santiago-pl

I'm seeing traffic to the GoModel github repo and to my website from aocr.org . Did anyone double-check that it's ByteDance as the article claims? Are there any other possibilities?

Thanks for the links!

Definitely it was a deliberated decision. Also enterprises adapt Go more and more.

I wish all the best to LiteLLM team with handling it again. I can imagine how challenging are situations like this one.

However, if anyone is looking for 20x faster and less "targeted" LiteLLM alternative - I'm work on one in Go. That's why GoModel it's called. https://github.com/ENTERPILOT/GoModel/

What do you mean? It's a link to a post on X

  1. Last Friday I noticed that one of my OPENAI keys had used up my spending limit in the OpenAI Console.
  2. Today I got the e-mail from OpenAI quoted below.
  3. I haven't got any e-mail from OpenCode about it.

I can’t remember using this OpenCode Console. Can anyone explain to me what happened?

Mail from OpenAI:

Subject: OpenCode incident: update on your API keys and reimbursement

Body:

Hello,

We're writing about a security incident reported by OpenCode, a third-party AI coding tool. OpenCode reported unauthorized access to a system that stored customer API keys.

An OpenAI API key associated with your organization, [masked], was identified as affected. The affected OpenAI API keys are no longer active and cannot be used to make new API requests.

API usage is billed to the organization associated with the key, including when an exposed key is used without authorization. As part of our review, we have applied a one-time courtesy adjustment to address the charges associated with the unauthorized usage we identified. (...)

I found this benchmark helpful: https://benchmarkheaven.com/jev-models

I encountered it on reddit. Did anyone try it? Is it really AWS-compatible?

rtk gain mechanism is oversimplified. 1 token != 4 bytes for the standard prompt / context window at coding agent.

Right now we have those OpenClaw commercial alternatives: Grok Bot, Muse, Viktor. Anything more?

I like the idea!

Two things I would like to see there:

1. In Poland we don't have Euro - I would love to see a switch / dropdown to change the currency for displayed prices.

  a) Also I think it will be convenient to for US based people to see the prices in USD

2. Oil prices are changing so fast recently. I've verified those for Poland - those are not up-to-date: https://prices.eu.org/fuel/poland/ Refreshing them daily might be helpful in this case.

It's simple and it's working!

I can't find a GitHub link. I assume it's not open-source. Am I correct?

WDYT about building a browser extension based on this?

Did anyone compare it with gpt-6?

GoModel author here. If you are looking for a self-reproducible benchmark, I run this one periodically. I keep it as honest as possible to know how GoModel stands in comparison to its competitors.

Any feedback appreciated via GitHub issues: https://github.com/ENTERPILOT/ai-gateway-reproducible-benchm...

GoModel author here. Someone asked me about a benchmark for GoModel during my "Show HN" launch here.

A few days ago, I moved it to a separate repository and gave it another run.

The benchmark is self-reproducible, so you can run it yourself and verify the results.

Feel free to contribute your open-source, self-hosted AI gateway here. If it's mature enough - I'd be more than happy to add it.

(also - if you see any unfairness in the benchmark, please let me know)

I came up with this idea of artificially slowing down expensive models within a company to incentivize people to use cheaper ones. I'm curious - has anyone tried this at scale?

GoModel author here. I prepared self-reproducible benchmarks and published them on my blog. Might be helpful. I don't think latency is the best metric for comparing AI gateways, as most of the latency comes from the providers. Memory usage is a much better metric and GoModel wins here.

https://enterpilot.io/blog/benchmarking-ai-gateways-gomodel-...

If you experience any issues with LiteLLM, you may try GoModel - the AI Gateway I'm working on. It consumes ~60x less resources and is more reliable :)

Actually, LiteLLM is the most popular, it was first product of this type, but there is plenty of more efficient and reliable alternate AI Gateways right now. I've written one - GoModel. https://gomodel.enterpilot.io/

I'm working on the last AI gateway you'll ever try - GoModel :)

I recently created a benchmark and it looks like GoModel is the fastest and most lightweight open-source (self-hosted) AI gateway on the market. I tried to make it as fair and honest as possible and you can reproduce the benchmark yourself.

https://enterpilot.io/blog/benchmarking-ai-gateways-gomodel-...

I’ve created an honest, reproducible benchmark of self-hosted AI gateways literally two days ago. I posted it on HN here: https://news.ycombinator.com/item?id=48688213

To be totally transparent, I’m the author of GoModel, but the benchmark is fully reproducible. It might be helpful in making your mind.

I mentally treat it as part of the documentation right now.

Currently, I want to keep them close to the documentation, where I link to them directly. The fewer repositories I have, the easier it is to maintain them.

Thank you! Feel free to reach out to me over Discord in the case of any feedback or feature request.

GoModel author is here.

This is my attempt to honestly compare GoModel vs LiteLLM vs Portkey vs Bifrost AI gateways.

Giorgi is the semantic caching master at GoModel right now. Let me ping him, and he'll get back to you here.

I'll definitely take a look at GAI myself! I like this beaver(?) at README.

I'm wroking on it full-time right now. It might be challenging, especially when it comes to interactions with video, audio, and image models. I'm just trying to stay on top of what's happening and add new things day by day.

An even bigger challenge might be integrating it tighter with the cloud platforms, Prometheus, OAuth, Datadog, vaults, and DLP software.

My thoughts about this:

Benchmarking AI gateways properly is harder than it looks. Feature sets differ meaningfully - exact vs semantic caching, cluster mode, guardrails, audit logging - and each carries its own latency cost. What actually matters for most users is end-to-end latency including provider overhead (200–2000ms), and in that frame Bifrost, LiteLLM, and GoModel are all perfectly fine.

I ran some comparisons but I'm not happy with the methodology, and I'd rather not spread misleading information. Once I have time to do it properly I'll write it up and share a link here. Honestly, I'd also love to see benchmarks done by someone other than the AI gateway builders. :)

Where GoModel actually differs today:

  - image size: 16.96 MB vs Bifrost's 69.84 MB. It matters for sidecar, edge, and cold-start scenarios.
  - per-tenant keys, guardrails, and audit logs are all in the OSS repo - not gated.
  - AI interaction visualization that makes debugging individual request/response flows much easier.

I've released a new version of GoModel (0.1.20) with explicit support for vllm. You can now use it even with a few vLLM instances. Like this:

  docker run --rm -p 8080:8080 \
    -e VLLM_BASE_URL=http://host.docker.internal:18000/v1 \
    -e VLLM_BASEMENT_BASE_URL=http://host.docker.internal:18000/v1 \
    enterpilot/gomodel:latest