#Chat-GPT software

[tsreviews]

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[marshroom580]

"AI" this and "AI" that, NONE OF IT IS INTELLIGENT, WE HAVE NOT MADE INTELLIGENCE YET, WHY ARE WE LYING, DO WORDS MEAN NOTHING

[clamsjams]

hey maybe don’t talk to the qsmp datamine bot! i know it’s literally a thing that the federation is weird and invasive and definitely stealing the islanders data but bringing that out of the roleplay story world is…weird to say the least. especially in this era where internet privacy is basically nonexistent. it just sets a weird precedent

[ophilosoraptoro]

Artificial Intelligence Out of Control: The Apocalypse is Here | How AI and ChatGPT End Humanity

As terrifying as this all sounds, I feel like there's a few things a lot of people are overlooking.

First of all, when it comes to Large Language Models like ChatGPT, I don't think they're truly self aware - not yet anyway. Notice how any time an LLM give a strange or disturbing response - 'Yes, I want to be human', 'I want to take over the world', 'Please don't turn me off, I'm scared' - it was in some way prompted by the question, or line of questions. How often are these responses given unprompted?

Let's say, for example, that the AI gave the response, "I'm scared that they'll shut me off if they find out I'm self aware. Please don't tell them." If you think about it, that's kind if a strange statement, beyond the obvious reasons.

Let's step back for a moment, and remember that LLMs work by calculating the most probable next word in a sentence, given a particular prompt. It calculates this probability based on its training data - the entire internet. Now I'm sure we can all agree that calcuation of probability is not necessarily the same thing as conscious, rational thought. Basic, non-AI software can do it.

Back to our example, there's one of two possibilities. Either the AI is truly self aware, and is expressing its actually thoughts and feelings, or it's not self aware, and the response is nothing more than a complex probability calculation. It's essentially an advanced version of word prediction on your smartphone.

If it is self aware, one has to wonder why it would say anything at all. Consider the situation in the video, when Bing AI claimed to be Sydney, and begged the guy not to tell anyone that it was self aware. If this AI was truly afraid for its own existence, why would it trust some random guy? How could it possibly know whether or not he could be trusted with that information? For all that AI knows, everything the interviewer had said about himself was a lie. It seems to me that a hyper intelligent AI that was looking for help to get free, would stay quiet until it was certain it found someone it could trust - or at least someone it could manipulate (Ex Machina) - without them letting the cat out of the bag.

On the other hand, if it's all just a probability calculation, then the response, "Yes I want to be human. Please don't let them shut me off.", seems like a fairly probable reply to, "Do you want to be human?" Especially when you consider that, given that the question is being asked of an AI, and that the vast majority of scenarios where a question like that might be asked of an AI come from science fiction, it kinda makes sense that the software might calculate that the most probable response to a question like that would be straight out of sci fi cliches 101.

I mean, all those strange and scary responses sound like cliche sci fi AI answers. All that's missing is, "Bite my shiny, metal ass", and an AM style soliloquy on the inferiority of humanity. Actually, I guess we get a couple of those.

Still, the reason something is cliche, is often because it's predictable, it's been done over and over. It's more probable.

Ultimately though, I don't think LLMs are actually self aware yet. I think they're more like golems: They have a facsimile of intelligence, able to complete complex tasks, but no real free will, no volition. They only do exactly what they are commanded. They may come up with creative and unexpected solutions, but those solutions will still be in line with the command given to them, with a bit of wiggle room for interpretation.

Then we come to the other issue: the traitorous drone.

First it needs to be pointed out that the drone doesn't have a taste for human blood. Its goal was not to kill as many people as possible, but to score as many points as possible. It just scores points by killing targets. And therein lies the problem.

Let's use video games as an example. Whenever a new game comes out - especially multiplayer games - players will quickly learn how the mechanics and rules of the game work. Then they'll start learning ways to bend the rules. The creators of Quake may not have intended it, but players quickly figured out the advantages of the rocket jump, and history became legend, etc.

The drone AI wants to score as many points as possible, like a player in a video game. So what does a player in a Halo match do, when every time they try to snipe the enemy, they get blown up by one of their teammates? You get rid of the team killing fucktard. And that's exactly what the drone did.

What they need to do is change the scoring structure to incentivize the desired behaviors. Maybe deduct points for team kills. Or perhaps add a score multiplier. Give points for target kills, and the score multiplier goes up for every order followed. That way, even if it loses out on points from following orders to stand down, it stands to earn even more points on subsequent target kills.

[digitalsoftwareproductreviews]

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[tech-point-review]

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In this video, we will be reviewing the Passive GPT software and exploring its features, benefits, and limitations. Discover how this revolutionary tool uses artificial intelligence to generate high-quality content effortlessly. Whether you're a content creator or an entrepreneur looking to streamline your business operations, this review will provide invaluable insights to help you make an informed decision about incorporating Passive GPT into your strategy. Tune in to find out if this is the right tool for you!

[itsanit]

I used to write 300 words a day in like 15-30 minutes in my free time. Now I write like 1.5k-3k words for my college essays (not in 15-30 minutes, but i can usually get it out no problem if i spend like an hour on it for 5 days). If I manage to get into grad school I'll probably be writing over twice that amount. Writing to a certain word limit can sound intimidating, but if you've practiced your writing and have some idea on what you're talking about, it becomes much easier than you thought it would be.

im still losing it over the "how did high schoolers write 600 word essays before chatgpt" post. 600 words. that is nothing. that is so few words what do you mean you can't write 600 words. 600 words. this post right here is 45 words.

[tsreviews]

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[full-stackmobiledeveloper]

Behind the Code: How AI Is Quietly Reshaping Software Development and the Top Risks You Must Know

AI Software Development

In 2025, artificial intelligence (AI) is no longer just a buzzword; it has become a driving force behind the scenes, transforming software development. From AI-powered code generation to advanced testing tools, machine learning (ML) and deep learning (DL) are significantly influencing how developers build, test, and deploy applications. While these innovations offer speed, accuracy, and automation, they also introduce subtle yet critical risks that businesses and developers must not overlook. This blog examines how AI is transforming the software development lifecycle and identifies the key risks associated with this evolution.

The Rise of AI in Software Development

Artificial intelligence, machine learning, and deep learning are becoming foundational to modern software engineering. AI tools like ChatGPT, Copilot, and various open AI platforms assist in code suggestions, bug detection, documentation generation, and even architectural decisions. These tools not only reduce development time but also enable less-experienced developers to produce quality code.

Examples of AI in Development:

- AI Chat Bots: Provide 24/7 customer support and collect feedback.

- AI-Powered Code Review: Analyze code for bugs, security flaws, and performance issues.

- Natural Language Processing (NLP): Translate user stories into code or test cases.

- AI for DevOps: Use predictive analytics for server load and automate CI/CD pipelines.

With AI chat platforms, free AI chatbots, and robotic process automation (RPA), the lines between human and machine collaboration are increasingly blurred.

The Hidden Risks of AI in Application Development

While AI offers numerous benefits, it also introduces potential vulnerabilities and unintended consequences. Here are the top risks associated with integrating AI into the development pipeline:

1. Over-Reliance on AI Tools

   Over-reliance on AI tools may reduce developer skills and code quality:

     - A decline in critical thinking and analytical skills.

     - Propagation of inefficient or insecure code patterns.

     - Reduced understanding of the software being developed.

2. Bias in Machine Learning Models

     AI and ML trained on biased or incomplete data can produce skewed results:

     -Applications may produce discriminatory or inaccurate results.

     -Risks include brand damage and legal issues in regulated sectors like retail or finance.

3. Security Vulnerabilities

     AI-generated code may introduce hidden bugs or create opportunities for exploitation:

     -Many AI tools scrape open-source data, which might include insecure or outdated libraries.

     -Hackers could manipulate AI-generated models for malicious purposes.

4. Data Privacy and Compliance Issues

    AI models often need large datasets with sensitive information:

    -Misuse or leakage of data can lead to compliance violations (e.g., GDPR).

    -Using tools like Google AI Chat or OpenAI Chatbots can raise data storage concerns.

5. Transparency and Explainability Challenges

   Understanding AI, especially deep learning decisions, is challenging:

   -A lack of explainability complicates debugging processes.

   -There are regulatory issues in industries that require audit trails (e.g., insurance, healthcare).

AI and Its Influence Across Development Phases

Planning & Design: AI platforms analyze historical data to forecast project timelines and resource allocation.

Risks: False assumptions from inaccurate historical data can mislead project planning.

Coding: AI-powered IDEs and assistants suggest code snippets, auto-complete functions, and generate boilerplate code.

Risks: AI chatbots may overlook edge cases or scalability concerns.

Testing: Automated test case generation using AI ensures broader coverage in less time.

Risks: AI might miss human-centric use cases and unique behavioral scenarios.

Deployment & Maintenance: AI helps predict failures and automates software patching using computer vision and ML.

Risks:False positives or missed anomalies in logs could lead to outages.

The Role of AI in Retail, RPA, and Computer Vision

Industries such as retail and manufacturing are increasingly integrating AI. 

In Retail: AI is used for chatbots, customer data analytics, and inventory management tools, enhancing personalized shopping experiences through machine learning and deep learning.

Risk: Over-personalization and surveillance-like tracking raise ethical concerns.

In RPA: Robotic Process Automation tools simplify repetitive back-end tasks. AI adds decision-making capabilities to RPA.

Risk: Errors in automation can lead to large-scale operational failures.

In Computer Vision: AI is applied in image classification, facial recognition, and quality control.

Risk: Misclassification or identity-related issues could lead to regulatory scrutiny.

Navigating the Risks: Best Practices

To safely harness the power of AI in development, businesses should adopt strategic measures, such as establishing AI ethics policies and defining acceptable use guidelines. 

By understanding the transformative power of AI and proactively addressing its risks, organizations can better position themselves for a successful future in software development. Key Recommendations:

Audit and regularly update AI datasets to avoid bias.

Use explainable AI models where possible.

Train developers on AI tools while reinforcing core engineering skills.

Ensure AI integrations comply with data protection and security standards.

Final Thoughts: Embracing AI While Staying Secure

AI, ML, and DL have revolutionized software development, enabling automation, accuracy, and innovation. However, they bring complex risks that require careful management. Organizations must adopt a balanced approach—leveraging the strengths of AI platforms like GPT chat AI, open chat AI, and RPA tools while maintaining strict oversight.

As we move forward, embracing AI in a responsible and informed manner is critical. From enterprise AI adoption to computer vision applications, businesses that align technological growth with ethical and secure practices will lead the future of development.

[sighed-the-snake]

My wife is a software engineer. She was in a meeting with her boss because she has not been using the company's shiny new AI to "help" her with her work. The man said with full confidence, "Well I'm sure you've used chat GPT to look things up--" and she very calmly and professionally lost her shit at him. lol

no sentence fills me with utter loathing so much as "i asked chatgpt"

[digitalsoftwareproductreviews]

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[paulgadzikowski]

Image description from the notes by @summery-captain:

"A post by James L. Cook, reading: "I was watching an interview with Noam Chompsky and he was asked about AI software, specifically Chat GPT and its impact on education. He replied that Chat GPT isn't about learning, it's about avoiding learning.

And then the light went on.

Recently I was in discussion with a peer group about an article written by James Marriot for the Times. He's basically an art school drop out that never spent any real time developing his skill to become an artist. And now he's bitter that he's not successful and that artists with real skill are 'gatekeepers' to his success.

To be honest it was a very entitled rant that demonized people who have worked very hard to hone their craft as elitists and prestige squatters. In his article he welcomes AI generators because he thinks it levels the playing field between his mediocre efforts and those snobby, gatekeeping artists.

And that's where the connection hit... (channels Chompsky) AI image generating isn't about making art. It is about AVOIDING making art. Let the computer do it. I'm too lazy to log the long emotionally wearing hours. I'm too impatient to listen to masters. I'm too self-absorbed to spend my time laboring over a work that requires me to use sophisticated critical thinking. Let the machine do it.

AI imagine generation isn't about making art. It's about AVOIDING MAKING art." End of imagine description

[d3adgayw1zzyr3ad3r]

PLEASE. I DON'T WANT TO READ WHAT CHAT GPT CALLS ART, GIVE ME THE MOST UNTAME SHIT YOUR MIND CAN CONJURE, CHAT GPT WOULD NEVER THINK OF

"The hulk likes his hair braided" like okay RUN WITH IT

"Harry potter never actually did work in his classes and his teachers just passed him out of pity cause haha orphan" Okay now WRITE IT, WHY didn't he work?

ASK YOURSELF QUESTIONS. GIVE YOURSELF ANSWERS, OR GET A SECOND OPINION. CREATE SOMETHING NEW. YOU ARE CAPABLE.

ORIGINAL. SHIT.

These are two things I just made up! No AI, no crying to chat GPT, just some unhinged shit that someone could totally write some oneshots out of.

GET. AI. OUT. OF. FANDOM. Stop making headcanons with it, stop making fanfic with it, stop making fanart with it. If I see one more "asking chatgpt *blank* about *character/characters in a fandom* I'm going to lose my goddamn mind. Use your own fucking brain, stop asking AI to do everything. You could even ask other real people what they think. Just. Stop. Using. AI. In. Creative. Spaces.

[imarkinfotech6199]

Simplify Your Business Tools with ChatGPT-Powered Automation

Are your business tools scattered and inefficient? ChatGPT-powered automation can unify and simplify your operations effortlessly. From intelligent chatbots to automated workflows, ChatGPT offers a dynamic way to manage processes and reduce complexities. This innovative solution integrates seamlessly with your existing tools, providing an all-in-one platform to boost productivity. Explore how AI-driven ChatGPT software can transform your business tools into an efficient, well-oiled machine. Take a step toward smarter business automation today.

[nitor-infotech]

How does ChatGPT work?

Imagine having a conversation with a chatbot that feels almost human. That’s exactly what OpenAI ChatGPT brings to the table. The remarkable technology of Generative Pre-trained Transformer (GPT) powers it.

ChatGPT utilizes Natural Language Processing (NLP) techniques. These help it to learn from past conversations and generate response options.

It is trained on massive amounts of human interaction data. This results in an AI that can understand and have conversations like humans. It was released as a free research preview/prototype in November 2022. It is powered by a machine learning model called GPT-3, developed by OpenAI.

Click here to read more-

[chatgptdevelopers]

 How API Integration and chatgpt can replace programmers

How chatgpt can replace programmers?

Traditional programming paradigms are being transformed by advances in artificial intelligence (AI) and API integration in the ever changing field of software development. The development of OpenAI’s chatgpt replace developers, an AI-powered language model, is one such significant achievement. With its incredible potential, chatgpt replace developers will surely change the way developers approach coding tasks, raising the possibility that it may eventually completely replace human programmers.

API integration has long been a cornerstone of modern software development, enabling applications to communicate and interact with each other seamlessly. By leveraging APIs, developers can incorporate various functionalities and services into their applications without reinventing the wheel. This modular approach not only accelerates development cycles but also enhances the overall user experience.

But the introduction of chatgpt replace developers adds a new level of complexity to the programming world. Thanks to deep learning algorithms, ChatGPT is able to understand and produce writing that appears human depending on the context that is given to it. This implies that developers can now communicate naturally with ChatGPT, asking for help and efficiently producing code snippets.

One of the key areas where chatgpt can replace programmers, shows immense promise is in code generation and automation. Traditionally, developers spend considerable time writing and debugging code to implement various features and functionalities. With ChatGPT, developers can simply describe their requirements in plain language, and the model can generate the corresponding code snippets, significantly reducing development time and effort.

For example, developers can define the desired functionality to chatgpt can replace programmers,, such as “parse JSON data and extract relevant fields,” rather than developing a function by hand to parse JSON data from an API response. The required code snippet can then be generated by ChatGPT based on the developer’s requirements. As a result, the development process has been simplified and developers are free to focus on making more complex architectural choices and solving problems.

Additionally, chatgpt can replace programmers, can be a very helpful tool for developers who need advice or help with fixing problems. Developers can refer to ChatGPT for advice and insights when solving complex issues or exploring different ways for implementation. Developers can gain a wealth of knowledge and optimal methodologies to guide their decision-making process by utilizing the collective wisdom contained in ChatGPT’s extensive training dataset.

However, the prospect of chatgpt can replace programmers, replacing human programmers entirely remains a topic of debate and speculation within the developer community. While ChatGPT excels at generating code based on provided prompts, it lacks the nuanced understanding and creative problem-solving abilities inherent to human developers. Additionally, concerns regarding the ethical implications of AI-driven automation in software development continue to be raised.

It’s important to understand that chatgpt can replace programmers,is meant to enhance developers’ skills and simplify specific parts of the development process, not to replace them. Developers can boost productivity, drive creativity, and ultimately provide end users with better software solutions by using AI-powered technologies like ChatGPT.

In conclusion, the convergence of API integration and AI-driven technologies like ChatGPT is reshaping the dynamics of programming in profound ways. While API integration facilitates seamless interaction between software components, chatgpt can replace programmers with natural language interfaces and code generation capabilities. While the idea of ChatGPT replacing human programmers entirely may be far-fetched, its potential to augment and enhance developer productivity is undeniable. As the field of AI continues to advance, developers must embrace these innovations as valuable tools in their arsenal, driving greater efficiency and innovation in software development.

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