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2026-10-31 @ 20:56:12Points: 5Comments: 0
With 86% of the words, you might understand only 38% of an anime's lines
2026-10-31 @ 20:46:39Points: 11Comments: 8
Surviving "The Graduate"
2026-10-31 @ 20:02:06Points: 9Comments: 0
HackerTube: YouTube built from videos recommended by HN
2026-10-31 @ 19:56:53Points: 209Comments: 94
Anthropic blocks users for anything touching training larger models
2026-10-31 @ 19:39:19Points: 51Comments: 8
Show HN: NES Contra (1987) port (ASM –> C and then C –> WebAssembly)
2026-10-31 @ 19:18:48Points: 21Comments: 12
https://github.com/vermiceli/nes-contra-us. Thanks for the great work.
I ported it in two steps. First to C: the game logic is translated routine by routine from the 6502 assembly, and checked frame by frame against the original in the Mesen emulator. Then the C code to WebAssembly so it runs in the browser.
The cherry on top: online multiplayer, so two players can play co-op over the internet.
TCP Use-After-Free: CVE-2026-98432 / CVSS 9.8
2026-10-31 @ 19:16:30Points: 151Comments: 74
The Duty of a Scholar
2026-10-31 @ 19:07:36Points: 59Comments: 12
GPT-5.6 Sol can beat Factorio on peaceful mode
2026-10-31 @ 18:50:34Points: 125Comments: 128
It's the Memory Latency, Stupid
2026-10-31 @ 18:38:16Points: 55Comments: 10
I post trained a model on Demoscenes
2026-10-31 @ 18:08:24Points: 37Comments: 21
William Nordhaus has died
2026-10-31 @ 17:59:41Points: 37Comments: 7
Show HN: A USB-C LTE modem that gives my computer a phone line
2026-10-31 @ 15:56:41Points: 31Comments: 23
Malware uses LLMs to rank victims by how much they're worth
2026-10-31 @ 14:29:03Points: 221Comments: 119
Jepsen: ParticleDB 1.1.4
2026-10-31 @ 13:39:52Points: 135Comments: 71
Show HN: A language model that lives in a boot sector
2026-10-31 @ 12:58:29Points: 28Comments: 0
Entire inference engine is 510 bytes of MBR, weights, vocab, and strings reside in the sectors following MBR. Small char-level neural network was trained on dmesg generated by machine, so machine produces Linux boot messages infinitely.
train.py performs precisely the same integer arithmetic operations as the asm code does and thus produces an output identical character-by-character to the one produced by QEMU. The difference analysis revealed my favorite bug. The characters at the beginning matched those generated by the simulator, but then the output diverged. I retrieved the hidden layer via QEMU’s debug port and compared the neurons' values with those of the simulator, one neuron at a time. Only the value of the first neuron was correct. The activation involves idiv, and idiv stores its remainder in edx. The pointer to the biases table was stored in edx, which resulted in the first neuron being biased correctly and the following ones using garbage. Now the bias' address is determined from the loop counter, and no pointers have to survive the division. Another bug I found simply by looking at the code before I did my first run-through: the BIOS print routine zeros out bx, but bx contains the predicted character number. This explains the push and pop of bx surrounding the print.
sectorllm is the most similar to my project, as it runs an actual 5-layer llama2 with attention mechanism in just 1.2K of assembly code. It is the trade-off in the opposite direction, as my engine needs to fit into one sector and therefore my network has just one hidden layer of int8 operations for dreaming the dmesg.