DrawSQL Reviews

Langfuse Reviews

DrawSQL mentions (12)

• AI assistance in Development

  With this, I went for designing the db. I went to http://drawsql.app/ and created my first draft. Then exported the DDL and did a bit of back and forth with AI. This is the final draft of the database:. - Source: dev.to / 8 months ago

• How Changing Requirements Shape the Infrastructure of a Software Project

  So I started designing the DB using this cool tool. The project has 2 tables, users and categories . The user can create many categories as he wants so the first approach I took was creating a third table, a union table to store user_id and category_id. With this solution the users are able to create x numbers of categories and we can see assign the category to the user. - Source: dev.to / over 1 year ago

• Creating Diagrams and Databases with Online Tools

  Once you have generated the SQL code, you can convert it into a relational schema (the graphical table model) using DrawSQL. This tool offers:. - Source: dev.to / over 1 year ago

• 🖌️ 5+1 Online Tools for Sketches, Wireframes, Drawings, and Diagrams

  DrawSQL makes it easy for teams to collaborate on creating and maintaining schema diagrams. With a single source of truth, there's no need for manually syncing diagram files between different developers and offline tools anymore. Source: about 3 years ago

• Newbie: Trying to use Supabase Auth fully with its database.

  To be honest, since you are just getting started, I think you should reconsider simplifying this app to begin with. Built something easier and get some more experience before jumping in the ocean. Maybe start by focusing only on the parent company and sub-companies. However, I strongly recommend you to try and make a diagram of your database with relations and columns as it can you a lot of time. I personally use... Source: about 3 years ago

Langfuse mentions (28)

• Strands Agents + Langfuse Evaluations

  In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse — step by step. - Source: dev.to / 11 days ago

• Best AI Monitoring Tools in 2026: LLM, Agent, and MCP Observability Compared

  Langfuse is the open-source standard for LLM observability. It traces every LLM interaction — prompts, completions, latency, token usage, cost — and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / 30 days ago

• What is an LLM evaluation harness? A deep dive into lm-eval-harness

  You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / about 1 month ago

• How to track LLM costs per customer in production

  Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / about 1 month ago

• Per-user cost attribution for your AI APP

  Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline — the metadata.userId pattern is the universal part. - Source: dev.to / about 2 months ago