Ask HN: What are you working on?

Ever since the pandemic, a lot of facilitators (design thinking, corporate workshops, internal brainstorms) were forced to move online. We found a gap in the video conferencing market for this group of niche users, and decided to fully focus down on that niche.

What started out as a React + Firebase app has now evolved into a complex full stack system. The industry is still ripe for innovation and ideas that we can play around with.

2. A neurosymbolic-processing system (in other words, a symbolic knowledge-based AI system that coordinates a raft of neural-network and machine-learning systems) for machine control for several U.S. federally-funded projects.

3. Version 0.7 of Bard, a small Lisp with a few novel properties that I've been working on for, oh, about 18 years or so.

4. With my (grown) children helping out, a new web-based extension of my Mac list-management app, Delectus, with some new features and plans for using them in new ways.

5. My fourth science fiction novel. It's tougher going than the first three; it's taking a long time.

6. A few musical recordings. One entirely new composition, and two or three new arrangements of some older material.

7. An illustration for the album cover of my older offspring's latest record.

8. Various essays that are likely to wind up on my blog sooner or later.

For small-scale examples of this, I highly recommend reading up on basic stuff like Karnaugh map simplification and compiler optimization. Once you realize that all computation is just mapping inputs to outputs, and that the main task is actually pruning the search space (not working the problem), everything changes.

Of course, knowing all that, setting up the initial conditions, figuring out the types/ranges/valid values of input, and what the output should be, is half the battle. And it turns out that these are just exactly the tasks you have to do for stuff like training neural nets. It's all going to be machine learning in 10 years is what I'm saying.

Oh and also for anyone interested, today's processors are almost perfectly wrong for this sort of work. Since we're just piping data around (like video games), the cache is borderline useless. Data locality is important though, so just about every core needs its own memory.

My feeling is that AI hasn't improved appreciably in our lifetimes because we're so focused on serial computation that we completely missed the fact that parallel is the way to go. Our brains are 100 billion neurons running at 1 kHz. Kinda hilarious/sad to think about IMHO, but I digress.

The tool uses the asm library to scan class files and to extract annotations. it can detect annotations with retention policy CLASS and RUNTIME. It can't detect annotations with retention policy SOURCE that are not put into bytecode, for example @Override is one of these.