The best Hacker News stories from Show from the past day
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Show HN: Inkeep (YC W23) – Agent Builder to create agents in code or visually
Hi HN! I'm Nick from Inkeep. We built an agent builder with true 2-way sync between code and a drag-and-drop visual editor, so devs and non-devs can collaborate on the same agents. Here’s a demo video: <a href="https://go.inkeep.com/video">https://go.inkeep.com/video</a>.<p>As a developer, the flow is:
1) Build AI Chat Assistants or AI Workflows with the TypeScript SDK 2) Run `inkeep push` from your CLI to publish 3)Edit agents in the visual builder (or hand off to non-technical teams) 4) Run `inkeep pull to edit in code again.<p>We built this because we wanted the accessibility of no-code workflow builders (n8n, Zapier), but the flexibility and devex of code-based agent frameworks (LangGraph, Mastra). We also wanted first-class support for chat assistants with interactive UIs, not just workflows. OpenAI got close, but you can only do a one-time export from visual builder to code and there’s vendor lock-in.<p>How I've used it: I bootstrapped a few agents for our marketing and sales teams, then was able to hand off so they can maintain and create their own agents. This has enabled us to adopt agents across technical and non-technical roles in our company on a single platform.<p>To try it, here’s the quickstart: <a href="https://go.inkeep.com/quickstart">https://go.inkeep.com/quickstart</a>.<p>We leaned on open protocols to make it easy to use agents anywhere:
An MCP endpoint, so agents can be used from Cursor/Claude/ChatGPT
A Chat UI library with interactive elements you can customize in React
An API endpoint compatible with the Vercel AI SDK `useChat` hook
Support for Agent2Agent (A2A) so they work with other agent ecosystems<p>We made some practical templates like a customer_support, deep_research, and docs_assistant. Deployment is easy with Vercel/Docker with a fair-code license and there's a traces UI and OTEL logs for observability.<p>Under the hood, we went all-in on a multi-agent architecture. Agents are made up of LLMs, MCPs, and agent-to-agent relationships. We’ve found this approach to be easier to maintain and more flexible than traditional “if/else” approaches for complex workflows.<p>The interoperability works because the SDK and visual builder share a common underlying representation, and the Inkeep CLI bridges it with a mix of LLMs and TypeScript syntactic sugar. Details in our docs: <a href="https://docs.inkeep.com">https://docs.inkeep.com</a>.<p>We’re open to ideas and contributions! And would love to hear about your experience building agents - what works, hasn’t worked, what’s promising?
Show HN: Inkeep (YC W23) – Agent Builder to create agents in code or visually
Hi HN! I'm Nick from Inkeep. We built an agent builder with true 2-way sync between code and a drag-and-drop visual editor, so devs and non-devs can collaborate on the same agents. Here’s a demo video: <a href="https://go.inkeep.com/video">https://go.inkeep.com/video</a>.<p>As a developer, the flow is:
1) Build AI Chat Assistants or AI Workflows with the TypeScript SDK 2) Run `inkeep push` from your CLI to publish 3)Edit agents in the visual builder (or hand off to non-technical teams) 4) Run `inkeep pull to edit in code again.<p>We built this because we wanted the accessibility of no-code workflow builders (n8n, Zapier), but the flexibility and devex of code-based agent frameworks (LangGraph, Mastra). We also wanted first-class support for chat assistants with interactive UIs, not just workflows. OpenAI got close, but you can only do a one-time export from visual builder to code and there’s vendor lock-in.<p>How I've used it: I bootstrapped a few agents for our marketing and sales teams, then was able to hand off so they can maintain and create their own agents. This has enabled us to adopt agents across technical and non-technical roles in our company on a single platform.<p>To try it, here’s the quickstart: <a href="https://go.inkeep.com/quickstart">https://go.inkeep.com/quickstart</a>.<p>We leaned on open protocols to make it easy to use agents anywhere:
An MCP endpoint, so agents can be used from Cursor/Claude/ChatGPT
A Chat UI library with interactive elements you can customize in React
An API endpoint compatible with the Vercel AI SDK `useChat` hook
Support for Agent2Agent (A2A) so they work with other agent ecosystems<p>We made some practical templates like a customer_support, deep_research, and docs_assistant. Deployment is easy with Vercel/Docker with a fair-code license and there's a traces UI and OTEL logs for observability.<p>Under the hood, we went all-in on a multi-agent architecture. Agents are made up of LLMs, MCPs, and agent-to-agent relationships. We’ve found this approach to be easier to maintain and more flexible than traditional “if/else” approaches for complex workflows.<p>The interoperability works because the SDK and visual builder share a common underlying representation, and the Inkeep CLI bridges it with a mix of LLMs and TypeScript syntactic sugar. Details in our docs: <a href="https://docs.inkeep.com">https://docs.inkeep.com</a>.<p>We’re open to ideas and contributions! And would love to hear about your experience building agents - what works, hasn’t worked, what’s promising?
Show HN: Inkeep (YC W23) – Agent Builder to create agents in code or visually
Hi HN! I'm Nick from Inkeep. We built an agent builder with true 2-way sync between code and a drag-and-drop visual editor, so devs and non-devs can collaborate on the same agents. Here’s a demo video: <a href="https://go.inkeep.com/video">https://go.inkeep.com/video</a>.<p>As a developer, the flow is:
1) Build AI Chat Assistants or AI Workflows with the TypeScript SDK 2) Run `inkeep push` from your CLI to publish 3)Edit agents in the visual builder (or hand off to non-technical teams) 4) Run `inkeep pull to edit in code again.<p>We built this because we wanted the accessibility of no-code workflow builders (n8n, Zapier), but the flexibility and devex of code-based agent frameworks (LangGraph, Mastra). We also wanted first-class support for chat assistants with interactive UIs, not just workflows. OpenAI got close, but you can only do a one-time export from visual builder to code and there’s vendor lock-in.<p>How I've used it: I bootstrapped a few agents for our marketing and sales teams, then was able to hand off so they can maintain and create their own agents. This has enabled us to adopt agents across technical and non-technical roles in our company on a single platform.<p>To try it, here’s the quickstart: <a href="https://go.inkeep.com/quickstart">https://go.inkeep.com/quickstart</a>.<p>We leaned on open protocols to make it easy to use agents anywhere:
An MCP endpoint, so agents can be used from Cursor/Claude/ChatGPT
A Chat UI library with interactive elements you can customize in React
An API endpoint compatible with the Vercel AI SDK `useChat` hook
Support for Agent2Agent (A2A) so they work with other agent ecosystems<p>We made some practical templates like a customer_support, deep_research, and docs_assistant. Deployment is easy with Vercel/Docker with a fair-code license and there's a traces UI and OTEL logs for observability.<p>Under the hood, we went all-in on a multi-agent architecture. Agents are made up of LLMs, MCPs, and agent-to-agent relationships. We’ve found this approach to be easier to maintain and more flexible than traditional “if/else” approaches for complex workflows.<p>The interoperability works because the SDK and visual builder share a common underlying representation, and the Inkeep CLI bridges it with a mix of LLMs and TypeScript syntactic sugar. Details in our docs: <a href="https://docs.inkeep.com">https://docs.inkeep.com</a>.<p>We’re open to ideas and contributions! And would love to hear about your experience building agents - what works, hasn’t worked, what’s promising?
Show HN: Inkeep (YC W23) – Agent Builder to create agents in code or visually
Hi HN! I'm Nick from Inkeep. We built an agent builder with true 2-way sync between code and a drag-and-drop visual editor, so devs and non-devs can collaborate on the same agents. Here’s a demo video: <a href="https://go.inkeep.com/video">https://go.inkeep.com/video</a>.<p>As a developer, the flow is:
1) Build AI Chat Assistants or AI Workflows with the TypeScript SDK 2) Run `inkeep push` from your CLI to publish 3)Edit agents in the visual builder (or hand off to non-technical teams) 4) Run `inkeep pull to edit in code again.<p>We built this because we wanted the accessibility of no-code workflow builders (n8n, Zapier), but the flexibility and devex of code-based agent frameworks (LangGraph, Mastra). We also wanted first-class support for chat assistants with interactive UIs, not just workflows. OpenAI got close, but you can only do a one-time export from visual builder to code and there’s vendor lock-in.<p>How I've used it: I bootstrapped a few agents for our marketing and sales teams, then was able to hand off so they can maintain and create their own agents. This has enabled us to adopt agents across technical and non-technical roles in our company on a single platform.<p>To try it, here’s the quickstart: <a href="https://go.inkeep.com/quickstart">https://go.inkeep.com/quickstart</a>.<p>We leaned on open protocols to make it easy to use agents anywhere:
An MCP endpoint, so agents can be used from Cursor/Claude/ChatGPT
A Chat UI library with interactive elements you can customize in React
An API endpoint compatible with the Vercel AI SDK `useChat` hook
Support for Agent2Agent (A2A) so they work with other agent ecosystems<p>We made some practical templates like a customer_support, deep_research, and docs_assistant. Deployment is easy with Vercel/Docker with a fair-code license and there's a traces UI and OTEL logs for observability.<p>Under the hood, we went all-in on a multi-agent architecture. Agents are made up of LLMs, MCPs, and agent-to-agent relationships. We’ve found this approach to be easier to maintain and more flexible than traditional “if/else” approaches for complex workflows.<p>The interoperability works because the SDK and visual builder share a common underlying representation, and the Inkeep CLI bridges it with a mix of LLMs and TypeScript syntactic sugar. Details in our docs: <a href="https://docs.inkeep.com">https://docs.inkeep.com</a>.<p>We’re open to ideas and contributions! And would love to hear about your experience building agents - what works, hasn’t worked, what’s promising?
Show HN: Wispbit - Linter for AI coding agents
Hey HN! Ilya and Nikita here. We're building wispbit (<a href="https://wispbit.com" rel="nofollow">https://wispbit.com</a>) - a tool that helps keep codebase standards alive.<p>With the help of AI coding tools, engineers are writing more code than ever. Code output has increased, but the tooling to manage this hasn't improved. Background agents still write bad code, and your IDE still writes slop without the right context.<p>So we built wispbit. It works by scanning your codebase for patterns you already use, and coming up with rules. Rules are kept up to date as standards change, and you can edit rules any time.<p>You can enforce these rules during code review, and because we have this rules system, you can run a CLI locally to review using these rules. You can think of it as a portable rules file that you can bring anywhere.<p>We put a lot of work into making a system that produces good rules and avoids slop. For repository crawling, we have an agent that dispatches subagents, similar to Anthropic's research agent. These subagents will go through and look for common patterns within modules and directories, and report back to the main agent, which synthesizes the results. We also do a historical scan on your pull request comments, determine which ones were addressed, filter out comments that wouldn't make a good rule, and use that to create or update rules.<p>Our early users are seeing 80%+ resolution rates, meaning that 80% of comments that wispbit makes are resolved.<p>Long-term, we see ourselves being a validation layer for AI-written code. With tools like Devin and Cursor, we find ourselves having to re-prompt the same solution many times. We still don't know the long-term implications on AI-assisted codebases, so we want to get in front of that as soon as possible.<p>We've opened up signups for free to HN folks at <a href="https://wispbit.com" rel="nofollow">https://wispbit.com</a>. We're also around to chat and answer questions!
Show HN: Trott – search,sort,extract social media videos(ig,yt,tiktok)
I built Trott out of frustration with my own “Saved” folders on Instagram and YouTube. I’d save reels and shorts—workout tips, recipes, travel spots—thinking I’d find them again later. But, like most people, I ended up with a black hole of unsorted videos: no search, no filters, and if I ever did find the right video again, I’d have to dig through the whole thing just to get that one detail I needed.<p>When I tried looking for solutions, I found only genre-specific apps or tools that demanded manual uploads or new workflows. None felt like they understood how real users behave or save content. So I decided to build Trott.<p>What makes Trott different?<p>You can share any Instagram Reel or YouTube Short (TikTok support launches next week) to Trott, straight from your phone’s native share menu—no manual uploads.<p>Trott uses AI to extract relevant info automatically (ingredients, places, products, etc.) and sorts everything for you.<p>It’s fully searchable with natural language. Just type something like “that Kyoto café from Instagram” and Trott finds it.<p>For travel videos, it can even produce Google Maps routes from extracted locations.<p>App Store: <a href="https://apps.apple.com/in/app/trott/id6751728352">https://apps.apple.com/in/app/trott/id6751728352</a>
Play Store: <a href="https://play.google.com/store/apps/details?id=in.hattimatimlabs.trott&hl=en_IN">https://play.google.com/store/apps/details?id=in.hattimatiml...</a><p>I’d love to hear how you organize your own saved content—or if you’ve just given up and let it pile up like I used to. Open to all questions, feedback, and bug reports. Happy to discuss the tech details behind Trott if you’re interested!
Show HN: Trott – search,sort,extract social media videos(ig,yt,tiktok)
I built Trott out of frustration with my own “Saved” folders on Instagram and YouTube. I’d save reels and shorts—workout tips, recipes, travel spots—thinking I’d find them again later. But, like most people, I ended up with a black hole of unsorted videos: no search, no filters, and if I ever did find the right video again, I’d have to dig through the whole thing just to get that one detail I needed.<p>When I tried looking for solutions, I found only genre-specific apps or tools that demanded manual uploads or new workflows. None felt like they understood how real users behave or save content. So I decided to build Trott.<p>What makes Trott different?<p>You can share any Instagram Reel or YouTube Short (TikTok support launches next week) to Trott, straight from your phone’s native share menu—no manual uploads.<p>Trott uses AI to extract relevant info automatically (ingredients, places, products, etc.) and sorts everything for you.<p>It’s fully searchable with natural language. Just type something like “that Kyoto café from Instagram” and Trott finds it.<p>For travel videos, it can even produce Google Maps routes from extracted locations.<p>App Store: <a href="https://apps.apple.com/in/app/trott/id6751728352">https://apps.apple.com/in/app/trott/id6751728352</a>
Play Store: <a href="https://play.google.com/store/apps/details?id=in.hattimatimlabs.trott&hl=en_IN">https://play.google.com/store/apps/details?id=in.hattimatiml...</a><p>I’d love to hear how you organize your own saved content—or if you’ve just given up and let it pile up like I used to. Open to all questions, feedback, and bug reports. Happy to discuss the tech details behind Trott if you’re interested!
Show HN: Trott – search,sort,extract social media videos(ig,yt,tiktok)
I built Trott out of frustration with my own “Saved” folders on Instagram and YouTube. I’d save reels and shorts—workout tips, recipes, travel spots—thinking I’d find them again later. But, like most people, I ended up with a black hole of unsorted videos: no search, no filters, and if I ever did find the right video again, I’d have to dig through the whole thing just to get that one detail I needed.<p>When I tried looking for solutions, I found only genre-specific apps or tools that demanded manual uploads or new workflows. None felt like they understood how real users behave or save content. So I decided to build Trott.<p>What makes Trott different?<p>You can share any Instagram Reel or YouTube Short (TikTok support launches next week) to Trott, straight from your phone’s native share menu—no manual uploads.<p>Trott uses AI to extract relevant info automatically (ingredients, places, products, etc.) and sorts everything for you.<p>It’s fully searchable with natural language. Just type something like “that Kyoto café from Instagram” and Trott finds it.<p>For travel videos, it can even produce Google Maps routes from extracted locations.<p>App Store: <a href="https://apps.apple.com/in/app/trott/id6751728352">https://apps.apple.com/in/app/trott/id6751728352</a>
Play Store: <a href="https://play.google.com/store/apps/details?id=in.hattimatimlabs.trott&hl=en_IN">https://play.google.com/store/apps/details?id=in.hattimatiml...</a><p>I’d love to hear how you organize your own saved content—or if you’ve just given up and let it pile up like I used to. Open to all questions, feedback, and bug reports. Happy to discuss the tech details behind Trott if you’re interested!
Show HN: Greenonion.ai – AI-Powered Design Assistant
Hi HN<p>I’m excited to launch GreenOnion.ai
— a platform that helps anyone create beautiful, editable design layouts instantly using AI.<p>A lot of “AI design” tools today generate images. GreenOnion doesn’t. You bring your own images — our AI handles the layout, composition, colors, and typography to turn them into cohesive, ready-to-use designs.<p>Every element is structured and editable — text, spacing, colors, hierarchy — not just pixels on a canvas. It’s real design generation, not image generation.<p>What it does:<p>Describe what you want (e.g. “modern poster for a coffee brand”)<p>AI builds a layout around your content and image<p>Edit and refine everything in the browser<p>Export for web, print, or campaigns<p>Why we built it:
Design shouldn’t be locked behind complex tools or templates. If you can describe an idea, you should be able to see it take form — and still have full control to adjust it.<p>It’s live and working today: <a href="https://greenonion.ai" rel="nofollow">https://greenonion.ai</a><p>I’d love your feedback — whether it’s about the product, the concept, or where you think AI-driven design should go next.<p>Thanks for reading,
— Yanjie
Founder, GreenOnion.ai
Show HN: Firm, a text-based work management system
Show HN: Firm, a text-based work management system
Show HN: Firm, a text-based work management system
Show HN: Firm, a text-based work management system
Show HN: Firm, a text-based work management system
Show HN: Scriber Pro – Offline AI transcription for macOS
Hey HN! Built this because I was tired of waiting hours for transcription
services and didn't want to upload sensitive recordings to the cloud.<p><pre><code> Real metrics from my M1 Max: 4.5hr video file transcribed in 3 minutes 32
seconds. Works completely offline.
First 5 HN users who click the button on the page get it free. Literally promo code straight to the app sore
Key differences vs Rev/Otter:
- No 2-hour file limits (handles any length)
- Timecodes stay accurate on long files (no drift from chunking)
- Supports MP3, WAV, MP4, MOV, M4A, FLAC
- Exports to SRT, VTT, JSON, PDF, DOCX, CSV, Markdown
Built for macOS. Happy to answer questions!</code></pre>
Show HN: Scriber Pro – Offline AI transcription for macOS
Hey HN! Built this because I was tired of waiting hours for transcription
services and didn't want to upload sensitive recordings to the cloud.<p><pre><code> Real metrics from my M1 Max: 4.5hr video file transcribed in 3 minutes 32
seconds. Works completely offline.
First 5 HN users who click the button on the page get it free. Literally promo code straight to the app sore
Key differences vs Rev/Otter:
- No 2-hour file limits (handles any length)
- Timecodes stay accurate on long files (no drift from chunking)
- Supports MP3, WAV, MP4, MOV, M4A, FLAC
- Exports to SRT, VTT, JSON, PDF, DOCX, CSV, Markdown
Built for macOS. Happy to answer questions!</code></pre>
Show HN: Scriber Pro – Offline AI transcription for macOS
Hey HN! Built this because I was tired of waiting hours for transcription
services and didn't want to upload sensitive recordings to the cloud.<p><pre><code> Real metrics from my M1 Max: 4.5hr video file transcribed in 3 minutes 32
seconds. Works completely offline.
First 5 HN users who click the button on the page get it free. Literally promo code straight to the app sore
Key differences vs Rev/Otter:
- No 2-hour file limits (handles any length)
- Timecodes stay accurate on long files (no drift from chunking)
- Supports MP3, WAV, MP4, MOV, M4A, FLAC
- Exports to SRT, VTT, JSON, PDF, DOCX, CSV, Markdown
Built for macOS. Happy to answer questions!</code></pre>
Show HN: Halloy – Modern IRC client
I started working on Halloy back in 2022, with the goal of giving something back to the community I’ve been a part of for the past two decades. I wanted to create a modern, multi-platform IRC client written in Rust.<p>Three years later, I’ve made new friends who have become core contributors, and there are now over 200 people idling in our #halloy channel on Libera.<p>My hope is that this client will outlive me and that IRC will live on.
Show HN: Halloy – Modern IRC client
I started working on Halloy back in 2022, with the goal of giving something back to the community I’ve been a part of for the past two decades. I wanted to create a modern, multi-platform IRC client written in Rust.<p>Three years later, I’ve made new friends who have become core contributors, and there are now over 200 people idling in our #halloy channel on Libera.<p>My hope is that this client will outlive me and that IRC will live on.
Show HN: Halloy – Modern IRC client
I started working on Halloy back in 2022, with the goal of giving something back to the community I’ve been a part of for the past two decades. I wanted to create a modern, multi-platform IRC client written in Rust.<p>Three years later, I’ve made new friends who have become core contributors, and there are now over 200 people idling in our #halloy channel on Libera.<p>My hope is that this client will outlive me and that IRC will live on.