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🚂 Introducing Agent Prompt Train 🚂 An open-sourced high-performance proxy that works with Claude Code to help turn it from a single-developer tool into a collaborative development platform. Claude Code changed the game. Now, Agent Prompt Train is here to help teams: ✅ Log every conversation for review ✅... show more

Moonsong Labs

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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!

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!

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🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

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Introducing az8 studio — the Agentic Studio for AI video creation. AI video is no longer just about typing a prompt and hoping for the best. It is about creating in a workspace where your agent understands the whole project, remembers the context, and helps before you ask. Meet Zumi — your creative agent inside az8. Zumi sees your assets, prompts, scenes, results, and decisions in one visual studio. It can spot what is missing, suggest what to try next, and help turn vanilla ideas into finished videos.

Introducing az8 studio — the Agentic Studio for AI video creation. AI video is no longer just about typing a prompt and hoping for the best. It is about creating in a workspace where your agent understands the whole project, remembers the context, and helps before you ask. Meet Zumi — your creative agent inside az8. Zumi sees your assets, prompts, scenes, results, and decisions in one visual studio. It can spot what is missing, suggest what to try next, and help turn vanilla ideas into finished videos.

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"Our best features come from engineers prototyping an idea, shipping it to dogfood, and just listening to feedback." Here's my new episode with cat (Claude Code product lead) where she shared exactly how her AI-native team operates: ✅ Engineers own features end-to-end ✅ Designers commit production code ✅ Demos over Google docs ✅ Rapid feedback loops every 10 minutes Cat also shared her 3 best Claude Code tips and what's next for the product. 📌 Watch now:

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Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

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THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this

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prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding

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Claude Code cracked something open for us Every 📧. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

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Gamma is now on Claude! You can generate a presentation with Gamma directly in your Claude chat. Connect Gamma and instantly turn any conversation into something you'd share with your team or present to the world. You can also: - Connect to Gmail, Slack, or HubSpot ("turn this email into a deck") - Fully edit your presentation in Gamma for any tweaks Take that research task, strategy proposal, or project summary and turn it into a polished presentation. Think with Claude. Visualize with Gamma.

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This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. Of course, it feels obvious after you see it: • You write a prompt • The system evaluates it across different scenarios • Based on the results, it refines it to improve results I recorded a quick video to show you how it works. It's pretty cool stuff! Here are some of the problems and best practices for teams building AI applications: 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. Here is the link: Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!

This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. Of course, it feels obvious after you see it: • You write a prompt • The system evaluates it across different scenarios • Based on the results, it refines it to improve results I recorded a quick video to show you how it works. It's pretty cool stuff! Here are some of the problems and best practices for teams building AI applications: 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. Here is the link: Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!

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Before software engineers even begin writing code, they have to set the stage of the entire development process. This process requires engineers to make complex tradeoffs between requirements, system design, and implementations details. Current IDEs that rely on AI features, like chat and inline coding, can help engineers get the job done quickly on small development tasks. Still, engineers spend much more time on larger projects—even after the initial code is generated—by conducting rigorous testing and creating documentation. This is where today’s AI IDEs can do more to accelerate the development lifecycle—and this is why we built Kiro. Kiro is an AI IDE that helps you go from prototype to production with spec-driven development and agent hooks. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs, then into working code, docs, and test so what you build is exactly what you want and ready to share with your team. After a developer builds the code with Kiro, Kiro’s agent hooks help engineers solve challenging problems and automate tasks like generating documentation and unit tests. Kiro brings structure and mature engineering practices to AI coding, so you can go from concept to application while being in the driver’s seat every step of the way. Kiro is free during preview, and supports Mac, Windows, and Linux, and most popular programming languages. We're excited for you to try it out and let us know what you think ➡️

Before software engineers even begin writing code, they have to set the stage of the entire development process. This process requires engineers to make complex tradeoffs between requirements, system design, and implementations details. Current IDEs that rely on AI features, like chat and inline coding, can help engineers get the job done quickly on small development tasks. Still, engineers spend much more time on larger projects—even after the initial code is generated—by conducting rigorous testing and creating documentation. This is where today’s AI IDEs can do more to accelerate the development lifecycle—and this is why we built Kiro. Kiro is an AI IDE that helps you go from prototype to production with spec-driven development and agent hooks. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs, then into working code, docs, and test so what you build is exactly what you want and ready to share with your team. After a developer builds the code with Kiro, Kiro’s agent hooks help engineers solve challenging problems and automate tasks like generating documentation and unit tests. Kiro brings structure and mature engineering practices to AI coding, so you can go from concept to application while being in the driver’s seat every step of the way. Kiro is free during preview, and supports Mac, Windows, and Linux, and most popular programming languages. We're excited for you to try it out and let us know what you think ➡️

Swami Sivasubramanian

154,343 Aufrufe • vor 1 Jahr

$Grok Code just literally cloned the entire Netflix UI with a one shot prompt From the iconic Hero section to the interactive Carousel, all with a single prompt and a few web-sourced image links It’s not just fast, it's the most cost-effective SOTA model out there ➝ Input: Grok $0.20 vs Claude $3 (15x cheaper) ➝ Output: Grok $1.50 vs Claude $15 (10x cheaper) Here’s how you can do it in VS Code with Kilo Code Grok Code matches Claude Sonnet 4 in performance, but is 10-15x more cost-effective All this for less than $1 SOTA power, fractional cost$

Grok Code just literally cloned the entire Netflix UI with a one shot prompt From the iconic Hero section to the interactive Carousel, all with a single prompt and a few web-sourced image links It’s not just fast, it's the most cost-effective SOTA model out there ➝ Input: Grok $0.20 vs Claude $3 (15x cheaper) ➝ Output: Grok $1.50 vs Claude $15 (10x cheaper) Here’s how you can do it in VS Code with Kilo Code Grok Code matches Claude Sonnet 4 in performance, but is 10-15x more cost-effective All this for less than $1 SOTA power, fractional cost

X Freeze

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Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

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Introducing the Legora Agent: the execution engine at the core of the Legora aOS™. Most legal AI responds to prompts. The Agent executes objectives. You give it a goal and it builds a plan, presents it for approval, then carries the work out end-to-end — selecting the right tools as it goes, whether that's research, drafting, or review. It keeps working while you review, instead of waiting for the next prompt — while you're with clients, with family, or even while you sleep. It isn’t a tool you use. It's a super-associate you supervise. The Legora Agent is live today. Read the announcement here:

Introducing the Legora Agent: the execution engine at the core of the Legora aOS™. Most legal AI responds to prompts. The Agent executes objectives. You give it a goal and it builds a plan, presents it for approval, then carries the work out end-to-end — selecting the right tools as it goes, whether that's research, drafting, or review. It keeps working while you review, instead of waiting for the next prompt — while you're with clients, with family, or even while you sleep. It isn’t a tool you use. It's a super-associate you supervise. The Legora Agent is live today. Read the announcement here:

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