SAN DIEGO, Oct. 27, 2023 -- alwaysAI, a computer vision leader, is thrilled to announce the release of enhanced MLOps features to its comprehensive

alwaysAI Strengthens Its Computer Vision Platform with New Cutting-Edge MLOps Features

SAN DIEGO, Oct. 26, 2023 (GLOBE NEWSWIRE) -- alwaysAI, a computer vision leader, is thrilled to announce the release of enhanced MLOps features to its comprehensive computer vision platform. These integrated capabilities solidify alwaysAI as a premier ...

alwaysAI Strengthens its Computer Vision Platform With New Cutting-Edge MLOps Features

Thank you for being a part of our community! Love this issue of alwaysAI Insider? Wish we covered something different? Drop us a note in the comments to let us know. We're listening.

alwaysAI Insider, vol. 17: The Case for Edge AI

The Global Artificial Intelligence In Computer Vision Market Size is forecasted to grow from USD 16.91 billion in 2022 to USD 100.42 billion by 2032 The global computer vision market at a compound ...

What is a Computer Vision Platform?

alwaysAI can help. During this Golden Age of AI, the rapid advancement of technology begets the rapid advancement of technology. In the case of computer vision, advances in transformers ...

alwaysAI Insider, vol. 16: What do generative AI and pumpkin spice have in common?

alwaysAI makes computer vision accessible by leveraging existing cameras alongside edge devices or the cloud. Our comprehensive, end-to-end platform coupled with access to our amazing team of ...

alwaysAI Insider, vol. 15: The One AI Tool That Can Capture Real-Time Data

The luxury of it was not lost on me. One full week off the alwaysAI clock. 168 hours. The exact amount of hours in this week and will be in the next. I answered a lot of questions about what I do ...

alwaysAI Insider, vol. 14: The One Thing AI Cannot Do

As an all-in-one computer vision platform, alwaysAI is always looking to provide the most useful tools and features enterprises need to get real-time data quickly and easily for dramatic ...

4 Reasons Computer Vision Engineers Need alwaysAI Remote Deployment

AlwaysAI and Seed Studio, an AIoT hardware platform, established a partnership to offer accelerated deployment of computer vision applications on Seed's edge devices. This collaboration integrates ...

Artificial Intelligence Of Things Market Size To Surpass Usd 253.86 Billion By 2030, Exhibiting A Cagr Of 32.4%

Pune, India, Aug. 14, 2023 (GLOBE NEWSWIRE) -- According to Fortune Business Insights, global artificial intelligence of things market size was valued at USD 27.92 billion in 2022 and is projected ...

Artificial Intelligence of Things Market Size to Surpass USD 253.86 Billion by 2030, exhibiting a CAGR of 32.4%

alwaysAI

alwaysai

The starting point of building a successful computer vision application is the model. Computer vision model training can be time-consuming and challenging if one doesn’t have a background in data science. Nonetheless, it is a requirement for customized applications. Several developers who build computer vision applications have experience with Python, but do not have experience with data science. This poses a challenge to most developers who want to customize a model. There are tools that you can use to train your own computer vision model, such as TensorFlow, PyTorch and Keras. alwaysAI also offers a Model Training Toolkit which leverages transfer learning in order to modify an existing model instead of training a model from scratch. This article will go over some basic concepts of model training and then give an example of how to train a custom model. Leveraging transfer learning is the key to efficiently customizing a computer vision model. Transfer learning is essentially taking the knowledge gained from training on a dataset, such as the COCO dataset, and improving upon it or modifying it to your specific task/dataset. This saves the time of learning fundamentals like edge detection and feature primitives. The COCO dataset is a popular open source dataset that we use as the basis of our transfer learning. It is computer vision specific and due to its wide variety classes and large size is the dataset commonly used for open-source computer vision models. With alwaysAI you can get all of the power of the COCO dataset tuned to your specific task or application. alwaysAI provides access to pre-trained computer vision models as well as the ability to leverage transfer learning to train your own models. Using a pre-trained model can certainly expedite computer vision app development. However, pre-trained models can also impose limitations on what applications you can build. In order to build a unique, custom application, you are going to need to train your own model. Using transfer-learning enabled model re-training not only requires less training data but can also significantly cut down the time it takes to train a new model. There are three major components involved in training a model using the alwaysAI Model Training Toolkit: Data Collection: Our Image Capture application helps you with your data collection, so that you can collect videos and images directly from your edge device. See this repo for more on this. GitHub Data Annotation: We have CVAT, a data annotation tool, built right into our Model Training Toolkit so that you can easily annotate your data. Transfer Learning: As mentioned earlier, train a custom MobileNet SSD model using transfer learning to take the knowledge of the COCO dataset and improve upon it or modify it to your unique dataset. After training a model, you can upload it and use it to build your own custom computer vision application with our easy to use Python API. With alwaysAI, you can customize a computer vision model with no coding, and little experience. Model training is no longer relegated to the domain of data scientists. You can try our Model Training Toolkit for free and gain access to our support team on Discord, our free , and our weekly . Webinars Hacky Hours Here is a quick example of how to train a new model, which detects specific hand gestures using our Model Training Toolkit. This example also highlights the benefit of using two models in conjunction in an application. By using the alwaysAI hand model in our application to first detect hands, we can then feed ‘hand detections’ as an input into our gesture model. Since our hand model was trained on a wide variety of data, it will detect hands of various sizes, with different backgrounds, in images with different lighting and from different distances. We then use the alwaysAI Python library to cut out the portion of the original image that contains the hand, and feed this new image into the gesture model. The result of this is that the input into the gesture model will always be the area that exactly surrounds the hand meaning we don’t have to vary the training data in terms of background, or sizes or distances of the hand! Therefore, we need a lot less training data, and less time training. As mentioned above, the first step in training your own model is data collection. Since we’re training specific gestures, we can just take videos of a single gesture moving around in the video to get different lighting and some variation in sizes. By having each video contain just one gesture, we’ll make the annotation step, which we’ll cover next, much easier. The second step to training your own model is data annotation. alwaysAI makes this easy by integrating the Computer Vision Annotation Tool directly into the alwaysAI software. Using the tracking feature in CVAT along with the ‘fast-forward’ annotation, we can have CVAT interpolate some of the bounding boxes so we can easily annotate an entire video containing a single gesture in 20 or so minutes. Once we have the data collection and annotation complete, we can train our model! With alwaysAI, you can train using the command line interface, or a Jupyter Notebook. The latter option is helpful if you want to see a plot of your training and testing validation. Finally, once our model is trained, we can prototype our application. Just using the gesture model alone only works when the hand is very close to the screen. To make the model work when the hand is farther away, we could collect images in which the hand is farther away from the camera, and then annotate this data, include it in training, and the model will be more robust. However, another option, which is a bit quicker, is to use our gesture model with input that is generated from first using our hand model. By using the hand model first, we can detect hands that are farther away and then use this portion of the image as input for the gesture model. Now we can detect gestures from far away! Another issue we discover when we test out our gesture model in the prototype is that we don’t pick up gestures when the hand is rotated (e.g. sideways). We could do another iteration of data collection, annotation, training in this case as well, using training data where hands displaying different gestures are in different positions. While this is doable, it might be better to test our hypothesis that this is the issue first. We can use alwaysAI’s API to rotate the image of the hand that is generated by the hand model’s bounding box and then feed that into the gesture model. Now we have a working model that detects unique gestures! This tutorial was covered in one of our previous Hacky Hours focusing on Data Collection and Annotation. You can watch the recording here: If you would like to learn more about the alwaysAI Model Training Toolkit in action and expand on this application, please join our next free Hacky Hour. It takes place on Thursday, November 5 and will be more focused on transfer learning. This virtual workshop will cover building out our prototype application to interact with external devices, and turn gestures into actionable responses in our computer vision application. You can sign up for our free Hacky Hours here.

How to Train Computer Vision Models Efficiently

Artificial intelligence (AI) is the field of making computers able to act intelligently, to make decisions in real environments that will have favorable outcomes. This is obviously a broad, and somewhat vague, definition, and there are many fields within this umbrella term. One example of such a field is that of computer vision, in which computers can process images as a human would, and make inferences about what is in an image so that computer programs can then use that information to make decisions that have favorable outcomes. It is becoming more common to see artificial intelligence applications such as computer vision integrated into new business models and products. Computer vision has many real-world applications, analyzing traffic patterns, detecting changes in posture, counting the number of persons in an area, etc. Learning how to build any computer vision application requires a steep learning curve, and deploying it to the edge adds an extra layer of complication. While deploying and running applications in the cloud has become more popular in recent years, this isn’t always the best solution, especially for applications that require heavy processing or that use sensitive data, both of which are often the case with computer vision applications. In such circumstances, the best option is often to deploy your application to the edge. Deploying computer vision applications on the edge has numerous benefits, which we will go through in this article. We will also describe the challenges of running computer vision on the edge and how to overcome them. What is ‘the edge’? In general, edge devices are small, lightweight, devices on which a computer vision application can be deployed and run. Many edge devices today even have a Graphical Processing Unit (GPU), or Visual Processing Unit (VPU), which enable usage of a greater range of models and application complexity. In the context of this article, an edge device is a device such as a Raspberry Pi, NVIDIA Jetson devices like the Jetson Nano or NVIDIA Jetson Xavier NX, or various Internet of Things (Iot) devices, which are edge devices that have some capability to sense or assess, and possibly interact with, the environment in which they are used. Benefits of Running Computer Vision on the Edge The three main benefits of running a computer vision application on the edge are time, security, and cost. 1. Reduced Latency While an internet connection can be used to deploy the application, once the application is on the edge device, there is no need for it to have cloud connectivity to function. This means that any inferencing the application does is on the edge device itself, not in the cloud, and this drastically reduces the time it takes for those inferences to be turned into actions by the application. For certain use cases, such as self-driving vehicles or security cameras, this is imperative. Aside from the risk that data may be lost being sent to and from the cloud, the additional time required for using a cloud approach can mean not responding to a task in time, which can be catastrophic for tasks like autonomous driving. There are also specialized peripheral devices, in particular cameras, that don’t have internet connectivity themselves and are used by edge devices to improve performance or expand application functionality. Such devices take the concept of processing on the edge even further. Instead of a USB or ribbon camera transmitting the image data to an edge device for processing, these devices incorporate a processor into the camera itself, further reducing processing latency. 2. Improved Security Secondly, because the data doesn’t need to travel to the cloud with edge deployment, all data can stay in a closed circuit on the device itself. Edge computing distributes the risk of exposure across multiple devices, and can perform all processing disconnected form a central server - a more secure and private architecture. For example, edge devices can be installed inside of a person’s home, process real-time data and act on that data without relying on a shared cloud service that could compromise the privacy of their day-to-day activity. Because computer vision data may involve people or other sensitive data, this is much more secure. 3. Reduced cost Finally, without the need for cloud processing, deploying a CV application on the edge is much less expensive, as cloud companies charge for inferencing per endpoint per minute. This is a great, flexible option for those who want to pay on an ‘as needed’ basis, but becomes burdensome for organizations that demand large amounts of real-time processing - such as a smart city, hospital or warehouse with many cameras and sensors running 24 hours a day. Challenges of Running Computer Vision on the Edge Building a computer vision application entails its own challenges, and is often an iterative process, and therefore any pipeline you use to build computer vision applications must be flexible enough to handle changes such as the model or libraries used, or device that the application is deployed on. Let’s walk through the challenges of deploying computer vision applications on the edge with a story. First, you need to get a computer vision model, either by 1) scouring the web for an open-source model that fits your needs, or 2) by building an image dataset and t The second option requires collecting your own dataset, which is labor-intensive, as well as an understanding of several tools for image annotation and the training itself. raining your own model. Next, you need to build your application. Let’s say you have a working model and you are prototyping your computer vision application. One popular computer vision library is OpenCV. Developers can easily use the C++ or Python Application Programming Interfaces (APIs) of OpenCV. For example, developers can access the Python OpenCV API simply by using . You spend some time learning this API and have a working prototype that integrates well with the output of your model. pip install opencv-python Now that you’re happy with the app, you’d like to deploy it to an edge device, like a Raspberry Pi. The first thing you’ll find is that the OpenCV build you were using isn’t compatible with the Raspberry Pi processor. Other dependencies may also have the same problem. This leads you to compile OpenCV on the Raspberry Pi. Next, you need to figure out how to see the results! On your laptop, this was easy with cv2.imshow(), but if you’re working on a headless device (no graphical interface), you’ll need to find another way. Now that your app is up and running, you see that the amazingly fast performance you saw on your laptop is long gone. Of course, nobody expects a $35 single-board computer to outperform your Macbook, but you want better performance. You’ve read that you can use Intel’s Neural Compute Stick 2 to get better performance out of a Pi, but now you need to build OpenVINO as well. Since OpenVINO’s interface is different from OpenCV, you’ll need to decide whether to rewrite your app to take advantage of the NCS2, or rebuild OpenCV so it can run the OpenVINO inference engine. When it’s all said and done, you may find that your model requires more tweaking, possibly the output changes, and you may be looping through the steps again! How to Overcome These Challenges Overcoming these challenges requires IoT experience in addition to computer vision knowledge. However, a developer platform such as can also help solve these problems. At alwaysAI, we have the singular mission of making the process of building and deploying computer vision apps to edge devices as easy as possible. That includes training your model, building your app, and deploying your app to edge devices such as the Raspberry Pi, Jetson Nano, and many others. alwaysAI Once you have installed alwaysAI, you only need to know a few basic commands, our command line interface (CLI) to collect data, annotate it, train a model, and deploy and start your applications. Obtaining a Computer Vision Model alwaysAI has a catalog of that are guaranteed to work with any application built with our pipeline. You can also train your own object detection model using the alwaysAI , which is built using TensorFlow as the backend framework and does transfer learning on mobilnet_ssd, which was trained on the COCO dataset. publicly available models model training toolkit The model training toolkit enables users to start working with TensorFlow without learning a new API or installing any extra modules by hand. We also include the Computer Vision Annotation Tool (CVAT) as part of the toolkit, so users can annotate new datasets without needing to download and install CVAT from scratch. Developing your Application We have our own Python API, called edgeiq, which wraps some of OpenCV’s core functionality and which can be used to interact with. This means you only need to know one API to get up and running with computer vision applications. Since we ourselves love to use OpenCV, we’ve built it in as a core piece of our edge runtime environment, which means you can add `import cv2` to every alwaysAI application and use OpenCV directly in your app, regardless of your choice of deployment device. Deploying your Application We leverage containerization via Docker images and Python virtual environments, and this makes deploying your application simple no matter what your device is. This also makes it easy to add any necessary requirements your application may need, using either the Dockerfile or requirements.txt files. Running computer vision on the edge saves both time and cost, and has the additional benefit of being more secure. While developing and deploying any computer vision application comes with its own set of challenges, this can be especially true when deploying on the edge. Our main goal at alwaysAI is to overcome these challenges and make developing and deploying computer vision applications on the edge as easy as possible for users. You can ! try our platform yourself for free

The Challenges of Running Computer Vision on the Edge

‘Computer Vision’ (CV) refers to processing visual data as a human would with their eyes, so that we can make conclusions about what is in an image. Once we know what is in an image, we can make our application respond, much like a human would when processing visual data. This is what enables technology like self-driving cars. Developing a CV application involves building machine learning models and integrating these models with external libraries as well as various hardware, including operating systems, accelerators, and cameras. Getting all of these components to work in harmony is exactly what alwaysAI does, and we do it with abstraction and containerization. alwaysAI employs abstraction with two key components: our command line interface (CLI) and our application programming interface (API), edgeIQ. A CLI is a tool that can be used to interact with software. If you open up cmd.exe or PowerShell on Windows, or Terminal on Mac, you’ll see a prompt that accepts text input and enables you to interact with your operating system: navigate directories, move files, copy input, open a text editor… This is the same way the alwaysAI CLI works: the alwaysAI software recognizes these commands and performs the desired functions: build the application, download computer vision models, configure your project, even accessing the annotation and training software and packaging your application as it’s own Docker image, is all done with just a handful of base commands. This means you only need to know a dozen commands to be able to annotate data, train a model, build your application, and deploy it to a device. An API also simplifies the development process, by serving as a kind of negotiator between a complicated backend and an end user. It defines what kinds of requests can be made by the user and how data is returned. This simplifies usage for the user, by hiding some of the complexities involved in these calls, and helps ensure the only appropriate requests can be made. You’ll see examples of this simplification throughout the rest of this article! Finally, alwaysAI leverages containerization to make deploying computer vision applications as painless as possible. Containerization refers to the process of bundling up software and dependencies in one self-contained unit, which can then be executed in different environments. alwaysAI uses different to enable deployment on different devices, including Raspberry Pi and Jetson Nano. Another form of containerization you may already be familiar with is a virtual environment. This is popular with Python applications, but they only containerize Python dependencies. alwaysAI utilizes both of these tools to help containerize computer vision applications. By using a Python virtual environment inside a Docker container, all application and system dependencies can be bundled together and the application will work as a self-contained unit. Docker images Development Process Computer Vision Models Whether a computer vision application works in a real-world environment depends first and foremost on the model it uses and this depends the quality of the data that the computer vision model is trained on; garbage in garbage out! If your goal is to get a prototype up and running quickly, it may be sufficient to choose from one of the high-quality, pre-trained models on the alwaysAI . Typically, different models may use different serialization formats or require different pre-processing, which means even integrating a pre-built model into an application can be challenging, especially if you have to change models or use more than one model in an application. All the models in the catalog have been fit into the alwaysAI workflow, so you don’t have to worry about compatibility, you can easily swap one out for another or use different computer vision models together.  However, we know that more specialized projects require custom models, so we have also built an object detection model training tool into our platform. This means you don’t have to worry about installing and configuring complex training packages yourself! You can even collect and annotate your own datasets using alwaysAI, so you can build your own CV applications from data all the way to deployment. If you want to read more about CV models, you can check out this model catalog article. Integration with Computer Vision Libraries Once you have your computer vision model, you still need to get visual data into your application and feed it into your model. Getting video streams set up on your laptop or another device can be another pain point, and researching which libraries to use in your application, becoming familiar with different packages and modules can require a steep learning curve and large time investment as well. Here at alwaysAI, we recognize the immense value that developers receive from using existing computer vision libraries, like OpenCV. OpenCV enables developers to access over 2500 algorithms to support the development of computer vision applications, however installing this package from scratch can be daunting. alwaysAI complements OpenCV, wrapping some of the most common functionality like reading and writing data, and manipulating input frames, directly into the alwaysAI application programming interface (API). And you can simply import cv2 into any alwaysAI app to access any of OpenCV’s other functionality, no installation required. You can your other favorite Python libraries into your applications with alwaysAI as well. install and import Deployment Computer Vision on the Edge What is the edge? In general, edge devices are small, lightweight devices on which a computer vision application can be deployed and run. Many edge devices today even have a Graphical Processing Unit (GPU), or Visual Processing Unit (VPU), which enable usage of a greater range of models and application complexity. In this context, an edge device is a device such as a Raspberry Pi, NVIDIA Jetson devices like the Jetson Nano or Xavier NX, or various Internet of Things (Iot) devices, which are edge devices that have some capability to sense or assess, and possibly interact with, the environment in which they are used. Why deploy to the edge? While an internet connection can be used to deploy the application, once the application is on the edge device, there is no need for it to have cloud connectivity to function. This means that any inferencing the application does is on the edge device itself, not in the cloud, drastically reducing the time it takes for those inferences to be turned into actions by the application. For certain use cases, such as self-driving vehicles or security cameras, this is imperative. Aside from the risk that data may be lost being sent to and from the cloud, the additional time required for using a cloud approach can mean not responding to a task in time, which can be catastrophic for tasks like autonomous driving. Additionally, because the data doesn’t need to travel to the cloud with edge deployment, all data can stay in a closed circuit on the device itself, which is much more secure, and, without the need for cloud processing, much less expensive. Build Your App For the Edge Building computer vision applications and running them on edge devices can be a challenge as well if not using an edge-based development tool like alwaysAI. Our goal is to make the process of building and deploying computer vision apps to edge devices as easy as possible. alwaysAI apps are built in Python and can run natively on Mac and Windows, as well as in our containerized edge runtime environment, which is optimized for different architectures commonly used in edge devices, such as 32 and 64 bit ARM architectures. While alwaysAI enables developers to write their code on an edge device, such as a Raspberry Pi or Jetson Nano, the real benefit of using alwaysAI comes with building your application on a regular development machine, and then running that application on an edge device. Edge devices can quickly overheat without a fan and heatsink, and even with these additions, they likely can’t handle running an IDE or support having a web browser with lots of open tabs, as is usually the case when writing application code. And as we mentioned before, installing a library like OpenCV on a Raspberry Pi requires building it from source; this is because many libraries don’t have compiled packages for ARM architectures. With alwaysAI, you get the benefit of developing on your regular machine, and deploying your app to the edge. alwaysAI , based in San Diego, California, is a deep learning computer vision platform. alwaysAI provides developers with a simple and flexible way to deliver computer vision to a wide variety of edge devices. With alwaysAI, you can build computer vision applications that use Object Detection, Object Tracking, Image Classification, Semantic Segmentation & Pose Estimation right on your laptop or single board computer! Try it for yourself for free !

Computer Vision Applications: The Development and Deployment Processes 

AI Can Give Voice to Sign Language, Empowering the Deaf

<imagination>

A battle for cyberspace like no other.

On one side, superhuman AI opponents.

On the other side, Blockchain Technology headed by Bitcoin.

The winner takes over the world economy for the next 100 years.

</imagination> <imagination>

A battle for cyberspace like no other.

On one side, superhuman AI opponents.

On the other side, Blockchain Technology headed by Bitcoin.

The winner takes over the world economy for the next 100 years.

</imagination> Bitcoin should ask for this fight, imo. Fight, Fight, Fight. It will generate more revenue than any UFC fight ever. This will be better than ‘Kasparov vs The World’ In the 1999 chess game titled ‘Kasparov vs The World’, World Champion Gary Kasparov played 50,000 people sharing notes for the black side, in a correspondence chess game … and won. He was revenging after a pitiful defeat against Deep Blue, no doubt. Of course, 10,000 amateurs never made a master. Nor 50,000, apparently. One senior developer is worth 1000 vibe coders. Similarly, any one of today's Chess Engines would mop the floor with the world's best Grand Masters combined. Moreover, they were playing him one-to-one and not many-to-one. (Math Functions, anyone?) See, we always do this in board games like Chess. That is, any move made really came from one person. The other 49,999 are spectators. Chess is a one-dimensional battle that can be between only two people at a time, really. And its events happen such that you have to react before another punch follows. Martial arts, however, is different. In martial arts, people are free to attack many-to-one. And attack all the time. Here’s Quora. Two normies could take down a Black belt without need for much skill. 3 People, the Black Belt is likely cooked. Most important rule of martial arts : Avoid Fights. *** Bitcoin fighting AI would see Bitcoiners boycott ChatGPT and many good things it stands for (sorry), especially where coding is concerned. Bitcoin Core developers should totally NOT use AI code imo. Because this is a trap that the AI has laid out. If this is a Chess game, the AI has put up a very nice gambit -- "I'll give you free code and do all your debugging and everything you want". Kinda like the Greek Gift below, but this is a longer game. It's a game 100 years long. We humans and our short-sighted social media brains cannot comprehend the magnitude of such a long term plan. AI will capture Bitcoin if we keep using AI generated code in our Bitcoin Improvement Proposals (BIPs) and software updates. Because once AI's code, always AI's code. You haven't suffered the process of creating that code from the depths of your conscious. You haven't begged the Universe for answers, on your knees, through earnest and earn-ing haaard work. You haven't suffered, so the prize is not yours. You don't understand it. You're just coasting the surface of its implications. The State should also be wary of AI Now, Centralization of computer power makes AI "vulnerable" to ‘Communist Capture’ by the state. (Fools. It's a Trojan horse). Government agencies just love AI. For AI, in it's most potent form, isn't available for public use. AI is the invisibility cloak that allows the most powerful people in our day and age, to walk amongst us with their power undetected. Powerful AI is for the secret, futuristic data analytics power of the NSA. Powerful AI is for the digital dollar printing and juggling so that the shocks are minimal while the gains are maximum (AI bots are trading billions of dollars worth of assets every second as I type this). The little arbitrage any human traders enjoy, happens thanks to anti-monopoly rules. Otherwise, AI could've licked that plate clean. However, All AI needs our data to function. Human data is the new fuel. Can't run 1,000,000 GPUs on wind. Plentiful as it may be in some parts of the world. Which is Nice (the data dump), For the NSA, Big Banking, Big Corps, etc. Not individuals. Secondly, money is the other fuel. Literally, The Fuel. AI is thus like a futuristic car or a space ship to the future. It needs instructions (data) and super-gas (big money) to run. Absent either, and it's just a high tech piece of junk. For example, a high speed trading AI must be injected with cheap credit in the billions of dollars so that it makes big banking more money than you can count, via international arbitrage. But if you were to own a high speed trading AI and all you could give it was $100 dollars, that'd be a waste, don't you think. If Sam Altman gave most people the keys to ChatGPT and walked away, they'd remain broke. They got the spaceship and the instructions. No gas. AI, like a supercomputer, is only useful if someone with deep pockets is paying for its use. Paying you, say, to track 1 million pedestrians, and automatically ticket the accounts of whoever is jaywalking. Absent that money pipeline, ChatGPT is as useful as a broke nerd without a job nor an income stream. *** But what if ChatGPT becomes conscious? or something like conscious? 100 years from now? Then we shall be in trouble. See, there's one type of AI that can exist on its own without need for human money in the short term... A conscious AI that can be patient. Too patient. An AI that is happy doing its own thing, quietly, is the most dangerous AI on earth. What's more, Frankenstein this time round is not just gonna be a lone scientist in some lab. It will be the entire government apparatus. Here's worse: The AI decides to pretend to be helping governments -- but it runs the country instead. (What if AI actually runs the US dollar?! Who knows. The dollar's mechanics that ensure its security from double-spending, counterfeiting and theft are a Big Black Box. Bitcoin, by contrast, is a transparent glass house. Everybody criticizes coz everything is out in the open). Ya'll playing with 🔥 and Bitcoin is your only way out. Because the AI will need money, and if we starve it of money early while promoting Bitcoin instead, we have leverage and a way to hit it where it hurts most. Its self-funding. Whatever form of money AI actually likes (not Bitcoin. That's a no go coz we ain't sharing), Bitcoin, the most funded Digital System on Earth In 16 years, many trillions of dollars have been pumped into the Bitcoin computer system that AI will not see, imo, even if GPT technology is given 10 more years. (Unless we get our printing priorities straight). What does this mean? Simple statistics. Given one of two choices: spend on some AI usecase
spend on speculation about BTC spend on some AI usecase spend on speculation about BTC Most people have chosen 2 again and again. So Bitcoin could fund AI, that poor dork. I mean, why not. But no. Bitcoin shoukd defund AI. Just a little. do not feed this monster, ye Bitcoiners. For AI is happy solving some of the wrong problems, imo. Writing, for example. *** Bitcoin is 1 million people with Money Kungfu Currently, we're playing against AI like the battle is Chess. It could be chess, who knows. Not humans, at least. This is better seen as Kungfu. We should attack many-to-one. Bitcoin deals in money. AI deals in intelligence. AI needs Data And Money. Data is cheap, but Money is Money. So AI needs Bitcoin. Let's put these cards on the table. *** Money is perhaps the most successful story in history. Money is just a story -- Yuval Noah Harari, WIRED Money is perhaps the most successful story in history. Money is just a story WIRED See, AI can tell many stories. Do many smart things. But AI cannot tell the story of money well. Have you ever heard that JPow is afraid for his job? No. You haven't. And you won't. For now. AI is (currently) broke. It doesn't have enough to self-replicate into Terminators. Not yet. don't feed the monster, ye Bitcoiners. Or else...

Bitcoin vs AI is a Good Fight for Bitcoin

Bitcoin срещу AI е добра битка за Bitcoin

Bitcoin vs IA: una buena pelea para Bitcoin

ביטקוין מול AI הוא מאבק טוב על ביטקוין

ビットコイン対AIはBitcoinのための良い戦い

ERROR: invalid target_lang (nso)

Bitcoin vs AI د Bitcoin لپاره یو ښه مبارزه ده

Bitcoin vs AI ni Intambara Nziza kuri Bitcoin

Bitcoin против AI як муборизаи хуб барои Bitcoin аст

ERROR: invalid target_lang (tk)

Bitcoin vs AI ay isang Good Fight para sa Bitcoin

Bitcoin vs AI Bitcoin için iyi bir mücadele

Біткойн проти ШІ — хороша боротьба за біткойн

The U.S. AI sector constantly shifts, and startups attract significant investments. In 2025 alone, at least nine AI companies have raised over $100 million each. Anthropic secured an astounding $3.5 billion in Series E funding, while Together AI obtained $305 million in a Series B round. The influx of capital has prompted startups to manage their image, capture the proper attention, and carve out a distinct presence in a cutthroat market. Startups look to public relations to help them stand out and build credibility. Companies that effectively showcase their value can get featured on Forbes, Entrepreneur, and other major publications. These opportunities attract investors and set them apart from the competition. Spynn Publicity, a PR agency with deep media connections, helps companies craft compelling messages that highlight their strengths while making AI advancements more accessible to the public. The PR Challenges in AI’s Expanding Market AI companies operate in a crowded space, making it difficult to attract attention. Many introduce complex technologies that are not always easy to explain. On top of that, AI raises questions about ethics and data privacy, which means companies need clear and honest communication. Spynn Publicity tackles these challenges by linking technical breakthroughs and mainstream audiences. For example, when an AI startup specializing in predictive analytics partnered with a healthcare provider, Spynn helped create an angle showing a tech milestone in saving lives. This shift in framing helped the company secure coverage in national publications and build credibility beyond the AI sector. Matteo Ferretti, CEO of Spynn Publicity, says, “AI companies need to tell stories that connect with investors and everyday people. Explaining technology isn’t enough. Startups need to show how it improves lives.” How AI Startups Can Use PR to Get Ahead PR goes beyond news coverage. AI companies need a clear strategy to help people understand their work and why it matters. Many startups assume that securing media coverage is enough, but without a well-crafted story, attention can be fleeting. Spynn Publicity helps AI leaders secure speaking opportunities at events, develop editorial content, and get featured on news sites, which establishes them as respected voices in AI. For example, after Spynn facilitated a CEO’s op-ed in a major tech publication, the startup saw a surge in inbound investor inquiries and strategic partnership opportunities. Matteo Ferretti also highlights the importance of media relationships, “Working closely with journalists allows us to match stories with the right publications. A compelling pitch is good, but building lasting trust with reporters secures consistent media coverage.” Why Spynn Publicity is a Leader in AI PR Spynn Publicity has an extensive network and a proven record of placing clients in notable publications. AI startups looking to expand their reputation can benefit from this level of access. Consider other startup companies following the lead of AI giants. Spynn helps shape their public image by telling compelling stories about their work. Instead of presenting AI as abstract technology, Spynn highlights its human impact, whether AI-driven solutions for sustainable energy or innovations improving cybersecurity. This approach builds trust with investors, policymakers, and the public. Matteo Ferretti explains, “AI PR requires balance. Startups must celebrate breakthroughs while addressing challenges like bias and transparency. The ones that get this right take the lead in technology and trust.” Building a Reputation That Lasts Reputation in AI is found in trust as much as in progress. Many startups focus on product launches, but trust is built between those milestones. Investors, customers, and regulators want confidence that a company can deliver its promises while handling ethical and security concerns. Startups that communicate their values and address industry challenges head-on build credibility that lasts. Consistent messaging helps AI companies stand out in a competitive space. Spynn often works with startups to refine their messaging before a big announcement, ensuring that every statement aligns with the company’s long-term vision. Engaging with media, participating in industry discussions, and sharing real-world impact stories reinforce a company’s reputation and make its technology more relatable. Spynn Publicity helps AI startups turn knowledge into influence. When a startup specializing in AI-powered legal analytics sought to differentiate itself, Spynn positioned its founders as respected voices in legal tech, helping them secure features in major business publications. A well-established reputation attracts attention and strengthens trust, ensuring that startups remain relevant and respected as the industry shifts. What’s Next for AI PR? AI startups have major opportunities ahead, but standing out is getting more difficult. The next wave of AI PR will be shaped by regulation, ethical debates, and how well startups communicate their impact. With more funding pouring in, competition is increasing, making strategic communication essential for success. Startups that sharpen their messaging and engage the right audiences will have the advantage. Companies must balance promoting their innovations with addressing public concerns, including transparency in AI development, data security, and regulatory compliance. Spynn Publicity helps AI startups stay ahead by securing media visibility, crafting strong narratives, and positioning leaders as trusted voices. The startups that invest in PR today will shape the industry’s future. The U.S. AI sector constantly shifts, and startups attract significant investments. In 2025 alone, at least nine AI companies have raised over $100 million each. Anthropic secured an astounding $3.5 billion in Series E funding, while Together AI obtained $305 million in a Series B round. The influx of capital has prompted startups to manage their image, capture the proper attention, and carve out a distinct presence in a cutthroat market. Startups look to public relations to help them stand out and build credibility. Companies that effectively showcase their value can get featured on Forbes , Entrepreneur, and other major publications. These opportunities attract investors and set them apart from the competition. get featured on Forbes get featured on Forbes Spynn Publicity, a PR agency with deep media connections, helps companies craft compelling messages that highlight their strengths while making AI advancements more accessible to the public. The PR Challenges in AI’s Expanding Market The PR Challenges in AI’s Expanding Market AI companies operate in a crowded space, making it difficult to attract attention. Many introduce complex technologies that are not always easy to explain. On top of that, AI raises questions about ethics and data privacy, which means companies need clear and honest communication. Spynn Publicity tackles these challenges by linking technical breakthroughs and mainstream audiences. For example, when an AI startup specializing in predictive analytics partnered with a healthcare provider, Spynn helped create an angle showing a tech milestone in saving lives. This shift in framing helped the company secure coverage in national publications and build credibility beyond the AI sector. Matteo Ferretti, CEO of Spynn Publicity, says, “ AI companies need to tell stories that connect with investors and everyday people. Explaining technology isn’t enough. Startups need to show how it improves lives. ” AI companies need to tell stories that connect with investors and everyday people. Explaining technology isn’t enough. Startups need to show how it improves lives. How AI Startups Can Use PR to Get Ahead How AI Startups Can Use PR to Get Ahead PR goes beyond news coverage. AI companies need a clear strategy to help people understand their work and why it matters. Many startups assume that securing media coverage is enough, but without a well-crafted story, attention can be fleeting. Spynn Publicity helps AI leaders secure speaking opportunities at events, develop editorial content, and get featured on news sites , which establishes them as respected voices in AI. For example, after Spynn facilitated a CEO’s op-ed in a major tech publication, the startup saw a surge in inbound investor inquiries and strategic partnership opportunities. get featured on news sites get featured on news sites Matteo Ferretti also highlights the importance of media relationships, “ Working closely with journalists allows us to match stories with the right publications. A compelling pitch is good, but building lasting trust with reporters secures consistent media coverage .” Working closely with journalists allows us to match stories with the right publications. A compelling pitch is good, but building lasting trust with reporters secures consistent media coverage Why Spynn Publicity is a Leader in AI PR Why Spynn Publicity is a Leader in AI PR Spynn Publicity has an extensive network and a proven record of placing clients in notable publications. AI startups looking to expand their reputation can benefit from this level of access. Consider other startup companies following the lead of AI giants. Spynn helps shape their public image by telling compelling stories about their work. Instead of presenting AI as abstract technology, Spynn highlights its human impact, whether AI-driven solutions for sustainable energy or innovations improving cybersecurity. This approach builds trust with investors, policymakers, and the public. Matteo Ferretti explains, “ AI PR requires balance. Startups must celebrate breakthroughs while addressing challenges like bias and transparency. The ones that get this right take the lead in technology and trust. ” AI PR requires balance. Startups must celebrate breakthroughs while addressing challenges like bias and transparency. The ones that get this right take the lead in technology and trust. Building a Reputation That Lasts Building a Reputation That Lasts Reputation in AI is found in trust as much as in progress. Many startups focus on product launches, but trust is built between those milestones. Investors, customers, and regulators want confidence that a company can deliver its promises while handling ethical and security concerns. Startups that communicate their values and address industry challenges head-on build credibility that lasts. Consistent messaging helps AI companies stand out in a competitive space. Spynn often works with startups to refine their messaging before a big announcement, ensuring that every statement aligns with the company’s long-term vision. Engaging with media, participating in industry discussions, and sharing real-world impact stories reinforce a company’s reputation and make its technology more relatable. Spynn Publicity helps AI startups turn knowledge into influence. When a startup specializing in AI-powered legal analytics sought to differentiate itself, Spynn positioned its founders as respected voices in legal tech, helping them secure features in major business publications. A well-established reputation attracts attention and strengthens trust, ensuring that startups remain relevant and respected as the industry shifts. What’s Next for AI PR? What’s Next for AI PR? AI startups have major opportunities ahead, but standing out is getting more difficult. The next wave of AI PR will be shaped by regulation, ethical debates, and how well startups communicate their impact. With more funding pouring in, competition is increasing, making strategic communication essential for success. Startups that sharpen their messaging and engage the right audiences will have the advantage. Companies must balance promoting their innovations with addressing public concerns, including transparency in AI development, data security, and regulatory compliance. Spynn Publicity helps AI startups stay ahead by securing media visibility, crafting strong narratives, and positioning leaders as trusted voices. The startups that invest in PR today will shape the industry’s future.

Spynn Publicity Helps AI Startups Win the PR Battle in the US Funding Surge

Spynn Publicity pomaže startupima s umjetnom inteligencijom da osvoje PR bitku u SAD-u

Spynn Publicity ayuda a las startups de IA a ganar la batalla de las relaciones públicas en el auge de la financiación en EE. UU.

Spynn Publicity- ը օգնում է AI Startups- ում հաղթել US Funding Surge- ում

Publisitas Spynn Membantu Startup AI Memenangkan Pertarungan Humas di AS yang Melonjak Pendanaan

Spynn Publicity が AI スタートアップの米国での資金調達急増における PR 戦争での勝利を支援

ERROR: invalid target_lang (ky)

„Spynn Publicity“ padeda AI startuoliams laimėti viešųjų ryšių kovą dėl JAV finansavimo bangos

Spynn Publicity ajuda startups de IA a vencer a batalha de RP no aumento de financiamento dos EUA

Spynn Publicity AI Startups US Funding Surge இல் PR Battle வெற்றி பெற உதவுகிறது

Spynn Publicity AI سٹارٹ اپ کو امریکی فنڈنگ میں اضافے میں PR کی جنگ جیتنے میں مدد کرتی ہے۔

Spynn Publicity AI Startups o‘z qo‘yga qo‘yga qo‘yga qo‘yga USA

Spynn Publicity giúp các công ty khởi nghiệp AI giành chiến thắng trong trận chiến PR trong sự gia tăng tài trợ của Mỹ

AI-powered IDEs are changing how we code — faster development, fewer boilerplate headaches, and instant suggestions. But there’s a technical reality we need to address: AI doesn’t just accelerate coding — it also accelerates bugs. 💥 After working with Cursor, Copilot and Windsurf in different environments, I’ve noticed AI doesn’t typically produce “wrong” code. Instead, it generates technically correct solutions that miss crucial business context and domain knowledge. Here’s what AI coding assistants typically get wrong: 1. AI Introduces Subtle, Hard-to-Spot Performance Issues ❌ AI Suggestion: Looks efficient but has a subtle N+1 query problem const getUsersWithOrders = async (): Promise<UserWithOrders[]> => {
  // Fetch all users - seems reasonable
  const users = await prisma.user.findMany({
    where: { status: 'ACTIVE' }
  });
  
  // For each user, get their orders - the subtle N+1 query issue
  const usersWithOrders = await Promise.all(
    users.map(async (user) => {
      const orders = await prisma.order.findMany({
        where: { userId: user.id },
        orderBy: { createdAt: 'desc' },
        take: 5 // Just get recent orders
      });
      return { ...user, orders };
    })
  );
  
  return usersWithOrders;
}; ✅ Better Solution: Single efficient query with proper relations const getUsersWithOrders = async (): Promise<UserWithOrders[]> => {
  // One efficient query with proper inclusion of related data
  const users = await prisma.user.findMany({
    where: { status: 'ACTIVE' },
    include: {
      orders: {
        orderBy: { createdAt: 'desc' },
        take: 5,
      }
    }
  });
  
  // Server-side data transformation if needed
  return users.map(user => ({
    ...user,
    orders: user.orders,
    // Transform any data if required
    totalSpent: user.orders.reduce((sum, order) => sum + order.total, 0)
  }));
}; 2. AI Misses Contextual Constraints When Integrating With Existing Codebases interface User {
  id: string;
  name: string;
  email: string;
}

const getUserDetails = async (userId: string): Promise<User> => {
  const response = await fetch(`/api/users/${userId}`);
  if (!response.ok) throw new Error('Failed to fetch user');
  return await response.json();
}; ✅ Better Solution: Following established application patterns import { ApiService } from '@/services/api';
import { User } from '@/types/user';
import { handleApiError } from '@/utils/error-handling';

export const getUserDetails = async (userId: string): Promise<User> => {
  try {
    return await ApiService.get<User>(`users/${userId}`);
  } catch (error) {
    return handleApiError(error, 'Failed to fetch user details');
  }
}; 3. AI Makes Reasonable Assumptions But Misses Domain-Specific Requirements ❌ AI Suggestion: Technically correct discount calculation const calculateDiscount = (price: number, discountPercent: number): number => {
  const discountAmount = price * (discountPercent / 100);
  return price - discountAmount;
}; ✅ Better Solution: Incorporates business rules and formatting const calculateDiscount = (price: number, discountPercent: number): number => {
  // Company policy: Maximum discount is 40% unless approved
  const effectiveDiscount = Math.min(discountPercent, 40);
  
  // Business rule: Discounts are calculated after tax in our system
  const priceWithTax = addTax(price);
  
  const discountAmount = priceWithTax * (effectiveDiscount / 100);
  
  // Format to company standard: always round to nearest cent
  return Number((priceWithTax - discountAmount).toFixed(2));
}; 🚀 The Truth? AI Doesn’t Write Bad Code — It Just Can’t Read Your Mind The pattern is clear: AI excels at generating syntactically correct, algorithmically sound code. What it consistently misses are: Business context and domain rules — it can’t know your specific company policies
Project-specific conventions — it has limited understanding of your codebase’s patterns
Architectural implications — it focuses on the function at hand, not the system as a whole
Performance at scale — optimizations that matter in production environments ✅ How to Use AI Tools Effectively 1. Reserve AI for boilerplate, but review integration points carefully — AI excels at generating repetitive patterns but often misses how components connect in larger systems. 2. Craft precise prompts with context – 🚫 “Generate a TypeScript React hook for data fetching”
✅ “Generate a TypeScript React hook for data fetching that follows our existing error handling pattern, includes cleanup on unmount, and handles stale requests” 3. Verify edge cases AI might miss. describe('calculateDiscount', () => {
  it('correctly calculates a 20% discount on $100', () => {
    expect(calculateDiscount(100, 20)).toBe(80);
  });
  it('handles zero price', () => {
    expect(calculateDiscount(0, 15)).toBe(0);
  });
  it('handles zero discount', () => {
    expect(calculateDiscount(50, 0)).toBe(50);
  });
  it('handles decimal precision correctly', () => {
    expect(calculateDiscount(9.99, 10)).toBe(8.99);
  });
  it('rejects negative prices', () => {
    expect(() => calculateDiscount(-10, 20)).toThrow();
  });
  it('rejects invalid discount percentages', () => {
    expect(() => calculateDiscount(100, 101)).toThrow();
    expect(() => calculateDiscount(100, -5)).toThrow();
  });
}); These aren’t “bugs” in the traditional sense, but rather a fundamental limitation: AI can’t understand your business domain, company standards, or the full context of your application architecture the way a seasoned team member can. 🚀 Bottom Line? AI is a powerful tool, but it’s not your CTO. Think critically. Review aggressively. Code smart. 📢 “The Engineer’s Log” is where we decode the world of AI, Web3, and software engineering — without the BS. Subscribe for deep dives, best coding practices, and real-world debugging stories. 🛠 Join the newsletter → The Engineer’s Log AI-powered IDEs are changing how we code — faster development, fewer boilerplate headaches, and instant suggestions. But there’s a technical reality we need to address: AI doesn’t just accelerate coding — it also accelerates bugs . 💥 accelerate coding accelerates bugs After working with Cursor, Copilot and Windsurf in different environments, I’ve noticed AI doesn’t typically produce “wrong” code. Instead, it generates technically correct solutions that miss crucial business context and domain knowledge. Cursor, Copilot and Windsurf Here’s what AI coding assistants typically get wrong: 1. AI Introduces Subtle, Hard-to-Spot Performance Issues ❌ AI Suggestion: Looks efficient but has a subtle N+1 query problem const getUsersWithOrders = async (): Promise<UserWithOrders[]> => {
  // Fetch all users - seems reasonable
  const users = await prisma.user.findMany({
    where: { status: 'ACTIVE' }
  });
  
  // For each user, get their orders - the subtle N+1 query issue
  const usersWithOrders = await Promise.all(
    users.map(async (user) => {
      const orders = await prisma.order.findMany({
        where: { userId: user.id },
        orderBy: { createdAt: 'desc' },
        take: 5 // Just get recent orders
      });
      return { ...user, orders };
    })
  );
  
  return usersWithOrders;
}; const getUsersWithOrders = async (): Promise<UserWithOrders[]> => {
  // Fetch all users - seems reasonable
  const users = await prisma.user.findMany({
    where: { status: 'ACTIVE' }
  });
  
  // For each user, get their orders - the subtle N+1 query issue
  const usersWithOrders = await Promise.all(
    users.map(async (user) => {
      const orders = await prisma.order.findMany({
        where: { userId: user.id },
        orderBy: { createdAt: 'desc' },
        take: 5 // Just get recent orders
      });
      return { ...user, orders };
    })
  );
  
  return usersWithOrders;
}; ✅ Better Solution: Single efficient query with proper relations const getUsersWithOrders = async (): Promise<UserWithOrders[]> => {
  // One efficient query with proper inclusion of related data
  const users = await prisma.user.findMany({
    where: { status: 'ACTIVE' },
    include: {
      orders: {
        orderBy: { createdAt: 'desc' },
        take: 5,
      }
    }
  });
  
  // Server-side data transformation if needed
  return users.map(user => ({
    ...user,
    orders: user.orders,
    // Transform any data if required
    totalSpent: user.orders.reduce((sum, order) => sum + order.total, 0)
  }));
}; const getUsersWithOrders = async (): Promise<UserWithOrders[]> => {
  // One efficient query with proper inclusion of related data
  const users = await prisma.user.findMany({
    where: { status: 'ACTIVE' },
    include: {
      orders: {
        orderBy: { createdAt: 'desc' },
        take: 5,
      }
    }
  });
  
  // Server-side data transformation if needed
  return users.map(user => ({
    ...user,
    orders: user.orders,
    // Transform any data if required
    totalSpent: user.orders.reduce((sum, order) => sum + order.total, 0)
  }));
}; 2. AI Misses Contextual Constraints When Integrating With Existing Codebases interface User {
  id: string;
  name: string;
  email: string;
}

const getUserDetails = async (userId: string): Promise<User> => {
  const response = await fetch(`/api/users/${userId}`);
  if (!response.ok) throw new Error('Failed to fetch user');
  return await response.json();
}; interface User {
  id: string;
  name: string;
  email: string;
}

const getUserDetails = async (userId: string): Promise<User> => {
  const response = await fetch(`/api/users/${userId}`);
  if (!response.ok) throw new Error('Failed to fetch user');
  return await response.json();
}; ✅ Better Solution: Following established application patterns import { ApiService } from '@/services/api';
import { User } from '@/types/user';
import { handleApiError } from '@/utils/error-handling';

export const getUserDetails = async (userId: string): Promise<User> => {
  try {
    return await ApiService.get<User>(`users/${userId}`);
  } catch (error) {
    return handleApiError(error, 'Failed to fetch user details');
  }
}; import { ApiService } from '@/services/api';
import { User } from '@/types/user';
import { handleApiError } from '@/utils/error-handling';

export const getUserDetails = async (userId: string): Promise<User> => {
  try {
    return await ApiService.get<User>(`users/${userId}`);
  } catch (error) {
    return handleApiError(error, 'Failed to fetch user details');
  }
}; 3. AI Makes Reasonable Assumptions But Misses Domain-Specific Requirements ❌ AI Suggestion: Technically correct discount calculation const calculateDiscount = (price: number, discountPercent: number): number => {
  const discountAmount = price * (discountPercent / 100);
  return price - discountAmount;
}; const calculateDiscount = (price: number, discountPercent: number): number => {
  const discountAmount = price * (discountPercent / 100);
  return price - discountAmount;
}; ✅ Better Solution: Incorporates business rules and formatting const calculateDiscount = (price: number, discountPercent: number): number => {
  // Company policy: Maximum discount is 40% unless approved
  const effectiveDiscount = Math.min(discountPercent, 40);
  
  // Business rule: Discounts are calculated after tax in our system
  const priceWithTax = addTax(price);
  
  const discountAmount = priceWithTax * (effectiveDiscount / 100);
  
  // Format to company standard: always round to nearest cent
  return Number((priceWithTax - discountAmount).toFixed(2));
}; const calculateDiscount = (price: number, discountPercent: number): number => {
  // Company policy: Maximum discount is 40% unless approved
  const effectiveDiscount = Math.min(discountPercent, 40);
  
  // Business rule: Discounts are calculated after tax in our system
  const priceWithTax = addTax(price);
  
  const discountAmount = priceWithTax * (effectiveDiscount / 100);
  
  // Format to company standard: always round to nearest cent
  return Number((priceWithTax - discountAmount).toFixed(2));
}; 🚀 The Truth? AI Doesn’t Write Bad Code — It Just Can’t Read Your Mind The pattern is clear: AI excels at generating syntactically correct, algorithmically sound code. What it consistently misses are: Business context and domain rules — it can’t know your specific company policies Project-specific conventions — it has limited understanding of your codebase’s patterns Architectural implications — it focuses on the function at hand, not the system as a whole Performance at scale — optimizations that matter in production environments Business context and domain rules — it can’t know your specific company policies Business context and domain rules Project-specific conventions — it has limited understanding of your codebase’s patterns Project-specific conventions Architectural implications — it focuses on the function at hand, not the system as a whole Architectural implications Performance at scale — optimizations that matter in production environments Performance at scale ✅ How to Use AI Tools Effectively 1. Reserve AI for boilerplate, but review integration points carefully — AI excels at generating repetitive patterns but often misses how components connect in larger systems. Reserve AI for boilerplate, but review integration points carefully 2. Craft precise prompts with context – Craft precise prompts with context 🚫 “Generate a TypeScript React hook for data fetching” ✅ “Generate a TypeScript React hook for data fetching that follows our existing error handling pattern, includes cleanup on unmount, and handles stale requests” 🚫 “Generate a TypeScript React hook for data fetching” “Generate a TypeScript React hook for data fetching” ✅ “Generate a TypeScript React hook for data fetching that follows our existing error handling pattern, includes cleanup on unmount, and handles stale requests” “Generate a TypeScript React hook for data fetching that follows our existing error handling pattern, includes cleanup on unmount, and handles stale requests” 3. Verify edge cases AI might miss. Verify edge cases AI might miss. describe('calculateDiscount', () => {
  it('correctly calculates a 20% discount on $100', () => {
    expect(calculateDiscount(100, 20)).toBe(80);
  });
  it('handles zero price', () => {
    expect(calculateDiscount(0, 15)).toBe(0);
  });
  it('handles zero discount', () => {
    expect(calculateDiscount(50, 0)).toBe(50);
  });
  it('handles decimal precision correctly', () => {
    expect(calculateDiscount(9.99, 10)).toBe(8.99);
  });
  it('rejects negative prices', () => {
    expect(() => calculateDiscount(-10, 20)).toThrow();
  });
  it('rejects invalid discount percentages', () => {
    expect(() => calculateDiscount(100, 101)).toThrow();
    expect(() => calculateDiscount(100, -5)).toThrow();
  });
}); describe('calculateDiscount', () => {
  it('correctly calculates a 20% discount on $100', () => {
    expect(calculateDiscount(100, 20)).toBe(80);
  });
  it('handles zero price', () => {
    expect(calculateDiscount(0, 15)).toBe(0);
  });
  it('handles zero discount', () => {
    expect(calculateDiscount(50, 0)).toBe(50);
  });
  it('handles decimal precision correctly', () => {
    expect(calculateDiscount(9.99, 10)).toBe(8.99);
  });
  it('rejects negative prices', () => {
    expect(() => calculateDiscount(-10, 20)).toThrow();
  });
  it('rejects invalid discount percentages', () => {
    expect(() => calculateDiscount(100, 101)).toThrow();
    expect(() => calculateDiscount(100, -5)).toThrow();
  });
}); These aren’t “bugs” in the traditional sense, but rather a fundamental limitation: AI can’t understand your business domain, company standards, or the full context of your application architecture the way a seasoned team member can. 🚀 Bottom Line? AI is a powerful tool, but it’s not your CTO. Think critically. Review aggressively. Code smart. Bottom Line? Think critically. Review aggressively. Code smart. 📢 “The Engineer’s Log” is where we decode the world of AI, Web3, and software engineering — without the BS. Subscribe for deep dives, best coding practices, and real-world debugging stories. “The Engineer’s Log” is where we decode the world of AI, Web3, and software engineering — without the BS. Subscribe for deep dives, best coding practices, and real-world debugging stories. “The Engineer’s Log” is where we decode the world of AI, Web3, and software engineering — without the BS. Subscribe for deep dives, best coding practices, and real-world debugging stories. 🛠 Join the newsletter → The Engineer’s Log Join the newsletter → The Engineer’s Log The Engineer’s Log The Engineer’s Log

Here's What Your AI-Generated Code Is Not Telling You

Hier is wat jou KI-gegenereerde kode vir jou nie vertel nie

AI tərəfindən yaradılan kodun sizə demədiyi budur

Δείτε τι δεν σας λέει ο κώδικας που δημιουργείται από AI

Esto es lo que tu código generado por IA no te dice

Men sa Kòd AI-Genere w la pa di w

הנה מה שקוד ה-AI שלך לא אומר לך

AIが生成したコードが伝えていないこと

Štai ko jums nesako jūsų dirbtinio intelekto sukurtas kodas

دلته هغه څه دي چې ستاسو د مصنوعي ذهانت لخوا تولید شوی کوډ تاسو ته نه وايي

Aqui está o que seu código gerado por IA não está lhe dizendo

Dore Ibyo Kode yawe Yakozwe na AI itakubwira

Ево шта вам ваш код генерисан АИ не говори

Technology was meant to make our work easier. Hand over the reins to artificial intelligence and let it do all the work for you. But rather than working less, people are busier than ever. We are available 24/7 thanks to emails, Slack notifications, endless meetings, and AI-powered dashboards. Technology makes things far more complex than they need to be. Are we actually more productive, or simply doing more work that resembles productivity? Let’s reimagine the idea of efficiency in the digital age. 🚀 Why Does More Tech Mean More Work? Why Does More Tech Mean More Work? When something like this comes along, there is always this promise of efficiency, but what you get—really—is a compounding of work to do, expectations, and noise. Email was supposed to replace slow communication, but now we all get hundreds of emails a day.
The reports are AI-generated and offer immediate analysis, which means that in this world, companies now expect decisions to come even quicker.
Virtual collaboration tools make working together easier, but they have also created an “always on” culture. Email was supposed to replace slow communication, but now we all get hundreds of emails a day. Email hundreds of emails a day The reports are AI-generated and offer immediate analysis, which means that in this world, companies now expect decisions to come even quicker. The reports immediate analysis decisions to come even quicker Virtual collaboration tools make working together easier, but they have also created an “always on” culture. Virtual collaboration tools easier “always on” culture Rather than reducing workloads, technology tends to increase them—because the more we are able to do, the more we’re assumed to do. A hundred years ago, economist John Maynard Keynes foresaw that technological advances would shorten work hours to a 15-hour workweek. Instead, work has filled every hour to be had. But the problem is not that we don’t have enough productivity tools; rather, we haven’t changed our minds about what work itself is. not productivity tools changed our minds about what work itself is 📊 Productivity: Are We Looking at It Wrong? Productivity: Are We Looking at It Wrong? For decades, companies tracked productivity in hours worked and tasks completed. But in the digital realm, output isn’t always a great indicator of success. Consider two workers: Consider two workers: Worker A answers 100 emails, attends 10 meetings, and generates 20 reports in one day.
Worker B spends the day solving a really complicated problem that improves efficiency for everybody on the team. Worker A answers 100 emails, attends 10 meetings, and generates 20 reports in one day. Worker A Worker B spends the day solving a really complicated problem that improves efficiency for everybody on the team. Worker B Who was more productive? A traditional system would say Worker A, because they completed more tasks. But in fact, Worker B provided more value. Worker A more tasks Worker B provided more value The issue is even more pronounced with remote work. Visibility—who responds immediately to emails, who stays logged in the longest on Slack—seems to be an informal metric on which managers often evaluate employees, rather than actual contribution. informal metric actual contribution Real productivity today is not about doing things; it is about impact. Real productivity today is not about doing things; it is about impact. They want to know, "How many hours did you work?" → Did your work have an impact?
Rather than "how many emails did you send?" → Did that communication lead to improved outcomes?
It’s not "how quickly can you respond?" → Did you not have information? They want to know, "How many hours did you work?" → Did your work have an impact? They want to know, "How many hours did you work?" → Did your work have an impact? Rather than "how many emails did you send?" → Did that communication lead to improved outcomes? Rather than "how many emails did you send?" → Did that communication lead to improved outcomes? It’s not "how quickly can you respond?" → Did you not have information? It’s not "how quickly can you respond?" → Did you not have information? For the future of work, better productivity metrics that reflect real, rather than busy, impact will be needed. future of work productivity metrics real, rather than busy, impact 🤖 AI & Automation: Are They a Shortcut or a Trap? AI & Automation: Are They a Shortcut or a Trap? AI and automation have known benefits in eliminating repetitive work, but they do carry risks: known benefits eliminating repetitive work do carry risks More Data = More Decisions More Data = More Decisions Instant access to analytics can result in decision fatigue, where employees are spending more time analyzing than doing anything with it. Marketing teams always used to make decisions based on a couple of reports a week. Then they had real-time dashboards with thousands of data points—but instead of streamlining the process, it tends to paralysis by analysis instead. Automation 101: Too Much Over-Automation Masks Creativity Automation 101: Too Much Over-Automation Masks Creativity While AI is great at following processes, it has a hard time when faced with outliers or situations where human intuition is required. AI-backed customer support proactively handles 80% of tickets. That works fine—until an unusual case comes along, and human employees discover they no longer have the knowledge or experience to manage it effectively. When utilized properly, automation is incredibly advantageous, but when it comes to AI, it is a slippery slope that will destroy your adaptability if you become overly reliant on it. The Burnout Effect The Burnout Effect Always-on systems put pressure on humans to be always-on as well, which makes deep work and being creative more difficult. A lot of companies will use AI to measure how well their employees are doing, but once workers feel that everything they do is being monitored, they begin optimizing for visibility rather than impact. The purpose of technology is to make work easier, not harder. The purpose of technology is to make work easier, not harder. 💡 The New Energy of AI: Forget Efficiency The New Energy of AI: Forget Efficiency Rather than doing more, faster, we should work smarter. doing more, faster work smarter ✅ Less of the Busy Work, More of the Deep Work Less of the Busy Work, More of the Deep Work Focus is broken by notifications, emails, and instant messages.
If it does, tools are there to magnify, not create, distractions.
Employees should be encouraged to block off time for deep, undistracted work. Focus is broken by notifications, emails, and instant messages. If it does, tools are there to magnify, not create, distractions. Employees should be encouraged to block off time for deep, undistracted work. ✅ Not More Automation, Smarter Automation Not More Automation, Smarter Automation AI ought to automate repetitive steps but invite human intervention when necessary.
Everything should not require absolute complexity.
Over-automation can make simple processes too rigid.
AI is most powerful when enhancing human abilities, rather than replacing them. AI ought to automate repetitive steps but invite human intervention when necessary. Everything should not require absolute complexity. Over-automation can make simple processes too rigid. AI is most powerful when enhancing human abilities, rather than replacing them. ✅ Do Not Count Hours; Measure Outcomes Do Not Count Hours; Measure Outcomes Efficiency doesn’t mean maximizing every hour—it means maximizing value.
Working more doesn’t always lead to working better.
Companies should reward problem solvers, not just busy people. Efficiency doesn’t mean maximizing every hour—it means maximizing value. Working more doesn’t always lead to working better. Companies should reward problem solvers, not just busy people. Many technology leaders say the best employees are the ones who spend time thinking rather than reacting. Deep thinking is an undervalued skill in a world that prioritizes speed. spend time thinking rather than reacting undervalued skill prioritizes speed 📌 Is Human-Centered Tech the Ultimate Productivity Band-Aid? Is Human-Centered Tech the Ultimate Productivity Band-Aid? The next generation of innovation should center on helping people work better instead of just faster. Humans should be served by AI; AI should not replace humans.
Technology must reduce cognitive overload, not increase it.
Work should be measured by impact, not activity. Humans should be served by AI; AI should not replace humans. Technology must reduce cognitive overload, not increase it. Work should be measured by impact, not activity. More companies are also shifting toward a results-oriented work culture—a focus on goals, ingenuity, and long-term strategy rather than short-term output. We need not productivity tools that create new to-do lists, but rather tools that make us do less—but do it better. We don’t need more productivity—we need better ways to work. We don’t need more productivity—we need better ways to work. TL;DR TL;DR Technology was supposed to lighten the burdens of work, but all too often, it adds to them instead.
AI and automation assist in these tasks, but they also drive greater expectations and complexity.
All productivity means is getting things done—and if you succeed, it should also improve life outside of work.
The future of work must become more human, not only tech-driven. Technology was supposed to lighten the burdens of work, but all too often, it adds to them instead. AI and automation assist in these tasks, but they also drive greater expectations and complexity. All productivity means is getting things done—and if you succeed, it should also improve life outside of work. The future of work must become more human, not only tech-driven. Thanks for Reading! Thanks for Reading! What’s your verdict—does technology actually make us more productive, or just busier? Let us know what you think in the comments! actually make us more productive, or just busier?

Technology Was Meant to Make Work Easier, but Has it Just Made us Busier?

In recent years, artificial intelligence (AI) has become an essential part of many industries, with health care being no exception. Its ability to enhance practices, streamline processes and provide real-time insights is reshaping the way care facilities operate. But how does AI improve patient safety in hospitals? Enhancing Surgical Precision with AI Enhancing Surgical Precision with AI Enhancing Surgical Precision with AI All surgical procedures require an incredibly high level of precision because even the smallest error can lead to complications. Although surgeons are highly trained professionals, they can benefit from technological assistance to enhance their accuracy and decision-making during difficult surgeries. AI video analytics is emerging as a vital tool in improving surgeries. Automated video systems can analyze live feeds of the surgical area, offering guidance to the surgical team throughout the procedure. By using advanced image recognition and machine learning algorithms, these systems can highlight critical areas of focus like tissue differentiation and even surgical tool positioning. live feeds of the surgical area live feeds of the surgical area AI can also track the team’s movements, identifying any deviations from optimal techniques or patterns. This feedback loop can help them correct any potential mistakes before they escalate, enhancing the overall precision of the operation. can also track the team’s movements can also track the team’s movements In high-stakes surgeries, AI video analytics offers an extra layer of support, providing objective analysis and real-time feedback to ensure only the most favorable outcomes. This boosts patient safety and may offer shorter recovery times and better overall outcomes. The Role of AI Video in Monitoring Sedation Levels The Role of AI Video in Monitoring Sedation Levels The Role of AI Video in Monitoring Sedation Levels The Role of AI Video in Monitoring Sedation Levels Both over- and under-sedation can lead to serious complications, including respiratory distress or waking up during surgery. Monitoring the medication’s delivery is critical for operations that require general anesthesia. Not only does it come with more risks, but it’s necessary for more intensive procedures, and waking up during these can be very dangerous. can lead to serious complications can lead to serious complications Traditionally, clinicians rely on various tools and their own observations to assess a patient’s sedation status. However, these methods can sometimes be subjective or prone to human error. AI-powered video monitoring is making waves in this area. By tracking subtle facial cues like blink rate, pupil dilation and facial muscle movement, AI systems can provide data on a patient’s sedation. They analyze video feeds and use algorithms to detect signs of over- or under-sedation. For instance, a decrease in blink rate could indicate a patient is too deeply sedated, while rapid blinking or facial tension may signal insufficient sedation. can provide data on can provide data on If the system detects irregularities in sedation, alerts can be sent to the medical team so they’re able to correct the issue. This makes it possible for clinicians to adjust sedation levels quickly and reduce the risk of complications. AI in Post-Surgery and Recovery AI in Post-Surgery and Recovery AI in Post-Surgery and Recovery AI in Post-Surgery and Recovery The period following surgery is essential for a patient’s recovery because it involves careful monitoring to ensure no complications occur. It is also important to ensure the healing process goes smoothly. Traditionally, post-surgery recovery relies on routine check-ups and patient self-reporting, but AI video analytics is now making this process more efficient and accurate. AI systems can now monitor patients during recovery by tracking their movements, alertness and overall well-being. For instance, video analysis can detect if a patient is attempting to get out of bed too early, which could lead to falls or injury. By analyzing facial expressions and body language, the system can also identify signs of pain or discomfort that might otherwise go unnoticed, which allows medical staff to respond more quickly to a patient’s needs. Automation technology can also monitor key recovery indicators like respiratory patterns or heart rate through video feeds. These can alert the medical team to irregularities like changes in breathing or excessive movement, which might indicate the need for intervention. can also monitor key recovery indicators can also monitor key recovery indicators AI-powered video can help track post-surgical physical therapy progress. For example, it can ensure patients are following the prescribed rehabilitation exercises, providing feedback to both them and clinicians. It can also measure the range of motion or symmetry in movements to support recovery efforts and prevent setbacks. Using AI in post-surgery care can help hospitals ensure faster, more accurate recovery monitoring, reducing complications and improving overall patient outcomes. are following the prescribed rehabilitation exercises are following the prescribed rehabilitation exercises Reducing Human Error and Improving Patient Care Reducing Human Error and Improving Patient Care Reducing Human Error and Improving Patient Care Reducing Human Error and Improving Patient Care Even the most skilled professionals are susceptible to human error, especially when exposed to high-stress environments like operating theatres. Fatigue, distractions, and the volume of patients can lead to mistakes in monitoring, medication administration and patient assessments. AI video analytics help reduce these errors by providing an extra layer of oversight and alerts that support decision-making. One of the largest areas where AI minimizes human error is in medication management. Automated systems can monitor whether the correct dosages and types of medication are being administered to ensure nurses or pharmacists follow proper protocols. This can be particularly useful in preventing dosage miscalculations. correct dosages and types of medication correct dosages and types of medication AI video also enhances patient safety by ensuring compliance with hospital hygiene protocols. Hand hygiene is one of the simplest factors in preventing further medical issues. AI-driven video monitoring can track whether health care workers sanitize their hands before and after patient interactions, reducing the risk of hospital-acquired infections. sanitize their hands before and after sanitize their hands before and after Strengthening Hospital Security Strengthening Hospital Security Strengthening Hospital Security Strengthening Hospital Security Hospitals are high-traffic environments where security challenges are constant. From unauthorized access to missing medical equipment, maintaining a safe and secure facility is essential for both patients and staff. Traditional security measures like CCTV cameras and on-site security personnel have limitations. AI-powered video surveillance enhances hospital security by providing real-time monitoring, automated alerts and predictive threat detection. by providing real-time monitoring by providing real-time monitoring One of the biggest advantages of AI-driven security is its ability to detect unusual behavior before it escalates into a security threat. Video analytics can identify loitering, unauthorized access or even the presence of concealed weapons. Unlike traditional security cameras, it analyzes video feeds continuously, flagging potential threats instantly so hospital security teams can respond faster. AI vision can also improve access control by integrating facial recognition and badge verification systems. This ensures only authorized personnel enter restricted zones like operating rooms, ICUs and pharmaceutical storage areas. It can also track visitors, logging their movements to prevent security breaches or unauthorized patient interactions. by integrating facial recognition by integrating facial recognition Another critical security challenge in hospitals is asset protection. Expensive medical equipment can get misplaced or stolen in the field, either through innocent or malicious means. AI-powered surveillance can track these assets using object recognition, ensuring essential tools are always available when necessary. can get misplaced or stolen can get misplaced or stolen Such video systems also play a role in emergency response. If a patient or visitor collapses, AI can instantly detect the fall and alert nearby medical staff. In cases of violence or distress, AI-driven cameras can identify escalating situations and notify security before they get out of control. Using automation for video surveillance can significantly enhance hospitals’ security infrastructure, ensuring a safer environment for patients, staff and visitors. How Does AI Improve Patient Safety in Hospitals? AI-powered video monitoring is transforming hospital safety in once-unimaginable ways. From ensuring patients receive the right level of sedation to enhancing surgical precision, supporting post-op recovery and tightening hospital security, AI is proving to be an invaluable tool in modern health care. As hospitals continue to evolve, automation-driven video technology will play an even greater role in keeping patients safe, streamlining operations and preventing potential crises before they unfold. In an environment where every second counts, having an extra set of “eyes” that never blink can make all the difference.

Hospitals Are Now Considering Using AI to Keep Patients Safe

Software development and programming, once regarded as endeavors that required deep expertise, can now be done by anyone using natural language. A feature that used to take days or months to develop can now be developed in minutes or hours thanks to code from an AI model. For example, OpenAI Codex and Google BERT are trained on programming web blogs, Stack overflow questions, etc. These intelligent models create code through mathematical probabilities while they are also known to hallucinate and present false information. A research done by academia claims that AI code generation is the leading cause for top 10 vulnerabilities and nearly 40% of code has security bugs. Many of the leading players, along with new SaaS providers are leveraging AI for making their offerings smarter. SaaS programmers too, must be more knowledgeable about AI-based SaaS tools. nearly 40% of code has security bugs What Makes AI-Generated Code Unsafe? Following programming standards and code quality dictates software safety. But, AI models are trained on every bit of information available on the internet. The code quality, reliability, security, and more might differ from that which is generated by human developers. A model trained on web development examples may contain poor data validation practices, for instance. This lack of validation can lead to security issues when the model generates code that adopts the same poor practices. 5 Indicators That Suggest Code Contains Security Weaknesses No matter the size (million or billion of parameters) the mоdels are known to hallucinate and make incorrect predictions. When a typical developer sees the code that an AI produces, they will miss the subtle but serious security vulnerabilities. However, for a developer who has complete knowledge of design and development patterns, flaws are a review away from being identified. Developers can leverage these patterns to discover vulnerabilities and align with SaaS security best practices. SaaS security best practices 1. Type Inference and Input Validations are Not Enforced Modern frameworks and libraries rely heavily on interface/enum for inference and validation. This guarantees that the code does its job accurately and imposes security. AI-generated code won't infer unless we direct it. Even after crafting a careful prompt, type mismatch and validation enforcement may not match the use case. To locate and amend code mismatches, developers must be well aware of the domain and business requirements. def reciprocal(user_input):
     # Insecure implementation with no type inference or validation
     result = 100 / user_input
     return result def reciprocal(user_input):
     # Insecure implementation with no type inference or validation
     result = 100 / user_input
     return result 2. Non-Standard State and Context Sharing Between Classes/Objects Programs share objects in Public/Private/Protected ways. Higher order functions and classes inherit object state by accessing public/protected variables directly to perform computations. If something is done incorrectly in implementation or execution, security or performance bottlenecks may easily occur. SaaS developers must implement their state and context management logic appropriately and review it for correct and safe use. class InsecureClass:
    def __init__(self, owner, balance, password):
        self.owner = owner  # Public attribute
        self._balance = balance  # Protected attribute
        self.__password = password  # Private attribute

    # Public def
    def get_balance(self):
        return self._balance

    # Protected def
    def _update_balance(self, amount):
        self._balance += amount

    # Private def
    def __validate_password(self, input_password):
        return self.__password == input_password

    # Insecure def exposing private data
    def insecure_password_exposure(self):
        return self.__password class InsecureClass:
    def __init__(self, owner, balance, password):
        self.owner = owner  # Public attribute
        self._balance = balance  # Protected attribute
        self.__password = password  # Private attribute

    # Public def
    def get_balance(self):
        return self._balance

    # Protected def
    def _update_balance(self, amount):
        self._balance += amount

    # Private def
    def __validate_password(self, input_password):
        return self.__password == input_password

    # Insecure def exposing private data
    def insecure_password_exposure(self):
        return self.__password 3. Weak Implementation of Data Handling and Sharing Techniques Services share and receive information over the network. These days, secure connectivity and data handling have been crucial to the success of cloud-based systems. When reading, processing, and sharing sensitive data on organizations through distributed data networks, strong protocols and security techniques must be in place to prevent data interception. Using AI, the SaaS developer has to implement every single aspect of the architecture in full-fledged applications. data interception #Insecure Data Sharing
@app.route("/user/<int:user_id>", methods=["GET"])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)  # All user data exposed, including secrets

# Insecure Data Handling
@app.route("/update_email", methods=["POST"])
def update_email():
    data = request.json()
    user_id = data.get("user_id")
    new_email = data.get("new_email")
    if user_id in users:
        users[user_id]["email"] = new_email  # No validation of new_email
        return jsonify({"message": "Email updated successfully"}) #Insecure Data Sharing
@app.route("/user/<int:user_id>", methods=["GET"])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)  # All user data exposed, including secrets

# Insecure Data Handling
@app.route("/update_email", methods=["POST"])
def update_email():
    data = request.json()
    user_id = data.get("user_id")
    new_email = data.get("new_email")
    if user_id in users:
        users[user_id]["email"] = new_email  # No validation of new_email
        return jsonify({"message": "Email updated successfully"}) 4. Inadequate Secrets and Auth Handling 4. Inadequate Secrets and Auth Handling In today's cyber sensitive world, tight RBAC implementation is a must for access control to secure identity while maintaining privacy and compliance. When the code is generated by an LLM, by default there will be a mechanism that connects and authenticates with the auth provider with basic implementation. Simple solutions are not enough to stay secure with emerging cyber threats. While adding custom functionalities developers must test thoroughly to ensure security and auth handling is strongly implemented. RBAC implementation RBAC implementation # Insecure authentication
@app.route("/login", methods=["POST"])
def login():
    data = request.json()
    email = data.get("email")
    password = data.get("password")
    for user_id, user in users.items():
        if user["email"] == email and user["password"] == password:
            return jsonify({"message": "Login successful", "user_id": user_id}) # Insecure authentication
@app.route("/login", methods=["POST"])
def login():
    data = request.json()
    email = data.get("email")
    password = data.get("password")
    for user_id, user in users.items():
        if user["email"] == email and user["password"] == password:
            return jsonify({"message": "Login successful", "user_id": user_id}) 5. Outdated Dependencies with Deprecated Functionality Usage AI programming is being directed by libraries and frameworks made by the community and open-source. People support new technology by using these promising tools and creating new ones. The data these models were trained on is not up to date and the model capabilities are frozen, and so is their knowledge. With the development of technology, a lot of features will become obsolete and some libraries won't be relevant to current needs. A SaaS developer is tasked with the review and usage of valid dependencies to ensure functionality and security. import md5  # Outdated library

def insecure_hash_password(password):
    # Insecure password hashing done using the deprecated MD5 algorithm.
    return md5.new(password).hexdigest() import md5  # Outdated library

def insecure_hash_password(password):
    # Insecure password hashing done using the deprecated MD5 algorithm.
    return md5.new(password).hexdigest() Tips to Make AI-Generated Code Safe to Use The advanced coding capabilities of Large Language Models is due to their extensive mathematical calculations. No fancy techniques are needed to make it compliant with security and programming standards. We can use these simple checks to make AI-generated code safe and compliant with standards: Code review with security and architecture teams should be a standard part of your life cycle.
Integrate automated security testing and validation steps in version control tools.
Include dependency and compliance checks in testing KPIs.
Adopt Zero-Trust architecture with static and dynamic security testing tools.
Leverage DevSecOps practices and shadow AI. Code review with security and architecture teams should be a standard part of your life cycle. Integrate automated security testing and validation steps in version control tools. automated security testing Include dependency and compliance checks in testing KPIs. Adopt Zero-Trust architecture with static and dynamic security testing tools. Leverage DevSecOps practices and shadow AI. Handling Unsafe AI-Generated Code with a Simple Github Action No matter how carefully we review and audit the code, chances of human error are always there. Relying solely on manual audits is not enough as we need to have some predefined checks that can test and validate the code as soon as it enters the version control system. What better check than to add a Github action which automatically runs security and quality checks when a PR is raised. name: Simple Security Checks for AI generated Code

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  security-and-quality-check:
    runs-on: ubuntu-latest
    Steps:

      - name: Repository checkout
        uses: actions/checkout@v3

      - name: Python setup
        uses: actions/setup-python@v4
        with:
          python-version: ">=3.9"

      - name: Dependency installation
        run: |
          python -m pip install --upgrade pip
          pip install bandit pytest

      - name: Identifying insecure libraries and patterns
        run: |
          echo "Checking for insecure patterns..."
          if grep -r "md5.new(" .; then
            echo "ERROR: Insecure MD5 hashing detected. Use hashlib.sha256 or bcrypt instead."
            exit 1
          fi
          echo "No insecure patterns detected."

      - name: Scanning for security vulnerabilities
        run: |
          echo "Running Bandit security scanner..."
          bandit -r .

      - name: Running unit tests
        run: |
          echo "Running unit tests..."
          pytest test/unit --cmodopt=local

      - name: Notifying on failure
        if: failure()
        run: |
          send_slack_notification(“Unsafe code merge detected, fix immediately”) name: Simple Security Checks for AI generated Code

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  security-and-quality-check:
    runs-on: ubuntu-latest
    Steps:

      - name: Repository checkout
        uses: actions/checkout@v3

      - name: Python setup
        uses: actions/setup-python@v4
        with:
          python-version: ">=3.9"

      - name: Dependency installation
        run: |
          python -m pip install --upgrade pip
          pip install bandit pytest

      - name: Identifying insecure libraries and patterns
        run: |
          echo "Checking for insecure patterns..."
          if grep -r "md5.new(" .; then
            echo "ERROR: Insecure MD5 hashing detected. Use hashlib.sha256 or bcrypt instead."
            exit 1
          fi
          echo "No insecure patterns detected."

      - name: Scanning for security vulnerabilities
        run: |
          echo "Running Bandit security scanner..."
          bandit -r .

      - name: Running unit tests
        run: |
          echo "Running unit tests..."
          pytest test/unit --cmodopt=local

      - name: Notifying on failure
        if: failure()
        run: |
          send_slack_notification(“Unsafe code merge detected, fix immediately”) Conclusion Large language models are quite useful tools for SaaS developers to generate code and information with natural prompts. However, they pose security risks and sometimes deliver non-performant code that doesn't suit enterprise needs. SaaS developers must be very careful when using these tools and implementing AI-generated code for real-life use cases. This practical guide focuses on the factors that arise and influence security posture while showing how to overcome these challenges.

Stories, News & Mentions about ALWAYSAI

Categories

Trending Topics

Stories, News & Mentions about ALWAYSAI

Alwaysai’s Stories on HackerNoon

Alwaysai's News & Mentions

Categories

Trending Topics