Finding Tiny Earthquakes by Their Fingerprints
A three-station seismic matched-filter experiment shows how repeating waveform shapes reveal small earthqua...
Utpal Kumar, PhD
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- 154+ Posts
- 12 Topics
- Nov 2026 Updated
RAG: Giving AI Agents Access to the Knowledge They Need
How retrieval-augmented generation helps AI agents find and use the knowledge they need.
Recent posts
Recent technical articles on seismic analytics, scientific computing, numerical modeling, and production data workflows.
What an agent harness does
Follow a CSV export task to see how an agent harness connects model decisions to tools, permissions and checks.
Mcp explained: how ai connects to the tools you already use
A simple earthquake-catalog example explains what the Model Context Protocol does, who introduced it, and how it connects AI applications to outside tools.
What happens when you open a website? tcp, tls, and http explained
A simple shop-and-courier analogy explains how TCP delivers data, TLS protects it, and HTTP gives a website request its meaning.
Rlhf explained: how human feedback shapes ai agents
How human comparisons become a training signal, where DPO fits, and why useful AI agents still need independent checks.
What ai agents can learn from the bayesian brain
How neuroscience, autonomous laboratories, and language-model research can inform agents that preserve evidence and revise their beliefs.
From github to zenodo: make your code and data citable
Turn a GitHub release into a citable research archive, share supporting data, and choose the right DOI for your paper.
How ai agents choose what to learn next: expected information gain explained
How expected information gain helps AI agents choose the next observation, with Shannon entropy, a worked building-monitoring example, and practical limits.
Agentic harness explained: how ai agents get work done
An agentic harness gives an AI model tools, context, memory, permissions, and feedback. See how the pieces work together in a scientific Python workflow.
Autoencoders for seismic waveforms: compress, learn, and generate
Autoencoders learn compact representations by reconstructing seismic data. This tutorial explains their use in compression, denoising, representation learnin...
When frequency changes signal structural damage
An unexpected drop in a building’s natural frequency is the structure reporting that it has lost stiffness — usually the first sign of damage. Here is why to...
From phase pickers to seismic foundation models
Foundation models ask whether one pretrained representation of seismic waveforms can support picking, detection, classification, and other downstream tasks—w...
Caffeinate on and off controls for macos with alfred and swiftbar
A compact macOS setup with two Alfred workflows for turning caffeinate on and off, plus SwiftBar for keeping the current status visible in the menu bar.
What an agent harness does
Follow a CSV export task to see how an agent harness connects model decisions to tools, permissions and checks.
Mcp explained: how ai connects to the tools you already use
A simple earthquake-catalog example explains what the Model Context Protocol does, who introduced it, and how it connects AI applications to outside tools.
What happens when you open a website? tcp, tls, and http explained
A simple shop-and-courier analogy explains how TCP delivers data, TLS protects it, and HTTP gives a website request its meaning.
Rlhf explained: how human feedback shapes ai agents
How human comparisons become a training signal, where DPO fits, and why useful AI agents still need independent checks.
What ai agents can learn from the bayesian brain
How neuroscience, autonomous laboratories, and language-model research can inform agents that preserve evidence and revise their beliefs.
From github to zenodo: make your code and data citable
Turn a GitHub release into a citable research archive, share supporting data, and choose the right DOI for your paper.
How ai agents choose what to learn next: expected information gain explained
How expected information gain helps AI agents choose the next observation, with Shannon entropy, a worked building-monitoring example, and practical limits.
Agentic harness explained: how ai agents get work done
An agentic harness gives an AI model tools, context, memory, permissions, and feedback. See how the pieces work together in a scientific Python workflow.
Autoencoders for seismic waveforms: compress, learn, and generate
Autoencoders learn compact representations by reconstructing seismic data. This tutorial explains their use in compression, denoising, representation learnin...
When frequency changes signal structural damage
An unexpected drop in a building’s natural frequency is the structure reporting that it has lost stiffness — usually the first sign of damage. Here is why to...
From phase pickers to seismic foundation models
Foundation models ask whether one pretrained representation of seismic waveforms can support picking, detection, classification, and other downstream tasks—w...
Caffeinate on and off controls for macos with alfred and swiftbar
A compact macOS setup with two Alfred workflows for turning caffeinate on and off, plus SwiftBar for keeping the current status visible in the menu bar.