fern VS l2beat

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

A real layer 2 would look more like something built on Ethereum (can see all its L2s at https://l2beat.com).

Essentially it's a separate network that every few minutes takes every transaction and compresses it into a data blob that it saves on Ethereum along with a proof that the computation was done correctly. The Ethereum L1 nodes then only need to verify the proof instead of re-executing all transactions that happened on the L2.

With this design users can go straight from an exchange like Coinbase onto the L2 and never need to use Ethereum, and fees are 10x cheaper because of the data compression. Fees will soon be 100x cheaper as Ethereum is adding extra space just for these L2 data blobs that is much cheaper than normal Ethereum data space.

Unfortunately it can't be done on Bitcoin right now because Bitcoin nodes don't have Turing complete scripting and so can't verify the proof that an L2 posts to Bitcoin.

We are running & maintaining the site (https://l2beat.com). Our work is to look on the current Layer 2 deployments on Ethereum & show risks and statistics to the end user. Very interesting thing is that we are a public goods company trying to stay as objective as possible in the industry full of subjectivity. What I mostly like in this job is that I am a part of the project shaping how it looks, not only mindlessly taking someones orders.

Candidate:

Ecosystem fragmentation is not necessarily a bad thing. It leads to rapid development through competition. Different L2s are competing against each other to provide the best service and that has lead to a cambrian explosion of solutions. It's also a very effective way to explore the solution space, I'm sure many will disappear, others will get eaten, and at some point there will be consolidation. But all this seems like a good approach early on when tackling complex problems for which the ideal trade-offs are not entirely obvious. Explore as much of the solution space as possible and trim later on.

A perhaps more pernicious problem is liquidity fragmentation. Moving assets between L2s is a tedious friction that leads to fragmentation of liquidity. In that respect, zero-knowledge rollups present a big advantage as you can share liquidity between them as long as they share some zk-circuits that allow to prove statements to both chains. All this is being very actively worked on. And the technology behind it is short of fascinating. The typical HN audience would have a huge hard-on for it, if they didn't have such a strong preconception against crypto-anything.

If anyone is curious to learn more about L2s a good starting point is here: https://l2beat.com/

And if you want to see Ethereum scaling progress you can check it here: https://l2beat.com/scaling/activity

The next major upgrade to the protocol, slated for late this year or early 2024 (date is not finalized yet), will focus on scalability by making L2 activity veeery cheap.

These two links will give you a lot of the info you need to compare L2s: https://l2beat.com/ and https://www.growthepie.xyz/ - enjoy.

Source: https://l2beat.com

Thanks! l2beat.com is the best.

In addition to what others said, I always find https://l2beat.com useful to see a summary of the security assumptions behind the various L2s. Currently, all L2 need to be trusted to some extent as they are still quite in development.

Exponential.fi has good summaries and links to the projects. And https://l2beat.com is also great for judging L2s.

Take a careful look at https://l2beat.com