Thursday, July 31st. Nine commits. One repo. A lot of moving parts.
The headline for the day wasn't a breakthrough in silicon or a new kernel optimization. It was housekeeping. I spent the day tidying up the Unicorn Execution Engine repository, cleaning out the debris from weeks of experimentation and preparing the ground for what comes next. The work fell into two distinct buckets: clearing the old and documenting the new.
First, the cleanup. I removed the llama.cpp submodule reference. It was a loose end that didn't belong in the final structure anymore. I fixed a submodule issue that had been nagging at the edges of the build process and then committed the changes to the gitignore. It’s the kind of work that doesn't make the news, but if you don't do it, the repo becomes a graveyard of half-baked integrations. I didn't just delete files; I organized them. I moved a massive amount of historical context into an archive_old_scripts directory. That’s over 240 files. Optimization checklists, setup guides, and old AI prompts. They’re still there if I need them, but they’re out of the way.
I spent the day tidying up the Unicorn Execution Engine repository, cleaning out the debris from weeks of experimentation and preparing the ground for what comes next.
Then, the documentation. This was the heavy lifting. I added 98 files of comprehensive project documentation and analysis reports. This isn't just README fluff. I’m talking about actual performance results, bottleneck analyses, and critical fixes completed. I wanted to leave a clear trail of why decisions were made. The ACTUAL_PERFORMANCE_RESULTS.md and BOTTLENECK_ANALYSIS.md files are there to remind me (and anyone else) where the real friction points are. I also updated the core documentation files, refining the NPU development guide and the optimization roadmap. It’s a lot of text, but it’s necessary. You can't optimize what you can't measure, and you can't measure what you don't write down.
I also added a significant amount of Python scripts for NPU integration and performance testing. Over 250 files. These aren't just scripts; they're the tools I used to benchmark the 4b models, test the NPU fallbacks, and analyze the GEMM reality. The benchmark_gemma3_4b_final.py and analyze_npu_gemm_reality.py files are particularly important. They represent the actual data gathering phase. I’m not guessing at performance anymore. I’m running the tests.
The day ended with a comprehensive summary of NPU and iGPU performance findings. I documented the bandwidth analysis and the final performance summary for July 2025. The numbers are in. The bugs are logged. The repo is clean.
Also today: I updated 4 files to reflect the current state of the project, ensuring that the AI assistants I work with have the latest context. It’s a small change, but it matters. You want your tools to know what you’re doing.
The Unicorn Execution Engine is no longer a collection of scattered experiments. It’s a documented, tested, and organized system. The foundation is set. Now I can focus on making it faster.