Suvrakamal Das
Machine Learning Engineer
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Suvrakamal is an ML Engineer at Mattoboard. He works on AI for interior design. He is passionate about applying machine learning and data engineering to bring real design workflows to life.
He has given talks and published papers across AI, and recently presented a poster/papers at PyCon US 2026, Pytorch 2025.
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How We built reliable & Self Evolving Agent Skills
Needless to say the recent advances in autonomous agents like OpenClaw & Hermes Agent. Agent frameworks have made developer life easier with tool calling, skills & MCPs adding scaffold and orchestration harness. Almost all agentic architecture layers are prompt driven. Prompts are a great way of interacting with LLMs, but often leads to unreliable outputs. Tuning prompts is quite a manual intrinsic work, given the non-deterministic nature of LLMs.
Hence the next wave of prompting is through reflection driven methodologies that don’t essentially need explicitly RL or fine tuning but can still invoke the self evolving nature in agents. Can we build a system that learns through its own mistakes and makes it better at each turn?
We essentially need to optimize and quantify which change leads to affecting the overall system. One way to achieve this is through text space optimization by refining the behavioral patterns, validation and feedback loops via evaluation structures.
Lightning-Fast Knowledge Graphs in Python: Real-Time Multi-Hop Reasoning with NVIDIA cuGraph
Imagine querying complex knowledge graphs in real time—right from Python—with all the performance of a GPU supercomputer and none of the usual code headaches. This session reveals how NVIDIA cuGraph turbocharges single-hop, multi-hop, and traversal operations on giant knowledge graphs, cutting response times from seconds to milliseconds. We’ll break down how cuGraph’s GPU-accelerated algorithms work seamlessly with popular Python tools and how you can combine cuGraph with deep learning frameworks like PyTorch for ultra-scalable AI and retrieval-augmented generation (RAG) pipelines. Join us for practical demos, hands-on advice, and approachable insights—whether you’re building enterprise reasoning engines, interactive agents, or next-gen graph-powered search. Unlock the full speed of your data, from Python, with just a few lines of code!
Sandboxed Doesn't Mean Safe: Threat-Modeling MCP Apps and the New UI Attack Surface
Working in payment and auth integrations teaches you fast that "runs in a sandbox" isn't the same as "safe." When MCP Apps landed, letting servers render interactive HTML UIs right inside the chat, it needed stress-testing the same way any new payment flow does: assume the server is hostile, assume the channel is being watched, and trace exactly where trust is handed over versus just assumed.
This talk walks through three ways it breaks, with live demos. First, postMessage trust, what happens when origin validation is wrong or missing. Second, tool poisoning through the UI, where instructions the user never sees quietly end up in the model's context. Third, a confused-deputy case where a UI-triggered tool call uses more of the host's access than the user ever agreed to.
For each one, the failure gets mapped against what the spec says should stop it, CSP, consent prompts, auditable JSON-RPC, showing honestly what holds and what you as the implementer are still on the hook for. Leave with a checklist to run before shipping any MCP App.
AGNTCon + MCPCon China 2026 Sessionize Event Upcoming
PyTorch Conference 2025 Sessionize Event
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