Deep dive into a product that had the world in its clutches just a week ago, and now that the noise has died out a bit, lets actually explore if it is worth your while – regardless of who you are. A bit of history and setting the stage So far, it seems that the tech world – specifically the field of AI – is making insane progress in whatever it touches. But if you zoom out, most […]
Running a local LLM and making it available to any device on your network — no cloud required. Update (Feb 2026): The SSE transport has been deprecated in newer versions of the MCP SDK and replaced by streamable-http. The code in this article has been updated to reflect this. If you’re on an older version of the SDK, transport=”sse” still works but you may see a deprecation warning. The Idea What if your Mac could act as a private AI […]
Paper-explained Series 1 Modern language models face a fundamental trade-off: accuracy versus efficiency. Full-attention Transformers deliver strong accuracy but scale quadratically with context length O(n²), making long-context inference expensive. Linear-attention models reduce complexity to O(n) but often sacrifice reasoning ability and precision. In Jet-Nemotron, NVIDIA introduces a new family of hybrid-architecture language models that match or exceed full-attention model accuracy while delivering dramatic throughput gains — without relying on MoE tricks. This article explains how. What Is Jet-Nemotron? Jet-Nemotron is a family of […]
This paper proposes a lightweight full-stack execution framework integrating Serverless architecture with WebAssembly runtime optimization to enhance performance and energy efficiency in edge deployments. The system employs modular task decomposition and Light-Container Isolation (LCI) technology to achieve cross-node function reuse on AWS Lambda and Cloudflare Workers platforms. An Reinforcement Learning Scheduler (RL-Scheduler) predicts request distribution in real-time, dynamically allocating CPU cycles and memory limits. Targeted testing demonstrates a 52% reduction in cold start time, a 33% decrease in […]
The last three posts have been about Stan Wagon’s algorithm for finding x and y satisfying x² + y² = p where p is an odd prime. The first post in the series gives Gauss’ formula for a solution, but shows why it is impractical for large p. The bottom of this post introduces Wagon’s algorithm and says that it requires two things: finding a quadratic non-residue mod p and a variation on the Euclidean algorithm. The next […]
The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]
APIs are the backbone of modern enterprises, connecting services, data, and business logic across microservices and cloud environments. Traditional perimeter-based security is no longer enough, as remote work, cloud adoption, and constant machine-to-machine communication blur internal and external boundaries. Zero Trust shifts the security model from “trust but verify” to “never trust, always verify,” treating every API request as potentially malicious. By enforcing strong identity verification through OAuth2, OpenID Connect, and mTLS, and applying granular authorization with attribute-based […]
arXiv:2601.05262v1 Announce Type: new Abstract: Modern dense information retrieval (IR) models usually rely on costly large-scale pretraining. In this paper, we introduce LLM2IR, an efficient unsupervised contrastive learning framework to convert any decoder-only large language model (LLM) to an information retrieval model. Despite its simplicity, the effectiveness is proven among different LLMs on multiple IR benchmarks including LoCo, LongEmbed and BEIR. We also find that models with a longer context length tend to have a stronger IR capacity […]
Mitchell Hashimoto: My AI Adoption Journey Some really good and unconventional tips in here for getting to a place with coding agents where they demonstrably improve your workflow and productivity. I particularly liked: Reproduce your own work – when learning to use coding agents Mitchell went through a period of doing the work manually, then recreating the same solution using agents as an exercise: I literally did the work twice. I’d do the work manually, and then I’d […]
Accurate prediction of student academic performance distributions is essential for institutional planning and decision support. This paper introduces a novel Monte Carlo (MC) framework for forecasting cohort-level grade distributions using historical data. The MC model extracts empirical probability distributions and generates predictions via stochastic sampling, explicitly capturing uncertainty and variability in student outcomes. Evaluated across engineering, medical, and aggregated student cohorts, the MC model outperformed a linear regression benchmark, particularly in capturing nonlinear grade transitions and stochastic variability. […]