import { unstable_noStore as noStore } from "next/cache"; import Link from "next/link"; import { CreatePost } from "~/app/_components/create-post"; import { getServerAuthSession } from "~/server/auth"; import { api } from "~/trpc/server"; export default async function Home() { noStore(); const hello = await api.post.hello.query({ text: "from tRPC" }); const session = await getServerAuthSession(); return (

Create T3span> App h1>

First Steps →h3>

Just the basics - Everything you need to know to set up your database and authentication. div> Link>

Documentation →h3>

Learn more about Create T3 App, the libraries it uses, and how to deploy it.div> Link> div>

{hello ? hello.greeting : "Loading tRPC query..."}p>

{session && Logged in as {session.user?.email}span>} p> {session ? "Sign out" : "Sign in"} Link> div> div> div> main> ); } async function CrudShowcase() { const session = await getServerAuthSession(); if (!session?.user) return null; const latestPost = await api.post.getLatest.query(); return (

{latestPost ? (

Your most recent post: {latestPost.name}p> ) : (

You have no posts yet.p> )} div> ); }

I thought he was making stuff up - "t3 stack vs t4 stack". But about 2 minutes in, I realized that I'd heard of a few things that he's talking about so I looked it up and they're real!

I think it's possible everything he's saying is true, more or less. LOL

t3: https://create.t3.gg

t4: https://t4stack.com

We start with a project that was bootstrapped with create-t3-app. For internationalization we use next-intl and set it up as described in the getting started guide. With this initial project setup we can jump into implementing localized error messages.

I used create-t3-app to scaffold the Next.js app, with TypeScript, Prisma, and "app router" enabled in the options. You can also use create-next-app for the job and install Prisma manually.

I agree that the quantity of publicly available code isn't the most reliable indicator of someone's seniority.

My issue with this individual arises from the discrepancy between his public claims of significant expertise in the content he produces. He positions himself as a highly experienced developer, attracting a large following of junior developers who take his advice at face value.

I am trying to collect data points supporting his claims of seniority. For instance, his website prominently features a statement that he is the creator of the T3 Stack. However, a review of the contributor statistics for the T3 Stack (https://github.com/t3-oss/create-t3-app/graphs/contributors)... minimal contributions from him, which raises questions about the validity of his claims.

I would recommend - https://create.t3.gg/

It uses the following, which as of late are pretty well know and common, so you can punch in your problems to stackoverflow, google, or chatgpt and get some pretty good answers.

It uses:

Nextjs (React), typescript, trpc (typescript rpc), auth, tailwind, and Prisma (ORM)

Though of course these could go out of fashion tomorrow, but I don't think the essential idea behind these libs and frameworks are that wacky, unique or unordinary.

Prisma ORM, is a little opinionated, and you could swap it out for Drizzle, which is basically typescript side sugared SQL.

TRPC might be also be a little sticky because it is tied to typescript, this is the tradeoff for the buttery smooth coupling for the fullstack experience. I think there exists a typed-rest solution out there, but haven't used it.

Personal anecdata, I used this stack for a little hobby project and it was FUN.

Open SaaS was built with Typescript, and because it’s a full-stack app, type safety from the back-end to the front-end can be a real lifesaver. I mean, some opinionated stacks have gotten hugely popular on this basis.

Let's start by creating a Next.js application. We will use the T3 stack (TypeScript, TailwindCSS, and Prisma ORM) for this tutorial to skip installing and configuring all the dependencies which is out of the scope of this article. You can find more information about the T3 stack.

https://github.com/t3-oss/create-t3-app

You still would need to add Stripe but there are so many examples publicly available that it should be straight forward

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