The new model however attracted criticism for using Kimi 2.5 – an open source model by the Chinese company Moonshot AI with just additional reinforcement learning according to the X user Fynn. They said:

“[A]t least rename the model ID,”

At first, the company did not mention Moonshot AI or Kimi in its announcement, which quickly raised questions about the role of the AI “arms race” between the United States and China in the situation.

Lee Robinson, Cursor’s vice president of developer education, responded by saying, that Cursor’s use of Kimi was consistent with the terms of its license. He also clarified

“Only ~1/4 of the compute spent on the final model came from the base, the rest is from our training.”

As a result, he said Composer 2’s performance on various benchmarks is “very different” from Kimi’s.

The company Kimi also joined the conversations posting:

“We are proud to see Kimi-k2.5 provide the foundation. Seeing our model integrated effectively through Cursor’s continued pretraining & high-compute RL training is the open model ecosystem we love to support.”

Cursor co-founder Aman Sanger said in a post,

“It was a miss to not mention the Kimi base in our blog from the start. We’ll fix that for the next model.”

But like any emerging practice, vibe coding has its sweet spots—and its traps. Here’s a clear-eyed guide to the dos and don’ts of vibe coding, for developers who want speed and substance.

What Is Vibe Coding?

Vibe coding is a style of development driven by momentum and intuition. Instead of rigid upfront planning, developers rely on rapid feedback loops, conversational AI tools, and a strong sense of direction to “feel” their way through a solution.

It thrives in:

• Prototyping and MVPs
• Hackathons and side projects
• Early-stage product exploration
• Creative problem-solving sessions

But vibe coding isn’t an excuse for chaos. The difference between magic and mess comes down to how you practice it.

The Dos of Vibe Coding

Do Use Vibe Coding for Exploration

Vibe coding shines when the problem space is fuzzy. Let yourself experiment, generate ideas quickly, and test assumptions without overthinking architecture too early.

Think:

What if we try this?

instead of

What’s the perfect solution?

Do Lean on AI, But Stay in Control

AI copilots are core to modern vibe coding. They help you scaffold, refactor, and brainstorm at speed. The key is intentional prompting and active review.

You’re still the architect. AI is your amplifier, not your replacement.

Do Protect the Flow State

Minimize friction:

• Reduce context switching
• Avoid premature optimization
• Keep feedback loops tight

When you’re in the zone, velocity compounds. Vibe coding works best when interruption is the exception, not the norm.

Do Refactor After the Vibe

Messy first drafts are fine—as long as you clean them up. Schedule time to:

• Rename variables
• Improve structure
• Add comments and tests

Vibe coding is phase one. Professional coding is phase two.

Do Communicate the “Why”

If you’re working on a team, document intent. Vibe-driven decisions can look arbitrary to others unless you explain the reasoning behind them.

A short README or commit message can save hours of confusion later.

The Don’ts of Vibe Coding

Don’t Skip Fundamentals

Vibe coding doesn’t override:

• Security best practices
• Performance considerations
• Data integrity
• Accessibility

If you don’t understand what the code is doing, you’re not vibe coding—you’re gambling.

Don’t Ship Vibes to Production

Production systems need reliability, observability, and maintainability. Pure vibe code without review, testing, and structure becomes technical debt—fast.

Rule of thumb:
If users depend on it, it deserves rigor.

Don’t Confuse Speed with Progress

Writing a lot of code quickly feels productive—but velocity without direction is just motion.

Pause occasionally and ask:

• Are we solving the right problem?
• Is this still aligned with the goal?

Don’t Ignore Your Future Self

Vibe coding today shouldn’t punish you tomorrow. If the code will live longer than a weekend, invest at least minimal effort in clarity.

Future-you is a teammate. Treat them well.

Don’t Assume Vibe Coding Is for Everyone

Some developers thrive in structured environments. Others flourish in creative flow. High-performing teams respect both styles and apply them deliberately.

Vibe coding is a tool—not a mandate.

Vibe first. Validate fast. Engineer responsibly.

Material by Iva Abadjievа

Image: AI generated, Deep Infra

Zuckerberg emphasized that AI will soon be capable of writing code, performing tasks that are currently done by human engineers.

“Probably in 2025,” he stated, “we at Meta, as well as the other companies that are basically working on this, are going to have an AI that can effectively be a sort of mid-level engineer that you have at your company that can write code”.

Furthermore, Zuckerberg envisions a future where AI engineers will play a dominant role in building and generating code for apps, including AI systems themselves.

“Over time, we’ll get to the point where a lot of the code in our apps, and including the AI that we generate, is actually going to be built by AI engineers instead of people engineers,” he added.

These statements suggest that the tech industry is on the cusp of a significant transformation, one that could potentially displace human jobs in software engineering. While this may seem like a daunting prospect, it also highlights the incredible potential of AI to drive innovation and efficiency in the industry.

In addition to Meta, other companies are also exploring the use of AI in software development. For example:

• Microsoft has developed an AI-powered tool called IntelliCode that can help developers write code more efficiently and accurately.
• Google has introduced an AI-powered platform called AutoML that allows developers to build machine learning models without extensive coding knowledge.
• Amazon has developed an AI-powered tool called CodeGuru that can review code and provide recommendations for improvement.
• GitHub has introduced an AI-powered tool called Copilot that can help developers write code by suggesting lines of code and completing tasks.

These examples demonstrate that the trend of using AI in software development is not limited to Meta, but is a broader industry shift. As the development of AI technology continues to accelerate, it will be interesting to see how these advancements unfold and what implications they may have for the future of work in software engineering. One thing is certain, however: the future of tech is likely to be shaped by the rapid evolution of AI capabilities.

Sources:

• Mark Zuckerberg’s conversation with Joe Rogan on YouTube
• Microsoft IntelliCode
• Google AutoML
• Amazon CodeGuru
• GitHub Copilot

Photo: Screenshot from the video on YouTube – Joe Rogan Experience #1863 Mark Zuckerberg

“Determination is what we look for the most. It turns out that it’s even more important than being smart”
–
Sam Altman

Today we will introduce you to the path and story of Sam Altman, CEO of Open AI and also a venture capitalist. You will find out who he really is and how it all begins.

Who is Sam Altman?

Sam Altman is an American entrepreneur and investor. From a young age, he was fascinated by computers and programming. A curious fact is that at the age of 8, he already knows how to write code and disassemble computers, reported The New Yorker.

Career path

Sam Altman started his career as an app developer. Enrolled to study Computer Science at Stanford University. Drops out of university just before receiving his degree. The reason for this is his goal to create his company together with his colleagues. A little later, he managed to realize this venture of his.

First steps in entrepreneurship and the technological world

The first startup he created was Loopt. This application skyrockets his career. Loopt was created in 2005 and allowed smartphone owners to share their location with other users of their choice. However, in 2012, the founders sold the company to Green Dot Corporation for $43.4 million.

For a period of time, Sam Altman gave up running technology companies and ventured into the management of a venture capital company that finances startups.

It is interesting that the famous programmer and entrepreneur who co-founded Y Combinator, Paul Graham, chose Sam Altman as his successor in 2014. The move puts Altman on Forbes’ 30 under 30 list. Y Combinator invests in companies like Airbnb, (which is valued at $105.82 billion) and Dropbox, (which is valued at $8.31 billion).

Creation of OpenAI, purpose and goals of the company

The young entrepreneur stepped down as a president of Y Combinator to focus his efforts entirely on OpenAI. An interesting fact is that the company, established in 2015, was originally a non-profit research center. It was later transformed into a “capped profit company”.

It is curious that Microsoft invested more than $10 billion dollars in the company led by Altman.

OpenAI aims to create artificial general intelligence (AGI) that is either equal to human intelligence or smarter than humans. However, this decision is sparking wide-ranging discussions among experts, tech industry leaders, politicians, investors, and citizens alike, as it raises the question of how AI might be regulated.

AI regulation

The American entrepreneur calls for a worldwide systemic regulation of AI, saying that the regulation of this field is still in the initial stage of discussions, and it is time to move on to actions.

“We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models”, Sam Altman told the Senate Oversight Committee.

According to the CEO, AI will affect all of us, so world leaders need to come together and start taking steps towards regulating this area.

“A misaligned superintelligent AGI could cause grievous harm to the world”, added the founder of Open AI.

In conclusion, Sam Altman considers that education is one of the key areas where AI can be useful. He believes strongly in the idea that everyone will be able to improve the quality of their lives thanks to the limitless possibilities that AI provides.

The new chatbot is currently active and its subscription costs $20 per month for each user. Reportedly, Microsoft Copilot Pro supports code writing, writing, exploring and even learning. It also provides a few unique features that are not included in the free edition, however.

Here are the features that differentiate Copilot Pro from Microsoft Copilot:

• Priority access to the latest models, including OpenAI’s new GPT-4 Turbo. With Copilot Pro, you’ll have access to GPT-4 Turbo during peak hours for faster performance, and soon you’ll be able to switch between models to optimize your experience as you choose, Microsoft explains.
• Ability to create your own Copilot GPT, customized for a specific Copilot theme.
• A single AI experience that runs across your devices, understanding your context on the web, on your PC, across your apps and soon on your phone to bring the right skills to you when you need them.
• Access to Copilot in Word, Excel, PowerPoint, Outlook, and OneNote on PC, Mac, and iPad for Microsoft 365 Personal and Family subscribers.
• Enhanced AI image creation with Image Creator from Designer (formerly Bing Image Creator) – ensuring it’s faster with 100 boosts per day while bringing you more detailed image quality as well as landscape image format.

Microsoft has introduced several enhancements related to Copilot, including:

• Customizable Copilot GPTs: Tailor the behavior of Microsoft Copilot to focus on specific topics of interest with Copilot GPTs.
• Copilot Mobile App: The Copilot mobile app is now accessible on both Android and iOS platforms.
• Integration with Microsoft 365 Mobile App: Copilot is now integrated into the Microsoft 365 mobile app for Android and iOS, catering to individuals with a Microsoft account.

Today, we’ve chosen to introduce you to the top 5 artificial intelligence tools for programming, presented by Analytics Insight.

Top 5 Artificial Intelligence Programming Tools

Replit Ghostwriter
Replit Professional Writer is an ML-powered apparatus that gives code fulfillment, age, change, and in-manager search capacities. There are four main parts to it: Complete Code, which looks at your code and suggests where it should go next; Generate Code, which uses your suggestions to create new code; Transform Code, which helps you meet standards by refactoring or modernizing code; what’s more, Make sense of Code, which investigates existing code and makes sense of its capability utilizing normal language, as well as Semantic Pursuit.

TabNine
TabNine is an AI code completion tool for languages like Java, Python, and C++ that uses deep learning algorithms to intelligently complete code. It makes customized suggestions based on your writing patterns and automatically indexes your code. Features like automated code completion, auto-generated tests, syntax highlighting, and integration with popular IDEs are provided by its advanced AI capabilities.

Codex of OpenAI
Based on OpenAI GPT-3, Codex is a natural language processing model that can generate working code in more than a dozen programming languages, including Shell and the following: Python, JavaScript, Go, Perl, PHP, Ruby, Swift, and TypeScript. The data used to train Codex includes natural language and billions of lines of source code from GitHub repositories that are publicly accessible. The Python code can be stored in 14 KB of memory. Codex is a powerful system powered by transformers.

CodeWP
CodeWP is optimized and trained specifically for WordPress website builders. It is compatible with JS and PHP, as well as WooCommerce and major page builders-specific modes. CodeWP incorporates highlights like live coordinated effort, constant code criticism, and an extensive variety of modules for various programming dialects.

MutableAI
MutableAI is an AI-powered coding assistant that uses natural language input to help automate repetitive coding tasks and accelerate production. The open-ended query dialogue, which allows users to ask more difficult questions, is one of its most exciting features. MutableAI offers explicit changes that comprehend your code flawlessly, making it more straightforward to effectively achieve errands.

In the January edition of this year’s Go Developer Survey, 5,844 Go developers were asked to share how they use the language, what their biggest challenges are in using it, and what their top priorities are for future improvements to the language.

Error handling, the readability and verbosity of errors in Go, and the difficulty in learning Go programming best practices were the most commonly reported challenges. Generics, which simplify coding by allowing developers to write functions that are independent of the types used, officially appeared with Go 1.18 in March 2022. After that introduction, comments about generics waned.

Other survey results indicate that maintainers of Go’s open source modules report challenges in keeping dependencies up-to-date and avoiding outages due to version changes and downtime. It is expected that this area will be investigated further to help maintainers ensure a stable and robust ecosystem. The survey also found that novice Go developers are interested in using Go for web development.

But what is it that makes us rely on these devices and apps and not imagine life without them? The answer to this question is the provision of sensory experiences. This is also why companies are focusing their efforts on the task of using the best techniques in developing these solutions so that the level of engagement remains at its highest.

And in that aspect of thoughts, there are two platforms on which developers usually create apps. One is Android and the other is iOS.

That’s why today we have chosen to present to you a list of a few programming languages that are the best choices when developing native Android apps according to ReadWrite.

Top 5 Programming Languages for Native Application Development

Java
Java is a computer coding language whose release was done by Sun Microsystems in the 1990s. Subsequently, it got acquired by Oracle and has since then built its name as a multi-purpose coding language that helps developers to develop Android apps for services like games alongside building tools.

It also has a diverse range of features that makes it the developers’ favorite.

-Enables developers with the concept of OOPS, also known as Object Oriented Programming to allow them to create reusable codes.

-It gives access to open-source libraries to allow the app development cost to go down and the procedure to get accelerated.

-It is easy to learn due to the presence of English punctuation, which most developers are familiar with, followed by containing angle brackets in the generics to make coding easy to read.

Kotlin
60% of developers recommend Kotlin next when they build an Android app. This is due to the support it provides them to boost their productivity, followed by ensuring that the code is safe from incidents like crashes. Additionally, it is packed with features like structured concurrency to make network calls and database updates easy to perform. This is followed by including nullability within the type system to ensure NullPointer Exceptions get avoided in the best possible manner.

Here are some other constituents present in the programming language that make it perfect for this task.

-Due to interoperability support with Java as a result of supporting Java libraries and tools, it helps developers in compiling an Android project in the two languages with swiftness.

-Helps the development experts in writing an equivalent of the Java code simplistically, requiring lesser code.

-It makes it easy to determine and predict the constituents of the resulting list through the provision of collection filtering.

Dart
Developed by Google in 2011, Dart has earned its name as an object-oriented client-optimized programming language that makes web and mobile app development seamless across different platforms. Particularly when it comes to building a native Android app, the language streamlines this task greatly due to its aid to developers in converting the Dart code into a native code. The language especially renders aid to developers in UI development by enabling them with features like hot-reload so that changes are easy to view as they occur.

Compiled with the below-mentioned elements make, Dart is perfect for developing an app for the Android platform.

-Possessing a syntax similar to Java, C#, etc., it is easy to learn.

-Code compilation happens at an accelerated pace due to the support it renders to the two kinds of compilation processes like AOT (Ahead of Time) and JIT (Just-in-Time).

-Has a large community of developers. Therefore, it ensures any problems that the development experts face during coding are easily resolved

C#
Microsoft developed C# as an object-oriented programming language that simplifies app management and accelerates the development pace. It is particularly known for supporting the task of developing mobile apps, desktop apps, cloud-based services, enterprise software, and games.

Coming to the task of developing Android apps, as a result of containing an android SDK (software development kit), C# helps in streamlining coding-related tasks to a great extent. This is followed by possessing the features listed below.

-Shareable code across multiple platforms.

-Garbage collection tool to prevent chances of memory leaks.

-Android SDK to enable developers to code comfortably using C3.

Python
Known as an interpreted, object-oriented, high-level language containing dynamic semantics, Python accelerates the Android app development time. Alongside this, it also contains third-party tools to allow the conversion of Python apps into Android packages to take place conveniently.

Nevertheless, containing the Kivy library ensures that developers can create these apps at a reasonably faster pace. Alternatively, it comes pre-loaded with the features such as:

-Code that is easy to read, thereby making app design and software update an easy process.

-Comprehensive libraries that enable developers to include functions within the app during the later stages of development mitigate the requirement to add code separately.

-Test-driven development to accelerate the app development pace, streamline the task of prototype creation, and, most importantly, make it easy for developers to assess the design of an app, ensuring its user-friendly nature.

“Torc aims to empower developers with data and intelligence, protect IP, increase upskilling and career opportunities, and make the remote workforce indispensable. Understanding performance data is the best way to do that”,

said Michael P. Morris, CEO and Co-founder of Torc.

Torc has expanded Codealike’s data and reporting capabilities as part of the reboot. Members of the Torc community can visualize programming results at no cost, create compelling quantitative assets for self marketers to increase their revenue and advance and develop their careers.

Technical debt (short term technical decisions that distract developers from coding new features) costs companies tens of billions of dollars per year. Analysts at McKinsey found “tech debt” to take up 15% – 60% of a company’s entire IT budget.

According to Gartner, companies that find ways to reduce tech debt will lower service delivery times by at least 50%. With Codealike, Torc is uniquely positioned to help companies minimize tech debt, while giving developers control of their coding and performance.

Personalized coding performance data is organized in a visual way that makes it easy to understand developer strengths and improvement opportunities. Developers decide when and what Codealike statistics to share for gamified recognition, peer ranking, team interaction and virtual collaboration.

“Torc takes privacy seriously and is committed to protecting developer data; Torc Community Members decide whether or not to share information generated from using Codealike. Integrity values like this are what differentiate Torc from others in the talent network space, and we’ll continue investing in the advancement of the software community’s skills and careers”,

said David Messinger, CTO and Co-founder of Torc.

“Torc is already an amazing nearshore talent network that cares about my career, but the addition of Codealike really makes it stand out from the crowd. Access to my coding data is so important and sharing it gives even more opportunity to expand my skills and earning potential.”

said Paulo Vitor Magacho, a Torc Community Member from Brazil who uses Codealike.

New Codealike features developed by Torc bring a human-centered approach to developer productivity and maximize career and earning potential. For a limited time, developers can access Codealike’s Premium features for free by creating a Torc Community Member account and completing at least 80% of their Torc profile.

Copilot has the ability to learn the programming style of individual developers. By analyzing the code written by a developer over time, the tool can learn their coding patterns and suggest code snippets that match their style.

“With artificial intelligence available at every step, we can fundamentally redefine developer productivity. We’re reducing templates and manual tasks and making complex work easier throughout the developer lifecycle,”

explains Thomas Domke, CEO of GitHub.

Some developers worry about the impact of AI-assisted programming on the job market. Questions have also been raised about Copilot’s potential to copy code verbatim and the potential copyright implications.

Copilot X supports all languages that appear in public repositories, but works particularly well with Python, Java, JavaScript, TypeScript, Ruby and Go.