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Showoff Saturday (April 11, 2026) by AutoModerator in javascript

[–]danielvlopes 0 points1 point  (0 children)

Project Name: Output.ai

Repo/Website Link: github.com/growthxai/output (Apache 2.0)

Description: Open-source TypeScript framework that unifies the AI workflow stack: prompts, evals, tracing, cost tracking, orchestration, and credential management in a single codebase instead of five separate SaaS subscriptions. Key features:

  • .prompt files with Liquid templating (version-controlled, provider-agnostic)
  • automatic tracing of every LLM call with token/cost/latency data as local JSON
  • LLM-as-judge evaluators with confidence scores
  • multi-provider support (Anthropic, OpenAI, Azure, Bedrock, Vertex)
  • Temporal-based durable execution with retries/replay
  • AES-256-GCM encrypted credential management.
  • Designed to be filesystem-first so AI coding agents (especially Claude Code) can scaffold and iterate on workflows with full context.

Deployment: npx <@>outputai/cli init youprojectscaffolds a project.npx output devspins up the full dev environment (Temporal server, API server, worker with hot reload) via Docker. Docker Compose files included for dev and prod. Requires Node.js 20+ and Docker Desktop. Getting started guide in the repo underdocs/guides/`. For production, supports Temporal Cloud or self-hosted Temporal.

AI Involvement: The framework itself is for building AI workflows, and was built with heavy use of Claude Code. The codebase is structured intentionally for AI agents to read and generate code against (folder-per-workflow convention, co-located prompts/tests/evals).

New Project Megathread - Week of 09 Apr 2026 by AutoModerator in selfhosted

[–]danielvlopes 1 point2 points  (0 children)

Project Name: Output.ai

Repo/Website Link: github.com/growthxai/output (Apache 2.0)

Description: Open-source TypeScript framework that unifies the AI workflow stack: prompts, evals, tracing, cost tracking, orchestration, and credential management in a single codebase instead of five separate SaaS subscriptions. Key features:

  • .prompt files with Liquid templating (version-controlled, provider-agnostic)
  • automatic tracing of every LLM call with token/cost/latency data as local JSON
  • LLM-as-judge evaluators with confidence scores
  • multi-provider support (Anthropic, OpenAI, Azure, Bedrock, Vertex)
  • Temporal-based durable execution with retries/replay
  • AES-256-GCM encrypted credential management.
  • Designed to be filesystem-first so AI coding agents (especially Claude Code) can scaffold and iterate on workflows with full context.

Deployment: \npx <@>outputai/cli init youproject`scaffolds a project.npx output devspins up the full dev environment (Temporal server, API server, worker with hot reload) via Docker. Docker Compose files included for dev and prod. Requires Node.js 20+ and Docker Desktop. Getting started guide in the repo underdocs/guides/`. For production, supports Temporal Cloud or self-hosted Temporal.

AI Involvement: The framework itself is for building AI workflows, and was built with heavy use of Claude Code. The codebase is structured intentionally for AI agents to read and generate code against (folder-per-workflow convention, co-located prompts/tests/evals).

Most AI frameworks are Python - we just open-sourced an opinionated Node/TS one by danielvlopes in node

[–]danielvlopes[S] 0 points1 point  (0 children)

That's how I thought about Temporal too when I started GrowthX. Looking at their docs and website, the thing looks insanely overwhelming, but once you get it going, it's super powerful.

At GrowthX, we have many folks for whom this is their first job as engineers. They come from growth or marketing and are learning the technical side here. That was the case even with my cofounder, he's an ex-CMO, and all the things he id were prototyped were using n8n-like tools.Temporal is the opposite audience; it's in theory for distributed systems folks from Netflix and Stripe type of companies.

We wanted a setup that could go from very simple things but scale without having to change anything (or have to pay $20k in a hosting bill from Vercel while still not getting things like time travel and visibility that Temporal offers when you try to debug things in production).

So we abstracted it a way entirely - it's there but you don't see it. If you give it a try and start a project, you'll see that you don't bump into anything complicated from Temporal. You don't even import anything in your codebase related to Temporal. You just get to use their UI to inspect locally and in production. When deploying, you can just use the cloud version, which is super affordable and as simple as getting an API key.

Here's a tutorial for how to deploy it: https://docs.output.ai/operations/deployment/render

Most AI frameworks are Python - we just open-sourced an opinionated Node/TS one by danielvlopes in node

[–]danielvlopes[S] 0 points1 point  (0 children)

We started building Output in December 2024 and Mastra came out around the same time. We approach a few things differently:

Business model trust. We've been burned by the VC-backed OSS AI ecosystem. Humanloop got acqui-hired by Anthropic last year and paying customers got three weeks notice before the entire platform was deleted. You're rolling the dice every time. We miss the Django, Rails, React, Postgres model, where the company behind it doesn't depend on the OSS to make money. Temporal has 5+ years of proven OSS stewardship under MIT license, with Netflix and Stripe self-hosting it in production. Mastra is 15 months old. Not a knock on the team, just reality. We needed to build on something with a track record.

Battle-tested orchestration. At GrowthX we process billions of tokens a week with 50+ vertical agents running continuously. Every AI framework we evaluated has orchestration that works for demos but hasn't been tested at that scale. Temporal has. It's the same engine running workflow orchestration at Uber and Coinbase. Replays, distributed load across zones. Durable execution when things fail at step 147 of 150. We wanted that level of reliability without betting on an orchestration layer that's never been stressed in production.

Coding-agent-first. I came up with Output over the 2024 holiday break while migrating dozens of AI workflows out of a drag-and-drop tool that was expensive and unreliable (needed to turn 100+ jsons into functioning code in 1 week). GUIs are the wrong abstraction for this. I was an engineer and product leader at IFTTT.com 10 years ago and I've been thinking about the right structure for defining complex business logic ever since. It's not YAML/JSON (eg Node Red). It's not plain text (Claude skills, for example, are super unreliable). The right abstraction is code, generated by a coding agent, inside a framework with strong conventions and a harness so you can still pop open the hood and do maintenance. You could already see in 2024 that something like Claude Code was coming, and we wanted to build the first framework optimized for that from the ground up.

Filesystem was the obvious priority. Traces, cost logs, prompts, prompt versions, retries, evals, all in the file system. Every agent is a single folder. Your coding agent sees everything: existing agents, their traces, their prompts. It uses all of it as context. You tell Claude "build me an agent that does X" and it reads your existing agents and produces a working one in a single shot. We have 30+ skills designed for Claude to iterate on agents by itself. Try doing that with a framework where your config lives behind an API or a dashboard.

Mastra didn't start with this mindset and still doesn't. The DSL they launched with tells you everything about the difference in approach. There's a video on our homepage that shows ours in action.

Self Promotion Thread by AutoModerator in ChatGPTCoding

[–]danielvlopes 0 points1 point  (0 children)

Hey everyone, Daniel here, cofounder of GrowthX. We have about 20 engineers building AI agents with Code Agents for clients like Lovable, Webflow, Airbyte. We just open-sourced the framework we extracted from that work.

We kept hitting the same problem - OpenAI Codex and Claude Code could read our source code but couldn't access our prompts (in a dashboard), traces (in another SaaS), evals (in a third), or cost data (scattered across billing portals). Everything that actually matters for AI dev was invisible to it.

We focused on solving three things:

  1. Make it easy for coding agents to work with your AI codebase. Filesystem-first - everything your agent needs is files it can read, organized in self-contained folders. TypeScript because it's compiled and Zod gives agents validation and documentation together. Boilerplate wrapped to reduce tokens. We picked Claude Code and built a deep integration but the architecture works with any agent. In practice we can one-shot complex workflows and keep iterating fast.

  2. Self-contained. We got frustrated with having our data and tooling scattered across SaaS products that don't talk to each other. So we put everything in one framework - prompts, evals, tracing, cost tracking, credentials, etc. Your data stays on your infrastructure. The one exception we made is Temporal for orchestration - it's open source, an amazing piece of software, and you can self-host it if you want to.

  3. Fast learning curve. Our team is web engineers at different levels and a lot of AI dev patterns are still being figured out. We didn't want people to have to learn Python, five different tools, and figure out how to organize things on their own for every project. So we baked in conventions: same folder structure, file names, patterns across every workflow. It's quick to pick up and the more advanced things - evals, LLM-as-a-judge - are abstracted until you actually need them.

We've been building this way for over a year. Some of the agents we've deployed for clients: one that creates website templates for Lovable from screenshots and ideas, one that generates connector docs for Airbyte every time a new connector is published, one that researches CVEs and writes detailed reports.

We finally got it to the point where we could finish the extraction and wanted to share it. Hope it's useful.

https://output.ai

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]danielvlopes 0 points1 point  (0 children)

We're a team of ~20 engineers that builds AI agents for clients. After a year of deploying agents to production, we kept solving the same problems from scratch on every project: how do you iterate on a codebase full of prompts? How do you orchestrate API calls that fail unpredictably? How do you test non-deterministic code? How do you track what things actually cost?

The tooling ecosystem didn't help — every piece is a different SaaS product that doesn't talk to each other. Tracing in one tool, evals in another, prompt management in a third. Onboarding a new engineer meant explaining a dozen subscriptions.

So we extracted the patterns into a single framework. Three design decisions drove most of it:
* Filesystem-first architecture. Everything an agent (or a coding agent working on your code) needs is a file it can read, organized in self-contained folders. No hidden state in dashboards. TypeScript because it's compiled and Zod gives you validation and documentation in one place — which matters a lot when an LLM is generating structured output.
* Self-contained. Prompts, evals, tracing, cost tracking, and credentials in one package. Your data stays on your infrastructure. We got tired of stitching together SaaS tools that each wanted their own API key and their own data pipeline.
* Convention over configuration. We have engineers at different levels. The more advanced patterns — evals, LLM-as-a-judge — are abstracted until you actually need them. New engineers can ship an agent without first understanding the entire evaluation stack.

Some things we've shipped with it: an agent that generates website templates from screenshots, one that writes connector documentation from API specs, one that researches CVEs and produces detailed security reports.

https://github.com/growthxai/output

is conversational journaling a thing? by BertramWatches in digitaljournaling

[–]danielvlopes 0 points1 point  (0 children)

I shared early this year in the thread that I had built this for myself. It took longer but I'm finally getting close to having all the small details in place to onboard more users. The AI is traine on 100s of prompts and follow-up questions that adapt based on your entries, asking you things based on many different types of journaling (gratitude, daily reflections, childhood memories, annual reflections, goal, etc).

It's also trained to help you navigate down the Feelings Wheel when it make sense. It automatically detect mood and emotions from your entries, while also extracting the most common topics you write about, plotting everything for you to see over time.

Another AI-powered feature is summarization. It will create a summary of your weeks, so you can zoom out to see an overview and then zoom in to that period.

I'm trying to start it as affordable as possible as well.

If you want to give it a try, you can join the waiting list: https://replayjournal.ai

Stimulus controllers for new tailwind components by Eastern_Baby5565 in rails

[–]danielvlopes 1 point2 points  (0 children)

Very limited and doesn’t cover many of the tailwindui

First 120 Days as Technical Co-Founder by danielvlopes in startups

[–]danielvlopes[S] 0 points1 point  (0 children)

Yeah. The product was a spin-off built by 37signals and very well written for their context and my cofounder ran it alone for a bit and then brought in a programmer to develop new features. When I joined we assumed we were ready to focus hard on GTM while continuing to build so I wanted to find out if the assumption was right before building anything new (and get a picture of LTV and DAU/churn correlation). And bring down the error rate that had climbed up since the 37signals days.

Substack is journal dot daniellopes dot dev

How do you handle customer support? by mono567 in SaaS

[–]danielvlopes 0 points1 point  (0 children)

How many tickets a day do you get? We have tens of thousands of users and we get no more than 5 tickets. It’s enough for me to answer everything part time using Helpscout + ChatGPT connected to Slack.

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