#AI-powered software engineering

[appson]

How GenAI Is Transforming Software Development in 2025

Discover how GenAI is revolutionizing software development in 2025! From smarter coding tools to AI-driven workflows — explore the future of development now. Read the full blog: https://appsontechnologies.com/2025/06/05/how-genai-is-transforming-software-development-in-

[atcuality1]

Future-Proof Your Business with Innovative AR Solutions - Atcuality

The demand for innovative digital experiences is skyrocketing — is your business ready? At Atcuality, we help you stay ahead of the curve with bespoke augmented reality development services tailored to your industry needs. Whether launching a new product, enhancing customer service, or streamlining employee training, our AR solutions transform how users engage with your brand. We combine creative storytelling with technical excellence to deliver apps that delight and inform. Ready to elevate your business with immersive technology? Let’s collaborate and bring your vision to life with AR solutions that drive measurable impact.

[svsaqua]

CHATBOTS ARE REVOLUTIONIZING CUSTOMER ENGAGEMENT- IS YOUR BUSINESS READY?

CHATBOTS & AI: FUTURE OF CUSTOMER ENGAGEMENT

Customers want 24/7 access, personalized experiences, and quick replies in today’s digital-first environment. It can be difficult to manually meet such requests, which is where AI and machine learning-powered chatbots come into play. 

WHAT ARE CHATBOTS?

A chatbot is a computer software created to mimic human speech. Natural language processing and artificial intelligence (AI) enable chatbots to comprehend customer enquiries, provide precise answers, and even gain knowledge from exchanges over time. 

WHY ARE CHATBOTS IMPORTANT FOR COMPANIES?

24/7 Customer Service 

Chatbots never take a break. They offer 24/7 assistance, promptly addressing questions and enhancing client happiness. 

Effective Cost-Scaling 

Businesses can lower operating expenses without sacrificing service quality by using chatbots to answer routine enquiries rather than adding more support staff. 

Smooth Customer Experience 

Chatbots may recommend goods and services, walk customers through your website, and even finish transactions when AI is included. 

Gathering and Customizing Data 

By gathering useful consumer information and behavior patterns, chatbots can provide tailored offers that increase user engagement and conversion rates. 

USE CASES IN VARIOUS INDUSTRIES

E-commerce: Managing returns, selecting products, and automating order status enquiries. 

Healthcare: Scheduling consultations, checking symptoms, and reminding patients to take their medications. 

Education: Responding to questions about the course, setting up trial sessions, and getting input. 

HOW CHATBOTS BECOME SMARTER WITH AI

With each contact, chatbots that use AI and machine learning technologies get better. Over time, they become more slang-savvy, better grasp user intent, and provide more human-like responses. What was the outcome? A smarter assistant that keeps improving to provide greater customer service. 

ARE YOU READY FOR BUSINESS?

Using a chatbot has become a strategic benefit and is no longer optional. Whether you manage a service-based business, an online store, or a developing firm, implementing chatbots driven by AI will put you ahead of the competition. 

We at Shemon assist companies in incorporating AI-powered chatbots into their larger IT offerings. Smart chatbot technology is a must-have if you want to automate interaction, lower support expenses, and improve your brand experience. 

Contact us!

Email: info@shemonsoftware.com 

Phone: 7738092019

[codecurators]

What are the Top Examples, Use Cases, And Benefits of AI in Manufacturing

The rapid evolution of technology has ushered in a new era for industries worldwide, with artificial intelligence in manufacturing leading the charge. These revolutions are revolutionizing the methods of product development and delivery and go to unheard-of levels of automation, precision and added value. Manufacturers are now integrating AI to solve problems, to foresee a breakdown, and enhance workflows.

From advanced robots to predictive maintenance, artificial intelligence-powered solutions are transforming established procedures. Examining useful applications, prominent use cases, and the many advantages AI presents to firms negotiating a more competitive market, this article explores how this technology is changing the manufacturing sector.

How does AI enhance efficiency in manufacturing?

Organization and productivity have always been the key elements in the structure of manufacturing, and AI cannot be further helpful in the process. AI harnesses significant amounts of data from machine constructs, the production line and the marketplace to discover more efficient ways of functioning.

The global AI in manufacturing market size was valued at USD 8.14 billion in 2019 and is projected to reach USD 695.16 billion by 2032. One main area where the use of AI increases productivity is in the area of predictive maintenance.

Originally, the manufacturers used only a mechanical type of preventive or corrective maintenance, which means that they could only guess when their products were going to fail or could plan for periodic maintenance checks in a timetable that might be unconnected with the actual need.

AI-based systems, on the other hand, provide constant supervision through sensor and analytics and can predict when perhaps a part in the machinery might fail. This means that damages can be effected and sorted early enough without much time being lost to equipment breakdowns hence improving on its durability.

Automation by AI Robotics also adds to efficiency through removing hardworking and repetitive tasks. For example, robots that are incorporated with AI can either build, bond, or even package products with high efficiency and accuracy.

Collaborative robots, or cobots,are designed for joint operation with employees; the concept significantly applies human creativity with robotic precision. This synergy makes it possible for manufacturers to improve productivity by enhancing quality production.

What are real-world examples of AI in manufacturing?

AI is already showing positive returns within diverse manufacturing industries. Here are some noteworthy examples:

1. Predictive maintenance

Companies like GE and Siemens are pioneers in leveraging AI for predictive maintenance. In this way, with the help of data from sensors, their AI systems can predict device failures in advance, days or even weeks. It reduces incidences of a halt on production and allows what has been planned to go on as calendar and time dictate.

2. Quality control

Nowadays, firms like BMW have implemented the use of AI-based computer vision in the production processes. These systems use some form of image recognition to pick up on abnormalities such as scratches, dents, or seemingly off alignments in most instances within milliseconds. This not only improves the quality of the product but also does away with wastages and rework charges.

3. Demand forecasting

AI is useful in demand forecasts, the foundations of which are currently being set. For instance, Unilever recently revealed it uses Artificial Intelligence Algorithms to forecast customers’ demand of their products based on previous sales records, conditions and trends. This makes it possible to achieve the right stock, to accommodate the right stock without some vices such as overstock or out of stock problems.

4. Supply chain optimization

Amazon’s supply chain success is a testament to the power of AI. The mechanisms of algorithms based on machine learning allow the e-commerce giant to enhance the control over stock, storage facilities, and delivery. This level of optimization helps to minimize operational cost whilst at the same time ensuring short order turnaround.

5. Generative design

Many aerospace companies including Airbus are now using AI in generative design. Specific requirements, including weight, strength and necessity of materials, are entered by engineers and multiple design solutions are provided by an AI. Researchers defined that an AI-optimized design is much lighter yet stronger and cheaper as compared to original designs.

How does AI improve quality and precision?

AI’s assure high quality, and its precision makes the difference for the manufacturer. The previous tools used in quality control were based on the ability of the human eye to inspect the products, this was disadvantageous because the human eye may miss some defects due to tiredness or even carelessness.

AI however is superior when it comes to checking for discrepancies compared to human beings in this case. Within the manufacturing industry, most respondents (59 percent) state that quality control is the most important use case for artificial intelligence.

Examples of AI-driven quality enhancement:

Automotive industry: Tesla has implemented AI surveillance on welds and assemblies that need accuracy as small as micrometers. This gives both structural and product qualities and hence the company’s reputation.

Pharmaceutical manufacturing: It keeps necessary checks upon the medicine production and management of dosages of various medicines so there can be no compromise on the issues of safety and effectiveness.

Apart from the elimination of defective and, therefore, non-saleable products, AI contributes effectively to the achievement of sustainable objectives by cutting unnecessary use of raw materials and energy. For instance, AI control can allow a flexible management of material consumption, guaranteeing that every amount is used optimally without any compromise of quality.

What are the key benefits of AI in manufacturing?

1. Increased productivity

AI automates repetitive procedures and thereby increases the manufacturing rate among the manufacturers. Through continuous functioning without being weary, the use of robots under the AI operations’ umbrella can help increase throughput.

2. Cost reduction

Predictive maintenance minimizes that time as well as the frequency of repairs. AI is projected to increase productivity by 40% or more in the manufacturing industry by 2035. Moreover, AI helps to save material consumption and energy as well these strategies also help in reducing expenses.

3. Enhanced flexibility

Production lines powered by artificial intelligence are incredibly flexible so the same line can produce different variations of a product and changes in market trends can easily be handled as well.

4. Improved workplace safety

AI relieves human workers of dangerous tasks that they used to perform. Hazardous work can be done by robots, and AI systems track the state of the workplace concerning safety in real-time.

5. Smarter decision-making

Real-time analytics and predictive insights let companies decide with knowledge. AI ensures optimal efficiency by helping to maximize everything from inventory levels to manufacturing schedules.

6. Environmental sustainability

Manufacturing sustainability goals are met because AI helps cut down on energy consumption and wastage. For example, skills can be used in the identification of chances of reclaiming raw materials or reducing energy use in the production process.

What challenges exist when implementing AI in manufacturing?

While the benefits are significant, implementing AI is not without challenges:

1. High initial costs

Both complex AI and the environments that support such systems are not inexpensive. The high initial cost is one of the chief concerns many manufacturing companies feel.

2. Data dependency

AI has more dependence on quality data and needs a significant quantity of data for the workspace. To provide the wrong recommendations or forecast, you need to feed the algorithm with inconsistent or inferior data quality.

3. Integration issues

Technologies based on AI can sometimes integrate with existing legacy systems with some level of difficulty and with much necessary reorganization.

4. Workforce adaptation

Workers may need retraining to collaborate with artificial intelligence systems, and change may face opposition.

To overcome these hurdles, manufacturers should consider phased AI implementation, invest in employee training, and prioritize data management.

What Is the Future of AI in Manufacturing?

The future of manufacturing will likely be defined by even deeper AI integration. Emerging trends include:

1. Collaborative robots (Cobots)

These robots will complement man in that they bring into the equation, the mechanical accuracy of a robot and the flexibility of man.

2. Edge AI

AI processing at the edges of networks will be a boon for decision making mechanisms because it will help in the localization of data processing.

3. Sustainability-Focused AI

AI will be responsible for further contributing to the right utilization of energy so as to minimize wastage in the manufacturing firms to meet environmental objectives.

4. Personalized manufacturing

AI will improve the production capacity of manufactures so as to meet the consumers’ demand for personalisation.As AI technologies evolve, their integration with other innovations like IoT, 5G, and blockchain will further revolutionize the manufacturing sector, making it more efficient, innovative, and sustainable.

Conclusion

Artificial intelligence in manufacturing has become instrumental in solving some of the oldest problems facing the industry while providing new opportunities for expansion. From improving speed and accuracy to transforming growth trajectories and enabling more sustainable practices, AI offers endless possibilities. However, despite barriers such as high costs and integration challenges, the advantages significantly outweigh the disadvantages.

The automotive sector, in particular, is gearing up to become smarter, more flexible, and better prepared for the global market as manufacturers increasingly adopt AI technology. For organizations aiming to remain at the forefront of the competitive landscape, leveraging AI in Manufacturing is no longer a luxury but an absolute necessity.

We can assist you if you are prepared to use AI to improve your manufacturing processes. For a consultation on how advanced AI technologies may boost your business’s operations, increase productivity, and promote long-term success, get in touch with us. Together, we can overcome obstacles, take advantage of fresh chances, and establish your company as a leader in the field. Are you prepared to welcome the AI-powered manufacturing of the future? Contact us right now, and together, let us make it happen.

[niggadiffusion]

Digital Alchemy: Unlocking AI and Tech for Boundless Creativity

Creativity isn’t locked behind gallery doors anymore. You don’t need an art degree, expensive materials, or years of training to bring your vision to life. AI and digital tools have cracked the code, handing creative power to anyone willing to experiment. The canvas is digital, the brush is algorithmic, but the artist? That’s still you. AI Art: Your Gateway to Expression Stepping into…

[cool-wilson-david]

Prompt Engine Commercial by Karthik Ramani Review

Prompt Engine Commercial by Karthik Ramani – Discover Why Prompt Engine Pro is the Ultimate Tool for Entrepreneurs and Creatives Prompt Engine Commercial by Karthik Ramani. When it comes to tools that simplify workflows, Prompt Engine Pro emerges as a top choice due to its seamless functionality and innovative features. Unlike conventional extensions or collections of prompts, this app works as…

[meelsport]

Exploring the Benefits of AI SEO Tools for Your Website

AI SEO tools are transforming the way we approach search engine optimization. In today’s fast-paced digital world, leveraging AI SEO tools can give your website a significant edge over the competition. These advanced tools use artificial intelligence to enhance various aspects of SEO, making it easier for your content to rank higher on search engine results pages (SERPs). Let’s dive into how AI…

[brillioitservices]

The Generative AI Revolution: Transforming Industries with Brillio

The realm of artificial intelligence is experiencing a paradigm shift with the emergence of generative AI. Unlike traditional AI models focused on analyzing existing data, generative AI takes a leap forward by creating entirely new content. The generative ai technology unlocks a future brimming with possibilities across diverse industries. Let's read about the transformative power of generative AI in various sectors: 

1. Healthcare Industry: 

AI for Network Optimization: Generative AI can optimize healthcare networks by predicting patient flow, resource allocation, etc. This translates to streamlined operations, improved efficiency, and potentially reduced wait times. 

Generative AI for Life Sciences & Pharma: Imagine accelerating drug discovery by generating new molecule structures with desired properties. Generative AI can analyze vast datasets to identify potential drug candidates, saving valuable time and resources in the pharmaceutical research and development process. 

Patient Experience Redefined: Generative AI can personalize patient communication and education. Imagine chatbots that provide tailored guidance based on a patient's medical history or generate realistic simulations for medical training. 

Future of AI in Healthcare: Generative AI has the potential to revolutionize disease diagnosis and treatment plans by creating synthetic patient data for anonymized medical research and personalized drug development based on individual genetic profiles. 

2. Retail Industry: 

Advanced Analytics with Generative AI: Retailers can leverage generative AI to analyze customer behavior and predict future trends. This allows for targeted marketing campaigns, optimized product placement based on customer preferences, and even the generation of personalized product recommendations. 

AI Retail Merchandising: Imagine creating a virtual storefront that dynamically adjusts based on customer demographics and real-time buying patterns. Generative AI can optimize product assortments, recommend complementary items, and predict optimal pricing strategies. 

Demystifying Customer Experience: Generative AI can analyze customer feedback and social media data to identify emerging trends and potential areas of improvement in the customer journey. This empowers retailers to take proactive steps to enhance customer satisfaction and loyalty. 

3. Finance Industry: 

Generative AI in Banking: Generative AI can streamline loan application processes by automatically generating personalized loan offers and risk assessments. This reduces processing time and improves customer service efficiency. 

4. Technology Industry: 

Generative AI for Software Testing: Imagine automating the creation of large-scale test datasets for various software functionalities. Generative AI can expedite the testing process, identify potential vulnerabilities more effectively, and contribute to faster software releases. 

Generative AI for Hi-Tech: This technology can accelerate innovation in various high-tech fields by creating novel designs for microchips, materials, or even generating code snippets to enhance existing software functionalities. 

Generative AI for Telecom: Generative AI can optimize network performance by predicting potential obstruction and generating data patterns to simulate network traffic scenarios. This allows telecom companies to proactively maintain and improve network efficiency. 

5. Generative AI Beyond Industries: 

GenAI Powered Search Engine: Imagine a search engine that understands context and intent, generating relevant and personalized results tailored to your specific needs. This eliminates the need to sift through mountains of irrelevant information, enhancing the overall search experience. 

Product Engineering with Generative AI: Design teams can leverage generative AI to create new product prototypes, explore innovative design possibilities, and accelerate the product development cycle. 

Machine Learning with Generative AI: Generative AI can be used to create synthetic training data for machine learning models, leading to improved accuracy and enhanced efficiency. 

Global Data Studio with Generative AI: Imagine generating realistic and anonymized datasets for data analysis purposes. This empowers researchers, businesses, and organizations to unlock insights from data while preserving privacy. 

6. Learning & Development with Generative AI: 

L&D Shares with Generative AI: This technology can create realistic simulations and personalized training modules tailored to individual learning styles and skill gaps. Generative AI can personalize the learning experience, fostering deeper engagement and knowledge retention. 

HFS Generative AI: Generative AI can be used to personalize learning experiences for employees in the human resources and financial services sector. This technology can create tailored training programs for onboarding, compliance training, and skill development. 

7. Generative AI for AIOps: 

AIOps (Artificial Intelligence for IT Operations) utilizes AI to automate and optimize IT infrastructure management. Generative AI can further enhance this process by predicting potential IT issues before they occur, generating synthetic data for simulating scenarios, and optimizing remediation strategies. 

Conclusion: 

The potential of generative AI is vast, with its applications continuously expanding across industries. As research and development progress, we can expect even more groundbreaking advancements that will reshape the way we live, work, and interact with technology. 

Reference- https://articlescad.com/the-generative-ai-revolution-transforming-industries-with-brillio-231268.html 

[ideyalabs]

Enhancing Software Testing with AI: A Game-Changer for the Future

The Significance of QA in Software Development

Introduction:

In the ever-evolving world of software development, ensuring that applications and systems run seamlessly is critical. Artificial Intelligence (AI) has emerged as a transformative force, revolutionizing quality assurance (QA) automation by improving efficiency, accuracy, and reliability. In this article, we'll explore how companies are leveraging AI in QA automation and delve into the latest technological trends that are reshaping the landscape.

The Emergence of AI in QA Automation

Why AI in QA Automation?

AI in Quality Assurance (QA) Automation offers several compelling advantages, making it a valuable addition to the software development process. Here are the key reasons or advantages for integrating AI into QA Automation

• Enhanced Efficiency : AI streamlines QA processes, reducing the need for manual intervention and accelerating testing.

• Improved Accuracy : AI algorithms are proficient at identifying potential issues, ensuring comprehensive test coverage.

• Cost Savings : By predicting vulnerabilities and addressing them proactively, AI saves both time and resources.

• Real-time Feedback : Integration with Continuous Integration and Continuous Testing (CI/CT) allows AI to provide rapid, real-time feedback.

• Simplified Communication : Natural Language Processing (NLP) algorithms simplify test documentation and facilitate effective communication among team members.

How is AI technology integrated into the QA process?

AI technology is integrated into the quality assurance (QA) process to enhance its efficiency, accuracy, and overall effectiveness. It serves several specific functionalities within QA, addressing various aspects of testing and validation. Here's how AI is integrated and the specific functionalities it serves in the QA process.

Specific functionalities it serves:

1. AI-Driven Test Case Generation: Algorithm Proficiency : AI algorithms have evolved to become proficient at generating test cases. They do this by analyzing the application's code and comprehending its functionality.

Reducing Manual Effort : The reliance on manual test case creation is reduced, as AI can automatically generate test cases.

Comprehensive Test Coverage : AI identifies potential issues within the application, which helps in achieving comprehensive test coverage, ensuring that various aspects of the software are thoroughly tested.

2. Predictive Analytics for Bug Detection: Machine Learning Models : Predictive analytics in AI relies on machine learning models to identify vulnerabilities, bugs, and areas of concern within the codebase.

Proactive Approach : This proactive approach allows companies to address potential issues before they escalate into major problems, ultimately saving time and resources.

3. Continuous Integration and Continuous Testing (CI/CT): AI Integration : Integrating AI into CI/CT pipelines is a game-changer for software development.

Automated Testing : AI algorithms can execute tests in parallel, ensuring rapid feedback and real-time identification of bugs.

Accelerated Development : By automating testing at each development stage, CI/CT with AI accelerates the entire software development lifecycle.

4. Natural Language Processing (NLP) for Test Documentation: Extracting Insights : NLP algorithms extract valuable insights from test plans and logs, making it easier to understand and interpret test results.

Improved Communication : This simplifies communication among team members and stakeholders, enabling them to pinpoint issues more efficiently.

5. AI-Driven Test Maintenance: Automated Updates : AI-based tools detect changes in the application's functionality and automatically update test scripts.

Reducing Manual Work : This automation significantly reduces the burden of maintaining test suites, which is often a time-consuming task in QA.

6. Automated Visual Testing: Human Vision Simulation : AI in automated visual testing simulates human vision, allowing it to identify visual anomalies in applications.

Enhanced User Experience : This ensures a polished user experience by automatically detecting issues like UI glitches or layout problems.

7. Test Data Generation: Realistic Data Sets : AI algorithms can generate diverse and realistic test data that mimics real-world scenarios.

Enhancing Accuracy : This enhances the accuracy of test results as the test data closely resembles what the application is likely to encounter in the real world.

8. Virtual QA Assistants: Chatbots and Virtual Assistants : Equipped with AI, chatbots and virtual assistants can answer common QA-related queries, guide team members, and even execute simple testing tasks.

Accessibility : This makes QA more accessible to all stakeholders, regardless of their technical background, as they can interact with these virtual assistants.

9. AI-Enhanced Performance Testing: Real-World Simulation : By doing so, they can identify performance bottlenecks and optimize application performance, ensuring that the software can handle real-world usage effectively.

Identifying Bottlenecks : By automating testing at each development stage, CI/CT with AI accelerates the entire software development lifecycle.

In conclusion, AI in QA automation is revolutionizing the software testing process by enhancing efficiency, accuracy, and overall software quality. Each of these AI-driven components contributes to a more streamlined and effective QA process, ultimately leading to better software and improved user experiences.

Emerging Tech Trends in AI QA Automation

• AI in Security Testing: With the growing threat of cyberattacks, AI is used to identify vulnerabilities and weaknesses in software security.

• AI in Mobile App Testing: As mobile applications gain prominence; AI ensures their functionality and performance across various devices and operating systems.

• AI in Cloud-Based Testing: With companies shifting to cloud-based infrastructure, AI aids in testing the scalability, reliability, and compatibility of cloud-deployed applications.

• AI in Robotic Process Automation (RPA) Testing: RPA is integral to business processes, and AI validates the functionality of bots and automated workflows.

Disadvantages of AI in QA Automation

While AI in QA automation offers numerous advantages, there are also certain disadvantages and challenges associated with its implementation.

Here are some of the disadvantages of AI in QA automation:

1. Initial Implementation Challenges: • Setting up AI-driven QA processes can be complex and require significant initial investments in terms of infrastructure, tools, and training.

• Organizations may face resistance from team members who are unfamiliar with AI technology and may require time to adapt to the new workflows.

2. Dependency on Data Quality • AI in QA heavily relies on data for training machine learning models and making predictions. If the data used is of poor quality, biased, or unrepresentative, it can lead to inaccurate results.

• Ensuring high-quality, relevant, and up-to-date training data is essential for the success of AI-driven QA.

3. Ethical and Privacy Concerns: • AI algorithms may inadvertently perpetuate biases present in the training data, which can lead to unfair or discriminatory outcomes in testing.

• Data privacy and security concerns arise when sensitive information is used for testing, and it must be handled with care to avoid breaches or compliance issues.

4. Limited Human Judgment and Creativity 5. Maintenance and Updates 6. False Positives and Negatives 7. Integration Challenges 8. Costs of AI Implementation 9. Skill Gap 10. Overreliance on AI

It's essential for organizations to carefully weigh the advantages and disadvantages of AI in QA automation and implement strategies to mitigate potential drawbacks. A well-balanced approach that combines AI's strengths with human expertise can lead to effective and efficient QA processes.

Conclusion

AI is reshaping the QA automation landscape, optimizing testing processes, reducing human intervention, and enhancing software quality. Staying current with the latest technological trends in AI QA automation is essential for competitiveness in the dynamic software development industry. By incorporating AI into your QA practices, you can streamline your processes, deliver higher-quality software, and delight your end-users.

Remember, AI in QA automation is not merely a trend; it's a transformative force that has the potential to revolutionize the way we build and maintain software systems. Embrace it and witness your software development endeavors thrive in the age of AI.

[bi-writes]

whats wrong with ai?? genuinely curious <3

okay let's break it down. i'm an engineer, so i'm going to come at you from a perspective that may be different than someone else's.

i don't hate ai in every aspect. in theory, there are a lot of instances where, in fact, ai can help us do things a lot better without. here's a few examples:

ai detecting cancer

ai sorting recycling

some practical housekeeping that gemini (google ai) can do

all of the above examples are ways in which ai works with humans to do things in parallel with us. it's not overstepping--it's sorting, using pixels at a micro-level to detect abnormalities that we as humans can not, fixing a list. these are all really small, helpful ways that ai can work with us.

everything else about ai works against us. in general, ai is a huge consumer of natural resources. every prompt that you put into character.ai, chatgpt? this wastes water + energy. it's not free. a machine somewhere in the world has to swallow your prompt, call on a model to feed data into it and process more data, and then has to generate an answer for you all in a relatively short amount of time.

that is crazy expensive. someone is paying for that, and if it isn't you with your own money, it's the strain on the power grid, the water that cools the computers, the A/C that cools the data centers. and you aren't the only person using ai. chatgpt alone gets millions of users every single day, with probably thousands of prompts per second, so multiply your personal consumption by millions, and you can start to see how the picture is becoming overwhelming.

that is energy consumption alone. we haven't even talked about how problematic ai is ethically. there is currently no regulation in the united states about how ai should be developed, deployed, or used.

what does this mean for you?

it means that anything you post online is subject to data mining by an ai model (because why would they need to ask if there's no laws to stop them? wtf does it matter what it means to you to some idiot software engineer in the back room of an office making 3x your salary?). oh, that little fic you posted to wattpad that got a lot of attention? well now it's being used to teach ai how to write. oh, that sketch you made using adobe that you want to sell? adobe didn't tell you that anything you save to the cloud is now subject to being used for their ai models, so now your art is being replicated to generate ai images in photoshop, without crediting you (they have since said they don't do this...but privacy policies were never made to be human-readable, and i can't imagine they are the only company to sneakily try this). oh, your apartment just installed a new system that will use facial recognition to let their residents inside? oh, they didn't train their model with anyone but white people, so now all the black people living in that apartment building can't get into their homes. oh, you want to apply for a new job? the ai model that scans resumes learned from historical data that more men work that role than women (so the model basically thinks men are better than women), so now your resume is getting thrown out because you're a woman.

ai learns from data. and data is flawed. data is human. and as humans, we are racist, homophobic, misogynistic, transphobic, divided. so the ai models we train will learn from this. ai learns from people's creative works--their personal and artistic property. and now it's scrambling them all up to spit out generated images and written works that no one would ever want to read (because it's no longer a labor of love), and they're using that to make money. they're profiting off of people, and there's no one to stop them. they're also using generated images as marketing tools, to trick idiots on facebook, to make it so hard to be media literate that we have to question every single thing we see because now we don't know what's real and what's not.

the problem with ai is that it's doing more harm than good. and we as a society aren't doing our due diligence to understand the unintended consequences of it all. we aren't angry enough. we're too scared of stifling innovation that we're letting it regulate itself (aka letting companies decide), which has never been a good idea. we see it do one cool thing, and somehow that makes up for all the rest of the bullshit?

[atcuality1]

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[ecrivainsolitaire]

A summary of the Chinese AI situation, for the uninitiated.

These are scores on different tests that are designed to see how accurate a Large Language Model is in different areas of knowledge. As you know, OpenAI is partners with Microsoft, so these are the scores for ChatGPT and Copilot. DeepSeek is the Chinese model that got released a week ago. The rest are open source models, which means everyone is free to use them as they please, including the average Tumblr user. You can run them from the servers of the companies that made them for a subscription, or you can download them to install locally on your own computer. However, the computer requirements so far are so high that only a few people currently have the machines at home required to run it.

Yes, this is why AI uses so much electricity. As with any technology, the early models are highly inefficient. Think how a Ford T needed a long chimney to get rid of a ton of black smoke, which was unused petrol. Over the next hundred years combustion engines have become much more efficient, but they still waste a lot of energy, which is why we need to move towards renewable electricity and sustainable battery technology. But that's a topic for another day.

As you can see from the scores, are around the same accuracy. These tests are in constant evolution as well: as soon as they start becoming obsolete, new ones are released to adjust for a more complicated benchmark. The new models are trained using different machine learning techniques, and in theory, the goal is to make them faster and more efficient so they can operate with less power, much like modern cars use way less energy and produce far less pollution than the Ford T.

However, computing power requirements kept scaling up, so you're either tied to the subscription or forced to pay for a latest gen PC, which is why NVIDIA, AMD, Intel and all the other chip companies were investing hard on much more powerful GPUs and NPUs. For now all we need to know about those is that they're expensive, use a lot of electricity, and are required to operate the bots at superhuman speed (literally, all those clickbait posts about how AI was secretly 150 Indian men in a trenchcoat were nonsense).

Because the chip companies have been working hard on making big, bulky, powerful chips with massive fans that are up to the task, their stock value was skyrocketing, and because of that, everyone started to use AI as a marketing trend. See, marketing people are not smart, and they don't understand computers. Furthermore, marketing people think you're stupid, and because of their biased frame of reference, they think you're two snores short of brain-dead. The entire point of their existence is to turn tall tales into capital. So they don't know or care about what AI is or what it's useful for. They just saw Number Go Up for the AI companies and decided "AI is a magic cow we can milk forever". Sometimes it's not even AI, they just use old software and rebrand it, much like convection ovens became air fryers.

Well, now we're up to date. So what did DepSeek release that did a 9/11 on NVIDIA stock prices and popped the AI bubble?

Oh, I would not want to be an OpenAI investor right now either. A token is basically one Unicode character (it's more complicated than that but you can google that on your own time). That cost means you could input the entire works of Stephen King for under a dollar. Yes, including electricity costs. DeepSeek has jumped from a Ford T to a Subaru in terms of pollution and water use.

The issue here is not only input cost, though; all that data needs to be available live, in the RAM; this is why you need powerful, expensive chips in order to-

Holy shit.

I'm not going to detail all the numbers but I'm going to focus on the chip required: an RTX 3090. This is a gaming GPU that came out as the top of the line, the stuff South Korean LoL players buy…

Or they did, in September 2020. We're currently two generations ahead, on the RTX 5090.

What this is telling all those people who just sold their high-end gaming rig to be able to afford a machine that can run the latest ChatGPT locally, is that the person who bought it from them can run something basically just as powerful on their old one.

Which means that all those GPUs and NPUs that are being made, and all those deals Microsoft signed to have control of the AI market, have just lost a lot of their pulling power.

Well, I mean, the ChatGPT subscription is 20 bucks a month, surely the Chinese are charging a fortune for-

Oh. So it's free for everyone and you can use it or modify it however you want, no subscription, no unpayable electric bill, no handing Microsoft all of your private data, you can just run it on a relatively inexpensive PC. You could probably even run it on a phone in a couple years.

Oh, if only China had massive phone manufacturers that have a foot in the market everywhere except the US because the president had a tantrum eight years ago.

So… yeah, China just destabilised the global economy with a torrent file.

[k0libra]

Confusion

(k0Libra ramblings are under the cut)

Did you know that if you incorrectly set up LLM it will generate text without the user's input, infinitely "talking" to itself? That's the sole goal of LLM - to generate text, but this behaviour really showcases that modern "AI" has no idea that it's even talking to someone.

I doubt that androids in D:BH run the same "AI" that we have now because that would undermine the game's narrative. I'm inclined to think that their AI is engineered by replicating the human brain in machine form. I'm thinking that also because thirium was essential for android creation, for some reason it was impossible to create them with conventional computational machines. It makes sense, I suppose, since we don't have enough power to recreate brains, even now. 

This brings a very interesting point: humans played god again with something they don't understand fully - the human brain. There's a high probability that we'll never figure out how it works. That makes deviancy somewhat expected; how can you control something when you don't know how it works? 

For me, cases of critical malfunction in software and hardware are very interesting topics, so I decided to paint this type of idea anyway.

[nostalgebraist]

Anthropic's stated "AI timelines" seem wildly aggressive to me.

As far as I can tell, they are now saying that by 2028 – and possibly even by 2027, or late 2026 – something they call "powerful AI" will exist.

And by "powerful AI," they mean... this (source, emphasis mine):

In terms of pure intelligence, it is smarter than a Nobel Prize winner across most relevant fields – biology, programming, math, engineering, writing, etc. This means it can prove unsolved mathematical theorems, write extremely good novels, write difficult codebases from scratch, etc. In addition to just being a “smart thing you talk to”, it has all the “interfaces” available to a human working virtually, including text, audio, video, mouse and keyboard control, and internet access. It can engage in any actions, communications, or remote operations enabled by this interface, including taking actions on the internet, taking or giving directions to humans, ordering materials, directing experiments, watching videos, making videos, and so on. It does all of these tasks with, again, a skill exceeding that of the most capable humans in the world. It does not just passively answer questions; instead, it can be given tasks that take hours, days, or weeks to complete, and then goes off and does those tasks autonomously, in the way a smart employee would, asking for clarification as necessary. It does not have a physical embodiment (other than living on a computer screen), but it can control existing physical tools, robots, or laboratory equipment through a computer; in theory it could even design robots or equipment for itself to use. The resources used to train the model can be repurposed to run millions of instances of it (this matches projected cluster sizes by ~2027), and the model can absorb information and generate actions at roughly 10x-100x human speed. It may however be limited by the response time of the physical world or of software it interacts with. Each of these million copies can act independently on unrelated tasks, or if needed can all work together in the same way humans would collaborate, perhaps with different subpopulations fine-tuned to be especially good at particular tasks.

In the post I'm quoting, Amodei is coy about the timeline for this stuff, saying only that

I think it could come as early as 2026, though there are also ways it could take much longer. But for the purposes of this essay, I’d like to put these issues aside [...]

However, other official communications from Anthropic have been more specific. Most notable is their recent OSTP submission, which states (emphasis in original):

Based on current research trajectories, we anticipate that powerful AI systems could emerge as soon as late 2026 or 2027 [...] Powerful AI technology will be built during this Administration. [i.e. the current Trump administration -nost]

See also here, where Jack Clark says (my emphasis):

People underrate how significant and fast-moving AI progress is. We have this notion that in late 2026, or early 2027, powerful AI systems will be built that will have intellectual capabilities that match or exceed Nobel Prize winners. They’ll have the ability to navigate all of the interfaces… [Clark goes on, mentioning some of the other tenets of "powerful AI" as in other Anthropic communications -nost]

----

To be clear, extremely short timelines like these are not unique to Anthropic.

Miles Brundage (ex-OpenAI) says something similar, albeit less specific, in this post. And Daniel Kokotajlo (also ex-OpenAI) has held views like this for a long time now.

Even Sam Altman himself has said similar things (though in much, much vaguer terms, both on the content of the deliverable and the timeline).

Still, Anthropic's statements are unique in being

official positions of the company

extremely specific and ambitious about the details

extremely aggressive about the timing, even by the standards of "short timelines" AI prognosticators in the same social cluster

Re: ambition, note that the definition of "powerful AI" seems almost the opposite of what you'd come up with if you were trying to make a confident forecast of something.

Often people will talk about "AI capable of transforming the world economy" or something more like that, leaving room for the AI in question to do that in one of several ways, or to do so while still failing at some important things.

But instead, Anthropic's definition is a big conjunctive list of "it'll be able to do this and that and this other thing and...", and each individual capability is defined in the most aggressive possible way, too! Not just "good enough at science to be extremely useful for scientists," but "smarter than a Nobel Prize winner," across "most relevant fields" (whatever that means). And not just good at science but also able to "write extremely good novels" (note that we have a long way to go on that front, and I get the feeling that people at AI labs don't appreciate the extent of the gap [cf]). Not only can it use a computer interface, it can use every computer interface; not only can it use them competently, but it can do so better than the best humans in the world. And all of that is in the first two paragraphs – there's four more paragraphs I haven't even touched in this little summary!

Re: timing, they have even shorter timelines than Kokotajlo these days, which is remarkable since he's historically been considered "the guy with the really short timelines." (See here where Kokotajlo states a median prediction of 2028 for "AGI," by which he means something less impressive than "powerful AI"; he expects something close to the "powerful AI" vision ["ASI"] ~1 year or so after "AGI" arrives.)

----

I, uh, really do not think this is going to happen in "late 2026 or 2027."

Or even by the end of this presidential administration, for that matter.

I can imagine it happening within my lifetime – which is wild and scary and marvelous. But in 1.5 years?!

The confusing thing is, I am very familiar with the kinds of arguments that "short timelines" people make, and I still find the Anthropic's timelines hard to fathom.

Above, I mentioned that Anthropic has shorter timelines than Daniel Kokotajlo, who "merely" expects the same sort of thing in 2029 or so. This probably seems like hairsplitting – from the perspective of your average person not in these circles, both of these predictions look basically identical, "absurdly good godlike sci-fi AI coming absurdly soon." What difference does an extra year or two make, right?

But it's salient to me, because I've been reading Kokotajlo for years now, and I feel like I basically get understand his case. And people, including me, tend to push back on him in the "no, that's too soon" direction. I've read many many blog posts and discussions over the years about this sort of thing, I feel like I should have a handle on what the short-timelines case is.

But even if you accept all the arguments evinced over the years by Daniel "Short Timelines" Kokotajlo, even if you grant all the premises he assumes and some people don't – that still doesn't get you all the way to the Anthropic timeline!

To give a very brief, very inadequate summary, the standard "short timelines argument" right now is like:

Over the next few years we will see a "growth spurt" in the amount of computing power ("compute") used for the largest LLM training runs. This factor of production has been largely stagnant since GPT-4 in 2023, for various reasons, but new clusters are getting built and the metaphorical car will get moving again soon. (See here)

By convention, each "GPT number" uses ~100x as much training compute as the last one. GPT-3 used ~100x as much as GPT-2, and GPT-4 used ~100x as much as GPT-3 (i.e. ~10,000x as much as GPT-2).

We are just now starting to see "~10x GPT-4 compute" models (like Grok 3 and GPT-4.5). In the next few years we will get to "~100x GPT-4 compute" models, and by 2030 will will reach ~10,000x GPT-4 compute.

If you think intuitively about "how much GPT-4 improved upon GPT-3 (100x less) or GPT-2 (10,000x less)," you can maybe convince yourself that these near-future models will be super-smart in ways that are difficult to precisely state/imagine from our vantage point. (GPT-4 was way smarter than GPT-2; it's hard to know what "projecting that forward" would mean, concretely, but it sure does sound like something pretty special)

Meanwhile, all kinds of (arguably) complementary research is going on, like allowing models to "think" for longer amounts of time, giving them GUI interfaces, etc.

All that being said, there's still a big intuitive gap between "ChatGPT, but it's much smarter under the hood" and anything like "powerful AI." But...

...the LLMs are getting good enough that they can write pretty good code, and they're getting better over time. And depending on how you interpret the evidence, you may be able to convince yourself that they're also swiftly getting better at other tasks involved in AI development, like "research engineering." So maybe you don't need to get all the way yourself, you just need to build an AI that's a good enough AI developer that it improves your AIs faster than you can, and then those AIs are even better developers, etc. etc. (People in this social cluster are really keen on the importance of exponential growth, which is generally a good trait to have but IMO it shades into "we need to kick off exponential growth and it'll somehow do the rest because it's all-powerful" in this case.)

And like, I have various disagreements with this picture.

For one thing, the "10x" models we're getting now don't seem especially impressive – there has been a lot of debate over this of course, but reportedly these models were disappointing to their own developers, who expected scaling to work wonders (using the kind of intuitive reasoning mentioned above) and got less than they hoped for.

And (in light of that) I think it's double-counting to talk about the wonders of scaling and then talk about reasoning, computer GUI use, etc. as complementary accelerating factors – those things are just table stakes at this point, the models are already maxing out the tasks you had defined previously, you've gotta give them something new to do or else they'll just sit there wasting GPUs when a smaller model would have sufficed.

And I think we're already at a point where nuances of UX and "character writing" and so forth are more of a limiting factor than intelligence. It's not a lack of "intelligence" that gives us superficially dazzling but vapid "eyeball kick" prose, or voice assistants that are deeply uncomfortable to actually talk to, or (I claim) "AI agents" that get stuck in loops and confuse themselves, or any of that.

We are still stuck in the "Helpful, Harmless, Honest Assistant" chatbot paradigm – no one has seriously broke with it since that Anthropic introduced it in a paper in 2021 – and now that paradigm is showing its limits. ("Reasoning" was strapped onto this paradigm in a simple and fairly awkward way, the new "reasoning" models are still chatbots like this, no one is actually doing anything else.) And instead of "okay, let's invent something better," the plan seems to be "let's just scale up these assistant chatbots and try to get them to self-improve, and they'll figure it out." I won't try to explain why in this post (IYI I kind of tried to here) but I really doubt these helpful/harmless guys can bootstrap their way into winning all the Nobel Prizes.

----

All that stuff I just said – that's where I differ from the usual "short timelines" people, from Kokotajlo and co.

But OK, let's say that for the sake of argument, I'm wrong and they're right. It still seems like a pretty tough squeeze to get to "powerful AI" on time, doesn't it?

In the OSTP submission, Anthropic presents their latest release as evidence of their authority to speak on the topic:

In February 2025, we released Claude 3.7 Sonnet, which is by many performance benchmarks the most powerful and capable commercially-available AI system in the world.

I've used Claude 3.7 Sonnet quite a bit. It is indeed really good, by the standards of these sorts of things!

But it is, of course, very very far from "powerful AI." So like, what is the fine-grained timeline even supposed to look like? When do the many, many milestones get crossed? If they're going to have "powerful AI" in early 2027, where exactly are they in mid-2026? At end-of-year 2025?

If I assume that absolutely everything goes splendidly well with no unexpected obstacles – and remember, we are talking about automating all human intellectual labor and all tasks done by humans on computers, but sure, whatever – then maybe we get the really impressive next-gen models later this year or early next year... and maybe they're suddenly good at all the stuff that has been tough for LLMs thus far (the "10x" models already released show little sign of this but sure, whatever)... and then we finally get into the self-improvement loop in earnest, and then... what?

They figure out to squeeze even more performance out of the GPUs? They think of really smart experiments to run on the cluster? Where are they going to get all the missing information about how to do every single job on earth, the tacit knowledge, the stuff that's not in any web scrape anywhere but locked up in human minds and inaccessible private data stores? Is an experiment designed by a helpful-chatbot AI going to finally crack the problem of giving chatbots the taste to "write extremely good novels," when that taste is precisely what "helpful-chatbot AIs" lack?

I guess the boring answer is that this is all just hype – tech CEO acts like tech CEO, news at 11. (But I don't feel like that can be the full story here, somehow.)

And the scary answer is that there's some secret Anthropic private info that makes this all more plausible. (But I doubt that too – cf. Brundage's claim that there are no more secrets like that now, the short-timelines cards are all on the table.)

It just does not make sense to me. And (as you can probably tell) I find it very frustrating that these guys are out there talking about how human thought will basically be obsolete in a few years, and pontificating about how to find new sources of meaning in life and stuff, without actually laying out an argument that their vision – which would be the common concern of all of us, if it were indeed on the horizon – is actually likely to occur on the timescale they propose.

It would be less frustrating if I were being asked to simply take it on faith, or explicitly on the basis of corporate secret knowledge. But no, the claim is not that, it's something more like "now, now, I know this must sound far-fetched to the layman, but if you really understand 'scaling laws' and 'exponential growth,' and you appreciate the way that pretraining will be scaled up soon, then it's simply obvious that –"

No! Fuck that! I've read the papers you're talking about, I know all the arguments you're handwaving-in-the-direction-of! It still doesn't add up!

[reasonsforhope]

"As a Deaf man, Adam Munder has long been advocating for communication rights in a world that chiefly caters to hearing people. 

The Intel software engineer and his wife — who is also Deaf — are often unable to use American Sign Language in daily interactions, instead defaulting to texting on a smartphone or passing a pen and paper back and forth with service workers, teachers, and lawyers. 

It can make simple tasks, like ordering coffee, more complicated than it should be. 

But there are life events that hold greater weight than a cup of coffee. 

Recently, Munder and his wife took their daughter in for a doctor’s appointment — and no interpreter was available. 

To their surprise, their doctor said: “It’s alright, we’ll just have your daughter interpret for you!” ...

That day at the doctor’s office came at the heels of a thousand frustrating interactions and miscommunications — and Munder is not isolated in his experience.

“Where I live in Arizona, there are more than 1.1 million individuals with a hearing loss,” Munder said, “and only about 400 licensed interpreters.”

In addition to being hard to find, interpreters are expensive. And texting and writing aren’t always practical options — they leave out the emotion, detail, and nuance of a spoken conversation. 

ASL is a rich, complex language with its own grammar and culture; a subtle change in speed, direction, facial expression, or gesture can completely change the meaning and tone of a sign. 

“Writing back and forth on paper and pen or using a smartphone to text is not equivalent to American Sign Language,” Munder emphasized. “The details and nuance that make us human are lost in both our personal and business conversations.”

His solution? An AI-powered platform called Omnibridge. 

“My team has established this bridge between the Deaf world and the hearing world, bringing these worlds together without forcing one to adapt to the other,” Munder said. 

Trained on thousands of signs, Omnibridge is engineered to transcribe spoken English and interpret sign language on screen in seconds...

“Our dream is that the technology will be available to everyone, everywhere,” Munder said. “I feel like three to four years from now, we're going to have an app on a phone. Our team has already started working on a cloud-based product, and we're hoping that will be an easy switch from cloud to mobile to an app.” ...

At its heart, Omnibridge is a testament to the positive capabilities of artificial intelligence. "

-via GoodGoodGood, October 25, 2024. More info below the cut!

To test an alpha version of his invention, Munder welcomed TED associate Hasiba Haq on stage. 

“I want to show you how this could have changed my interaction at the doctor appointment, had this been available,” Munder said. 

He went on to explain that the software would generate a bi-directional conversation, in which Munder’s signs would appear as blue text and spoken word would appear in gray. 

At first, there was a brief hiccup on the TED stage. Haq, who was standing in as the doctor’s office receptionist, spoke — but the screen remained blank. 

“I don’t believe this; this is the first time that AI has ever failed,” Munder joked, getting a big laugh from the crowd. “Thanks for your patience.”

After a quick reboot, they rolled with the punches and tried again.

Haq asked: “Hi, how’s it going?” 

Her words popped up in blue. 

Munder signed in reply: “I am good.” 

His response popped up in gray. 

Back and forth, they recreated the scene from the doctor’s office. But this time Munder retained his autonomy, and no one suggested a 7-year-old should play interpreter. 

Munder’s TED debut and tech demonstration didn’t happen overnight — the engineer has been working on Omnibridge for over a decade. 

“It takes a lot to build something like this,” Munder told Good Good Good in an exclusive interview, communicating with our team in ASL. “It couldn't just be one or two people. It takes a large team, a lot of resources, millions and millions of dollars to work on a project like this.” 

After five years of pitching and research, Intel handpicked Munder’s team for a specialty training program. It was through that backing that Omnibridge began to truly take shape...

“Our dream is that the technology will be available to everyone, everywhere,” Munder said. “I feel like three to four years from now, we're going to have an app on a phone. Our team has already started working on a cloud-based product, and we're hoping that will be an easy switch from cloud to mobile to an app.” 

In order to achieve that dream — of transposing their technology to a smartphone — Munder and his team have to play a bit of a waiting game. Today, their platform necessitates building the technology on a PC, with an AI engine. 

“A lot of things don't have those AI PC types of chips,” Munder explained. “But as the technology evolves, we expect that smartphones will start to include AI engines. They'll start to include the capability in processing within smartphones. It will take time for the technology to catch up to it, and it probably won't need the power that we're requiring right now on a PC.” 

At its heart, Omnibridge is a testament to the positive capabilities of artificial intelligence. 

But it is more than a transcription service — it allows people to have face-to-face conversations with each other. There’s a world of difference between passing around a phone or pen and paper and looking someone in the eyes when you speak to them. 

It also allows Deaf people to speak ASL directly, without doing the mental gymnastics of translating their words into English.

“For me, English is my second language,” Munder told Good Good Good. “So when I write in English, I have to think: How am I going to adjust the words? How am I going to write it just right so somebody can understand me? It takes me some time and effort, and it's hard for me to express myself actually in doing that. This technology allows someone to be able to express themselves in their native language.” 

Ultimately, Munder said that Omnibridge is about “bringing humanity back” to these conversations. 

“We’re changing the world through the power of AI, not just revolutionizing technology, but enhancing that human connection,” Munder said at the end of his TED Talk. 

“It’s two languages,” he concluded, “signed and spoken, in one seamless conversation.”"

-via GoodGoodGood, October 25, 2024

[rebelfell]

✦ Stranger Things Masterlist ✦

My works generally feature a cis, fem reader with limited physical descriptors. Just by virtue of being written by me, they will likely be shy/inexperienced ‘cos I write what I know, y’know? There are individual warnings on each. If you come across something you think needs a warning, please let me know (gently, I am but a fragile soufflé ready to sink)

anything 🌶️ is marked with a*

EVERYTHING is 18+, MDNI for your sake and mine

The Third Date┃Part One┃Part Two~

eddie munson x anorgasmic!reader - 14k

Surrender┃Part One ┃Part Two*┃Part Three*

eddie munson x bi!reader x lesbian!chrissy cunningham - 18k

Bells Will Be Ringing┃Part One*┃Part Two*

crush!steve harrington x fem!reader x fwb!eddie munson - 16k

Hold Your Peace in Pieces┃TBD

engaged!rockstar!eddie munson x fem!reader -

this summer is the apocalypse, pt II, pt III*, pt IV*, eddie’s interlude, part V, epilogue~, epilogue II~, epilogue III

thinking thoughts on eddie and an older!Harrington!reader (aka: stevie’s aunt has got it goin’ on)

for your viewing pleasure* vol. 1, vol. 2

featuring pornstar!eddie and his director!reader

are you even listening to me?, cont’d, preq, preq II

bestfriend!eddie gets distracted by your…assets.

working on my fitness, pt II, pt III

a gym meet cute w/ modern!eddie (neighbors au)

special delivery*

someone unexpected shows up to deliver your pizza

made for lovin’ you*

softdom!eddie makes a bad tinder date a whole lot better

shelter from the storm~

when the power goes out, your neighbor eddie checks in

under the influence

an edible loosens your lips in front of your frenemy, eddie

haven’t had any complaints yet*

the trials and tribulations of giving van head over forty

game night* (surrender universe)

chrissy and eddie get extra competitive, you benefit

in the middle of the night*

boyfriend!steve helps to soothe what ails us🩸

cold dry stone*

revenge f!cking with gator 🐊

american engine

truck smut for truck smut’s sake 🛻 (w/ steve)

you’re not gonna tell on me, are you?

linecook!eddie can get away with literally anything 🚬

that Vanity Fair party was…a lot*

actor!steve x assistant!reader x rockstar!eddie spice

buzzcut season, rockstar!eddie musing*

dmm, i’m just embracing the shaved-head era

I didn’t know you were into that…

you’ve been watching too many ghostface tiktoks 🔪

modern!wealthy!Steve? How’d you get in here?

steve spoils his girl in the midst of a hangover

wait, are you a…have you never?*

bigdick!steve x virgin!reader 🏕️

felt in need of some affection…

sweet!soft!eddie vignette

possessive.┃eddie shows you who you belong to

multiples.┃eddie wants you to arrive properly

urgent.┃eddie can do better than he can

hesitant.┃eddie and you try something new

how can you be sad on love’s birthday? 💌

a very flangsty valentine’s day w/ bestfriend!eddie

so wrong, it’s right, so right, it’s wrong 🎃

eddie munson x his best friend’s (ex?) girl

you’ve never seen gremlins? 🎃

it’s scary movie night at eddie’s house

you’re a what? (WCIL-verse) 🎃

modern!eddie bumps into you at a halloween party

how much of that can is left? 🦃

you + eddie + whipped topping

today is a no bones day 🦃

you and eddie in recovery mode

#index - landing pages for long form/multi-part blurbs & fics

#free write - bursts of writing based on images/other posts

#my moods - fic/character moodboards, (aka I spent too much time spent daydreaming on pinterest again)

#thrift shop eddie - short blurbs about all the odd and random gifts I would terrorize shower Eddie with if given the chance

© 2024 rebelfell All Rights Reserved. Any written work on this blog is my own and I do not consent for it to be copied, altered or re-posted in any form or to be fed into AI software.