Thoughts/Notes

LeetCode Style Interviews Are Over. It’s Not “Can You Code?” but “How Do You Build?

The technical interview is evolving. Instead of abstract LeetCode puzzles, companies now favour practical assessments that reflect real work. AI is central, candidates are expected to use tools like Copilot, demonstrating judgement and collaboration rather than memorisation. “How do you build?” not just “Can you code?”

Is DeFi Finally the Real Deal?

This article explores how DeFi has evolved from speculative beginnings to become a foundational, low-risk financial infrastructure. It highlights the shift towards safer protocols, growing institutional and regulatory support, and the merging of traditional and decentralised finance. Key developments include regulatory changes in the UK, the role of Ethereum as the Internet of Value, and new interoperability between banks and blockchains. The piece argues that DeFi is now delivering real-world impact and marks the start of a new era for global finance.

Preparing Deep Research for enterprise

Exploring how to prepare Deep Research agents for enterprise use cases, focusing on three core ingredients: secure and reliable web search, robust orchestration architectures, and rigorous quality assurance (QA) pipelines. It compares leading search providers on enterprise readiness, discusses orchestration patterns (workflow, agentic, FSM), and details practical QA strategies to ensure trustworthy, auditable outputs at scale. The article provides actionable recommendations for building enterprise-grade research systems that balance flexibility, control, and trust.

So are Deep Research Agents Enterprise ready?

The rapidly reshaping landscape of Deep Research agents this year has been incredible to the point where they look enterprise ready. Thoughts on benchmarks, APIs, and the real challenges for business use.

Overview Agentic AI Frameworks, January 2025

The agentic AI framework landscape is experiencing rapid growth, presenting developers with a diverse array of choices. To navigate this evolving space, this post offers a comparative analysis of six prominent frameworks: LangGraph, Pydantic AI Agents, Smol Agents, AutoGen, CrewAI, and LlamaIndex.

Optimise Your Research Workflow using GenAI, Part 2: Agents

Continuing the exploration of how Generative AI (GenAI) tools can enhance research workflows. In Part 1, we discussed foundational GenAI tools that streamline research processes like chatGPT & Perplexity. Here in Part 2, we focus on “agentic workflows” Combining tools and agents to automate complex workflows, and interact with large data sets and multiple sources, to enhance productivity through innovative capabilities.

Optimise Your Research Workflow using GenAI, Part 1: Search (No-Code)

Imagine embarking on a research project where the tedious tasks of data collection and preliminary analysis are seamlessly handled by an intelligent assistant. Picture having the ability to quickly uncover trends and insights from vast amounts of information. Today, we can “Use Gen AI to Optimize Your Research Workflow.” I’m sharing some examples and actionable tips on how you can make your research more effective, insightful, and rewarding using Gen AI.

Unlocking the Potential of Gen AI Innovation within the Enterprise through Inner Source

Dive deep into web performance metrics, including tools and techniques for measuring and optimizing loading times. Discuss the significance of metrics like First Contentful Paint, Time to Interactive, and more.