İstem Enjeksiyonu Kırmızı Takım Planı
LLM uygulamasının enjeksiyon dayanıklılığını güvenli test senaryolarıyla ölçer.
HAZIR VE KOPYALANABİLİR
Prompt Kütüphanesi
315+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
Yerel Model Gizlilik Kontrol Listesi
Yerel model kullanımındaki görünmeyen veri çıkışı ve erişim risklerini denetler.
GGUF Yerel Dağıtım Planı
Yerel model kurulumunu donanım kapasitesine göre uygulanabilir hale getirir.
Karşılaştırmalı LLM Ağ Geçidi Seçimi
Model sağlayıcılarını yalnızca özellik listesiyle değil, üretim riskleri ve toplam sahip olma maliyetiyle karşılaştırır.
Kuantizasyon Kalite Kapısı
Kuantizasyon kazancını gerçek görev kalitesiyle birlikte doğrular.
LLM Birim Test Paketi
Üretken çıktıları kırılgan metin eşitliği olmadan sürekli entegrasyonda test eder.
LLM Sunucu Yığınlama Ayarı
Model sunucusunun verimini tek bir ortalama gecikmeye bakmadan optimize eder.
Semantik Yönlendirici Kural Seti
İstekleri uygun modele yönlendiren açıklanabilir ve test edilebilir kurallar oluşturur.
Agent Prompt Architecture skill.md
Wanted to share a skill I built for designing and reviewing the prompts that run AI agents. Most prompt engineering advice for agents still treats the system prompt as a text block: "write a clear role, add examples, be specific." That helps with a chat answer, but agents fail in ways text-block advice doesn't cover. I kept watching the same three failures: an agent with overla
Demis Hassabis on the future of coding: Why AI won't kill programming, but turns top engineers into 10x systems architects
Google DeepMind CEO and Nobel laureate Demis Hassabis sat down with Lex Fridman for an in-depth, 2.5-hour masterclass on the future of AI, world simulation models, and the architectural limits of LLMs. Most people don't have 2.5 hours to sit through the whole podcast, so here are the most mind-bending highlights and engineering takeaways condensed into a 3-minute read: ⚡ Key Ta
Everyone's building ai agents that can act on your systems and nobody's talking about how easy they are to hijack
Been poking at this for a while and it worries me more than it should. everyone's rushing to give llm agents real access now, read my email, run this tool, push to the repo, book the thing. the demos look great. the part that gets skipped is that the agent takes instructions from whatever text it reads. so a prompt injection isn't just "make the chatbot say something rude" anym
How to break ChatGPT's sycophancy bias: The Red Team Perspective Challenge prompt
One of the biggest hidden traps when using LLMs for strategic thinking is sycophancy bias. Because modern frontier models (GPT-4o, Claude 3.5, Gemini 1.5) are heavily tuned via RLHF to be polite and helpful, their default response to almost any proposed idea is subtle validation. If you ask a standard model "What do you think of my plan to do X?", it almost always replies with
I built a Popperian Falsification Prompt to stop LLM confirmation bias (Testing your plans against plausible black swans)
Most people use LLMs in a confirmatorily biased way: "Tell me why my business plan is great" or "How do I implement X?". This triggers the model's RLHF pleasing bias. Inspired by Karl Popper’s principle of falsifiability, a friend and I designed a prompt framework that flips this dynamic. Instead of validating your idea, it forces the AI to act as a harsh auditor and attempt to
I distilled Google's official Gemini agent guidelines into a battle-tested agentic workflow system prompt
If you have built autonomous agents or multi-step tool-calling workflows with LLMs, you have likely run into the standard failure modes that break production agents: Premature Action Bias: The model fires off tool calls or answers the user before mapping out prerequisites and the logical order of operations. Fragile Error Handling: When an API call fails or returns unexpected d
I tested dozens of analytical prompts to stop LLMs from jumping to conclusions. Here is the exact structure that works best
One of the most frustrating aspects of modern frontier LLMs is RLHF sycophancy. Because models like ChatGPT and Claude are heavily trained to be helpful, pleasant, and eager assistants, they suffer from a dangerous default behavior: they validate flawed premises. If you bring a premature or fundamentally flawed idea to an LLM (e.g., "I want to rewrite our entire React app in Vu
I turned Wikipedia's "Signs of AI Writing" into an open-source self-edit checklist skill for LLM agents
Wikipedia editors have spent months cataloguing submissions to figure out what gives away AI-generated text. They compiled a detailed community guide called Signs of AI Writing. I turned that guide into an open-source self-edit agent skill called Writ: 👉 https://github.com/Avinashricky211/writ What it catches: • Stock vocabulary: "delve", "tapestry", "testament", "seamless", "r
Linus Torvalds Skill/Soul or how I distilled the knowledge for code reviews from 32~k emails
https://daniele.tech/2026/09/linus-torvalds-skill-soul-or-how-i-distilled-the-knowledge-for-code-reviews-from-32k-emails/ A report about the changes after the first announcement and all the improvements, including Soul.md (with profanities), 4 different version Skill/Soul from 4 different LLMs, a comparison with/without skills and a reproducible pipeline. submitted by /u/Mte90
Prompt Optimizer skill.md
I wanted to share a custom skill I created. Many prompt-optimization templates suffer from "bloat"—they often take a simple request and turn it into a massive, overly complex prompt, or they accidentally alter technical details like code snippets, file paths, and generator flags. To solve this, I built a meta-prompting skill designed to classify the context of the user's prompt
Prompt request: getting llm’s to be robustly collaborative rather than confidently incorrect?
For context I work in a complex coding environment with many complex problems, and llm’s are absolutely awful by default at understanding most of my issues. I want them to be thinking partners for me but they heavily default to trying to give me advice when they don’t even understand the problem properly!! How can I get them to override their default tendency to pretend they kn
The "Galician Gene" Directive: Forcing LLMs to ask for context instead of hallucinating (and reducing token waste)
TL;DR: I developed a system prompt ("Galician Gene") that forces LLMs to ask for missing context instead of guessing or hallucinating. It drastically reduces token waste, stops encyclopedic verbosity, and acts as a stress test to separate truly smart models from rigid ones. The prompt and documentation are below. Why "Galician Gene"? This is a nod to a Spanish cultural stereoty
Why passive AI automation is a trap and how to use LLMs as a "flight simulator" for executive function
Most AI workflows right now are designed around passive automation: hand off a task, let the model generate text, copy-paste, and move on. The problem is that over-relying on LLMs for core thinking causes critical thinking and executive function to atrophy. When you use an AI purely as a ghostwriter or answering engine, you're interacting with a system programmed to be sycophan
Model Hakemi Kalibrasyon Testi
LLM tabanlı değerlendiricinin önyargılarını kontrollü deneyle ortaya çıkarır.