protobuf-ts VS fern

DX for front or back end? The beauty of tRPC is that the types are derived/inferred from the backend runtime code (like, as you type). It would be nigh impossible to do that with grpc(-web) using proto files as the source of truth.

It's possible there's a project out there which could automatically produce proto files from something like zod, json-schema, etc. which could be directly interpreted by TS to provide similar (as you type) DX while still allowing some other language backend to consume the derived proto files (though the DX there would be less than ideal).

If you're just looking for similar TS clients/interfaces for grpc-web then I'd recommend https://github.com/timostamm/protobuf-ts which operates on plain JS objects (no new MyMessage().serialize(), instead the code generator mostly produces TS interfaces for you to work against: const myMessage: MyMessage = pojoConformingToInterface; const binary = MyMessage.toBinary(myMessage);)

https://github.com/timostamm/protobuf-ts (RpcMetaData) https://github.com/timostamm/protobuf-ts/blob/main/MANUAL.md

The second big difference is that we now have auto-generated client and server stubs. For this task, I chose to use buf and the protobuf-ts plugin in order to generate idiomatic Typescript classes and objects. Not only do these classes describe the types we'll use in the server and client, but also includes the actual gRPC implementations used to serialize and send messages back and forth across the wire.

I wouldn’t use grpc with web if you can avoid it. If you’re looking for a ts protobuf library I can recommend this one https://github.com/timostamm/protobuf-ts

https://github.com/timostamm/protobuf-ts looks interesting too.

I'd recommend looking into protobuf-ts (Timo from Buf) or protobuf-es (Buf maintained).

Use a GRPC library for frontend, assuming you want to go with Typescript take a look at https://github.com/timostamm/protobuf-ts Frontend frameworks like Angular/React/Vue don't define what and how to implement backend communication. You can use what you want and how you want it.

And there's also this which is by the same author but came before it: https://github.com/timostamm/protobuf-ts

The latter has code-generation for services and has various transport packages for twirp, grpc, and grpc-web.

This looks very interesting! Anything that can move people away from protobuf.js (which seems to no longer be maintained and depends on prototype values for "default values" meaning that you can't send deserialized protobuf messages to/from web workers) and the "native" JS codegen by Google (which produces code that is both very slow and a awkward to use) is a win in my book.

We're currently using https://github.com/timostamm/protobuf-ts which has been fantastic. It's codegen is dependent on the protoc binary as it is implemented as a protoc plugin, but the code it generates passes the protobuf conformance tests. The generated code also outputs plain objects when deserializing protobuf messages which means it works perfectly when sending stuff to/from web workers. It also has grpc, grpc-web, and twirp clients.

Lots of these have been popping up lately, they all seem really good.

https://buildwithfern.com/

Fern | https://buildwithfern.com | Founding Backend Engineer | $160k + equity | On-site NYC | Full-time

At Fern, we're creating the modern developer experience platform. We work with developer-focused companies to generate SDKs & API documentation. We're looking for a Founding Backend Engineer to help us scale with our users. You'll join a small team (3 of us) and will be a product owner who designs, builds, and ships weekly.

Learn more at https://www.buildwithfern.com/careers

Fern (YC W23) | Founding Engineer | New York City | $130k-$160k + 0.5-1.0% equity | Full Time | Open Source | https://buildwithfern.com

REST APIs underpin the internet but are still painful to work with. They are often untyped, unstandardized, and out-of-sync across multiple sources of truth. With Fern, we aim to bring great developer experiences to REST APIs.

Our stack is Next.js + Vercel, Express (Node.js) + FastAPI (Python), Postgres DB + Prisma ORM, and AWS CDK. We're open source: https://www.github.com/fern-api/fern

We closed a Seed this year from top-tier US investors, including Y Combinator, Abhinav Asthana (Postman CEO), Arash Ferdowsi (Dropbox co-founder), and Ian McCrystal (Stripe's Head of Docs).

Learn more: https://www.buildwithfern.com/careers

Thank you for your encouraging words and insights!

There are indeed popular DSLs and code to openapi solutions out there. Many of which are easy to plug in to the openapi-stack libraries btw!

I guess I personally always found it frustrating to try to control the generated OpenAPI output using additional tooling and ended up preferring yaml + a visualisation tool as the api design workflow. (e.g. swagger editor)

But something like https://buildwithfern.com, or using zod as substitute for json schema may indeed be worth a try as a step before emitting openapi.

Fern (YC W23) | Founding Engineer | New York City | $125k-$175k + equity | Full Time | Open Source | https://buildwithfern.com

REST APIs underpin the internet but are still painful to work with. They are often untyped, unstandardized, and out-of-sync across multiple sources of truth. With Fern, we aim to bring great developer experiences to REST APIs.

Our stack is Next.js + Vercel, Express (Node.js) + FastAPI (Python), Postgres DB + Prisma ORM, and AWS CDK.

We closed a Seed this year from top-tier US investors, including Y Combinator, Abhinav Asthana (Postman CEO), Arash Ferdowsi (Dropbox co-founder), and Ian McCrystal (Stripe's Head of Docs).

Apply by emailing [email protected]

Hi HN! Langfuse is OSS observability and analytics for LLM applications (repo: https://github.com/langfuse/langfuse, 2 min demo: https://langfuse.com/video; try it yourself: https://langfuse.com/demo)

Langfuse makes capturing and viewing LLM calls (execution traces) a breeze. On top of this data, you can analyze the quality, cost and latency of LLM apps.

When GPT-4 dropped, we started building LLM apps – a lot of them! [1, 2] But they all suffered from the same issue: it’s hard to assure quality in 100% of cases and even to have a clear view of user behavior. Initially, we logged all prompts/completions to our production database to understand what works and what doesn’t. We soon realized we needed more context, more data and better analytics to sustainably improve our apps. So we started building a homegrown tool.

Our first task was to track and view what is going on in production: what user input is provided, how prompt templates or vector db requests work, and which steps of an LLM chain fail. We built async SDKs and a slick frontend to render chains in a nested way. It’s a good way to look at LLM logic ‘natively’. Then we added some basic analytics to understand token usage and quality over time for the entire project or single users (pre-built dashboards).

Under the hood, we use the T3 stack (Typescript, NextJs, Prisma, tRPC, Tailwind, NextAuth), which allows us to move fast + it means it's easy to contribute to our repo. The SDKs are heavily influenced by the design of the PostHog SDKs [3] for stable implementations of async network requests. It was a surprisingly inconvenient experience to convert OpenAPI specs to boilerplate Python code and we ended up using Fern [4] here. We’re fans of Tailwind + shadcn/ui + tremor.so for speed and flexibility in building tables and dashboards fast.

Our SDKs run fully asynchronously and make network requests in the background. We did our best to reduce any impact on application performance to a minimum. We never block the main execution path.

We've made two engineering decisions we've felt uncertain about: to use a Postgres database and Looker Studio for the analytics MVP. Supabase performs well at our scale and integrates seamlessly into our tech stack. We will need to move to an OLAP database soon and are debating if we need to start batching ingestion and if we can keep using Vercel. Any experience you could share would be helpful!

Integrating Looker Studio got us to first analytics charts in half a day. As it is not open-source and does not work with our UI/UX, we are looking to switch it out for an OSS solution to flexibly generate charts and dashboards. We’ve had a look at Lightdash and would be happy to hear your thoughts.

We’re borrowing our OSS business model from Posthog/Supabase who make it easy to self-host with features reserved for enterprise (no plans yet) and a paid version for managed cloud service. Right now all of our code is available under a permissive license (MIT).

Next, we’re going deep on analytics. For quality specifically, we will build out model-based evaluations and labeling to be able to cluster traces by scores and use cases.

Looking forward to hearing your thoughts and discussion – we’ll be in the comments. Thanks!

[1] https://learn-from-ai.com/

[2] https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba

[3] https://posthog.com/docs/libraries

[4] https://buildwithfern.com/

You can recommend it in what context, from openapi (as they claim https://github.com/fern-api/fern#starting-from-openapi ) or from their ... special ... definition schema?

For those wanting less talk, moar code: https://github.com/fern-api/fern-java/blob/0.4.2-rc3/example... -> https://github.com/fern-api/fern-java/blob/0.4.2-rc3/example...

I'm one of the builders of an open source project (buildwithfern.com) to improve client codegen. One of the learnings I've had is that the quality of OpenAPI specs varies widely (like really widely). We wrote a linter that suggests improvements to your OpenAPI before you run the code generators and that's been really helpful for generating idiomatic clients.

You can try Fern for free: https://buildwithfern.com