Pattern Monster: A simple online pattern generator to create repeatable SVG patterns. Perfect for website backgrounds, apparel, branding, packaging design and more. - Source: dev.to / about 2 years ago

SVG Patterns (if it counts as illustration:) https://pattern.monster/. Source: over 3 years ago

For the back of the card I'll use and SVG pattern, feel free to use the one you like the most from Pattern Monster. Also center the background image with background-position, and make it cover the full card by adding background-size: cover;. - Source: dev.to / over 3 years ago

I'd like to put together a POC to show some possible solutions. I know there are some tools out there such as https://pattern.monster/ and https://haikei.app/ which can generate images, but ideally this would be a library or at least an API I can hook into, so admins can just create templates in our admin application without having to go to an external site and manually knock a thumbnail up. Although not required,... Source: over 3 years ago

Bonus points for adding a custom background - pattern.monster is a must-use, in my personal, humble opinion. Source: about 4 years ago

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 / 26 days ago

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 / about 1 month ago

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 2 months ago

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 2 months ago

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