DEV Community: Siya Jain

Use Google's AI tools without turning every task into an "AI project."

Siya Jain — Mon, 07 Sep 2026 12:34:12 +0000

I've been looking at how small teams can use Google's AI tools without turning every task into an "AI project."

A few tools caught my attention:

NotebookLM — useful when you're dealing with a lot of internal documents or research.

Google Vids — potentially useful when a small team needs to turn an idea, process or explanation into video content without building a complicated production workflow.

MusicFX — more of a creative experimentation tool, but interesting for anyone working with content.

The bigger lesson for me is that AI adoption doesn't necessarily mean adding AI everywhere.

A better approach is:

Find repetitive/expensive task → test one AI tool → measure the result → keep it only if it actually improves the workflow.

I'm currently learning these tools through a structured Google AI resource covering NotebookLM, Vids, MusicFX and others.

If anyone here has experimented with these tools, I'd be interested to hear which ones actually saved you time rather than just being interesting to try.

Don't Use the Same AI Tool for Every Problem

Siya Jain — Mon, 07 Sep 2026 12:31:25 +0000

One thing I've noticed while exploring AI tools is that tool selection matters almost as much as prompting.

A chatbot might be great for brainstorming, but it isn't necessarily the best choice when you're working with a large collection of source documents, creating a presentation, or experimenting with audio.

Here is a simple way to think about Google's AI ecosystem:

Task	Tool worth exploring
Working with research/document sources	NotebookLM
Creating structured video content	Google Vids
Generative music experimentation	MusicFX

The interesting part is the workflow rather than the individual products.

For example, if I were researching a technical topic, I could:

Collect reliable source material.
Organize it in NotebookLM.
Ask questions to identify the important concepts.
Turn the resulting explanation into a structured presentation.
Experiment with different ways of communicating the same information.

That is a much more useful AI skill than simply knowing a list of tools.

For anyone wanting to explore this workflow further, I've been using a Google AI learning resource that covers NotebookLM, Vids, MusicFX and additional Google AI tools.

Is AI Actually Saving Time? Try Measuring the Workflow

Siya Jain — Mon, 07 Sep 2026 12:29:20 +0000

One mistake with AI productivity is measuring the speed of generating an output while ignoring the time spent fixing it.

A better experiment is to measure the whole workflow.

For example, take a research task:

Without AI

Research → read documents → take notes → organize information → create summary

With AI assistance

Collect sources → analyze with AI → verify important points → organize → create summary

The second workflow isn't automatically better.

The real question is:

Did the AI reduce the total amount of useful work required?

I've been experimenting with Google's AI tools from this perspective.

NotebookLM is interesting for source-based research, Google Vids for turning information into video, and MusicFX for creative experimentation.

The tools are different, but the principle is the same: start with the problem, then choose the tool.

I'm also working through a structured Google AI learning resource covering these tools. I'm finding the hands-on approach more useful than simply collecting AI tool names.

Don't Use the Same AI Tool for Every Problem

Siya Jain — Mon, 07 Sep 2026 12:24:54 +0000

One thing I've noticed while exploring AI tools is that tool selection matters almost as much as prompting.

Here is a simple way to think about Google's AI ecosystem:

Task Tool worth exploring
Working with research/document sources NotebookLM
Creating structured video content Google Vids
Generative music experimentation MusicFX

The interesting part is the workflow rather than the individual products.

For example, if I were researching a technical topic, I could:

Collect reliable source material.
Organize it in NotebookLM.
Ask questions to identify the important concepts.
Turn the resulting explanation into a structured presentation.
Experiment with different ways of communicating the same information.

That is a much more useful AI skill than simply knowing a list of tools.

For anyone wanting to explore this workflow further, I've been using a Google AI learning resource that covers NotebookLM, Vids, MusicFX and additional Google AI tools.

AI Agents: Where the Real Engineering Challenge Begins

Siya Jain — Wed, 12 Aug 2026 12:44:38 +0000

AI agents are getting a lot of attention, but building a basic one isn't necessarily the difficult part anymore. With today's LLM APIs and frameworks, developers can create agents that use tools and complete simple tasks relatively quickly.

The harder question is: can an agent reliably complete a task without doing something unexpected?

A normal chatbot usually follows:

User → Prompt → LLM → Response

An agent adds another layer:

Goal → Reason → Tool → Result → Decide → Repeat

For example, instead of simply answering "Why is our API slow?", an agent could check monitoring data, retrieve logs, compare recent deployments, and then explain the likely cause.

While exploring these topics, I recently came across a resource covering generative AI, AI agents, machine learning, and data science. What I found useful was seeing these areas together rather than treating them as completely separate subjects.

Tools are what make agents particularly useful. An agent might have functions such as get_logs(),search_database(), or check_deployment(). But giving an agent more tools doesn't automatically make it better. Each tool should have a clear purpose, predictable output, and limited permissions.

This becomes especially important when agents can perform real actions. Reading an order is relatively low risk. Changing customer information may require confirmation. Issuing a large refund or deleting data might require human approval.

Another interesting question is whether we actually need multiple agents. A research agent, coding agent, reviewer agent, and manager agent can sound impressive, but every additional component adds complexity, latency, and potential failure points.

Sometimes one well-designed agent with a few reliable tools is enough.

I think this is where AI agent development is becoming less about clever prompts and more about traditional software engineering: validation, permissions, retries, logging, monitoring, and evaluation.

The real question isn't "Can an AI agent do this?"

It's "Should it do this, how do we know it did it correctly, and what happens when it fails?"

I'm curious what other developers think: what's currently the biggest challenge with AI agents — reliability, security, tool use, or evaluation?