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For developers, by developers
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Gen AI interview questions: What recruiters are looking for
The blog covers a Generative AI skills roadmap guiding learners from novice to expert, covering essential knowledge and practical experience. The blog includes insights into Google’s and Meta’s interview processes, strategies to stand out, and Gen AI interview questions based on real-world AI challenges. Finally, it outlines the next steps to help aspiring professionals break into top AI roles.
Kamran Lodhi
Jan 29 · 2025
Unveiling the LangChain Suite
In this blog, we dive into how LangChain, LangSmith, and LangGraph can turn that chaos into harmony. Imagine building AI applications as effortlessly as snapping together LEGO blocks—that's what LangChain offers with its interoperable components. LangSmith steps in as your seasoned mechanic, helping you test, debug, and fine-tune your AI models for peak performance. And when it comes to orchestrating multiple AI agents without losing your mind, LangGraph is your go-to conductor, ensuring everything runs in perfect harmony.
Whether you're a beginner or an expert, these tools not only simplify AI development but also make it downright enjoyable.
Usama Ahmed
Jan 29 · 2025
Top AI coding copilots: Why Codeium is the best choice?
This blog explores how AI coding copilots assist developers, compares popular options like GitHub Copilot, Tabnine, and Replit, and explains why Codeium is the best choice. With fast, accurate code suggestions, multi-language support, and easy integration, Codeium enhances coding productivity. The blog also includes a setup guide of Codeium and usage examples to help developers maximize their efficiency with AI-powered tools.
lfrah Dar
Jan 28 · 2025
What are Small Language Models (SLM)?
Small Language Models (SLMs) are efficient, resource-friendly AI models ideal for real-time tasks on devices with limited memory and processing power. Unlike Large Language Models (LLMs), which excel at complex, deep-context tasks, SLMs like DistilBERT and TinyBERT are faster, smaller, and more cost-effective. They’re often used in mobile apps, chatbots, and virtual assistants. With advancements in AI, SLMs are becoming vital for low-power, high-efficiency applications.
Kamran Lodhi
Jan 22 · 2025
Google layoffs: Everything developers need to know
Google’s recent layoffs indicate where the industry is headed: AI, cloud infrastructure, and automation. For developers globally, these layoffs are more than just buzzwords: they’re a reality check for what it will take to get hired, and succeed, at Google. This blog will explore what's next for developers who are setting their sights on the company.
Zarish Khalid
Jan 15 · 2025
2025 Free AI Resources: Create Images & Videos from Text
Discover how free AI resources are revolutionizing visual content creation by turning simple text prompts into stunning images and videos. With just a few words, these innovative platforms can generate high-quality visuals tailored to your needs, whether for imaginative storytelling or project design. Learn how to leverage this technology for practical applications, exploring its versatility and transformative potential in modern creative workflows.
Nimra Zaheer
Jan 13 · 2025
AI and coding: How AI is redefining learning to code
AI isn't replacing any developer — it's an assistant that will help you do more. In fact, by reducing tedium, AI tools will help junior developers make a stronger impact, faster.
Fahim ul Haq
Jan 7 · 2025
Making sense of Kolmogorov-Arnold Networks (KANs)
This blog explores Kolmogorov-Arnold Networks (KANs), an innovative neural network architecture that resembles traditional fully-connected neural networks but replaces weights and node-based activation functions with edge-based activation functions. We examine the learnability of these functions, compare KANs with traditional neural networks based on early experimental findings, and investigate their potential for greater interpretability and continual learning.
Mehvish Poshni
Jan 6 · 2025
Mastering advanced prompt engineering: Part 2
The Graph of Thoughts (GoT) is an advanced reasoning framework designed for large language models (LLMs), offering more flexible and complex cognitive structures than traditional methods like Chain-of-Thought (CoT) or Tree of Thoughts (ToT). Unlike linear (CoT) or hierarchical (ToT) representations, GoT organizes thoughts as interconnected nodes in a graph. This allows for non-linear, iterative, and multi-directional connections between ideas, mimicking the complexity of human cognition more closely.
Saif Ali
Dec 26 · 2024