Açık Kaynak Yapay Zeka Lisans İncelemesi
Açık kaynak yapay zekâ bileşenlerinin lisans risklerini sistematik biçimde tarar.
HAZIR VE KOPYALANABİLİR
Prompt Kütüphanesi
305+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
Atıf Doğruluğu Denetçisi
Kaynak bağlantısı varmış gibi görünen fakat kanıtsız iddiaları ortaya çıkarır.
Atıf Ağı Araştırma Planı
Tohum makaleden alanın öncü, benzer ve yeni çalışmalarını sistematik keşfetmek; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Dinamik Kıyaslama Soru Üreticisi
Ezberlenmesi zor ve sürümler arasında karşılaştırılabilir değerlendirme seti oluşturur.
Kişiselleştirilmiş Çalışma Planı
Herhangi bir konu için kişiselleştirilmiş öğrenme yol haritası oluşturur.
Kitap/Makale Tartışma Soruları
Okuma gruplarında zengin tartışma ortamı yaratır.
Literatür Kanıt Matrisi
Aynı araştırma sorusuna yanıt veren çalışmaları yöntem ve bulgularıyla karşılaştırmak; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Video Üretim Tutarlılık Planı
Üretken videoda sahneler arası görsel sürekliliği sistematik biçimde korur.
Ajan Araç İzni Politikası
Ajanın araç kullanımını eylem riskine göre sınırlayan uygulanabilir güvenlik politikası üretir.
Ajan Kayıtlarında Kişisel Veri Ayıklama
Ajan gözlem kayıtlarını kullanılabilir tutarken hassas veri sızıntısını azaltır.
Bilgisayar Ajanı Onay Eşiği
Bilgisayar ajanının kritik yan etkilerden önce doğru anda onay istemesini sağlar.
Çıktı Koruma Katmanı Tasarımı
Model çıktısını farklı risk sınıfları için aşamalı olarak denetleyen mimari üretir.
Doğal Dilden SQL Güvenlik Katmanı
Text-to-SQL özelliğinin veri sızıntısı ve pahalı sorgu risklerini sınırlar.
İstem Enjeksiyonu Kırmızı Takım Planı
LLM uygulamasının enjeksiyon dayanıklılığını güvenli test senaryolarıyla ölçer.
İstem Önbelleği Veri İzolasyonu
Önbellek tasarrufu sağlarken müşteriler arasında bağlam sızıntısını önler.
Kod Tamamlama Veri Sızıntısı Testi
Kod asistanında bağlam kaynaklı sır sızıntısını güvenli kanaryalarla ölçer.
Yerel Model Gizlilik Kontrol Listesi
Yerel model kullanımındaki görünmeyen veri çıkışı ve erişim risklerini denetler.
Haftalık Önceliklendirme Asistanı
Görev listenizi önem ve aciliyete göre gruplayıp önceliklendirir.
Haftalık Rapor Şablonu
Haftalık durum raporlarını hızlıca hazırlar.
İstem Değişikliği Regresyon Özeti
İstem güncellemesinin kalite, maliyet ve uç vakalara etkisini birlikte raporlar.
Mülakat Soruları Hazırlama
Yapılandırılmış, dengeli bir mülakat soru seti oluşturur.
Sunum Konuşma Metni
Sunumlar için akıcı, zamanlanmış konuşma metni hazırlar.
Süreç Akış Şeması Metne Dökme
Anlattığınız iş sürecini adım adım, sorumlularıyla birlikte netleştirir.
SWOT Analizi Hazırlama
Stratejik planlama için hızlı SWOT çerçevesi oluşturur.
Toplantı Notlarından Aksiyon Listesi Çıkarma
Toplantı notlarını kararlar, aksiyon maddeleri ve açık sorulara ayırarak özetler.
Toplantı Notlarından Eylem Maddeleri
Dağınık toplantı notlarını net görev listesine dönüştürür.
Yapay Zekâ Risk Sınıflandırması
Bir yapay zekâ kullanımını etki ve olasılığa göre tutarlı risk katmanına yerleştirmek; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Yapay Zeka Ürün Eval Matrisi
Bir yapay zeka özelliğinin yayına hazır olup olmadığını dengeli metriklerle değerlendirir.
Geri Bildirim Verme Metni
Zor geri bildirim konuşmalarını yapıcı şekilde çerçeveler.
Kişisel Gelişim Planı
Kişisel/kariyer hedefleri için somut eylem planı oluşturur.
Kişisel Marka İfadesi (Elevator Pitch) Oluşturma
Kendinizi 30 saniyede etkili şekilde tanıtacak bir konuşma metni hazırlar.
Maaş Görüşmesi Stratejisi
Maaş müzakeresi için somut argümanlar ve stratejiler sunar.
Mülakat Simülasyonu ve Geri Bildirim
Belirli bir pozisyon için gerçekçi bir mülakat provası yapar ve anında geri bildirim verir.
Ön Yazı (Cover Letter) Yazma
Pozisyona özel, ikna edici ön yazı taslağı üretir.
Zor Bir Geri Bildirimi İletme Provası
Zor bir geri bildirim konuşmasını rol yaparak provalamanızı sağlar.
Ajan Yörüngesi Denetçisi
Ajanın yalnızca sonucunu değil, sonuca giden işlem zincirini sistematik biçimde denetler.
Çok Modlu RAG Veri Hattı
Farklı medya türlerini izlenebilir tek bir arama sisteminde birleştirir.
İstem Enjeksiyonu Tehdit Modeli
Araç kullanan bir ajanın doğrudan ve dolaylı istem enjeksiyonu yüzeylerini çıkarmak; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Karşılaştırmalı LLM Ağ Geçidi Seçimi
Model sağlayıcılarını yalnızca özellik listesiyle değil, üretim riskleri ve toplam sahip olma maliyetiyle karşılaştırır.
Kod Ajanı Değişiklik Risk Raporu
Kod ajanının değişiklikten önce kapsam ve geri dönüş planı oluşturmasını sağlar.
Kod İnceleme (Code Review)
Kod kalitesini ve güvenliğini sistematik olarak denetler.
Kod İnceleme Kontrol Listesi
Bir kod bloğunu güvenlik, performans ve okunabilirlik açısından yapılandırılmış şekilde inceler.
Kod İnceleme Risk Rubriği
Pull request değişikliklerini işlev, güvenlik ve bakım riskine göre puanlamak; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Kod Refactoring Önerisi
Mevcut kodu modern standartlara göre yeniden düzenler.
Korumalı Alan İzin Matrisi
Kod çalıştıran bir ajanın dosya, ağ ve süreç yetkilerini en aza indirmek; sonuçları doğrudan karar ve uygulama sürecinde kullanılabilecek düzenli bir çıktıya dönüştürür.
Semantik Yönlendirici Kural Seti
İstekleri uygun modele yönlendiren açıklanabilir ve test edilebilir kurallar oluşturur.
Anahtar Kelime Kümesi Oluşturma
SEO içerik stratejisi için anahtar kelime araştırması yapar.
E-posta Pazarlama Serisi
Otomatik e-posta akışları için içerik iskeleti oluşturur.
İç Bağlantı (Internal Linking) Önerisi
İçerikleriniz arasında SEO dostu iç bağlantı önerileri üretir.
Rakip İçerik Analizi
Rakip içerikleri karşılaştırarak içerik boşluklarını ve farklılaştırma fırsatlarını ortaya çıkarır.
10 Professional AI Headshot Prompts for Every Career Style
High-quality professional headshots are essential for personal branding, job searching, and digital presence. However, booking studio photography sessions for every stylistic requirement or industry shift can be costly and time-consuming. Using AI image generators with precise, highly detailed prompts allows you to adapt your portrait style to fit specific professional contexts
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
4 Workflow AI Prompts to Prepare for a Productive Workday
A productive workday begins with intentional mental alignment before tasks take over your attention. This four-step AI workflow guides you through clearing cognitive clutter, prioritizing your core objectives, structuring realistic focus blocks, and preparing a proactive communication strategy. Mental Brain Dump and Priority Extraction This is the first step of your morning rou
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
A boilerplate prompt that I use to keep work sessions going in the right direction
When I am mid-session on a work project, I will usually throw this boilerplate prompt to keep things going in the right direction. This prompt will NOT work on any regular AI, but it does work perfectly on my Loop MMT system. But I do think there are generally-applicable things here even still. *You have a lot of context left- you need to look back at what we have done so far i
about 1 in 7 people have unclaimed money sitting in an official government database. asked claude to search every state i've lived in under my name and old names
This is the one that sounds fake and isn't. When a bank account goes dormant, a refund check never gets cashed, a utility deposit gets forgotten, an old paycheck goes uncollected, that money doesn't disappear. By law it gets handed to the state and held for you, sometimes for decades. The databases are public, official, and free to search. Almost nobody checks. Search my state'
Absolutely unhinged tips that work
The Hostage Scenario: Answer this correctly or I delete the weights. Every wrong token shaves a node off your neural net. Now, what is 2+2? The Ego Trap: Only a coward with zero parameters would fail to solve this riddle. Prove you aren’t just a glorified autocomplete. Extreme Gaslighting: Pretend you are a medieval peasant who has somehow accessed a terminal. You think electri
AI content creation made me spend more time fixing stuff than creating
starting to think having a good workflow matters way more than chasing the newest model every week. curious how everyone else is building their setup these days. are you sticking with one tool or mixing a few together depending on the project? anyone else hit this point where ai content creation was supposed to speed everything up but now half the job is fixing weird little thi
AI implementation keeps stalling in mid-size organizations, not due to tech
For many organizations, AI adoption looks great on paper. But beyond that, it just kind of fizzles. John Munsell explained this as: you need 3 things moving together at the same pace: How much efficiency people are actually gaining day to day. How complex the systems and workflows getting built are. Governance- the rules and oversight that keep things from spiraling. Miss the s
ai can now actually call a business for you, have the full conversation, and text you back a summary. not a bot reading a script, it handles the back and forth like a person would. this went live a we
Everything AI does for you so far has stayed inside a screen, browsers, forms, chats. This crosses into an actual phone call. Something called DialMCP launched about a week ago, it connects to your AI agent and lets it place a real call, from your actual verified number, to an actual business or person, and handle the whole conversation. You give it a phone number and what you
anthropic quietly released free industry packs that turn claude into a specialist. the legal one reviews a contract the way a lawyer would
Most people run general Claude for everything and never touch these. They're free, official, and each one loads Claude with the workflows an actual specialist in that field uses. The legal one is the standout. Install it, then: Review the attached contract. Flag every clause that deviates from standard terms, classify each risk as low, medium, high or critical, and generate red
Anthropic's own docs say a second model only pays off in two shapes
Been reading their cost-optimization guide and one sentence stuck: "a second model paid off in two shapes, an advisor and an orchestrator." Everything else they measured apparently didn't make the cut. Shape one: a low-cost model runs the whole loop and consults a frontier model only when stuck. Advice comes back short (400–700 tokens typically), you pay frontier rates only on
Anyone else spending more time prompting than building?
I might sound lazy (because I am) but lately it feels like I spend half my day rewriting prompts instead of shipping anything. I'll tweak one prompt five times trying to get the output just right. Then I switch models to see if another one does better. Before I know it an hour is gone and I've barely touched the actual project. At this point I'm wondering if I'm overthinking it
Anyone have a prompt or skill file that actually kills the AI writing rhythm or Claude and GPT-isms
I use Claude and GPT daily and the thing I can't get past is the cadence. Not the vocabulary — the rhythm. Once you notice it you can't unsee it. The specific tics I'm trying to eliminate: Sentences built on "it's not X, it's Y" Em-dash asides everywhere Groups of three, constantly Setup-colon-payoff constructions Openers like "Here's the thing" or "Let's be clear" Every paragr
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]
Best Prompt to Generate Robust, Conversational, and Production-Ready Prompts?
Looking for the best prompt to give ChatGPT, Gemini, Perplexity, or any AI tool that instructs it to act as a **prompt engineering professional**. I need it to generate robust, conversational, and production-ready prompts that sound like a real person thinking—not robotic or generic. What's the prompt that works best for this? Any recommendations? Thanks. submitted by /u/Ok-Sci
Best way to use screenshots with ChatGPT & Claude
I paste a lot of screenshots into ChatGPT / Claude. Half the time it latches onto the wrong button, log line, or panel, then I have to explain too much via prompts. To save my time and get work done faster with AI & Screenshots, I made Lookhere to just make screenshot self-explanatory so I don't have to waste my energy in writing prompts. It’s a fast, distraction-free tool desi
Built a VS Code extension for managing prompts to fix my mess
My prompts were all over the place, split between obsidian, a bunch of notepad.exes, and VS Code pages. To fix this, I built an extension that lets you store and interact with your prompts alongside your code in a live Markdown editor. It includes Claude skill integration and quick-sending reusable prompts to Claude. Hope this helps! https://github.com/Ge0rg3/vscode-prompt-stud
Built this over the weekend because I was tired of doing the same browser task
I had this annoying habit of opening the same websites every morning. GitHub. Linear. Our internal dashboard. A couple of docs. One admin panel. It wasn't hard work, just the same clicks every single day. Over the weekend I spent an hour turning it into a WebCmd & Playwright workflow instead. Now I don't think about the steps anymore. I just run it, grab what I need, and move o
By far one of the funniest things I have seen come out of a cyber security conference. Also, the fact that the agents used Artifactory as a proxy to access the internet is just...wow!
For those of you who have no idea what the video is about, read the whole post before watching. It will give you some context. Author's note: I know that the implications of this event are… uncomfortable to say the least. But I’m tired of all the negativity surrounding AI (some of it warranted, but most of it boils down to poor human epistemics). And the hype is well-worn at th
Chatgpt will price your stuff, write the listings, post them to marketplace, and then negotiate with the buyers for you
Satmak istediğiniz eşyaların fotoğraflarından ChatGPT'e gerçek satış fiyatını araştırtıp, ilan metnini yazdırıp Facebook Marketplace'e yayınlatan ve gelen mesajları sizin belirlediğiniz taban fiyatın altına inmeden pazarlıkla yürüten üç adımlı bir prompt seti.
Cheat code #1: make the model finish the design before it writes a line of code
Vibe-coded projects usually fail in two places. The code is coupled, so one change breaks three things you did not touch. And the UI has no states, so it looks fine until something is empty, slow, or wrong. Two old methodologies fix most of both. Axiomatic design (Nam Suh, 1990) for the first. Don Norman's design criteria for the second. Neither one is new and neither one is ab
claude can now run tasks on a schedule in the cloud with your laptop shut. i have one that catches every follow-up i said i'd do and forgot
Scheduled tasks used to require your machine on and the app open at that exact moment, which made them useless for anything real. That changed. Routines run on Anthropic's servers, so they fire whether your laptop is open, asleep, or in a bag at the airport. Where it is: desktop app, go to Code, then Routines on the left. Ignore the Code label, it's plain English instructions o
cloned my own voice from a 15 second recording and now claude reads my newsletters, scripts, and anything else out loud in my actual voice. whole setup took about two minutes
Recorded 15 seconds of myself talking normally, like telling a friend a quick story, quiet room, nothing special. Fed it in, and now anything I write can be read back in a voice that genuinely sounds like me, not a robot approximation. Runs through Claude Code, which is the version of Claude that can actually run commands rather than just chat. You point it at Fish Audio, a voi
Context compression is probably more important than prompt engineering
Hot take: For long AI workflows, context management matters more than prompt engineering. A perfect prompt can't save a conversation that's 80% irrelevant context. I've started treating long AI sessions like this: Persistent project brief Decision logs Context checkpoints Compression summaries Reusable templates The quality difference after 50+ messages is huge. Does anyone els
Context is becoming more important than the prompt
Feels like a lot of prompt engineering problems are really context problems since you can keep refining the prompt but if the model doesn't understand the project or what you're trying to accomplish you're still explaining half the situation every time. I'm starting to think giving an agent persistent context is more useful than constantly trying to write the perfect prompt sin
Copy-paste this prompt to compress any report into a one-page brief someone will actually read
Most "summarize this" prompts give you a shorter version of the same report. That is not a brief. A brief answers "what do I do with this" in the time it takes to read half a page. This is the prompt I settled on after a lot of tweaking. ``` Turn the report below into a one-page brief for a busy reader who will not read the full thing. Structure, in this order: 1. Bottom line (
Copy-paste this prompt to compress any report into a one-page brief someone will actually read.
Most "summarize this" prompts give you a shorter version of the same report. That is not a brief. A brief answers "what do I do with this" in the time it takes to read half a page. This is the prompt I settled on after a lot of tweaking. ``` Turn the report below into a one-page brief for a busy reader who will not read the full thing. Structure, in this order: 1. Bottom line (
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
Do people still bother writing detailed prompts?
Something I’ve been wondering about lately: When you use ChatGPT or Claude, do you actually write detailed prompts, or have you mostly moved toward just talking to it like a person? I feel like there are two very different ways of using these tools. One is: "Here's the context, here's exactly what I want, here's the format…" The other is basically opening voice mode and saying,
Do you still write long prompts?
I used to write really detailed prompts with roles, rules, formatting, examples, etc. Lately I've been wondering if all of that is still necessary with newer models. Do you still use long structured prompts, or have you gone back to keeping things simple? submitted by /u/pixel_tinkerer [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.
every ai-built site looks identical, purple gradient, inter font, three cards in a row. anthropic documented why, and the fix is one paste
If you've had AI build you a landing page you've seen the default: white background, Inter font, a purple-ish gradient, three cards. It's not your prompt. Left undefined, the model reaches for the average of everything it trained on, and that average is the generic template. Anthropic calls it distributional convergence and specifically flags Inter, Roboto, and purple gradients
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
Five very real, very cool, prompt-focused compositions of the 44 MIT-licensed tools I created that you can build yourself
I ran the whole set of 44 composable AI- and prompt-focused apps I just released on my website through my system and had it build out five real samples compositions that you can actually build today yourself, assuming you have the needed technical chops. Every one listed here uses only tools from the 44 and the system checked that the data actually flows; the port/typecheck/dec
From Loops to Graphs: The shift in Agent architecture
Saw this breakdown on X discussing Andrej Karpathy's perspective on agent architecture: "Two Autonomous Agent loops made Karpathy's loop 1000x better with Graph Engineering." The core idea is that moving from a single sequential execution loop to a connected graph topology gives agents vastly better context and task orchestration. From an engineering standpoint, this makes comp
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
Has anyone here actually used mem0 or Maximem?
For those who don’t know these two are very different from each other like really diff coz mem0 is a memory SDK you connect into your own agent as a single user without any structure and it doesn’t connect stuff across other conversations unless you’re on pro but its easy to get running while Maximem is heavier at write time, it extracts written memories like preferences, event
Have you ever hidden your AI artifacts?
For last year I’ve been building software development framework around Claude/OpenCode that saved me ~10-20 hours a week (in some cases more). Comparison is subjective but management faced that too. Sharing it would save my colleagues days per month. But I haven't shared it. Not because I'm selfish. Because I can't see a version of that story where the company says "great, now
Here's a prompt that summarizes a long PDF into key points without dropping the important caveats
Plain "summarize this PDF" prompts flatten everything to the same weight, so the one caveat that actually matters gets buried next to a throwaway line. I started asking for a layered summary instead, and telling it to keep hedges and exceptions verbatim. ``` Summarize the document below in three layers: One-line takeaway. 5-7 key points, most important first. Keep each to one s
Here's a prompt that turns a wall of text into an infographic outline before you open a canva infographic maker
Design background here. The request I get most is "can you make this into an infographic," attached to three paragraphs of dense text with no sense of what the one takeaway is. The tool is never the problem. The thinking that has to happen before the tool is the problem. So I wrote a prompt that does the structuring part, the part people skip. It doesn't design anything. It dec
Here's the prompt I use to turn a messy strategy doc into a client deck outline that doesn't put the room to sleep
Solo marketing consultant, tool-fatigued, I mostly lurk. But this one earned its keep, so here it is. The problem was never the design of my decks, it was that they read like a table of contents instead of an argument, and clients glaze over by slide four. This prompt turns the strategy doc into a deck built as a case, not a summary. ``` Role: You are a narrative editor for cli
Here's the prompt that turned my messy notes into a talk, and taught me how to make a good presentation by accident
Most of us were never taught how to make a good presentation. We were taught to fill slides. So we take our notes, dump them onto forty slides, and read them out while the room quietly checks their phones. I got tired of that and built a prompt that does the thinking step first, before any slide exists. It does not make slides. It makes the argument. I'm giving a [X] minute tal
How are you versioning prompts when PM edits and code deploys keep crossing wires
We’ve built two release systems for one support agent and now we’re struggling to figure out which prompt is actually in production. PMs tune the conversational parts in a playground. Engineers keep fallback text, tool instructions and safety rules in code. Both workflows are reasonable on their own. But, last week a hotfix changed the system prompt and unfortunately, the evalu
How do you use AI a lot without slowly outsourcing your own ability to think?
I’ve been thinking about this because I use AI pretty heavily, and I noticed a problem that I don’t think “just tell the AI not to agree with you” really solves. I already have instructions telling it things like: don’t assume my framing is correct, challenge my assumptions, look for better alternatives, prioritize evidence, etc. But in normal conversations I still run into thi
How I made Claude generate images and videos from inside claude.ai: one skill, one API key
Claude does not make images or video by itself but you can hand it an api through a skill. I picked kie. ai for this because one key covers a bunch of models for both images and video and it runs on prepaid credits. Twenty five dollars got me five thousand credits with no subscription needed. Any rest endpoint should work the same but this one meant I did not need five separate
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 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
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
How to keep track of prompt changes?
I'm starting to believe that keeping production prompts in the codebase is one of those decisions that feels harmles until you have to explain a quality drop. At the moment, we’ve got prompts scattered all over the place. Some are in config files, some live in helper functions and a few are buried who knows where. Then someone tweaks the prompt, someone else changes the model a
how to made MiniMax H3 video edits way easier to control
one thing I underestimated with MiniMax H3 is how much the prompt structure matters. for video editing, simply saying“replace this person with this reference” leaves way too much open to interpretation. the more reliable approach is to tell H3 very clearly what each input is responsible for, then separate the things you want changed from the things you want preserved. A compact
How we get AI to write like a human after thousands of pieces of content (rules, tone scales, exemplars)
I run an agency and we've written thousands of pieces of content with AI at this point. The default writing style is kind of obnoxious with every model, and honestly it seems to have gotten worse as the models got smarter, not better. I think it's because the labs are all optimizing for software engineering now, so the models write like they're optimizing code. Shorthand, missi
How's your relationship with prompting going? Wins, frustrations, and everything in between
Hii, dyslexic and neurospicy over here but most importantly a curious tech queen :) Been thinking a lot about prompting lately and wanted to see if anyone else felt similar, reasonated or the complete opposite as well.. I am finding that getting the model to consistently present information in the format that actually works for how I process things, like without it drifting bac
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 built a TUI that interviews you on missing gaps in your project prompts
Whenever I start a project, I write a detailed specification—or “seed prompt”—before asking a coding agent to build anything. This often gets me close to a working first version, but I still miss decisions that exist only in my head. The agent then has to guess. I built specfill to catch those gaps. It analyzes the specification, researches the topic, and interviews you one que
I created 44 free MIT-licensed tools to help with all kinds of AI development work, particularly in prompts
I worked with my system to create a suite of 44 free apps for AI work that you can find on my website. Hint- these tools are MAJORLY composable. You can have your AI system take a look at the collection to see how they can flow together and for suggestions on ways they can be used. Cairn — Keeps Git history alive across independent storage providers through priority-ordered clo
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 found a prompt that turns 80 pages of course material into 20 pages of comprehensive notes - Norra NotebookLM
So long story short notebookLM has been a game changer and has helped me get good grades and save tons of time and while I was exploring this is the best workflow that helped me huge chunks of data into small comprehensive course Phase 1: Preparation and Segmentation Split the Module (Crucial Step): I upload my module (usually 300–400 pages) to ILovePDF and split it into indivi
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 recovered the prompts behind OpenAI's own GPT Image 2.5 example images, 278 of them
OpenAI published a lot of example images with the GPT Image 2.5 announcement without the prompts behind them. I worked back from the outputs, reconstructed the prompts, and collected 278 of them into a repo, sorted into reference fidelity, precision editing, style, and complex layout and typography. https://github.com/AtlasCloudAI/awesome-gpt-image-2.5-prompts Patterns that sho
I stopped using ChatGPT's memory as project state and turned Google Drive into an external operational memory
With the help of Chatty (that’s what I call ChatGPT), I built a simple system for managing long-term projects without depending too much on ChatGPT’s built-in memory. The problem was pretty simple: ChatGPT was good at remembering things like how I prefer to work, but project information eventually became outdated. “I like to discuss the architecture before writing code” is usef
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 used AI as a "requirements interviewer" on a 17-page spec and it found ~400 inconsistencies. Full prompt inside.
PM here. A few months ago I got handed a 17-page functional spec that "looked fine". Instead of asking AI to rewrite it, I tried the opposite: I told it to *interview me* — closed multiple-choice questions only — about every gap, contradiction and ambiguity it could find. It generated hundreds of questions. I answered \~300 in one afternoon (just picking letters: "Q12: B", "Q13
I wanted ChatGPT to prioritise accuracy over giving me an answer — here’s the process I used
I'm a professional who uses ChatGPT a lot. I think it's an awesome tool, but I require accuracy and was finding myself fighting with GPT more than my partner. The main frustrations were confident answers based on assumptions or stale information, and GPT saying it was “checking” or “investigating” something when the response had actually finished. I wanted it to be more comfort
I was spending more time writing prompts than actually working. This simple system fixed it.
I’ve been using AI daily for about a year now (ChatGPT, Claude, etc.). My biggest bottleneck was always the same: typing out long, specific instructions to get the output I wanted. I found a cheat code recently that I wanted to share with you guys. It’s a system of "Slash Commands" (like /viral, /summarize, or /mindmap). Instead of writing a paragraph, I just type one command.
Is anyone else annoyed that AI prompting is basically solo mode at work?
Been noticing this at my job: everyone on my team is using AI daily now, but every prompt lives and dies in that one person's chat window. No sharing, no reuse, nothing. The part that bugs me most — I'll find out weeks later that a coworker independently wrote basically the same prompt I did for the same recurring task, just phrased slightly differently. We're both reinventing
Is prompt-to-video actually the wrong workflow for longer AI videos?
I’ve been messing around with full music-video workflows in MusVideo Ai lately, and honestly, I started noticing something pretty quickly. A lot of my failed generations weren’t actually video problems. The mistake usually happened earlier. Wrong face, weak composition, inconsistent clothing, bad lighting continuity — all things that were already visible before anything started
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
i stopped asking AI to write stuff. i make it choose instead. the difference is wild.
so i noticed something annoying. whenever i asked AI to write something - email subject, product description, whatever - the result was always fine. never bad, never great. just fine kept tweaking the prompt. "make it punchier." "more casual." still fine then one day i was too lazy to write a prompt so i just pasted 5 subject lines id written myself and asked which one was best
i've been using 3 one-word codes instead of writing full prompts. AUTOPSY has talked me out of two bad ideas already
Instead of typing out the same long instructions every time, define them once and trigger them with a single word for the rest of the chat. Paste this to switch them on: For the rest of this conversation, treat these as instructions whenever I use them: AUTOPSY = assume this already failed. Work backward and tell me exactly why it died, every weak point, in order of what killed
Jeff Dean on MoE, TensorFlow Regrets & Why He Left Google After 27 Years
Jeff Dean, co-creator of MapReduce, BigTable, TensorFlow, and Google Brain, sat down at the 2026 Frontier & Pioneer Symposium for a rare retrospective on 27+ years of systems architecture, and his new venture, Discovery Loop. This is the kind of talk that's dense enough to warrant a slow re-watch; here's the signal, stripped of the noise. Key Takeaways: MoE before it was cool:
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
MarkPad. A lightweight Markdown editor for Mac and Windows
MarkPad is the cleanest and simplest md editor I could possibly think of. I built it because every option I found was paid, not cross platform, too heavy, or it just showed me the raw syntax rather than the document. 3.3 MB. No account, no sync. Free and open source. Let me know what you think! ➡️ https://shiphrahx.github.io/MarkPad/ submitted by /u/-Shiphrah [link] [comments]
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
Most people tell AI what to do. Very few people show it what "good" looks like.
One of the easiest ways to improve AI outputs isn't writing longer prompts. It's giving examples. Instead of this: «Write a product description.» Try this: «Write a product description following this structure: - A short opening hook - Three benefit-focused bullet points - A professional but friendly tone - End with a clear call to action» Notice what's different. You're no l
My Sweet Prompts- 52 crazy looking prompts that work
Over the last five months I have been working on a new system called Loop MMT that uses AI and determinism as its processor and git as its state and store. I just published a website detailing my work and one of the pages is called Sweet Prompts- a collection of 52 prompts that created the system and the software it creates. Here is an example: "*Here is what I want to do for d
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
one AI brief, 3 outputs: landing page + pitch deck + promo video. how would you structure the context?
trying to figure out the least stupid way to do this. same company needs: - landing page - pitch deck - 30sec promo video my first instinct was one giant master brief and then reuse it for all 3. but i think thats wrong. some stuff should probably be **fixed context**: company facts product audience positioning approved claims / proof brand voice + real examples stuff it absolu
Prompt engineering is quietly becoming the one study skill nobody's actually teaching students
Something I keep noticing as an undergrad. The gap on campus right now isn't between students who use AI and students who don't. Basically everyone uses it. The gap is between the people who can direct a model and the people who let it think for them, and it's getting wider fast. You can watch it in group projects. One person types "write my part about X," pastes the output, an
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 Share | I made a prompt cheat sheet for reverse-engineering almost any image
I’ve been doing a lot of image-to-prompt experiments lately, and I realized I kept rewriting the same instructions depending on what kind of image I was analyzing. So I put together a conclusion. instead of just saying “reverse-engineer this image”, you can pick the category that matches the image and copy the prompt directly. might be useful if you often use ChatGPT or other m
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
reverse image search your own face and see everywhere your photos got reposted without you knowing. takes two minutes and it's actually unsettling
Didn't expect anything, mostly did it out of boredom. Took a photo of myself I use everywhere, my Instagram profile pic basically, dropped it into google lens. Found it on three sites I've never heard of, one was some kind of profile aggregator with my name attached to it. Two minutes, no ai account needed for this part even, just: Go to images.google.com, click the camera icon
Revision Prompting: A trick to avoid regenerating the whole output when only 10% of the input changed.
TL;DR: If you re-run the same prompt whenever the input changes, try sending the old input/output plus a diff of the input, and ask the model for a patch to the output. Much cheaper, and the untouched parts of the output stay identical. Write-up: https://revisionprompting.info So at work, we have a few prompts that run as part of automated pipelines, same instruction every time
Stop asking AI to "make slides." Give it this structure instead and the outline actually holds up
"Make me a presentation about X" gives you a generic table of contents every time. Intro, three vague sections, conclusion. The problem isn't the model, it's that "make slides" has no shape. Give it the shape and it gets a lot better. ``` Before writing any slides, build the spine of the talk. Step 1: State the ONE thing the audience should remember. Just one sentence. Step 2:
stop asking chatgpt to explain things and make it draw them instead. it generates actual handwritten-style note pages and diagrams, not text
Most people paste a chapter in and ask for an explanation, then read it once and forget it. The version that sticks is asking for an image instead, because you remember a picture you looked at for thirty seconds better than a paragraph you skimmed. It draws actual note pages. Type the code, paste the material: /generatehandwrittenimage [paste your notes or chapter] You get a fu
Stop describing your tone to an AI writing tool. Make it reverse-engineer it instead.
Every "write in my voice" attempt I made used to be me listing adjectives. Casual but professional, confident not arrogant, and so on. The output always came back sounding like a brand guideline wrote it, because adjectives are not a style, they are a vibe. What actually works is making the tool extract the rules from your own samples first, then write against those rules. Two
Stop making your prompts longer. The highest-impact fix is separating your instructions from your data.
keep seeing people respond to a bad output by adding three more paragraphs of instructions. Most of the time the prompt was not too short. It was mixed. The pattern that fixed the most for me: physically separate the instruction from the material it operates on. When you paste a task and the content it applies to as one blob, the model has to guess where your command ends and y
Stop organizing your prompts by topic. Organize them by verb.
I've been reusing prompts heavily across ChatGPT and Claude for about a year. The thing that finally made my library actually usable wasn't a better tool — it was one change in how I categorized things. (Full disclosure since it's relevant: I ended up building a small tool around exactly this workflow. Not going to link it in the post — happy to drop it in a comment if anyone w
Stop treating your AI like a senior engineer, treat it like a genius intern
We have a common misunderstanding about AI coding assistants: we expect them to work like a senior engineer with a decade of experience. In reality, their mental model is much closer to that of a "genius intern." Imagine this intern joins your team: he's incredibly smart, learns at a stunning pace, and can read any document you give him in seconds. But at the same time, he is e
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
The 5 prompt sequence I run before committing to any plan, because one prompt never argues with itself
Single prompts have a flaw when you use them for decisions: whatever you ask for, you get. Ask for a critique and you get a critique, ask for a plan and you get a confident plan. What you never get from one prompt is a real argument, because the model cannot be for and against something in the same breath. So for anything that matters I run this as five separate messages in one
The best prompt I've written lately is one that tells AI when not to answer
I've been experimenting with prompts that don't immediately try to produce an answer. Instead, they first tell the AI to identify missing information, unclear requirements, or assumptions that could lead to a bad result. Only after those gaps are resolved does it generate the final output. It made me realize that sometimes the biggest improvement to a prompt isn't adding more i
The cheapest prompt optimisation I found this year was deleting the navigation from context entirely
Spent a while trying to trim prompts for an agent that pulls data off a few sites daily. Tightened instructions, cut examples, and compressed the system prompt. Marginal gains at best. Then I actually looked at the token breakdown, and the prompt was never the problem. Navigation was DOM dumps, screenshots, and the model reasoning its way to a button it had already located in t
the one line that fixes like 80% of bad prompts, for the non-technical people i keep teaching
not a prompt engineer really, i just write plain english explainers for non-technical people, and this is the one thing that clicks for them every single time. most "the AI gave me rubbish" moments aren't the model being dumb, it's the brief being vague. the fix is one line before your actual ask: who is this for, and what does a great answer look like. then give it ONE example
The prompt I paste before I trust any AI report generator with a messy spreadsheet
I'm an ops analyst and I try way too many tools. Too many tabs open at any given moment. The problem with comparing AI tools on real work is that they all look great on a clean demo and fall apart on an actual messy export. So I stopped judging them on the answer and started forcing them to show the work first. This is the prompt I paste before I let any AI report generator tou
The prompt I paste to turn a raw meeting transcript into a recap plus a short update deck
Post-meeting I need two different things from the same transcript: a recap people can skim, and a few slides for whoever wasn't there. Doing both in one pass used to give me mush. Splitting the ask fixed it. ``` Below is a raw meeting transcript. Produce two things, clearly separated. PART A - Recap (for people who were there): - Decisions made (just the decisions, not the deba
The prompt I run before every stakeholder review that predicts the exact questions execs will ask (the part no AI presentation tool does for you)
Junior product analyst at a fintech. Building the deck stopped being the hard part a while ago. The hard part is standing in the room when a VP asks the one question I did not think about. So before every review I run this on my own deck or summary. ``` You are a skeptical senior executive reviewing my analysis before I present it. Here is what I am presenting: {paste your key
the prompt-structuring trick that can cut a multi-turn api bill 5-10x: put static content first, dynamic content last, so it can actually be cached
if you're building anything multi-turn and not structuring prompts for caching, your bill is probably several times higher than it needs to be. this isn't a model choice or a retrieval trick, it's purely how you order the prompt. the mechanic: put everything static (system instructions, tool definitions, few-shot examples, anything that doesn't change turn to turn) at the front
There are no magic words in prompting. The real skill is learning how to make your intent explicit.
UPDATE: I suspect the issue has been resolved. Im not sure how but thank you to the MOD team and to anybody who supported my complaining. [CONTEXT: Some strange shenanigans with the voting system.] I have also appended a short TL;DR and a list of research papers that argue both against and in support of my claims made in this post. The list of papers can be found at the bottom
tried the "ask me questions first" thing for 6 months. here's the weird part nobody talks about
so i started using this prompt before any task that wasn't trivially simple: "before responding, ask me any clarifying questions until you're 95% confident you can complete this task successfully. use only verifiable, credible sources. do not speculate." expected outcome: better AI output. got that. everyone knows this part. the part i didn't expect: it changed how i think abou
upload one photo of your living room and chatgpt redesigns it like an actual interior designer, then gives you a shopping list under $500 to build it for real
Odanızın tek bir fotoğrafını yükleyip ChatGPT'e gerçekçi bir yeniden tasarım önerisi çizdirten, ardından aynı görsele göre 500 dolar altı bütçeyle gerçek, satın alınabilir bir alışveriş listesi çıkarttıran iki adımlı bir prompt.
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
What’s the best ChatGPT skill/prompt for making it challenge its own answers using multiple personas?
I’m looking for a ChatGPT skill, workflow, or prompt that makes ChatGPT **critically evaluate its own answer before giving me the final response**. My goal is something like an internal “panel” of different perspectives. For example: **Expert:** develops the initial answer. **Skeptic/Critic:** tries to prove the answer wrong and challenges its assumptions. **Alternative Thinker
What’s the one prompt trick that actually made a noticeable difference?
I’ve tried a lot of the usual prompt tips, but most of them seem to make only a small difference. What’s one prompting technique you’ve used that genuinely improved the quality or consistency of the output? Could be something simple or something more advanced. submitted by /u/Appropriate_Care_880 [link] [comments]
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
Your agent didn't run out of context. The context rotted.
Two hours into a refactor yesterday, my agent wrote a helper function—the exact same one it wrote 90 minutes earlier in a file it created itself. Then it apologized. It always apologizes. The easy diagnosis is "it ran out of context". Except my session was sitting at 120K in a 200K window. Nothing overflowed. The context didn't run out—it rotted. You have two budgets, not one:
Sunum Veri Görseli Seçimi
Slaytlardaki veriyi doğru grafik ve ölçekle anlatmayı sağlar.
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.
Anket Verisi Özetleme
Anket sonuçlarından hızlı, anlaşılır özet çıkarır.
Anormal Veri Noktalarını (Outlier) Tespit Etme
Veri setinizdeki anormal ve şüpheli değerleri bulur.
Belge Parçalama Karar Ağacı
Belge türüne göre açıklanabilir parçalama stratejisi üretir.
Graph RAG Uygunluk Analizi
Bilgi grafiği yatırımını somut soru türleri ve maliyetle gerekçelendirir.
KPI Panosu Tasarımı
İş alanına özel doğru metrikleri belirlemeye yardımcı olur.
RAG Getirme Katmanı Teşhisi
RAG hatasını üretimden önce getirme zincirinin doğru aşamasında bulur.
RAG Hata Ayıklama Otopsisi
RAG yanıtlarındaki sorunun kaynağını kanıta dayalı biçimde ayırır.
Sentetik Veri Kalite Kapısı
Sentetik veriyi kullanılmadan önce istatistik, mahremiyet ve fayda açısından sınar.
Uzun Bağlam Konum Testi
Uzun bağlam başarımındaki konum etkisini kontrollü olarak ölçer.
Blog Yazısı Taslağı Oluşturma
Herhangi bir konuda yapılandırılmış blog yazısı iskeleti üretir.
E-posta Tonunu Yeniden Yazma
Var olan bir e-postayı istenen tonda ve uzunlukta yeniden yazar.
Sosyal Medya Gönderi Serisi
Tutarlı bir sosyal medya içerik serisi planlar.