The University of Arkansas for Medical Sciences (UAMS) is hosting a traveling exhibit that explores the magic of the “Harry Potter” series and its roots in Renaissance discovery. “Renaissance Science, Magic, and Medicine in Harry Potter’s World” is on display at the UAMS Library through ...

UAMS Library Hosts Exhibit on Roots of Renaissance Science in ‘Harry Potter’ | UAMS News

SecurityBrief New Zealand - Technology news for CISOs & cybersecurity decision-makers

Proofpoint named Governance leader in 2025 Gartner Magic Quadrant

Oracle's Cloud ERP platform, serving 10,000+ organizations, earns leadership recognition for its advanced AI capabilities and comprehensive finance solutions across multiple sectors.

Oracle Dominates Cloud ERP Market, Named Leader in Three 2024 Gartner Magic Quadrant Reports | ORCL Stock News

CX Today covers Customer Data Platform news including Automation, Enterprise, ERP, Magic Quadrant and more.

Gartner Magic Quadrant for Cloud ERP for Product-Centric Enterprises 2024: The Rundown - CX Today

Revolutionize Your Gaming Experience! Discover how Circle of Games is changing the world at Dubai's Global Game Show with web3!

Unveiled in Dubai: The Mysterious Platform Transforming Casual Gaming with Web3 Magic! - West Island Blog

VCs are excited about generative AI startups that can make coding more efficient. Poolside, Magic, and Augment have raised the most funding.

AI coding startups are becoming a hotspot for VCs. Here are the 12 copilots that have landed funding from funds like a16z, Lightspeed, and Menlo Ventures.

NBA player Jonathan Isaac criticized President Biden for making March 31 Transgender Day of Visibility during Easter Sunday, saying the move &quot;promotes division.&quot;

Magic's Jonathan Isaac criticizes White House over Transgender Day of Visibility | Fox News

Live coverage of the Memphis Grizzlies vs. Orlando <strong>Magic</strong> NBA game on ESPN, including live score, highlights and updated stats.

Grizzlies vs. Magic (Mar 30, 2024) Live Score - ESPN

The Orlando <strong>Magic</strong> have hit the home stretch of the regular season vying for home-court advantage in the first round of the playoffs, as they host the struggling Portland Trail Blazers on Monday.

Magic, buoyed by stout defense, take on depleted Blazers - CBSSports.com

Magic

magic

My ability to remember things can drive my wife crazy. Earlier this week, she asked me two questions after I finished telling her a story that I had recalled from my long-term memory: How do you remember such things? Why do you even remember that memory? Honestly, I have no clue how to answer her questions, only that I recognized this ability earlier in my life. We both agreed that if there was a “useless trivia show,” I would be the world champion … hands down. I feel like some events that I experience for the first time are often indexed in the long-term chambers of my brain, later becoming the source of my stories that I feel compelled to share with her. In terms of career implications, it does not take much effort to recall the first time I worked in a programming language or started using a service or framework. Reading about the service provided by —one that promises “Zero DevOps”—made me want to take it for a test drive. Looking over the list of supported languages, I saw an opportunity to create a service in a programming language that is new to me. Render As a result, I decided to give the programming language a shot, in hopes of adding a new deposit to my long-term memory bank. Go The Magic 8-Ball Service To make things fun, consider an example where asks for the creation of a RESTful API that mimics the toy that was invented in 1946: Mattel Magic 8-Ball For those who are not aware, the Magic 8-Ball is a novelty item that performs basic fortune-telling tasks. With the 8-Ball image facing up, the person holding the ball asks a yes-no question, then turns it over to reveal an answer. The reply magically floats to the top out of the dark black ocean inside the toy. The original design contained 20 unique answers, and the acceptance criteria is that only these responses will be allowed in the RESTful API: Ten positive answers are noted with green bullets, and the remaining ten are an equal combination of non-committal (yellow) and negative (red) answers. Consumers of the API simply need to make a standard GET request, which will return a random answer from the list of possible options. For validation purposes, Mattel would also like a second endpoint to retrieve a list of all available answers from this new service. With my design, we can meet these requirements with the following endpoints: GET - randomly returns one of the twenty available answers /answer GET - returns a list of all twenty available answers /answers Getting Started With Go While visiting the on the Go site, I noticed the installation process was very similar to getting started with the Java programming language. The installer used a wizard-like design where I basically just had to click buttons to navigate through the process. Once finished, I used the following terminal command to verify a successful installation: download page ╭─me@johnjvester ~ 
╰─$ go version
go version go1.17.6 darwin/amd64 Using IntelliJ IDEA, I added the and started a new project using the command: Go plug-in File | New | Project … The project name of just seemed right for this endeavor: magic-eight-ball After some research, I found that the provides the functionality needed for a RESTful API written in Go, so I installed it with the following command: Gin web framework $ go get -u github.com/gin-gonic/gin To reference Gin inside my source code, I simply needed to include the following import: import "github.com/gin-gonic/gin" Now we are ready to start building the Magic 8-Ball API. Creating the Magic 8-Ball API The first thing I want to do is establish the data side of the API. For this example, the following JSON structure will make up the response body when an answer is provided: {
  "id" : number,
  "response" : string
} In Go, this JSON payload is defined (as shown below) and added to the file: main.go type response struct {
	ID       int    `json:"id"`
	Response string `json:"response"`
} Since all of the data is static, I added the standard answers to the file: main.go var responses = []response{
	{ID: 1, Response: "It is certain."},
	{ID: 2, Response: "It is decidedly so."},
	{ID: 3, Response: "Without a doubt."},
	{ID: 4, Response: "Yes definitely."},
	{ID: 5, Response: "You may rely on it."},
	{ID: 6, Response: "As I see it, yes."},
	{ID: 7, Response: "Most likely."},
	{ID: 8, Response: "Outlook good."},
	{ID: 9, Response: "Yes."},
	{ID: 10, Response: "Signs point to yes."},
	{ID: 11, Response: "Reply hazy, try again."},
	{ID: 12, Response: "Ask again later."},
	{ID: 13, Response: "Better not tell you now."},
	{ID: 14, Response: "Cannot predict now."},
	{ID: 15, Response: "Concentrate and ask again."},
	{ID: 16, Response: "Don't count on it."},
	{ID: 17, Response: "My reply is no."},
	{ID: 18, Response: "My sources say no."},
	{ID: 19, Response: "Outlook not so good."},
	{ID: 20, Response: "Very doubtful."},
} In order to leverage the responses variable, I added two functions to the file: main.go func getAllAnswers(c *gin.Context) {
	c.IndentedJSON(http.StatusOK, responses)
}

func getRandomAnswer(c *gin.Context) {
	rand.Seed(time.Now().Unix())
	c.IndentedJSON(http.StatusOK, responses[rand.Intn(len(responses))])
} The function simply returns the responses list in JSON format, and the function randomly picks one of the twenty options and returns the value in JSON format. getAllAnswers() getRandomAnswer() Finally, the function sets up the endpoints and the port used by the service: main() func main() {
	router := gin.Default()
	router.GET("/answers", getAllAnswers)
	router.GET("/answer", getRandomAnswer)

	port := os.Getenv("PORT")
	if port == "" {
		port = "8080"
	}
	if err := router.Run(":" + port); err != nil {
		log.Panicf("error: %s", err)
	}
} That’s all that is required to meet the acceptance criteria established by Mattel. To start the application, we run the following command from the terminal: ╭─me@johnjvester ~/projects/jvc/go/magic-eight-ball 
╰─$ go run . The logs that displayed included those noted below: [GIN-debug] GET    /answers        --> main.getAllAnswers (3 handlers)
[GIN-debug] GET    /answer         --> main.getRandomAnswer (3 handlers)
[GIN-debug] Listening and serving HTTP on :8080 I can test the API by sending curl requests with the following command: ╭─me@johnjvester ~ 
╰─$ curl http://localhost:8080/answer This is the response I received: {
    "id": 6,
    "response": "As I see it, yes."
}% With the Go service running without any issues, I uploaded the code as a new project on GitLab: https://gitlab.com/johnjvester/magic-eight-ball Now I’m ready to see how Render works. Deploying to Render Via GitLab Getting started with Render was quick, easy, and free. I simply opted to use my GitLab credentials, which made things easy when selecting projects to deploy. To get started, I selected the New button on the Render dashboard and chose the Web Service option: I selected my project on GitLab: magic-eight-ball Within the Render platform, I decided to call my service and kept the defaults for all of the other options: jvc-magic-eight-ball Scrolling down, I opted to keep this service running on the free plan: Then, I clicked the Create Web Service button. Within a few minutes, the showed me that my service was up and running: Render dashboard I sent a request to the URL for my service on Render ( ), and I received the following response: https://jvc-magic-eight-ball.onrender.com/answer {
    "id": 5,
    "response": "You may rely on it."
} Within just a few clicks, I deployed my first RESTful API written in Go to the Render platform—and this didn’t incur any costs. More importantly, the Render approach did not require any DevOps skills or knowledge for me to complete my task. Conclusion The introduction of this article talked about my ability to retain and recall historical events several years (if not decades) later. The fact that this entire experiment took less than an hour of my time—from start to finish—is certainly worthy of becoming another one of those events. Starting in 2021, I have been trying to live by the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality which extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester In this article, not only was I able to produce a functional API quickly with very little source code, but I was also able to deploy the project to a cloud provider simply by performing a series of small steps. Zero DevOps was promised, and Zero DevOps was delivered. This allowed me to focus my time on providing features to my customers. If you are interested in the full source code, just navigate over to GitLab: https://gitlab.com/johnjvester/magic-eight-ball Knowing that I currently have APIs and applications running in the Heroku ecosystem, I plan to start looking into what a conversion from Heroku to Render looks like in the next few weeks. So far, I am quite impressed with what Render has to offer. Have a really great day! First published here

Spend Zero Time on DevOps with Render PaaS

They’re pretty incredible, you can use any Invocation name to open your custom app and then any utterance word as the first layer, you can then apply inline ‘slots’ to your command to give you unlimited triggers for different pieces of code. ‘Alexa Ask Your Mind to tell me what they’re thinking.’ ‘Alexa Ask Your Mind to read this persons mind using AI.’ That’s , running different programs. one app two ‘Alexa Ask Your AI Engine to read this persons mind using AI.’ That’s a different app, running a program. completely different What I am trying to show here is that there are 100’s of ways of asking the same question, and each variant can trigger a different signal to the Alexa, which will run a seperate piece of code. This method would mean I could personally read someones mind, then tell the Alexa what card they have and the Alexa would just read it out. Simple. The average consumer is completely unaware of this; in fact, I performed this for the CCO’s (chief cybersecurity officers) of many major tech companies, the kind that have a direct line to goverment officials, and they too were oblivious as to what was going on. You will be surprised how many people in technology have no idea about the scope of some low end consumer tech products we take for granted every day. That’s the output part of my deception. But it’s way too boring for a hacker. I need to work out how to tell the Alexa what card the person is currently thinking of (without signaling it first). More so, I need to find a way to work out what card the person is thinking of, using real magic? There are lots of magic tricks that allow me to peek the card someone is looking at, or to force them to pick a card, however I want to have zero involvement in this routine and also have the option of repeating it multiple times, it has to fool the biggest sceptic. Electronic card NFC reader I built a NFC reader, using an Arduino Uno and a simple RFID reader, it cost no more than 30 bucks. Before this, I hadn’t done any electronics, maybe a few LEDs and resistors back in high school but that was it. I was shocked how easy the Arduino stuff is to work with, I watched a few tutorials and took some code from the Arduino CC library. I then placed RFID chips in between the two paper layers of each playing card. These things are completely undetecable, and can store endless amounts of data. I didn’t need that, I just used the unique IDs stored on the chip, created a json object and paired them to the playing cards. The result was 52 RFID playing cards, made of paper. This worked well, you could ask the spectator to place the card on a table, hide the RFID reader under the table and then the RFID reader would ping the card over the internet to my node server. When you ask the Alexa what the card is, it runs a script on Lambda, queries the node server for the JSON object which stores the current playing card and then the Alexa literally READS OUT THE TEXT. I found that the coolest part, the Alexa can read out text stored on a NFC chip. This all happens in a few 100ms. It was cool enough for , WIRED magazine who wrote about my performance of this hack. (I wanted to be in WIRED ever since Paramount’s remake of The Seth Green’s character claimed to be “ The Real ” and got on the cover. lol) Italian Job, Napster Sadly, this still isn’t good enough, the NFC reader has a 10–20cm range and who wants to put a card down on a table for that? If the magician has to deliver a set of rules to the spectator then he’s already leading them through a maze. I don’t want to make a maze, I want to make The Matrix. $300 and 5 days later, Sparkfun sent this bad boy to me. It’s the M6e simultaneous card reader. It can read multiple UHF RFID chips and from what they have told me, when used with an antenna, has a 10 meter range. Boom. I wired the Arduino to a Ethernet shield and plugged that into a wi-fi hotspot to make it mobile. I used PubNub to send the RFID data from the chip to my node server, it’s lightweight and I also used them for all my America’s Got Talent hacks, but that’s another story (I explained in my TED talk). I wanted to wear this thing in my jacket, so I had a USB bank too. Sadly, the m6e needs a HUGE amount of power, like 2–5amps at full power draw to get 10 meters range. Outsourcing a hacker The whole thing packed into a small case. Yes, I took this on a plane once. I was in over my head, I don’t know about the technical power ratings and numbers and things, but someone else does. Jumped onto Upwork (hire a freelancer website) and put a job up for someone who can make Arduino stuff, explained the chip etc. a few hours later someone came back to me. Robert, my god, what a gangsta. He worked at the US Navy or something, builds radios and stuff, such a solid guy. After $40 he had sent me a full wiring diagram and links to Amazon eletronics I need to buy, he even re-wrote my Arduino code. At this point, I had the entire thing wired up to a power converter pumping out 10amps of current when I needed it. I had to build it into a PC case with multiple fans to keep it cool. Back to Magic trick theory. If I can read someone’s mind just by looking at them ( I can’t, but I can make it look like that, by using magic) then by using this same method, it’s entirely feasible that I can teach this machine to do it, right? A company called saw my WIRED performance and sent me the Nova, a face tracking AI kit that uses OpenCV to control a robotic head. The robot would follow the user, tracking it’s eye movement and animating itself to look creepy. It runs on Arduino tech, allowing me to dynamically code it’s facial movements to sync with the Alexa speech triggers. This offered an emotional level to the AI along with with the camera placed in its right eye, it provided the perfect misdirection. Creoqode If a robot can track your face, moving its head to maintain eye contact, how hard is it then to convince people that it can also read your eye movements and work out what you’re thinking… especially if I do the same thing using ‘magic’ two minutes prior. The result. My RFID box can pretty much cover the entire stage with radio waves. Once a playing card enters that space, you just ask Alexa and she will read out what card is in the space. I had the spectator take a pack of cards, shuffle them, look at one and hide it so that they are the only person that can tell the audience what card they are imagining. What the didn’t know is that I had lined the playing card box with aluminium foil, creating a faraday cage. This blocks all the radio waves from the NFC reader I had placed on the side of the stage. After they take their card I ask them to put the cards back in the box. Essentially, the only only radio waves on stage at this point are the ones coming from the spectators chosen card, the other cards radio waves get blocked out. This prevented anyone from thinking I was using NFC because as we all know, you can’t read more than one NFC chip at once (but I can). Upon approaching the robotic head, the NFC reader is pinging the chip on the card and sending it up to the cloud for my Alexa to read out. However, what it would actually do is get the Alexa to read out all of the cards in the deck, then identify which card the spectator had chosen, pretending that it was looking at movements in the spectators eyes to decide what card they had picked. This final piece of misdirection gave the audience the last piece of the puzzle to lead them down the wrong trail, making them think the AI is looking at the spectator’s eyes to identify what they were thinking. In truth. I was performing a very advanced, never before seen or performed mind trick that magicians and spectators still fail to work out. Once layered under the showmanship of my robotic AI, I had the perfect deception. Why am I telling you this? Think about it for a second. A magician, a trickster, was able to use technology and our ignorance towards it to completely deceive a reputable and trusted news source. I was in New York at the time and it was aired on BBC World News several times over the period of a week, and even saw it at the airport when I was boarding. This is just the tip of the iceberg; my deceptions go darker into our inability to identify the line between real technology and trickery. I don’t do this because I think it’s funny, I don’t do it because I have a hate for ‘the system’. I do it because I genuinely think that most people have no idea what is possible with technology, how it works, or what the people who run most of it are actually doing. We live in a time where communication is driven by one company online. I sit and watch people use apps that are designed to make them do shocking things, whether it’s a free to play game or a social network, and I am blown away at how easy it is to people with them. control Whilst my passion is to shine a light on these phenonum through light hearted entertainment, some magicians have a darker purpose. Some build apps, websites and products that have been designed to change the way we think. I’m just one guy, in his flat, building new technologies to see if he can convince people what he does is magical and mystical. Just one guy. I pose a simple question. If we’re so distracted by the ‘neon lights’ emitted from mankind’s sci-fi creations, will we ever be able see the real intentions behind those inventing them?

How I hacked a news channel to report fake news.

Every day, every minute, and every second, AI is digging deeper into data analytics. It automates routine tasks, writes SQL queries, cleans messy datasets, builds charts — and, at first glance, even seems to understand what it's doing. Well... almost. This raises a very real, slightly unsettling question: Will AI replace data analysts? Or at least those junior analysts still wrangling spreadsheets in Excel? On the surface, it makes sense. Analytics is about spotting patterns, finding correlations, and building scripts. Can AI find correlations? Can it structure data? It can. Put it all together, and AI starts to look like less of an assistant — and more like a real contender on the analytics team. But there’s a catch. Several, in fact. AI in Analytics: Between Expectations and Reality AI in Analytics: Between Expectations and Reality If you take a closer look at what’s being written about AI in data analytics today, you’ll notice a clear divide in perspectives. On one side — there are the overly optimistic takes, usually aimed at beginners. Here, AI is positioned as a universal solution: it quickly finds patterns, cleans data, writes SQL queries, and even draws conclusions. After reading a few of these, you might start to believe analysts are on the verge of becoming obsolete. On the other side — there are experienced professionals raising very valid concerns. These articles focus on AI’s weaknesses: how it struggles with complex cases, generates questionable insights, and doesn’t always grasp what it’s dealing with. The takeaway often sounds like: “AI isn’t a replacement for humans — it’s just a lightweight helper.” But both sides are missing the complete picture. The first tends to overestimate what AI can do. The second risks underestimating the real value it already brings to day-to-day analytical work. So, what are we left to do? We either buy into the hype or ignore a tool that’s already capable of saving hours of effort. The goal of this article is to offer a third perspective: a practical take from those already using AI in real workflows. Let’s see where it helps, where it fails, and how to actually make it work without expecting miracles. a practical take from those already using AI in real workflows. We’ll walk through real-world use cases with ChatGPT, taking a clear look at how AI can complement the analyst’s work and why thoughtful integration is no longer just a trend but a new layer of professional reality. Simple Technical Tasks: How Good Is AI, Really? Simple Technical Tasks: How Good Is AI, Really? Before we dive into the examples, here’s a bit of context. I’m the founder and former CTO of Infolek (until 2022) and currently the CTO of OMNI Digital. I'm focused on building innovative solutions for automating and optimizing business processes in pharmacies and pharmaceutical distribution, including in Kazakhstan. The examples below come from real-world challenges we’ve encountered — drawn directly from actual projects. Example 1: Parsing Kazakhstan Addresses into Segments Example 1: Parsing Kazakhstan Addresses into Segments Imagine a dataset with 30,000 addresses from Kazakhstan. The task is to split each address into separate components: region, district, locality, street, and building number. A straightforward prompt like “split the address into these parts” doesn’t quite work — the structure of the data is too complex. There are many nuances. For instance, if you try to split by delimiter, the number of segments can vary widely — from 3 (e.g., Almaty, Baizakov Street, bld.7) to 8 or more (e.g., Kazakhstan, Aktobe Region, Aktobe, Almaty District, Kargaly Residential Area, Yeset Batyr Microdistrict, 1st Microdistrict, bld.34). Some addresses even contain multiple nested localities. In short — the rules aren’t consistent. Almaty, Baizakov Street, bld.7 Kazakhstan, Aktobe Region, Aktobe, Almaty District, Kargaly Residential Area, Yeset Batyr Microdistrict, 1st Microdistrict, bld.34 The solution? Iteration. Prompt → result → refinement → another prompt. Rinse and repeat — until it works. Solving this manually takes a lot more time. But here’s where AI shines: Given a precise prompt, it can quickly test hypotheses, generate scripts or regular expressions, and adapt to complex conditions. It easily picks out specific subtasks when they’re clearly formulated — like this one: precise prompt, “Create a new column adr1_hc2 that contains a substring composed of the final address segments, joined by commas, that were not previously classified. Extract the digits starting from the first encountered number, up to the first non-numeric character…” adr1_hc2 With clear instructions, AI becomes a powerful helper — fast, consistent, and surprisingly accurate in handling technical logic. With clear instructions, AI becomes a powerful helper — fast, consistent, and surprisingly accurate in handling technical logic. Bulk Data Validation: Where AI Hits Its Limits (and Then Pushes Past Them) Bulk Data Validation: Where AI Hits Its Limits (and Then Pushes Past Them) Example 2: Validating Kazakh Rural Districts Example 2: Validating Kazakh Rural Districts Let’s say you have a spreadsheet with 1,000 rural districts in Kazakhstan — and a list of assumed matches to the administrative regions they belong to. The problem? There’s no reliable open-source table you can just plug into your model. A human would have to search for each entry manually — one by one. ChatGPT handles single-row validation pretty well. Give it one district, and it can cross-check, reason, and suggest corrections. But try to scale that to 1,000 rows in the standard interface, and things get tricky. Fast. But ChatGPT will politely (and sometimes not so politely) refuse. It starts throwing limitations and safety warnings or just says, "I’m sorry, I can’t help with that." The workaround? Use the API. By switching to the API and automating the prompt flow, the task became totally manageable — and was completed successfully. What took hours of manual effort could now be done consistently at scale. The key takeaway: AI can absolutely assist with bulk data validation — but it needs the right infrastructure. For anything beyond small-scale checks, you’ll need to go beyond the chat window and tap into automation. AI can absolutely assist with bulk data validation — but it needs the right infrastructure. For anything beyond small-scale checks, you’ll need to go beyond the chat window and tap into automation. Advanced Analytics: When AI Starts to Struggle Advanced Analytics: When AI Starts to Struggle Example 3: Investigating Data Discrepancies in Pharmaceutical Reports Example 3: Investigating Data Discrepancies in Pharmaceutical Reports A pharmaceutical distributor sends monthly reports. One month, something’s off — 95% of customer names and addresses no longer match any previous reports. It’s not just a formatting issue — it looks like a completely different dataset. A quick manual review showed that about 25% of the records can be partially matched using semi-automated methods. The rest? They needed full-on human review. So, we decided to put AI to the test. First Attempt: Let AI figure it out on its own. We uploaded both files and asked ChatGPT to identify discrepancies and explain what was happening. This resulted in a complete failure. The model doesn't even come close to identifying the issue — it just highlights the obvious mismatches without meaningful analysis.


Second Attempt: Help it along. We gave ChatGPT detailed context: data structure, business logic, reporting rules, possible scenarios, and hypotheses to test. We guided it step by step. Still no luck. Here, the model either offers vague generalizations or latches on to irrelevant details. No progress.


Final Attempt: Use AI as a tool, not a brain. A human analyst takes over. Using ChatGPT for routine checks, hypothesis scripting, and basic structuring, the analyst tested several theories in about 30–40 minutes. Eventually, a pattern emerges: the issue lies in the distributor’s reporting system — confirmed later by their tech support. First Attempt: Let AI figure it out on its own. We uploaded both files and asked ChatGPT to identify discrepancies and explain what was happening. This resulted in a complete failure. The model doesn't even come close to identifying the issue — it just highlights the obvious mismatches without meaningful analysis. First Attempt: Let AI figure it out on its own. We uploaded both files and asked ChatGPT to identify discrepancies and explain what was happening. This resulted in a complete failure. The model doesn't even come close to identifying the issue — it just highlights the obvious mismatches without meaningful analysis. Second Attempt: Help it along. We gave ChatGPT detailed context: data structure, business logic, reporting rules, possible scenarios, and hypotheses to test. We guided it step by step. Still no luck. Here, the model either offers vague generalizations or latches on to irrelevant details. No progress. Second Attempt: Help it along. We gave ChatGPT detailed context: data structure, business logic, reporting rules, possible scenarios, and hypotheses to test. We guided it step by step. Still no luck. Here, the model either offers vague generalizations or latches on to irrelevant details. No progress. Final Attempt: Use AI as a tool, not a brain. A human analyst takes over. Using ChatGPT for routine checks, hypothesis scripting, and basic structuring, the analyst tested several theories in about 30–40 minutes. Eventually, a pattern emerges: the issue lies in the distributor’s reporting system — confirmed later by their tech support. Final Attempt: Use AI as a tool, not a brain. A human analyst takes over. Using ChatGPT for routine checks, hypothesis scripting, and basic structuring, the analyst tested several theories in about 30–40 minutes. Eventually, a pattern emerges: the issue lies in the distributor’s reporting system — confirmed later by their tech support. The takeaway: The takeaway: When it’s about structuring or visualizing known data, AI does well. But when the task requires identifying hidden patterns, dealing with non-linear relationships, or forming new hypotheses — AI hits its current limits. When it’s about structuring or visualizing known data, AI does well. But when the task requires identifying hidden patterns, dealing with non-linear relationships, or forming new hypotheses — AI hits its current limits. It’s not that it’s terrible. It’s just not a replacement for experience, context, and human intuition — at least not yet. AI is great at the boring stuff. It handles repetitive tasks like text parsing, pattern recognition, and basic structuring and even writes scripts and regex faster than most junior analysts. It can even run simple forecasts — but let’s be clear: the quality of those forecasts still depends on a human analyst setting the direction, checking the assumptions, and approving the results. AI can also help visualize data — as long as the metrics are well-defined. But when it comes to identifying non-standard KPIs, it often misses the point entirely. Why? Because novelty isn’t its strength — it's still pattern-matching inside known boundaries. Need to scale? That’s where APIs come in. With the right setup, you can automate large-scale tasks that would normally eat up hours (or days) of manual work. Bulk validation, mass transformations, structured prompting — all of it becomes doable. But there’s a catch. AI still throws out false correlations and misleading conclusions — especially when your data is messy, incomplete, or when the problem isn’t clearly defined. That’s when a human needs to step in. Not just to review the output but to ask the right questions in the first place. The hard truth? You still need a human brain for anything nuanced, nonlinear, or business-specific. Analysts know how to adapt methods to context, create custom metrics, and see patterns no model would notice. Humans bring intuition. AI brings speed. And right now, that’s the real power combo.

AI is Neither the Magical Replacement for Human Analysts Nor a Useless Gimmick

Authors:
(1) Opher Lieber, with Equal contribution; (2) Barak Lenz, with Equal contribution; (3) Hofit Bata; (4) Gal Cohen; (5) Jhonathan Osin; (6) Itay Dalmedigos; (7) Erez Safahi; (8) Shaked Meirom; (9) Yonatan Belinkov; (10) Shai Shalev-Shwartz; (11) Omri Abend; (12) Raz Alon; (13) Tomer Asida; (14) Amir Bergman; (15) Roman Glozman; (16) Michael Gokhman; (17) Avashalom Manevich; (18) Nir Ratner; (19) Noam Rozen; (20) Erez Shwartz; (21) Mor Zusman; (22) Yoav Shoham. Table of Links Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 5. Evaluation In general we approach benchmarks cautiously, as they correlate only partially with what matters in real applications, and furthermore invite gaming the system in order to boast vanity numbers. Nevertheless, we present several indicative results. 5.1 Academic Benchmarks We report results with a wide range of standard academic benchmarks: Common sense reasoning: HellaSwag (10-shot) [47], WinoGrande (5-shot) [37], ARC-E (0-shot) and ARC-Challenge (25-shot) [9], and PIQA (zero-shot) [3]. Reading Comprehension: BoolQ (10-shots) [8] and QuAC (zero-shot) [5]. Others: GSM8K (3-shot CoT) [10], HumanEval (pass@1) [4], Natural Questions closed-book (NQ; 5-shot) [26], and TruthfulQA (zero-shot) [27]. Aggregate benchmarks: MMLU (5-shot) [20] and BBH (3-shot) [43]. Table 2 compares Jamba to several publicly available models on common academic benchmarks for evaluating language models. We compare with Llama-2 13B [45], which has about the same number of active paramters as our model, Llama-2 70B, which is larger than our model, Gemma [44], which has 7B parameters, and Mixtral [23], which has about the same number of active and total parameters as our model. Noticebly, Jamba performs comparably to the leading publicly available models of similar or larger size, including Llama-2 70B and Mixtral. At the same time, our model has a smaller number of total available parameters than Llama-2 (52B compared to 70B). Moreover, as a sparse model, Jamba has only 12B active parameters, similar to Mixtral’s 12.9B active parameters. However, as a fullyattentional model, Mixtral has a large memory footprint with long sequences, requiring 32GB for the KV cache with 256K tokens. In contrast, thanks to its hybrid Attention-Mamba architecture, Jamba’s KV cache takes only 4GB even at such a long context (Section 2). Importantly, our Jamba achieves such a strong performance while having much better throughput than Llama-2 70B and Mixtral, up to 3x improvement (Section 3.2). In summary, Jamba demostrates the ability of hybrid architectures to reach the performance of state-of-the-art Transformer based models of the same size class, while having the benefits of an SSM. 5.2 Long-Context Evaluations We have successfully trained Jamba models with context lengths of up to 1M tokens. The released model handles context lengths of up to 256K tokens. In this section, we evaluate it on synthetic and naturalistic benchmarks that test is long-context capabilities. 5.2.1 Needle-in-a-haystack As Figure 4 shows, Jamba has excellent performance in the needle-in-a-haystack evaluation, which requires retrieving a simple statement planted in a long context window [24]. This result is noteworthy especially given that our implementation of Jamba uses only 4 attention layers. 5.2.2 Naturalistic long-context evaluation We evaluate Jamba’s ability to handle long contexts using question-answering benchmarks, consisting of long inputs. To this end, we repurpose five of the longest-context datasets from L-Eval [2], by structuring them in a few-shot format (we use 3-shots in all experiments here). Specifically, we evaluated the models on the following datasets: NarrativeQA (QA on narratives; [25]), LongFQA (finance; [2]), Natural Questions (NQ; Wikipedia; [26]), CUAD (law; [21]), and SFiction (science fiction). The average input length in these datasets ranges from 6K to 62K tokens. These context lengths are further highly expanded by the few-shot format. Table 3 summarizes the evaluation results, in terms of F1. Jamba outperforms Mixtral on most of the datasets as well as on average. In addition, as these long-context tasks require substantial computation, here Jamba’s efficiency shines, with much better throughput with long contexts (Section 3.2). This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Opher Lieber, with Equal contribution; (2) Barak Lenz, with Equal contribution; (3) Hofit Bata; (4) Gal Cohen; (5) Jhonathan Osin; (6) Itay Dalmedigos; (7) Erez Safahi; (8) Shaked Meirom; (9) Yonatan Belinkov; (10) Shai Shalev-Shwartz; (11) Omri Abend; (12) Raz Alon; (13) Tomer Asida; (14) Amir Bergman; (15) Roman Glozman; (16) Michael Gokhman; (17) Avashalom Manevich; (18) Nir Ratner; (19) Noam Rozen; (20) Erez Shwartz; (21) Mor Zusman; (22) Yoav Shoham. Authors: Authors: (1) Opher Lieber, with Equal contribution; (2) Barak Lenz, with Equal contribution; (3) Hofit Bata; (4) Gal Cohen; (5) Jhonathan Osin; (6) Itay Dalmedigos; (7) Erez Safahi; (8) Shaked Meirom; (9) Yonatan Belinkov; (10) Shai Shalev-Shwartz; (11) Omri Abend; (12) Raz Alon; (13) Tomer Asida; (14) Amir Bergman; (15) Roman Glozman; (16) Michael Gokhman; (17) Avashalom Manevich; (18) Nir Ratner; (19) Noam Rozen; (20) Erez Shwartz; (21) Mor Zusman; (22) Yoav Shoham. Table of Links Part 1 Part 1 Part 2 Part 2 Part 3 Part 3 Part 4 Part 4 Part 5 Part 5 Part 6 Part 6 5. Evaluation In general we approach benchmarks cautiously, as they correlate only partially with what matters in real applications, and furthermore invite gaming the system in order to boast vanity numbers. Nevertheless, we present several indicative results. 5.1 Academic Benchmarks We report results with a wide range of standard academic benchmarks: Common sense reasoning: HellaSwag (10-shot) [47], WinoGrande (5-shot) [37], ARC-E (0-shot) and ARC-Challenge (25-shot) [9], and PIQA (zero-shot) [3]. Common sense reasoning: Reading Comprehension: BoolQ (10-shots) [8] and QuAC (zero-shot) [5]. Reading Comprehension: Others : GSM8K (3-shot CoT) [10], HumanEval (pass@1) [4], Natural Questions closed-book (NQ; 5-shot) [26], and TruthfulQA (zero-shot) [27]. Others Aggregate benchmarks: MMLU (5-shot) [20] and BBH (3-shot) [43]. Aggregate benchmarks: Table 2 compares Jamba to several publicly available models on common academic benchmarks for evaluating language models. We compare with Llama-2 13B [45], which has about the same number of active paramters as our model, Llama-2 70B, which is larger than our model, Gemma [44], which has 7B parameters, and Mixtral [23], which has about the same number of active and total parameters as our model. Noticebly, Jamba performs comparably to the leading publicly available models of similar or larger size, including Llama-2 70B and Mixtral. At the same time, our model has a smaller number of total available parameters than Llama-2 (52B compared to 70B). Moreover, as a sparse model, Jamba has only 12B active parameters, similar to Mixtral’s 12.9B active parameters. However, as a fullyattentional model, Mixtral has a large memory footprint with long sequences, requiring 32GB for the KV cache with 256K tokens. In contrast, thanks to its hybrid Attention-Mamba architecture, Jamba’s KV cache takes only 4GB even at such a long context (Section 2). Importantly, our Jamba achieves such a strong performance while having much better throughput than Llama-2 70B and Mixtral, up to 3x improvement (Section 3.2). In summary, Jamba demostrates the ability of hybrid architectures to reach the performance of state-of-the-art Transformer based models of the same size class, while having the benefits of an SSM. 5.2 Long-Context Evaluations We have successfully trained Jamba models with context lengths of up to 1M tokens. The released model handles context lengths of up to 256K tokens. In this section, we evaluate it on synthetic and naturalistic benchmarks that test is long-context capabilities. 5.2.1 Needle-in-a-haystack 5.2.1 Needle-in-a-haystack As Figure 4 shows, Jamba has excellent performance in the needle-in-a-haystack evaluation, which requires retrieving a simple statement planted in a long context window [24]. This result is noteworthy especially given that our implementation of Jamba uses only 4 attention layers. 5.2.2 Naturalistic long-context evaluation 5.2.2 Naturalistic long-context evaluation We evaluate Jamba’s ability to handle long contexts using question-answering benchmarks, consisting of long inputs. To this end, we repurpose five of the longest-context datasets from L-Eval [2], by structuring them in a few-shot format (we use 3-shots in all experiments here). Specifically, we evaluated the models on the following datasets: NarrativeQA (QA on narratives; [25]), LongFQA (finance; [2]), Natural Questions (NQ; Wikipedia; [26]), CUAD (law; [21]), and SFiction (science fiction). The average input length in these datasets ranges from 6K to 62K tokens. These context lengths are further highly expanded by the few-shot format. Table 3 summarizes the evaluation results, in terms of F1. Jamba outperforms Mixtral on most of the datasets as well as on average. In addition, as these long-context tasks require substantial computation, here Jamba’s efficiency shines, with much better throughput with long contexts (Section 3.2). This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

256K Tokens on One GPU? Jamba’s Engineering Magic Explained

Authors:
(1) Simone Silvestri, Massachusetts Institute of Technology, Cambridge, MA, USA;
(2) Gregory Wagner, Massachusetts Institute of Technology, Cambridge, MA, USA;
(3) Christopher Hill, Massachusetts Institute of Technology, Cambridge, MA, USA;
(4) Matin Raayai Ardakani, Northeastern University, Boston, MA, USA;
(5) Johannes Blaschke, Lawrence Berkeley National Laboratory, Berkeley, CA, USA;
(6) Valentin Churavy, Massachusetts Institute of Technology, Cambridge, MA, USA;
(7) Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, MA, USA;
(8) Navid Constantinou, Australian National University, Canberra, ACT, Australia;
(9) Alan Edelman, Massachusetts Institute of Technology, Cambridge, MA, USA;
(10) John Marshall, Massachusetts Institute of Technology, Cambridge, MA, USA;
(11) Ali Ramadhan, Massachusetts Institute of Technology, Cambridge, MA, USA;
(12) Andre Souza, Massachusetts Institute of Technology, Cambridge, MA, USA;
(13) Raffaele Ferrari, Massachusetts Institute of Technology, Cambridge, MA, USA. Authors: Authors: (1) Simone Silvestri, Massachusetts Institute of Technology, Cambridge, MA, USA; (2) Gregory Wagner, Massachusetts Institute of Technology, Cambridge, MA, USA; (3) Christopher Hill, Massachusetts Institute of Technology, Cambridge, MA, USA; (4) Matin Raayai Ardakani, Northeastern University, Boston, MA, USA; (5) Johannes Blaschke, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; (6) Valentin Churavy, Massachusetts Institute of Technology, Cambridge, MA, USA; (7) Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, MA, USA; (8) Navid Constantinou, Australian National University, Canberra, ACT, Australia; (9) Alan Edelman, Massachusetts Institute of Technology, Cambridge, MA, USA; (10) John Marshall, Massachusetts Institute of Technology, Cambridge, MA, USA; (11) Ali Ramadhan, Massachusetts Institute of Technology, Cambridge, MA, USA; (12) Andre Souza, Massachusetts Institute of Technology, Cambridge, MA, USA; (13) Raffaele Ferrari, Massachusetts Institute of Technology, Cambridge, MA, USA. Table of Links Abstract and 1 Justification Abstract and 1 Justification 2 Performance Attributes 2 Performance Attributes 3 Overview of the Problem 3 Overview of the Problem 4 Current State of the Art 4 Current State of the Art 5 Innovations 5 Innovations 5.1 Starting from scratch with Julia 5.1 Starting from scratch with Julia 5.2 New numerical methods for finite volume fluid dynamics on the sphere 5.2 New numerical methods for finite volume fluid dynamics on the sphere 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability 6 How performance was measured 6 How performance was measured 7 Performance Results and 7.1 Scaling Results 7 Performance Results and 7.1 Scaling Results 7.2 Energy efficiency 7.2 Energy efficiency 8 Implications 8 Implications 9 Acknowledgments and References 9 Acknowledgments and References 5.1 Starting from scratch with Julia Oceananigans.jl is an open-source library for ocean-flavored fluid dynamics written from scratch in Julia [7]. Julia is a dynamic high-level programming language that leverages Just-In-Time (JIT) compilation and LLVM [24] to achieve performance competitive with traditional HPC languages like C or Fortran. Julia has gathered interest as potential language for HPC [17, 10, 16, 21, 27] and provides easy integration with MPI [47, 38]. Most of Oceananigans.jl software is hardware-agnostic through the Julia package KernelAbstractions.jl [10], which enables performance portability targeting CPUs and different GPU vendors using the JuliaGPU [6, 5] software stack, similar to the capabilities provided by Kokkos [9], OCCA [30], and HIP [1]. To our knowledge, Oceananigans is the first ocean model written from scratch for GPUs, rather than ported from existing CPU code. Starting from scratch and using the Julia programming language allowed us to rethink the typical patterns used in ocean and atmosphere dynamical cores. In particular, we developed a system of composable atomic operators that leverages Julia’s functional programming paradigm and effective inlining capabilities to recursively construct large expression trees for calculus on staggered finite volume grids. Using this composable operator system, we fuse the entire tendency computation for each prognostic variable into a single compute-heavy kernel, each of which depends on only two intermediate diagnostic variables representing hydrostatic pressure and vertical diffusivity (which is treated implicitly using a predictor-corrector method). Such a high degree of abstraction yields a number of innovations: first, kernel fusion maximizes efficiency on GPUs. Second, almost all intermediate quantities are computed on-the-fly, so that Oceananigans is extremely memory efficient and can perform global ocean simulations at resolutions up to 1/4th degree on a single Nvidia V100. Finally, because all compute-heavy kernels rely on a single “tendency kernel function” applied at each grid index i, j, k, we can easily optimize performance by rapidly prototyping techniques to overlap computation and communication. The sparsity of kernels per time-step and small number of temporary variables mean that Oceananigans’ algorithmic structure is markedly different from current ocean models, which typically allocate 10 to 100 times the minimum necessary memory [4] and distribute computations across many small kernels [51]. We argue these algorithmic differences are a major factor in Oceananigans’ energy-efficiency and time-to-solution on GPU systems. 5.2 New numerical methods for finite volume fluid dynamics on the sphere Our results use Oceananigans.HydrostaticFreeSurfaceModel, which solves the hydrostatic Boussinesq equations in a finite volume framework on staggered C-grids [3]. Oceananigans’ hydrostatic model employs an implicit-explicit second-order Adams-Bashforth time stepping scheme. Vertically implicit diffusion is implemented with a backward Euler time-discretization and tridiagonal solver. A major innovation is a new adaptive-order scheme based on weighted essentially non-oscillatory (WENO) reconstructions [42] for advecting momentum and tracers on curvilinear finite-volume grids [43]. This new scheme automatically adapts to changing spatial resolution and permits stable, high-fidelity simulations of ocean turbulence without explicit dissipation or hyper-dissipation. This innovation reduces setup time when changing or increasing resolution while guaranteeing high-fidelity solutions that exhibit the minimum necessary dissipation of sharp, near-grid scale features. 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability In hydrostatic ocean models with a free surface, the vertically-averaged, two-dimensional “barotropic mode” has dynamics orders of magnitude faster than the three-dimensional “baroclinic” component, and must be treated by a special “barotropic solver”. Due to communication overhead, barotropic solvers in current ocean models — whether implicit or explicit — are a major bottleneck that accounts for between 40% [22] to 60% [48, 36] of the cost of a typical IPCC-class ocean simulations. Oceananigans’ excellent scalability is enabled by an innovative optimization of the parallel barotropic solver. An increase in computation is traded in for decreased communication latency by leveraging the two-dimensionality of the barotropic problem. Our new barotropic solver is based on explicit subcycling of the barotropic mode. Increasing the width of the barotropic halo to equal the number of explicit subcycles (typically between 10–30) greatly decreases the frequency of communication. As a result, communication is required once per time-step rather than every subcycle, reducing the frequency of communication by a factor of 10 to 30. The cost of the barotropic solver is therefore less than 10% of the total cost of a time step. Due to the sparsity of communication enabled by our novel barotropic solver, all communication operations can be overlapped with computational workloads as sketched in figure 2. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

From Code to Currents: Julia and GPU Magic Transform Ocean Modeling Efficiency

Authors:
(1) S M Rakib Hasan, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh (sm.rakib.hasan@g.bracu.ac.bd);
(2) Aakar Dhakal, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh (aakar.dhakal@g.bracu.ac.bd). Table of Links Abstract and I. Introduction II. Literature Review III. Methodology IV. Results and Discussion V. Conclusion and Future Work, and References **Abstract—**In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this type of malware, as it leaves no discernible traces on the system. In this research, we propose a simple and cost-effective obfuscated malware detection system through memory dump analysis, utilizing diverse machine-learning algorithms. The study focuses on the CIC-MalMem-2022 dataset, designed to simulate real-world scenarios and assess memory-based obfuscated malware detection. We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps. Our analysis spans multiple malware categories, providing insights into algorithmic strengths and limitations. By offering a comprehensive assessment of machine learning algorithms for obfuscated malware detection through memory analysis, this paper contributes to ongoing efforts to enhance cybersecurity and fortify digital ecosystems against evolving and sophisticated malware threats. The source code is made open access for reproducibility and future research endeavors. It can be accessed at https://bit.ly/MalMemCode I. INTRODUCTION The rise of internet connectivity and smart devices has transformed various sectors, but it has also led to an evolving threat landscape, including sophisticated malware targeting interconnected systems. Obfuscated malware, adept at concealing itself, presents a significant challenge to conventional cybersecurity methods. Traditional heuristic-based or signature-based systems struggle to identify such elusive threats, necessitating a shift towards innovative and adaptive detection mechanisms. This paper explores obfuscated malware detection through multiclass classification, aiming to bridge the gap between evolving threats and advanced detection methods using machine learning. We analyze various algorithms, including decision trees, ensemble methods, support vector machines, and neural networks, to uncover their capabilities and limitations in identifying obfuscated malware. Acknowledging the significance of class imbalance in real-world datasets, especially in malware detection, we investigate techniques such as under sampling (Edited Nearest Neighbor Rule, Near Miss Rule, Random Under sampling, and All KNN Under sampling) and synthetic data generation using the ADASYN method to address this challenge. Our research, based on the CIC-MalMem-2022 dataset, simulates real-world scenarios for memory-based obfuscated malware detection. By meticulously analyzing machine learning algorithms and data balancing techniques, we contribute to fortifying cybersecurity against evolving malware threats. In the following sections, we delve into our dataset, methodologies, and results, aiming to provide valuable insights that can shape the future of malware detection and cybersecurity strategies amidst the challenges posed by obfuscated malware and class imbalance. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) S M Rakib Hasan, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh (sm.rakib.hasan@g.bracu.ac.bd); (2) Aakar Dhakal, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh (aakar.dhakal@g.bracu.ac.bd). Authors: Authors: (1) S M Rakib Hasan, Department of Computer Science and Engineering, BRAC University , Dhaka, Bangladesh (sm.rakib.hasan@g.bracu.ac.bd); Department of Computer Science and Engineering, BRAC University (2) Aakar Dhakal, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh (aakar.dhakal@g.bracu.ac.bd). Department of Computer Science and Engineering, BRAC University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Literature Review II. Literature Review III. Methodology III. Methodology IV. Results and Discussion IV. Results and Discussion V. Conclusion and Future Work, and References V. Conclusion and Future Work, and References ** Abstract —**In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this type of malware, as it leaves no discernible traces on the system. In this research, we propose a simple and cost-effective obfuscated malware detection system through memory dump analysis, utilizing diverse machine-learning algorithms. The study focuses on the CIC-MalMem-2022 dataset, designed to simulate real-world scenarios and assess memory-based obfuscated malware detection. We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps. Our analysis spans multiple malware categories, providing insights into algorithmic strengths and limitations. By offering a comprehensive assessment of machine learning algorithms for obfuscated malware detection through memory analysis, this paper contributes to ongoing efforts to enhance cybersecurity and fortify digital ecosystems against evolving and sophisticated malware threats. The source code is made open access for reproducibility and future research endeavors. It can be accessed at https://bit.ly/MalMemCode Abstract I. INTRODUCTION The rise of internet connectivity and smart devices has transformed various sectors, but it has also led to an evolving threat landscape, including sophisticated malware targeting interconnected systems. Obfuscated malware, adept at concealing itself, presents a significant challenge to conventional cybersecurity methods. Traditional heuristic-based or signature-based systems struggle to identify such elusive threats, necessitating a shift towards innovative and adaptive detection mechanisms. This paper explores obfuscated malware detection through multiclass classification, aiming to bridge the gap between evolving threats and advanced detection methods using machine learning. We analyze various algorithms, including decision trees, ensemble methods, support vector machines, and neural networks, to uncover their capabilities and limitations in identifying obfuscated malware. Acknowledging the significance of class imbalance in real-world datasets, especially in malware detection, we investigate techniques such as under sampling (Edited Nearest Neighbor Rule, Near Miss Rule, Random Under sampling, and All KNN Under sampling) and synthetic data generation using the ADASYN method to address this challenge. Our research, based on the CIC-MalMem-2022 dataset, simulates real-world scenarios for memory-based obfuscated malware detection. By meticulously analyzing machine learning algorithms and data balancing techniques, we contribute to fortifying cybersecurity against evolving malware threats. In the following sections, we delve into our dataset, methodologies, and results, aiming to provide valuable insights that can shape the future of malware detection and cybersecurity strategies amidst the challenges posed by obfuscated malware and class imbalance. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Hide and Seek in Memory: Outsmarting Sneaky Malware with Data Magic

Errors in a PowerShell script can prevent successful execution. Implementing try-catch blocks enables error management and response. This post covers try/catch basics and how to handle custom error messages in PowerShell. Effective error handling saves troubleshooting time and improves user experience. PowerShell offers robust options for exception management with try, catch, and finally blocks. Learn to use try-catch for graceful error handling and to add custom error messages for enhanced feedback. Why Use Exception Handling in PowerShell? Scripts can fail for many reasons: missing files, invalid input, or network issues, to name a few. With exception handling, you can capture these issues, inform users in a friendly way, and potentially recover from errors without crashing your script. Using try-catch, you can: Catch specific errors.
Display user-friendly messages.
Log errors for debugging. Syntax overview of Try/Catch Like similar in other programming languages, the try-catch block syntax is very simple and syntax will be the same. It is framed with two sections enclosed in curly brackets (the first block is a try and the second is the catch block). try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} The main purpose of using the try-catch block, we can start to manipulate the error output and make it more friendly for the user. Example 1: After executing the below script, the below error will be shown on the screen as output and it would occupy some space and the problem may not be immediately visible to the User. So you can use a try-catch block to manipulate the error output and make it more friendly. without Try-Catch block Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” with Try Catch block In the below script, we added the ErrorAction parameter with a value of Stop to the command. Not all errors are considered “terminating”, so sometimes we need to add this bit of code in order to properly terminate into the catch block. try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} Example 2: Using the $Error Variable To improve the code and follow best practices, consider the following improvements: Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user.
Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0], consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Here's a refined version of how you might handle errors using $Error: try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
} Errors in a PowerShell script can prevent successful execution. Implementing try-catch blocks enables error management and response. This post covers try/catch basics and how to handle custom error messages in PowerShell. Effective error handling saves troubleshooting time and improves user experience. PowerShell offers robust options for exception management with try , catch , and finally blocks. Learn to use try-catch for graceful error handling and to add custom error messages for enhanced feedback. PowerShell script PowerShell script try catch finally try-catch error error Why Use Exception Handling in PowerShell? Scripts can fail for many reasons: missing files, invalid input, or network issues, to name a few. With exception handling, you can capture these issues, inform users in a friendly way, and potentially recover from errors without crashing your script. Using try-catch , you can: try-catch Catch specific errors. Display user-friendly messages. Log errors for debugging. Catch specific errors. Display user-friendly messages. Log errors for debugging. Syntax overview of Try/Catch Syntax overview of Try/Catch Like similar in other programming languages, the try-catch block syntax is very simple and syntax will be the same. It is framed with two sections enclosed in curly brackets (the first block is a try and the second is the catch block). try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} The main purpose of using the try-catch block, we can start to manipulate the error output and make it more friendly for the user. Example 1: Example 1: After executing the below script, the below error will be shown on the screen as output and it would occupy some space and the problem may not be immediately visible to the User. So you can use a try-catch block to manipulate the error output and make it more friendly. without Try-Catch block without Try-Catch block without Try-Catch block Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” with Try Catch block with Try Catch block with Try Catch block In the below script, we added the ErrorAction parameter with a value of Stop to the command. Not all errors are considered “terminating”, so sometimes we need to add this bit of code in order to properly terminate into the catch block. ErrorAction Stop try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} Example 2: Example 2: Using the $Error Variable Using the $Error Variable Using the $Error Variable $Error To improve the code and follow best practices, consider the following improvements: Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user. Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0], consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user. Use More Descriptive Error Messages: Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0] , consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Avoid Hardcoding Indexes: $Error[0] .First() Here's a refined version of how you might handle errors using $Error : $Error try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
} try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
}

How to Stop Your Scripts from Crashing with Try-Catch Magic

Hello 👋 How often have you wished for a method that doesn't exist on collections or string helpers? You start chaining methods, only to hit a wall when one of them turns out to be missing. Honestly, it's understandable; frameworks, you know, are a one-size-fits-all thing. I found myself in this situation multiple times. Every time, before diving into how to extend the framework, I check to see if what I want to extend is macroable or not. But what does that mean? That's exactly what we'll be exploring! WTF Are Macros? 🍏 Let's say we have this JWT: $jwt = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c'; And we need to extract the headers: str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson(); // does not exist 😞

//  BadMethodCallException  Method Illuminate\Support\Stringable::fromJson does not exist. The fromJson() doesn't exist 😔 Sure, one could simply do: json_decode(str($jwt)->before('.')->fromBase64()); But where's the fun in that? Plus, it is my article 🤷 So, we need a way to extend the Stringable class. There are a few ways to do this, but Laravel thought ahead, it knew that developers might want to add custom methods, so it made the class macroable, or as I like to call it, extendable. If you inspect the Illuminate\Support\Stringable class, you'll see it uses a Macroable trait. Let's go ahead and extend the class. In the AppServiceProvider, add the following: <?php

namespace App\Providers;

use Illuminate\Support\Stringable;
use Illuminate\Support\ServiceProvider;

class AppServiceProvider extends ServiceProvider
{
    public function boot(): void
    {
        Stringable::macro('fromJson', function (bool $associative = true) {
            return json_decode($this->value, $associative);
        });
    }
} Now, let's rerun the code: str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson();

// ["alg" => "HS256", "typ" => "JWT"] It works perfectly 🎉 But now, you might be wondering, how did this work? And what exactly is $this->value? What in the Harry Potter is going on? Unveiling the Magic 🪄 We know that the Stringable class uses the Macroable trait, which provides the macro() method. Let's take a closer look at what it does: // src/Illuminate/Macroable/Traits/Macroable.php

/**
 * Register a custom macro.
 *
 * @param  string  $name
 * @param  object|callable  $macro
 *
 * @param-closure-this static  $macro
 *
 * @return void
 */
public static function macro($name, $macro)
{
    static::$macros[$name] = $macro;
} It's pretty straightforward; it just saves the callback to a static macros array. Now, if we inspect the trait further, we will find the __call method, which is triggered every time a non-existent method is called. In our case, that's fromJson(). Let's dive in: /**
 * Dynamically handle calls to the class.
 *
 * @param  string  $method
 * @param  array  $parameters
 * @return mixed
 *
 * @throws \BadMethodCallException
 */
public function __call($method, $parameters)
{
    if (! static::hasMacro($method)) {
        throw new BadMethodCallException(sprintf(
            'Method %s::%s does not exist.', static::class, $method
        ));
    }

    $macro = static::$macros[$method];

    if ($macro instanceof Closure) {
        $macro = $macro->bindTo($this, static::class);
    }

    return $macro(...$parameters);
} First, it checks if a macro is registered, which is the case with fromJson(), it then fetches the callback (or object) from the macros array. Now, for the magic trick; if the macro is a closure (as in our case), it calls bindTo() which essentially tells the closure that $this should refer to whatever is passed as the first argument. In this case, it is the Stringable instance, which happens to have the $value attribute. // $this here is the stringable
// $this inside the closure is now referencing the stringable class
$macro->bindTo($this, static::class); And this is why we can do $this->value. We Can Do Better: Mixins 🧩 There is one more thing I want to show you! When we extend the same class a couple of times, the service provider might get messy very quickly. We can extract all our custom macros to a class called a Mixin. Let's create a StringableMixin: <?php

namespace App\Macros;

use Closure;

class StringableMixin
{
    public function fromJson(): Closure
    {
        return function (bool $associative = true) {
            json_decode($this->value, $associative);
        };
    }

    // Add more macros here as needed
} Now, in AppServiceProvider, we can register this mixin: use App\Macros\StringableMixin;
use Illuminate\Support\Stringable;

class AppServiceProvider extends ServiceProvider
{
    public function boot(): void
    {
        Stringable::mixin(new StringableMixin);
    }
} And that's it! Now, we can do: str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson(); Which is basically the same, just a bit cleaner. If you are curious about how this works, the mixin() method on the Macroable trait uses the reflection API. It retrieves all the public methods from the Mixin class, expects each to return a closure, and then registers the closure as a macro just like we have seen earlier. The Poor IDE 🥲 Well, as you have seen, there is a lot of magic going on, and the IDE wouldn't know about the defined macros. If you're working within a team, other developers won't know about these macros either, which is not good. Luckily, there are tools to help you with that. A free and open-source option is the Laravel IDE helper package. You can install the package and generate the _ide_helper.php file, and you should be good to go. And So It Ends.. Our example is fairly simple, but you can push macros much further than this, as most of the common classes Laravel ships with are macroable. For instance, you can add a new apiResponse() macro, or anything that you feel is very common in your app's logic and is being repeated more than it should be. But don't overdo it. Macros add a new layer of complexity, and when working in a team, they could be confusing. So, whenever you feel something is missing from your application, but not from the framework itself, use macros 🪄 Hello 👋 How often have you wished for a method that doesn't exist on collections or string helpers? You start chaining methods, only to hit a wall when one of them turns out to be missing. Honestly, it's understandable; frameworks, you know, are a one-size-fits-all thing. I found myself in this situation multiple times. Every time, before diving into how to extend the framework, I check to see if what I want to extend is macroable or not. But what does that mean? That's exactly what we'll be exploring! macroable WTF Are Macros? 🍏 Let's say we have this JWT: $jwt = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c'; $jwt = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c'; And we need to extract the headers: str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson(); // does not exist 😞

//  BadMethodCallException  Method Illuminate\Support\Stringable::fromJson does not exist. str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson(); // does not exist 😞

//  BadMethodCallException  Method Illuminate\Support\Stringable::fromJson does not exist. The fromJson() doesn't exist 😔 Sure, one could simply do: fromJson() json_decode(str($jwt)->before('.')->fromBase64()); json_decode(str($jwt)->before('.')->fromBase64()); But where's the fun in that? Plus, it is my article 🤷 So, we need a way to extend the Stringable class. There are a few ways to do this, but Laravel thought ahead, it knew that developers might want to add custom methods, so it made the class macroable , or as I like to call it, extendable. Stringable macroable If you inspect the Illuminate\Support\Stringable class, you'll see it uses a Macroable trait. If you inspect the Illuminate\Support\Stringable class, you'll see it uses a Macroable trait. Illuminate\Support\Stringable Macroable Let's go ahead and extend the class. In the AppServiceProvider , add the following: AppServiceProvider <?php

namespace App\Providers;

use Illuminate\Support\Stringable;
use Illuminate\Support\ServiceProvider;

class AppServiceProvider extends ServiceProvider
{
    public function boot(): void
    {
        Stringable::macro('fromJson', function (bool $associative = true) {
            return json_decode($this->value, $associative);
        });
    }
} <?php

namespace App\Providers;

use Illuminate\Support\Stringable;
use Illuminate\Support\ServiceProvider;

class AppServiceProvider extends ServiceProvider
{
    public function boot(): void
    {
        Stringable::macro('fromJson', function (bool $associative = true) {
            return json_decode($this->value, $associative);
        });
    }
} Now, let's rerun the code: str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson();

// ["alg" => "HS256", "typ" => "JWT"] str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson();

// ["alg" => "HS256", "typ" => "JWT"] It works perfectly 🎉 But now, you might be wondering, how did this work? And what exactly is $this->value ? What in the Harry Potter is going on? $this->value Unveiling the Magic 🪄 We know that the Stringable class uses the Macroable trait, which provides the macro() method. Let's take a closer look at what it does: Stringable Macroable macro() // src/Illuminate/Macroable/Traits/Macroable.php

/**
 * Register a custom macro.
 *
 * @param  string  $name
 * @param  object|callable  $macro
 *
 * @param-closure-this static  $macro
 *
 * @return void
 */
public static function macro($name, $macro)
{
    static::$macros[$name] = $macro;
} // src/Illuminate/Macroable/Traits/Macroable.php

/**
 * Register a custom macro.
 *
 * @param  string  $name
 * @param  object|callable  $macro
 *
 * @param-closure-this static  $macro
 *
 * @return void
 */
public static function macro($name, $macro)
{
    static::$macros[$name] = $macro;
} It's pretty straightforward; it just saves the callback to a static macros array. Now, if we inspect the trait further, we will find the __call method, which is triggered every time a non-existent method is called. In our case, that's fromJson() . Let's dive in: macros __call fromJson() /**
 * Dynamically handle calls to the class.
 *
 * @param  string  $method
 * @param  array  $parameters
 * @return mixed
 *
 * @throws \BadMethodCallException
 */
public function __call($method, $parameters)
{
    if (! static::hasMacro($method)) {
        throw new BadMethodCallException(sprintf(
            'Method %s::%s does not exist.', static::class, $method
        ));
    }

    $macro = static::$macros[$method];

    if ($macro instanceof Closure) {
        $macro = $macro->bindTo($this, static::class);
    }

    return $macro(...$parameters);
} /**
 * Dynamically handle calls to the class.
 *
 * @param  string  $method
 * @param  array  $parameters
 * @return mixed
 *
 * @throws \BadMethodCallException
 */
public function __call($method, $parameters)
{
    if (! static::hasMacro($method)) {
        throw new BadMethodCallException(sprintf(
            'Method %s::%s does not exist.', static::class, $method
        ));
    }

    $macro = static::$macros[$method];

    if ($macro instanceof Closure) {
        $macro = $macro->bindTo($this, static::class);
    }

    return $macro(...$parameters);
} First, it checks if a macro is registered, which is the case with fromJson() , it then fetches the callback (or object) from the macros array. Now, for the magic trick; if the macro is a closure (as in our case), it calls bindTo() which essentially tells the closure that $this should refer to whatever is passed as the first argument. In this case, it is the Stringable instance, which happens to have the $value attribute. fromJson() bindTo() $this Stringable $value // $this here is the stringable
// $this inside the closure is now referencing the stringable class
$macro->bindTo($this, static::class); // $this here is the stringable
// $this inside the closure is now referencing the stringable class
$macro->bindTo($this, static::class); And this is why we can do $this->value . $this->value We Can Do Better: Mixins 🧩 There is one more thing I want to show you! When we extend the same class a couple of times, the service provider might get messy very quickly. We can extract all our custom macros to a class called a Mixin. Let's create a StringableMixin : StringableMixin <?php

namespace App\Macros;

use Closure;

class StringableMixin
{
    public function fromJson(): Closure
    {
        return function (bool $associative = true) {
            json_decode($this->value, $associative);
        };
    }

    // Add more macros here as needed
} <?php

namespace App\Macros;

use Closure;

class StringableMixin
{
    public function fromJson(): Closure
    {
        return function (bool $associative = true) {
            json_decode($this->value, $associative);
        };
    }

    // Add more macros here as needed
} Now, in AppServiceProvider , we can register this mixin: AppServiceProvider use App\Macros\StringableMixin;
use Illuminate\Support\Stringable;

class AppServiceProvider extends ServiceProvider
{
    public function boot(): void
    {
        Stringable::mixin(new StringableMixin);
    }
} use App\Macros\StringableMixin;
use Illuminate\Support\Stringable;

class AppServiceProvider extends ServiceProvider
{
    public function boot(): void
    {
        Stringable::mixin(new StringableMixin);
    }
} And that's it! Now, we can do: str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson(); str($jwt)
    ->before('.')
    ->fromBase64()
    ->fromJson(); Which is basically the same, just a bit cleaner. Which is basically the same, just a bit cleaner. If you are curious about how this works, the mixin() method on the Macroable trait uses the reflection API . It retrieves all the public methods from the Mixin class, expects each to return a closure, and then registers the closure as a macro just like we have seen earlier. mixin() Macroable reflection API The Poor IDE 🥲 Well, as you have seen, there is a lot of magic going on, and the IDE wouldn't know about the defined macros. If you're working within a team, other developers won't know about these macros either, which is not good. Luckily, there are tools to help you with that. A free and open-source option is the Laravel IDE helper package. Laravel IDE helper You can install the package and generate the _ide_helper.php file, and you should be good to go. _ide_helper.php And So It Ends.. Our example is fairly simple, but you can push macros much further than this, as most of the common classes Laravel ships with are macroable. For instance, you can add a new apiResponse() macro, or anything that you feel is very common in your app's logic and is being repeated more than it should be. But don't overdo it. Macros add a new layer of complexity, and when working in a team, they could be confusing. apiResponse() So, whenever you feel something is missing from your application, but not from the framework itself, use macros 🪄

Laravel Under The Hood - The Magic of Macros

Have you ever found yourself frustrated by distracting people, logos, or background clutter that disrupts the visual harmony of your photos? This problem can be particularly aggravating for photographers and content creators who strive for perfection. Introducting MagicEraser.Pro, an innovative AI-powered tool designed to tackle these challenges head-on. By leveraging advanced algorithms, MagicEraser.Pro allows users to effortlessly remove unwanted objects from their images, transforming them into polished works of art. In this review, we will explore how this tool works, its key features, and whether it truly lives up to the hype in enhancing your photo editing experience. What is MagicEraser.Pro? MagicEraser.Pro is an advanced AI tool designed to simplify the process of removing unwanted elements from images. It allows users to effortlessly eliminate distractions such as people, objects, and text, ensuring that the focus remains on the intended subject. With a user-friendly interface, MagicEraser.Pro caters to both novices and experienced editors, making it accessible for various applications, from real estate to e-commerce. The tool supports multiple image formats and boasts features like batch editing, enabling users to modify several photos simultaneously. Its precision in object removal is powered by sophisticated algorithms that ensure clean edits without leaving behind artifacts. Additionally, the Pro version offers high-resolution downloads and priority support, enhancing the overall user experience. Overall, it stands out as a versatile asset for anyone looking to enhance their photo editing workflow with efficiency and ease. When Can You Use MagicEraser? MagicEraser.Pro offers a seamless solution for refining your photographs and removing unwanted elements. Explore the numerous ways you can utilize MagicEraser.Pro to elevate your images. Removing People from Photos Swiftly clear out unwanted figures, like random tourists or event spectators, to keep the spotlight on your intended subject. Eliminating Text and Logos Wipe away any distracting text, captions, or logos to craft images that are clean and visually striking. Enhancing Image Focus Trim away background distractions such as buildings, trees, or clutter to accentuate the main subject of your photograph. Getting Rid of Watermarks Effortlessly erase obtrusive watermarks, date stamps, or location markers that detract from the overall beauty of your images. Improving Image Quality Seamlessly eliminate eyesores like litter, power lines, or unwanted shadows, resulting in a polished and professional appearance. Correcting Imperfections Refine your photos by removing minor flaws like scratches, dust, or stains, ensuring every detail shines with clarity. MagicEraser.Pro is designed for photographers, graphic designers, and hobbyists seeking an efficient and user-friendly tool to optimize their images. What's The Standout Benefits of This Magic Eraser? The following features collectively make MagicEraser.Pro a versatile and powerful tool for anyone looking to enhance their photography with ease. Free to Use: Completely free with unlimited usage after a simple sign-up, making it accessible for everyone. High-Quality Results: Utilizes advanced image processing technology to deliver clear and precise edits, ensuring excellent image quality. Quick and Simple: Streamlined interface allows for fast removal of unwanted elements, perfect for busy professionals and casual users alike. User-Friendly Interface: Intuitive design that makes it easy for anyone to navigate and use the tool effectively, regardless of their technical skill level. Batch Processing: Ability to edit multiple images at once, saving time for users who need to enhance several photos quickly. Real-Time Preview: See changes in real-time as you edit, allowing for immediate adjustments and ensuring satisfaction with the final result. Supports Various Formats: Compatible with popular image formats like JPG, PNG, and WebP, providing flexibility in editing. How to Remove Unwanted Objects from Photo With MagicEraser.Pro? Follow these simple steps to experience the magic of MagicEraser.Pro: Step 1: Upload Your Image Begin by uploading your image to the MagicEraser.Pro platform. The tool supports popular formats such as JPG, PNG, AVIF, and WEBP, ensuring compatibility with a wide range of image files. Please note that the maximum file size allowed is 50MB. Step 2: Select the Element to Erase Using the intuitive selection tool, mark the area or object you wish to remove from your photo. The AI-powered algorithm will automatically detect and select the marked area, making the process quick and easy. Step 3: Review and Refine Once the AI has selected the marked area, review the selection to ensure accuracy. You can refine the selection using the available tools, such as the Brush or Lasso tool, for precise adjustments. Step 4: Erase and Save After finalizing your selection, let MagicEraser.Pro work its magic by clicking the "Erase" button. In just a few moments, the unwanted element will be seamlessly removed from your photo. Save your edited image and admire the stunning results! Wrap Ups MagicEraser.Pro is the perfect solution for photographers, graphic designers, and hobbyists looking to optimize their images without the hassle of traditional editing software. Experience the future of image editing with MagicEraser.Pro – visit the website today and discover a world of endless creative possibilities. This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author Have you ever found yourself frustrated by distracting people, logos, or background clutter that disrupts the visual harmony of your photos? This problem can be particularly aggravating for photographers and content creators who strive for perfection. Introducting MagicEraser.Pro , an innovative AI-powered tool designed to tackle these challenges head-on. By leveraging advanced algorithms, MagicEraser.Pro allows users to effortlessly remove unwanted objects from their images, transforming them into polished works of art. MagicEraser.Pro MagicEraser.Pro In this review, we will explore how this tool works, its key features, and whether it truly lives up to the hype in enhancing your photo editing experience. What is MagicEraser.Pro? MagicEraser.Pro is an advanced AI tool designed to simplify the process of removing unwanted elements from images. It allows users to effortlessly eliminate distractions such as people, objects, and text, ensuring that the focus remains on the intended subject. With a user-friendly interface, MagicEraser.Pro caters to both novices and experienced editors, making it accessible for various applications, from real estate to e-commerce. The tool supports multiple image formats and boasts features like batch editing, enabling users to modify several photos simultaneously. Its precision in object removal is powered by sophisticated algorithms that ensure clean edits without leaving behind artifacts. Additionally, the Pro version offers high-resolution downloads and priority support, enhancing the overall user experience. Overall, it stands out as a versatile asset for anyone looking to enhance their photo editing workflow with efficiency and ease. When Can You Use MagicEraser? MagicEraser.Pro offers a seamless solution for refining your photographs and removing unwanted elements. Explore the numerous ways you can utilize MagicEraser.Pro to elevate your images. Removing People from Photos Swiftly clear out unwanted figures, like random tourists or event spectators, to keep the spotlight on your intended subject. Eliminating Text and Logos Wipe away any distracting text, captions, or logos to craft images that are clean and visually striking. Enhancing Image Focus Trim away background distractions such as buildings, trees, or clutter to accentuate the main subject of your photograph. Getting Rid of Watermarks Effortlessly erase obtrusive watermarks, date stamps, or location markers that detract from the overall beauty of your images. Improving Image Quality Seamlessly eliminate eyesores like litter, power lines, or unwanted shadows, resulting in a polished and professional appearance. Correcting Imperfections Refine your photos by removing minor flaws like scratches, dust, or stains, ensuring every detail shines with clarity. MagicEraser.Pro is designed for photographers, graphic designers, and hobbyists seeking an efficient and user-friendly tool to optimize their images. What's The Standout Benefits of This Magic Eraser? The following features collectively make MagicEraser.Pro a versatile and powerful tool for anyone looking to enhance their photography with ease. Free to Use: Completely free with unlimited usage after a simple sign-up, making it accessible for everyone. Free to Use: Completely free with unlimited usage after a simple sign-up, making it accessible for everyone. High-Quality Results: Utilizes advanced image processing technology to deliver clear and precise edits, ensuring excellent image quality. High-Quality Results: Utilizes advanced image processing technology to deliver clear and precise edits, ensuring excellent image quality. Quick and Simple: Streamlined interface allows for fast removal of unwanted elements, perfect for busy professionals and casual users alike. Quick and Simple: Streamlined interface allows for fast removal of unwanted elements, perfect for busy professionals and casual users alike. User-Friendly Interface: Intuitive design that makes it easy for anyone to navigate and use the tool effectively, regardless of their technical skill level. User-Friendly Interface: Intuitive design that makes it easy for anyone to navigate and use the tool effectively, regardless of their technical skill level. Batch Processing: Ability to edit multiple images at once, saving time for users who need to enhance several photos quickly. Batch Processing: Ability to edit multiple images at once, saving time for users who need to enhance several photos quickly. Real-Time Preview: See changes in real-time as you edit, allowing for immediate adjustments and ensuring satisfaction with the final result. Real-Time Preview: See changes in real-time as you edit, allowing for immediate adjustments and ensuring satisfaction with the final result. Supports Various Formats: Compatible with popular image formats like JPG, PNG, and WebP, providing flexibility in editing. Supports Various Formats: Compatible with popular image formats like JPG, PNG, and WebP, providing flexibility in editing. How to Remove Unwanted Objects from Photo With MagicEraser.Pro? Follow these simple steps to experience the magic of MagicEraser.Pro: Step 1: Upload Your Image Begin by uploading your image to the MagicEraser.Pro platform. The tool supports popular formats such as JPG, PNG, AVIF, and WEBP, ensuring compatibility with a wide range of image files. Please note that the maximum file size allowed is 50MB. Step 2: Select the Element to Erase Using the intuitive selection tool, mark the area or object you wish to remove from your photo. The AI-powered algorithm will automatically detect and select the marked area, making the process quick and easy. Step 3: Review and Refine Once the AI has selected the marked area, review the selection to ensure accuracy. You can refine the selection using the available tools, such as the Brush or Lasso tool, for precise adjustments. Step 4: Erase and Save After finalizing your selection, let MagicEraser.Pro work its magic by clicking the "Erase" button. In just a few moments, the unwanted element will be seamlessly removed from your photo. Save your edited image and admire the stunning results! Wrap Ups MagicEraser.Pro is the perfect solution for photographers, graphic designers, and hobbyists looking to optimize their images without the hassle of traditional editing software. Experience the future of image editing with MagicEraser.Pro – visit the website today and discover a world of endless creative possibilities. This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Stories, News & Mentions about MAGIC

Categories

Trending Topics

Stories, News & Mentions about MAGIC

Magic’s Stories on HackerNoon

Magic's News & Mentions

Categories

Trending Topics