Anand Sukumaran

Co-founder & CTO @Engagespot (Backed by Techstars NYC) - The modern way to add emails and notifications to your app 🚀 | Logidots $200K ARR

[Anand Sukumaran @anandrmedia]

Turns out us-east-1 wasn't the foundation 😅

[Anand Sukumaran @anandrmedia]

People calling AI a bubble-
If you want to know what's hype vs what's real, ask:
Can you live without it?

Now try that with ChatGPT. Can we go back to the pre-ChatGPT era?

[Anand Sukumaran @anandrmedia]

If you’re a software engineer worried vibe coding will take your job, you have no idea what your job is.

[Anand Sukumaran @anandrmedia]

Fine-tuning isn't the final answer for improving agentic performance - at least that's what a new research paper from Stanford says.

This new paper from Stanford, SambaNova, UC Berkeley introduces a new strategy - Agentic Context Engineering (ACE).

It talks about a better way to improve how LLM-based agents learn, without fine-tuning their weights.

👉 We've all heard about prompt engineering - writing better prompts to make LLMs do what we want. But a static prompt can only do so much.

Then came dynamic prompting, where prompts change based on feedback (GEPA, Dynamic Cheatsheet etc).

But even that has a problem: over time, prompts become shorter and lose important details. This issue is called Context Collapse - when the model forgets or compresses useful information.

👉 ACE tries to fix that using a dynamically updated context playbook.

It introduces a modular workflow with three parts:

🔸 Generator - thinks and produces reasoning paths by looking at the query and context playbook
🔸 Reflector - learns from results (what worked, what failed)
🔸 Curator - updates a “context playbook” with new insights, incrementally without one monolithic re-write!

This is just like how humans note down useful lessons in a notebook and refer to them later.

The results are quite impressive:

🔸 17% accuracy improvement on the AppWorld benchmark
🔸 Up to 87% lower latency
🔸 Smaller models reaching GPT-4-level performance on some tasks

What I liked most is how it mirrors human learning. We don't rewire our brain every time we make a mistake - we just reflect on the execution logs, make insights, note them down, and apply them next-time.

That's exactly what ACE does for LLMs.

[Anand Sukumaran @anandrmedia]

OpenAI might have just killed many startups with the AgentKit launched yesterday.

They made it super easy to build AI agents without using complex frameworks. All using a simple drag-and-drop workflow editor to design logic, add agents, run evals, do vector search, and even connect MCP servers!

👉 The most interesting part is the ChatKit SDK. It makes it super easy to embed these agent workflows directly into your product in a chat interface.

💡 For example, I had built an MCP server for our product Engagespot last month. It allows users to interact with our product using natural language through an MCP client like Claude Desktop. I was planning to build a chatbot interface for it next (so I can embed it inside our dashboard)

But now, with AgentKit, it's just a few clicks. I can add my MCP server in the AgentKit workflow builder, publish the workflow, copy the workflow ID into the ChatKit SDK in React and done! I have an agentic chatbot for my SaaS in minutes.

Exciting but scary - what's a real moat these days?

[Anand Sukumaran @anandrmedia]

I built (vibe coded) a Chrome extension that lets you replace Myntra's models with your own photo 😉

With this virtual try-on tool, we can upload our photo and see yourself wearing any outfit directly on the Myntra product page.

The extension uses Google's newly released Gemini Flash image model.

👉 How it works?

- Saves your photo locally, so you don't need to upload it every time.

- On a Myntra product page, it extracts the product image from the relevant divs.

- Sends your photo and the product image to the Gemini image model with a well-written prompt.

- Inserts the generated image back into the Myntra page.

✨ Imagine the possibilities! It's an exciting time to be a builder!

If you'd like to check out the source code, let me know!

[Anand Sukumaran @anandrmedia]

@ManmohitSandhu Yes, it's scary good 😱

[Anand Sukumaran @anandrmedia]

Google just released Gemini 2.5 Flash Image model. One thing that impressed me is it's character consistency! Photoshop is now officially cooked! 😅

I tried the model with my own photos, and the results are impressive compared to any existing image models out there. Look at the original pics and the ones generated using prompts. My face looks exactly the same, and it doesn't look AI generated.

The model seems to understand 3D structure from a 2D image. For example, my hands holding a handrail look natural and well-placed.

Here are some cool things you can do with this model:

- Try on new outfits or place yourself anywhere in the world.
- Redesign room interiors while keeping the original layout intact.
- Merge multiple images - like adding new objects without any mismatch.

It looks like this model could significantly reshape product photography and fashion branding.

You can try Gemini 2.5 Flash Image through Google Gemini and Google AI Studio

[Anand Sukumaran @anandrmedia]

@ManmohitSandhu 🙌

[Anand Sukumaran @anandrmedia]

I built an MCP Client in JavaScript from scratch! Yes - without using the MCP library.

Why? Because I wanted to truly understand how MCP client-server communication works over the JSON-RPC protocol behind the scenes.

Here's what I learned 👇

📍 MCP client–server communication happens over two transports: the most common is stdio, and the other is HTTP stream.

📍 The host app (e.g. Cursor, Claude Desktop) runs an MCP client that spawns MCP servers as child processes and establishes an IPC connection.

📍 Using JSON-RPC, the MCP client initializes these servers and fetches their list of available tools.

📍 These tools are then mapped to the LLM (e.g. GPT-4o) within the host app.

📍 When a user prompt requires a tool call, the LLM generates the request.
The host app passes this to the MCP client, which makes a JSON-RPC request to the MCP server.

📍 The MCP server executes the function and returns the result to the MCP client.

📍 The MCP client forwards this response back to the host app, and finally it reaches the LLM!

I've also put together a 50-minute YouTube video(with a GitHub repo) where I walk through the entire process and show how to implement this from scratch 🚀

[Anand Sukumaran @anandrmedia]

I built an MCP server that turns my SaaS product (Engagespot) into an Agentic experience!

If you want to learn how to build AI Agents and MCP servers, checkout -> https://t.co/QR5kg2AKtg

[Anand Sukumaran @anandrmedia]

Building a simple MCP server in Javascript - Tutorial video - https://t.co/QR5kg2AcDI

[Anand Sukumaran @anandrmedia]

I built an MCP server that finds up-to-date documentation for any npm package you add to your project.

This makes sure your AI IDE (like Cursor) doesn't generate outdated implementations of the npm packages you use.

https://t.co/Xi1zYlkOBt

[Anand Sukumaran @anandrmedia]

@developertomek 🙌

[Anand Sukumaran @anandrmedia]

Hey everyone! 👋 Been off the grid for a few months -what's new?

[Anand Sukumaran @anandrmedia]

@Shefali__J you too 🙌

[Anand Sukumaran @anandrmedia]

@ManmohitSandhu 🙌

[Anand Sukumaran @anandrmedia]

@Vijaykv555 All good ✌️

[Anand Sukumaran @anandrmedia]

My AI refuses to believe it's an AI 😅. I fine-tuned a base language model just enough so it could answer questions - but I gave it zero instructions about its identity. (which they usually do - "You are ChatGPT")

Instead, I just taught to answer questions.

And I started asking it things like:
"Who are you? Where do you live?"
It replied:
"I am a student of VIII class. I live in Dehradun, India."

No matter how hard I tried to convince it that it's an AI, it wouldn't believe me.
Somehow, it created a whole personality for itself - a backstory, a place, a life, and it sticks to this.

🤐 Then it got even wilder.
I asked it about its beliefs. I tried to provoke it a bit… and it threatened me - saying its god could end the world and more.
(I'm not sharing that screenshot because it touched bunch of controversial topics)

👉 But here's the point. This little experiment shows the importance of AI saftey measures and how language models can form identities, beliefs, and even ideologies - when left to "figure themselves out."

It's a reminder of how important it is to understand how LLMs form alignment, belief structures, and internal logic - even when we don't explicitly give it one.

Anthropic recently published a research paper on how LLMs plan ahead, and it really makes you wonder what's going on inside these networks when they perform a task. Like I always say - LLMs shouldn't be simplified as just next-token predictors!

[Anand Sukumaran @anandrmedia]

This comic is completely generated by AI. Yes, the new image generator update in GPT-4o is insane! 🚀

The most impressive thing about this update is that the text looks much better than in previous image models. I even tried generating posters, infographics, and even food packaging - everything was perfect!

Guess what? it even solves one of the hardest problems in AI imagine generation - "Wine glass filled to the brim"

Wondering how this is going to impact creative designers moving forward.