#it's essentially a parsing error

[getvalentined]

The result of that flash poll I did the other day, Riv wound up winning so here he is!

Random OC lore below for anyone interested.

Riv is the oldest of the aur, a species unintentionally formed from the energetic aftershocks of the creation of his planet. Because there was only so much of that energy to go around, there are a limited number of "souls" available for their species, and thus the aur have a static population. Although functionally immortal, they do lose neuroelasticity over time, which eventually makes living pretty unpleasant, so they inevitably opt to pass away and allow a new member of the species to be born.

Several thousand years ago, Riv contracted a particularly dangerous magical condition that left him discolored—he used to be a very pale apricot color and his hair was opalescent white—and with chronic pain, but also keeps him from losing neuroelasticity, allowing him to live basically forever without experiencing the ennui that is the literal death of the rest of his species.

Travelers of other species who came across the aur in ancient times wound up essentially engaging in a millennia-long game of telephone that led to a gross misunderstanding of what they actually looked like, which is where the concept of unicorns comes from. When the aur finally went public as a species to get people to stop killing each other, everyone was very surprised to find that they look nothing like horses or deer. (Although they do have hooves, which is what led to the mistranslation that brought about that misconception in the first place.)

[mariacallous]

One phrase encapsulates the methodology of nonfiction master Robert Caro: Turn Every Page. The phrase is so associated with Caro that it’s the name of the recent documentary about him and of an exhibit of his archives at the New York Historical Society. To Caro it is imperative to put eyes on every line of every document relating to his subject, no matter how mind-numbing or inconvenient. He has learned that something that seems trivial can unlock a whole new understanding of an event, provide a path to an unknown source, or unravel a mystery of who was responsible for a crisis or an accomplishment. Over his career he has pored over literally millions of pages of documents: reports, transcripts, articles, legal briefs, letters (45 million in the LBJ Presidential Library alone!). Some seemed deadly dull, repetitive, or irrelevant. No matter—he’d plow through, paying full attention. Caro’s relentless page-turning has made his work iconic.

In the age of AI, however, there’s a new motto: There’s no need to turn pages at all! Not even the transcripts of your interviews. Oh, and you don’t have to pay attention at meetings, or even attend them. Nor do you need to read your mail or your colleagues’ memos. Just feed the raw material into a large language model and in an instant you’ll have a summary to scan. With OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude as our wingmen, summary reading is what now qualifies as preparedness.

LLMs love to summarize, or at least that’s what their creators set them about doing. Google now “auto-summarizes” your documents so you can “quickly parse the information that matters and prioritize where to focus.” AI will even summarize unread conversations in Google Chat! With Microsoft Copilot, if you so much as hover your cursor over an Excel spreadsheet, PDF, Word doc, or PowerPoint presentation, you’ll get it boiled down. That’s right—even the condensed bullet points of a slide deck can be cut down to the … more essential stuff? Meta also now summarizes the comments on popular posts. Zoom summarizes meetings and churns out a cheat sheet in real time. Transcription services like Otter now put summaries front and center, and the transcription itself in another tab.

Why the orgy of summarizing? At a time when we’re only beginning to figure out how to get value from LLMs, summaries are one of the most straightforward and immediately useful features available. Of course, they can contain errors or miss important points. Noted. The more serious risk is that relying too much on summaries will make us dumber.

Summaries, after all, are sketchy maps and not the territory itself. I’m reminded of the Woody Allen joke where he zipped through War and Peace in 20 minutes and concluded, “It’s about Russia.” I’m not saying that AI summaries are that vague. In fact, the reason they’re dangerous is that they’re good enough. They allow you to fake it, to proceed with some understanding of the subject. Just not a deep one.

As an example, let’s take AI-generated summaries of voice recordings, like what Otter does. As a journalist, I know that you lose something when you don’t do your own transcriptions. It’s incredibly time-consuming. But in the process you really know what your subject is saying, and not saying. You almost always find something you missed. A very close reading of a transcript might allow you to recover some of that. Having everything summarized, though, tempts you to look at only the passages of immediate interest—at the expense of unearthing treasures buried in the text.

Successful leaders have known all along the danger of such shortcuts. That’s why Jeff Bezos, when he was CEO of Amazon, banned PowerPoint from his meetings. He famously demanded that his underlings produce a meticulous memo that came to be known as a “6-pager.” Writing the 6-pager forced managers to think hard about what they were proposing, with every word critical to executing, or dooming, their pitch. The first part of a Bezos meeting is conducted in silence as everyone turns all 6 pages of the document. No summarizing allowed!

To be fair, I can entertain a counterargument to my discomfort with summaries. With no effort whatsoever, an LLM does read every page. So if you want to go beyond the summary, and you give it the proper prompts, an LLM can quickly locate the most obscure facts. Maybe one day these models will be sufficiently skilled to actually identify and surface those gems, customized to what you’re looking for. If that happens, though, we’d be even more reliant on them, and our own abilities might atrophy.

Long-term, summary mania might lead to an erosion of writing itself. If you know that no one will be reading the actual text of your emails, your documents, or your reports, why bother to take the time to dig up details that make compelling reading, or craft the prose to show your wit? You may as well outsource your writing to AI, which doesn’t mind at all if you ask it to churn out 100-page reports. No one will complain, because they’ll be using their own AI to condense the report to a bunch of bullet points. If all that happens, the collective work product of a civilization will have the quality of a third-generation Xerox.

As for Robert Caro, he’s years past his deadline on the fifth volume of his epic LBJ saga. If LLMs had been around when he began telling the president’s story almost 50 years ago—and he had actually used them and not turned so many pages—the whole cycle probably would have been long completed. But not nearly as great.

[uroboros-if]

ChoiceScript Savepoint System Very Quickly

Hey guys,

@hpowellsmith made a great template for save points! It requires you to create another variable for every variable you have in your ChoiceScript game, so that it can store the old values to essentially "save"! This won't rely on third-party saving systems but is rather hard-coded into the game itself.

I realize that it can be a daunting task to create a whole other set of variables, especially if you already have many, many of them. (Looking at TSS' code, there are thousands!)

But I propose two super quick ways to automatically create all the variables you need for save points.

Find and replace.

Copy all your *create variables

Paste it into a Google Docs

On, PC, Ctrl+H to open up the dialog box for Find and Replace (link on how to find and replace on different platforms)

Search for "*create " (space included at the end) and replace it with *create save_

Hit "Replace All" and there you have your duplicated variables to paste into your startup (do so without replacing any of your old variables).

Bonus: you can instead replace it with *create save1_ , *create save2_ , etc. to have multiple save slots.

You can create all your needed variables in startup quickly with this, but there is still the issue of having to *set the variables to the new variables (when you're saving) or vice versa (when loading).

Hence the other way:

Save System Generator

I also made a program where, if you copy and paste all of your *create variables, it will automatically:

Give you code to put in your startup (the duplicated save variables)

Give you code that you use to save.

Give you code that you use to load.

I recommend you do it the way Hannah PS does in their template by calling a *gosub_scene.

Here are the step by step instructions on how to do this:

1. Prepare your *create variables. To clarify, you will only put in *create stuff into the program. Copy from your very first *create to your very last *create (the variables you want to save at least). Do not add any comments or additional code that is NOT *create. Do not have any additional spaces at the end (line breaks in between *create should be fine, but be more aware for potential errors).

2. Create a .txt file. In Hannah's template, the file is called "savegame.txt". You will want to make a *label save and a *label load that each *return (as depicted above).

3. Load up the program. Here is the link.

4. Pasting in your code. Paste in your code and immediately after your last *create, press enter, press $, and press enter again.

Note 1: You cannot use Ctrl+V or shortcut keys to paste in the code. You have to right click and paste it. Do not do this on mobile.

Note 2: You might want to do this in segments, as the program might have difficulty parsing through it, and you will more easily find errors in case they happen. Maybe every 30-50 variables to keep them bite-sized. I've tested inputting up to 70 unique variables to success.

5. Startup variables. After reading your input, it will give you code that you then have to add to your startup. Copy it by highlighting and right-clicking on it (do not use shortcut keys or do this on mobile).

6. Save. If you press S and enter, it will give you the code that you need to put in your savegame.txt under your *label save .

7, Load. If you press L and enter, it will give you the code you need to put in your savegame.txt under your *label load .

8. Using it. As in the template, you'll want to call on this with a *gosub_scene savegame load (if you want to load) or *gosub_scene savegame save (if you want to save).

And that's it! Please let me know if the program works incorrectly! 💕💕

[rametarin]

Psychosis is not a mark of intellect.

Psychopathic people mistake the ability to manipulate other people and exploit their emotions to be a mark of intelligence. They have a belief in their own superiority and flatter themselves with the idea that because they can break the rules of how feelings work, trick people into thinking they're emotionally neuro typical only to deceive them for their own ends, it makes them a smarter, more mature person.

That's not how intelligence works. People like this aren't smarter, they're broken. Just as the ability to lie and disrupt communications doesn't make you more intelligent, it makes you a violent predator. Just using a different means to exploit, trap and deprive your prey. And when it's your own family or species, that's just virtually cannibalism.

Exploiting somebody's trust is not a mark of intelligence, it's a mark of someone that does not have those inhibitions natural in a functioning brain. The willingness to suspend them for selfish reasons is not something to praise. And that's kind of why you have all these disgusting assholes calling themselves empaths or "dark empaths." You aren't some gifted genius, you're a monster. And because of people like yours predations, others have to learn to reign in their emotions in disbelief you could act like this, just to deal with you.

It's easy as pie to deceive and manipulate people that trust you or think you also share those healthy social and emotional inhibitions. The same ones that go off like error messages in your brain if you kill someone. Those same ones that make you sleepless if you unknowingly engage in cannibalism- even if it's necessary to survive. You can rationalize it all you want, but objectively speaking, we're animals. We're hard wired for certain things, and to not do certain things. People not missing these essential things have to cultivate violating them in order to condition themselves to continue doing them. It's not a mark of supremacy or cleverness to exploit another person by deception or manipulation. It comes natural to people that are broken and willing to engage in that sort of behavior.

Often I've come across people that thought they were superior for their willingness to exploit someone else. That being able to extract something from another and get away with it was proof of their supremacy, or at least, that of another's inferiority. If you confront them and tell them you know they're being dishonest and deceptive, their brains interpret that as, "Hey! You took advantage of how I'm too dumb to comprehend what you did!" And take it as a compliment. The inexperienced person confronting the deceiver expects the person receiving this to come clean or acknowledge they did wrong and panic because they've been caught. But that's not how a person built like this reacts, unless it's also another form of manipulation.

I'm lucky enough that as a child I had a firsthand experience with a peer like this that was a rowdy little boy. Because it meant, not only did I get the hard, cold life lessons of what dealing with a manipulative psychopath meant pushed on me, and the time to parse it out, it also meant I got to beat his fucking ass for being a manipulative and violent shit. So badly, he screamed hysterically for his mother. And then I never saw his disgusting, psychotic self again.

[zerosecurity]

New Android Malware SoumniBot Employs Innovative Obfuscation Tactics

Banking Trojan Targets Korean Users by Manipulating Android Manifest

A sophisticated new Android malware, dubbed SoumniBot, is making waves for its ingenious obfuscation techniques that exploit vulnerabilities in how Android apps interpret the crucial Android manifest file. Unlike typical malware droppers, SoumniBot's stealthy approach allows it to camouflage its malicious intent and evade detection. Exploiting Android Manifest Weaknesses According to researchers at Kaspersky, SoumniBot's evasion strategy revolves around manipulating the Android manifest, a core component within every Android application package. The malware developers have identified and exploited vulnerabilities in the manifest extraction and parsing procedure, enabling them to obscure the true nature of the malware. SoumniBot employs several techniques to obfuscate its presence and thwart analysis, including: - Invalid Compression Method Value: By manipulating the compression method value within the AndroidManifest.xml entry, SoumniBot tricks the parser into recognizing data as uncompressed, allowing the malware to evade detection during installation. - Invalid Manifest Size: SoumniBot manipulates the size declaration of the AndroidManifest.xml entry, causing overlay within the unpacked manifest. This tactic enables the malware to bypass strict parsers without triggering errors. - Long Namespace Names: Utilizing excessively long namespace strings within the manifest, SoumniBot renders the file unreadable for both humans and programs. The Android OS parser disregards these lengthy namespaces, facilitating the malware's stealthy operation.

Example of SoumniBot Long Namespace Names (Credits: Kaspersky) SoumniBot's Malicious Functionality Upon execution, SoumniBot requests configuration parameters from a hardcoded server, enabling it to function effectively. The malware then initiates a malicious service, conceals its icon to prevent removal, and begins uploading sensitive data from the victim's device to a designated server. Researchers have also highlighted SoumniBot's capability to search for and exfiltrate digital certificates used by Korean banks for online banking services. This feature allows threat actors to exploit banking credentials and conduct fraudulent transactions. Targeting Korean Banking Credentials SoumniBot locates relevant files containing digital certificates issued by Korean banks to their clients for authentication and authorization purposes. It copies the directory containing these digital certificates into a ZIP archive, which is then transmitted to the attacker-controlled server. Furthermore, SoumniBot subscribes to messages from a message queuing telemetry transport server (MQTT), an essential command-and-control infrastructure component. MQTT facilitates lightweight, efficient messaging between devices, helping the malware seamlessly receive commands from remote attackers. Some of SoumniBot's malicious commands include: - Sending information about the infected device, including phone number, carrier, and Trojan version - Transmitting the victim's SMS messages, contacts, accounts, photos, videos, and online banking digital certificates - Deleting contacts on the victim's device - Sending a list of installed apps - Adding new contacts on the device - Getting ringtone volume levels With its innovative obfuscation tactics and capability to target Korean banking credentials, SoumniBot poses a significant threat to South Korean Android users. Read the full article

[running2reanimation]

Minecraft Crew

(Background/side characters that I have essentially made into OCs)

Name: Teal Pronouns: She/they Occupation: Team Lead - The Minecraft Experience Age: Mid-30s A hard worker with a strong sense of duty. Has desperately searched for the cause of the glitch or error that happened that evening. She is not the one who invented the technology, but she's the most well-versed in its operation.

Name: Indigo Pronouns: He/they Occupation: Operator - The Minecraft Experience Age: Early 30s A sensitive fellow, he is very loyal to Teal, they have been friends since college. If it weren't for Teal he probably would have walked away from the business altogether after what happened that evening, but has instead joined Teal in her investigation. He's better at parsing code than Teal is.

Name: Fern Pronouns: They/them Occupation: Intern - The Minecraft Experience Age: Early 20's They really wish they had actually read the waiver they were having people sign. This was supposed to be a simple job! Has felt disillusioned with the industry but doesn't know how to escape it - it's what their education is in.

Name: Sky Pronouns: They/them Occupation: Operator - The Minecraft Experience Age: Mid-20's Another operator who works the booths sometimes. Sneaks into the game to steal game objects to sell on the black market. Not particularly concerned about the incident at Booth 30. Cocky, but their confidence isn't completely unwarranted. Friends with Purple.

[tonydaddingham]

So something terrible happens which makes future Crowley go back to try to fix it and there's just 2 Crowleys running around in the present? Oh, and thanks for explaining!

Regarding not taking yourself seriously: I may not be entirely convinced by this particular theory - or any, yet - but I don't think time travel is completely out there or impossible either. Considering the way Adam resets things after the failed Apocalypse, the timeline clearly can be messed with, as can time itself, as Crowley repeatedly demonstrates. I saw the post you reblogged about the rugs and we are rapidly moving out of the territory of plausible deniability regarding the sheer number of bizarre continuity errors. Any one or two of them on their own, yes, but collectively?

If you do go looking back through the minisodes, Crowley's hair seems to go shorter-longer-shorter in Job and his sideburns look like they get quite a bit shorter in the crypt in the Resurrectionists. I didn't see anything in the Nazis minisode, but that doesn't meant nothing's there.

further ask:

hi anon!!!✨ first of all, im so sorry for not getting round to your asks until now!!!

re: first ask - mhm that's the half-baked idea, anyhow!!! and tbh 💀 im not completely convinced either but i like to entertain the possibility just out of Fun, so here we are!!!✨ oh god The Rugs - so the red one, that appears during the ball? okay sure i can accept that it is part of the Austen Aesthetic, and once the magic lifts it shifts back to the normal s2.

as for the s1 one... im torn. because i saw the amazing post where they hand-painted the mf sink tiles bc they would be in the background of a couple of shots, and wanted to at least be as close to the s1 ones as possible (GO crew honestly do the Mostest). and yeah okay, re: the difference between the s1 and s2 rugs, maybe it's that they thought 'well it's going to be on the floor most of the time and therefore out of shot' but. there are two shots that literally focus on it. as the focal point. so to my mind, they either literally couldn't find a like for like replacement (completely valid), or something Fishy is going on.

ive seen a couple of people remark on the flashbacks potentially being skewed because they're from aziraphale's perspective, but ive genuinely had the half-baked idea that the whole season is. there's so many in-story indicators, to my mind - biased red/yellow colour grading, the cartoony loch ness animation in ep3, and tbh the whole ball thing - and i do wonder if this whole rug sitch (as well as other Unexplained Things) might be chalked up to this very thing; that we are seeing s2 for the mostly part literally through aziraphale's eyes, and that what we see is a little... altered. magicked. as i said, half-baked idea, but there we are.

i did end up going through ACtO, and it's currently sat in my drafts at the moment because... well, idk what to make of it. the scenes where - by my estimation - he has the longer, more defined-curl wig, is every shot in job's house (three scenes, iirc), and so it might actually, if you consider that these scenes were likely filmed in alternative days to the other ACtO scene, a plain continuity/wig-availability issue. plus, when looking at the dialogue, all the scenes in some way link together (so i don't, essentially, think it can feasibly be the same time-travel theory). the only thing, i guess, that still remains valid is that we are seeing a recount of the events of ACtO as per aziraphale's retelling... but even then, there are plenty of scenes where they are very heavy in the crowley perspective (ie it doesn't feel like aziraphale is fudging anything), so this doesn't 100% feel like a true explanation either imo.

i do still need to look at the resurrectionists minisode though, so may well be able to parse some crackpot musing once ive done that!!!✨

[weareallfromearth]

I would also like to add regarding these tags: the sentiments about creative failure being indelible and cancelling out all your good work are not true and accurate to all or even most environments. There are a couple environments where they may feel true or where some bad actors may behave in a way where they become as good as true:

- in abusive relationships, including caregiver relationships you might experience early in life, you may have received the kinds of criticism that teach you never to try because failure cancels out success

- on the internet in extremely high visibility contexts - viral posts or for celebrities - some people will take it upon themselves to mock or cancel people just for making understandable errors, particularly where those errors can be parsed as a failure to care enough about the needs of everyone else on the damn planet everywhere, OR where those errors may involve having some trait that is mockable according to conventional societal standards of various flavours (eg “oh look this person screwed up while being non-normative in their social performance or gender or looks, we are shitty bullies so we’re gonna mock them”)

The vast majority of functional human beings do not agree that “if you draw a line wrong you’re a fraud and an impostor” or “you are your mistakes” or “everyone hates you forever”. These are beliefs that arise from a distorted world view, potentially arising from negative prior experiences but sometimes just arising from your brain fucking with you by way of anxiety disorder.

Running events in a way where there’s an error is a matter of scale. If you mess up something about the physical safety of an event and people are injured that may be a big deal, but if you undercater, or forget to invite someone, or your accessibility could use improvement, these are not indelible failures, they’re errors where we can learn iteratively.

If you give advice and you give incorrect advice, the scale of the error matters a lot, but often your prior training can help you heaps. If giving advice as a hobby or calling stresses you out enormously because of the potential risk to others, it’s ok to not prioritise that option. But there are low stakes areas where an error is just not a big deal, or where the advice you’re giving is a matter of taste.

In terms of combatting the belief that any failure is essentially terminal, cognitive strategies - the kind you might find in therapies like CBT or ACT - can be really helpful. Another thing that can help is low stakes practice - trying out failure in a controlled safe environment in small doses with people you trust, to give your brain and nervous system the experience of feeling, over and over again, that failure can be ok.

I think people get mixed up a lot about what is fun and what is rewarding. These are two very different kinds of pleasure. You need to be able to tell them apart because if you don't have a balanced diet of both then it will fuck you up, and I mean that in a "known cause of persistent clinical depression" kind of way.

[grapheneai]

Employee Feedback Analysis: Unlock Key Insights with AI

Understanding employee sentiment is more critical than ever. Feedback analysis provides companies with valuable insights into their workforce’s morale, engagement, and overall job satisfaction. But traditional methods of gathering and analyzing feedback can be slow and labor-intensive, often failing to capture timely issues.  

Enter AI-driven employee feedback analysis—a transformative solution that offers companies the ability to identify key themes from surveys, interviews, and social media data in as little as 24 hours.

The Role of Employee Feedback in Business Success 

Effective employee feedback is essential for organizational success. It serves as a direct line of communication between employees and management, helping to uncover potential issues and areas for improvement. When organizations actively listen to and address employee concerns, they foster an environment of trust and engagement, which in turn boosts productivity, morale and retention. 

However, analyzing vast amounts of feedback manually, whether in text, voice, or video formats, is a daunting task. HR teams may find themselves overwhelmed by the volume of data, causing delays in identifying critical issues that need immediate attention. This is where AI-based employee feedback analysis steps in to offer a powerful, efficient solution. 

AI-Based Employee Feedback Analysis: How It Works 

AI-driven employee feedback analysis leverages machine learning (ML) and natural language processing (NLP) to automatically process large amounts of data. Whether the feedback is in the form of text from surveys, transcripts from interviews, or even social media posts, AI can swiftly analyze the content and extract key themes, sentiments, and actionable insights. Here’s how it works: 

Data Collection: Feedback can be gathered from multiple sources—employee surveys, interviews, internal communication channels, or public platforms such as social media. 

Analysis: AI models analyze this data using advanced NLP techniques to detect recurring themes, identify sentiment (positive, negative, or neutral), and highlight urgent issues or topics that may require management intervention. 

Insights Delivery: Within 24 hours, the system generates comprehensive reports that provide HR professionals and leaders with a clear overview of key findings. These insights allow for timely decision-making, helping companies address concerns before they escalate into larger problems. 

By automating this process, AI enables businesses to act on employee feedback almost immediately, turning insights into action without the delays associated with manual analysis. 

The Benefits of AI-Driven Feedback Analysis 

The advantages of using AI for employee feedback analysis are numerous: 

Speed and Efficiency

Traditional methods of analyzing employee feedback are often slow and laborious. AI-based solutions provide results within 24 hours, allowing HR teams and management to respond to issues in real time. This quick turnaround is critical for addressing urgent concerns and maintaining a positive work environment. 

Ability to Parse Multiple Data Inputs

AI tools can process vast amounts of data from diverse formats (text, voice, video), offering a holistic view of employee sentiment. By analyzing multiple sources, companies can gain deeper insights into their workforce’s mood and engagement levels, leading to more informed decision-making. 

Improved Accuracy

Manual analysis is prone to human error and can miss subtle patterns in feedback. AI is unbiased as opposed to humans. So, it avoids cases where bias can creep in when humans analyse the data, especially when it is not in favor of certain organizational teams. 

Real-Time Sentiment Tracking

With AI-based systems, companies can continuously monitor employee sentiment over time. This enables HR teams to detect shifts in morale early, preventing potential disengagement and turnover before they become widespread issues. 

Actionable Recommendations

AI feedback systems don’t just present raw data; they provide actionable insights. Companies can prioritize which areas need attention and take steps to resolve problems, leading to a more engaged and satisfied workforce. 

Applications in Modern Workplaces 

AI-driven feedback analysis is particularly useful in remote or hybrid work environments, where it can be challenging to gauge employee satisfaction through traditional means. Since remote employees may not have regular face-to-face interactions with HR or management, AI-based analysis ensures their voices are still heard, even from a distance. 

Furthermore, AI-based tools are valuable for tracking employee sentiment during major organizational changes such as mergers, acquisitions, or company restructuring. By monitoring feedback in real-time, businesses can manage change more effectively, addressing employee concerns as they arise. 

Transforming HR Practices with AI 

The future of feedback analysis lies in AI, its impact on human resources is profound, and GrapheneAI has a solution to analyse employee feedback. By automating the feedback analysis process, companies can save time and resources while improving employee engagement and retention. 

AI-based employee feedback analysis tools enable HR teams to move beyond reactive responses to problems. With the ability to detect trends and emerging issues in real time, businesses can adopt a proactive approach, addressing concerns before they escalate into larger challenges. 

Conclusion 

Employee feedback analysis has always been a crucial aspect of workforce management, but AI is now revolutionizing how companies handle this vital task. AI-powered solutions provide fast, accurate, and comprehensive insights into employee sentiment, allowing businesses to make data-driven decisions that enhance engagement, improve productivity, and foster a positive work environment. 

With the ability to analyze feedback from multiple formats��including text, voice, and video—within 24 hours, AI-driven employee feedback analysis is transforming the way companies interact with their workforce.  

Contact us at GrapheneAI to ensure you stay ahead of issues and continue building strong, engaged teams. 

[webdatacrawler0]

What Are the Best Practices to Scrape Wikipedia With Python Efficiently?

Introduction

Wikipedia, the treasure trove of knowledge, is a go-to source for data across various fields, from research and education to business intelligence and content creation. Leveraging this wealth of information can provide a significant advantage for businesses and developers. However, manually collecting data from Wikipedia can be time- consuming and prone to errors. This is where you can Scrape Wikipedia With Python, an efficient, scalable, and reliable method for extracting information.

This blog will explore best practices for web scraping Wikipedia using Python, covering essential tools, ethical considerations, and real-world applications. We’ll also include industry statistics for 2025, examples, and a case study to demonstrate the power of Wikipedia Data Extraction.

Why Scrape Wikipedia With Python?

Wikipedia is one of the largest repositories of knowledge on the internet, providing a vast array of information on diverse topics. For businesses, researchers, and developers, accessing this data efficiently is crucial for making informed decisions, building innovative solutions, and conducting in-depth analyses. Here’s why you should consider Scrape Wikipedia With Python as your go-to approach for data extraction.

Efficiency and Flexibility

Web scraping Wikipedia using Python allows quick and efficient structured and unstructured data extraction. Python’s powerful libraries, like BeautifulSoup, Requests, and Pandas, simplify the process of extracting and organizing data from Wikipedia pages. Unlike manual methods, automation significantly reduces time and effort.

Access to Rich Data

From tables and infoboxes to article content and references, Wikipedia Data Extraction provides a goldmine of information for industries like education, market research, and artificial intelligence. Python’s versatility ensures you can extract exactly what you need, tailored to your use case.

Cost-Effective Solution

Leveraging Web scraping Wikipedia eliminates the need for expensive third-party services. Python scripts allow you to collect data at minimal costs, enhancing scalability and sustainability.

Applications Across Industries

Researchers use Wikipedia Data Extraction to build datasets in natural language processing and knowledge graphs.

Businesses analyze trends and competitor information for strategy formulation.

Developers use Web scraping Wikipedia for content creation, chatbots, and machine learning models.

Ethical and Efficient

Python enables compliance with Wikipedia’s scraping policies through APIs and structured extraction techniques. This ensures ethical data use while avoiding legal complications.

Scrape Wikipedia With Python to unlock insights, streamline operations, and power your projects with precise and reliable data. It’s a game- changer for organizations looking to maximize the potential of data.

Key Tools for Web Scraping Wikipedia Using Python

When you set out to Scrape Wikipedia With Python, having the right tools is crucial for efficient and effective data extraction. Below are some of the essential libraries and frameworks you can use:

1. BeautifulSoup

BeautifulSoup is one of the most popular Python libraries for web scraping Wikipedia. It allows you to parse HTML and XML documents, making navigating and searching the page structure easier. BeautifulSoup helps extract data from Wikipedia page tables, lists, and text content. It is known for its simplicity and flexibility in handling complex web structures.

2. Requests

The Requests library is used to send HTTP requests to Wikipedia and retrieve the HTML content of the page. It simplifies fetching data from a website and is essential for initiating the scraping process. With Requests, you can interact with Wikipedia’s servers and fetch the pages you want to scrape while seamlessly handling session management, authentication, and headers.

3. Pandas

Once the data is scraped, Pandas come in handy for organizing, cleaning, and analyzing the data. This library provides powerful data structures, like DataFrames, perfect for working with structured data from Wikipedia. Pandas can handle data transformation and cleaning tasks, making it an essential tool for post-scraping data processing.

4. Wikipedia API

Instead of scraping HTML pages, you can use the Wikipedia API to access structured data from Wikipedia directly. This API allows developers to request information in a structured format, such as JSON, making it faster and more efficient than parsing raw HTML content. The Wikipedia API is the recommended way to retrieve data from Wikipedia, ensuring compliance with the site's usage policies.

5. Selenium

When scraping pages with dynamic content, Selenium is the go-to tool. It automates web browsers, allowing you to interact with JavaScript-heavy websites. If Wikipedia pages load content dynamically, Selenium can simulate browsing actions like clicking and scrolling to extract the necessary data.

6. Scrapy

For larger, more complex scraping projects, Scrapy is a powerful and high-performance framework. It’s an open-source tool that enables scalable web scraping, allowing users to build spiders to crawl websites and gather data. Scrapy is ideal for advanced users building automated, large-scale scraping systems.

Utilizing these tools ensures that your Wikipedia Data Extraction is efficient, reliable, and scalable for any project.

Best Practices for Efficient Wikipedia Data Extraction

Regarding Wikipedia Data Extraction, adopting best practices ensures that your web scraping is efficient but also ethical and compliant with Wikipedia’s guidelines. Below are the key best practices for effective scraping:

1. Use the Wikipedia API

Rather than scraping HTML directly, it is best to leverage the Wikipedia API for structured data retrieval. The API allows you to request data in formats like JSON, making it faster and more reliable than parsing raw HTML. It also reduces the likelihood of errors and ensures you abide by Wikipedia's scraping guidelines. The API provides access to detailed articles, infoboxes, categories, and page revisions, making it the optimal way to extract Wikipedia data.

2. Respect Wikipedia’s Robots.txt

Always check Wikipedia's robots.txt file to understand its scraping policies. This file defines the rules for web crawlers, specifying which sections of the site are allowed to be crawled and scraped. Adhering to these rules helps prevent disruptions to Wikipedia’s infrastructure while ensuring your scraping activity remains compliant with its policies.

3. Optimize HTTP Requests

When scraping large volumes of data, optimizing HTTP requests is crucial to avoid overloading Wikipedia’s servers. Implement rate limiting, ensuring your scraping activities are paced and don’t overwhelm the servers. You can introduce delays between requests or use exponential backoff to minimize the impact of scraping on Wikipedia’s resources.

4. Handle Edge Cases

Be prepared for pages with inconsistent formatting, missing data, or redirects. Wikipedia is a vast platform with a wide range of content, so not all pages will have the same structure. Implement error-handling mechanisms to manage missing data, broken links, or redirects. This will ensure your script doesn’t break when encountering such anomalies.

5. Parse Tables Effectively

Wikipedia is filled with well-structured tables that contain valuable data. Pandas is an excellent library for efficiently extracting and organizing tabular data. Using Pandas, you can easily convert the table data into DataFrames, clean it, and analyze it as required.

6. Focus on Ethical Scraping

Lastly, ethical scraping should always be a priority. Respect copyright laws, provide proper attribution for extracted data, and avoid scraping sensitive or proprietary information. Ensure that the data you collect is used responsibly, complies with Wikipedia’s licensing terms, and contributes to the greater community.

By following these best practices, you can ensure that your web scraping activities on Wikipedia using Python are both practical and ethical while maximizing the value of the extracted data.

Real-Life Use Cases for Web Scraping Wikipedia

1. Academic Research

Web scraping Wikipedia can be valuable for academic researchers, especially in linguistics, history, and social sciences. Researchers often need large datasets to analyze language patterns, historical events, or social dynamics. With its vast structured information repository, Wikipedia provides an excellent source for gathering diverse data points. For instance, linguists might scrape Wikipedia to study language usage across different cultures or periods, while historians might gather data on events, figures, or periods for historical analysis. By scraping specific articles or categories, researchers can quickly build extensive datasets that support their studies.

2. Business Intelligence

Wikipedia data extraction plays a crucial role in competitive analysis and market research for businesses. Companies often scrape Wikipedia to analyze competitors' profiles, industry trends, and company histories. This information helps businesses make informed strategic decisions. Organizations can track market dynamics and stay ahead of trends by extracting and analyzing data on companies' growth, mergers, key executives, or financial milestones. Wikipedia pages related to industry sectors or market reports can also provide real-time data to enhance business intelligence.

3. Machine Learning Projects

Wikipedia serves as a rich source of training data for machine learning projects. For natural language processing (NLP) models, scraping Wikipedia text enables the creation of large corpora to train models on tasks like sentiment analysis, language translation, or entity recognition. Wikipedia's diverse and well-structured content makes it ideal for building datasets for various NLP applications. For example, a machine learning model designed to detect language nuances could benefit significantly from scraping articles across different topics and languages.

4. Knowledge Graphs

Extract Wikipedia data to build knowledge graphs for AI applications. Knowledge graphs organize information in a structured way, where entities like people, places, events, and concepts are connected through relationships. Wikipedia's well-organized data and links between articles provide an excellent foundation for creating these graphs. Scraping Wikipedia helps populate these knowledge bases with data that can power recommendation systems, semantic search engines, or personalized AI assistants.

5. Content Creation

Content creators often use Wikipedia data collection to streamline their work. By scraping Wikipedia, content creators can quickly generate fact-checks, summaries, or references for their articles, blogs, and books. Wikipedia's structured data ensures the information is reliable and consistent, making it a go-to source for generating accurate and up-to-date content. Bloggers and journalists can use scraped data to support their writing, ensuring their content is well-researched and informative.

Through these use cases, it is clear that web scraping Wikipedia offers numerous possibilities across various industries, from academia to business intelligence to AI development.

Statistics for 2025: The Impact of Data Scraping

By 2025, the global web scraping market is anticipated to reach a staggering $10.7 billion, fueled by the increasing need for automated data collection tools across various industries. As businesses rely more on data to drive decisions, the demand for efficient and scalable scraping solutions continues to rise, making this a key growth sector in the tech world.

Wikipedia plays a significant role in this growth, as it receives over 18 billion page views per month, making it one of the richest sources of free, structured data on the web. With millions of articles spanning virtually every topic imaginable, Wikipedia is a goldmine for businesses and researchers looking to collect large amounts of information quickly and efficiently.

The impact of web scraping on business performance is substantial. Companies leveraging scraping tools for data-driven decision-making have reported profit increases of up to 30%. By automating the collection of crucial market intelligence—such as competitor pricing, product availability, or customer sentiment—businesses can make quicker, more informed decisions that lead to improved profitability and competitive advantage.

As the web scraping industry continues to evolve and expand, the volume of accessible data and the tools to harvest it will grow, further shaping how businesses and researchers operate in the future.

Case Study: Extracting Data for Market Analysis

Challenge

A leading media analytics firm faced a significant challenge in tracking public opinion and historical events for its trend analysis reports. They needed to gather structured data on various topics, including social issues, historical events, political figures, and market trends. The firm’s existing process of manually collecting data was time-consuming and resource-intensive, often taking weeks to gather and process relevant information. This delay affected their client’s ability to provide timely insights, ultimately hindering their market intelligence offerings.

Solution

The firm leveraged Python and the Wikipedia API for large-scale data extraction to overcome these challenges. Using Python’s powerful libraries, such as Requests and BeautifulSoup, combined with the Wikipedia API, the firm could automate the data extraction process and pull structured data from Wikipedia’s vast repository of articles. This allowed them to access relevant content from thousands of Wikipedia pages in a fraction of the time compared to traditional methods. The firm gathered data on historical events, public opinion trends, and key industry topics. They set up an automated system to scrape, clean, and organize the data into a structured format, which could then be used for in-depth analysis.

Outcome

The results were significant. The firm was able to build a dynamic database of market intelligence, providing clients with real-time insights. By automating the data collection process, they saved approximately 60% of the time it previously took to gather the same amount of data.

The firm was able to deliver trend analysis reports much faster, improving client satisfaction and strengthening its position as a leader in the media analytics industry. The successful implementation of this solution not only streamlined the firm’s data collection process but also enhanced its ability to make data-driven decisions and offer more actionable insights to its clients.

Challenges in Web Scraping Wikipedia

While web scraping Wikipedia offers great potential for data collection and analysis, several challenges need to be addressed to ensure an effective and compliant scraping process.

1. Dynamic Content

Wikipedia pages often contain dynamic content, such as tables, infoboxes, and images, which may not always be easily accessible through traditional scraping methods. In some cases, these elements are rendered dynamically by JavaScript or other scripting languages, making extracting the data in a structured format more difficult. To handle this, advanced parsing techniques or tools like Selenium may be required to interact with the page as it loads or to simulate user behavior. Additionally, API calls may be needed to retrieve structured data rather than scraping raw HTML, especially for complex elements such as tables.

2. Data Volume

Wikipedia is a vast repository with millions of articles and pages across various languages. Scraping large volumes of data from Wikipedia can quickly become overwhelming in terms of the data size and the complexity of processing it. Efficient data handling is essential to avoid performance bottlenecks. For example, optimizing scraping scripts to manage memory usage, store data efficiently, and perform incremental scraping can significantly improve the overall process. Additionally, large datasets may require robust storage solutions, such as databases or cloud storage, to organize and manage the extracted data.

3. Compliance

Wikipedia operates under strict ethical guidelines, and scraping must comply with these standards. This includes respecting robots.txt directives, which specify which pages or sections of the site are off-limits for scraping. Furthermore, adhering to Wikipedia’s licensing policies and giving proper attribution for the data extracted is vital to avoid copyright violations. Ensuring compliance with legal standards and maintaining ethical practices throughout the scraping process is crucial for long-term success and avoiding potential legal issues.

By understanding and addressing these challenges, businesses and researchers can scrape Wikipedia efficiently and responsibly, extracting valuable insights without compromising data quality or compliance.

Mobile App Scraping: An Extension of Data Collection

While web scraping services have long been famous for gathering data from websites, mobile app scraping is rapidly becoming an essential extension of modern data collection techniques. As mobile applications dominate the digital landscape, businesses realize the immense potential of extracting data directly from apps to enhance their competitive advantage and drive informed decision-making.

Unlike websites, mobile apps often feature data not publicly available on their corresponding websites, such as real-time inventory information, user reviews, personalized recommendations, and even app-specific pricing models. This unique data can give businesses a more granular view of their competitors and market trends, offering insights that are often harder to obtain through traditional scraping methods. For example, mobile apps for grocery delivery services, e-commerce platforms, and ride-sharing apps frequently have detailed information about pricing, promotions, and consumer behavior not displayed on their websites.

Mobile app scraping can also benefit industries that rely on real-time data. For instance, travel and tourism companies can scrape mobile apps for flight availability, hotel prices, and rental car data. Similarly, the e- commerce sector can extract product data from mobile shopping apps to keep track of stock levels, prices, and seasonal discounts.

However, scraping mobile apps presents unique challenges, such as dealing with app-specific APIs, handling dynamic content, and overcoming security measures like CAPTCHAs or rate limits. Despite these challenges, businesses that implement effective mobile app scraping strategies gain a competitive edge by accessing often overlooked or unavailable data through traditional web scraping.

By incorporating mobile app scraping into their data collection processes, businesses can unlock valuable insights, stay ahead of competitors, and ensure they have the most up-to-date information for market analysis and decision-making.

Conclusion

Web scraping is a powerful tool for businesses, and scraping Wikipedia with Python offers unparalleled opportunities to collect and analyze data efficiently. Whether you’re a researcher, business analyst, or developer, following the best practices outlined in this blog ensures successful data extraction while respecting Wikipedia’s guidelines.

Ready to streamline your data collection process? Partner with Web Data Crawler today for efficient, ethical, customizable solutions. From Web Scraping Services to APIs, we have the tools to meet your business needs. Explore our services and take your data strategy to the next level!

Originally Published At :

[albertpeter]

What Are the Best Approaches to Data Preprocessing in LLM Development?

The development of large language models (LLMs) like OpenAI's GPT, Google's Bard, and others is a complex process requiring meticulous attention to detail at every stage. Among these stages, data preprocessing plays a pivotal role, as it directly impacts the quality, accuracy, and overall performance of the resulting model. Proper preprocessing ensures that the input data is clean, relevant, and optimized for the training process.

This blog explores the best approaches to data preprocessing in LLM development, emphasizing its importance, key techniques, and challenges faced during the process.

Why Is Data Preprocessing Crucial for LLMs?

LLMs are trained on massive datasets to understand and generate human-like text. However, raw data collected from the internet or other sources is often noisy, redundant, or irrelevant. Without preprocessing, the model might learn from flawed or biased data, leading to suboptimal performance or unintended consequences like perpetuating stereotypes.

Key Goals of Data Preprocessing for LLMs:

Data Quality Enhancement: Remove noise, errors, and irrelevant content to improve data reliability.

Bias Mitigation: Address imbalances or stereotypes in the data to reduce harmful outputs.

Optimization: Convert raw data into a format suitable for efficient model training.

Scalability: Prepare data for the scale and complexity required by LLM architectures.

Best Approaches to Data Preprocessing

1. Data Collection and Source Validation

Before diving into preprocessing, it's essential to start with robust data collection and validate the sources. High-quality training data leads to better model performance.

Diversified Sources: Gather data from a mix of domains, including books, research papers, websites, and open datasets, to ensure the model has broad knowledge.

Source Credibility: Evaluate the authenticity of sources to filter out misinformation.

Multilingual Data: For multilingual LLMs, include diverse languages and dialects to improve linguistic capabilities.

2. Data Cleaning

Data cleaning involves removing unwanted artifacts, errors, and irrelevant content from the raw dataset.

Key Techniques:

Duplicate Removal: Ensure no redundant examples exist, which could waste computational resources.

Noise Reduction: Eliminate advertisements, boilerplate text, and formatting errors.

Outlier Detection: Remove sentences or paragraphs that are nonsensical, extremely biased, or irrelevant.

HTML Parsing: For web-sourced data, strip away HTML tags and metadata that don't contribute to meaningful language understanding.

Tools to Use:

Regex for pattern-based cleaning.

Python libraries like Beautiful Soup for web scraping cleanup.

3. Text Normalization

Normalization ensures that text data is consistent in structure and style.

Key Steps:

Lowercasing: Standardize all text to lowercase unless case sensitivity carries meaning (e.g., proper nouns).

Punctuation Handling: Remove or standardize punctuation depending on the dataset's focus.

Tokenization: Break text into smaller units like words, phrases, or subwords.

Spelling Corrections: Correct misspelled words using libraries like Hunspell or custom dictionaries.

4. Language-Specific Processing

When working with multilingual datasets, it is crucial to address language-specific nuances.

Examples:

Stopword Removal: Remove common words like "the," "is," or "and," which don't contribute much semantic value.

Stemming and Lemmatization: Reduce words to their base forms for better consistency.

Accent Normalization: Convert accented characters to standard forms, e.g., "café" to "cafe."

For languages like Chinese, segmenting characters correctly is critical since words are not separated by spaces.

5. Removing Sensitive or Biased Content

LLMs can inadvertently perpetuate biases present in the training data. Proactively identifying and removing such content is crucial.

Methods:

Bias Identification: Use tools or manual audits to detect biased language or stereotypes.

Sensitive Content Filtering: Identify and remove data that includes hate speech, explicit content, or harmful ideologies.

Data Balancing: Introduce counterexamples or additional data from underrepresented groups to balance the dataset.

6. Data Annotation and Labeling

In some cases, labeled data is necessary to fine-tune LLMs or train smaller supervised models.

Steps:

Manual Annotation: Employ experts to annotate data for tasks like sentiment analysis or named entity recognition.

Crowdsourced Annotation: Use platforms like Amazon Mechanical Turk to scale the annotation process.

Automated Labeling: Leverage pre-trained models to label data, followed by human validation.

7. Deduplication and Redundancy Removal

Training datasets for LLMs often exceed terabytes in size, and duplicates can create inefficiencies or reinforce biases.

Techniques:

Hash-Based Matching: Identify duplicates using hash functions.

Semantic Matching: Use cosine similarity or embeddings to detect semantically similar entries.

8. Data Augmentation

Augmentation techniques expand the dataset by generating new examples, increasing the model's robustness.

Methods:

Paraphrasing: Rewrite sentences to introduce variability.

Back Translation: Translate text to another language and back to the original.

Synonym Replacement: Replace words with their synonyms to diversify input.

9. Format Conversion

LLMs require data to be in a specific format, such as JSON or plain text, with structured metadata.

Steps:

Convert PDFs, HTML, or other formats into plain text.

Add metadata tags (e.g., source, timestamp, or language) to enable better context management during training.

10. Data Sampling

Not all data is equally important. Sampling ensures that the training dataset remains diverse and representative.

Methods:

Stratified Sampling: Ensure all categories or domains are proportionally represented.

Importance Sampling: Focus on high-quality data subsets for training.

11. Dataset Documentation

Documenting the preprocessing pipeline is essential for transparency, reproducibility, and debugging.

Documentation Includes:

Sources of data.

Steps taken during cleaning and normalization.

Decisions about inclusion or exclusion criteria.

Known limitations or biases.

Challenges in Data Preprocessing for LLMs

While preprocessing is critical, it is not without its challenges:

Scale: Managing and preprocessing datasets that span terabytes requires robust infrastructure and efficient algorithms.

Bias Detection: Identifying subtle biases in massive datasets is difficult and often requires domain expertise.

Linguistic Diversity: Handling multiple languages with unique structures and idioms demands tailored preprocessing techniques.

Evolving Standards: Preprocessing methods must adapt as societal norms and ethical standards evolve.

Conclusion

Data preprocessing is a cornerstone of large language model development. A well-prepared dataset can significantly enhance the model's performance, reduce biases, and ensure robustness across diverse tasks. By following the best practices outlined above ranging from data cleaning to normalization, bias mitigation, and sampling developers can lay a strong foundation for successful LLM training.

However, preprocessing is not a one-size-fits-all process. Each project may require adjustments based on the model's objectives, the nature of the dataset, and the ethical considerations involved. Continuous refinement, transparency, and adherence to ethical principles are key to building language models that serve a diverse and inclusive audience.

[anilpal]

Revolutionizing Software Testing in 2024

Why Genqe is the Future

The software development world is moving at lightning speed, and so is the demand for reliable, efficient, and innovative testing solutions. In this era of digital transformation, where AI and automation dominate the landscape, staying ahead in software testing requires tools that can think, adapt, and deliver — tools like Genqe.

Let’s dive deep into how Genqe aligns with modern trends in software testing and why it’s the tool you need to elevate your testing game.

The Evolution of Software Testing

Gone are the days when manual testing could ensure product quality. Today’s applications demand faster releases, continuous integration, and seamless functionality across platforms. According to leading experts in software testing:

AI-Powered Automation is no longer optional; it’s the norm.

The shift-left approach ensures defects are caught early, reducing costs and improving efficiency.

Comprehensive test coverage, from functional to non-functional testing, is essential for delivering robust applications.

Genqe embodies all these principles, offering cutting-edge solutions that redefine software testing.

Meet Genqe: The Next-Generation Testing Tool

At Genqe, we believe software testing should be smart, seamless, and scalable. Our platform leverages AI and machine learning to automate, optimize, and enhance your testing processes. Here’s what makes us stand out:

1. Automated Test Case Generation

With Genqe, creating test cases becomes effortless:

From Requirement Documents: Upload Word or Excel files, and Genqe will parse them to generate accurate and thorough test cases.

From Jira User Stories: No more manual creation. Genqe integrates with Jira to ensure all your stories are covered.

From Visual Media: Generate test cases directly from application walkthroughs or recorded sessions.

This unparalleled flexibility means you spend less time writing test cases and more time ensuring application quality.

2. AI-Powered Efficiency

Our platform uses advanced AI to:

Predict defects early in the pipeline.

Self-heal test cases when application changes occur.

Optimize testing strategies with combinatorial test designs, ensuring maximum coverage with minimal effort.

By automating these processes, Genqe empowers QA teams to focus on innovation rather than tedious tasks.

3. Visual Business Flow Representation

Complex workflows? No problem. Genqe translates your application processes into interactive flow diagrams, making it easy to identify gaps and optimize your testing scenarios.

4. Comprehensive Testing Coverage

Whether it’s functional testing, performance testing, or security validation, Genqe has you covered. Its AI-powered capabilities ensure that every edge case and scenario is addressed.

The Benefits of Genqe in Your Workflow

A. Speed and Efficiency

By automating repetitive tasks, Genqe reduces testing time significantly. Need to validate a critical feature before a release? Genqe makes it happen in minutes.

B. Cost Savings

Manual testing can be expensive. Genqe’s automation reduces costs while delivering higher accuracy and reliability.

C. Seamless Integration

Whether you use Jira, GitLab, Jenkins, or other CI/CD tools, Genqe integrates smoothly to provide a unified testing ecosystem.

D. User-Friendly Interface

With its low-code/no-code approach, even non-technical users can participate in test creation and management.

How Genqe Aligns with Industry Trends

Shift-Left Testing: Catch defects early by integrating Genqe into your CI/CD pipelines.

AI in Testing: Reduce human error and enhance accuracy with self-healing test scripts and defect prediction.

Agile and DevOps: Accelerate releases without compromising quality, thanks to Genqe’s real-time insights and automated workflows.

Real-World Applications of Genqe

E-Commerce

Handle peak loads and ensure secure transactions with Genqe’s performance and security testing features.

Mobile Apps

Test across devices and platforms effortlessly, ensuring a flawless user experience.

Enterprise Software

Validate complex integrations and workflows, guaranteeing that your software meets business needs.

What Industry Experts Say

Experts agree that tools like Genqe are transforming the testing landscape. They praise its ability to adapt to evolving application requirements and deliver exceptional value to businesses of all sizes.

Why Genqe is the Future of Testing

The world of software testing is at a crossroads. Traditional methods are no longer sufficient to meet the demands of modern applications. Genqe bridges this gap by combining the best of AI, automation, and human intelligence.

From automating test case creation to providing actionable insights through advanced analytics, Genqe is more than a tool — it’s a revolution.

Start Your Genqe Journey Today

Ready to see the difference Genqe can make? Visit Genqe.ai to learn more, or book a demo to experience the future of software testing firsthand.

💡 Follow us for more insights on software testing, AI, and automation.

📢 Share this post to help others transform their testing processes too!

[fabzen123]

 Testing WebSocket APIs: Ensuring Reliable Communication

 Introduction

In the realm of modern web development, WebSocket APIs have become pivotal for enabling real-time, bidirectional communication between clients and servers. This technology is crucial for applications that require instantaneous data updates, such as live chat, gaming, and financial trading platforms. However, ensuring the reliability and performance of WebSocket APIs is challenging. This article explores the essential aspects of testing WebSocket APIs to guarantee reliable communication.

 Understanding WebSocket APIs

WebSocket APIs provide a persistent connection that allows for continuous data exchange without the overhead of repeatedly establishing and tearing down connections, as seen in traditional HTTP. This efficiency makes WebSockets ideal for real-time applications where latency and performance are critical.

 Challenges in Testing WebSocket APIs

1. Connection Management: WebSocket connections need to be established, maintained, and closed properly. Issues can arise if connections are dropped or if the server or client fails to handle reconnections effectively.

2. Concurrency and Scalability: WebSocket applications often handle numerous concurrent connections. Testing must ensure that the server can manage high concurrency levels without degrading performance.

3. Data Integrity and Consistency: Ensuring that messages are correctly transmitted, received, and processed is crucial. This includes maintaining the order of messages and verifying that no data is lost or corrupted during transmission.

4. Error Handling: Robust error handling mechanisms are essential. This involves testing how the application responds to various types of errors, such as connection timeouts, unexpected disconnections, and message parsing errors.

5. Security: WebSocket APIs must be tested for security vulnerabilities, including unauthorized access, data breaches, and denial-of-service attacks.

 Strategies for Effective WebSocket API Testing

1. Unit Testing: Start with unit tests to validate the core functionalities of your WebSocket API. This includes testing individual components, such as message serialization and deserialization, connection handling, and business logic processing.

2. Integration Testing: Conduct integration tests to ensure that different parts of the WebSocket API work together seamlessly. This involves testing the interaction between the client and server, including connection establishment, message exchange, and connection termination.

3. Load Testing: Perform load testing to evaluate how your WebSocket API handles high levels of concurrent connections. Tools like Apache JMeter, Gatling, or custom scripts can simulate thousands of simultaneous connections to assess performance and scalability.

4. Stress Testing: Stress testing helps determine the breaking point of your WebSocket API. This involves pushing the system beyond its normal operational capacity to see how it handles extreme conditions, such as sudden spikes in traffic.

5. End-to-End Testing: Conduct end-to-end tests to validate the complete workflow of your application. This includes testing real-world scenarios to ensure that the WebSocket API performs as expected under various conditions.

6. Automated Testing: Implement automated testing to streamline the testing process and ensure consistent coverage. Tools like Selenium, TestCafe, and custom scripts can automate WebSocket interactions and verify the results.

7. Monitoring and Logging: Implement automated WebSocket testing to streamline the testing process and ensure consistent coverage. Tools like Selenium, TestCafe, and custom scripts can automate WebSocket interactions and verify the results.

8. Security Testing: Conduct security tests to identify vulnerabilities in your WebSocket API. This includes penetration testing, vulnerability scanning, and implementing secure coding practices to protect against common threats.

 Best Practices for Reliable WebSocket Communication

1. Connection Management: Implement robust connection management strategies, such as automatic reconnection, heartbeat mechanisms, and connection timeouts, to ensure persistent and reliable connections.

2. Data Validation: Validate incoming and outgoing messages to ensure data integrity. Use schemas to enforce data structure and format, and perform thorough testing to catch any inconsistencies.

3. Error Handling: Design comprehensive error handling procedures to manage different types of errors gracefully. This includes providing meaningful error messages and implementing retry mechanisms where appropriate.

4. Security Measures: Employ security best practices, such as using secure WebSocket (WSS) for encrypted communication, authenticating users, and authorizing actions to protect against unauthorized access and data breaches.

 Conclusion

Testing WebSocket APIs is essential for ensuring reliable and secure real-time communication in modern applications. By adopting a comprehensive testing strategy that includes unit, integration, load, stress, end-to-end, automated, and security testing, including using WebSocket testing online tools, developers can identify and address potential issues before they impact users. Implementing best practices for connection management, data validation, error handling, and security will further enhance the reliability of WebSocket APIs, providing a robust foundation for real-time applications.

[itcubesolution]

Why Data Visualization Matters for Businesses: Essential Benefits You Need to Know

According to a survey, in 2023, about 328.77 million terabytes of data were created daily- equating to 120 zettabytes per year- and this figure is expected to rise to 181 zettabytes annually by 2025. These numbers are proof of an increasingly data-driven world. Businesses are using this data more than ever to make informed decisions. However, without proper visual structure, raw data has limited value. This is where data visualization becomes essential.

When executed effectively, data visualization and data analytics goes beyond basic charts and graphs, empowering organizations to make sense of extensive data effortlessly. So, businesses need to interpret complex datasets, identify trends, and gain actionable insights quickly.

In this blog, we will explore the top reasons why data visualization is essential and beneficial for companies. Let’s get started!

Remarkable Benefits of Data Visualization for Businesses

1- Enhanced Data Comprehension

Data visualization transforms complex datasets into easily digestible formats, allowing users to grasp patterns and relationships quickly. Unlike lengthy reports, visual tools use graphs, charts, and infographics to make data intuitive, so teams across departments — whether or not they’re data experts — can interpret insights seamlessly. This leads to data-driven decision-making at all levels of the organization.

2- Improved Communication Across Teams

Visualization fosters better communication and understanding among cross-functional teams by conveying data in a clear, visual format. When departments work together on projects or strategic initiatives, visually accessible data makes it easier to align on objectives, allocate resources, and prioritize tasks. Visualizations encourage meaningful discussions and unify teams around shared goals, reducing silos and misunderstandings.

3- Better Decision-Making

Visualization simplifies data-driven decision-making by highlighting key insights, risks, and opportunities. It’s easier for leaders to identify patterns, outliers, and trends in visual form than by parsing raw data alone. With these actionable insights, organizations can make more confident, timely decisions that are supported by data rather than guesswork, leading to more successful outcomes.

4- Higher Data Accuracy

When data is presented visually, it’s easier to identify discrepancies, inconsistencies, or patterns that would be less obvious in raw data form. This enhanced clarity reduces the likelihood of misinterpretation and human error, which is critical for high-stakes industries where accuracy impacts outcomes, such as healthcare, finance, manufacturing, etc. Data visualization tools provide reliable data insights that teams can confidently base decisions on.

5- Identification of Trends and Patterns

Visual data analysis reveals trends, correlations, etc. that may not be apparent in traditional reporting. For example, sales data visualizations can show seasonality or regional preferences that support targeted marketing efforts. By making these insights accessible, businesses can strategically adjust their operations, marketing strategies, or resource allocation to better align with observed patterns, gaining a competitive edge in their industry.

6- Faster Time-to-Insight

Data visualization significantly reduces analysis time. Visual dashboards allow users to interpret real-time data instantly rather than spending hours combing through spreadsheets. This speed is invaluable in dynamic business environments where prompt responses to shifts can mean the difference between capitalizing on a trend and missing out entirely. With faster insights, organizations can improve agility and stay ahead in competitive markets.

7- Increased Engagement and Retention

People are naturally drawn to visuals, and data presented through charts, graphs, and interactive dashboards is more engaging than standard reports. This engagement helps teams retain information better, leading to more informed discussions and proactive decisions. In an age where data overload can lead to fatigue, well-designed visualizations keep users focused and invested, ultimately promoting smarter, data-driven actions across the organization.

ITLytics: An Effective Data Visualization Tool

For businesses looking to enhance their data visualization capabilities, ITLytics stands out as an intuitive and robust tool. This tool is designed to help users generate meaningful insights from large datasets, transforming data into actionable visualizations for diverse business needs. From dynamic dashboards to customizable reporting tools, ITLytics simplifies data interpretation, enabling teams to focus on making strategic decisions.

ITLytics has ensured a 30% boost in operational efficiency and 25% savings on implementation costs for the clients.

Its key features include:

Zero Licensing Fees, being an Open-source Platform

Proprietary Analytics Product with Robust Data-Handling Capabilities

Intuitive Drag-and-drop Interface, Extensive Customizable Visualization Options

Ability to Integrate AI Capabilities and Several Advanced Features

Using ITLytics, we, at ITCube, have provided analytics/forecasting services to several global organizations from domains such as construction, healthcare, legal, etc. We created customized dashboards for various user groups or teams such as operations teams, senior management, business analysts, etc. from these organizations.

How ITCube Can Help you in Data Visualization and Analytics

At ITCube, we understand the importance of visual data representation for informed decision-making. Our team specializes in helping organizations deploy data visualization tools like ITLytics that are tailored to industry-specific needs. With over 20 years of experience across sectors, we help businesses implement solutions that bring data to life, providing clarity, efficiency, and value. We can help you too.

For details, get in touch with us at: https://www.itcube.net/contact-us or mail at: inquiry@itcube.net

[outsourcebigdata]

Top 6 Scraping Tools That You Cannot Miss in 2024

In today's digital world, data is like money—it's essential for making smart decisions and staying ahead. To tap into this valuable resource, many businesses and individuals are using web crawler tools. These tools help collect important data from websites quickly and efficiently. 

What is Web Scraping? 

Web scraping is the process of gathering data from websites. It uses software or coding to pull information from web pages, which can then be saved and analyzed for various purposes. While you can scrape data manually, most people use automated tools to save time and avoid errors. It’s important to follow ethical and legal guidelines when scraping to respect website rules. 

Why Use Scraping Tools? 

Save Time: Manually extracting data takes forever. Web crawlers automate this, allowing you to gather large amounts of data quickly. 

Increase Accuracy: Automation reduces human errors, ensuring your data is precise and consistent. 

Gain Competitive Insights: Stay updated on market trends and competitors with quick data collection. 

Access Real-Time Data: Some tools can provide updated information regularly, which is crucial in fast-paced industries. 

Cut Costs: Automating data tasks can lower labor costs, making it a smart investment for any business. 

Make Better Decisions: With accurate data, businesses can make informed decisions that drive success. 

Top 6 Web Scraping Tools for 2024 

APISCRAPY

APISCRAPY is a user-friendly tool that combines advanced features with simplicity. It allows users to turn web data into ready-to-use APIs without needing coding skills. 

Key Features: 

Converts web data into structured formats. 

No coding or complicated setup required. 

Automates data extraction for consistency and accuracy. 

Delivers data in formats like CSV, JSON, and Excel. 

Integrates easily with databases for efficient data management. 

ParseHub

ParseHub is great for both beginners and experienced users. It offers a visual interface that makes it easy to set up data extraction rules without any coding. 

Key Features: 

Automates data extraction from complex websites. 

User-friendly visual setup. 

Outputs data in formats like CSV and JSON. 

Features automatic IP rotation for efficient data collection. 

Allows scheduled data extraction for regular updates. 

Octoparse

Octoparse is another user-friendly tool designed for those with little coding experience. Its point-and-click interface simplifies data extraction. 

Key Features: 

Easy point-and-click interface. 

Exports data in multiple formats, including CSV and Excel. 

Offers cloud-based data extraction for 24/7 access. 

Automatic IP rotation to avoid blocks. 

Seamlessly integrates with other applications via API. 

Apify

Apify is a versatile cloud platform that excels in web scraping and automation, offering a range of ready-made tools for different needs. 

Key Features: 

Provides pre-built scraping tools. 

Automates web workflows and processes. 

Supports business intelligence and data visualization. 

Includes a robust proxy system to prevent access issues. 

Offers monitoring features to track data collection performance. 

Scraper API

Scraper API simplifies web scraping tasks with its easy-to-use API and features like proxy management and automatic parsing. 

Key Features: 

Retrieves HTML from various websites effortlessly. 

Manages proxies and CAPTCHAs automatically. 

Provides structured data in JSON format. 

Offers scheduling for recurring tasks. 

Easy integration with extensive documentation. 

Scrapy

Scrapy is an open-source framework for advanced users looking to build custom web crawlers. It’s fast and efficient, perfect for complex data extraction tasks. 

Key Features: 

Built-in support for data selection from HTML and XML. 

Handles multiple requests simultaneously. 

Allows users to set crawling limits for respectful scraping. 

Exports data in various formats like JSON and CSV. 

Designed for flexibility and high performance. 

Conclusion 

Web scraping tools are essential in today’s data-driven environment. They save time, improve accuracy, and help businesses make informed decisions. Whether you’re a developer, a data analyst, or a business owner, the right scraping tool can greatly enhance your data collection efforts. As we move into 2024, consider adding these top web scraping tools to your toolkit to streamline your data extraction process. 

[yoma123]

Optimizing HR Efficiency: How Top HR Outsourcing Companies and Staffing Solutions Transform Workforce Management

In today's fast-paced business world, efficient workforce management is critical. As companies strive to stay competitive, they increasingly turn to specialized solutions, including HR outsourcing, staff augmentation team, and contract staffing software. The demand for skilled professionals in various industries is rapidly changing, which makes it necessary to adopt flexible workforce strategies. This blog will discuss how top HR outsourcing companies, contract staffing software, industrial recruiting, and managed service providers in India can streamline HR functions, cut costs, and enhance business performance.

The Role of Top HR Outsourcing Companies

HR outsourcing has emerged as a vital tool for companies seeking to focus on core functions while delegating HR-related activities. Top HR outsourcing companies provide a comprehensive suite of HR services, including recruitment, payroll, compliance management, and employee development. These services ensure that companies can maintain a productive workforce without getting bogged down in administrative complexities.

One of the most significant advantages of partnering with a top HR outsourcing company is the ability to access expertise in areas that may otherwise require a dedicated internal team. By outsourcing, businesses can leverage the latest technology, industry insights, and compliance expertise, which is especially beneficial for companies operating in heavily regulated industries.

Benefits of HR Outsourcing

Cost Reduction: HR outsourcing minimizes the need for an in-house HR department, reducing operational costs.

Access to Top Talent: Outsourcing companies have vast networks and talent pools, allowing businesses to access the best candidates for specific roles quickly.

Improved Compliance: Staying compliant with employment laws can be challenging, especially for companies with operations in multiple regions. Top HR outsourcing companies help ensure that all HR practices comply with local regulations.

Increased Efficiency: HR outsourcing allows companies to focus on strategic goals and core business activities, as the HR provider takes over recruitment, payroll, and other time-consuming tasks. The Rise of Contract Staffing Software in Workforce Management

With businesses facing constantly evolving staffing needs, contract staffing software has become essential. Contract staffing software allows companies to manage short-term staffing requirements effectively. This software is designed to streamline the hiring process, match candidates to roles efficiently, and manage contract-based employees. By integrating contract staffing software, companies can reduce time-to-hire and improve workforce flexibility.

Contract staffing software solutions provide functionalities such as candidate sourcing, resume parsing, automated onboarding, and tracking employee contracts. These features save time and reduce human error in the hiring process. In sectors where project-based hiring is prevalent, like IT and construction, contract staffing software is an invaluable tool for maintaining an agile workforce.

Advantages of Contract Staffing Software

Enhanced Recruitment Process: The software speeds up recruitment, with AI-driven tools that match candidates to job requirements and automate resume screening.

Efficient Onboarding: Contract staffing software often includes onboarding features, enabling new hires to start contributing quickly without unnecessary delays.

Time and Cost Savings: By automating repetitive tasks, contract staffing software reduces the cost and time associated with recruitment, allowing HR teams to focus on other critical functions.

Scalability: Contract staffing software provides the flexibility to scale hiring up or down depending on business demands, which is particularly useful for seasonal or project-based roles. Industrial Recruiting: The Key to Filling Specialized Roles

Industrial recruiting plays a crucial role in sectors such as manufacturing, logistics, and construction, where specialized skills are essential. Recruiting for industrial roles presents unique challenges, as it requires expertise in sourcing candidates who meet specific skill and safety qualifications. Industrial recruiting often demands deep industry knowledge to find candidates who are not only skilled but also familiar with the regulatory requirements of the field.

To ensure that these needs are met, industrial recruiting firms work closely with clients to understand job requirements and source the right talent. Many industrial recruiting firms leverage databases and job boards specific to these fields, streamlining the sourcing process and connecting companies with highly qualified candidates quickly.

How Industrial Recruiting Services Benefit Businesses

Specialized Talent Pools: Industrial recruiting firms have access to extensive networks of skilled workers, making it easier for companies to find talent quickly.

Reduced Training Costs: By sourcing candidates with relevant skills and certifications, industrial recruiting helps reduce the training burden on employers.

Compliance with Industry Regulations: Industrial recruiters understand the specific compliance requirements for each sector, ensuring candidates meet necessary legal and safety standards.

Quick Response to Demand: Industrial recruiting firms offer quick and effective solutions, especially valuable for businesses experiencing sudden spikes in demand or seasonal workforce requirements. Staff Augmentation Teams: Meeting Project Demands Efficiently

Staff augmentation provides companies with the flexibility to add skilled professionals to their teams on an as-needed basis. Unlike traditional hiring, staff augmentation allows businesses to fill skill gaps or meet project-specific demands without long-term commitments. A staff augmentation team is especially valuable in industries like IT, where specific expertise may be needed for limited periods.

By utilizing a staff augmentation team, companies can bring in specialized talent to work alongside internal teams, ensuring that projects are completed efficiently. This approach is ideal for businesses looking to reduce recruitment costs and maintain agility in their workforce strategy.

Advantages of a Staff Augmentation Team

Access to Specialized Skills: Staff augmentation enables companies to bring in experts with niche skills for specific projects, avoiding the costs of hiring permanent staff.

Increased Flexibility: A staff augmentation team can be scaled up or down as needed, allowing companies to respond quickly to changes in project demands.

Reduced Overhead Costs: Staff augmentation reduces the overhead associated with full-time employees, such as benefits and long-term salaries, making it a cost-effective solution for short-term projects.

Seamless Integration: Staff augmentation providers ensure that temporary staff integrate smoothly into existing teams, maintaining productivity and collaboration. The Role of Managed Service Providers in India

India has become a global hub for managed services, with companies worldwide seeking to leverage cost-effective and high-quality managed service providers in India. Managed service providers (MSPs) offer a range of services, from IT support to HR management, providing companies with the expertise they need to streamline operations.

Managed service providers in India specialize in delivering efficient, scalable solutions that enhance business performance. Whether it’s through technical support, HR outsourcing, or supply chain management, MSPs offer businesses the opportunity to focus on their core competencies while delegating essential functions.

Benefits of Partnering with Managed Service Providers in India

Cost Efficiency: Managed service providers in India offer competitive pricing, making it possible for companies to access high-quality services at a fraction of the cost.

Access to Skilled Professionals: Indian MSPs provide access to a highly skilled talent pool, from IT specialists to HR experts, ensuring that companies receive top-notch support.

Scalable Solutions: Managed service providers in India offer scalable solutions, allowing businesses to adjust services based on demand and growth.

24/7 Support: Many MSPs in India provide round-the-clock support, enabling businesses to resolve issues promptly and maintain continuity. Combining HR Outsourcing, Staffing Solutions, and Managed Services for Maximum Efficiency

By combining the services of top HR outsourcing companies, contract staffing software, industrial recruiting, staff augmentation teams, and managed service providers in India, companies can create a dynamic workforce strategy that aligns with business goals. Each solution offers unique benefits, and together they enable businesses to:

Reduce Costs: Outsourcing, staffing software, and managed services allow businesses to reduce expenses associated with traditional recruitment and in-house teams.

Access Skilled Talent Quickly: Each service model provides access to skilled professionals, ensuring that businesses have the right talent for each project or role.

Enhance Flexibility: Staff augmentation, contract staffing, and managed services offer scalable solutions that can be adjusted based on business needs.

Focus on Core Competencies: By outsourcing HR and other essential functions, companies can focus on strategic initiatives and growth, leaving administrative tasks to specialized providers. Conclusion

In a competitive business environment, leveraging top HR outsourcing companies, contract staffing software, industrial recruiting, staff augmentation teams, and managed service providers in India allows companies to build a responsive and efficient workforce strategy. These solutions not only reduce costs but also provide access to skilled professionals, improve compliance, and offer the flexibility necessary to adapt to changing market demands. Embracing these workforce management solutions is a strategic move for companies looking to thrive in today's global economy.