This post proposes a framework for thinking about AI and its impact on the creative industry. I work at Adobe, but these views are my own. Over the past three years, the conversation around AI in the creative community has centered almost entirely on generative AI (specifically image and video generation). For many creators, generative AI feels threatening because it appears to automate work that was once theirs alone. At the same time, many models are trained on unlicensed content, raising ethical, legal, and fairness concerns.

Adobe has been doing a lot of work over the past year and a half, integrating generative AI functionality inside their tools, with things like Generative Fill in Photoshop, Generative Remove in Lightroom and Generative Extend in Premiere Pro. However, the world of AI is much larger than just Generative AI and video/image generation. Specifically, agentic AI and more general-purpose AI with MCP (such as Claude / ChatGPT) provide a much larger opportunity to help creatives across their entire workflow.

One of the advantages of working on the community team at Adobe is I get to help solve real world problems for super creative people (inside and outside Adobe). Chatting with my team this week, we had a discussion about how big of a hassle it is managing and searching screenshots. I thought this might be a fun project to tackle over the weekend. My first thought was to just throw the image and the search at AI and have it give me the results, but that would be slow, and would not work at all if you are searching lots of screenshots.

As I expect most people in the tech industry have been doing, I’ve been spending a lot of time lately thinking about AI and its impact. As I work with the community for Adobe, I’ve specifically been thinking about how it will affect the creative industry, and its implications for Adobe and Adobe’s creative tools. Adobe makes industry leading creative tools such as Photoshop, Illustrator and Premiere Pro, and I have been curious about exploring how / if these tools fit in within an AI first world.

Since I was in college, I have been an avid reader of The Economist. I read (and listen) to it cover to cover every week, and essentially have a set schedule for it through the week to ensure I get through it, and keep time for additional reading. However, over the past couple of years, The Economist has made it more and more difficult to consume the content in a flexible manner.

DARCI is a self hosted platform for aggregating, viewing and analyzing Destiny 2 PVP stats. Released as an open source project in 2023, it is available for download from the DARCI Github project page. DARCI provides similar functionality to Ascendant, but where as Ascendant focuses on deep data views of a single player, DARCI can track and compare multiple players (perfect for friends or a clan.) DARCI is built on top of three key parts:

Ascendant is a MacOS desktop application for collecting and analyzing your Destiny 2 PVP stats. Originally released in 2023 it is available for download at ascendantapp.info. The application provides what is probably the most comprehensive view, breaks downs and analysis of individual player data available in the Destiny 2 community. Features Include: General Display current activity and map as you are loading into match Detailed experience data, including latest gains / losses Chance of going flawless based on current card on historic win rate Trials card status and progress Session mode to track stats during specific game play sessions Track overall Glory gained / losses during competitive session Track your fireteam members performance as they play with you Latest news and alerts from Bungie Quickly link out to player stats to popular Destiny 2 community sites (right click on names) Stats Match stats, win rates and status, current and all time streaks Detailed break down of in game stats across games Kills breakdown by weapon and ability types Detailed weapon usage and performance Overview of matchmaking balance and teammate and opponents performance Weapon meta for your specific matches Breakdown of top medals Data Syncs all PVP activity data for player Slice data by time period and game mode Export to CSV and JSON Integrate with ChatGPT data analysis via CSV export Fix missing and corrupt data from API Add support for modes not supported by API, such as Checkmate, Iron Banner Tribute and competitive sub-modes Charting Visualize game play performance and results over time View changes in game status and match results over time Key meta shifts marked on charts so you can see impact on performance View per map information and stats Heatmap of playtime intensity over time Screenshots You can find more information including an FAQ, as well as download the app at:

DCLI (Destiny Command Line Interface) is a suite of command line tools for syncing and analyzing Destiny 2 PVP data. Originally released in 2001, it is written in RUST, and available for Mac, Windows and Linux. The code for the project is released under an MIT License. The latest release, as well as code is available on the DCLI Github project page. Utilities dclim : Manages and syncs the remote Destiny 2 API manifest database dclisync : Downloads and syncs Destiny 2 Crucible activity history into a local sqlite3 database file dclitime : Generates date / time stamps for Destiny 2 weekly event moments Apps dclia : Displays information on player’s current activity within Destiny 2 dcliah : Displays Destiny 2 activity history and stats dcliad : Displays Destiny 2 Crucible activity / match details dclistat : Displays specified Destiny 2 PVP stats The core of the suite is built around dclisync which syncs all Destiny 2 PVP data for the specified player.

The WoW TCG Utility is a native Mac OS X application for searching and managing cards for the World of Warcraft Trading Card game. This was an app I worked on from 2009 to 2010, and released as a free, open source project (released under an MIT License). You can view the project page as well as download and view all of the code from the WoW TCG Utility Github Page.

Continuing my posts as I learn about the Bevy game engine for Rust, this post covers the basics of working with windows in Bevy, including how to set the initial size, and handle window resize events. Setting The Initial Window Size In order to set the initial window size, you need to specify the settings via a WindowDescriptor resource added before DefaultPlugins are added. Here is some example init code that sets the window size to 400 x 400 and sets the title of the window: