Why I Started Learning FastAPI (The Real Story) Read more ›
You built the pipeline. You chunked the documents. You picked a solid embedding model. You stood up a vector database. You tested a few… Read more ›
Infrastructure, evaluation, and operations: the layers where production AI quietly lives or dies. Read more ›
In my previous blogs, we explored AI fundamentals, the end-to-end ML + MLOps lifecycle, and why most models never make it to production… Read more ›
Welcome to the third article in this series. Read more ›
Why modern AI systems don’t search for words anymore they search for meaning. Read more ›
Policies must operate across diverse conditions, yet a single policy is often conservative while fully adaptive schemes can be complex. We study zero-shot generalization in contextual dynamical systems and introduce a performance-centric, directional task dissimilarity--the signed divergence--that upper bounds the generalization gap from a source context to a target context. The signed divergence induces $\varepsilon$-tolerance sets that certify... Read more ›
Video diffusion has quickly grown into a key generative serving workload, yet producing each clip demands many denoising iterations over large spatio-temporal latents, which puts low-latency inference out of reach on a single device. A denoising step is therefore typically distributed across multiple accelerators, and TPU sub-slices have become an attractive and practical fabric for doing so. Current auto-parallel systems, however, search almost... Read more ›
This is the third piece in a series on a prompt injection vector that leaked a system-prompt secret from GPT-4o using nothing but a… Read more ›
Microsoft Foundry Observability lets you trace, evaluate, monitor, and optimize AI agents on any framework, then measure their real… Read more ›
Continue reading on Medium » Read more ›
WhatsApp is no longer just a messaging app for personal conversations. With over 2 billion users worldwide, it has become one of the most… Read more ›
A practical, production-tested guide for engineers, technologists, and business leaders on Prompt Engineering, Context Engineering, RAG… Read more ›
When an LLM operates as a standalone agent writing SQL queries, invoking APIs, or running terminal commands it relies heavily on the… Read more ›
When the Next Step Is Not One Step: Distribution-Aware Execution Modeling for Concurrent Go Programs
Training a model to predict the next step in a concurrent program is harder than it looks: two runs of the same program from the same trace prefix can produce different next events, both valid, because the scheduler is nondeterministic. A model trained against a single label is learning to guess one outcome of a random process. We turn this around and use the nondeterminism as a training signal. We run each program many times, aggregate the obse... Read more ›
Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and connected human-driven vehicles. Non-connected vehicles, such as non-connected autonomous or human-driven vehicles, are not incorporated. As a result, these platooning approaches may not properly reflect real-world mixed traffic conditions at the current stage. To address this limitation, this study ... Read more ›
whereVectorSimilarTo(), embeddings, pgvector, cosine similarity, chunking strategies, and the difference between keyword search, full-text… Read more ›
Modern deep neural network architectures are trained via backpropagation, which requires errors to be sequentially propagated through all layers before parameters can be updated. This introduces two limitations: locking, where layer-wise updates are strictly interdependent and cannot proceed in parallel, and the weight transport problem, which requires symmetric forward and backward pathways for exact gradient computation. These constraints re... Read more ›
Qualcomm published figures for the X Elite, not the X Plus, regarding the Llama 3.2 Read more ›