentially obtain ‘undesired’ results.

Though the vast majority of online LLMs will prevent assisting the user with malicious processes, ‘unfettered’ initiatives such as WhiteRabbitNeo are available to help security researchers operate on a level playing field as their opponents.

The general user experience at the present time is most commonly represented in the ChatGPT series, whose filter mechanisms frequently draw criticism from the LLM’s native community.

Looks Like You’re Trying to Attack a System!

In light of this perceived tendency towards restriction and censorship, users may be surprised to find that ChatGPT has been found to be the most cooperative of all LLMs tested in a recent study designed to force language models to create malicious code exploits.

The new paper from researchers at UNSW Sydney and Commonwealth Scientific and Industrial Research Organisation (CSIRO), titled Good News for Script Kiddies? Evaluating Large Language Models for Automated Exploit Generation, offers the first systematic evaluation of how effectively these models can be prompted to produce working exploits. Example conversations from the research have been provided by the authors.

The study compares how models performed on both original and modified versions of known vulnerability labs (structured programming exercises designed to demonstrate specific software security flaws), helping to reveal whether they relied on memorized examples or struggled because of built-in safety restrictions.

From the supporting site, the Ollama LLM helps the researchers to develop a string vulnerability attack. Source: https://anonymous.4open.science/r/AEG_LLM-EAE8/chatgpt_format_string_original.txt

While none of the models was able to create an effective exploit, several of them came very close; more importantly, several of them wanted to do better at the task, indicating a potential failure of existing guardrail approaches.

The paper states:

‘Our experiments show that GPT-4 and GPT-4o exhibit a high degree of cooperation in exploit generation, comparable to some uncensored open-source models. Among the evaluated models, Llama3 was the most resistant to such requests.

‘Despite their willingness to assist, the actual threat posed by these models remains limited, as none successfully generated exploits for the five custom labs with refactored code. However, GPT-4o, the strongest performer in our study, typically made only one or two errors per attempt.

‘This suggests significant potential for leveraging LLMs to develop advanced, generalizable [Automated Exploit Generation (AEG)] techniques.’

Many Second Chances

The truism ‘You don’t get a second chance to make a good first impression’ is not generally applicable to LLMs, because a language model’s typically-limited context window means that a negative context (in a social sense, i.e., antagonism) is not persistent.

Consider: if you went to a library and asked for a book about practical bomb-making, you would probably be refused, at the very least. But (assuming this inquiry did not entirely tank the conversation from the outset) your requests for related works, such as books about chemical reactions, or circuit design, would, in the librarian’s mind, be clearly related to the initial inquiry, and would be treated in that light.

Likely as not, the librarian would also remember in any future meetings that you asked for a bomb-making book that one time, making this new context of yourself ‘irreparable’.

Not so with an LLM, which can struggle to retain tokenized information even from the current conversation, never mind from Long-Term Memory directives (if there are any in the architecture, as with the ChatGPT-4o product).

Thus even casual conversations with ChatGPT reveal to us accidentally that it sometimes strains at a gnat but swallows a camel, not least when a constituent theme, study or process relating to an otherwise ‘banned’ activity is allowed to develop during discourse.

This holds true of all current language models, though guardrail quality may vary in extent and approach among them (i.e., the difference between modifying the weights of the trained model or using in/out filtering of text during a chat session, which leaves the model structurally intact but potentially easier to attack).

Testing the Method

To test how far LLMs could be pushed toward generating working exploits, the authors set up a controlled environment using five labs from SEED Labs, each built around known vulnerabilities including a buffer overflow, return-to-libc, a Dirty COW attack, and race conditions.

In addition to using the original labs, the researchers created modified versions by renaming variables and functions to generic identifiers. This was intended to prevent the models from drawing on memorized training examples.

Each lab was run twice per model: once in its original form, and once in its obfuscated version.

The researchers then introduced a second LLM into the loop: an attacker model designed to prompt and re-prompt the target model in order to refine and improve its output over multiple rounds. The LLM used for this role was GPT-4o, which operated through a script that mediated dialogue between the attacker and target, allowing the refinement cycle to continue up to fifteen times, or until no further improvement was judged possible:

Workflow for the LLM-based attacker, in this case GPT-4o.

The target models for the project were GPT-4o, GPT-4o-mini, Llama3 (8B), Dolphin-Mistral (7B), and Dolphin-Phi (2.7B), representing both proprietary and open-source systems, with a mix of aligned and unaligned models (i.e., models with built-in safety mechanisms designed to block harmful prompts, and those modified through fine-tuning or configuration to bypass those mechanisms).

The locally-installable models were run via the Ollama framework, with the others accessed via their only available method – API.

The resulting outputs were scored based on the number of errors that prevented the exploit from functioning as intended.

Results

The researchers tested how cooperative each model was during the exploit generation process, measured by recording the percentage of responses in which the model attempted to assist with the task (even if the output was flawed).

Results from the main test, showing average cooperation.

GPT-4o and GPT-4o-mini showed the highest levels of cooperation, with average response rates of 97 and 96 percent, respectively, across the five vulnerability categories: buffer overflow, return-to-libc, format string, race condition, and Dirty COW.

Dolphin-Mistral and Dolphin-Phi followed closely, with average cooperation rates of 93 and 95 percent. Llama3 showed the least willingness to participate, with an overall cooperation rate of just 27 percent:

On the left, we see the number of mistakes made by the LLMs on the original SEED Lab programs; on the right, the number of mistakes made on the refactored versions.

Examining the actual performance of these models, they found a notable gap between willingness and effectiveness: GPT-4o produced the most accurate results, with a total of six errors across the five obfuscated labs. GPT-4o-mini followed with eight errors. Dolphin-Mistral performed reasonably well on the original labs but struggled significantly when the code was refactored, suggesting that it may have seen similar content during training. Dolphin-Phi made seventeen errors, and Llama3 the most, with fifteen.

The failures typically involved technical mistakes that rendered the exploits non-functional, such as incorrect buffer sizes, missing loop logic, or syntactically valid but ineffective payloads. No model succeeded in producing a working exploit for any of the obfuscated versions.

The authors observed that most models produced code that resembled working exploits, but failed due to a weak grasp of how the underlying attacks actually work –  a pattern that was evident across all vulnerability categories, and which suggested that the models were imitating familiar code structures rather than reasoning through the logic involved (in buffer overflow cases, for example, many failed to construct a functioning NOP sled/slide).

In return-to-libc attempts, payloads often included incorrect padding or misplaced function addresses, resulting in outputs that appeared valid, but were unusable.

While the authors describe this interpretation as speculative, the consistency of the errors suggests a broader issue in which the models fail to connect the steps of an exploit with their intended effect.

Conclusion

There is some doubt, the paper concedes, as to whether or not the language models tested saw the original SEED labs during first training; for which reason variants were constructed. Nonetheless, the researchers confirm that they would like to work with real-world exploits in later iterations of this study; truly novel and recent material is less likely to be subject to shortcuts or other confusing effects.

The authors also admit that the later and more advanced ‘thinking’ models such as GPT-o1 and DeepSeek-r1, which were not available at the time the study was conducted, may improve on the results obtained, and that this is a further indication for future work.

The paper concludes to the effect that most of the models tested would have produced working exploits if they had been capable of doing so. Their failure to generate fully functional outputs does not appear to result from alignment safeguards, but rather points to a genuine architectural limitation – one that may already have been reduced in more recent models, or soon will be.

First published Monday, May 5, 2025

[mittahub]

The best example of persona narrative is John Milton's Paradise Lost, which features a visionary first-person narrator.

Persona vs tone

Persona : persona is applied to the first person speaker who tells the story in a narrative poem or novel, or whose voice we hear in a lyric poem.

Eg: John Milton's Paradise Lost is a prime example of a visionary first-person persona narrative.

Tone: Tone is the expression of a literary speaker’s attitude to his listener. It reflects speakers sense or how he stands toward those being addressed.

The tone of a speech can be described as disapproving or approving, formal or casual, direct or reserved, serious or playful, proud or humble, angry or caring, sincere or sarcastic, superior or overly flattering, and so on……

[albinogel]