Daily Wrap-Up

Some days the timeline is a firehose, and some days it's a slow drip. October 24th was firmly in the latter camp, with the AI conversation taking a collective breather. But the one signal that did cut through the noise was telling: @aakashgupta sharing a resource on building AI agents from scratch, framed not as an advanced technique but as a fundamental skill developers need to acquire. It's the kind of post that reflects where the industry's center of gravity has shifted. We've moved past "what are agents?" and into "okay, but how do I actually build one without a framework doing all the thinking for me?"

The timing tracks with a broader pattern that's been building for weeks. As agent frameworks proliferate and every SaaS product bolts on an "AI agent" feature, there's a counter-movement of developers who want to understand the underlying mechanics. It's reminiscent of the early React days when the best engineers weren't the ones who could install Create React App, but the ones who understood the virtual DOM, reconciliation, and state management at a conceptual level. The same dynamic is playing out with agents: the developers who understand tool-calling patterns, memory architectures, and orchestration loops from first principles will have a significant edge over those who only know how to configure a framework.

The most practical takeaway for developers: if you haven't yet built a simple agent from scratch, without LangChain, without CrewAI, without any framework, now is the time. Start with a basic loop that takes user input, calls an LLM, parses tool calls, executes them, and feeds results back. Understanding that core cycle will make every framework you touch afterward more transparent and debuggable.

Quick Hits

• @aakashgupta shared a resource on building AI agents from scratch, calling it "literally how to build AI agents" and pointing to the growing demand for foundational agent development skills. (link)

Agents & Automation

The agent-building discourse has entered an interesting phase. Early 2025 was dominated by announcements of agent frameworks and platforms, each promising to abstract away the complexity of orchestrating LLM-powered workflows. But as @aakashgupta's post suggests, there's a growing appetite for the opposite: stripping away the abstractions and understanding what's actually happening under the hood.

"This is literally how to build AI agents from scratch" — @aakashgupta

The word "literally" is doing real work in that sentence. It signals a frustration with the gap between marketing-speak about agents and the practical reality of constructing them. When someone frames a from-scratch tutorial as noteworthy content worth sharing, it tells you that the ecosystem has a knowledge gap. Too many developers have used agent frameworks without understanding the patterns those frameworks implement: the ReAct loop, tool-call parsing, context window management, memory retrieval, and error recovery.

This matters because the agent space is still young enough that frameworks change rapidly, APIs shift, and best practices evolve month to month. Developers who only know the framework-level API are stuck when that framework makes a breaking change or doesn't support their use case. Developers who understand the from-scratch approach can adapt, switch tools, or build custom solutions as requirements demand. It's the difference between knowing how to drive and knowing how an engine works. Both are useful, but when the car breaks down on a remote road, only one of those skill sets gets you home.

The broader trend here is that agent development is following the classic technology adoption curve. The initial excitement phase produced a flood of high-level tools. Now we're entering the "actually, let me understand this properly" phase, where the serious practitioners go deeper. Expect to see more from-scratch tutorials, more architectural deep-dives, and more opinionated takes on which patterns actually work in production versus which ones only look good in demos.