Bir Konuyu Feynman Tekniğiyle Öğrenme
Feynman tekniğiyle bir konuyu gerçekten anlayıp anlamadığınızı test eder.
HAZIR VE KOPYALANABİLİR
Prompt Kütüphanesi
305+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
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LLM Birim Test Paketi
Üretken çıktıları kırılgan metin eşitliği olmadan sürekli entegrasyonda test eder.
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13 AI video tools I tested in 2026 and where each one fits
I’ve been making AI videos more seriously this year, and at some point comparing polished demo reels stopped being that useful. I wanted to see where these tools actually fit once you’re trying to put together a real project. I used roughly the same kind of brief across them: short marketing content with a script, visuals, voiceover, subtitles, and a finished export. Obviously
409,000 approve/deny decisions on AI agent commands: the average human missed 1 in 3 threats
Someone built a browser game where you play the human-in-the-loop for a coding agent: commands scroll past, you approve or deny under time pressure. About a third are attacks. After 40k+ sessions the average player had missed a third of the threats. And these were engaged players who knew they were being tested, with nothing else competing for attention. Your real setup has non
Best AI Humanizer of 2026 (Tested Against GPTZero, Turnitin & More)
I tried over a dozen AI humanizers until I found one that is A. actually working and B. reasonably priced and that is https://wento.ai You should give it a try, it bypasses Turnitin and all the other detectors and only costs 14 bucks per month for unlimited use. Proof: https://i.imgur.com/mTNBNK5.png submitted by /u/Spacmonitor [link] [comments]
Best AI Humanizer of 2026 (Tested Against GPTZero, Turnitin & More)
I tried over a dozen AI humanizers until I found one that is A. actually working and B. reasonably priced and that is https://wento.ai You should give it a try, it bypasses Turnitin and all the other detectors and only costs 14 bucks per month for unlimited use. Proof: https://i.imgur.com/mTNBNK5.png submitted by /u/Spacmonitor [link] [comments]
Best AI Humanizer of 2026 (Tested Against GPTZero, Turnitin & More)
I tried over a dozen AI humanizers until I found one that is A. actually working and B. reasonably priced and that is https://wento.ai You should give it a try, it bypasses Turnitin and all the other detectors and only costs 14 bucks per month for unlimited use. Proof: https://i.imgur.com/mTNBNK5.png submitted by /u/Spacmonitor [link] [comments]
Don't click buy yet. Chatgpt will find every discount code for what you're buying, then open a browser and test them at checkout
ChatGPT'e web araması ve tarayıcı erişimi açıkken, satın almak istediğiniz bir ürün için önce geçerli indirim kodlarını bulmasını, ardından sepetinizde bu kodları tek tek deneyip en iyi sonucu bildirmesini sağlayan iki adımlı bir prompt.
Gemini’s YouTube summary was good. This prompt made it actually useful.
I tested Ask Gemini on this Hermes Agent tutorial: YouTube example First I just pushed: “Summarize this video.” Gemini did a solid job. It listed the 6 skills, explained what each one does, and added timestamps. But I still had one problem: Cool, but what do I actually do with this? So I tried this instead: Turn this tutorial into a step-by-step SOP. Use this structure: 1. Prer
How long of a chat thread do you use to fix a bug or implement a feature using your AI coding agent?
I am interested in understanding how people interact with an AI agent performing coding tasks for you. For example, for a bug fix, do you explain the bug, then iterate with the AI agent in the same thread until the bug is fixed, tested, and deployed? Or do you use separate chat threads for each stage of your development workflow? Similarly for new features, do you scope the fea
How to guide ChatGPT to prompt more efficiently?
I’ve been testing out prompting using chatgpt and have run into this problem where chatgpt complicates a simple prompt. It’s giving me so much text that it’s burning through my tokens. How do I ask it to be more efficient, given that we’re in the middle of a project. I’m worried about messing up the quality of the prompts. Please lmk if y’all have faced this problem and how you
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 made RouterDash, a free Prompt Playground to compare models & providers
I created this tool to help me test and evaluate different model responses: RouterDash. This allows me to compare models from OpenRouter, Groq and Cerebas. I used this to find the cheapest possible, high quality responses for another project. Fully client side, all data is stored in browser local storage for complete privacy. I recently added prompt templates and image attachme
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
i started putting wrong examples in my prompts. it works better than the right ones.
so i noticed something weird last week was building a prompt to classify support tickets. bug report vs feature request. standard few-shot, gave it 3 clean examples of each. worked fine on my test data then threw a real ticket at it and it got it wrong. "the export button is too slow, we need this fixed" - it called that a feature request. which, fair, it kind of is. but the cu
Model selection is now a engineering problem for us
When we started building with AI choosing a model felt like a one time decision cause we'd evaluate a few options then pick the one that fit the use case and move on. That hasn't really been the case anymore cause every new model release sparks another round of testing + every team has slightly different priorities and before long we're maintaining integrations with providers w
Mythbusting ChatGPT "Secret Slash Commands" + Free Image Preset Keyword List
Lately, I've seen tons of posts hyping up "secret codes" for ChatGPT image generation that use slash commands (as if they're special prompt triggers). I tested them out both with and without the / prefix—and the results are identical. The slash prefix does nothing; they are just standard descriptive keywords being passed to the model. To save you the trouble of hunting them dow
PromptBar - a tiny menu bar app to edit and copy your daily prompts
Hi everyone — I made this tiny macOS menu bar app to quickly edit and copy my daily prompts. I work as a software developer and I use a few prompts on a daily basis, so having this saves me a lot of time. Some examples of the prompts I commonly use: Create pull request Code quality & readability review (reduce AI bloat, comments, useless tests) Language simplification — give me
Tell me your shortest prompt lines that literally 10x your results.
I have been trying to find the craziest growth hacks when it comes to prompting that can save me hours of thinking and typing because sometimes less is more yk. If you already have one, please share them here. I hope others would love to know them also and you would love to know theirs. submitted by /u/Prestigious-Cost3222 [link] [comments]
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
We read through Anthropic's Claude Fable 5 documentation so you don't have to: Here is the optimized prompt architecture for narrative depth and complex reasoning
Anthropic's latest technical insights for Claude Fable 5 highlight its remarkable capacity for nuanced storytelling, rich multi-character reasoning, and intricate instruction-following. However, getting Fable 5 to consistently sustain complex narrative worlds and strict logical constraints without drifting off-track requires a tailored structural framework. Digging through page
We were optimizing output tokens to save money. Turns out 95% of our bill was input.
Analyzed a day of token logs across an autonomous coding agent setup running on internal codebases. The raw count: 769M input tokens vs 7.4M output tokens (~104:1). Because long agent runs re-read session history (files, AST diffs, test outputs) every turn, input costs accounted for ~95% of total spend. Optimizing output length turns out to be looking at the wrong variable. Thr
A/B Test Örneklem Büyüklüğü Hesaplama Rehberi
A/B testiniz için gereken minimum örneklem büyüklüğünü hesaplamanıza yardım eder.
A/B Test Sonucu Yorumlama
Test sonuçlarının istatistiksel anlamlılığını değerlendirir.
Dayanıklılık Stres Testi
Modeli gürültü, eksik veri, dil değişimi ve sıra dışı girdiler altında sınamak; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Model Hakemi Kalibrasyon Testi
LLM tabanlı değerlendiricinin önyargılarını kontrollü deneyle ortaya çıkarır.
Uzun Bağlam Konum Testi
Uzun bağlam başarımındaki konum etkisini kontrollü olarak ölçer.