Praveen Vaddadi @Densebit
Generalist happiest with problems bigger than my grasp. Low-level tinkerer, slow walker/thinker. UMD CS. AI, DBs, PLs. Making things faster, better, simpler. densebit.com India Joined June 2020-
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@arpit_bhayani >but as a tangent bridging #18 and #12: if you don't need strict AOF crash semantics, persistence works right at the allocator level. I built a single-header file-backed C malloc for zero-cost ser/deserialization: github.com/xtellect/serde
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The problems in tech aren't really about the tech.
You can write a surprisingly large class of software under 1.44Mb (a floppy): editors, databases, games, even operating systems. Beyond that, it is usually more abstraction paying for excess.
Natural language is little-endian: lower-significance bits come first as shared context, and higher-significance bits come later as the informative payload.
Praveen Vaddadi retweeted
This 1-pager from Xusheng Li on GDB internals of how watchpoints are implemented is a delight to read! (especially that double-write behaviour false positive - I did not know about that)
github.com/xtellect/spaces is a C allocator with explicit heap regions. It can work as a drop-in allocator, or give each subsystem its own heap, cap it, inspect it, and destroy it in one step.
out of ignorance, comes certainty - Anonymous
codegen: ai no longer makes things, it makes things up.
Fabs have massive fixed costs but near-zero marginal costs and strong long-term pricing power. AI software is hyper-competitive, supply-constrained today but structurally deflationary. Capital may compound better at the chip layer than in crowded model apps.
In AI, a fab/hardware bet may actually be cheaper than backing yet another software startup.
Santa Claws is a good name for an ecommerce startup of persistent job bots/agents
A timeless gem jstor.org/stable/259121
Understanding isn’t like flipping a light switch; it’s more like slowly turning up the dimmer knob
...and uncalibrated reward/gain scales in the graphs....
Results on standard tasks/tests are relegated to the appendix due to page limits. While the main body seems focused on finding a problem for a specific tool rather than solving a fundamental issue.
Spotting selective evidence in papers (CS): The performance graph shows the new algorithm/data-structure and the baseline tangled together rather than clearly separated.
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