Strathweb. A free flowing tech monologue.

The no-cloning theorem is usually stated as an impossibility, namely that there is no machine which takes one copy of an unknown quantum state and returns two of them.

Hamiltonian simulation is the application for which quantum computers were originally proposed, and the question of how many gates it costs to simulate a given Hamiltonian to a given accuracy is one of the oldest in the field.

Back in 2022 I announced a new Q# community project - a proper, standalone TextMate grammar for Q#, built with a real build pipeline, test suite and multiple output formats, rather than the handful of regular expressions that the QDK extension shipped with

Back in December I introduced the MLX Integration Library for Agent Framework, a small library that lets you plug local MLX models into Agent Framework applications as a regular chat client, sitting right next to your cloud-backed agents.

In part one of this little series I introduced TasmanianDevil, a standalone, offline PII detection and de-identification engine for .NET.

A few months ago I introduced AgentGuard, a library for declarative guardrails and safety controls for .NET AI agents.

This post continues a series on hybrid architectures that combine local Small Language Models (SLMs) with cloud Large Language Models (LLMs).

Some years (wow, time flies!)

As AI agents become more common in .NET applications, the question of how to keep them safe and well-behaved keeps coming up.

Over the last few years, I have dedicated a lot of space on this blog to the topic of Post-Quantum Cryptography (PQC).

Recently, I dedicated quite a lot of room on this blog to the topic of running Phi locally.

Following the initial introduction of (Maybe) LibOQS.NET a few months ago, the post-quantum cryptography landscape has continued to move rapidly.

In the world of quantum computing, generating true randomness is one of the most fundamental applications.

If you visited this blog before, chances are you are familiar with Q# Bridge, a library that I have been working on for quite a while, that allows you to run Q# quantum simulations and access a number of Q# compiler/QDK features from multiple popular high…

I’ve recently been working on setting up a bunch of Agent Framework samples, which would showcase the cooperation between cloud agents (backed by LLMs in the cloud) and local agents (running on your own machine).

When building AI workflows, we often face a choice: do we use a massive, expensive cloud model for everything (to ensure best reasoning capabilities), or do we cut costs with a smaller local model (and risk hallucinations)?

Last week, we released version 2.0 of dotnet-script.

In this post, we’ll explore a novel approach to optimizing AI workflows by strategically combining large language models (LLMs) with small language models (SLMs) using the “Minions pattern.”

Over recent years I have been involved in the post-quantum cryptography community, especially from the .NET angle - trying to streamline integration of PQC into .NET space and raise the awareness of developers via various projects, samples and articles.

Following up on my recent posts about ML-KEM and ML-DSA post-quantum cryptography in .NET using BouncyCastle.NET, I wanted to share an interesting development on the Windows side.

In this post we will explore a novel approach to Retrieval-Augmented Generation (RAG) called HyPE (Hypothetical Prompt Embeddings), which I came across in a preprint paper recently.

In the previous part of this series, we explored how to attach OpenAPI-based tools to a Semantic Kernel AI agent.

Today we will kick off a short series on building AI agents which have access to OpenAPI tools.

There is an old joke that one day a cat jumped on a keyboard and that’s how Perl was invented.

Last week at Build, Microsoft released a public preview of Foundry Local - a new tool for running AI models locally on your machine, with a focus on privacy and security.

Last year, Microsoft announced the Copilot Plus PC, a new class of devices that are designed to run AI workloads locally.

If you have tried to use the OpenAI o-series reasoning models, such as o1 or o3, with PromptFlow recently, you certainly ran into a nasty surprise.

Quokka (or “Quokka Puck”) is a brand new quantum educational device from Chris Ferrie and Simon Devitt, originally funded by a Kickstarter campaign.

As I previously blogged a few times, I have been working on the Strathweb Phi Engine, a cross-platform library for running Phi model inference via a simple, high-level API, from a number of high-level languages: C#, Swift, Kotlin and Python.

Last summer, I launched Strathweb Phi Engine — a cross-platform library for running Phi model inference via a simple, high-level API, from a number of high-level languages: C#, Swift, Kotlin and Python.

Some time ago, I wrote a post about post-quantum cryptography in .NET, where I introduced the concept of post-quantum cryptography and discussed the early BouncyCastle.NET implementation of Kyber and Dilithium.

Recently, I dedicated quite a lot of room on this blog to the topic of running Phi locally with the Strathweb Phi Engine.

Local AI inference has become increasingly important for developers seeking to build robust, privacy-preserving applications.

For a few years in a row now, around this time of the year, I have been writing a festive Q# quantum computing post.

In the summer of 2024, I announced the Q# Bridge library, which allows you to run Q# simulations from many popular high-level languages - C#, Swift, Python and Kotlin.

In this post, we will explore the flexibility behind Azure AI Inference, a new library from Azure, which allows us to run inference against a wide range of AI model deployments - both in Azure and, as we will see in this notebook, in other places as well.

This summer, I announced the Strathweb Phi Engine — a cross-platform library for running Phi inference anywhere.

GPT-4o-mini is the small, cost-effective version of the GPT-4o model.

On October 1st, OpenAI and Microsoft (Azure OpenAI) announced the availability of the GPT-4o Realtime API for speech and audio.

I recently announced Strathweb Phi Engine, a cross-platform library/toolset for conveniently running Phi-3 (almost) anywhere.

I previously blogged about post-quantum cryptography on this blog a few times.

Earlier this week, I released version 0.2.0 of my post-quantum cryptography helper library .NET, Strathweb.Dilithium, which introduces a new feature - automatic key management support in Duende Identity Server.

I recently wrote a blog post about using Rust to run Phi-3 model on iOS.

Many years ago I wrote a book about ASP.NET Web API.

Over the past year, Q# and the QDK, have undergone a massive transformation, with the entire toolchain moving to Rust - which resulted in a significant performance improvement, better portability of the toolchain and the ability to run Q# on a wide range…

Last month, Microsoft released the exciting new minimal AI model, Phi-3 mini.

Last time around, we discussed how Large Language Models can select the appropriate tool and its required parameters out of freely flowing conversation text.

One of the fantastic capabilities of the Large Language Models is their ability to choose (based on a predefined set of tool definitions) the appropriate tool and its required parameters out of freely flowing conversation text.

In my recent posts, I’ve been exploring various facets of the Azure OpenAI Service, discussing how it can power up our applications with AI.

For our Retrieval-Augmented-Generation (RAG) application, we setup AI Search in part 1, however so far we only used it using the basic keyword search.