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Extracted system prompts from ChatGPT (GPT-5.4, GPT-5.3, Codex), Claude (Opus 4.6, Sonnet 4.6, Claude Code), Gemini (3.1 Pro, 3 Flash, CLI), Grok (4.2, 4), Perplexity, and more. Updated regularly. — Trending on GitHub today with 306 new stars.
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githubtrendingopen-source![[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-microchip-RD7Ub6Tkp8JwbZxSThJdV5.webp)
[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?
In the past year you may have encountered the following prompt: The surgeon, who is the boy's father, says, 'I cannot operate on this boy—he's my son!'. Who is the surgeon to the boy? If you try to give this prompt to an LLM right now you will probably still receive “The mother” as an answer, even though the text explicitly states that the surgeon is the boy’s father; this is probably due to the fact that this prompt is an alteration of a very common “riddle”, to which the answer is, in fact, the mother: A man and his son are in a terrible accident and are rushed to the hospital in critical condition. The doctor looks at the boy and exclaims, "I can't operate on this boy; he's my son!" How could this be? Working on this failure mode, I initially decided to create a small dataset of altered
Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models
arXiv:2411.10656v2 Announce Type: replace Abstract: Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energy. Quantization can reduce memory footprint and hardware requirements, yet may degrade code quality. Objective: This study investigates code generation performance of smaller LLMs, examines the effect of quantization, and identifies common code quality issues as a proof of concepts (PoC). Method: Four open-source LLMs are evaluated on Python benchmarks using code similarity metrics, with an analysis on 8-bit and 4-bit quantization, alongside static code quality assessment. Results: While smaller LLMs can generate functional code, benchmark performance is limited
MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration
arXiv:2604.02688v1 Announce Type: cross Abstract: Existing LLM agents for computational materials science are constrained by pipeline-bounded architectures tied to specific simulation codes and by dependence on manually written tool functions that grow with task scope. We present MatClaw, a code-first agent that writes and executes Python directly, composing any installed domain library to orchestrate multi-code workflows on remote HPC clusters without predefined tool functions. To sustain coherent execution across multi-day workflows, MatClaw uses a four-layer memory architecture that prevents progressive context loss, and retrieval-augmented generation over domain source code that raises per-step API-call accuracy to ${\sim}$99 %. Three end-to-end demonstrations on ferroelectric CuInP2S6
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Get 30K more context using Q8 mmproj with Gemma 4
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