Sunday, November 1, 2026
Show HN: AirFlick Drop – AirDrop for Windows with zero mobile app installs https://ift.tt/dAECBIn
Show HN: AirFlick Drop – AirDrop for Windows with zero mobile app installs https://ift.tt/tn1qZTA November 2, 2026 at 02:08AM
Show HN: Jik – a self-hosted language using region-based memory management https://ift.tt/qzRXWxP
Show HN: Jik – a self-hosted language using region-based memory management https://ift.tt/qBKTMzV November 2, 2026 at 01:21AM
Show HN: Offline project memory for coding agents – no API key, no vector DB https://ift.tt/2GMwuac
Show HN: Offline project memory for coding agents – no API key, no vector DB https://ift.tt/SFslLYZ November 1, 2026 at 11:27PM
Saturday, October 31, 2026
Show HN: NES Contra (1987) port (ASM –> C and then C –> WebAssembly) https://ift.tt/NjroaRV
Show HN: NES Contra (1987) port (ASM –> C and then C –> WebAssembly) Based on vermiceli's annotated disassembly of Contra: https://ift.tt/Swq57ZE . 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. Source: https://ift.tt/e3KIqCi https://ift.tt/rgPaA8u November 1, 2026 at 12:48AM
Show HN: A language model that lives in a boot sector https://ift.tt/eCycpaD
Show HN: A language model that lives in a boot sector All the ideas came from the boot sector demo scene where people had been writing chess and BASIC games in 512 bytes, even a C compiler. I wondered what one can do using a neural network instead of a program and generating boot logs for non-existent hardware was something fun to try. 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. https://ift.tt/LVKED6h October 31, 2026 at 06:28PM
Friday, October 30, 2026
Show HN: I rebuilt Kubernetes for ESP32 microcontrollers https://ift.tt/mlu1szK
Show HN: I rebuilt Kubernetes for ESP32 microcontrollers https://ift.tt/o9HQOfb October 29, 2026 at 10:05PM
Thursday, October 29, 2026
Show HN: SLCR – An open standard for agent-native software requirements https://ift.tt/DezpGQq
Show HN: SLCR – An open standard for agent-native software requirements https://slcr.dev/ October 29, 2026 at 10:13PM
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