Beautiful relaxing solo piano music. Evening Blues by Bill Whitfield. 
Bill's website: http://billwhitfieldmusic.com/ #SCFIRST
YouTube: https://www.youtube.com/watch?v=t07HwXtbPnc
Apple Music: https://music.apple.com/gb/artist/bill-whitfield/578479004
Spotify: https://open.spotify.com/artist/2vsmftXvzst7kEEK4sUWeg

Evening Blues

A piano cover of The Man Who Sold the World by David Bowie.

The Man Who Sold The World

Ichizu/一途 - KING GNU "Jujutsu Kaisen 0 movie theme song" piano cover

A War Without Casualties
The Republic of San Magnolia has long been under attack from the neighboring Giadian Empire's army of unmanned drones known as the Legion. After years of painstaking research, the Republic finally developed autonomous drones of their own, turning the one-sided struggle into a war without casualties-or at least, that's what the government claims.

In truth, there is no such thing as a bloodless war. Beyond the fortified walls protecting the eighty-five Republic territories lies the "nonexistent" Eighty-Sixth Sector. The young men and women of this forsaken land are branded the Eighty-Six and, stripped of their humanity, pilot the "unmanned" weapons into battle...

Available July 12, 2022 from Hachette Audio as a digital download from Yen Audio. 

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86--EIGHTY-SIX, Vol. 1 by Asato Asato, Shirabii Read by Suzie Yeung and Alejandro Saab - Audio

Beck's DEAR LIFE "Where It's At" Performance & Conversation

These are audio examples for the beats we've shown you how to build in our Beat Building tutorial series. Find the full tutorials at musicradar.com.

Berlin Techno

Unknown

Exactly what my night in SF sounded like...

Firewatch stand auf unserer Liste der meisterwarteten Spiele 2016. Jetzt ist die Zeit der Abrechnung gekommen: Ist das Indie-Abentuer wirklich unsere Vorfreude wert gewesen, oder verdammen wir es wie eifersüchtige Ex-Freundinen?

Runde #46: Firewatch (Some people just want to watch the world burn)

In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.

As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”

Simplifying Machine Learning Lifecycle Management

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Simplifying Machine Learning Lifecycle Management

O'Reilly Radar