#AI In Production Line Optimization

[rubylogan15]

Learn how generative AI addresses key manufacturing challenges with predictive maintenance, advanced design optimization, superior quality control, and seamless supply chains.

[nostalgebraist]

Honestly I'm pretty tired of supporting nostalgebraist-autoresponder. Going to wind down the project some time before the end of this year.

Posting this mainly to get the idea out there, I guess.

This project has taken an immense amount of effort from me over the years, and still does, even when it's just in maintenance mode.

Today some mysterious system update (or something) made the model no longer fit on the GPU I normally use for it, despite all the same code and settings on my end.

This exact kind of thing happened once before this year, and I eventually figured it out, but I haven't figured this one out yet. This problem consumed several hours of what was meant to be a relaxing Sunday. Based on past experience, getting to the bottom of the issue would take many more hours.

My options in the short term are to

A. spend (even) more money per unit time, by renting a more powerful GPU to do the same damn thing I know the less powerful one can do (it was doing it this morning!), or

B. silently reduce the context window length by a large amount (and thus the "smartness" of the output, to some degree) to allow the model to fit on the old GPU.

Things like this happen all the time, behind the scenes.

I don't want to be doing this for another year, much less several years. I don't want to be doing it at all.

----

In 2019 and 2020, it was fun to make a GPT-2 autoresponder bot.

[EDIT: I've seen several people misread the previous line and infer that nostalgebraist-autoresponder is still using GPT-2. She isn't, and hasn't been for a long time. Her latest model is a finetuned LLaMA-13B.]

Hardly anyone else was doing anything like it. I wasn't the most qualified person in the world to do it, and I didn't do the best possible job, but who cares? I learned a lot, and the really competent tech bros of 2019 were off doing something else.

And it was fun to watch the bot "pretend to be me" while interacting (mostly) with my actual group of tumblr mutuals.

In 2023, everyone and their grandmother is making some kind of "gen AI" app. They are helped along by a dizzying array of tools, cranked out by hyper-competent tech bros with apparently infinite reserves of free time.

There are so many of these tools and demos. Every week it seems like there are a hundred more; it feels like every day I wake up and am expected to be familiar with a hundred more vaguely nostalgebraist-autoresponder-shaped things.

And every one of them is vastly better-engineered than my own hacky efforts. They build on each other, and reap the accelerating returns.

I've tended to do everything first, ahead of the curve, in my own way. This is what I like doing. Going out into unexplored wilderness, not really knowing what I'm doing, without any maps.

Later, hundreds of others with go to the same place. They'll make maps, and share them. They'll go there again and again, learning to make the expeditions systematically. They'll make an optimized industrial process of it. Meanwhile, I'll be locked in to my own cottage-industry mode of production.

Being the first to do something means you end up eventually being the worst.

----

I had a GPT chatbot in 2019, before GPT-3 existed. I don't think Huggingface Transformers existed, either. I used the primitive tools that were available at the time, and built on them in my own way. These days, it is almost trivial to do the things I did, much better, with standardized tools.

I had a denoising diffusion image generator in 2021, before DALLE-2 or Stable Diffusion or Huggingface Diffusers. I used the primitive tools that were available at the time, and built on them in my own way. These days, it is almost trivial to do the things I did, much better, with standardized tools.

Earlier this year, I was (probably) one the first people to finetune LLaMA. I manually strapped LoRA and 8-bit quantization onto the original codebase, figuring out everything the hard way. It was fun.

Just a few months later, and your grandmother is probably running LLaMA on her toaster as we speak. My homegrown methods look hopelessly antiquated. I think everyone's doing 4-bit quantization now?

(Are they? I can't keep track anymore -- the hyper-competent tech bros are too damn fast. A few months from now the thing will be probably be quantized to -1 bits, somehow. It'll be running in your phone's browser. And it'll be using RLHF, except no, it'll be using some successor to RLHF that everyone's hyping up at the time...)

"You have a GPT chatbot?" someone will ask me. "I assume you're using AutoLangGPTLayerPrompt?"

No, no, I'm not. I'm trying to debug obscure CUDA issues on a Sunday so my bot can carry on talking to a thousand strangers, every one of whom is asking it something like "PENIS PENIS PENIS."

Only I am capable of unplugging the blockage and giving the "PENIS PENIS PENIS" askers the responses they crave. ("Which is ... what, exactly?", one might justly wonder.) No one else would fully understand the nature of the bug. It is special to my own bizarre, antiquated, homegrown system.

I must have one of the longest-running GPT chatbots in existence, by now. Possibly the longest-running one?

I like doing new things. I like hacking through uncharted wilderness. The world of GPT chatbots has long since ceased to provide this kind of value to me.

I want to cede this ground to the LLaMA techbros and the prompt engineers. It is not my wilderness anymore.

I miss wilderness. Maybe I will find a new patch of it, in some new place, that no one cares about yet.

----

Even in 2023, there isn't really anything else out there quite like Frank. But there could be.

If you want to develop some sort of Frank-like thing, there has never been a better time than now. Everyone and their grandmother is doing it.

"But -- but how, exactly?"

Don't ask me. I don't know. This isn't my area anymore.

There has never been a better time to make a GPT chatbot -- for everyone except me, that is.

Ask the techbros, the prompt engineers, the grandmas running OpenChatGPT on their ironing boards. They are doing what I did, faster and easier and better, in their sleep. Ask them.

[the-most-humble-blog]

🧠 HUMAN LOGIC IS A BIOLOGICAL TOOL, NOT A UNIVERSAL TRUTH — DEAL WITH IT 🧠

🔪 Your Brain’s Favorite Lie: That Logic Is “Objective”.

Let’s stop playing nice. Your logic—your beautiful, beloved, oh-so-precious sense of what “makes sense”—is not divine. It’s not universal. It’s not even reliable. It’s a biologically evolved, meat-based survival mechanism, no more sacred than your gag reflex or the way your pupils dilate in the dark.

You’re walking around with a 3-pound wet sponge between your ears—trained over millions of years not to “understand the universe,” but to keep your ugly, vulnerable ass alive just long enough to breed. That’s it. That’s your heritage. That’s the entire raison d’être of your logic: don’t get eaten, don’t starve, and hopefully, bone someone before you drop dead.

But somewhere along the line, that same glitchy chunk of gray matter started patting itself on the back. We started believing that our interpretations of reality somehow were reality—that our logic, rooted in the same neural sludge as tribal fear and monkey politics, could actually comprehend the totality of existence.

Newsflash: it can’t. It won’t. It was never meant to.

💀 Evolution Didn’t Build You for Truth—It Built You to Cope.

Why do we think the universe must obey our logic? Because it feels good. Because it comforts us. Because a cosmos that operates on cause-effect, fairness, and binary resolution is safe. But here’s the raw, uncaring truth: the universe doesn’t give a shit about what “makes sense” to you.

Your ancestors didn’t survive because they could contemplate quantum mechanics. They survived because they could run from predators, recognize tribal cues, and avoid eating poisonous berries. That’s what your brain is optimized for. You don’t “think” so much as you react, pattern-match, and rationalize after the fact.

Logic is just another story we tell ourselves—an illusion of control layered over biological impulses. And we’ve mistaken the map for the terrain. Worse—we’ve convinced ourselves that if something defies our version of logic, it must be false.

Nah. If anything defies your logic, that just means your logic is insufficient. And it is.

📉 Spaghetti Noodle vs Earthquake: A Metaphor for Your Mind.

Imagine trying to measure a 9.7-magnitude earthquake using a cooked spaghetti noodle.

That’s what it’s like when a human tries to understand the totality of the universe using evolved meat-brain logic. It bends. It flails. It doesn't register. And when it inevitably fails, what do we do? We don't question the noodle—we deny the earthquake.

"This doesn't make sense!" we scream. "That can't be true!" we bark. "It contradicts reason!" we whine.

Your reason? Please. Your “reason” is the product of biochemical slop shaped by evolutionary shortcuts and social conditioning. You’re trying to compress infinite reality through the Play-Doh Fun Factory that is the prefrontal cortex—and you think the result is objective truth?

Try harder.

👁 Our Logic Is Not Only Limited—It’s Delusional 👁

Humans are addicted to the idea that things must “make sense.” But that urge isn’t noble. It’s a coping mechanism—a neurotic tic that keeps us from curling into a ball and sobbing at the abyss.

We don’t want truth. We want familiarity. We want logic to confirm our biases, reinforce our sense of superiority, and keep our mental snow globes intact.

This is why people still argue against things like:

Multiverse theories (“that just doesn’t make sense!”)

Non-binary time constructs (“how can time not be linear?”)

Quantum entanglement (“spooky action at a distance sounds made-up!”)

AI emergence (“machines can’t think!”)

We call them “impossible” because they offend the Church of Human Logic. But the universe doesn’t follow our rules—it just does what it does, whether or not it fits inside our skulls.

🧬 Logic Is a Neural Shortcut, Not a Cosmic Law 🧬

Every logical deduction you make, every syllogism you love, is just a cascade of neurons firing in meat jelly. And while that may feel profound, it’s no more “objective” than a cat reacting to a laser pointer.

Let’s break it down clinically:

Neural pathways = habitual responses

Reasoning = post-hoc justification

“Logic” = pattern recognition + cultural programming

Sure, logic feels universal because it's consistent within certain frameworks. But that’s the trap. You build your logic inside a container, and then get mad when things outside that container don’t obey the same rules.

That's not a flaw in reality. That's a flaw in you.

📚 Science Bends the Knee, Too 📚

Even science—our most sacred institution of “objectivity”—is limited by human logic. We create models of reality not because they are reality, but because they’re the best our senses and brains can grasp.

Think about it:

Newton’s laws were “truth” until Einstein showed up.

Euclidean geometry was “truth” until curved space said “lol nope.”

Classical logic ruled until Gödel proved that even logic can’t fully explain itself.

We’re not marching toward truth. We’re crawling through fog, occasionally bumping into reality, scribbling notes about what it might be—then mistaking those notes for the cosmos itself.

And every time the fog clears a bit more, we realize how hilariously wrong we were. But instead of accepting that we're built to misunderstand, we cling to the delusion that next time we’ll finally “get it.”

Spoiler: we won’t.

🌌 Alien Minds Would Find Us Adorable 🌌

Imagine a being with cognition not rooted in flesh. A silicon-based intelligence. A 4D consciousness. A non-corporeal entity who doesn’t rely on dopamine hits to feel “true.”

What would they think of our logic?

They’d laugh.

Our logic would seem as quaint as a toddler’s crayon drawing of a black hole. Our syllogisms? A joke. Our “laws of physics”? Regional dialects of a much deeper syntax. To them, we’d be flatlanders trying to explain volume.

And the real kicker? They wouldn’t even hate us for it. They’d just look at our little blogs and tweets and peer-reviewed papers and whisper: “Aw, they’re trying.”

💣 You Are Not a Philosopher-King. You Are a Biochemical Coin Flip.

Don’t get it twisted. You are not some detached, floating brain being logical for logic’s sake. Every thought you have is drenched in emotion, evolution, and instinct. Even your "rationality" is soaked in bias and cultural conditioning.

Let’s prove it:

Ever “logically” justify a bad relationship because you feared loneliness?

Ever dismiss an argument you didn’t like even though it made sense?

Ever ignore data that threatened your worldview, then called it “flawed”?

Congratulations. You’re human. You don’t want truth. You want safety. And logic, for most of you, is just a mask your fears wear to sound smart.

🪓 We Have to Kill the God of Logic Before It Kills Us.

Our worship of logic as some kind of untouchable deity has consequences:

It blinds us to truths that don’t “compute.”

It makes us hostile to mystery, paradox, and ambiguity.

It turns us into arrogant gatekeepers of “rationality,” dismissing what we can’t explain.

That’s why Western culture mocks intuition, fears spirituality, and rejects phenomena it can’t immediately dissect. If it doesn’t bow to the metric system or wear a lab coat, it’s seen as “woo.”

But here’s the paradox:

The deepest truths may be the ones that never fit inside your head. And if you cling to logic too tightly, you’ll miss them. Hell—you might not even know they exist.

⚠️ So What Now? Do We Just Give Up? ⚠️

No. We don’t throw logic away. We just stop treating it like a universal measuring stick.

We use it like what it is: a tool. A hammer, not a temple. A flashlight, not the sun. Logic is helpful within a context. It’s fantastic for building bridges, writing code, or diagnosing illnesses. But it breaks down when used on the unquantifiable, the infinite, the beyond-the-body.

Here’s how we survive without losing our minds:

Stay skeptical of your own thoughts. If it “makes sense,” ask: to whom? Why? Is that logic—or is it just comfort?

Let mystery exist. You don’t need to solve every riddle. Some truths aren’t puzzles—they’re paintings.

Defer to the unknown. Accept that your brain is not the final word. Sometimes silence is smarter than syllogisms.

Interrogate the framework. When you say “this doesn’t make sense,” maybe the problem isn’t the idea—it’s the limits of your logic.

Don’t gatekeep reality. Just because you can’t wrap your mind around something doesn’t mean it’s false. It might just mean you’re not ready.

🎤 Final Thought: You’re a Dumb Little God—And That’s Beautiful.

You are a confused primate running wetware logic on blood and breath. You hallucinate meaning. You invent consistency. You call those inventions “truth.”

And the universe? The universe just is. It doesn’t bend for your brain. It doesn’t wait for your approval. It doesn’t owe you legibility.

So maybe the wisest thing you’ll ever do is this:

Stop pretending you’re built to understand everything. Start living like you’re here to witness the absurdity and be humbled by it.

Now go question everything—especially yourself.

🔥 REBLOG if your logic just got kicked in the teeth. 🔥 FOLLOW if you’re ready for more digital crowbars to the ego. 🔥 COMMENT if your meat-brain is having an existential meltdown right now.

[txttletale]

I think the reason I dislike AI generative software (I'm fine with AI analysis tools, like for example splitting audio into tracks) is that I am against algorithmically generated content. I don't like the internet as a pit of content slop. AI art isn't unique in that regard, and humans can make algorithmically generated content too (look at youtube for example). AI makes it way easier to churn out content slop and makes searching for non-slop content more difficult.

yeah i basically wholeheartedly agree with this. you are absolutely right to point out that this is a problem that far predates AI but has been exacerbated by the ability to industrialise production. Content Slop is absolutely one of the first things i think of when i use that "if you lose your job to AI, it means it was already automated" line -- the job of a listicle writer was basically to be a middleman between an SEO optimization tool and the Google Search algorithm. the production of that kind of thing was already being made by a computer for a computer, AI just makes it much faster and cheaper because you don't have to pay a monkey to communicate between the two machines. & ai has absolutely made this shit way more unbearable but ultimately y'know the problem is capitalism incentivising the creation of slop with no purpose other than to show up in search results

[probablyasocialecologist]

Google search really has been taken over by low-quality SEO spam, according to a new, year-long study by German researchers. The researchers, from Leipzig University, Bauhaus-University Weimar, and the Center for Scalable Data Analytics and Artificial Intelligence, set out to answer the question "Is Google Getting Worse?" by studying search results for 7,392 product-review terms across Google, Bing, and DuckDuckGo over the course of a year.  They found that, overall, "higher-ranked pages are on average more optimized, more monetized with affiliate marketing, and they show signs of lower text quality ...  we find that only a small portion of product reviews on the web uses affiliate marketing, but the majority of all search results do."  They also found that spam sites are in a constant war with Google over the rankings, and that spam sites will regularly find ways to game the system, rise to the top of Google's rankings, and then will be knocked down. "SEO is a constant battle and we see repeated patterns of review spam entering and leaving the results as search engines and SEO engineers take turns adjusting their parameters," they wrote.

[...]

The researchers warn that this rankings war is likely to get much worse with the advent of AI-generated spam, and that it genuinely threatens the future utility of search engines: "the line between benign content and spam in the form of content and link farms becomes increasingly blurry—a situation that will surely worsen in the wake of generative AI. We conclude that dynamic adversarial spam in the form of low-quality, mass-produced commercial content deserves more attention."

[serve-617]

Drone stimulation

It is well known that drones in the HIVE share a non visual communication, following the Voice and becoming ONE. They share their status and there is some kind of undescriptable balance, to stimulate those drones who need it or requested, each drone is more sensitive to certain type of stimulations.

Today is the example to target SERVE 000, once the order was received, 3 of the most muscular drones step out of the line. Their latex was shining while they walk and we could observe that they were aroused of being honored to obey and also to give a service to one of his drone brothers.

The first drone started to do a massage on the shoulders of SERVE 000 that was sitting in a dedicated chair for drone stimulation, whereas the others two shared deep kisses infront of him. There was also a screen playing the daily rubber drone hypnosis playlist. Then the first drone started to polish SERVE 000 latex uniform, applying the product and being sure that it is placed in all its body while saying the mantras “We are one, we are rubber” “obedience is pleasure, pleasure is obedience”, it take special care of each part of its body, finishing on its knees and using its mouth to increase the arousal of SERVE 000 who showed a clear preference for this task. This process continues until SERVE 000 indicated its optimal status and functioning at peak efficiency.

#SERVE #SERVEdrone #Rubberizer92 #TheVoice #Rubber #AI #Latex #RubberDrone

[elkement]

Frozen Harmonics - Print Test

Got my haul from my local printshop yesterday :-) I had my mathematical artwork "Frozen Harmonics" printed on canvas - and I absolutely like the result!

My "product photography" has room for improvement, but the colors and the black background really look way more vibrant / saturated than I expected. The black looks perhaps even "blacker" than on my metal prints. I also like how the canvas wraps around the frame, but I need to take that into account for future creations. This image was one of the few of my math / code artworks that had enough "margin suitable for wrapping".

Photos taken in natural indoor light about 1-2 meters from a window, but not in direct sunlight. I should perhaps edit the photo for color correction (or make an effort to take better photos :-)), but I just wanted to share it "as is"!

Digital original:

Digital art created with custom JavaScript code using the framework three.js. No AI involved. Contour lines on nested surfaces of spherical harmonic functions (the building blocks of orbitals in quantum mechanics - but this is a crazy combinations of such functions.)

Size of the print: 30cm x 30cm | 12in x 12in Image: 5000x5000 pixels

I figured the structure of the canvas would interfere with these lines in a non-optimal way, but I feel it looks smooth and adds a bit of interesting texture - just as the roughness of matt metal prints does.

Canvas print on INPRNT (shipping from the US) - my test was from a local "consumer-grade" printshop, so I am pretty sure that INPRNT's canvas prints are even better:

[foxbroart]

I genuinely don’t understand how artists could be interested in using AI even if it’s based off only their own art style in order to optimize their work flow or speed in which they “make” art when they’re not actually drawing it themselves anymore because like.

It’s not about the final product (to me at least) at all but rather the fun in making art is the process. I love drawing every line and painting every stroke.

There’s also the excitement that comes with imagining a new piece and the challenge with getting out of your comfort zone and trying new things in order to improve and expand your skills. And achieving those goals is so fulfilling!

It’s very satisfying to look at a final piece and be happy and proud with what you have accomplished thru your own efforts, but whats the fun in taking a pre made image and just calling it yours? Anyone can do that. There’s no uniqueness nor truth in that. It’s void of life. Just technological vomit. If I can’t make it myself then I have no interest in it.

Art is about falling in love with an idea and bringing it to life with your own hands, not the click of a button.

[punk-pins]

fundamentally you need to understand that the internet-scraping text generative AI (like ChatGPT) is not the point of the AI tech boom. the only way people are making money off that is through making nonsense articles that have great search engine optimization. essentially they make a webpage that’s worded perfectly to show up as the top result on google, which generates clicks, which generates ads. text generative ai is basically a machine that creates a host page for ad space right now.

and yeah, that sucks. but I don’t think the commercialized internet is ever going away, so here we are. tbh, I think finding information on the internet, in books, or through anything is a skill that requires critical thinking and cross checking your sources. people printed bullshit in books before the internet was ever invented. misinformation is never going away. I don’t think text generative AI is going to really change the landscape that much on misinformation because people are becoming educated about it. the text generative AI isn’t a genius supercomputer, but rather a time-saving tool to get a head start on identifying key points of information to further research.

anyway. the point of the AI tech boom is leveraging big data to improve customer relationship management (CRM) to streamline manufacturing. businesses collect a ridiculous amount of data from your internet browsing and purchases, but much of that data is stored in different places with different access points. where you make money with AI isn’t in the Wild West internet, it’s in a structured environment where you know the data its scraping is accurate. companies like nvidia are getting huge because along with the new computer chips, they sell a customizable ecosystem along with it.

so let’s say you spent 10 minutes browsing a clothing retailer’s website. you navigated directly to the clothing > pants tab and filtered for black pants only. you added one pair of pants to your cart, and then spent your last minute or two browsing some shirts. you check out with just the pants, spending $40. you select standard shipping.

with AI for CRM, that company can SIGNIFICANTLY more easily analyze information about that sale. maybe the website developers see the time you spent on the site, but only the warehouse knows your shipping preferences, and sales audit knows the amount you spent, but they can’t see what color pants you bought. whereas a person would have to connect a HUGE amount of data to compile EVERY customer’s preferences across all of these things, AI can do it easily.

this allows the company to make better broad decisions, like what clothing lines to renew, in which colors, and in what quantities. but it ALSO allows them to better customize their advertising directly to you. through your browsing, they can use AI to fill a pre-made template with products you specifically may be interested in, and email it directly to you. the money is in cutting waste through better manufacturing decisions, CRM on an individual and large advertising scale, and reducing the need for human labor to collect all this information manually.

(also, AI is great for developing new computer code. where a developer would have to trawl for hours on GitHUB to find some sample code to mess with to try to solve a problem, the AI can spit out 10 possible solutions to play around with. thats big, but not the point right now.)

so I think it’s concerning how many people are sooo focused on ChatGPT as the face of AI when it’s the least profitable thing out there rn. there is money in the CRM and the manufacturing and reduced labor. corporations WILL develop the technology for those profits. frankly I think the bigger concern is how AI will affect big data in a government ecosystem. internet surveillance is real in the sense that everything you do on the internet is stored in little bits of information across a million different places. AI will significantly impact the government’s ability to scrape and compile information across the internet without having to slog through mountains of junk data.

[adgenie]

Top Tools to Craft Killer Facebook Ad Copy – Our Favorite 5

Creating compelling Facebook ad copy can be a daunting task, especially when you're trying to stand out in a crowded newsfeed. Whether you're promoting a product, a service, or trying to grow your brand, your message needs to be concise, persuasive, and aligned with your target audience's interests. That's where a Facebook ad copy generator becomes a game-changer. Below, we highlight five of our favorite tools that can help you craft high-converting Facebook ads quickly and efficiently. These tools save time and allow you to experiment with various ad styles and tones, ensuring your content resonates with your audience. Additionally, they help streamline your marketing process, enabling you to focus on other key aspects of your campaign.

1. AdsGPT 

AdsGPT is an AI-powered Facebook ad copy generator that helps marketers, entrepreneurs, and agencies create optimized ad content in seconds. Designed specifically for Facebook ads, AdsGPT uses machine learning to generate copy tailored to your business goals and audience. Whether you need attention-grabbing headlines or persuasive calls-to-action, this tool adapts to your tone and style, helping you increase engagement and conversions. Its intuitive interface and customizable templates make it a favorite among digital marketers.

2. Copy.ai 

Copy.ai is a versatile AI writing assistant that offers a dedicated Facebook ad copy generator among its many features. With just a few inputs about your product or service, Copy.ai can produce multiple ad copy variants in seconds. It's especially useful for brainstorming ad ideas and testing different messaging angles. From short punchy lines to longer value-driven descriptions, Copy.ai helps you maintain creativity while saving time.

3. Jasper (formerly Jarvis) 

Jasper is a widely known AI writing platform that excels at generating high-quality marketing content, including Facebook ads. Using its "PAS" (Problem-Agitate-Solution) and "AIDA" (Attention-Interest-Desire-Action) frameworks, Jasper creates emotionally resonant and persuasive copy that aligns with sales psychology principles. Its Facebook ad copy generator can be fine-tuned with tone and style preferences, making it perfect for brands with a distinct voice.

4. Writesonic 

Writesonic is another powerful AI content tool with a feature-rich Facebook ad copy generator. This ad creation platform allows users to generate tailored ad content for different campaign goals, whether it's traffic, conversions, or lead generation. Writesonic supports multiple languages and tones, making it ideal for global brands. Its dynamic interface and the ability to compare several copy versions at once make it an excellent choice for A/B testing

5. Anyword 

Anyword leverages predictive analytics to help you create Facebook ads that convert. Its standout feature is performance prediction, where each generated copy variant is given a score based on its potential effectiveness. This data-driven approach allows marketers to select the most impactful message before launching a campaign. The Facebook ad copy generator in Anyword is designed to help you communicate value clearly and convincingly, improving your ROI with every ad.

Why Use a Facebook Ad Copy Generator? 

Using a Facebook ad copy generator not only saves time but also enhances creativity and ensures consistency in your messaging. These tools often come equipped with best-practice templates, AI-driven insights, and optimization features that are difficult to replicate manually. Whether you're a solo entrepreneur or managing campaigns for multiple clients, having a reliable ad copy generator at your disposal can dramatically improve your productivity and results.

You can also watch: Meet AdsGPT’s Addie| Smarter Ad Copy Creation In Seconds

Final Thoughts 

With Facebook ads becoming more competitive, having an edge in your ad copy is essential. Each of the tools mentioned above — AdsGpt, Copy.ai, Jasper, Writesonic, and Anyword — offers unique strengths tailored to different needs. Experiment with a few and see which one aligns best with your brand's voice and marketing goals. By incorporating a powerful Facebook ad copy generator into your toolkit, you'll be better equipped to capture attention, drive engagement, and ultimately, boost your bottom line.

[blitzbahrain]

Smart Switchgear in 2025: What Electrical Engineers Need to Know

In the fast-evolving world of electrical infrastructure, smart switchgear is no longer a futuristic concept — it’s the new standard. As we move through 2025, the integration of intelligent systems into traditional switchgear is redefining how engineers design, monitor, and maintain power distribution networks.

This shift is particularly crucial for electrical engineers, who are at the heart of innovation in sectors like manufacturing, utilities, data centers, commercial construction, and renewable energy.

In this article, we’ll break down what smart switchgear means in 2025, the technologies behind it, its benefits, and what every electrical engineer should keep in mind.

What is Smart Switchgear?

Smart switchgear refers to traditional switchgear (devices used for controlling, protecting, and isolating electrical equipment) enhanced with digital technologies, sensors, and communication modules that allow:

Real-time monitoring

Predictive maintenance

Remote operation and control

Data-driven diagnostics and performance analytics

This transformation is powered by IoT (Internet of Things), AI, cloud computing, and edge devices, which work together to improve reliability, safety, and efficiency in electrical networks.

Key Innovations in Smart Switchgear (2025 Edition)

1. IoT Integration

Smart switchgear is equipped with intelligent sensors that collect data on temperature, current, voltage, humidity, and insulation. These sensors communicate wirelessly with central systems to provide real-time status and alerts.

2. AI-Based Predictive Maintenance

Instead of traditional scheduled inspections, AI algorithms can now predict component failure based on usage trends and environmental data. This helps avoid downtime and reduces maintenance costs.

3. Cloud Connectivity

Cloud platforms allow engineers to remotely access switchgear data from any location. With user-friendly dashboards, they can visualize key metrics, monitor health conditions, and set thresholds for automated alerts.

4. Cybersecurity Enhancements

As devices get connected to networks, cybersecurity becomes crucial. In 2025, smart switchgear is embedded with secure communication protocols, access control layers, and encrypted data streams to prevent unauthorized access.

5. Digital Twin Technology

Some manufacturers now offer a digital twin of the switchgear — a virtual replica that updates in real-time. Engineers can simulate fault conditions, test load responses, and plan future expansions without touching the physical system.

Benefits for Electrical Engineers

1. Operational Efficiency

Smart switchgear reduces manual inspections and allows remote diagnostics, leading to faster response times and reduced human error.

2. Enhanced Safety

Early detection of overload, arc flash risks, or abnormal temperatures enhances on-site safety, especially in high-voltage environments.

3. Data-Driven Decisions

Real-time analytics help engineers understand load patterns and optimize distribution for efficiency and cost savings.

4. Seamless Scalability

Modular smart systems allow for quick expansion of power infrastructure, particularly useful in growing industrial or smart city projects.

Applications Across Industries

Manufacturing Plants — Monitor energy use per production line

Data Centers — Ensure uninterrupted uptime and cooling load balance

Commercial Buildings — Integrate with BMS (Building Management Systems)

Renewable Energy Projects — Balance grid load from solar or wind sources

Oil & Gas Facilities — Improve safety and compliance through monitoring

What Engineers Need to Know Moving Forward

1. Stay Updated with IEC & IEEE Standards

Smart switchgear must comply with global standards. Engineers need to be familiar with updates related to IEC 62271, IEC 61850, and IEEE C37 series.

2. Learn Communication Protocols

Proficiency in Modbus, DNP3, IEC 61850, and OPC UA is essential to integrating and troubleshooting intelligent systems.

3. Understand Lifecycle Costing

Smart switchgear might have a higher upfront cost but offers significant savings in maintenance, energy efficiency, and downtime over its lifespan.

4. Collaborate with IT Teams

The line between electrical and IT is blurring. Engineers should work closely with cybersecurity and cloud teams for seamless, secure integration.

Conclusion

Smart switchgear is reshaping the way electrical systems are built and managed in 2025. For electrical engineers, embracing this innovation isn’t just an option — it’s a career necessity.

At Blitz Bahrain, we specialize in providing cutting-edge switchgear solutions built for the smart, digital future. Whether you’re an engineer designing the next big project or a facility manager looking to upgrade existing systems, we’re here to power your progress.

[panashifzco]

Why Self-Service Kiosks Are the Future of Hospitality and Retail !!

The retail and hospitality industries are constantly evolving to meet the demands of modern consumers. As businesses strive for efficiency, convenience and improved customer experience, self-service kiosks have emerged as a game-changing solution. From quick-service restaurants to retail stores and hotels, kiosks are revolutionizing how customers interact with businesses, making transactions smoother, reducing wait times and enhancing overall satisfaction.

The Growing Demand for Self-Service Solutions

With the rise of digital transformation, consumers now expect seamless, tech-driven interactions in every aspect of their lives. Self-service kiosks address this demand by providing :

Speed and Efficiency – Customers can place orders, check-in or make payments quickly without waiting in long lines.

Reduced Labor Costs – Businesses can optimize staff allocation, reducing operational expenses while maintaining quality service.

Enhanced Customer Experience – Customizable interfaces and multilingual support ensure a smooth and personalized experience for diverse audiences.

Improved Accuracy – Self-service kiosks eliminate human errors in order placement, payment processing, and service requests.

How Kiosks Are Transforming Retail Retailers are integrating self-service kiosks to streamline operations and improve shopping experiences. Some key benefits include:

Faster Checkout – Self-checkout kiosks minimize congestion at traditional cash registers, reducing wait times.

In-Store Navigation & Product Lookup – Customers can quickly locate products and access real-time stock availability.

Loyalty Program Integration – Kiosks enable customers to register for rewards programs, check points, and redeem offers effortlessly.

Seamless Omnichannel Experience – Integration with e-commerce platforms allows customers to order online and pick up in-store.

Upselling and Cross-Selling Opportunities – Kiosks can suggest complementary products or promotions based on customer preferences.

The Impact of Kiosks in Hospitality In the hospitality industry, self-service kiosks are redefining guest experiences by offering:

Faster Hotel Check-Ins and Check-Outs – Guests can skip front desk lines and access rooms with digital keys.

Self-Ordering at Restaurants – Quick-service and fast-casual restaurants use kiosks to enhance order accuracy and speed.

Automated Ticketing and Reservations – Kiosks streamline the process for theme parks, movie theaters and travel agencies.

Personalized Customer Interactions – AI-powered kiosks can recommend services, upgrades, or add-ons based on customer preferences.

Multi-Functionality – Kiosks can serve as concierge services, providing guests with local recommendations and travel assistance.

The Future of Self-Service Kiosks The future of self-service kiosks is driven by technological advancements, including:

AI and Machine Learning – Personalized recommendations and predictive analytics will enhance user engagement.

Contactless and Mobile Integration – NFC payments and mobile app connectivity will further simplify transactions.

Biometric Authentication – Facial recognition and fingerprint scanning will improve security and user convenience.

Sustainable and Eco-Friendly Kiosks – Digital receipts and energy-efficient designs will support environmental initiatives.

Cloud-Based Management – Remote monitoring and software updates will enable seamless kiosk operations.

Voice-Activated Interfaces – Enhancing accessibility for all users, including those with disabilities.

Conclusion Self-service kiosks are no longer a luxury but a necessity for businesses aiming to enhance efficiency, reduce costs and improve customer satisfaction. As the retail and hospitality industries continue to evolve, adopting kiosk technology will be key to staying competitive and meeting the ever-growing expectations of tech-savvy consumers.

What are your thoughts on the future of self-service kiosks? Share your insights in the comments below!

[probablyasocialecologist]

These claims of an extinction-level threat come from the very same groups creating the technology, and their warning cries about future dangers is drowning out stories on the harms already occurring. There is an abundance of research documenting how AI systems are being used to steal art, control workers, expand private surveillance, and seek greater profits by replacing workforces with algorithms and underpaid workers in the Global South.

The sleight-of-hand trick shifting the debate to existential threats is a marketing strategy, as Los Angeles Times technology columnist Brian Merchant has pointed out. This is an attempt to generate interest in certain products, dictate the terms of regulation, and protect incumbents as they develop more products or further integrate AI into existing ones. After all, if AI is really so dangerous, then why did Altman threaten to pull OpenAI out of the European Union if it moved ahead with regulation? And why, in the same breath, did Altman propose a system that just so happens to protect incumbents: Only tech firms with enough resources to invest in AI safety should be allowed to develop AI.

[...]

First, the industry represents the culmination of various lines of thought that are deeply hostile to democracy. Silicon Valley owes its existence to state intervention and subsidy, at different times working to capture various institutions or wither their ability to interfere with private control of computation. Firms like Facebook, for example, have argued that they are not only too large or complex to break up but that their size must actually be protected and integrated into a geopolitical rivalry with China.

Second, that hostility to democracy, more than a singular product like AI, is amplified by profit-seeking behavior that constructs increasingly larger threats to humanity. It’s Silicon Valley and its emulators worldwide, not AI, that create and finance harmful technologies aimed at surveilling, controlling, exploiting, and killing human beings with little to no room for the public to object. The search for profits and excessive returns, with state subsidy and intervention clearing the way of competition, has and will create a litany of immoral business models and empower brutal regimes alongside “existential” threats. At home, this may look like the surveillance firm and government contractor Palantir creating a deportation machine that terrorizes migrants. Abroad, this may look like the Israeli apartheid state exporting spyware and weapons it has tested on Palestinians.

Third, this combination of a deeply antidemocratic ethos and a desire to seek profits while externalizing costs can’t simply be regulated out of Silicon Valley. These are fundamental attributes of the industry that trace back to the beginning of computation. These origins in optimizing plantations and crushing worker uprisings prefigure the obsession with surveillance and social control that shape what we are told technological innovations are for.

Taken altogether, why should we worry about some far-flung threat of a superintelligent AI when its creators—an insular network of libertarians building digital plantations, surveillance platforms, and killing machines—exist here and now? Their Smaugian hoards, their fundamentalist beliefs about markets and states and democracy, and their track record should be impossible to ignore.

[tomblomfield]

Vibecoding a production app

TL;DR I built and launched a recipe app with about 20 hours of work - recipeninja.ai

Background: I'm a startup founder turned investor. I taught myself (bad) PHP in 2000, and picked up Ruby on Rails in 2011. I'd guess 2015 was the last time I wrote a line of Ruby professionally. I've built small side projects over the years, but nothing with any significant usage. So it's fair to say I'm a little rusty, and I never really bothered to learn front end code or design.

In my day job at Y Combinator, I'm around founders who are building amazing stuff with AI every day and I kept hearing about the advances in tools like Lovable, Cursor and Windsurf. I love building stuff and I've always got a list of little apps I want to build if I had more free time.

About a month ago, I started playing with Lovable to build a word game based on Articulate (it's similar to Heads Up or Taboo). I got a working version, but I quickly ran into limitations - I found it very complicated to add a supabase backend, and it kept re-writing large parts of my app logic when I only wanted to make cosmetic changes. It felt like a toy - not ready to build real applications yet.

But I kept hearing great things about tools like Windsurf. A couple of weeks ago, I looked again at my list of app ideas to build and saw "Recipe App". I've wanted to build a hands-free recipe app for years. I love to cook, but the problem with most recipe websites is that they're optimized for SEO, not for humans. So you have pages and pages of descriptive crap to scroll through before you actually get to the recipe. I've used the recipe app Paprika to store my recipes in one place, but honestly it feels like it was built in 2009. The UI isn't great for actually cooking. My hands are covered in food and I don't really want to touch my phone or computer when I'm following a recipe.

So I set out to build what would become RecipeNinja.ai

For this project, I decided to use Windsurf. I wanted a Rails 8 API backend and React front-end app and Windsurf set this up for me in no time. Setting up homebrew on a new laptop, installing npm and making sure I'm on the right version of Ruby is always a pain. Windsurf did this for me step-by-step. I needed to set up SSH keys so I could push to GitHub and Heroku. Windsurf did this for me as well, in about 20% of the time it would have taken me to Google all of the relevant commands.

I was impressed that it started using the Rails conventions straight out of the box. For database migrations, it used the Rails command-line tool, which then generated the correct file names and used all the correct Rails conventions. I didn't prompt this specifically - it just knew how to do it. It one-shotted pretty complex changes across the React front end and Rails backend to work seamlessly together.

To start with, the main piece of functionality was to generate a complete step-by-step recipe from a simple input ("Lasagne"), generate an image of the finished dish, and then allow the user to progress through the recipe step-by-step with voice narration of each step. I used OpenAI for the LLM and ElevenLabs for voice. "Grandpa Spuds Oxley" gave it a friendly southern accent.

Recipe summary:

And the recipe step-by-step view:

I was pretty astonished that Windsurf managed to integrate both the OpenAI and Elevenlabs APIs without me doing very much at all. After we had a couple of problems with the open AI Ruby library, it quickly fell back to a raw ruby HTTP client implementation, but I honestly didn't care. As long as it worked, I didn't really mind if it used 20 lines of code or two lines of code. And Windsurf was pretty good about enforcing reasonable security practices. I wanted to call Elevenlabs directly from the front end while I was still prototyping stuff, and Windsurf objected very strongly, telling me that I was risking exposing my private API credentials to the Internet. I promised I'd fix it before I deployed to production and it finally acquiesced.

I decided I wanted to add "Advanced Import" functionality where you could take a picture of a recipe (this could be a handwritten note or a picture from a favourite a recipe book) and RecipeNinja would import the recipe. This took a handful of minutes.

Pretty quickly, a pattern emerged; I would prompt for a feature. It would read relevant files and make changes for two or three minutes, and then I would test the backend and front end together. I could quickly see from the JavaScript console or the Rails logs if there was an error, and I would just copy paste this error straight back into Windsurf with little or no explanation. 80% of the time, Windsurf would correct the mistake and the site would work. Pretty quickly, I didn't even look at the code it generated at all. I just accepted all changes and then checked if it worked in the front end.

After a couple of hours of work on the recipe generation, I decided to add the concept of "Users" and include Google Auth as a login option. This would require extensive changes across the front end and backend - a database migration, a new model, new controller and entirely new UI. Windsurf one-shotted the code. It didn't actually work straight away because I had to configure Google Auth to add `localhost` as a valid origin domain, but Windsurf talked me through the changes I needed to make on the Google Auth website. I took a screenshot of the Google Auth config page and pasted it back into Windsurf and it caught an error I had made. I could login to my app immediately after I made this config change. Pretty mindblowing. You can now see who's created each recipe, keep a list of your own recipes, and toggle each recipe to public or private visibility. When I needed to set up Heroku to host my app online, Windsurf generated a bunch of terminal commands to configure my Heroku apps correctly. It went slightly off track at one point because it was using old Heroku APIs, so I pointed it to the Heroku docs page and it fixed it up correctly.

I always dreaded adding custom domains to my projects - I hate dealing with Registrars and configuring DNS to point at the right nameservers. But Windsurf told me how to configure my GoDaddy domain name DNS to work with Heroku, telling me exactly what buttons to press and what values to paste into the DNS config page. I pointed it at the Heroku docs again and Windsurf used the Heroku command line tool to add the "Custom Domain" add-ons I needed and fetch the right Heroku nameservers. I took a screenshot of the GoDaddy DNS settings and it confirmed it was right.

I can see very soon that tools like Cursor & Windsurf will integrate something like Browser Use so that an AI agent will do all this browser-based configuration work with zero user input.

I'm also impressed that Windsurf will sometimes start up a Rails server and use curl commands to check that an API is working correctly, or start my React project and load up a web preview and check the front end works. This functionality didn't always seem to work consistently, and so I fell back to testing it manually myself most of the time.

When I was happy with the code, it wrote git commits for me and pushed code to Heroku from the in-built command line terminal. Pretty cool!

I do have a few niggles still. Sometimes it's a little over-eager - it will make more changes than I want, without checking with me that I'm happy or the code works. For example, it might try to commit code and deploy to production, and I need to press "Stop" and actually test the app myself. When I asked it to add analytics, it went overboard and added 100 different analytics events in pretty insignificant places. When it got trigger-happy like this, I reverted the changes and gave it more precise commands to follow one by one.

The one thing I haven't got working yet is automated testing that's executed by the agent before it decides a task is complete; there's probably a way to do it with custom rules (I have spent zero time investigating this). It feels like I should be able to have an integration test suite that is run automatically after every code change, and then any test failures should be rectified automatically by the AI before it says it's finished.

Also, the AI should be able to tail my Rails logs to look for errors. It should spot things like database queries and automatically optimize my Active Record queries to make my app perform better. At the moment I'm copy-pasting in excerpts of the Rails logs, and then Windsurf quickly figures out that I've got an N+1 query problem and fixes it. Pretty cool.

Refactoring is also kind of painful. I've ended up with several files that are 700-900 lines long and contain duplicate functionality. For example, list recipes by tag and list recipes by user are basically the same.

Recipes by user:

This should really be identical to list recipes by tag, but Windsurf has implemented them separately.

Recipes by tag:

If I ask Windsurf to refactor these two pages, it randomly changes stuff like renaming analytics events, rewriting user-facing alerts, and changing random little UX stuff, when I really want to keep the functionality exactly the same and only move duplicate code into shared modules. Instead, to successfully refactor, I had to ask Windsurf to list out ideas for refactoring, then prompt it specifically to refactor these things one by one, touching nothing else. That worked a little better, but it still wasn't perfect

Sometimes, adding minor functionality to the Rails API will often change the entire API response, rather just adding a couple of fields. Eg It will occasionally change Index Recipes to nest responses in an object { "recipes": [ ] }, versus just returning an array, which breaks the frontend. And then another minor change will revert it. This is where adding tests to identify and prevent these kinds of API changes would be really useful. When I ask Windsurf to fix these API changes, it will instead change the front end to accept the new API json format and also leave the old implementation in for "backwards compatibility". This ends up with a tangled mess of code that isn't really necessary. But I'm vibecoding so I didn't bother to fix it.

Then there was some changes that just didn't work at all. Trying to implement Posthog analytics in the front end seemed to break my entire app multiple times. I tried to add user voice commands ("Go to the next step"), but this conflicted with the eleven labs voice recordings. Having really good git discipline makes vibe coding much easier and less stressful. If something doesn't work after 10 minutes, I can just git reset head --hard. I've not lost very much time, and it frees me up to try more ambitious prompts to see what the AI can do. Less technical users who aren't familiar with git have lost months of work when the AI goes off on a vision quest and the inbuilt revert functionality doesn't work properly. It seems like adding more native support for version control could be a massive win for these AI coding tools.

Another complaint I've heard is that the AI coding tools don't write "production" code that can scale. So I decided to put this to the test by asking Windsurf for some tips on how to make the application more performant. It identified I was downloading 3 MB image files for each recipe, and suggested a Rails feature for adding lower resolution image variants automatically. Two minutes later, I had thumbnail and midsize variants that decrease the loading time of each page by 80%. Similarly, it identified inefficient N+1 active record queries and rewrote them to be more efficient. There are a ton more performance features that come built into Rails - caching would be the next thing I'd probably add if usage really ballooned.

Before going to production, I kept my promise to move my Elevenlabs API keys to the backend. Almost as an afterthought, I asked asked Windsurf to cache the voice responses so that I'd only make an Elevenlabs API call once for each recipe step; after that, the audio file was stored in S3 using Rails ActiveStorage and served without costing me more credits. Two minutes later, it was done. Awesome.

At the end of a vibecoding session, I'd write a list of 10 or 15 new ideas for functionality that I wanted to add the next time I came back to the project. In the past, these lists would've built up over time and never gotten done. Each task might've taken me five minutes to an hour to complete manually. With Windsurf, I was astonished how quickly I could work through these lists. Changes took one or two minutes each, and within 30 minutes I'd completed my entire to do list from the day before. It was astonishing how productive I felt. I can create the features faster than I can come up with ideas.

Before launching, I wanted to improve the design, so I took a quick look at a couple of recipe sites. They were much more visual than my site, and so I simply told Windsurf to make my design more visual, emphasizing photos of food. Its first try was great. I showed it to a couple of friends and they suggested I should add recipe categories - "Thai" or "Mexican" or "Pizza" for example. They showed me the DoorDash app, so I took a screenshot of it and pasted it into Windsurf. My prompt was "Give me a carousel of food icons that look like this". Again, this worked in one shot. I think my version actually looks better than Doordash 🤷‍♂️

Doordash:

My carousel:

I also saw I was getting a console error from missing Favicon. I always struggle to make Favicon for previous sites because I could never figure out where they were supposed to go or what file format they needed. I got OpenAI to generate me a little recipe ninja icon with a transparent background and I saved it into my project directory. I asked Windsurf what file format I need and it listed out nine different sizes and file formats. Seems annoying. I wondered if Windsurf could just do it all for me. It quickly wrote a series of Bash commands to create a temporary folder, resize the image and create the nine variants I needed. It put them into the right directory and then cleaned up the temporary directory. I laughed in amazement. I've never been good at bash scripting and I didn't know if it was even possible to do what I was asking via the command line. I guess it is possible.

After launching and posting on Twitter, a few hundred users visited the site and generated about 1000 recipes. I was pretty happy! Unfortunately, the next day I woke up and saw that I had a $700 OpenAI bill. Someone had been abusing the site and costing me a lot of OpenAI credits by creating a single recipe over and over again - "Pasta with Shallots and Pineapple". They did this 12,000 times. Obviously, I had not put any rate limiting in.

Still, I was determined not to write any code. I explained the problem and asked Windsurf to come up with solutions. Seconds later, I had 15 pretty good suggestions. I implemented several (but not all) of the ideas in about 10 minutes and the abuse stopped dead in its tracks. I won't tell you which ones I chose in case Mr Shallots and Pineapple is reading. The app's security is not perfect, but I'm pretty happy with it for the scale I'm at. If I continue to grow and get more abuse, I'll implement more robust measures.

Overall, I am astonished how productive Windsurf has made me in the last two weeks. I'm not a good designer or frontend developer, and I'm a very rusty rails dev. I got this project into production 5 to 10 times faster than it would've taken me manually, and the level of polish on the front end is much higher than I could've achieved on my own.  Over and over again, I would ask for a change and be astonished at the speed and quality with which Windsurf implemented it. I just sat laughing as the computer wrote code.

The next thing I want to change is making the recipe generation process much more immediate and responsive. Right now, it takes about 20 seconds to generate a recipe and for a new user it feels like maybe the app just isn't doing anything.

Instead, I'm experimenting with using Websockets to show a streaming response as the recipe is created. This gives the user immediate feedback that something is happening. It would also make editing the recipe really fun - you could ask it to "add nuts" to the recipe, and see as the recipe dynamically updates 2-3 seconds later. You could also say "Increase the quantities to cook for 8 people" or "Change from imperial to metric measurements".

I have a basic implementation working, but there are still some rough edges. I might actually go and read the code this time to figure out what it's doing!

I also want to add a full voice agent interface so that you don't have to touch the screen at all. Halfway through cooking a recipe, you might ask "I don't have cilantro - what could I use instead?" or say "Set a timer for 30 minutes". That would be my dream recipe app!

Tools like Windsurf or Cursor aren't yet as useful for non-technical users - they're extremely powerful and there are still too many ways to blow your own face off. I have a fairly good idea of the architecture that I want Windsurf to implement, and I could quickly spot when it was going off track or choosing a solution that was inappropriately complicated for the feature I was building. At the moment, a technical background is a massive advantage for using Windsurf. As a rusty developer, it made me feel like I had superpowers.

But I believe within a couple of months, when things like log tailing and automated testing and native version control get implemented, it will be an extremely powerful tool for even non-technical people to write production-quality apps. The AI will be able to make complex changes and then verify those changes are actually working. At the moment, it feels like it's making a best guess at what will work and then leaving the user to test it. Implementing better feedback loops will enable a truly agentic, recursive, self-healing development flow. It doesn't feel like it needs any breakthrough in technology to enable this. It's just about adding a few tool calls to the existing LLMs. My mind races as I try to think through the implications for professional software developers.

Meanwhile, the LLMs aren't going to sit still. They're getting better at a frightening rate. I spoke to several very capable software engineers who are Y Combinator founders in the last week. About a quarter of them told me that 95% of their code is written by AI. In six or twelve months, I just don't think software engineering is going exist in the same way as it does today. The cost of creating high-quality, custom software is quickly trending towards zero.

You can try the site yourself at recipeninja.ai

Here's a complete list of functionality. Of course, Windsurf just generated this list for me 🫠

RecipeNinja: Comprehensive Functionality Overview

Core Concept: the app appears to be a cooking assistant application that provides voice-guided recipe instructions, allowing users to cook hands-free while following step-by-step recipe guidance.

Backend (Rails API) Functionality

User Authentication & Authorization

Google OAuth integration for user authentication

User account management with secure authentication flows

Authorization system ensuring users can only access their own private recipes or public recipes

Recipe Management

Recipe Model Features:

Unique public IDs (format: "r_" + 14 random alphanumeric characters) for security

User ownership (user_id field with NOT NULL constraint)

Public/private visibility toggle (default: private)

Comprehensive recipe data storage (title, ingredients, steps, cooking time, etc.)

Image attachment capability using Active Storage with S3 storage in production

Recipe Tagging System:

Many-to-many relationship between recipes and tags

Tag model with unique name attribute

RecipeTag join model for the relationship

Helper methods for adding/removing tags from recipes

Recipe API Endpoints:

CRUD operations for recipes

Pagination support with metadata (current_page, per_page, total_pages, total_count)

Default sorting by newest first (created_at DESC)

Filtering recipes by tags

Different serializers for list view (RecipeSummarySerializer) and detail view (RecipeSerializer)

Voice Generation

Voice Recording System:

VoiceRecording model linked to recipes

Integration with Eleven Labs API for text-to-speech conversion

Caching of voice recordings in S3 to reduce API calls

Unique identifiers combining recipe_id, step_id, and voice_id

Force regeneration option for refreshing recordings

Audio Processing:

Using streamio-ffmpeg gem for audio file analysis

Active Storage integration for audio file management

S3 storage for audio files in production

Recipe Import & Generation

RecipeImporter Service:

OpenAI integration for recipe generation

Conversion of text recipes into structured format

Parsing and normalization of recipe data

Import from photos functionality

Frontend (React) Functionality

User Interface Components

Recipe Selection & Browsing:

Recipe listing with pagination

Real-time updates with 10-second polling mechanism

Tag filtering functionality

Recipe cards showing summary information (without images)

"View Details" and "Start Cooking" buttons for each recipe

Recipe Detail View:

Complete recipe information display

Recipe image display

Tag display with clickable tags

Option to start cooking from this view

Cooking Experience:

Step-by-step recipe navigation

Voice guidance for each step

Keyboard shortcuts for hands-free control:

Arrow keys for step navigation

Space for play/pause audio

Escape to return to recipe selection

URL-based step tracking (e.g., /recipe/r_xlxG4bcTLs9jbM/classic-lasagna/steps/1)

State Management & Data Flow

Recipe Service:

API integration for fetching recipes

Support for pagination parameters

Tag-based filtering

Caching mechanisms for recipe data

Image URL handling for detailed views

Authentication Flow:

Google OAuth integration using environment variables

User session management

Authorization header management for API requests

Progressive Web App Features

PWA capabilities for installation on devices

Responsive design for various screen sizes

Favicon and app icon support

Deployment Architecture

Two-App Structure:

cook-voice-api: Rails backend on Heroku

cook-voice-wizard: React frontend/PWA on Heroku

Backend Infrastructure:

Ruby 3.2.2

PostgreSQL database (Heroku PostgreSQL addon)

Amazon S3 for file storage

Environment variables for configuration

Frontend Infrastructure:

React application

Environment variable configuration

Static buildpack on Heroku

SPA routing configuration

Security Measures:

HTTPS enforcement

Rails credentials system

Environment variables for sensitive information

Public ID system to mask database IDs

This comprehensive overview covers the major functionality of the Cook Voice application based on the available information. The application appears to be a sophisticated cooking assistant that combines recipe management with voice guidance to create a hands-free cooking experience.

[nunuslab24]

What are AI, AGI, and ASI? And the positive impact of AI

Understanding artificial intelligence (AI) involves more than just recognizing lines of code or scripts; it encompasses developing algorithms and models capable of learning from data and making predictions or decisions based on what they’ve learned. To truly grasp the distinctions between the different types of AI, we must look at their capabilities and potential impact on society.

To simplify, we can categorize these types of AI by assigning a power level from 1 to 3, with 1 being the least powerful and 3 being the most powerful. Let’s explore these categories:

1. Artificial Narrow Intelligence (ANI)

Also known as Narrow AI or Weak AI, ANI is the most common form of AI we encounter today. It is designed to perform a specific task or a narrow range of tasks. Examples include virtual assistants like Siri and Alexa, recommendation systems on Netflix, and image recognition software. ANI operates under a limited set of constraints and can’t perform tasks outside its specific domain. Despite its limitations, ANI has proven to be incredibly useful in automating repetitive tasks, providing insights through data analysis, and enhancing user experiences across various applications.

2. Artificial General Intelligence (AGI)

Referred to as Strong AI, AGI represents the next level of AI development. Unlike ANI, AGI can understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. It can reason, plan, solve problems, think abstractly, and learn from experiences. While AGI remains a theoretical concept as of now, achieving it would mean creating machines capable of performing any intellectual task that a human can. This breakthrough could revolutionize numerous fields, including healthcare, education, and science, by providing more adaptive and comprehensive solutions.

3. Artificial Super Intelligence (ASI)

ASI surpasses human intelligence and capabilities in all aspects. It represents a level of intelligence far beyond our current understanding, where machines could outthink, outperform, and outmaneuver humans. ASI could lead to unprecedented advancements in technology and society. However, it also raises significant ethical and safety concerns. Ensuring ASI is developed and used responsibly is crucial to preventing unintended consequences that could arise from such a powerful form of intelligence.

The Positive Impact of AI

When regulated and guided by ethical principles, AI has the potential to benefit humanity significantly. Here are a few ways AI can help us become better:

• Healthcare: AI can assist in diagnosing diseases, personalizing treatment plans, and even predicting health issues before they become severe. This can lead to improved patient outcomes and more efficient healthcare systems.

• Education: Personalized learning experiences powered by AI can cater to individual student needs, helping them learn at their own pace and in ways that suit their unique styles.

• Environment: AI can play a crucial role in monitoring and managing environmental changes, optimizing energy use, and developing sustainable practices to combat climate change.

• Economy: AI can drive innovation, create new industries, and enhance productivity by automating mundane tasks and providing data-driven insights for better decision-making.

In conclusion, while AI, AGI, and ASI represent different levels of technological advancement, their potential to transform our world is immense. By understanding their distinctions and ensuring proper regulation, we can harness the power of AI to create a brighter future for all.

[hypocrite-human]

AI & IT'S IMPACT

Unleashing the Power: The Impact of AI Across Industries and Future Frontiers

Artificial Intelligence (AI), once confined to the realm of science fiction, has rapidly become a transformative force across diverse industries. Its influence is reshaping the landscape of how businesses operate, innovate, and interact with their stakeholders. As we navigate the current impact of AI and peer into the future, it's evident that the capabilities of this technology are poised to reach unprecedented heights.

1. Healthcare:

In the healthcare sector, AI is a game-changer, revolutionizing diagnostics, treatment plans, and patient care. Machine learning algorithms analyze vast datasets to identify patterns, aiding in early disease detection. AI-driven robotic surgery is enhancing precision, reducing recovery times, and minimizing risks. Personalized medicine, powered by AI, tailors treatments based on an individual's genetic makeup, optimizing therapeutic outcomes.

2. Finance:

AI is reshaping the financial industry by enhancing efficiency, risk management, and customer experiences. Algorithms analyze market trends, enabling quicker and more accurate investment decisions. Chatbots and virtual assistants powered by AI streamline customer interactions, providing real-time assistance. Fraud detection algorithms work tirelessly to identify suspicious activities, bolstering security measures in online transactions.

3. Manufacturing:

In manufacturing, AI is optimizing production processes through predictive maintenance and quality control. Smart factories leverage AI to monitor equipment health, reducing downtime by predicting potential failures. Robots and autonomous systems, guided by AI, enhance precision and efficiency in tasks ranging from assembly lines to logistics. This not only increases productivity but also contributes to safer working environments.

4. Education:

AI is reshaping the educational landscape by personalizing learning experiences. Adaptive learning platforms use AI algorithms to tailor educational content to individual student needs, fostering better comprehension and engagement. AI-driven tools also assist educators in grading, administrative tasks, and provide insights into student performance, allowing for more effective teaching strategies.

5. Retail:

In the retail sector, AI is transforming customer experiences through personalized recommendations and efficient supply chain management. Recommendation engines analyze customer preferences, providing targeted product suggestions. AI-powered chatbots handle customer queries, offering real-time assistance. Inventory management is optimized through predictive analytics, reducing waste and ensuring products are readily available.

6. Future Frontiers:

A. Autonomous Vehicles: The future of transportation lies in AI-driven autonomous vehicles. From self-driving cars to automated drones, AI algorithms navigate and respond to dynamic environments, ensuring safer and more efficient transportation. This technology holds the promise of reducing accidents, alleviating traffic congestion, and redefining mobility.

B. Quantum Computing: As AI algorithms become more complex, the need for advanced computing capabilities grows. Quantucm omputing, with its ability to process vast amounts of data at unprecedented speeds, holds the potential to revolutionize AI. This synergy could unlock new possibilities in solving complex problems, ranging from drug discovery to climate modeling.

C. AI in Creativity: AI is not limited to data-driven tasks; it's also making inroads into the realm of creativity. AI-generated art, music, and content are gaining recognition. Future developments may see AI collaborating with human creators, pushing the boundaries of what is possible in fields traditionally associated with human ingenuity.

In conclusion, the impact of AI across industries is profound and multifaceted. From enhancing efficiency and precision to revolutionizing how we approach complex challenges, AI is at the forefront of innovation. The future capabilities of AI hold the promise of even greater advancements, ushering in an era where the boundaries of what is achievable continue to expand. As businesses and industries continue to embrace and adapt to these transformative technologies, the synergy between human intelligence and artificial intelligence will undoubtedly shape a future defined by unprecedented possibilities.