DEV Community: Sourabh Mourya

You Don’t Need More Tokens, You Need Better Thinking

Sourabh Mourya — Thu, 28 May 2026 12:52:42 +0000

I got my first Anthropic bill and genuinely thought it was a mistake.

It wasn't. I was just being wasteful without realizing it.

After a week of obsessing over token usage, I cut my costs by nearly 60% with better results. Here's what I learned.

What even is a token?

A token isn't a word. It's closer to a word-chunk.

As a rough rule: 1 token ≈ 4 characters in English. So "unbelievable" is about 3 tokens. A full paragraph might be 80–120 tokens.

Every API call counts both directions:

Input tokens — everything you send (your prompt + system prompt + conversation history)
Output tokens — everything the model sends back

Output tokens cost more. Always. Keep that in mind.

Where most people bleed tokens without knowing

When I audited my prompts, I found the same mistakes every time.

Bloated system prompts. I had one that was 800 words of "be helpful, be concise, be professional..." repeated five different ways. That runs on every single call.

Dumping full context when partial context works. I was sending entire documents and saying "answer this question about section 3." The model reads all of it. Every time.

Asking for long outputs when short ones do the job. "Explain this concept" gets you 400 words. "Explain this in 2 sentences" gets you 40 words and usually a cleaner answer.

Conversation history that never gets trimmed. In multi-turn chats, every previous message gets re-sent. A 20-turn conversation can be 3,000 tokens before you've even typed your next message.

What actually works to cut costs

1. Be specific about output length.
Add "in 1 paragraph" or "in under 100 words" to your prompt. Models respect this and it forces tighter answers.

2. Chunk your context.
Instead of sending a full document, extract and send only the relevant section. A focused 200-token excerpt beats a 2,000-token dump every time.

3. Shrink your system prompt ruthlessly.
Cut anything that's vague or repeated. "Be concise" said once beats "please ensure your responses are as concise as possible and avoid unnecessary verbosity" said once. Less is genuinely more.

4. Use a cheaper model for simple tasks.
Not every call needs your most powerful model. Routing classification, summarization, or formatting tasks to a smaller model can cut costs 5–10x with zero quality loss.

5. Cache repeated context.
If you're building something and sending the same instructions or documents repeatedly, look into prompt caching. Anthropic, OpenAI, and others support it — cached tokens cost a fraction of fresh ones.

6. Ask for structured output.
"Return JSON with keys: summary, action, confidence" is shorter to process and parse than "please write a detailed explanation followed by the recommended action and your confidence level."

The mindset shift that helped most

I stopped thinking "how do I write better prompts" and started thinking "what's the minimum context this model actually needs to answer correctly?"

That question changed everything.

The model doesn't need your life story. It needs the right facts, a clear goal, and a defined output format. Everything else is noise you're paying for.

Now I want to hear from you:

Have you ever actually looked at your token usage per call, or do you just watch the bill?
What's the dumbest token waste you've caught in your own prompts?
Have you found a trick that cut costs without hurting quality?
Are you using prompt caching yet — and did it actually help? Drop your answer below. Even "I had no idea tokens worked this way" counts — we've all been there.

AI Agents Changed My Workflow

Sourabh Mourya — Tue, 26 May 2026 11:33:11 +0000

I'll be honest I thought "agentic AI" was just a fancy way of saying chatbot.

Then I tried it. And I haven't gone back.

What even is an AI agent?

Here's the simplest way I can explain it.

A normal AI tool waits for you to ask something, answers, and stops. You're still driving every step.

An agent takes a goal not a task and figures out the steps itself. It reads files, runs commands, hits errors, self-corrects, and keeps going.

That's a completely different thing.

The moment it clicked for me

I had a broken API integration. Wrong payload format, 400 errors, the usual headache.

Normally that's 20–30 minutes of my life gone. Log pulling, doc checking, trace and patch and repeat.

I handed the goal to an agentic AI instead.

It read the stack trace, checked recent commits, found the exact line where the payload structure broke, wrote a fix, and ran the test.

Under four minutes.

I just sat there. Not impressed unsettled. Like watching someone else parallel park your car perfectly on the first try.

The tool I've been using

I went deep on OpenClaw specifically it's open-source, runs locally, brings your own API key, and it can read files, control your browser, send messages, and run shell commands autonomously.

180,000+ GitHub stars in three months. That's not hype. That's developers voting with their attention.

I wrote up my full experience with it what it did well, where it failed badly, and whether I'd actually trust it in a real workflow:

👉 OpenClaw and the Rise of Agentic AI for Faster Coding

But it's not magic let me be real

Agents break in weird ways.

I watched OpenClaw loop on the same wrong fix seven times without realizing it. I watched it "solve" a bug by deleting the test catching it. I watched it make a three-second architectural decision I'd have thought about for three days and get it completely wrong.

The demos you see online are cherry-picked. Real usage is messier.

Right now you still need a human who understands what's happening not to write every line, but to catch the agent when it confidently goes off the rails.

The real shift I'm noticing

The skill that's mattering now isn't "can you code fast."

It's "can you think clearly enough about a problem to give an agent a goal it won't misinterpret."

That's harder than it sounds. And most of us are figuring it out in real time.

I want to hear from you:

Have you tried any agentic AI tools in your actual workflow yet?
What's the most impressive thing one did for you?
What's the most embarrassingly wrong thing it did that you caught just in time?

Drop it below. Even one line counts.

I Let an AI Agent Loose on My Codebase. Here's What Actually Happened.

Sourabh Mourya — Mon, 25 May 2026 11:19:34 +0000

Okay, real talk.

I kept seeing "agentic AI" everywhere Twitter, YouTube, every second DEV post. And honestly? I thought it was just another buzzword people were using to feel ahead of the curve.

Then I actually tried it.

Wait, what even is an agentic AI?

Here's how I'd explain it to past-me.

A regular AI tool (think: Copilot autocomplete) waits for you to ask something, gives you a suggestion, and stops. You're still driving.

An agent is different. You give it a goal not a task, a goal and it figures out the steps itself. It reads your files, runs commands, hits errors, self-corrects, and keeps going until it's done or stuck.

It's less "smart autocomplete" and more "intern who actually reads the whole repo before touching anything."

The moment it clicked for me

I had a broken webhook integration. Nothing catastrophic, but annoying wrong payload format, 400 errors, the usual.

I would normally spend 20–30 minutes on it. Open logs, check docs, trace the request, patch, test, repeat.

I pointed an agent at it instead.

It read the stack trace. Checked the last three commits. Found the exact line where the payload structure changed. Wrote a fix. Ran the test. Done.

Under four minutes.

I just sat there staring at my screen. Not excited unsettled. Like watching someone else parallel park your car perfectly on the first try.

But here's where I need to be honest with you

Agents are not magic. They're more like a very confident junior dev who sometimes has no idea what they don't know.

I've also watched an agent:

Loop on the same broken fix seven times without realizing it was wrong
"Solve" a bug by deleting the test that was catching it
Make an architectural decision in 3 seconds that I'd have thought about for 3 days and get it completely wrong The demos you see online are cherry-picked. Production reality is messier.

Right now, according to Anthropic's own data, developers can fully hand off only 0–20% of tasks to agents without supervision. The rest still needs a human in the loop.

So no, agents aren't replacing you. But they are changing what "your job" actually means.

The real question I keep asking myself

If an agent can handle the doing part of coding the mechanical execution what exactly is the skill that matters now?

I think it's judgment. Knowing what to build. Knowing when the agent is confidently wrong. Knowing which 20% of decisions actually matter and can't be delegated.

That's not a junior skill. That's not even a mid-level skill. That's the stuff that takes years to develop.

Which makes me wonder are we about to see a massive gap open up between developers who can think clearly about problems and developers who are just really fast at typing code?

I genuinely want to hear from you

Have you used an AI agent in your actual workflow yet or just played with it?

And if you have what's the task it handled best? What's the most embarrassing thing it got wrong?

Drop it in the comments. Genuinely curious whether my experience is typical or if I just got unlucky with my first few tries.

Follow me if you want more unfiltered takes on building with AI no hype, no doom, just what's actually happening day-to-day.