Keeping up with AI

AI Writes More Code. Why Are Engineering Teams Shipping Slower?

Here's the paradox: AI can write code 5x faster than humans. Yet many engineering teams aren't shipping faster. Some are actually shipping slower.

In this episode, I break down why code generation speed doesn't translate to shipping speed. The cost of writing code has collapsed, which creates a new constraint: review capacity, architectural decisions, and judgment. These are the real bottlenecks now.

I explore what happens when you can generate thousands of lines of code in hours but need senior engineers to validate all of it. How architectural entropy spreads when each engineer uses AI to solve similar problems differently. Why the skill that matters most isn't code generation anymore. It's deciding which code is actually worth shipping.

We also talk about engineers who've never coded without AI. What happens when you can build features but can't explain how they work. And how to recognize if this shift is happening in your own organization.

The teams winning with AI aren't trying to maximize code output. They're maximizing decision quality. They're treating AI like a capable junior engineer who needs oversight, not autonomy.

If you're leading an engineering team, this episode is for you.

Substack article - https://swapniltalekar.substack.com/p/ai-writes-more-code-why-are-engineering?r=1v4ea

I keep meeting young developers who are frustrated with AI coding tools. The output is inconsistent, the context is missing, and fixing the first draft takes longer than writing it themselves. The common diagnosis is that the model is bad. I don't think that's it.

The real problem is the spec, and most developers skip writing one entirely.

In this episode, I break down spec-driven development: what it is, why it's gaining serious traction in engineering teams right now, and how companies like GitHub and AWS are building entire tooling ecosystems around it. I also talk about the difference between a Jira ticket, a plan, and an actual spec, because they are not the same thing.

And I walk through grill-me, a Claude Code skill that replaces cold spec-writing with a structured conversation. It's one of the most practical changes I've made to my workflow, and I share a real example of what that session looks like in practice.

If you've been getting inconsistent output from AI coding agents, this episode is worth your time.

If you prefer reading, I've also published this entire episode on my substack

Substack link - https://swapniltalekar.substack.com/p/why-better-prompts-wont-fix-your?r=1v4ea

The prompting vs. RAG vs. fine-tuning debate is not over. It just moved to the wrong level.

Most teams building with LLMs are still applying a 2023 framework to 2026 agent systems. The classic ladder model, start with prompting, add RAG for knowledge, fine-tune as a last resort, was built for single-turn systems. In an agent, all three techniques play completely different roles than that framework assumes.

In this episode, I break down what actually changed. We cover how RAG evolved from static pipelines to agentic self-correction, why Anthropic's Claude Code team abandoned vector RAG entirely in favor of grep-style search, and what the shift toward context architecture and MCP means for how you build today. We also get into where prompting actually breaks in multi-step agents, and which fine-tuning targets move the needle in agentic systems (hint: it's not response style).

The episode closes with a practical diagnostic framework for identifying which layer of your agent is failing before you reach for a solution.

If you're building agentic systems and finding that your architecture feels more expensive than it should be, this one is for you.

Substack link - https://swapniltalekar.substack.com/p/rag-for-agentic-era?r=1v4ea

What happens when a single developer, working alone over a weekend, ships something that leaves Microsoft, Google, and a dozen well-funded AI startups in the dust? That's the OpenClaw story, and it says a lot about where AI agents are actually headed.

In this episode, we break down how OpenClaw went from an unnamed GitHub repo to tens of thousands of waitlist signups in under 48 hours, and the four specific design decisions that made it explode when similar tools quietly fizzled.

We also cut through the influencer hype. Yes, people used it to order food from Swiggy, and no, that's not the point. We walk through three use cases where OpenClaw actually earns its setup cost: intelligent competitor monitoring, an always-on personal finance manager that sends you weekly WhatsApp digests, and an async personal shopper that watches prices so you don't have to.

Plus, the practical stuff: cloud hosting options from as little as six dollars a month, and the security basics you need in place before you connect anything real.

OpenClaw may not be the final form of AI agents. But the category it represents, persistent and proactive agents that live in your workflow rather than asking you to change it, is the category that matters.

If you've been trying to make sense of the AI agent landscape, this episode is your map.

Substack article - https://swapniltalekar.substack.com/p/the-openclaw-story?r=1v4ea

Are you drowning in digital files, spending hours on repetitive tasks that feel like they should be automated? In this episode of Keeping up with AI, we dive deep into Claude Code - one of my favourite and most versatile AI tools that's not just for programmers. Never mind the name. Whether you're a researcher organizing hundreds of PDFs, a content creator managing multiple projects, a business analyst wrestling with messy spreadsheets, or simply someone tired of their chaotic Downloads folder, this episode will fundamentally change how you think about productivity. We'll explore how Claude Code works directly with your actual files and folders, performing batch operations that would normally take hours or days and compressing them into minutes – all through simple, plain English instructions.

We'll walk through some real-world examples including document processing, data cleaning, content creation, and intelligent file organization. We'll also provide essential tips for first-time users. By the end of this episode, you'll understand why having an AI that can actually touch your files – not just advise you about them – is an absolute game-changer, and you'll have the knowledge to start automating your own workflows immediately even if you have no coding experience.

Artificial Intelligence is moving faster than any technology in history. Every week there’s a new breakthrough — and a flood of headlines, opinions, and hype. For many of us, keeping up can feel overwhelming.

Keeping Up with AI is a podcast designed to cut through that noise.

The podcast distills the most important developments in the AI world into clear, practical insights. Instead of trying to cover everything, each episode focuses on what actually matters — the tools, workflows, ideas, and breakthroughs that can meaningfully impact how we work, build, and think about technology.

Whether you're a developer, a product builder, a tech enthusiast, or simply someone curious about how AI is shaping the future, this podcast will help you stay informed without drowning in information.

In every episode, you’ll get:

• Clear explanations of the most important AI developments

• Practical tools and workflows you can start using today

• Insights into how AI systems and models actually work

• Thoughtful analysis on what changes matter — and what doesn’t

If AI feels like it’s moving too fast to follow, this podcast is here to help you keep up — calmly, clearly, and intelligently.