vast VS FFMpeg-Online

Most languages target C or LLVM, and C and LLVM have a fundamentally lossy compilation processes.

To get around this, you'd need a hodge podge of pre compiler directives, or take a completely different approach.

I found a cool project that uses a "Tower of IRs" that can restablish source to binary provenance, which, seems to me, to be on the right track:

https://github.com/trailofbits/vast

I'd definitely like to see the compilation processes be more transparent and easy to work with.

At Trail of Bits, we are creating a new compiler front/middle end for Clang called VAST [1]. It consumes Clang ASTs and creates a high-level, information-rich MLIR dialect. Then, we progressively lower it through various other dialects, eventually down to the LLVM dialect in MLIR, which can be translated directly to MLIR.

Our goals with this pipeline are to enable static analyses that can choose the right abstraction level(s) for their goals, and using provenance, cross abstraction levels to relate results back to source code.

Neither Clang ASTs nor LLVM IR alone meet our needs for static analysis. Clang ASTs are too verbose and lack explicit representations for implicit behaviours in C++. LLVM IR isn't really "one IR," it's a two IRs (LLVM proper, and metadata), where LLVM proper is an unspecified family of dialects (-O0, -O1, -O2, -O3, then all the arch-specific stuff). LLVM IR also isn't easy to relate to source, even in the presence of maximal debug information. The Clang codegen process does ABI-specific lowering takes high-level types/values and transforms them to be more amenable to storing in target-cpu locations (e.g. registers). This actively works against relating information across levels; something that we want to solve with intermediate MLIR dialects.

Beyond our static analysis goals, I think an MLIR-based setup will be a key enabler of library-aware compiler optimizations. Right now, library-aware optimizations are challenging because Clang ASTs are hard to mutate, and by the time things are in LLVM IR, the abstraction boundaries provided by libraries are broken down by optimizations (e.g. inlining, specialization, folding), forcing optimization passes to reckon with the mechanics of how libraries are implemented.

We're very excited about MLIR, and we're pushing full steam ahead with VAST. MLIR is a technology that we can use to fix a lot of issues in Clang/LLVM that hinder really good static analysis.

[1] https://github.com/trailofbits/vast

The AI Generators (on PC, websites) that I use include Dall-E 2, Playground.ai, Night Cafe, Hotpot.ai, Stable Diffusion, and Artbreeder. I also use Free Domain pictures from Freepik. On my mobile I use Wonder.ai.

https://creator.nightcafe.studio https://hotpot.ai https://www.starryai.com and https://neural.love

5. Hotpot: AI image tools

10. Hotpot.ai

Welcome to the new world Mahayana! I picked a random AI image generator using Google (https://hotpot.ai/) and fed it the ashe with a request to draw it in the future. That's what came back. Interesting eh?

If I ask ChatGPT to write a story about three cowboys who 'adopt' a little girl whose parents were killed in a tornado or I ask Hotpot to create a picture the includes a rose and a puppy, who owns the copyright? I ask because when these works are created no living person with rights is involved. After whose death after 70 years does the work enter into public domain?

Many ways to do it for free: hotpot.ai, cutout.pro, and r/Colorization.

Using https://hotpot.ai/ to colorize, this will probably unlock some more realistic results.