Ai on myvirtualcloud.net

The Answer Was Already in Teams. We Just Couldn't Find It.

Mon, 07 Sep 2026 09:00:00 +0000

Somewhere in your company's chat history is the exact fix for the problem you are debugging right now. Someone wrote it down two years ago, in a thread, at 11pm. Good luck finding it.

That was the problem I set out to solve at ASI Solutions. Our knowledge was not missing. It was scattered. Decisions lived in Microsoft Teams. Delivery history lived in Linear. Deal context lived in HubSpot. Documents lived in SharePoint. Meeting notes lived in Granola. Proposals lived in PandaDoc. Invoices lived in Xero. Code and pull requests lived in GitHub.

How our document classifier votes before it ever asks a language model

Thu, 11 Jun 2026 09:00:00 +1200

A district council we work with has about 4 TB of files in SharePoint and more on internal SMB shares, and they need to know what is in them: which documents are contracts, which hold personal information, and which should have been disposed of years ago under the Public Records Act 2005. Every one of those files has to be read, classified into one of roughly 30 document types, and scored for sensitivity.

How I Built ASI's Marketing Engine Around AI Agents

Mon, 11 May 2026 09:00:00 +0000

This is a long one, and I mean that as a recommendation rather than a warning. Most of what gets written about AI in marketing right now skips the part that actually matters — the specifics: what broke, what surprised you, what you'd do differently. This piece doesn't skip it. If you're building something similar, or wondering whether any of this is real, I think you'll find it useful.

From XGBoost to LightGBM: How Our ML Model Adapted to Two Market Regimes

Sun, 19 Apr 2026 09:00:00 +0000

When we first wrote about SignalScope's ML backtesting pipeline in March, the model of record was XGBoost. Six weeks and hundreds of experiments later, it isn't. The current production model is a single LightGBM regressor with forty trees and depth two, trained on three-day forward returns, pulling almost all of its out-of-sample skill from a single feature. Mean information coefficient — the rank correlation between how the model ranks tickers and how they actually perform, where 0 is random and higher is better — has climbed from 0.006 on our first Ridge baseline to 0.161 today, a roughly 27x improvement. But the path between those two numbers wasn't monotonic. Twice in the last six weeks, a regime shift in the underlying data quietly invalidated our best model and forced us to start over. This is the story of how we rebuilt the pipeline three times — and what those rebuilds taught us about model stewardship in markets that keep changing underneath you.

Announcing the Ollama GPU Calculator

Mon, 28 Oct 2024 05:37:02 +0000

If you have spent any time running local models with Ollama, you already know the awkward part: picking a model is easy; knowing whether your GPU can actually host it is not. Parameter count, quantization, context length, and VRAM interact in ways that are hard to eyeball — especially once you start mixing cards or looking at Apple Silicon unified memory.

Four Advantages of Capacity Forecasting and Analytics with Hyperconvergence

Tue, 14 Jun 2016 07:15:32 +0000

…read the full article at CIOReview Magazine here.

This article was first published by Andre Leibovici (@andreleibovici) at myvirtualcloud.net