punkpeye

Use one of the router services

Did we manage to accidentally make something live twice as long and then abandon efforts to replicate the results?

Serious question: With all the advancements in mice medicine, are we theoretically able to create immortal mice?

Someone explain to me why this is on the front page.

Never heard of it and found it to be an interesting read

Exciting! Particularly the emphasis on smoother MCP integrations.

Will be adding the Add to cursor button to Glama later today today.

https://glama.ai/mcp/servers

If anyone from Cursor is reading this, we are rolling out MCP server usage analytics where we aggregate (anonymous) usage data across several providers. Would be amazing to include Cursor (reach me at frank@glama.ai). The data will be used to help the community discover the most used (and therefore useful) clients and servers.

Shocked by the negativity around this.

I tested early versions of zod v4 and liked the new API, but was very concerned about what will be the migration path. I was even going to suggest to publish under a new package name.

But the author's approach is ingenious. It allows for someone like me to start adopting v4 immediately without waiting for every dependency to update.

Well done!

Just focusing on worst-case scenarios tends to spread more FUD than move things forward. If you have specific proposals for how the protocol could be designed differently, I’m sure the community would love to hear them – https://github.com/orgs/modelcontextprotocol/discussions

Yup. It's easy to focus on what’s missing or broken in early-stage tech, but I’m more excited about where this kind of standardization could take us. Sometimes you need to look beyond imperfections and see the possibilities ahead.

I think that transport is a non-issue.

Whatever the transport evolves to, it is easy to create proxies that convert from one transport to another, e.g. https://github.com/punkpeye/mcp-proxy

As an example, every server that you see on Glama MCP registry today is hosted using stdio. However, the proxy makes them available over SSE, and could theoretically make them available over WS, 'streamable HTTP', etc

Glama is just one example of doing this, but I think that other registries/tools will emerge that will effectively make the transport the server chooses to implement irrelevant.

I am the founder of one of the MCP registries (https://glama.ai/mcp/servers).

I somewhat agree with author’s comments, but also want to note that the protocol is in the extremely early stages of development, and it will likely evolve a lot over the next year.

I think that no one (including me) anticipated just how much attention this will get straight out the door. When I started working on the registry, there were fewer than a few dozen servers. Then suddenly a few weeks later there was a thousand, and numbers just kept growing.

However, lots and lots of those servers do not work. Majority of my time has gone into trying to identify servers that work (using various automated tests). All of this is in large part because MCP got picked up by the mainstream AI audience before the protocol reached any maturity.

Things are starting to look better now though. We have a few frameworks that abstract the hard parts of the protocol. We have a few registries that do a decent job surfacing servers that work vs those that do not. We have a dozen or so clients that support MCPs, etc. All of this in less than half a year is unheard of.

So yes, while it is easy to find flaws in MCP, we have to acknowledge that all of it happened in a super short amount of time – I cannot even think of comparisons to make. If the velocity remains the same, MCP future is very bright.

For those getting started, I maintain a few resources that could be valuable:

* https://github.com/punkpeye/awesome-mcp-servers/

* https://github.com/punkpeye/awesome-mcp-devtools/

* https://github.com/punkpeye/awesome-mcp-clients/

So what are the practical use cases for this?

What I am noticing with every new Gemini model that comes out is that the time to first token (TTFT) is not great. I guess it is because they gradually transfer computer power from old models to new models as the demand increases.

It is not without rate limits, but we do have elevated limits for our accounts through:

https://glama.ai/models/gemini-2.5-flash-preview-04-17

So if you just want to run evals, that should do it.

Though the first couple of days after a model comes out are usually pretty rough because everyone try to run their evals.

This is cool, but rate limits on all of these preview models are PITA

Big fan of Inngest! Most of http://glama.ai/mcp logic is built on top of Inngest.

Let me rephrase it.

MCP has the capability to add this functionality.

It would be nice to see MCP getting adoption in their web UI, as well easier UX, rather than more ad hoc features being added natively.

Awesome to see MCP related work surfacing on the front page of HN!

There is already a 100 ways of doing it using MCP

https://glama.ai/mcp/servers?searchTerm=search

What's the benefit of bringing native integration?

Random: I tried posting this as ShowHN, but I am guessing I don't have enough reputation, because it was hidden.

So if you are here for MCP, I will use the opportunity to share what I've been working on the last few months.

I've hand curated hundreds of MCP servers, which people can access and browse via https://glama.ai/mcp/servers and made those servers available via API https://glama.ai/mcp/reference

The API allows to search for MCP servers, identify their capabilities via API attributes, and even access user hosted MCP servers.

However, you can also try these servers using an inspector (available under every server) and also in the chat (https://glama.ai/chat)

This is all part of a bigger ambition to create an all encompassing platform for authoring, discovering and hosting MCP servers.

I am also the author of https://github.com/punkpeye/fastmcp framework and several other supporting open-source tools, like https://github.com/punkpeye/mcp-proxy

If you are also interested in MCP and want to chat about the future of this technology, drop me a message.

I've been working on MCP for the last several months.

I've hand curated hundreds of MCP servers, which people can access and browse via https://glama.ai/mcp/servers

However, today I am making the API available for everyone to use. https://glama.ai/mcp/reference

The API allows to search for MCP servers, identify their capabilities via API attributes, and even access user hosted MCP servers.

This is all part of a bigger ambition to create an all encompassing platform for authoring, discovering and hosting MCP servers.

I've bootstrapped this project to just over 4k users. Most users are private individuals, though I am seeing an uptick of adoption among small businesses.

I am also the author of https://github.com/punkpeye/fastmcp framework and several other supporting open-source tools, like https://github.com/punkpeye/mcp-proxy

You can already use most of these MCP servers directly through Glama –

* https://glama.ai/chat – if you just want to use MCPs as an end user or if you want to integrate them with Cursor, Windsurf, Roo, Cline, etc. I provide SSE url to connect directly to MCP servers.

* By using sandbox instances. Every server that is capable of being hosted online (e.g. https://glama.ai/mcp/servers/oge85xl22f) can be inspected in our sandbox, which also gives you SSE url.

The project is steadily growing and I am excited to see about increased awareness about MCP.

It is whatever their API privacy policy is, i.e. private by default.

Big fan of Roo. Highly recommend them. Easy to setup and easy to use.

I am keen to see if Roo integrates the new Anthropic's coding assistant or if they become competing offerings.

I have observed some unusual behavior.

I wonder if it simply due to reprioritization of resources.

Presumably, there is some parameter that determines how long a model is allowed to use resources for, which would get tapered in preparation for a demand surge of another model.

I kinda get how LLMs work with language, but it beyond blows me my mind trying to understand how an LLM can draw SVG. There are just so many dimensions to understanding how SVG converts to an image. Even as a human I don't think I could do anywhere close to that result in first attempt.

I cannot believe that others just casually dismiss this as 'basic', when just a few years ago this would have taken someone a full day of work.

Open router is great.

They have a very solid infrastructure.

Scaling infrastructure to handle billions of tokens is no joke.

I believe they are approaching 1 trillion tokens per week.

Glama is way smaller. We only recently crossed 10bn tokens per day.

However, I have invested a lot more into UX/UI of that chat itself, i.e. while OpenRouter is entirely focused on API gateway (which is working for them), I am going for a hybrid approach.

The market is big enough for both projects to co-exist.

Indeed we do https://glama.ai/models/deepseek-r1

It is provided by DeepSeek and Avian.

I am also midway of enabling a third-provider (Nebius).

You can see all models/providers over at https://glama.ai/models

As another commenter in this tread said, we are just a 'frontend wrapper' around other people services. Therefore, it is not particularly difficult to add models that are already supported by other providers.

The benefit of using our wrapper is that you can use a single API key and you get one bill for all your AI bills, you don't need to hack together your own logic for routing requests between different providers, failovers, keeping track of their costs, worry what happens if a provider goes down, etc.

The market at the moment is hugely fragmented, with many providers unstable, constantly shifting prices, etc. The benefit of a router is that you don't need to worry about those things.

The API still has limits. Even if you are on the highest tier, you will quickly run into those limits when using coding assistants.

The value proposition of Glama is that it combines UI and API.

While everyone focuses on either one or the other, I've been splitting my time equally working on both.

Glama UI would not win against Anthropic if we were to compare them by the number of features. However, the components that I developed were created with craft and love.

You have access to:

* Switch models between OpenAI/Anthropic, etc.

* Side-by-side conversations

* Full-text search of all your conversations

* Integration of LaTeX, Mermaid, rich-text editing

* Vision (uploading images)

* Response personalizations

* MCP

* Every action has a shortcut via cmd+k (ctrl+k)

If you are open to alternatives, try https://glama.ai/gateway

We currently serve ~10bn tokens per day (across all models). OpenAI compatible API. No rate limits. Built in logging and tracing.

I work with LLMs every day, so I am always on top of adding models. 3.7 is also already available.

https://glama.ai/models/claude-3-7-sonnet-20250219

The gateway is integrated directly into our chat (https://glama.ai/chat). So you can use most of the things that you are used to having with Claude. And if anything is missing, just let me know and I will prioritize it. If you check our Discord, I have a decent track record of being receptive to feedback and quickly turning around features.

Long term, Glama's focus is predominantly on MCPs, but chat, gateway and LLM routing is integral to the greater vision.

I would love feedback if you are going to give a try frank@glama.ai