Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

at Raindrop, we like to treat every prompt as a function: structured inputs, structured outputs every function used to prepare the input for the model lives in the same prompt file, and we have an extension for iterating on the prompt right inside our codebase.

benhylak's profile picture

ben hylak

50,551 subscribers

31,575 görüntüleme • 6 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Prompt tricks pay off. In this clip I show you how to *drastically* improve ChatGPT’s outputs for software generation. Compare the default prompt to my specialized prompt - night and day. Full prompt below!
3:06

Prompt tricks pay off. In this clip I show you how to *drastically* improve ChatGPT’s outputs for software generation. Compare the default prompt to my specialized prompt - night and day. Full prompt below!

Mckay Wrigley

924,171 görüntüleme • 3 yıl önce

2. Use the generated image as an image prompt for Runway or any video model that supports End-Frames. Use the image as the final/last frame and create a prompt that reveals the image in an ink-style animation. Example prompt:
0:21

2. Use the generated image as an image prompt for Runway or any video model that supports End-Frames. Use the image as the final/last frame and create a prompt that reveals the image in an ink-style animation. Example prompt:

Halim Alrasihi

14,596 görüntüleme • 1 yıl önce

Introducing Prompt-to-leaderboard (P2L): a real-time LLM leaderboard tailored exactly to your use case! P2L trains an LLM to generate "prompt-specific" leaderboards, so you can input a prompt and get a leaderboard specifically for that prompt. The model is trained on the 2M human preference votes from Chatbot Arena. P2L Highlights: 🔹Instant leaderboard for any prompt 🗿 🔹Optimal model routing (hit #1 on Chatbot Arena in Jan 2025 with 1395 score 🧏) 🔹Fine-grained model strength & weakness analysis 🤓 Check out our demo and thread below for more details!
1:06

Introducing Prompt-to-leaderboard (P2L): a real-time LLM leaderboard tailored exactly to your use case! P2L trains an LLM to generate "prompt-specific" leaderboards, so you can input a prompt and get a leaderboard specifically for that prompt. The model is trained on the 2M human preference votes from Chatbot Arena. P2L Highlights: 🔹Instant leaderboard for any prompt 🗿 🔹Optimal model routing (hit #1 on Chatbot Arena in Jan 2025 with 1395 score 🧏) 🔹Fine-grained model strength & weakness analysis 🤓 Check out our demo and thread below for more details!

Arena.ai

122,525 görüntüleme • 1 yıl önce

We worked a ton to get prompt adherence and best in class world understanding for Gen-4 right. So you can prompt for camera movements in the most efficient way possible
0:37

We worked a ton to get prompt adherence and best in class world understanding for Gen-4 right. So you can prompt for camera movements in the most efficient way possible

Cristóbal Valenzuela

11,884 görüntüleme • 1 yıl önce

ON THE APP: → Tap on 'Imagine' in the app → Choose 'Video' from the prompt box → Tap on 'Animate Photos' → Select up to 7 images → Write your prompt → Tap the reference image while the cursor is where you want it to appear in the prompt. Repeat for every image. → Check the settings before sending the prompt for aspect ratio, video duration, and resolution. → Hit send and wait. Now you can use the same character, object, scene, or anything else across different videos.
0:52

ON THE APP: → Tap on 'Imagine' in the app → Choose 'Video' from the prompt box → Tap on 'Animate Photos' → Select up to 7 images → Write your prompt → Tap the reference image while the cursor is where you want it to appear in the prompt. Repeat for every image. → Check the settings before sending the prompt for aspect ratio, video duration, and resolution. → Hit send and wait. Now you can use the same character, object, scene, or anything else across different videos.

Dogan Ural

27,008 görüntüleme • 3 ay önce

PROOF same model, same effort, same provider, same codebase, same prompt claude code vs pi + fff extension vs opencode 0.17.3
1:29

PROOF same model, same effort, same provider, same codebase, same prompt claude code vs pi + fff extension vs opencode 0.17.3

Dmitriy Kovalenko

603,494 görüntüleme • 23 gün önce

Grok 4 Heavy is better than any model available at identifying issues in your codebase. Here's the JS prompt I use with my game code to have Grok 4 Heavy find the bugs. Python prompt in Comments👇
1:08

Grok 4 Heavy is better than any model available at identifying issues in your codebase. Here's the JS prompt I use with my game code to have Grok 4 Heavy find the bugs. Python prompt in Comments👇

tetsuo

1,814,524 görüntüleme • 11 ay önce

🎇 Video Model Comparison: Image2Video Video Model Comparison: Image2Video Same input image + text prompt on each model: • Pika 2.0 • PixVerse 3.5 • Runway Gen-3 • Kling AI 1.6 • Luma Dream Machine • Hailuo MiniMax I used the same Midjourney image and text prompt in each model and chose my favorite results.
1:00

🎇 Video Model Comparison: Image2Video Video Model Comparison: Image2Video Same input image + text prompt on each model: • Pika 2.0 • PixVerse 3.5 • Runway Gen-3 • Kling AI 1.6 • Luma Dream Machine • Hailuo MiniMax I used the same Midjourney image and text prompt in each model and chose my favorite results.

Heather Cooper

36,110 görüntüleme • 1 yıl önce

I had a great time chatting with three of Anthropic's top prompt engineers: Amanda Askell, Zack Witten, and David Hershey. We covered a few topics like: - what makes a good prompt engineer - practical tips for writing better prompts - what's happening inside an LLM - jailbreaks - the future of prompt engineering - and more If you want to understand how we think about prompt engineering, this may be the best resource out there. Here's the entire one hour and sixteen minutes of our conversation. Enjoy!
1:16:42

I had a great time chatting with three of Anthropic's top prompt engineers: Amanda Askell, Zack Witten, and David Hershey. We covered a few topics like: - what makes a good prompt engineer - practical tips for writing better prompts - what's happening inside an LLM - jailbreaks - the future of prompt engineering - and more If you want to understand how we think about prompt engineering, this may be the best resource out there. Here's the entire one hour and sixteen minutes of our conversation. Enjoy!

Alex Albert

333,662 görüntüleme • 1 yıl önce

"A giant prehistoric beast wakes up in the desert. Opens its mouth. And roars." We ran the same prompt across different models inside Pollo — Seedance 1.5 Pro, Kling 3.0, Pollo 2.5, Sora 2, Veo 3.1 Same prompt. Different beasts. Same words. Different worlds. Which one actually directed the scene? Vote for your favorite model.
0:25

"A giant prehistoric beast wakes up in the desert. Opens its mouth. And roars." We ran the same prompt across different models inside Pollo — Seedance 1.5 Pro, Kling 3.0, Pollo 2.5, Sora 2, Veo 3.1 Same prompt. Different beasts. Same words. Different worlds. Which one actually directed the scene? Vote for your favorite model.

Pollo AI

15,000 görüntüleme • 3 ay önce

lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇
0:21

lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇

Farhan

324,211 görüntüleme • 2 ay önce

You can just ask Perplexity Comet to generate a video for you using Veo 3 inside Gemini 🙂 You can either give a detailed prompt with steps or just keep it simple like this, both work: Comet Prompt: You have access to the Gemini interface, use it to generate a video and wait for rendering to complete. Prompt: It figures out the rest. Where to paste the prompt, whether to click the video option, everything. Pretty smart.
0:21

You can just ask Perplexity Comet to generate a video for you using Veo 3 inside Gemini 🙂 You can either give a detailed prompt with steps or just keep it simple like this, both work: Comet Prompt: You have access to the Gemini interface, use it to generate a video and wait for rendering to complete. Prompt: It figures out the rest. Where to paste the prompt, whether to click the video option, everything. Pretty smart.

AshutoshShrivastava

35,501 görüntüleme • 1 yıl önce

The most important prompt Give me an answer. Then Destroy your own Answer. Add this to the end of every prompt you do. And see the result!
1:09

The most important prompt Give me an answer. Then Destroy your own Answer. Add this to the end of every prompt you do. And see the result!

Nuseir Yassin

15,910 görüntüleme • 2 ay önce

holy shit Kimi K2.6 literally cooked claude design🤯 and it's 7x cheaper try the same prompt on both Kimi and Claude design, and there are the outputs see the exact same prompt 👇
0:21

holy shit Kimi K2.6 literally cooked claude design🤯 and it's 7x cheaper try the same prompt on both Kimi and Claude design, and there are the outputs see the exact same prompt 👇

Guri Singh

303,140 görüntüleme • 2 ay önce

Every AI tool just waits for you to type a prompt. For the last year we built the opposite: an assistant that already did the thing before you wake up. Here's what that looks like in a real family. Mine. 👇
0:59

Every AI tool just waits for you to type a prompt. For the last year we built the opposite: an assistant that already did the thing before you wake up. Here's what that looks like in a real family. Mine. 👇

Kevin Li

423,255 görüntüleme • 1 ay önce

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:
2:05

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,703 görüntüleme • 1 yıl önce

Building apps used to require code, cloud accounts, and centralized providers watching your every prompt. That era just ended. Meet 0G App, the ultimate hub for decentralized AI. Prompt to app. Verified inference. 🐼 Try the Desktop App now →
0:36

Building apps used to require code, cloud accounts, and centralized providers watching your every prompt. That era just ended. Meet 0G App, the ultimate hub for decentralized AI. Prompt to app. Verified inference. 🐼 Try the Desktop App now →

0G Labs (Home of Infinite AI)

185,197 görüntüleme • 2 ay önce

New feature: Prompt Guard Every builder knows prompt injection is a big threat to production agents. That's why we are introducing Prompt Guard, which solves the problem for every user by default. • 3 layers of defense by default • 256 attack vectors tested, zero leaks, zero false positives The next era of AI infrastructure comes with security baked in as default. SERV is already shipping it.
0:30

New feature: Prompt Guard Every builder knows prompt injection is a big threat to production agents. That's why we are introducing Prompt Guard, which solves the problem for every user by default. • 3 layers of defense by default • 256 attack vectors tested, zero leaks, zero false positives The next era of AI infrastructure comes with security baked in as default. SERV is already shipping it.

OpenServ

26,160 görüntüleme • 1 ay önce

curl This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set. In this example, we copied the entire prompt into Anthropic Claude and asked it to grab every sentence from Hacker News front page and visualize them using UMAP with matplotlib. This task is nontrivial as it combines multiple APIs from our Search Foundation, like Reader and Embedding where Claude may not have knowledge of. So if you asked Claude directly, it probably wouldn't give an optimal answer. But with the meta-prompt, Claude now has good knowledge about our APIs and can generate much better code! We can copy paste the code directly to Google Colab and with minimum modification, the code just works!
1:42

curl This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set. In this example, we copied the entire prompt into Anthropic Claude and asked it to grab every sentence from Hacker News front page and visualize them using UMAP with matplotlib. This task is nontrivial as it combines multiple APIs from our Search Foundation, like Reader and Embedding where Claude may not have knowledge of. So if you asked Claude directly, it probably wouldn't give an optimal answer. But with the meta-prompt, Claude now has good knowledge about our APIs and can generate much better code! We can copy paste the code directly to Google Colab and with minimum modification, the code just works!

Jina AI

209,078 görüntüleme • 1 yıl önce