What Awesome Thanks 2 Actually Is
A few years back I picked up Awesome Thanks 2 for a routine project involving large batches of product photos, and it ended up becoming the default tool in my pipeline for texture and asset management work. It runs on Windows and macOS, and it's built around automated asset organization, metadata tagging, and batch file conversion. You drop a folder in, point it at your preset, and it goes through the queue. The interface is plain. There's a main window with a settings panel on the left, a file browser in the center, and an activity log at the bottom. That's it. No wizard, no onboarding screens, no "let me help you set things up" nonsense. Which is fine by me because I didn't want that kind of thing anyway.
Downloading and Installing Awesome Thanks 2
The official download page is straightforward enough. You go to their site, click the download button for your OS, and run the installer. On macOS you need to right-click and open it manually the first time because Gatekeeper will flag it if you haven't signed in with your developer account. I've seen this trip up people more than once. The installer size is around 140 MB, and after installation it takes up roughly 300 MB with libraries included. The free version handles up to 500 files per batch and supports the standard formats — PNG, JPG, TIFF, and PSD. If you're doing this professionally and working with raw camera files, you'll want the paid tier. The Pro license is a one-time purchase, not a subscription, which was the main reason I stuck with it instead of switching to something else every time a new version of something popular came out.
Setting Up Your First Workflow
When I first ran Awesome Thanks 2 on my machine, the default configuration was completely useless for anything real. The output folder pointed to a sample directory, the tag templates were generic, and the naming convention just appended a timestamp to every file. I spent about twenty minutes reconfiguring before I had something usable. The first thing I changed was the root input path. Instead of letting it scan my entire desktop or documents folder, I pointed it at a dedicated working directory. This matters more than people realize. When Awesome Thanks 2 picks the wrong source, it processes the same files twice because it doesn't track what it's already done unless you enable the skip-processed toggle in the advanced options. After that I set up the naming convention. I use a pattern like projectCode_clientName_assetType_001.ext and the field builder in the settings makes it simple to assemble. You pick from variables, type in static text, and arrange them in order. Once I saved that template, every file that came through my workflow got a consistent name automatically. No more renaming by hand.
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The Tagging System
This is where Awesome Thanks 2 actually separates itself from the alternatives I've tried. The tag engine runs on a local rules database, which means it doesn't need to call out to any cloud service to assign categories. Everything happens offline. For a lot of people working with proprietary or sensitive material, this is the deciding factor. I set up custom tag rules based on file dimensions, color profile, and the presence of embedded metadata. A simple rule: if the DPI is above 300 and the color space is sRGB, assign the tag "printReady". Another rule checks for embedded ICC profiles and flags files that have none. These rules fire during the import step, so you see the tags applied before the batch even finishes processing. The tag editor has a search function, but it's not great. You can type a partial tag name and it filters the list, but there's no fuzzy matching and it doesn't understand synonyms. I worked around this by creating a master tag list document and keeping it open while I worked, so I could copy-paste the exact tag names instead of trying to remember them.
Batch Processing in Practice
Here's how I typically run a batch. I load the source folder, apply the naming template, confirm the tags are being assigned correctly with a preview panel, and then hit process. A folder with about 1,200 images usually takes between eight and twelve minutes on my machine, which runs an 8-core processor with 32 GB of RAM. The bottleneck tends to be the disk read speed more than the CPU, so if your source files are on an older HDD you should expect it to take longer. The progress bar doesn't show elapsed time or a completion estimate. It just shows how many files have been processed out of the total. After the first couple of runs I learned to just wait. But if you're processing thousands of files and want a rough estimate, you can divide the total count by the rate you observed in the first hundred files.
Awesome Thanks 2 Plugin Ecosystem
There isn't much of a plugin ecosystem for this tool. The developer released a couple of community scripts on GitHub a while back — one for auto-rotating images based on EXIF orientation data, and another that exports the tag list to CSV. Both work, but neither has been updated in over a year. I forked the CSV exporter and made a small change so it also outputs the metadata JSON file, which I needed for a pipeline integration at the time. What the tool does support natively is scripting through its API. If you know Python or JavaScript, you can write automation scripts that call into Awesome Thanks 2 and chain operations together. I use a Python script that talks to the API to trigger processing jobs remotely from my CI system. This keeps the whole thing hands-off once the initial configuration is done.
A Real Problem I Ran Into
Last year I was processing a batch of about three thousand scanned archival photographs, most of them in TIFF format with varying color depths and some files corrupted from the scanning equipment. About two hundred of those files had truncated headers, which should have caused the batch to fail entirely. Instead, Awesome Thanks 2 would start processing, hit the corrupted files, skip them silently, and continue. By the time the batch finished I had no idea which files were skipped. The workaround was to run the batch twice. The first run I left the "skip on error" option enabled so it would get through everything. Then I compared the output folder against the source folder using a simple diff script to find the missing files. The second run I pointed it only at the missing files with error handling turned on so it would log every failure. That gave me a complete report of what was broken and what wasn't. This isn't documented anywhere that I could find. The help files mention the skip option but don't explain the behavior when it encounters multiple failures in a single batch. I figured it out through trial and error after the first run failed to produce the expected output count.
Limitations and When to Look Elsewhere
Awesome Thanks 2 has real constraints. The free version is limited to 500 files per batch, which sounds fine until you're working with a project that has several thousand assets. The Pro version lifts this limit but there's still a maximum folder depth of six levels, which caught me off guard on a nested project structure. Files beyond that depth are ignored silently, which is frustrating. The color profile management is adequate but not deep. It handles sRGB, Adobe RGB, and a handful of common profiles. If you're working with specialty color spaces like Pantone or printer-specific profiles, you'll need to handle that conversion outside of this tool. I use a separate script for that and then pipe the results into Awesome Thanks 2 for organization and tagging. For people who just need basic file organization and don't care about the tagging or metadata aspects, there are simpler tools that do the job faster. Ditto, for example, or even Automator on macOS. But if you need both organization and structured metadata workflows in a single application, Awesome Thanks 2 is one of the few options that actually works reliably without requiring constant manual intervention.
The current version is 2.4.3. The developer releases updates roughly every three to four months, and they tend to focus on bug fixes rather than new features. If you're fine with a stable tool that does what it says it does, this works well. If you're looking for cutting-edge features or a rapidly evolving product, you might want to check back in a few months or look at competing tools first.
