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Show HN: Uscope, a new Linux debugger written from scratch

Hi! I've been building a debugger on my nights and weekends because it's fun, and I personally need a better debugger for my work. GDB and LLDB pain me greatly; we can and will do better!<p>As explained in the README, it's still very early-days and it's not ready for use yet, but check back often because it's improving all the time!<p>Check out <a href="https://calabro.io/uscope" rel="nofollow">https://calabro.io/uscope</a> for a more detailed explanation.<p>Thanks for taking a look!

Show HN: Uscope, a new Linux debugger written from scratch

Hi! I've been building a debugger on my nights and weekends because it's fun, and I personally need a better debugger for my work. GDB and LLDB pain me greatly; we can and will do better!<p>As explained in the README, it's still very early-days and it's not ready for use yet, but check back often because it's improving all the time!<p>Check out <a href="https://calabro.io/uscope" rel="nofollow">https://calabro.io/uscope</a> for a more detailed explanation.<p>Thanks for taking a look!

Show HN: Uscope, a new Linux debugger written from scratch

Hi! I've been building a debugger on my nights and weekends because it's fun, and I personally need a better debugger for my work. GDB and LLDB pain me greatly; we can and will do better!<p>As explained in the README, it's still very early-days and it's not ready for use yet, but check back often because it's improving all the time!<p>Check out <a href="https://calabro.io/uscope" rel="nofollow">https://calabro.io/uscope</a> for a more detailed explanation.<p>Thanks for taking a look!

Show HN: TinyJs React like framework in 35 lines of code

Hi HN, I got to work yesterday and today and came up with a very simple way to create and manage state in TinyJS.<p>I end up creating a simple App to illustrate how to use the new createState function and custom components in a React like manner. Here's the code for it - <a href="https://github.com/victorqribeiro/tinyapp">https://github.com/victorqribeiro/tinyapp</a><p>Here's the PR for the createState function - <a href="https://github.com/victorqribeiro/TinyJS/pull/9">https://github.com/victorqribeiro/TinyJS/pull/9</a>

Show HN: TinyJs React like framework in 35 lines of code

Hi HN, I got to work yesterday and today and came up with a very simple way to create and manage state in TinyJS.<p>I end up creating a simple App to illustrate how to use the new createState function and custom components in a React like manner. Here's the code for it - <a href="https://github.com/victorqribeiro/tinyapp">https://github.com/victorqribeiro/tinyapp</a><p>Here's the PR for the createState function - <a href="https://github.com/victorqribeiro/TinyJS/pull/9">https://github.com/victorqribeiro/TinyJS/pull/9</a>

Show HN: Workflow86 - An AI business analyst and automation engineer

Hey HN,<p>We built Workflow86 to help teams build and automate their internal business processes and workflows using drag and drop components like forms, tasks, tables and nodes for business logic, API requests, running custom code etc. It works as a standalone process/workflow automation tool, or as a workflow customization layer on top of existing apps and systems like HRIS, CRM and ERP.<p>One common problem we hear from users is that no-code still has a significant learning curve, and it can take some time to understand how to properly build something. Users also needed help with knowing what to build in the first place, or what a process might or should look like.<p>To solve this, we've integrated an AI that acts as a business analyst/consultant and workflow automation engineer. This AI is powered by a combination of Large Language Models and lots of prompt engineering, RAG and prompt chaining techniques we developed along the way.<p>See a demo of it in action here: <a href="https://www.loom.com/share/fdbd5ad64c8f4071a062ecaa6a6d01f1?sid=a84ac71b-4639-4e2c-ae9e-33a3c30006d4" rel="nofollow">https://www.loom.com/share/fdbd5ad64c8f4071a062ecaa6a6d01f1?...</a><p>In business analyst/consultant mode, the AI helps users brainstorm ideas, identify and discover processes and draft what a process should look like. Like a business analyst/consultant, the AI works to pull and extract information and details from the user by asking the right questions rather than rely on the user's instructions alone.<p>Once the required information has been gathered, the AI goes into engineer mode: it will plan and then build the entire workflow by selecting the right nodes, connecting them together and then fully configuring every single node individually as well. This includes writing custom code and API requests using stored credentials when required.<p>Once a workflow is built, edits can be done manually or by asking the AI to adjust the workflow at any time (e.g., “Add a compensation band check before final approval”). The AI has full context of the current state of the workflow, so it can “patch” in any changes like adding new nodes, rewriting existing nodes and so on.<p>Some use cases we’ve seen from customers include building: - automated compliance checks for new CRM leads - custom international contractor onboarding workflows on top of a HRIS - automated vendor risk assessment before ERP updates<p>Try it out and let us know how the AI performs and any other feedback you have!<p>Full docs can be found at <a href="https://docs.workflow86.com">https://docs.workflow86.com</a>

Show HN: Workflow86 - An AI business analyst and automation engineer

Hey HN,<p>We built Workflow86 to help teams build and automate their internal business processes and workflows using drag and drop components like forms, tasks, tables and nodes for business logic, API requests, running custom code etc. It works as a standalone process/workflow automation tool, or as a workflow customization layer on top of existing apps and systems like HRIS, CRM and ERP.<p>One common problem we hear from users is that no-code still has a significant learning curve, and it can take some time to understand how to properly build something. Users also needed help with knowing what to build in the first place, or what a process might or should look like.<p>To solve this, we've integrated an AI that acts as a business analyst/consultant and workflow automation engineer. This AI is powered by a combination of Large Language Models and lots of prompt engineering, RAG and prompt chaining techniques we developed along the way.<p>See a demo of it in action here: <a href="https://www.loom.com/share/fdbd5ad64c8f4071a062ecaa6a6d01f1?sid=a84ac71b-4639-4e2c-ae9e-33a3c30006d4" rel="nofollow">https://www.loom.com/share/fdbd5ad64c8f4071a062ecaa6a6d01f1?...</a><p>In business analyst/consultant mode, the AI helps users brainstorm ideas, identify and discover processes and draft what a process should look like. Like a business analyst/consultant, the AI works to pull and extract information and details from the user by asking the right questions rather than rely on the user's instructions alone.<p>Once the required information has been gathered, the AI goes into engineer mode: it will plan and then build the entire workflow by selecting the right nodes, connecting them together and then fully configuring every single node individually as well. This includes writing custom code and API requests using stored credentials when required.<p>Once a workflow is built, edits can be done manually or by asking the AI to adjust the workflow at any time (e.g., “Add a compensation band check before final approval”). The AI has full context of the current state of the workflow, so it can “patch” in any changes like adding new nodes, rewriting existing nodes and so on.<p>Some use cases we’ve seen from customers include building: - automated compliance checks for new CRM leads - custom international contractor onboarding workflows on top of a HRIS - automated vendor risk assessment before ERP updates<p>Try it out and let us know how the AI performs and any other feedback you have!<p>Full docs can be found at <a href="https://docs.workflow86.com">https://docs.workflow86.com</a>

Show HN: Distr – open-source distribution platform for on-prem deployments

Distr is designed to help software engineers distribute and manage their applications or agents in customer-controlled or shared-responsibility environments. You only need a Docker Compose file or Helm chart—everything else for on-prem is handled by the platform.<p>We’re are an open source dev tool company. Over the past couple of months, we’ve spoken with dozens of software companies to understand their challenges with on-prem deployments. We analyzed the internal tools they’ve built and the best practices from existing solutions, combining them into a prebuilt, Open Source solution that works out of the box and integrates seamlessly.<p>Distr consists of two key components:<p>1. Hub - Provides a centralized view of all deployments and controls connected agents. - Comes with a simple GUI but also supports API and SDK access for seamless integration. - Fully Open- Surce and self-hostable, or you can use our fully managed platform.<p>2. Lightweight Agents - Pre-built agents for Helm (Kubernetes) and Docker Compose (VM) that run alongside your application. - Handle lifecycle tasks like guided installation, updates, and rollbacks. - Provide basic metrics (health status, application version) and logs<p>If you already have a customer portal or self-service interface for on-prem deployments, you can seamlessly integrate all features into your existing portal or application using our API or SDK. Alternatively, you can use our pre-built, white-labeled customer portal.<p>Here’s what an integration into your existing customer portal could look like:<p><pre><code> import {DistrService} from "@glasskube/distr-sdk"; const customerHasAutoUpdatesEnabled = false; // replace with your own logic const deploymentTargetId = 'da1d7130-bfa9-49a1-b567-c49728837df7'; const service = new DistrService({ apiKey: 'distr-8c24167aeb5fd4bb48b6d2140927df0f' }); const result = await service.isOutdated(deploymentTargetId); if(result.deploymentTarget.deployment?.latestStatus?.type !== 'ok') { // let the user decide whether to allow updates from an instable state, e.g. with: if(!confirm('The deployment is not in a stable state. Do you want to update anyway?')) { return; } } if(result.outdated) { if(customerHasAutoUpdatesEnabled) { await service.updateDeployment({deploymentTargetId}); // notify customer about the update } else { const newerVersionsAvailable = result.newerVersions; // notify customer about the newer versions, e.g. via email } } </code></pre> With the SDK/API, you can: - Display real-time deployed version and deployment status directly within the application, notifying customers when their deployed version is outdated. - Allow customers to trigger updates from within your app using a simple API call<p>If you’re distributing software and want to streamline updates or enhance monitoring, we’d love your feedback and are here to answer any questions.<p>Getting started is easy—just bring your Docker Compose file or Helm chart, and we’ll guide you through the rest.<p>Check out the fully managed version (<a href="https://app.distr.sh/register" rel="nofollow">https://app.distr.sh/register</a>) and explore our documentation (<a href="https://distr.sh/docs/" rel="nofollow">https://distr.sh/docs/</a>) to learn more.

Show HN: Distr – open-source distribution platform for on-prem deployments

Distr is designed to help software engineers distribute and manage their applications or agents in customer-controlled or shared-responsibility environments. You only need a Docker Compose file or Helm chart—everything else for on-prem is handled by the platform.<p>We’re are an open source dev tool company. Over the past couple of months, we’ve spoken with dozens of software companies to understand their challenges with on-prem deployments. We analyzed the internal tools they’ve built and the best practices from existing solutions, combining them into a prebuilt, Open Source solution that works out of the box and integrates seamlessly.<p>Distr consists of two key components:<p>1. Hub - Provides a centralized view of all deployments and controls connected agents. - Comes with a simple GUI but also supports API and SDK access for seamless integration. - Fully Open- Surce and self-hostable, or you can use our fully managed platform.<p>2. Lightweight Agents - Pre-built agents for Helm (Kubernetes) and Docker Compose (VM) that run alongside your application. - Handle lifecycle tasks like guided installation, updates, and rollbacks. - Provide basic metrics (health status, application version) and logs<p>If you already have a customer portal or self-service interface for on-prem deployments, you can seamlessly integrate all features into your existing portal or application using our API or SDK. Alternatively, you can use our pre-built, white-labeled customer portal.<p>Here’s what an integration into your existing customer portal could look like:<p><pre><code> import {DistrService} from "@glasskube/distr-sdk"; const customerHasAutoUpdatesEnabled = false; // replace with your own logic const deploymentTargetId = 'da1d7130-bfa9-49a1-b567-c49728837df7'; const service = new DistrService({ apiKey: 'distr-8c24167aeb5fd4bb48b6d2140927df0f' }); const result = await service.isOutdated(deploymentTargetId); if(result.deploymentTarget.deployment?.latestStatus?.type !== 'ok') { // let the user decide whether to allow updates from an instable state, e.g. with: if(!confirm('The deployment is not in a stable state. Do you want to update anyway?')) { return; } } if(result.outdated) { if(customerHasAutoUpdatesEnabled) { await service.updateDeployment({deploymentTargetId}); // notify customer about the update } else { const newerVersionsAvailable = result.newerVersions; // notify customer about the newer versions, e.g. via email } } </code></pre> With the SDK/API, you can: - Display real-time deployed version and deployment status directly within the application, notifying customers when their deployed version is outdated. - Allow customers to trigger updates from within your app using a simple API call<p>If you’re distributing software and want to streamline updates or enhance monitoring, we’d love your feedback and are here to answer any questions.<p>Getting started is easy—just bring your Docker Compose file or Helm chart, and we’ll guide you through the rest.<p>Check out the fully managed version (<a href="https://app.distr.sh/register" rel="nofollow">https://app.distr.sh/register</a>) and explore our documentation (<a href="https://distr.sh/docs/" rel="nofollow">https://distr.sh/docs/</a>) to learn more.

Show HN: Distr – open-source distribution platform for on-prem deployments

Distr is designed to help software engineers distribute and manage their applications or agents in customer-controlled or shared-responsibility environments. You only need a Docker Compose file or Helm chart—everything else for on-prem is handled by the platform.<p>We’re are an open source dev tool company. Over the past couple of months, we’ve spoken with dozens of software companies to understand their challenges with on-prem deployments. We analyzed the internal tools they’ve built and the best practices from existing solutions, combining them into a prebuilt, Open Source solution that works out of the box and integrates seamlessly.<p>Distr consists of two key components:<p>1. Hub - Provides a centralized view of all deployments and controls connected agents. - Comes with a simple GUI but also supports API and SDK access for seamless integration. - Fully Open- Surce and self-hostable, or you can use our fully managed platform.<p>2. Lightweight Agents - Pre-built agents for Helm (Kubernetes) and Docker Compose (VM) that run alongside your application. - Handle lifecycle tasks like guided installation, updates, and rollbacks. - Provide basic metrics (health status, application version) and logs<p>If you already have a customer portal or self-service interface for on-prem deployments, you can seamlessly integrate all features into your existing portal or application using our API or SDK. Alternatively, you can use our pre-built, white-labeled customer portal.<p>Here’s what an integration into your existing customer portal could look like:<p><pre><code> import {DistrService} from "@glasskube/distr-sdk"; const customerHasAutoUpdatesEnabled = false; // replace with your own logic const deploymentTargetId = 'da1d7130-bfa9-49a1-b567-c49728837df7'; const service = new DistrService({ apiKey: 'distr-8c24167aeb5fd4bb48b6d2140927df0f' }); const result = await service.isOutdated(deploymentTargetId); if(result.deploymentTarget.deployment?.latestStatus?.type !== 'ok') { // let the user decide whether to allow updates from an instable state, e.g. with: if(!confirm('The deployment is not in a stable state. Do you want to update anyway?')) { return; } } if(result.outdated) { if(customerHasAutoUpdatesEnabled) { await service.updateDeployment({deploymentTargetId}); // notify customer about the update } else { const newerVersionsAvailable = result.newerVersions; // notify customer about the newer versions, e.g. via email } } </code></pre> With the SDK/API, you can: - Display real-time deployed version and deployment status directly within the application, notifying customers when their deployed version is outdated. - Allow customers to trigger updates from within your app using a simple API call<p>If you’re distributing software and want to streamline updates or enhance monitoring, we’d love your feedback and are here to answer any questions.<p>Getting started is easy—just bring your Docker Compose file or Helm chart, and we’ll guide you through the rest.<p>Check out the fully managed version (<a href="https://app.distr.sh/register" rel="nofollow">https://app.distr.sh/register</a>) and explore our documentation (<a href="https://distr.sh/docs/" rel="nofollow">https://distr.sh/docs/</a>) to learn more.

Show HN: Audiocube – A 3D DAW for Spatial Audio

I’ve recently released my solo project Audiocube<p>I wanted to make a 3D DAW, where spatial audio, physics, and virtual acoustics are all directly integrated into the engine.<p>This makes it easy to create music in 3D, and experiment with new techniques which aren’t possible in traditional DAWs and plugins.<p>I’d love to get any feedback on this software (Mac/Windows) to make it better.<p>You can download it for free through the website.<p>Thanks, Noah

Show HN: Audiocube – A 3D DAW for Spatial Audio

I’ve recently released my solo project Audiocube<p>I wanted to make a 3D DAW, where spatial audio, physics, and virtual acoustics are all directly integrated into the engine.<p>This makes it easy to create music in 3D, and experiment with new techniques which aren’t possible in traditional DAWs and plugins.<p>I’d love to get any feedback on this software (Mac/Windows) to make it better.<p>You can download it for free through the website.<p>Thanks, Noah

Show HN: Audiocube – A 3D DAW for Spatial Audio

I’ve recently released my solo project Audiocube<p>I wanted to make a 3D DAW, where spatial audio, physics, and virtual acoustics are all directly integrated into the engine.<p>This makes it easy to create music in 3D, and experiment with new techniques which aren’t possible in traditional DAWs and plugins.<p>I’d love to get any feedback on this software (Mac/Windows) to make it better.<p>You can download it for free through the website.<p>Thanks, Noah

Show HN: Mcp-Agent – Build effective agents with Model Context Protocol

Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](<a href="https://github.com/lastmile-ai/mcp-agent">https://github.com/lastmile-ai/mcp-agent</a>) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions.<p>For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and prompts.<p>mcp-agent provides a higher level interface to build apps with MCP. It handles the connection management of MCP servers so you don’t have to. It also implements the Building Effective Agents patterns: - Augmented LLM (an LLM with access to one or more MCP servers) - Router, Orchestrator-Worker, Evaluator-Optimizer, and more - Swarm<p>The key design principles are composability and reusability – every pattern is an AugmentedLLM itself, so you can chain them into more complex workflows.<p>Some background: I worked on LSP [5] and language servers at Microsoft, and saw firsthand how standards and protocols can revolutionize developer workflows. Before LSP every IDE had its own esoteric ways of providing language services. LSP changed all that, and arguably made every language server better, since they can focus on improving a single implementation for all clients.<p>I think AI development is in a similar pre-LSP space right now. There are tons of frameworks [6], every model provider has its own way of handling messages, tool calls, streaming, etc. I really think we need a protocol to standardize these patterns.<p>Pretty soon every service is going to expose an MCP interface, and mcp-agent is about letting developers orchestrate these services into applications (i.e. build “MCP apps”). This can cover any use of an AI model that needs to interact with the world around it: - RAG pipelines and Q&A chatbots - Process automation via AI workflows/async tasks - Multi-agent orchestration, with human in the loop<p>The repo contains examples [7] to build RAG agents, streamlit apps and more. There’s a lot left to build, like streaming support, server auth and tighter integration with MCP clients.<p>But I wanted to share early in the hopes that you can guide me: - If you find this useful, please let me know. If it’s useful to you, I will dedicate all my time to improving it. - I really welcome contributions. If you want to collaborate, please reach out on github to help take this forward.<p>I want to help standardize AI development, so developers a few years from now can look back with horror at the pre-MCP days.<p>[1] - <a href="https://github.com/lastmile-ai/mcp-agent">https://github.com/lastmile-ai/mcp-agent</a><p>[2] - <a href="https://modelcontextprotocol.io/introduction" rel="nofollow">https://modelcontextprotocol.io/introduction</a><p>[3] - <a href="https://www.anthropic.com/research/building-effective-agents" rel="nofollow">https://www.anthropic.com/research/building-effective-agents</a><p>[4] - <a href="https://github.com/openai/swarm">https://github.com/openai/swarm</a><p>[5] - <a href="https://microsoft.github.io/language-server-protocol/" rel="nofollow">https://microsoft.github.io/language-server-protocol/</a><p>[6] - <a href="https://xkcd.com/927/" rel="nofollow">https://xkcd.com/927/</a> (I understand the irony)<p>[7] - <a href="https://github.com/lastmile-ai/mcp-agent/tree/main/examples">https://github.com/lastmile-ai/mcp-agent/tree/main/examples</a>

Show HN: Mcp-Agent – Build effective agents with Model Context Protocol

Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](<a href="https://github.com/lastmile-ai/mcp-agent">https://github.com/lastmile-ai/mcp-agent</a>) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions.<p>For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and prompts.<p>mcp-agent provides a higher level interface to build apps with MCP. It handles the connection management of MCP servers so you don’t have to. It also implements the Building Effective Agents patterns: - Augmented LLM (an LLM with access to one or more MCP servers) - Router, Orchestrator-Worker, Evaluator-Optimizer, and more - Swarm<p>The key design principles are composability and reusability – every pattern is an AugmentedLLM itself, so you can chain them into more complex workflows.<p>Some background: I worked on LSP [5] and language servers at Microsoft, and saw firsthand how standards and protocols can revolutionize developer workflows. Before LSP every IDE had its own esoteric ways of providing language services. LSP changed all that, and arguably made every language server better, since they can focus on improving a single implementation for all clients.<p>I think AI development is in a similar pre-LSP space right now. There are tons of frameworks [6], every model provider has its own way of handling messages, tool calls, streaming, etc. I really think we need a protocol to standardize these patterns.<p>Pretty soon every service is going to expose an MCP interface, and mcp-agent is about letting developers orchestrate these services into applications (i.e. build “MCP apps”). This can cover any use of an AI model that needs to interact with the world around it: - RAG pipelines and Q&A chatbots - Process automation via AI workflows/async tasks - Multi-agent orchestration, with human in the loop<p>The repo contains examples [7] to build RAG agents, streamlit apps and more. There’s a lot left to build, like streaming support, server auth and tighter integration with MCP clients.<p>But I wanted to share early in the hopes that you can guide me: - If you find this useful, please let me know. If it’s useful to you, I will dedicate all my time to improving it. - I really welcome contributions. If you want to collaborate, please reach out on github to help take this forward.<p>I want to help standardize AI development, so developers a few years from now can look back with horror at the pre-MCP days.<p>[1] - <a href="https://github.com/lastmile-ai/mcp-agent">https://github.com/lastmile-ai/mcp-agent</a><p>[2] - <a href="https://modelcontextprotocol.io/introduction" rel="nofollow">https://modelcontextprotocol.io/introduction</a><p>[3] - <a href="https://www.anthropic.com/research/building-effective-agents" rel="nofollow">https://www.anthropic.com/research/building-effective-agents</a><p>[4] - <a href="https://github.com/openai/swarm">https://github.com/openai/swarm</a><p>[5] - <a href="https://microsoft.github.io/language-server-protocol/" rel="nofollow">https://microsoft.github.io/language-server-protocol/</a><p>[6] - <a href="https://xkcd.com/927/" rel="nofollow">https://xkcd.com/927/</a> (I understand the irony)<p>[7] - <a href="https://github.com/lastmile-ai/mcp-agent/tree/main/examples">https://github.com/lastmile-ai/mcp-agent/tree/main/examples</a>

Show HN: Mcp-Agent – Build effective agents with Model Context Protocol

Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](<a href="https://github.com/lastmile-ai/mcp-agent">https://github.com/lastmile-ai/mcp-agent</a>) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions.<p>For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and prompts.<p>mcp-agent provides a higher level interface to build apps with MCP. It handles the connection management of MCP servers so you don’t have to. It also implements the Building Effective Agents patterns: - Augmented LLM (an LLM with access to one or more MCP servers) - Router, Orchestrator-Worker, Evaluator-Optimizer, and more - Swarm<p>The key design principles are composability and reusability – every pattern is an AugmentedLLM itself, so you can chain them into more complex workflows.<p>Some background: I worked on LSP [5] and language servers at Microsoft, and saw firsthand how standards and protocols can revolutionize developer workflows. Before LSP every IDE had its own esoteric ways of providing language services. LSP changed all that, and arguably made every language server better, since they can focus on improving a single implementation for all clients.<p>I think AI development is in a similar pre-LSP space right now. There are tons of frameworks [6], every model provider has its own way of handling messages, tool calls, streaming, etc. I really think we need a protocol to standardize these patterns.<p>Pretty soon every service is going to expose an MCP interface, and mcp-agent is about letting developers orchestrate these services into applications (i.e. build “MCP apps”). This can cover any use of an AI model that needs to interact with the world around it: - RAG pipelines and Q&A chatbots - Process automation via AI workflows/async tasks - Multi-agent orchestration, with human in the loop<p>The repo contains examples [7] to build RAG agents, streamlit apps and more. There’s a lot left to build, like streaming support, server auth and tighter integration with MCP clients.<p>But I wanted to share early in the hopes that you can guide me: - If you find this useful, please let me know. If it’s useful to you, I will dedicate all my time to improving it. - I really welcome contributions. If you want to collaborate, please reach out on github to help take this forward.<p>I want to help standardize AI development, so developers a few years from now can look back with horror at the pre-MCP days.<p>[1] - <a href="https://github.com/lastmile-ai/mcp-agent">https://github.com/lastmile-ai/mcp-agent</a><p>[2] - <a href="https://modelcontextprotocol.io/introduction" rel="nofollow">https://modelcontextprotocol.io/introduction</a><p>[3] - <a href="https://www.anthropic.com/research/building-effective-agents" rel="nofollow">https://www.anthropic.com/research/building-effective-agents</a><p>[4] - <a href="https://github.com/openai/swarm">https://github.com/openai/swarm</a><p>[5] - <a href="https://microsoft.github.io/language-server-protocol/" rel="nofollow">https://microsoft.github.io/language-server-protocol/</a><p>[6] - <a href="https://xkcd.com/927/" rel="nofollow">https://xkcd.com/927/</a> (I understand the irony)<p>[7] - <a href="https://github.com/lastmile-ai/mcp-agent/tree/main/examples">https://github.com/lastmile-ai/mcp-agent/tree/main/examples</a>

Show HN: Design/build of some parametric speaker cabinets with OpenSCAD

Show HN: Design/build of some parametric speaker cabinets with OpenSCAD

Show HN: Open-Source Alternative to OpenAI Platform, for Local Models

Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.

Show HN: Open-Source Alternative to OpenAI Platform, for Local Models

Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.