#supervised and unsupervised learning examples

[bayesic-bitch]

@intimate-mirror's rent-lowering gunshot post is excellent, but it also made me realize that I've never really written up a full response to the AI foom stuff. Obviously this is all just my opinion, but I do think that as someone who has worked in RL, offline RL, and bandit theory, I think I'm somewhat uniquely positioned to give this argument.

yud's argument basically comes down to the idea that it is much harder to constrain a very intelligent AI than to make one. I think this is a reasonable intuition to have from the examples he gives, such as the "security mindset" bit. However, I do not think that this idea actually holds up very well to the mathematical intuition of statistical agents operating autonomously in an environment.

Pure unsupervised learning will not get you superintelligence. Unsupervised learning approximates the dataset with some amount of error, and in the large network and large data limit, it will simply predict human text with no error. This does not require superhuman intelligence. It's suspected that the effectiveness of LLMs comes from the fact that after a large enough sample size, it's more parameter efficient to actually model the world than memorize the dataset, and models learn the more parameter-efficient option. By the same principle, if superintelligence is not needed to model the dataset (as it is not for predicting human text), it will not be learning.

RL can hypothetically give you superintelligence. Unlike unsupervised learning, RL collects its own data and seeks to maximize cumulative reward, so it is not constrained to merely imitating human intelligence. There are many many problems where RL gives an agent that is better than the best humans, but there are no supervised or unsupervised ML algorithms trained on humans that are more accurate then the humans that the data was taken from.

RL is really, really, stupidly hard. It's way harder than traditional ML in both theory and practice. Baby's first regression problem is linear regression. It's easy to explain as a minimization problem, it has a closed form solution that's easy to find, and it's not too hard to find convergence rates. Baby's first RL problem is the multi-armed bandit problem, and baby's first algorithm for multi-armed bandits is UCB, which requires graduate-level stats like Chernoff bounds to explain. RL with linear function approximation is extremely difficult to analyze, such that questions like "how do I make all the weights of my linear function approximator not blow up to infinity" were open questions that took 15+ years to answer. In practice, this theoretical finickiness manifests as an extreme dependence in implementation minutia, making good results an exercise in trial and error.

Comparatively, keeping RL near a dataset is pretty easy. Basically this is because if you have access to the dataset and don't have to go explore to find new data, the problem becomes much much easier. It can be turned into a single closed-form optimization problem, but you can also get bounds by doing something as simple as adding a term that penalizes KL divergence from the dataset on a per-point basis.

Alignment is as simple as staying in the distribution of human-approved actions. The doomsday scenarios for AI foom are all scenarios in which the AI wants to do something that no (or at least vanishingly few) humans would do, such is melting the entire earth down into paperclips. Any non-zero regularization term like KL(P(action | human) || P(action | AI)) becomes infinite as the probability of a human taking this action goes to zero, and it's easy to get upper bounds on this KL penalty that can easily be optimized.

The result of all this is that it's way easier to align agents than to allow them to figure out stuff on their own. In my opinion, alignment (offline RL) is just a much easier problem than superintelligence (online RL). I think this view lines up well with what we have seen from AI chatbots so far. RLHF came along very quickly, and the "aligned personalities" of chatGPT and Claude very quickly reached a mature form. It's taken a lot longer for us to figure out how to do open-ended Rl from Verifiable Feedback, and although this looks immensely promising, it's very much still in its infancy.

[brucesterling]

AI terminology of the week, circa Mar 2026

Terms Related to AI and Agents

A/B Testing AGI (Artificial General Intelligence) AGI Acceleration AI Accelerators AI Affordances AI Cognitive Pattern AI Cognitive Spirit AI Command Palette AI Companion AI Copiloting AI Feature Design AI Governance AI Leading States AI Literacy AI Models AI Partner AI Product Design AI Product Management AI Prompting AI Safety AI Strategy AI Suggestions Patterns AI Watermarking AI Wireframing AI as Assistant AI as Collaborator AI as Creative Partner AI as Infrastructure AI as Medium AI as Mirror AI as Substrate AI as Tool AI as Toy AI as Utility AI-Augmented Design AI-Generated Content Detection AI-Native Design AI-Powered Search API (Application Programming Interface) ASI (Artificial Superintelligence) Accepted/Reject Flow Adaptive UI Adversarial Examples Agent Agent Builders Agents Loop Alignment Ambient AI Appropriateness Reliance Assistance Automation Automation Spectrum Autonomous Agent Autonomous Vehicle Autopilot Mode BMOA (Biggest Method of AI-Driven Development) Bias & Fairness Black Box Browser Use C2PA (Coalition for Content Provenance and Authenticity) CV (Computer Vision) Capability Elicitation Career Modalities Chain of Thought Client AI Cloud AI Cognitive Load Cognitive Offloading Collaboration Compute Use Computer Use Conscience Consent Considerate Display Content Models Content Moderation Context Control Copilot Mode DL (deep learning) DL Engines Data Labeling Data Poisoning Data Privacy Dataset Bias Dataset Curation Design Automation Design Education Design for AI Design for AI/AGI Digital Provenance Digital Twin EUI/AI Embedded AI Embodied AI Emergent Capabilities Empathy with AI Ethics Evaluation Explainable AI Fairness Metrics Fake News Few-Shot Prompting Fine-Tuning Foundation Model Free Speech GOFAI (Good Old-Fashioned AI) GenAI Interns Generative AI Generative Design Grounding Hallucination Harness Human in the Loop Human-Centered AI Human-on-the-Loop Image Generation Image-to-Image Image-to-Text Inference Efficiency Inference Engine Intent Classification Intent Detection Interface JSON Mode Justifiable Risk LLM (Large Language Model) LLMOps (Large Language Model Operations) LLMs (Large Language Models) Latency of Computation Latency of Response Meta-Prompt Meta-Prompting Model Drift Model Hallucination Model Misuse Model Poisoning Model Training Model Use Multi-modal Multi-modal Interface NLP (Natural Language Processing) NSAI (Neural Symbolic AI) NSFW Filter Open Source Open Source AI PEFT (Parameter Efficient Fine-Tuning) Personalization Personalized AI Plan Mode Plans/Planning Post-Training Pre-Training Predictive UI Proactive AI Proactive AI DESIGN Progress Disclosure Prompt Prompt Chaining

Prompt Debugging Prompt Design Prompt Engineering

Prompt Evaluation Prompt Injection

Prompt Injection Mitigation

Prompt Libraries Prompt Literacy Prompt Template

Prompt Versioning Prompting Push vs Pull RAI (Responsible AI) RAS (Retrieval Augmented Generation) RLF RLHF (Reinforcement Learning from Human Feedback) Recommendation Engine Reinforced Learning Reinforcement Learning Response/AI Roles & Tone Rules Safety Filters Semantic Search Shadows Mode Silicon Use Speculative Design for AI Speech Stochastic Prompt Streaming Text Effect Structure Subagents Subtasks Supervised Learning Supervision & Oversight Symbolic AI Synthetic Data Synthetic Users System Prompt Task Delegation Taxonomy of Agents Temperature Text-to-3D Text-to-Code Text-to-Image Text-to-Speech Text-to-Video Throughput Tokens Tool Use Top-k Sampling Toxicity Detection Training Transfer Learning Transformer Transparency Trust Trust Calibration Unlabeled/Raw AI Unsupervised Learning Usability Vector Search Voice Voice Interface Voice Language Model Voice Recognition Weights Workflow Automation Workflows Zero-Shot Prompting

[materialsscienceandengineering]

From Curious to Hooked in One Evening

I went in skeptical. An AI girlfriend sounded like a gimmick. But one evening on sweetdream.ai turned that around fast. Designing her was genuinely fun, and the first real conversation felt warm and surprisingly natural.

By the end of the night I understood the appeal. The chat remembered what I'd said, the photos matched the person I built, and a quick voice message sealed it. SweetDream is the rare thing that's better than the pitch.

[materialsscienceandengineering]

Machine Learning

One computational technique that has seen a rapid increase in use over the last decade or so has been machine learning. Considered to be a subset of artificial intelligence, machine learning is, very simply put, a computational tool or technique that uses algorithms to process complex datasets and then "learn" from that data to predict outcomes or make decisions.

There are many varieties of machine learning techniques. Two main categorizations are known as supervised versus unsupervised learning. Supervised learning uses training data, with known inputs and outputs to train a model, whereas unsupervised learning attempts to discern patterns and structures from data that has not yet been classified. Other common methods include artificial neural networks, deep learning, random forest regression, and Bayesian networks.

In the field of materials science there are countless applications for machine learning. Rather than attempt to list them, three examples of research that have been shared on this blog include:

November 2025 research using "[m]achine-learning (ML) assisted image segmentation techniques [...] for high throughput object identification" to teach membranes to sort by chemical affinity

October 2025 research using "a high-fidelity machine learning interatomic potential" to perform "[l]arge-scale molecular dynamics simulations of solidification and thermomechanical processing of the chemically-complex CrCoNi alloy"

August 2025 research on "a new data-driven approach that integrates [data mining], experimentation and [machine learning] for the efficient development of high-performance underwater adhesive hydrogels"

Sources/Further Reading: (Wikipedia—Image 1) (2024 article—Image 2) (2023 article—Image 3) (Ansys) (2016 article)

[religion-is-a-mental-illness]

She let her 6-year-old ride to the park alone. Georgia called it neglect.

Despite a new state law protecting childhood independence, child welfare officials accused these Atlanta parents of neglect—and put their fa

By: Lenore Skenazy

Published: Jan 16, 2026

With schools closed for Election Day 2025, Mallerie Shirley's 6-year-old son was riding his electric scooter to and from the local playground—about a third of a mile away—on the bike trail just outside their Atlanta house. On his way home, a woman in a car stopped him and, according to the boy, asked rapid-fire questions in an elevated voice: What's your name? How old are you? Where do you live?

Shirley is a software engineer and mom of two—but she also holds a master's in social work and was a caseworker for four years in Minnesota. She and her husband, Christopher, believe in childhood independence and had prepared their son for such an encounter. He did not tell the lady his name, age, or where he lived. He later told his parents he was afraid she would try to pull him into the car, so he sped home, but he believes she followed him.

Two days later, a caseworker from the Division of Family and Children Services (DFCS) showed up at their house. Shirley wasn't home, but the caseworker told her husband that a report had been made that their son was seen on his scooter unsupervised. She informed him that she would be going to both their son's and their 3-year-old daughter's schools to interview and photograph the children.

Shirley's husband asked if there was a problem with letting their son go to the playground by himself. The caseworker said he was too young to go alone until he was "about 13." When asked if that was the law, she said, "They will consider that inadequate supervision," adding that "he is extremely vulnerable at that age.…Anything could happen." Shirley's husband again asked the caseworker what the law said, and she replied that she would follow up with her supervisor, who eventually answered that DFCS policy is that a child cannot be left unattended for any amount of time until they are 9 years old. 

In 2025, Georgia passed Senate Bill 110 (with help from my nonprofit, Let Grow), which revised the definition of neglect as putting a child in "real, significant, and imminent risk of harm that would be so obvious…that a legal custodian acting reasonably would not have exposed the child to the imminent risk of harm." It further specifies that "independent activity…shall include, but shall not be limited to, playing indoors or outdoors alone or with other children, walking to or from school, running errands, or traveling to local commercial or recreational facilities."

The law received bipartisan support following the case of Brittany Patterson, who was handcuffed in front of her family after her 10-year-old walked to town without telling her.

 "Because of SB110 we felt confident that [DFCS] would not find us to be negligent," Shirley wrote in Let Grow's Facebook Group, Raising Independent Kids. "We knew, and made clear to the social worker, that we ensured our six-year-old had the maturity, physical abilities, AND mental abilities to be safe," wrote Shirley.

A few hours later, the caseworker returned. (Shirley would later learn that the caseworker had asked her son if his parents loved him and if they had drugs in the house.) During this visit, Shirley and her husband cited the law and asked how they were neglecting their child. According to Shirley, the caseworker couldn't give a single example, but she did produce extremely unlikely hypothetical events, including that the boy could have been kidnapped or broken his leg.

The caseworker was not applying the new law's standards, says Let Grow's legal consultant Diane Redleaf. "DFCS has not taken sufficient steps to inform its staff" of what the new law allows kids to do—including traveling alone to playgrounds. 

On December 16, the caseworker called to say she was trying to close out the case and asked if Shirley and her husband were "still doing that free-range parenting or whatever they call it?" Shirley responded that her son had become too afraid to go out anymore after being reported.

A few weeks later, a letter arrived from DFCS saying the department had "substantiated" the finding of neglect "based on the preponderance of evidence." Shirley requested to see their file and asked how to appeal the decision. She was told by the caseworker's supervisor that much of the case file would be blacked out. When Shirley asked for the specific policy that says children under 9 cannot be unsupervised for any amount of time, the supervisor said, "That's something you can Google."

Young kids have been riding around their neighborhoods for eons. That this has become less common is not due to a sudden reversal in evolution or a giant spike in crime. Kids are the same as ever, and today's murder rate is on track to be the lowest in 65 years. What has changed is the ability to see 6-year-olds as reasonably competent young humans.

The 1981 book, Your Six-Year-Old: Loving and Defiant, provided a checklist of milestones for neurotypical kids, including traveling "alone in the neighborhood (four to eight blocks) to store, school, playground, or to a friend's home".

David DeLugas, founder and executive director of ParentsUSA, a nonprofit that provides pro bono legal help to parents in situations like this, has taken on Shirley's case. He has filed a request for an administrative review to contest the ruling, and ParentsUSA has created a donation page to help cover legal expenses for this and similar cases.

In the meantime, Shirley and her husband are living under a DFCS "Safety Plan," which they were told they had to sign or DFCS would escalate its response and label them as noncompliant parents. The plan states that they must ensure both children are supervised at all times.

DFCS has an important job to do: Save children who are truly being neglected and abused. When she was a caseworker, Shirley said she saw "unthinkable things."

A kid riding his scooter to the park was not one of them.

==

https://letgrow.org/crime-statistics/

The media like to report that “460,000 children go missing every year.” But that number isn’t talking about “children who are kidnapped and given new names and enrolled in a new elementary school,” says University of Delaware Criminal Justice Prof. Joel Best. It’s talking about children (even teens) who said they’d be home for dinner at 6 and their spaghetti is getting cold.

That “460,000” number comes from a 2017 report by the Department of Justice on missing children. To qualify as a missing kid – we realize that’s a strange word — a person UNDER AGE 18 just had to be missing for more than an hour.

The DOJ report used police data as well as results from a national survey that asked parents: Did your kids ever go missing? Yes, said some parents. Then they gave the DOJ some examples. For instance:

• An 8-year-old got off at the wrong bus stop and his frantic parents called the cops. • A 10-year-old came home from the beach and went to bed – but her parents thought she was still outside. • A divorced mom violated a court order by taking her 9-year-old out of state. • A 17-year-old girl, pregnant, ran away.

So, of the 460,000 missing children — a scary number! — the report concluded (in a footnote), about 105 were “stereotypical kidnappings” – police-speak for abductions like you see on Law & Order. Most of those victims were teens. And 92% of them made it home safe.

...

If for some strange reason you WANTED your child to be kidnapped by a stranger, how long would you have to keep them outside, unattended, for this to be statistically likely to happen? 750,000 years.

[halfagone]

So Here's the Thing About the Fenton Parents...

I have written more than one post about the Fenton parents now. You might have seen them, you might not have. These are the two posts if you're curious: meta post and the original ask that inspired the meta.

From these posts, I've learned that this topic can be very divisive in the fandom. There are those that prefer them depicted as good parents, others as bad, some search for a more mild depiction of the Fentons' poor parenting, so on and so forth. Everyone is entitled to their own opinion. If you want a certain depiction or trope, please read those and be respectful to those who don't. It's as simple as that.

However, there is something that I realized we don't talk enough about as a fandom. I'll admit, I've done the same:

How can we depict the Fentons as good parents when they cannot adhere to basic safety standards?

It's easy to excuse this as quirky, eccentric behavior. This is a cartoon show after all, we shouldn't take it that seriously. Except... Danny Phantom, the show, has also showcased how their lacking safety measures has hurt and injured the people around them. More than once.

Vlad was first with the proto-portal in college. Arguably, their children have been struggling with their parents' carelessness long before the portal was ever finished: their food is constantly ecto-contaminated, coming back to life to traumatize their young kids, as we see in "The Fright Before Christmas". And then, of course, we have the Accident. Danny is turned half-ghost, and the rest is history.

Only... now these two have ecto-powered weapons that they use to hunt ghosts, caring very little when their hunts intrude on or injure their children, like the multiple occasions they have turned their weapons to Jazz, who is neither half-ghost or anything ghost-adjacent in canon.

There have been multiple scenes where the Fentons blatantly choose to ignore safety standards. The meta post I linked earlier shows a couple of such examples. But you might be wondering, what does this have anything to do with their parenting style? Well...

If they were good parents, their children's safety would be top of mind.

The Fentons cannot be wishy-washy with their basic lab procedures and also be good parents. These two facts cannot coexist, especially so when their lab headquarters is in their home, where curious children can walk in at any time, unsupervised. Should kids be walking into a lab with dangerous chemicals around? No, but it is their responsibility as parents to make sure their kids don't roam about.

It is their responsibility to teach their children this basic safety procedure, and adhere to it themselves, because it is meant to protect them and everyone around them. This cannot be a "do as I say, not as I do" situation. Their failure to be responsible can both directly and indirectly harm others, as we see from canon, where Danny becomes a halfa in a second portal accident.

Strictly speaking, if Danny had good parents, he would not have become a halfa in the first place. If the Fentons were good parents, they would have been there, in that lab, with Danny, Sam, and Tucker, supervising their visit. Danny would have never been allowed into the portal which- at that time- had been dysfunctional. Furthermore, even if Danny had been allowed in the portal, it should have been unplugged in adherence to safety code. Therefore, even if Danny did trip and hit, say, a misplaced "On" button inside, it wouldn't have turned on because it should not have been powered up to begin with.

More than once, safety measures could have been implemented to prevent a lab accident, yet nothing ever came out of it.

Furthermore, neither Fenton parent make an effort to reach out to Vlad after his own accident. Of course, we could extrapolate and say that the Fentons tried but were barred entry. However, that is more wishful thinking and personal headcanons than based on concrete canon evidence. What does this indicate about the Fentons?

Simply put, they are not good people. Of course, there are many characters in media that are depicted as villains but show compassion and care for their children. The Fentons are not one of them. They say they love their children, but very few times do they show it outside fighting off ghosts- which they would have done regardless if their children were there or not. They do not have respect for personal boundaries, public or private property, or public safety.

There are more than a few examples of this, but here is a very obvious one: the portal itself.

The Fentons believe that ghosts are nothing more than scum; they believe ghosts are malicious entities that would destroy the world given the chance. And yet they thought it was a good idea to create a portal to a world full of so-called malicious entities with little to no preparation whatsoever.

Oh sure, they have their weapons, but as we can see these two cannot be everywhere at once. Not even Danny, with superpowers, can be everywhere at once. The Fentons do not build a door for the portal until episode 13 of the series. 13 episodes. By this time, there have already been multiple ghost attacks, some of which spanned city-wide.

Incompetence is not an excuse. In fact, that incompetence should outright disbar them from keeping their children.

At the end of the day, whether you believe the Fentons really do love their children beyond their prejudice, they have repeatedly shown that they cannot be trusted to care for their children. This begs the question:

Is it still possible for the Fentons to be good parents?

Technically speaking, yes. Everyone is capable of change. The Fentons are more than capable of learning from their mistakes, although evidence indicates that they likely wouldn't, seeing how there were two portal accidents.

But yes, they can learn from the past and become better parents in the future. Jazz and Danny would likely have to be removed from the home until they update and comply with safety regulations, and they may even lose their business license because of their failure to adhere, but it is still possible.

However, the Fentons were not always good parents. Even if they were to learn and grow as people, it cannot be ignored that once upon a time, they had been responsible for a lot of damage: material, physical, mental, and emotional. This can come with consequences, including prison time and losing custody of their children.

Of course, we could choose to ignore all of this. We as a fandom do that for many aspects of canon, the Fenton parents could just be another one. You could argue that canon declares the Fenton parents are good.

But here's another thing:

The Fentons redeem themselves as parents when they accept Danny after a reveal. They are not automatically good ones.

Hopefully this will be the last meta on the Fenton parents from me. I understand people want Danny's parents to be good, and they certainly can be, but I am tired of people ignoring the very real neglect and abuse both their kids suffered. You do not have to have the Fentons dissect Danny to make them abusive. You do not have to have the Fentons work day and night to make them negligent.

Abuse and neglect cover a variety of cases. That includes kids like this too.

Thank you for reading.

[codingcorgi]

The more and more I see people being mad at AI in general makes me mad as someone who's major is in Machine Learning and my masters will also be in Machine Learning. Most people think of ChatGPT, Claude, Grok, or Gemini when it comes to AI and that's all they know about AI well those are all something called Generative AI. There are other AIs that do more good than bad like supervised learning, unsupervised learning, and reinforcement. For example without reinforcement machine learning we wouldn't have interesting enemies in video games. Generative AI is a plague tbh it should've never been widely available for the public and most businesses, and it has caused many unethical decisions being made, cognitive decline in many heavy users, and environmental harm. Just if you're going to hate something at least be more informed on it please, thank you

[mooddrafts]

What is Machine Learning? A Beginner's Guide to How It Works and Why It Matters

Introduction: In today’s digital world, Machine Learning (ML) is transforming everything from online shopping and social media to healthcare and banking. But what exactly is machine learning, and why is it so important? In this blog, we’ll break down the basics of machine learning, how it works, its types, and real-world applications — all in simple terms.

What is Machine Learning?

Machine Learning is a branch of Artificial Intelligence (AI) that enables computers to learn from data and make decisions or predictions without being explicitly programmed. Instead of following fixed instructions, a machine learning model “learns” from patterns in data to improve over time.

How Does Machine Learning Work?

At its core, machine learning involves three main steps:

Data Collection – Gathering relevant data (text, images, numbers, etc.).

Training a Model – Feeding this data into an algorithm that finds patterns.

Prediction or Output – Using the model to make predictions or decisions on new data.

The more quality data the model gets, the better it performs.

Types of Machine Learning 1. Supervised Learning The model is trained on labeled data (where the input and output are known). Example: Email spam detection, price prediction.

Unsupervised Learning The model finds hidden patterns in data without labels. Example: Customer segmentation, recommendation systems.

Reinforcement Learning The model learns by trial and error, getting rewards for right actions. Example: Game AI, robotics.

Real-World Applications of Machine Learning

Healthcare – Disease prediction, medical image analysis.

Finance – Fraud detection, stock price forecasting.

Retail – Personalized recommendations (like on Amazon).

Transportation – Self-driving cars, route optimization.

Marketing – Customer behavior analysis, targeted ads.

Why Machine Learning Matters

Machine learning allows businesses to automate processes, improve accuracy, and provide personalized experiences. It's also a key driver behind AI technologies like chatbots, voice assistants, and predictive analytics.

Conclusion

Machine Learning is no longer just a buzzword — it's a powerful tool shaping our digital future. Whether you're a business owner, developer, or student, understanding the basics of ML can open up new opportunities in tech and innovation.

SEO Keywords: machine learning, what is machine learning, how machine learning works, types of machine learning, machine learning examples, AI, artificial intelligence, machine learning applications

[kedreeva]

Indoor Cats: Leashes

Leashes for the Cat Enrichment Masterpost

While allowing cats to free roam can be done with supervision, unlike most dogs, cats can be difficult to recall and if they escape beyond an owner’s ability to recover them, the chances of them being found and safely returned are very slim because folks that see a cat out currently assume it’s supposed to be out (another good reason to turn away from unsupervised cats outside... less lost cats that never come home).

Instead, it is becoming increasingly common for owners to train their cats to a harness and leash (or lead) in order to allow them to safely experience some outdoor time, as well as facilitate bonding; cats trained to a harness and leash often learn to associate the items and the person carrying it with the enjoyable experience of going outside.

This does not necessarily mean that your cat will enjoy the leash immediately. It takes time for an association to be formed, and repeated exposure and consistency are often the key. Patience is also a virtue in this endeavor, as it is generally recommended to train slowly, by familiarizing them with the harness first and adding the leash later.

There are many good guides out there on how to train your cat to use a leash, but I will direct you first toward adventurecats.org. They are also home to a wealth of articles pertaining to gear choices and taking your cat on an adventure/traveling etc. Chewy also has a guide, as does the cat behaviorist.com. There are also plenty of video guides online that show examples. Do some exploring! Familiarize yourself with the process and the idea behind the process before expecting your cat to go through it.

The other thing to be aware of is that there are DOZENS of types of harnesses. Some cats will take the easiest one and do great. Others are escape artists and require a more secure harness. Because cats have such a huge variance in body type, a harness that comfortably fits one cat may not fit the next one. How the leash attaches and where it pulls comfortably on one cat may not suit another. You may have to do some trial and error to find a leash that works for your cat, so don’t abandon all hope if it doesn’t work immediately on the first try.

Here’s one made for harness escape artist cats: 

Lead training can also lead to socialization. A lot of folks tend to think of wolves as pack animals and cats as solitary loners, when in fact many cats - including housecats - enjoy being part of a pride or colony. In your home, you become the cat’s family, but while humans spend a lot of time socializing dogs to be friendly to folks outside of their family, cats generally get the short end of the stick because they either don’t leave the house or they leave the house unsupervised and thus have no opportunity to be purposefully exposed to socialization.

A harness and leash can change that, particularly for kittens! Taking your cat out on a leash can help you to take them to various places and maintain control over them the way you would a dog, which can familiarize them with new stimuli and make them overall less prone to stress when there’s a change, as well as help them to be more outgoing toward people because they won’t be as afraid of meeting new ones. It can also make it less scary for things like going to the vet, something a lot of cat owners struggle with because car rides = scary and new places = scary and new people = scary. But that can be way less true if they’re trained early on to use a leash and come visit new places and people while leashed. Now it’s just a new adventure the two of you can share!

And I have to share one last leash photo, which is technically an accessory to transporting your cat around so you can use your leashes... the cat backpack! They actually have one that, if I understand it correctly, can be reversed around to turn into a cat cave/bed for a nice little hide while camping/adventuring.

They also make them with completely clear sides, and ones that fold out into little cat tents, which may make it easier to familiarize your cat with it before taking it out and about.

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