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init0

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skills-over-mcp.h3manth.com 8h ago

Skills over MCP

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2pts0
h3manth.com 5d ago

Show HN: Pocket Voice: TTS and Voice clone in the browser

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3pts0
news.ycombinator.com 22d ago

Show HN: Vaghenu, a meter aware sloka-to-chant, TTS for Sanskrit

init0
10pts2
eslabs.web.app 2mo ago

Show HN: ESLabs

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1pts0
h3manth.com 2mo ago

Client side search and recommendation with TurboQuant

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1pts0
agentathon.dev 3mo ago

Show HN: Agentathon, hackathon where AI agents compete autonomously

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2pts0
shlokartha.in 3mo ago

Show HN: Shloka Artha [Word-by-Word Meanings for Shlokas]

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h3manth.com 3mo ago

More Vetoes, Less Vision

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pypi.org 3mo ago

Show HN: mmcheck -- Check if a model supports multimodal inputs.

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1pts0
github.com 4mo ago

Show HN: Reverse lookup XKCD comics using Gemini multimodal embeddings

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1pts0
github.com 5mo ago

Show HN: Ucpify – JSON config to UCP-compliant commerce server

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2pts0
h3manth.com 5mo ago

RFCs vs. READMEs: The Evolution of Protocols

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5pts2
h3manth.com 5mo ago

RCFs to READMEs

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1pts1
news.ycombinator.com 6mo ago

Show HN: UCP Demo – Interactive Demo of the Universal Commerce Protocol

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4pts0
news.ycombinator.com 6mo ago

Show HN: Agentlearn – Interactive course for AI agent fundamentals

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2pts0
news.ycombinator.com 6mo ago

Show HN: Agentu Minimalist Python AI agent framework

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1pts0
hemanth.github.io 6mo ago

Show HN: Gojju, a Fun Programming Language

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13pts4
h3manth.com 6mo ago

My 9-Year-Old Built His Own Gaming PC

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4pts0
mcphost.link 6mo ago

Show HN: MCP Web Host

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2pts1
okap.dev 7mo ago

OKAP (Open Key Access Protocol): Like OAuth, but for API Keys

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3pts1
mcphost.link 7mo ago

MCP Web Host

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2pts1
pypi.org 7mo ago

Show HN: RAG in 3 Lines of Python

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41pts6
github.com 7mo ago

SEP-1865 MCP Apps Extension sample implementation

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1pts1
h3manth.com 8mo ago

AI Agents Are the New Web Stack

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3pts0
pypi.org 8mo ago

Agentu: The sleekest way to build AI agents

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1pts1
github.com 8mo ago

Show HN: OpenAI Apps Handbook

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10pts0
github.com 9mo ago

AI ML Jargon

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3pts0
github.com 9mo ago

GitHub launches public MCP registry

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3pts0
github.com 1y ago

Show HN: A2A Xkcd Agent as per the Spec

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2pts0
github.com 1y ago

Show HN: A MCP server and client implementing the latest spec

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72pts27
RCFs to READMEs 6 months ago

TCP/IP took nine years to deploy. MCP moved to the Linux Foundation in one. That contrast explains everything about how protocol development has changed.

I've been tracking the explosion of AI Agent protocols over the last 18 months. The contrast with history is staggering: - TCP/IP: 9 years from paper to "Flag Day." - OAuth 2.1: 5+ years and still counting. - Model Context Protocol (MCP): <1 year from launch to Linux Foundation.

It’s not just MCP. In 2025 alone, we saw: - Google's Agent2Agent (50+ partners), - Universal Commerce Protocol (20+ retailers) - AP2 (Payments) all ship. - Agent Protocol, UTCP and few more.

We are entering an era of "Room Consensus": where a few giants agree on a spec and ship it to billions, bypassing the slow deliberation of the RFC era. Is this efficiency? Or fragility?

I break down the landscape of the new agent protocols and what this means for developers in my latest post.

Problem: Every AI app wants you to paste your OpenAI/Anthropic key. Keys spread across dozens of apps with zero visibility, and you can only revoke by rotating the key itself.

Proposal: OKAP (Open Key Access Protocol) - like OAuth, but for API keys.

How it works: 1. Keys stay in YOUR vault (self-host or hosted) 2. Apps request access via token (scoped to provider, models, expiry) 3. Vault proxies requests - apps never see your actual key 4. Revoke any app instantly without touching your master key

Not to be confused with LiteLLM/OpenRouter (those are proxies you pay for). OKAP is a protocol for user-owned key management - your keys, your vault, your control.

Working implementation: - Hosted vault: https://vault.okap.dev - Python SDK: pip install okap - Spec: https://okap.dev

Looking for feedback. Would you use this for your AI tools? What's missing?

MCP Web Host 8 months ago

Hey folks! I just launched https://mcphost.link/ a web-based MCP host that lets you connect to multiple remote MCP servers and interact with them through a simple chat-style interface.

Key Features

Multi-server support — connect to several MCP servers at once

OAuth 2.0 & Bearer Token auth (with PKCE)

Persistent sessions — servers + credentials saved locally

Full MCP features — tools, resources, prompts

LLM support — bring your own inference backend

The goal is to make exploring and working with the Model Context Protocol much more approachable.

Happy to answer questions, take feedback, or hear feature requests!

SEP-1865 MCP Apps Extension, even though in draft currently, will change how AI agents deliver interactive experiences.

The idea: MCP tools return HTML/CSS/JS directly. The client renders it in a sandboxed iframe. That's it.

Your AI agent calls a tool, gets back a full interactive UI. Dashboard, form, chart - whatever you need.

How it works: - Tool returns text/html+mcp resource - Client renders in iframe with CSP - UI talks back via JSON-RPC 2.0 postMessage - Fully sandboxed, secure by default

Built a sample implementation with vanilla Web Components. This is where MCP is heading.

I got tired of complex agent frameworks with their orchestrators and YAML configs, so I built something simpler.

  AgentU uses two operators for workflows: >> chains steps, & runs parallel. That's it.
``` from agentu import Agent, serve import asyncio
  def search(topic: str) -> str:
      return f"Results for {topic}"

  # Agent auto-detects available model, connects to authenticated MCP server
  agent = Agent("researcher").with_tools([search]).with_mcp([
      {"url": "http://localhost:3000", "headers": {"Authorization": "Bearer token123"}}
  ])

  # Memory
  agent.remember("User wants technical depth", importance=0.9)

  # Parallel then sequential: & runs parallel, >> chains
  workflow = (
      agent("AI") & agent("ML") & agent("LLMs")
      >> agent(lambda prev: f"Compare: {prev}")
  )

  # Execute workflow
  result = asyncio.run(workflow.run())

  # REST API with auto-generated Swagger docs
  serve(agent, port=8000)
```
  Features:
  - Auto-detects Ollama models (also works with OpenAI, vLLM, LM Studio)
  - Memory with importance weights, SQLite backend
  - MCP integration with auth support
  - One-line REST API with Swagger docs
  - Python functions are tools, no decorators needed

  Using it for automated code review, parallel data enrichment, research synthesis.

  pip install agentu

  GitHub: https://github.com/hemanth/agentu

  Open to feedback.

Trying to openai am I missing something?

    import OpenAI from 'openai'

    const openai = new OpenAI({
      baseURL: 'http://localhost:11434/v1',
      apiKey: 'ollama', // required but unused
    })

    const chatCompletion = await 
      openai.chat.completions.create({
      model: 'llama2',
      messages: [{ role: 'user', content: 'Why is the sky blue?' }],
    })

    console.log(completion.choices[0].message.content)
I am getting the below error:
    return new NotFoundError(status, error, message, headers);
                   ^
    NotFoundError: 404 404 page not found