#the half language is sql

[borealing]

"learn to code" as advice is such bullshit. i have learned and used four and a half different coding languages through my career (html/css, java, python+sql, c++) and when i say used i mean I've built things in every one but the things that i actually used these languages for??? these earn zero money (with the caveat of until you have seniority in, e.g. front end web dev) what people really mean when they say learn coding is "learn to code. go into investment banking or finance startups." coding does not inherently have money in it. my absolute favourite part of coding? my peak enjoyment? was when i was developing for a visual coding language (you put it together like a flowchart, so say youre using a temperature sensor and you want it to log the temperature once every four hours, you can put the blocks together to make it do that. i was writing the code behind the blocks for new sensors) and i was earning £24k a year and that wasn't even part of my main role. it was an extra voluntary thing i was doing (i was working as a research assistant in biosensors - sort of - at a university, and was developing the visual code for students who didnt want to learn c++) like. i want people to learn to code, i want people to know how their electrical equipment works and how coding works, but dont believe the myth that there is inherently money in coding. the valuable things, the things people are passionate about are still vulnerable to the passion tax (if you want to do it you dont have to be paid for it). skills arent where the money is, money is where the money is.

[mariacallous]

The flood of text messages started arriving early this year. They carried a similar thrust: The United States Postal Service is trying to deliver a parcel but needs more details, including your credit card number. All the messages pointed to websites where the information could be entered.

Like thousands of others, security researcher Grant Smith got a USPS package message. Many of his friends had received similar texts. A couple of days earlier, he says, his wife called him and said she’d inadvertently entered her credit card details. With little going on after the holidays, Smith began a mission: Hunt down the scammers.

Over the course of a few weeks, Smith tracked down the Chinese-language group behind the mass-smishing campaign, hacked into their systems, collected evidence of their activities, and started a months-long process of gathering victim data and handing it to USPS investigators and a US bank, allowing people’s cards to be protected from fraudulent activity.

In total, people entered 438,669 unique credit cards into 1,133 domains used by the scammers, says Smith, a red team engineer and the founder of offensive cybersecurity firm Phantom Security. Many people entered multiple cards each, he says. More than 50,000 email addresses were logged, including hundreds of university email addresses and 20 military or government email domains. The victims were spread across the United States—California, the state with the most, had 141,000 entries—with more than 1.2 million pieces of information being entered in total.

“This shows the mass scale of the problem,” says Smith, who is presenting his findings at the Defcon security conference this weekend and previously published some details of the work. But the scale of the scamming is likely to be much larger, Smith says, as he didn't manage to track down all of the fraudulent USPS websites, and the group behind the efforts have been linked to similar scams in at least half a dozen other countries.

Gone Phishing

Chasing down the group didn’t take long. Smith started investigating the smishing text message he received by the dodgy domain and intercepting traffic from the website. A path traversal vulnerability, coupled with a SQL injection, he says, allowed him to grab files from the website’s server and read data from the database being used.

“I thought there was just one standard site that they all were using,” Smith says. Diving into the data from that initial website, he found the name of a Chinese-language Telegram account and channel, which appeared to be selling a smishing kit scammers could use to easily create the fake websites.

Details of the Telegram username were previously published by cybersecurity company Resecurity, which calls the scammers the “Smishing Triad.” The company had previously found a separate SQL injection in the group’s smishing kits and provided Smith with a copy of the tool. (The Smishing Triad had fixed the previous flaw and started encrypting data, Smith says.)

“I started reverse engineering it, figured out how everything was being encrypted, how I could decrypt it, and figured out a more efficient way of grabbing the data,” Smith says. From there, he says, he was able to break administrator passwords on the websites—many had not been changed from the default “admin” username and “123456” password—and began pulling victim data from the network of smishing websites in a faster, automated way.

Smith trawled Reddit and other online sources to find people reporting the scam and the URLs being used, which he subsequently published. Some of the websites running the Smishing Triad’s tools were collecting thousands of people’s personal information per day, Smith says. Among other details, the websites would request people’s names, addresses, payment card numbers and security codes, phone numbers, dates of birth, and bank websites. This level of information can allow a scammer to make purchases online with the credit cards. Smith says his wife quickly canceled her card, but noticed that the scammers still tried to use it, for instance, with Uber. The researcher says he would collect data from a website and return to it a few hours later, only to find hundreds of new records.

The researcher provided the details to a bank that had contacted him after seeing his initial blog posts. Smith declined to name the bank. He also reported the incidents to the FBI and later provided information to the United States Postal Inspection Service (USPIS).

Michael Martel, a national public information officer at USPIS, says the information provided by Smith is being used as part of an ongoing USPIS investigation and that the agency cannot comment on specific details. “USPIS is already actively pursuing this type of information to protect the American people, identify victims, and serve justice to the malicious actors behind it all,” Martel says, pointing to advice on spotting and reporting USPS package delivery scams.

Initially, Smith says, he was wary about going public with his research, as this kind of “hacking back” falls into a “gray area”: It may be breaking the Computer Fraud and Abuse Act, a sweeping US computer-crimes law, but he’s doing it against foreign-based criminals. Something he is definitely not the first, or last, to do.

Multiple Prongs

The Smishing Triad is prolific. In addition to using postal services as lures for their scams, the Chinese-speaking group has targeted online banking, ecommerce, and payment systems in the US, Europe, India, Pakistan, and the United Arab Emirates, according to Shawn Loveland, the chief operating officer of Resecurity, which has consistently tracked the group.

The Smishing Triad sends between 50,000 and 100,000 messages daily, according to Resecurity’s research. Its scam messages are sent using SMS or Apple’s iMessage, the latter being encrypted. Loveland says the Triad is made up of two distinct groups—a small team led by one Chinese hacker that creates, sells, and maintains the smishing kit, and a second group of people who buy the scamming tool. (A backdoor in the kit allows the creator to access details of administrators using the kit, Smith says in a blog post.)

“It’s very mature,” Loveland says of the operation. The group sells the scamming kit on Telegram for a $200-per month subscription, and this can be customized to show the organization the scammers are trying to impersonate. “The main actor is Chinese communicating in the Chinese language,” Loveland says. “They do not appear to be hacking Chinese language websites or users.” (In communications with the main contact on Telegram, the individual claimed to Smith that they were a computer science student.)

The relatively low monthly subscription cost for the smishing kit means it’s highly likely, with the number of credit card details scammers are collecting, that those using it are making significant profits. Loveland says using text messages that immediately send people a notification is a more direct and more successful way of phishing, compared to sending emails with malicious links included.

As a result, smishing has been on the rise in recent years. But there are some tell-tale signs: If you receive a message from a number or email you don't recognize, if it contains a link to click on, or if it wants you to do something urgently, you should be suspicious.

[hfy-kot]

Knights of Terra prologue part 3 of ?

First Mate Jess Davis worked furiously, trying to help the AI LIBRA process and organize the fleet's sensor data into at least something remotely usable. LIBRA was doing its best on low power mode, only running at about 40% made everything feel very slow. Jess was writing codes to trying to bridge sensor data in half a dozen coding languages. If she just got it all into SQL+ then LIBRA could process it that much faster.

LIBRA pinged a message "The power surge likely came from engineering, the damage is in a wave out from that compartment."

Jess rolled her eyes "Thank you LIBRA but please focus on the sensor data, it doesn't matter where it came from right now, just making what we have work."

In 2 hours they were going to die. Jess had accepted this. But she was going to make damn sure to take as many of them down on the way down as she could. If this was New Eden's last stand, then so be it. But this was the Ark Class New Eden, one of the first Ark ships Sol built, and she would not go down quietly. So Jess Davis coded, and LIBRA processed, the fleet knowing the extinction is coming, and planning to go down swinging. There was no where to run and no help coming.

Captain Ides arrived at the door to the Sub-Space room right as the maintenance bot got the door open. Sam Kelly was leaning against the transmitter, which looked in surprisingly good condition. "Kelly I'm going to throw you out an air lock. Why the hell would you go against my orders, and screw us even harder. This might be treason for sabotage for all I know." Ides yelled across the room, getting louder as she got angrier. Sam looked triumphant "I did it, someone will answer, someone will come." They answered quietly, almost a whispered prayer as much as a defense.

"I really don't have time for this, you need to try to restore power, as far away from me as you can" Ides barked sharply. She was livid, no one answered from Sol, they never did, her parents wasted their lives trying to talk to Sol. vg had no such fantasy, but right now she needed her Chief Engineer, as angry as she was. She spun on her heel, she needed to get back to the bridge and see what she could do to fix this fuck up.

Right as Captain Ides went to walk out the door, the transmitter pinged. Someone had answered.

[kirshd]

Hey, Musk.

I'll pay 10$ cash for Twitter.

You don't have to report it as taxes. Hell you could report it as a loss so you don't have to pay taxes.

I'll help you get that Kitchen sink you brought out.

I'll even let you keep the new X name, it's yours, I'll take the Twitter brand and I'll glue it back together with some half assed JavaScript, and loads of recursive tables...it'll unbury repressed memories from anyone that remembers Internet Explorer 6. I mean who needs PHP, what is that anyways, Portable Hay Propulsion? What about this SQL thing, Some Quack Language? You can keep the Google servers, too. I've got a half a laptop that did okay as a minecraft server, it can handle things. I'm sure three drops of glue can hold up the old twitter sign. Yeah, I think I can manage it.

Tell ya what, I'll sweeten the deal 12$, that should cover a sandwich down the street, so, how 'bout it? ... 12.50$ for a little extra mustard, or mayo, on an extra cheese slice?

[nrsinfowaysin]

Reliable Website Maintenance Services In India | NRS Infoways

In today’s hyper‑connected marketplace, a website is far more than a digital brochure—it is the beating heart of your brand experience, your lead‑generation engine, and your most valuable sales asset. Yet many businesses still treat their sites as “launch‑and‑forget” projects, only paying attention when something breaks. At NRS Infoways, we understand that real online success demands continuous care, proactive monitoring, and seamless enhancements. That’s why we’ve built our Reliable Website Maintenance Services In India to deliver round‑the‑clock peace of mind, bulletproof performance, and measurable ROI for forward‑thinking companies like yours.

Why Website Maintenance Matters—And Why “Reliable” Makes All the Difference

Search engines reward fast, secure, and regularly updated sites with higher rankings; customers reward them with trust and loyalty. Conversely, a sluggish, outdated, or vulnerable site can cost you traffic, conversions, and brand reputation—sometimes overnight. Our Reliable Website Maintenance Services In India go beyond the basic “fix‑it‑when‑it‑breaks” model. We combine proactive health checks, performance tuning, security hardening, and content optimization into a single, cohesive program that keeps your digital storefront open, polished, and ready for growth.

What Sets NRS Infoways Apart?

1. Proactive Performance Monitoring

We leverage enterprise‑grade monitoring tools that continuously scan load times, server resources, and user journeys. By identifying bottlenecks before they escalate, we ensure smoother experiences and higher conversion rates—24/7.

2. Robust Security & Compliance

From real‑time threat detection to regular firewall updates and SSL renewals, your site stays impervious to malware, SQL injections, and DDoS attacks. We align with global standards such as GDPR and PCI‑DSS, keeping you compliant and trustworthy.

3. Seamless Content & Feature Updates

Launching a new product line? Running a seasonal promotion? Our dedicated team updates layouts, landing pages, and plugins—often within hours—to keep your messaging sharp and relevant without disrupting uptime.

4. Data‑Driven Optimization

Monthly analytics reviews highlight user behavior, bounce rates, and conversion funnels. We translate insights into actionable tasks—A/B testing CTAs, compressing heavy images, or refining navigation—all folded into our maintenance retainer.

5. Transparent Reporting & SLAs

Every client receives detailed monthly reports covering task logs, incident resolutions, and performance metrics. Our Service Level Agreements guarantee response times as low as 30 minutes for critical issues, underscoring the “Reliable” in our Reliable Website Maintenance Services In India.

Real‑World Impact: A Success Snapshot

A Delhi‑based B2B SaaS provider reached out to NRS Infoways after repeated downtime eroded user trust and slashed demo bookings by 18 %. Within the first month of onboarding, we:

Migrated their site to a high‑availability cloud cluster

Deployed a Web Application Firewall (WAF) to fend off bot attacks

Compressed multimedia assets, cutting average load time from 4.2 s to 1.3 s

Implemented weekly backup protocols with versioned restores

Result? Organic traffic climbed 27 %, demo sign‑ups rebounded 31 %, and support tickets fell by half—proving that consistent, expert care translates directly into revenue.

Flexible Plans That Scale With You

Whether you manage a lean startup site or a sprawling enterprise portal, we offer tiered packages—Basic, Professional, and Enterprise—each customizable with à‑la‑carte add‑ons like e‑commerce catalog updates, multi‑language support, or advanced SEO audits. As your business evolves, our services scale seamlessly, ensuring you never pay for overhead you don’t need or sacrifice features you do.

Partner With NRS Infoways Today

Your website is too important to leave to chance. Join the growing roster of Indian businesses that rely on NRS Infoways for Reliable Website Maintenance Services In India and experience the freedom to innovate while we handle the technical heavy lifting. Ready to protect your digital investment, delight your visitors, and outpace your competition?

Connect with our maintenance experts now and power your growth with reliability you can measure.

[hiringiosdevelopers]

Hire Data Scientists in a GenAI Era: What's Changed in 2025?

With the rapidly evolving nature of artificial intelligence, the role of data scientists too has been modified. With generative AI transforming sectors across the board, companies that want to hire data scientists have to deal with new challenges and opportunities. This article covers how the craft of recruiting leading data professionals has been modified in 2025 and what firms need to know to stay ahead.

The Changing Role of Data Scientists

Those were days when data scientists would just build models and sweep across sets of data. Today, as it is 2025, the data scientist operates at the nexus of foundation analysis and generation AI capabilities. When companies hire data scientists Today, they are looking for people who not only understand how to read data but are masters at how to leverage GenAI tools in order to drive business functions.

The technology acumen in data science has increased much higher. Python, R, and SQL are still crucial, but prompt engineering, large language model fine-tuning, and multimodal AI systems knowledge is now a necessity. Such types of organizations that have been employing data scientists today are looking for people who are aware of the most recent GenAI architecture and how such capabilities can be integrated into data processes today.

Critical Shifts in the Hiring Environment

From Model Creators to AI Conductors

Earlier years were focused to hire data scientists who could create models from scratch. With the advent of foundation models and pre-trained AI tools, the emphasis has shifted towards people with expertise in conducting, customizing, and utilizing these high-capacity tools in an effective manner.

Blend of Technical and Strategic Skills

Companies that hired data scientists in 2025 no longer hire merely for technical talent. The best of these candidates possess technical as well as business strategy acumen. Data scientists of today need to have the capability to communicate easily with stakeholders in various departments, taking very abstract AI concepts and making them deliverable business value.

Ethical AI Expertise

As AI continues to become more sophisticated and pervasive, firms hiring data scientists now put a high value on individuals with outstanding experience in developing AI responsibly. Understanding bias mitigation, transparency, and privacy technology is now a "must-have" instead of a "nice-to-have."

Real-World Strategies for GenAI Recruitment

Redesign Your Job Descriptions

When writing job postings to hire data scientists, make sure the descriptions accurately capture today's reality of the job. Leave behind vague requirements such as "machine learning experience" to more detailed ones such as "fine-tuning experience on large language models for domain use cases" or "experience with deploying retrieval-augmented generation systems."

Evaluate AI Fluency Through Practical Challenges

Legacy coding tests remain relevant but are no longer sufficient. Businesses that wish to hire data scientists must incorporate these challenges that test the candidate's ability to collaborate using generative AI tools. Attempt to measure prompt engineering ability, model choice ability, and critical assessment of GenAI output.

The rapid pace of technology advancement in AI is such that conventional education may not always reflect the best skills of a candidate. When you hire data scientists, hire unconventional candidates with practical experience with cutting-edge GenAI technologies, regardless of their bachelor's being related fields.

Focus on Continuous Learners

The knowledge half-life of AI is shrinking more and more. To prosper to hire data scientists In 2025, businesses need to realize the relevance of hiring candidates with proven track records of lifelong learning and adjustment. Look for candidates who engage significantly in AI forums, work on open-source initiatives, or present their work on evolving methods.

Key Skills to Search for While Hiring Data Scientists in 2025

1. Generative AI Knowledge

The capability to work effectively with large language models, diffusion models, and other generative models is now essential. When you hire data scientists, evaluate their skills in fine-tuning techniques, retrieval-augmented generation, and model testing methods.

2. Data Engineering in the GenAI Era

Data scientists must possess abilities to craft and handle data specifically for generative AI applications. Hiring organizations should test the applicants' familiarity with designing effective prompt datasets, synthetic data generation, and GenAI-oriented data augmentation techniques.

3. Integration of AI Systems

Because AI is being applied in increasing numbers of business processes, data scientists should be able to incorporate generative models into existing systems. Companies looking to hire data scientists need to hire people with the ability to bridge old infrastructure to new AI capabilities.

4. AI Risk Management

With increased regulatory monitoring of AI deployment, businesses recruiting data scientists must ensure the recruits are conversant with AI governance frameworks and possess the ability to implement appropriate risk mitigation controls on generative models.

Retention Strategies in a Competitive Market

To hire data scientists is only the beginning. In the competitive market of 2025, retention must be done with much planning:

Provide Next-generation AI Infrastructure Access

Data scientists thrive when given access to emerging tools and technologies. Companies who hire data scientists must spend on robust AI infrastructure that fosters experimentation and innovation.

Create Career Paths for Specialization

As the field continues to expand, companies that hire data scientists must create distinct career tracks for specializing in such domains as multimodal AI, time-series forecasting with generative models, or decision intelligence with AI.

Create an Ethical AI Development Culture

Top data scientists increasingly prefer to work for companies committed to ethical AI development. When you hire data scientists, emphasize your adherence to ethical principles and governance frameworks for deploying AI. Conclusion

The recruitment landscape for data scientists has transformed dramatically because of the generative AI phenomenon. Businesses hoping to hire data scientists in 2025 must prepare their hiring strategy to identify applicants that possess hybrid skill sets that they will be needing in this new landscape. By awareness of these alterations and implementation of carefully planned recruitment strategies, businesses can build data science teams with the potential to maximize the capabilities of generative AI technologies.

These will be the ones to make the hire of experts who not only are aware of the technical underpinnings of current AI but also aware of how to use these technologies strategically in order to develop real business value. Day by day, the most successful organizations will be those that hire data scientists able to constantly innovate and adjust in the rapidly changing realm of generative AI.

[lunarsilkscreen]

The typical Mobile-App Node/Firebase Model

Every mobile-app uses more or less the same model. And due to "Concurrent Connect Money Model" that is; the number of users that can be connected to your "backend" at any one time impacts the cost of your "database-like".

Backend-services charge *both* on data-storage AND simultaneous users. I suspect this has to do with the number of Millennials who downloaded [whole ass cars] so they could get to the movies or a concert or something.

The template they use is something like this;

[User ID]{ Name::string, FreeCurrency1::integer, FreeCurrency2::integer, ChatStats{complex object}, inventory::[array], etc...}

For logins, however; they have a supplemental datasheet:

[Login] {user::{id,password,email,phone number,social media, RMTCurrency(fake money you bought with real money)}

The business model requires that a lot of *stuff* is done on the user's device. Because of the [Concurrent Connections] thing.

So it's limited to transactional Commands sent to the server via {RESTful API}(which is just a fancy of way of saying HTTP).

So the commands to the server are kind of like;

[sign up,login,Google/Apple pay{me money}, perform action(action_name)]

With a lot of the in-game logic being performed user side and verified by the backend. I suspect that many newbie app developers aren't too concerned with security and so a lot of "verifications" are done dumbly.

Again; cuz of the concurrent Connection thing. Otherwise they run the risk of server-slowdown when they're at the max connection.

A few apps in particular even have "AI" users that a player can recruit that are simply externally connected NPCs. Players run by AI, instead of AI run on the backend.

Because of this you see MobileApp developers follow *the same* process when developing new content.

"Add a currency value related to new content, and then worry about all the frontend stuff later"

Because they're all connected to the [pay me money] button; pretty much all in-game currencies can be purchased *somehow*.

I highly suspect that the lack of "developer-user friendly interfaces" for modern backend-services *coughFireBasecoughcoughAWScough* effectively serve as a limiting factor for developer ability to use the platform creatively.

Limiting the kinds of apps that *can* be developed *because* most developers don't really understand the backend service of what it's doing.

There's a lack of good backend interface tools that would accomplish this.

Because; and I can't stress this enough; there's *no money* in customer service, and allowing developers to create their own *interfaces* is a potential security risk.

It's odd, because many devs already understand DataSheets(spreadsheets) AND the JSON (JavaScript object notation) model... Yet dealing with these services is harder than using Microsoft Excel... Which; I think that's a good metric; if your DataSheet software is harder to understand than Excel--that makes it bad.

Part of this has to deal with JSON being *more* complex than traditional SQL(talking to databases language) yet... It's also because of Large Software Enterprises buying as much as they can of the landscape.

Google, on their own, has *several* database-solutions ALL with their own notation, niche-usecases, and none of them are cross-compatible unless you pay a Google dev to build it.

And none of those solutions are *really focused* on... Being intuitive or usable.

Firebase, before Google, was on its way to being a respectable backend utility. Yet, I'm still wondering *why* the current ecosystem is *so much more of a mess* than traditional SQL solutions.

Half of the affor-mentioned services still use SQL after-all ... Why are they harder to understand than SQL?

Anyone familiar with JavaScript or Excel should be able to pick up your *backend service* and run with it. Yet... A lot of those people who *do* don't understand these services.

It's hard to say it's not intentional or a monopolized ecosystem that hinders growth and improvement.

[pcrtisuyog]

How a Full Stack Developer Course Prepares You for Real-World Projects

The tech world is evolving rapidly—and so are the roles within it. One role that continues to grow in demand is that of a full-stack developer. These professionals are the backbone of modern web and software development. But what exactly does it take to become one? Enrolling in a full-stack developer course can be a game-changer, especially if you're someone who enjoys both the creative and logical sides of building digital solutions.

In this article, we'll explore the top 7 skills you’ll master in a full-stack developer course—skills that not only make you job-ready but also turn you into a valuable tech asset.

1. Front-End Development

Let’s face it: first impressions matter. The front-end is what users see and interact with. You’ll dive deep into the languages and frameworks that make websites beautiful and functional.

You’ll learn:

HTML5 and CSS3 for content and layout structuring.

JavaScript and DOM manipulation for interactivity.

Frameworks like React.js, Angular, or Vue.js for scalable user interfaces.

Responsive design using Bootstrap or Tailwind CSS.

You’ll go from building static web pages to creating dynamic, responsive user experiences that work across all devices.

2. Back-End Development

Once the front-end looks good, the back-end makes it work. You’ll learn to build and manage server-side applications that drive the logic, data, and security behind the interface.

Key skills include:

Server-side languages like Node.js, Python (Django/Flask), or Java (Spring Boot).

Building RESTful APIs and handling HTTP requests.

Managing user authentication, data validation, and error handling.

This is where you start to appreciate how things work behind the scenes—from processing a login request to fetching product data from a database.

3. Database Management

Data is the lifeblood of any application. A full-stack developer must know how to store, retrieve, and manipulate data effectively.

Courses will teach you:

Working with SQL databases like MySQL or PostgreSQL.

Understanding NoSQL options like MongoDB.

Designing and optimising data models.

Writing CRUD operations and joining tables.

By mastering databases, you’ll be able to support both small applications and large-scale enterprise systems.

4. Version Control with Git and GitHub

If you’ve ever made a change and broken your code (we’ve all been there!), version control will be your best friend. It helps you track and manage code changes efficiently.

You’ll learn:

Using Git commands to track, commit, and revert changes.

Collaborating on projects using GitHub.

Branching and merging strategies for team-based development.

These skills are not just useful—they’re essential in any collaborative coding environment.

5. Deployment and DevOps Basics

Building an app is only half the battle. Knowing how to deploy it is what makes your work accessible to the world.

Expect to cover:

Hosting apps using Heroku, Netlify, or Vercel.

Basics of CI/CD pipelines.

Cloud platforms like AWS, Google Cloud, or Azure.

Using Docker for containerisation.

Deployment transforms your local project into a living, breathing product on the internet.

6. Problem Solving and Debugging

This is the unspoken art of development. Debugging makes you patient, sharp, and detail-orientated. It’s the difference between a good developer and a great one.

You’ll master

Using browser developer tools.

Analysing error logs and debugging back-end issues.

Writing clean, testable code.

Applying logical thinking to fix bugs and optimise performance.

These problem-solving skills become second nature with practice—and they’re highly valued in the real world.

7. Project Management and Soft Skills

A good full-stack developer isn’t just a coder—they’re a communicator and a team player. Most courses now incorporate soft skills and project-based learning to mimic real work environments.

Expect to develop:

Time management and task prioritisation.

Working in agile environments (Scrum, Kanban).

Collaboration skills through group projects.

Creating portfolio-ready applications with documentation.

By the end of your course, you won’t just have skills—you’ll have confidence and real-world project experience.

Why These Skills Matter

The top 7 skills you’ll master in a full-stack developer course are a balanced mix of hard and soft skills. Together, they prepare you for a versatile role in startups, tech giants, freelance work, or your own entrepreneurial ventures.

Here’s why they’re so powerful:

You can work on both front-end and back-end—making you highly employable.

You’ll gain independence and control over full product development.

You’ll be able to communicate better across departments—design, QA, DevOps, and business.

Conclusion

Choosing to become a full-stack developer is like signing up for a journey of continuous learning. The right course gives you structured learning, industry-relevant projects, and hands-on experience.

Whether you're switching careers, enhancing your skill set, or building your first startup, these top 7 skills you’ll master in a Full Stack Developer course will set you on the right path.

So—are you ready to become a tech all-rounder?

[shubhamjobsinlondon]

Top IT Jobs in London: High-Demand Roles & Salary Insights

London has long been a global center for business, finance, and innovation. Over the past decade, it has also emerged as one of the most attractive destinations for IT professionals. With a thriving tech ecosystem, diverse job opportunities, and competitive salaries, London is an excellent choice for those looking to build a career in IT.

Get More Job Opportunity:-https://jobsinlondon.com

Why London is the Ideal Destination for IT Jobs?

Booming Tech Industry: London is home to a rapidly growing tech sector, with numerous startups, established companies, and multinational corporations setting up offices in the city. Major players like Google, Microsoft, Amazon, and IBM have a strong presence in London, making it a hotbed for IT jobs.

Diverse Job Roles: Whether you're a software developer, cybersecurity expert, data analyst, or IT project manager, London offers opportunities across various domains. The demand for IT professionals continues to rise, driven by advancements in artificial intelligence, cloud computing, and data science.

Competitive Salaries: IT jobs in London offer some of the highest salaries in the UK. On average, a software engineer can expect to earn between £45,000 and £80,000 per year, while specialized roles like AI engineers and cybersecurity analysts can earn even more.

Networking & Growth Opportunities: London hosts numerous tech conferences, networking events, and meetups, providing IT professionals with excellent opportunities to connect, learn, and advance in their careers. Events like London Tech Week and AI & Big Data Expo bring together industry leaders and innovators.

Popular IT Job Roles in London

1. Software Developer/Engineer

Responsibilities: Designing, developing, and maintaining software applications.

Required Skills: Programming languages (Java, Python, C++, JavaScript), problem-solving, teamwork.

Average Salary: £50,000 - £90,000 per year.

2. Data Scientist

Responsibilities: Analyzing data, building predictive models, and helping businesses make data-driven decisions.

Required Skills: Python, R, machine learning, data visualization, SQL.

Average Salary: £55,000 - £100,000 per year.

3. Cybersecurity Analyst

Responsibilities: Protecting systems and networks from cyber threats and attacks.

Required Skills: Ethical hacking, risk assessment, network security, incident response.

Average Salary: £50,000 - £90,000 per year.

4. Cloud Engineer

Responsibilities: Managing cloud infrastructure, deploying cloud-based solutions, ensuring security and scalability.

Required Skills: AWS, Azure, Google Cloud, Kubernetes, DevOps.

Average Salary: £60,000 - £110,000 per year.

5. IT Project Manager

Responsibilities: Overseeing IT projects, managing budgets, timelines, and teams.

Required Skills: Agile methodology, communication, leadership, risk management.

Average Salary: £55,000 - £100,000 per year.

Top Companies Hiring IT Professionals in London

Google

Amazon

Microsoft

IBM

Accenture

Deloitte

Revolut

Monzo

Facebook (Meta)

Barclays & HSBC (for fintech roles)

Where to Find IT Jobs in London?

1. Online Job Portals

Jobs In London– A great platform for networking and job searching.

Indeed UK – Lists a variety of IT job openings.

Glassdoor – Provides insights into salaries and company reviews.

CW Jobs – A job board dedicated to IT and tech jobs.

2. Recruitment Agencies

Hays Technology

Robert Half Technology

Michael Page IT

Spring Technology

3. Company Career Pages

Most tech giants and startups post their job openings on their official career pages. Keeping an eye on these websites can help you land a job directly.

Tips to Land an IT Job in London

Build a Strong CV & Portfolio: Highlight your skills, experience, and projects. If you are a developer, having a GitHub portfolio can be beneficial.

Gain Certifications: Certifications such as AWS Certified Solutions Architect, Cisco CCNA, or CompTIA Security+ can boost your resume.

Network Actively: Attend London-based tech meetups, conferences, and LinkedIn networking events.

Stay Updated: The IT industry is ever-evolving, so continuous learning and upskilling are crucial.

Prepare for Interviews: Research common IT interview questions and practice coding challenges on platforms like LeetCode or HackerRank.

Conclusion

London offers an exciting and dynamic environment for IT professionals. With a booming tech industry, high salaries, and abundant job opportunities, it is one of the best places to build a rewarding career in IT. Whether you're a fresh graduate or an experienced tech expert, London has something to offer for everyone in the IT sector. Start your job search today and take advantage of the numerous opportunities available in this thriving city!

[shastra101]

How to Become a Full Stack Developer in 6 Months

The demand for skilled Full Stack Developers is growing rapidly as companies seek professionals who can handle both front-end and back-end development. If you’re looking to break into this exciting field, you might be wondering: Can I become a Full Stack Developer in just six months? The answer is YES — with the right approach, dedication, and a structured learning path, you can master full stack development in half a year.

Step 1: Understand the Basics (Month 1)

Before diving into full stack development, it’s essential to understand the fundamentals:

HTML, CSS, and JavaScript: These are the building blocks of web development.

Version Control (Git & GitHub): Learn to manage your code efficiently.

Basic Programming Concepts: Understanding loops, conditions, and functions is crucial.

Enrolling in a Full Stack Developer Course in Pune can help you build a solid foundation and get hands-on experience from the beginning.

Step 2: Learn Front-End Development (Month 2)

Now that you have the basics, start focusing on front-end technologies:

CSS Frameworks (Bootstrap, Tailwind CSS): Make your designs responsive and appealing.

JavaScript Libraries (React, Angular, or Vue.js): Choose one to specialize in.

Building Static Websites: Start creating simple projects to improve your skills.

A structured Full Stack Development Course in Pimpri Chinchwad can guide you through this phase with real-world projects and expert mentorship.

Step 3: Master Back-End Development (Month 3–4)

Once you’re comfortable with front-end technologies, it’s time to work on back-end development:

Learn a Backend Language: Popular choices include Node.js, Python (Django/Flask), or Java (Spring Boot).

Understand Databases: Work with SQL (MySQL, PostgreSQL) and NoSQL (MongoDB).

Build RESTful APIs: Learn how to connect your front end with the back end.

Authentication & Authorization: Implement user authentication using JWT or OAuth.

At this stage, working on real projects and enrolling in a hands-on Full Stack Developer Course in Pune will ensure you’re on the right track.

Step 4: Work on Full Stack Projects (Month 5)

By now, you should have a good grasp of both front-end and back-end development. Strengthen your skills by:

Building Real-World Projects: Create a portfolio with projects like a blog, e-commerce site, or task manager.

Using Cloud Services: Deploy applications using AWS, Firebase, or Heroku.

Working with APIs: Integrate third-party APIs to enhance your applications.

Step 5: Prepare for Job Interviews (Month 6)

Now that you have gained full stack development skills, focus on landing a job:

Revamp Your Resume & LinkedIn Profile: Highlight your projects and skills.

Practice Data Structures & Algorithms: Platforms like LeetCode and CodeChef can help.

Prepare for Technical Interviews: Learn commonly asked questions related to full stack development.

Apply for Jobs & Freelancing Gigs: Start building experience in the industry.

Start Your Journey with Testing Shastra

If you’re looking for a practical and structured learning experience, Testing Shastra offers a top-notch Full Stack Development Course in Pimpri Chinchwad. With expert trainers, hands-on projects, and industry-relevant curriculum, we help aspiring developers kickstart their careers. Enroll today and take the first step toward becoming a Full Stack Developer!

To know more about Testing Shastra,

Visit website: https://www.testingshastra.com/ Address: 504, Ganeesham E, Pimple Saudagar, Pune. Email: info@testingshastra.comGet directions

[inkzoidfoundation]

Shobhit Gupta: An Ambitious, Multifaceted IT Expert, Author and Social Worker.

INTRODUCTION

Shobhit Gupta is an ambitious, multifaceted IT expert, author, social worker and a versatile individual with a master’s degree in computer application and business administration, has carved a unique path in the literary world and beyond his academic pursuits, Shobhit’s diverse interests span travel, photography, culinary experimentation, language learning, and social activism.

In the grand tapestry of technological evolution, specific individuals emerge as luminous threads, weaving together innovation, resilience, and creativity.

Shobhit Gupta's multifaceted journey spanning over a decade and a half is a testament to this rare breed and this Renaissance technologist dances effortlessly between the binary rhythms of code and the vivid hues of human experience.

PROFESSIONAL CAREER

• Managing Director, AshoShila Group (July 2013 – Present)

Established and currently manages a company with diverse activities, including software development, website design, educational classes for coding and graphics, and eBook publishing.

• Senior Project Manager, Microsoft Corporation (June 2005 – June 2015)

Managed and delivered complex software projects on time and within budget.

Led and motivated cross-functional teams of engineers, designers, and other technical specialists.

Ensured clear communication and collaboration between all stakeholders.

• Programmer, Sun Microsystems (June 2003 – June 2005)

Designed, developed, and implemented software applications.

Troubleshooted and resolved technical issues.

TECHNICAL PROFICIENCY

• Programming Languages: C++, Visual Basic, Java, Python, PHP, HTML, CSS, JavaScript

• Database Management Systems: Microsoft SQL, MySQL, Oracle

• Communication: Strong verbal and written communication skills; adept at creating presentations and delivering information to technical and non-technical audiences.

• Leadership: Proven ability to lead and motivate teams; experience in setting goals, delegating tasks, and providing feedback.

• Management: Skilled in project, resource, and budget management.

EDUCATIONAL BACKGROUND

• Masters in Computer Application, College of Arts & Computers, Delhi University (2003)

• Masters in Business Administration (Part-time), Faculty of Management Studies, Delhi University (2006)

• Bachelor of Commerce, MJP Rohilkhand University (2000)

CONTACT INFORMATION

For those seeking to connect with Shobhit, here are the ways to reach out:

• Website: Visit www.ShobhitGupta.in to explore his literary endeavors.

• Phone: Reach him at +91-7838110755.

• Email: Contact Shobhit via email at mail4shobhit@gmail.com

LITERARY CONTRIBUTIONS

Shobhit’s passion for writing finds expression through various channels:

• Personal Website:

Shobhit regularly contributes poems, short stories, and reviews to his website, www.ShobhitGupta.in.

• Other Platforms:

He consistently shares his creative work on platforms like StoryMirror, YourQuote, and Pratilipi.

SOCIAL MEDIA PRESENCE

Shobhit actively engages with his audience on social media platforms:

• Facebook: Follow him at @shobhit2607 or @gupta.shobhit

• Instagram: Connect with him on Instagram @shobhit2607

• LinkedIn: Follow him at @gshobhit

LITERARY ACHIEVEMENTS

Throughout his career, Shobhit has garnered recognition in literary competitions. His works have been featured in various Radion Stations and Newspapers:

• RADIO

•Broadcast on Radio Deutsche Welle (Germany):

One of his short stories was broadcast on Radio Deutsche Welle, reaching audiences in Germany.

•Poetry Reading On Radio Lotus Fm (South Africa):

Shobhit’s poetic talent was showcased through a reading on Radio Lotus FM in South Africa.

• NEWSPAPER

•Dainik Hindustan Hindi:

His pieces have been featured in Dainik Hindustan Hindi.

•Dainik Navin Kadam:

Shobhit’s literary contributions have also found a place in Dainik Navin Kadam.

•Saptahik Vinod Varta:

Shobhit’s technical articles were applauded in the `weekly newspaper Vinod Varta.

MOTIVATIONAL BOOKS AND POETRY COLLECTIONS

Shobhit’s literary repertoire includes both English and Hindi works:

• “It’s Not Over Yet” (English):

A motivational book that inspires readers to persevere and overcome challenges.

• “Unsung Stories” (English):

A collection of motivational biographies that celebrate unsung heroes.

• “Bolte Shabd” (Hindi):

A poetry collection in Hindi, reflecting his emotional depth and creativity.

• “Ishqbaaziyan” (Hindi):

A collection of Hindi stories is currently in the process of being published.

As the curtain falls on this article, Shobhit Gupta remains an unfinished symphony—an overture that continues beyond the printed page. His legacy, a constellation of achievements, inspires us all. For in the dance of technology, he pirouettes with grace, leaving stardust in his wake.

Shobhit Gupta’s literary journey is a testament to his passion, creativity, and commitment to making a meaningful impact through words. Through prose, poetry, and essays, he continues to captivate readers across borders.

[govindhtech]

Pgvector: Rise PostgreSQL with Vector Similarity Search

What is pgvector

With the help of the open-source Pgvector extension for PostgreSQL, you may deal with vectors from inside the database. This implies that you can use PostgreSQL to store, search for, and analyse vector data in addition to structured data.

The following are some essential pgvector knowledge points:

Vector Similarity Search

Enabling vector similarity search is the primary purpose of pgvector. This is helpful for things like recommending products based on user behaviour or content or locating related items. Pgvector provides options for both exact and approximation searches.

Storing Embeddings

Vector embeddings, which are numerical representations of data points, can also be stored using Pgvector. Many machine learning tasks can make use of these embeddings.

Functions with Various Vector Data Types

Pgvector is compatible with binary, sparse, half-precision, and single-precision vector data types.

Rich Functionality

Pgvector offers a wide range of vector operations, such as addition and subtraction, as well as distance measurements (such as cosine similarity) and indexing for quicker search times.

PostgreSQL integration

Since pgvector is a PostgreSQL extension, it interacts with PostgreSQL without any problems. This enables you to use PostgreSQL’s built-in architecture and features for your AI applications.

All things considered, pgvector is an effective tool for giving your PostgreSQL database vector similarity search capabilities. Numerous applications in artificial intelligence and machine learning may benefit from this.

RAG Applications

In order to speed up your transition to production, Google Cloud is pleased to announce the release of a quickstart solution and reference architecture for Retrieval Augmented Generation (RAG) applications. This article will show you how to use Ray, LangChain, and Hugging Face to quickly deploy a full RAG application on Google Kubernetes Engine (GKE), along with Cloud SQL for PostgreSQL and pgvector.

Describe RAG

For a particular application, RAG can enhance the outputs of foundation modes, such as large language models (LLMs). AI apps with RAG support can extract the most pertinent information from an external knowledge source, add it to the user’s prompt, and then transmit it to the generative model instead of depending solely on knowledge acquired during training. Digital shopping assistants can access product catalogues and customer reviews, vector databases, relational databases, and customer service chabots can look up help centre articles using the knowledge base. AI-powered travel agents can also retrieve the most recent flight and hotel information from the knowledge base.Image Credit to Google Cloud

LLMs rely on their training data, which may not contain information pertinent to the application’s domain and can rapidly become outdated. Retraining or optimising an LLM to deliver new, domain-specific data can be a costly and difficult procedure. RAG provides the LLM with access to this data without the need for fine-tuning or training. but can also direct an LLM towards factual answers, minimising delusions and allowing applications to offer material that can be verified by a person.

AI Framework for RAG

An application architecture would typically consist of a database, a collection of microservices, and a frontend before Generative AI gained popularity. New requirements for processing, retrieving, and serving LLMs are introduced by even the most rudimentary RAG applications. Customers demand infrastructure that is specifically optimised for AI workloads in order to achieve these criteria.

Many clients decide to use a fully managed platform, like Vertex AI, to access AI infrastructure, such as TPUs and GPUs. Others, on the other hand, would rather use open-source frameworks and open models to run their own infrastructure on top of GKE. This blog entry is intended for the latter.

Making a lot of important decisions when starting from scratch with an AI platform includes choosing which frameworks to use for model serving, which machine models to use for inference, how to secure sensitive data, how to fulfil performance and cost requirements, and how to expand as traffic increases. Every choice you make pits you against an expansive and dynamic array of creative AI tools.

LangChain pgvector

For RAG applications, Google Cloud has created a quickstart solution and reference architecture based on GKE, Cloud SQL, and the open-source frameworks Hugging Face, Ray, and LangChain. With RAG best practices integrated right from the start, the Google Cloud solution is made to help you get started quickly and accelerate your journey to production.

RAG’s advantages for GKE and Cloud SQL

GKE with Cloud SQL expedite your deployment process through multiple means:

Load Data Quickly

Using GKE’s GCSFuse driver, you can easily access data in parallel from your Ray cluster by using Ray Data. To do low latency vector search at scale, load your embeddings into Cloud SQL for PostgreSQL and pgvector efficiently.

Fast deploy

Install Hugging Face Text Generation Inference (TGI), JupyterHub, and Ray on your GKE cluster quickly.

Simplified security

GKE provides move-in ready Kubernetes security. Use Sensitive Data Protection (SDP) to filter out anything that is hazardous or sensitive. Use Identity-Aware Proxy to take advantage of Google’s standard authentication and enable users to login to your LLM frontend and Jupyter notebooks with ease.

Cost effectiveness and lower management overhead

GKE simplifies the use of cost-cutting strategies like spot nodes through YAML configuration and lowers cluster maintenance.

Scalability

As traffic increases, GKE automatically allocates nodes, removing the need for human configuration to expand.

Pgvector Performance

The following are provided by the Google Cloud end-to-end RAG application and reference architecture:

Google Cloud project

The Google Cloud project setup provides the necessary setup for the RAG application to run, such as a GKE Cluster, Cloud SQL for PostgreSQL, and pgvector instance.

AI frameworks

Ray, JupyterHub, and Hugging Face TGI are implemented at GKE

RAG Embedding Pipeline

The RAG Embedding Pipeline creates embedding and loads the PostgreSQL and pgvector instance’s data into the Cloud SQL.

Example RAG Chatbot Application

A web-based RAG chatbot is deployed to GKE via the example RAG chatbot application.Image Credit to Google Cloud

Pgvector Postgres

An open source LLM can be interacted with by users through the web interface offered by the example chatbot programme. By utilising the data that is loaded into Cloud SQL for PostgreSQL with pgvector via the RAG data pipeline, it may provide users with more thorough and insightful answers to their queries.

The Google Cloud end-to-end RAG solution shows how this technology may be used for a variety of applications and provides a foundation for future development. With the strength of RAG, the scalability, flexibility, and security capabilities of GKE and Cloud SQL, along with the security features of Google Cloud, developers can create robust and adaptable apps that manage intricate processes and offer insightful data.

Read more on govindhtech.com

[jcmarchi]

Snowflake Arctic: The Cutting-Edge LLM for Enterprise AI

New Post has been published on https://thedigitalinsider.com/snowflake-arctic-the-cutting-edge-llm-for-enterprise-ai/

Snowflake Arctic: The Cutting-Edge LLM for Enterprise AI

Enterprises today are increasingly exploring ways to leverage large language models (LLMs) to boost productivity and create intelligent applications. However, many of the available LLM options are generic models not tailored for specialized enterprise needs like data analysis, coding, and task automation. Enter Snowflake Arctic – a state-of-the-art LLM purposefully designed and optimized for core enterprise use cases.

Developed by the AI research team at Snowflake, Arctic pushes the boundaries of what’s possible with efficient training, cost-effectiveness, and an unparalleled level of openness. This revolutionary model excels at key enterprise benchmarks while requiring far less computing power compared to existing LLMs. Let’s dive into what makes Arctic a game-changer for enterprise AI.

Enterprise Intelligence Redefined At its core, Arctic is laser-focused on delivering exceptional performance on metrics that truly matter for enterprises – coding, SQL querying, complex instruction following, and producing grounded, fact-based outputs. Snowflake has combined these critical capabilities into a novel “enterprise intelligence” metric.

The results speak for themselves. Arctic meets or outperforms models like LLAMA 7B and LLAMA 70B on enterprise intelligence benchmarks while using less than half the computing budget for training. Remarkably, despite utilizing 17 times fewer compute resources than LLAMA 70B, Arctic achieves parity on specialized tests like coding (HumanEval+, MBPP+), SQL generation (Spider), and instruction following (IFEval).

But Arctic’s prowess goes beyond just acing enterprise benchmarks. It maintains strong performance across general language understanding, reasoning, and mathematical aptitude compared to models trained with exponentially higher compute budgets like DBRX. This holistic capability makes Arctic an unbeatable choice for tackling the diverse AI needs of an enterprise.

The Innovation

Dense-MoE Hybrid Transformer So how did the Snowflake team build such an incredibly capable yet efficient LLM? The answer lies in Arctic’s cutting-edge Dense Mixture-of-Experts (MoE) Hybrid Transformer architecture.

Traditional dense transformer models become increasingly costly to train as their size grows, with computational requirements increasing linearly. The MoE design helps circumvent this by utilizing multiple parallel feed-forward networks (experts) and only activating a subset for each input token.

However, simply using an MoE architecture isn’t enough – Arctic combines the strengths of both dense and MoE components ingeniously. It pairs a 10 billion parameter dense transformer encoder with a 128 expert residual MoE multi-layer perceptron (MLP) layer. This dense-MoE hybrid model totals 480 billion parameters but only 17 billion are active at any given time using top-2 gating.

The implications are profound – Arctic achieves unprecedented model quality and capacity while remaining remarkably compute-efficient during training and inference. For example, Arctic has 50% fewer active parameters than models like DBRX during inference.

But model architecture is only one part of the story. Arctic’s excellence is the culmination of several pioneering techniques and insights developed by the Snowflake research team:

Enterprise-Focused Training Data Curriculum Through extensive experimentation, the team discovered that generic skills like commonsense reasoning should be learned early, while more complex specializations like coding and SQL are best acquired later in the training process. Arctic’s data curriculum follows a three-stage approach mimicking human learning progressions.

The first teratokens focus on building a broad general base. The next 1.5 teratokens concentrate on developing enterprise skills through data tailored for SQL, coding tasks, and more. The final teratokens further refine Arctic’s specializations using refined datasets.

Optimal Architectural Choices While MoEs promise better quality per compute, choosing the right configurations is crucial yet poorly understood. Through detailed research, Snowflake landed on an architecture employing 128 experts with top-2 gating every layer after evaluating quality-efficiency tradeoffs.

Increasing the number of experts provides more combinations, enhancing model capacity. However, this also raises communication costs, so Snowflake landed on 128 carefully designed “condensed” experts activated via top-2 gating as the optimal balance.

System Co-Design But even an optimal model architecture can be undermined by system bottlenecks. So the Snowflake team innovated here too – co-designing the model architecture hand-in-hand with the underlying training and inference systems.

For efficient training, the dense and MoE components were structured to enable overlapping communication and computation, hiding substantial communication overheads. On the inference side, the team leveraged NVIDIA’s innovations to enable highly efficient deployment despite Arctic’s scale.

Techniques like FP8 quantization allow fitting the full model on a single GPU node for interactive inference. Larger batches engage Arctic’s parallelism capabilities across multiple nodes while remaining impressively compute-efficient thanks to its compact 17B active parameters.

With an Apache 2.0 license, Arctic’s weights and code are available ungated for any personal, research or commercial use. But Snowflake has gone much farther, open-sourcing their complete data recipes, model implementations, tips, and the deep research insights powering Arctic.

The “Arctic Cookbook” is a comprehensive knowledge base covering every aspect of building and optimizing a large-scale MoE model like Arctic. It distills key learnings across data sourcing, model architecture design, system co-design, optimized training/inference schemes and more.

From identifying optimal data curriculums to architecting MoEs while co-optimizing compilers, schedulers and hardware – this extensive body of knowledge democratizes skills previously confined to elite AI labs. The Arctic Cookbook accelerates learning curves and empowers businesses, researchers and developers globally to create their own cost-effective, tailored LLMs for virtually any use case.

Getting Started with Arctic

For companies keen on leveraging Arctic, Snowflake offers multiple paths to get started quickly:

Serverless Inference: Snowflake customers can access the Arctic model for free on Snowflake Cortex, the company’s fully-managed AI platform. Beyond that, Arctic is available across all major model catalogs like AWS, Microsoft Azure, NVIDIA, and more.

Start from Scratch: The open source model weights and implementations allow developers to directly integrate Arctic into their apps and services. The Arctic repo provides code samples, deployment tutorials, fine-tuning recipes, and more.

Build Custom Models: Thanks to the Arctic Cookbook’s exhaustive guides, developers can build their own custom MoE models from scratch optimized for any specialized use case using learnings from Arctic’s development.

A New Era of Open Enterprise AI Arctic is more than just another powerful language model – it heralds a new era of open, cost-efficient and specialized AI capabilities purpose-built for the enterprise.

From revolutionizing data analytics and coding productivity to powering task automation and smarter applications, Arctic’s enterprise-first DNA makes it an unbeatable choice over generic LLMs. And by open sourcing not just the model but the entire R&D process behind it, Snowflake is fostering a culture of collaboration that will elevate the entire AI ecosystem.

As enterprises increasingly embrace generative AI, Arctic offers a bold blueprint for developing models objectively superior for production workloads and enterprise environments. Its confluence of cutting-edge research, unmatched efficiency and a steadfast open ethos sets a new benchmark in democratizing AI’s transformative potential.

Here’s a section with code examples on how to use the Snowflake Arctic model:

Hands-On with Arctic

Now that we’ve covered what makes Arctic truly groundbreaking, let’s dive into how developers and data scientists can start putting this powerhouse model to work. Out of the box, Arctic is available pre-trained and ready to deploy through major model hubs like Hugging Face and partner AI platforms. But its real power emerges when customizing and fine-tuning it for your specific use cases.

Arctic’s Apache 2.0 license provides full freedom to integrate it into your apps, services or custom AI workflows. Let’s walk through some code examples using the transformers library to get you started: Basic Inference with Arctic

For quick text generation use cases, we can load Arctic and run basic inference very easily:

from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("Snowflake/snowflake-arctic-instruct") model = AutoModelForCausalLM.from_pretrained("Snowflake/snowflake-arctic-instruct") # Create a simple input and generate text input_text = "Here is a basic question: What is the capital of France?" input_ids = tokenizer.encode(input_text, return_tensors="pt") # Generate response with Arctic output = model.generate(input_ids, max_length=150, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text)

This should output something like:

“The capital of France is Paris. Paris is the largest city in France and the country’s economic, political and cultural center. It is home to famous landmarks like the Eiffel Tower, the Louvre museum, and Notre-Dame Cathedral.”

As you can see, Arctic seamlessly understands the query and provides a detailed, grounded response leveraging its robust language understanding capabilities.

Fine-tuning for Specialized Tasks

While impressive out-of-the-box, Arctic truly shines when customized and fine-tuned on your proprietary data for specialized tasks. Snowflake has provided extensive recipes covering:

Curating high-quality training data tailored for your use case

Implementing customized multi-stage training curriculums

Leveraging efficient LoRA, P-Tuning orFactorizedFusion fine-tuning approaches

Optimizations for discerning SQL, coding or other key enterprise skills

Here’s an example of how to fine-tune Arctic on your own coding datasets using LoRA and Snowflake’s recipes:

from transformers import AutoModelForCausalLM, AutoTokenizer from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training # Load base Arctic model tokenizer = AutoTokenizer.from_pretrained("Snowflake/snowflake-arctic-instruct") model = AutoModelForCausalLM.from_pretrained("Snowflake/snowflake-arctic-instruct", load_in_8bit=True) # Initialize LoRA configs lora_config = LoraConfig( r=8, lora_alpha=16, target_modules=["query_key_value"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) # Prepare model for LoRA finetuning model = prepare_model_for_int8_training(model) model = get_peft_model(model, lora_config) # Your coding datasets data = load_coding_datasets() # Fine-tune with Snowflake's recipes train(model, data, ...)

This code illustrates how you can effortlessly load Arctic, initialize a LoRA configuration tailored for code generation, and then fine-tune the model on your proprietary coding datasets leveraging Snowflake’s guidance.

Customized and fine-tuned, Arctic becomes a private powerhouse tuned to deliver unmatched performance on your core enterprise workflows and stakeholder needs.

[enationstechnologyltd]

Free online database design tool softfactory

As we all know, good database design can significantly reduce the amount of maintenance work required later on, and it can also minimize the likelihood of errors in software projects. Therefore, an appropriate online database template design tool can achieve twice the result with half the effort.

What is online database design?

Online database design is a method of designing and managing databases over the Internet. It provides users with a friendly interface, allowing them to effortlessly create, modify, and manage databases. Compared to traditional database design methods, online database design offers greater flexibility and convenience.

Why choose online database design?

Online database design has many advantages, making it an ideal choice for enterprise data management:

Flexibility: Online database design tools offer a variety of features and options that can be operated according to your specific needs. You can add, delete, or modify database tables and fields based on business requirements. Ease of use: Online database design tools usually have a straightforward user interface, enabling non-technical personnel to easily create and manage databases. There’s no need to write complex code or have professional database knowledge. Collaboration: Online database design tools allow team members to collaboratively create and manage databases. You can invite others to join the project and share the rights to design and modify the database. Real-time updates: Online database design tools can update the database structure and data in real time, allowing you to get the latest information promptly. Security: Online database design tools often have security measures to protect your data from unauthorized access and damage.

How do you perform online database design?

AI Table Creation, can convert natural language into My SQL, Oracle, etc., defaulting to My SQL when no type is selected. Supports team collaboration, real-time communication, and seamless collaboration. Strong expandability, database types support My SQL, Oracle, MariaDB, PostgreSQL, SQL Server, SQLite, etc., and more database types will be supported in the future. Customized language library, these language names will tell AI how to generate CRUD code. Powerful data management capability, supporting management of data tables, fields, indexes, foreign keys, diagrams, relationship diagrams, etc.

Are you looking for a cutting-edge online database design tool on the market? Give SoftFactory a try! Arrange now: https://www.softfactory.cloud/"

[gnarlykickflip]

okay so i made it all day and only opened tumblr a couple times but closed it as soon as i remembered!! im going to try again tomorrow morning but like. just for the morning tho because i do genuinely enjoy using tumblr i just need to break my habit of twitching to it whenever i lose focus. and DEFINITELY not when im supposed to be doing work.

[temsikfates]

DOSSIER CHEAT SHEET

[For Main verse.]

LEGAL NAME: Yomiel

NICKNAME[S]: Shades, Man in Red, Pointy-head, Sunglasses, Banana-head, Mr. Red Suit, Yommy.

DATE OF BIRTH: November 2nd.

SEX: Male

PLACE OF BIRTH: [redacted]

CURRENTLY LIVING: (often l leave it up to interpreatation since Ghost Trick doesn't take place in any particular place, but I'd say mostly America since most muses are american)

SPOKEN LANGUAGES: English, a bit of French, German and Portuguese. Java, Javascript, PhP, Python, HTML, C++, CSS, SQL, C#, TypeScript, R, Swift, Binary

EDUCATION: Degrees in: Computer Science, Systems Engineering, Information Technology

HAIR COLOR: Blond.

EYE COLOR: Blue.

HEIGHT: 1,90m

WEIGHT: 70kg i guess? i have no idea sdsjhgdsf

FAMILY INFORMATION

SIBLING[S]: Three half-sisters from his father's side.

PARENT[S]: Tamara (mother/ deceased), Unnamed father (divorced)

RELATIVE[S]: An aunt from his mother's side and cousins, he is not close to any of them.

CHILDREN: None yet.

PET[S]: Sissel the black cat, Java the kitten

RELATIONSHIP INFORMATION

SEXUAL ORIENTATION: Female attracted. Leans toward demisexual.

RELATIONSHIP STATUS: Engaged to Sissel (the lady)

SINCE WHEN: Engaged since their 21 years old, dating since High School.

TAGGED BY: Stole it from @therapardalis

TAGGING: Steal it!