I want to click it so bad, but I can't bring myself to do it.
15 karma · joined June 29, 2026
One thing that doesn't seem clear to me from reading without signing up is how do you actually upload content? Do you have to have paper and pen and then take a picture of it, or do you provide some kind of excalidraw-like canvas for writing? I notice there isn't any paper background or line rules, which I think would also be nice for those who fancy a bit of structure when reading/writing.
1. Investigate the author. In this case, it is an administrative scrivener & independent researcher. Nothing wrong with that, but credibility just isn't there for such dense CS, math & "physics".
2. Check for peer review. In this case, there is none. Published on zenodo, a platform notorious for self-publishing authors plagued by runaway AI sycophantic psychosis.
3. Check the author's publishing cadence. In this case, we are already at version 8 in less than a week. In traditional research, iterating from v1 to v8 represents weeks/months of peer feedback, major revisions, and architectural redesigns.
4. Read the paper. Did you leave more informed or more confused? I left much more confused. Three disparate domains of computer science mashed together in a short abstract: (1) Deterministic finite-automaton equivalence, (2) Deep learning theory, (3) FPGA/VLSI Engineering. While cross domain papers exist, LLM's often mix high-level theoretical complexity bounds (like DPSACE) with hyper-specific low level vendor parameters without providing the connective tissue that bridges the math to the hardware synthesis.
5. Check the numbers. 3.81 x 10^-5 pJ/op. This is ultra precision, yet right after dropping these numbers, we get the classic "All energy figures are simulation-based tool estimates. No physical FPGA board measurement was performed". One of the earlier versions claimed a 2,129x speedup vs an RTX 3090 on bus-traffic reduction.
6. Consult real frontier LLM's. Straight up copy paste the link into "relatively" credible models like Claude, GPT, Grok, and Gemini and ask "Is this AI Slop?". In my case I did it with three separate models, and all 3 came back with a resounding yes, which confirmed my initial suspicion.
I am not claiming that the guy doesn't know what he is talking about. And no shame to the author, I too have fallen victim to grandiose claims from LLMs. I am just stating that this paper by itself is mega sus and doesn't belong in the top ten results of hackernews.