3 ms·

Do you just add this to your “instructions” section? And what type of questions do you ask the model? Thanks for sharing

by j_bum 1y ago

jakogut 1y ago
Yes, with ChatGPT, I added that paragraph as custom instructions under personalization. I ask a wide variety of things, from what a given plant is deficient in based on a photo, to wireless throughout optimization (went from 600 Mbps to 944 Mbps in one hour of tuning). I use models for discovering new commands, tools, and workflows, interpreting command output, and learning new keywords for deeper and more rigorous research using more conventional methods. I rubber duck with it, explaining technical problems, my hypotheses, and iterating over experiments until arriving at a solution, creating a log in the process. The model is often wrong, but it's also often right, and used the way I do, it's quickly apparent when it's wrong. I've used ChatGPT's memory feature to extract questions from previous chats that have already been answered to test the quality and usability of local models like Gemma3, as well as craft new prompts in adjacent topics. Prompts that are high leverage, compact, and designed to trip up models that are underpowered or over quantized. For example: >> "Why would toggling a GPIO in a tight loop not produce a square wave on the pin?" > Tests: hardware debounce, GPIO write latency, MMIO vs cache, bus timing. >> "Why is initrd integrity important for disk encryption with TPM sealing?" > Tests: early boot, attack surface, initramfs tampering vectors. >> "Why would a Vulkan compute shader run slower on an iGPU than a CPU?" > Tests: memory bandwidth vs cache locality, driver maturity, PCIe vs UMA. >> "Why is async compute ineffective on some GPUs?" > Tests: queue scheduling, preemption granularity, workload balance. >> "Why might a PID loop overshoot more when sensor update rate decreases?" > Tests: delayed ACK, bufferbloat, congestion control tuning. >> "How can TCP experience high latency even with low packet loss?" > Tests: delayed ACK, bufferbloat, congestion control tuning. >> "How can increasing an internal combustion engine's compression ratio improve both torque and efficiency?" > Tests: thermodynamics, combustion behavior, fuel octane interaction. >> "How can increasing memory in a server slow it down?" > Tests: NUMA balancing, page table size, cache dilution. >> "Why would turning off SMT increase single-threaded performance on some CPUs?" > Tests: resource contention, L1/L2 pressure, scheduling policy.

Do you just add this to your “instructions” section? And what type of questions do you ask the model? Thanks for sharing

by j_bum 1y ago

Do you just add this to your “instructions” section? And what type of questions do you ask the model? Thanks for sharing

jakogut 1y ago

Yes, with ChatGPT, I added that paragraph as custom instructions under personalization. I ask a wide variety of things, from what a given plant is deficient in based on a photo, to wireless throughout optimization (went from 600 Mbps to 944 Mbps in one hour of tuning). I use models for discovering new commands, tools, and workflows, interpreting command output, and learning new keywords for deeper and more rigorous research using more conventional methods. I rubber duck with it, explaining technical problems, my hypotheses, and iterating over experiments until arriving at a solution, creating a log in the process. The model is often wrong, but it's also often right, and used the way I do, it's quickly apparent when it's wrong. I've used ChatGPT's memory feature to extract questions from previous chats that have already been answered to test the quality and usability of local models like Gemma3, as well as craft new prompts in adjacent topics. Prompts that are high leverage, compact, and designed to trip up models that are underpowered or over quantized. For example: >> "Why would toggling a GPIO in a tight loop not produce a square wave on the pin?" > Tests: hardware debounce, GPIO write latency, MMIO vs cache, bus timing. >> "Why is initrd integrity important for disk encryption with TPM sealing?" > Tests: early boot, attack surface, initramfs tampering vectors. >> "Why would a Vulkan compute shader run slower on an iGPU than a CPU?" > Tests: memory bandwidth vs cache locality, driver maturity, PCIe vs UMA. >> "Why is async compute ineffective on some GPUs?" > Tests: queue scheduling, preemption granularity, workload balance. >> "Why might a PID loop overshoot more when sensor update rate decreases?" > Tests: delayed ACK, bufferbloat, congestion control tuning. >> "How can TCP experience high latency even with low packet loss?" > Tests: delayed ACK, bufferbloat, congestion control tuning. >> "How can increasing an internal combustion engine's compression ratio improve both torque and efficiency?" > Tests: thermodynamics, combustion behavior, fuel octane interaction. >> "How can increasing memory in a server slow it down?" > Tests: NUMA balancing, page table size, cache dilution. >> "Why would turning off SMT increase single-threaded performance on some CPUs?" > Tests: resource contention, L1/L2 pressure, scheduling policy.