Ideas for making AI useful.

A journey through on-device model deployment: how we benchmarked K2 Horizon against Llama 3.2 across 500 test cases,tackled mobile heating and battery drain on an 8GBAndroid device, and achieved a +25% performance edge by turning our SLM into an Ahead-of-Time Rule Compiler

In 2025, a 14-year-old named Vaibhav Suryavanshi walked out in the IPL and hit the fastest century the tournament had ever seen from an Indian, and one of the fastest from anyone. By the next morning every broadcast was asking the same thing: where did this kid come from?

A decade ago, people searched for information. Today, information searches for people. It appears in notifications, reels, swipe cards, AI summaries, YouTube Shorts, carousel posts, and algorithmic feeds. The modern user no longer “reads through” information. They scan, evaluate, react, and move on - often within seconds.

There’s a certain kind of freedom that comes with not spending on ads. No dashboards to obsess over. No rising Customer Acquisition Cost [CAC] to worry about. No panic when performance dips overnight. But more importantly, there’s something else you gain - something most teams miss when they rely on paid acquisition.

Building a product means making hundreds of decisions every week. Which feature to prioritize. Which page to simplify. Which flow is confusing users. Most teams make these decisions based on gut feeling and assumptions.

A few years ago, most company websites were static. Visitors would browse pages, maybe fill out a contact form, and wait for a response. Today that expectation has completely changed. When someone lands on your website, they expect the same responsiveness they get from messaging apps - quick answers, instant clarification, and sometimes even guidance while making decisions.

Enterprise deals rarely fail at the final stage. Most of them lose momentum much earlier - somewhere between the first promising conversation and the pilot agreement.

For a long time, we operated like many other companies: trapped in a "Frankenstein’s Monster" of disconnected tools. We had Github for tasks, Teams for chat, and gods knew about the documentation. Every month, we paid the "SaaS Tax" - hundreds of dollars for a team of 50 - only to deal with data silos, manual status updates, and a complete lack of universal visibility.

Artificial intelligence is entering a new phase moving beyond conversational responses and into autonomous action. OpenAI’s newly released Agents SDK is at the center of this shift, giving developers the ability to build intelligent agents that can reason, plan, use tools, execute tasks, manage state, and interact with UI components.

In the digital era, the way people are seen in photos and videos has a huge impact—from memorable movie moments to everyday tools like facial recognition or security checks.

Artificial intelligence has transformed the game of chess. This blog explores the current landscape of chess AI, providing insights into building a basic chess AI from scratch and demonstrating how to leverage existing powerful AI engines through free APIs and libraries.

The United Arab Emirates (UAE) is not only keeping pace with the AI revolution—it's leading the way. With forward-looking policies such as the **UAE Strategy for Artificial Intelligence 2031**, the nation has established itself as a global testbed for AI innovation**.

As a developer, choosing the right frontend framework can significantly impact the performance, maintainability, and scalability of your web applications. This article provides an in-depth comparison of three popular frameworks: React, Svelte, and Solid.js, from a developer's perspective .

In a data-driven world, effective visualization is essential for analyzing complex datasets. Well-crafted visuals simplify intricate information, enhance storytelling, and make insights more accessible. Among the many tools available, Matplotlib and Plotly stand out as two of the most widely used Python libraries for data visualization. Each offers distinct features catering to different user needs. Let's explore their strengths, differences, and ideal use cases.

The gaming industry is a multi-billion-dollar battlefield where player sentiment shifts rapidly. Every day, millions of gamers voice their opinions, frustrations, and desires on platforms like Reddit, Twitter, Discord, and Twitch. But are you truly listening?

The AI race has long been dominated by proprietary giants like OpenAI, but a new contender is making waves—DeepSeek. With its latest open-source models, DeepSeek V3 and DeepThink R1, this Chinese AI company is challenging OpenAI’s dominance by offering competitive performance at a fraction of the cost. DeepSeek’s Mixture of Experts (MoE) architecture, efficient GPU utilization, and strategic innovations have enabled it to deliver high-performance AI models with minimal computational expense. But how does it truly compare to OpenAI’s GPT-4o and GPT-o1? Let's break it down.

Reactive Programming is a paradigm that is gaining prominence in enterprise-level microservices. While it may not yet be a standard approach in every development workflow, its principles are essential for building efficient, scalable, and responsive applications. This blog explores the value of Reactive Programming, emphasizing the challenges it addresses and the solutions it offers. Rather than diving into the theoretical aspects of the paradigm, the focus will be on how Spring Boot simplifies the integration of reactive elements into modern applications.

In an interview with Joe Rogan, Elon Musk described his “Not a Flamethrower” as more of a quirky novelty than a real flamethrower, calling it a roofing torch with an air rifle cover. He also explained the reasoning behind its name—avoiding shipping restrictions and simplifying customs procedures in countries where flamethrowers are prohibited. When the OpenAI GPT-3.5 Turbo model was asked, "What are Elon Musk’s views on flamethrowers?" it captured this insight effortlessly, showcasing the potential of AI to extract meaningful information from complex datasets like interview transcripts. Now imagine using similar AI capabilities to query complex datasets like interview transcripts. What if you could upload a PDF, ask nuanced questions, and instantly uncover relevant insights—just as GPT models interpret context? This blog explores how to leverage AI and natural language processing (NLP) to create a system capable of analyzing and querying a PDF document—such as Elon Musk's interview with Joe Rogan transcript—with remarkable accuracy.

In an era where websites serve as the front door to most businesses, driving and maintaining user engagement is more critical than ever. Yet, many websites face challenges such as high bounce rates, short session durations, and low user retention. Visitors often leave after just a few clicks, failing to engage deeply with the content or take desired actions, such as signing up, making a purchase, or sharing the site with others. The root of the problem often lies in a lack of emotional connection and interactivity. Generic designs and impersonal experiences make it difficult for businesses to stand out and hold visitors' attention.

AI and games have been fundamentally intertwined from their inception, pushing one another to new frontiers. Games, with their precise rules and narrow subdomains, provide the perfect arena for game AI to excel. This blog offers a glimpse into one of our attempts at creating a multi-agent game and explores the complexities involved in designing a multi-agent system in the world of AI in gaming.

Optimizing a website for performance is essential to enhance user experience and boost search engine rankings. Two critical metrics from Google’s Core Web Vitals (CWV)—Largest Contentful Paint (LCP) and Cumulative Layout Shift (CLS)—play a significant role in measuring and improving a site’s performance. These metrics outline the key strategies for optimization and highlight the observed impact on both mobile and desktop performance.

Building efficient and scalable applications often requires balancing responsibilities between the frontend and backend. When tasks like report generation are managed solely on the frontend, it can lead to performance bottlenecks, scalability issues, and user experience challenges. Transitioning to a balanced architecture can address these limitations while improving overall system efficiency.

The Raspberry Pi, launched in 2012, has entered the vocabulary of all doers and makers of the world. It was designed as an affordable, accessible microcomputer for students and hobbyists. Over the years, Raspberry Pi has evolved from a modest credit card-sized computer into a versatile platform that powers innovations in fields as diverse as home economics to IoT, AI, robotics and industrial automation. Raspberry Pis are single board computers that can be found in an assortment of variations with models ranging from anywhere between $4 to $70. Here, we’ll trace the journey of the Raspberry Pi’s evolution and explore some of the innovations that it has spurred with examples and code snippets.

Text-to-speech (TTS) technology has evolved significantly in the past few years, enabling one to convert simple text to spoken words with remarkable accuracy and naturalness. From simple robotic voices to sophisticated, human-like speech synthesis, models offer specialized capabilities applicable to different use cases. In this blog, we will explore how different TTS models generate speech from text as well as compare their capabilities, models explored include MARS-5, Parler-TTS, Tortoise-TTS, MetaVoice-1B, Coqui TTS among others. The TTS process generally involves several key steps discussed later in detail: input text and reference audio, text processing, voice synthesis and then the final audio is outputted. Some models enhance this process by supporting few-shot or zero-shot learning, where a new voice can be generated based on minimal reference audio. Let's delve into how some of the leading TTS models perform these tasks.

A cursory prompt to chatGPT asking for guidance into the world of automated testing, spits out the words Selenium and Taiko. This blog post will explore our hands-on experience with these tools and share insights into how they performed in real-world testing scenarios. But first what is automated testing? Automated testing refers to the process of using specialized tools to run predefined tests on software applications automatically. It differs from manual testing, where human testers interact with the software to validate functionality and identify bugs. The key USPs of automated testing are efficiency in terms of multiple repeat runs of test cases, integration with CI/CD pipelines like Github actions and reliability.

In an era where Moore's Law—the observation that computing power doubles approximately every two years—seems to apply to every facet of technology, the pace of innovation has become exponential. What was cutting-edge yesterday is commonplace today, and tomorrow's breakthroughs are already on the horizon. This relentless progression presents businesses with a stark reality: innovate or risk obsolescence.  The advent of AI has only accelerated this trend, creating a new paradigm where the ability to harness AI can make the difference between market leadership and irrelevance. It's no longer enough to simply adopt new technologies; businesses must leverage them strategically to create sustainable competitive advantages.

Why sift through endless headlines when you can watch the news unfold on a live map? With MapyNews, real-time events come to life as hotspots, letting you track stories across the globe with just a glance. Staying informed about global events can be overwhelming, especially when you need to quickly locate relevant information, like what’s happening in London at this very moment. This is where MapyNews, our responsive web application, steps in. MapyNews offers a dynamic dashboard that visualizes news feeds as geographical hotspots and includes an interactive timeline feature that allows users to track how stories evolve in real-time. As older news becomes less relevant, it automatically fades away, making it easier to focus on current events. With real-time updates, MapyNews ensures you're always up-to-date, whether it’s local news or global headlines, all displayed in one comprehensive view. We utilized React.js to build the interactive dashboard, while news feeds are scraped from The Hindu as mock data using Puppeteer and Node.js. Now, let’s dive into the implementation details of MapyNews and explore its key features.

Large Language Models (LLMs) have proven effective in tasks like natural language processing, sentiment analysis, data extraction, and answering questions. According to ChatGPT, complex mathematical operations for it include advanced techniques such as calculus, matrix operations, differential equations, optimization, and probability, often used for solving real-world problems in fields like engineering, physics, and economics. And complex data analysis involves multivariate analysis, statistical modeling, time series analysis, machine learning, and big data handling, used for discovering patterns, making predictions, and drawing insights from large datasets. Recently, we've explored multiple LLMs’ ability to handle basic mathematical and analytical operations, including additions, subtractions, multiplication, division and percentage calculations and financial data analysis. While LLMs can manage basic arithmetic, While LLMs can manage basic arithmetic, we tested their ability to solve more complex tasks using a restaurant bill, and tried to convert the bill into a table, split the total amount, and calculate each person's percentage share.

Machine learning operations (MLOps) have evolved from being a niche practice to becoming an essential pillar in AI-driven enterprises. The integration of large language models (LLMs) into MLOps is proving to be a game-changer, helping businesses manage and optimize their machine learning (ML) lifecycle.

The open-source versus paid software debate has been a staple of tech discussions for years. While the conversation often centers on content creation tools, it’s important to realize that this comparison spans a far wider range of technological applications—from operating systems and development frameworks to content management systems and cloud services. Open-source software (OSS) has evolved tremendously, leading many to ask: how close are open-source tools to competing with their paid counterparts, not only in content creation but across various tech domains?

The sheer volume of content generated on the internet is astounding: 500 million tweets daily, 50 billion Instagram photos, and 700,000 hours of YouTube video each day. The World Economic Forum estimates that 463 exabytes of data are generated daily, with one exabyte equaling one billion gigabytes. This digital deluge presents an unprecedented challenge for content moderation. As we find ourselves at the intersection of free expression and responsible oversight, it’s evident that traditional content moderation methods are struggling to keep pace with the massive influx of information. In this critical moment, AI emerges as a promising solution to manage vast amounts of information. How can AI tackle the challenge of moderating content on a large scale, while safeguarding the content diversity?

Google’s AI operations recently made headlines due to their significant environmental impact, particularly regarding carbon emissions. The company's AI activities, including training and deploying large language models (LLMs), have led to a 48% increase in greenhouse gas emissions over the past five years. Google’s annual environmental report revealed that emissions from its data centers and supply chain were the main contributors to this rise. In 2023, emissions surged by 13% from the previous year, totaling 14.3 million metric tons, underscoring the pressing need to address the environmental effects of AI’s rapid growth.

LLMs like OpenAI’s GPT-4, Google’s Bard, and Meta’s LLaMA have ushered in new opportunities for businesses and individuals to enhance their services and automate tasks through advanced natural language processing (NLP) capabilities. However, this increased adoption also raises significant privacy concerns, particularly around WebLLM attacks. These attacks can compromise sensitive information, disrupt services, and expose businesses and individuals to substantial risks compromising enterprise and individual data privacy.

Information is readily available at our fingertips in the current digital age and the line between truth and fiction is becoming increasingly blurred. AI has introduced a new layer of complexity to this challenge. AI-generated content continues to advance, and there is a line between human-written and machine-generated work that has become increasingly blurred. This evolution challenges our ability to differentiate, highlighting the growing influence of AI in content creation.

At Random Walk, we’re always curious about the ways humans and technology interact, especially when it comes to interpreting and visualizing information. Our latest challenge was both fascinating and revealing: Can AI tools outperform humans in creating a map based on a story? We began with a passage from a book that provided a detailed description of a landscape, landmarks, and directions: At 7:35 A.M. Ishigami left his apartment as he did every weekday morning. Just before stepping out onto the street, he glanced at the mostly full bicycle lot, noting the absence of the green bicycle. Though it was already March, the wind was bitingly cold. He walked with his head down, burying his chin in his scarf. A short way to the south, about twenty yards, ran Shin-Ohashi Road. From that intersection, the road ran east into the Edogawa district, west towards Nihonbashi. Just before Nihonbashi, it crossed the Sumida River at the Shin-Ohashi Bridge. The quickest route from Ishigami’s apartment to his workplace was due south. It was only a quarter mile or so to Seicho Garden Park. He worked at the private high school just before the park. He was a teacher. He taught math. Ishigami walked south to the red light at the intersection, then he turned right, towards Shin-Ohashi Bridge. Using this description, we were tasked with manually sketching a map. It was a test of our ability to translate words into a visual representation, relying on our interpretation of the narrative. Then came the second part of the experiment: feeding the same description into AI tools like ChatGPT, Copilot, Ideogram, and Mistral AI, asking them to generate their versions of the map.

Sound pollution is a growing concern in urban and suburban areas worldwide. As cities expand and industrial activities increase, ambient noise levels rise, impacting the quality of life and health of inhabitants. Addressing this challenge requires innovative solutions, and we have developed an innovative solution to empower individuals and communities to monitor and manage sound pollution effectively. Our sound monitoring device combines advanced technology with user-friendly design, enabling users to easily set up and monitor their environment with minimal effort. Here’s a closer look at the key components and features that make our device stand out.

Without strong leadership buy-in, even the most promising AI initiatives are destined to falter. You need to identify the gap between the current state and the desired state of digital transformation of your

Finding specific rooms or locations in large and complex buildings can be a daunting task. Whether it’s locating a gym or restroom in a hotel, navigating to a specific store in a mall, or finding a meeting room in an office, traditional maps and signage often prove inefficient, leading to delays and frustration. Our indoor navigation web application, which uses Augmented Reality (AR), addresses this issue by guiding users to precise locations within a building. This technology enhances navigation accuracy and user convenience by overlaying digital directions onto the physical environment, ensuring efficient and intuitive wayfinding. We utilized WebXR, Three.js, and React to implement the navigation system. Let’s take a closer look at how we’ve designed and implemented this application for intuitive indoor navigation.

Efficient deployment pipelines are significant for delivering software features swiftly and smoothly. We have experimented with several tools and methods to address the challenges and streamline the deployment processes. Recently, we implemented Coolify, a self-hosted platform that has remarkably simplified our deployment workflows. This blog details our journey from implementing Coolify to experiencing how it simplifies our deployment workflows and enhances our overall development lifecycle.

Data is everywhere, from social media posts to financial records, but making sense of it all can be overwhelming. This stage necessitates the expertise of data analysts and data scientists, who play a crucial role in uncovering the hidden insights within the data. But have you ever found yourself puzzled by the distinctions between these roles? Don’t worry, let’s take a closer look at what they do and what skills they have, as well as where their careers can take them.

Finding and retaining top talent must be like an uphill battle for you in today’s competitive landscape. Resumes pile up, interviews take time, and yet, the perfect fit seems to slip through your grasp. Here’s the truth: traditional recruiting methods are stretched too thin. Posting job ads, sifting through resumes, and conducting interviews can be time-consuming and inefficient. The talent pool is evolving faster than ever, with new skills and roles emerging thanks to artificial intelligence (AI). The question isn’t whether AI exists – it’s how to utilize its power to find the talent that will propel your business forward. That’s where partnering with an AI company comes into play.

Human bias refers to the systematic errors in judgment or decision-making that result from subjective factors such as cultural influences, personal experiences, and societal stereotypes. It often leads individuals to make unfair or inaccurate judgments based on factors such as race, gender, or other irrelevant characteristics. Similarly, AI systems, designed by humans, can inherit and perpetuate biases due to flaws in the data used to train them or the algorithms themselves. AI bias can manifest in various forms, such as amplifying existing societal biases present in the training data or making inaccurate predictions for certain groups based on biased patterns learned during the training process. Unaddressed bias obstructs individuals’ economic and societal engagement while limiting AI’s efficiency. Distorted outcomes from biased systems hinder businesses and breed mistrust among marginalized groups. Addressing AI bias is crucial to ensure that AI systems make fair and equitable decisions across diverse populations.