openapi-typescript-codegen VS fern

I’d also add that if you use Typescript with an OpenAPI client generator (https://github.com/ferdikoomen/openapi-typescript-codegen) it can immensely alleviate some of the biggest pain points of seperate backend and front-end. It always used to be a major pain in the ass with the amount of overhead an API change would incur - updating documentation, postman, constant communication between backend and front-end devs, etc. Now I just npm run generate, I see new API changes in my Git client and Typescript errors for code that needs updating.

Also, using a library like Tanstack Query or Rdtk Query can almost completely eliminate manual state management, and kinda makes the whole development experience feel almost like SSR.

I have been in love with Loopback.io since v2 even though it was a bit of a rollercoaster.. Loopback v4 is a beautiful library. Its been around longer than nestjs but that's the easiest thing to compare it too. I recently have been creating lb4 servers that interface nextjs and react native clients. Initially, I identify my entities and use cases that I want to build. I then use the lb4 cli to auto generate models, relations, controllers, datasources, interceptors (add logic on methods/classes). I can start testing them with the OpenAPI explorer. With the openapi-typescript-codegen library I can generate services from my lb4 OpenAPI spec that I can use on the client side. From there, you can really query data easily with the loopback filter (which can be used on the client too). I initially started doing this with angular1/2+ but its been pleasant using many clients. Even though I have been leveraging it for years in production, I am still learning and exploring. There are many other awesome things I can expand on or explain if you are interested!

https://loopback.io/doc/en/lb4/

https://github.com/ferdikoomen/openapi-typescript-codegen/tr...

In our current project with a TS frontend and Python backend, we use an OpenAPI schema as the source of truth and openapi-typescript-codegen [0] to interface with it on the client side. While not perfect, it provides a very nice interface to our API with request/response typings.

I also wrote a 10-line mock API wrapper that you can call as mockApi((request) => response), and it will type-check that your mock function implements the API correctly.

[0]: https://github.com/ferdikoomen/openapi-typescript-codegen

Second step is to generate typescript types from the backend's spec. You can use a library like this.

openapi-typescript-codegen Generates a Typescript client with interfaces from an OpenAPI spec.

I'm not familiar with all the options openapi-generator has. I tried it awhile ago and found it quite buggy, and more of a pain to run, especially if you're not already doing Java development. I ended up preferring OpenAPI Typescript Codegen, which is written in Typescript. One option it has which would solve the problem you're running into here, is that you can tell it to use union types instead of enums. So your interfaces would be generated as

I'm currently working on two separate projects, the first is a Django project with DRF and I codegen with drf-spectacular [1] and openapi-typescript-codegen [2]. The other project also uses Django, with the API through Hasura and codegen with graphql-codegen [3]. In both of these cases I've been able to largely avoid duplicating my models clientside, or at least it isn't manual.

1: https://github.com/ferdikoomen/openapi-typescript-codegen

2: https://drf-spectacular.readthedocs.io/en/latest/

3: https://github.com/dotansimha/graphql-code-generator

For typescript client code generation, I typically use something like openapi-typescript-codegen, but there are a lot more generators (like the openapi-generator project) that are imperfect in their own ways, I'm sure you can find one that works for you.

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