Prisma Reviews

1. shah7

   · Admin at Lensa Mod · over 3 years ago
   Lensa

   Prisma AI is a computer vision and AI company that specializes in developing deep learning algorithms for image and video processing. It offers a range of products for object recognition, image and video classification, and style transfer. Many users have praised its ease of use and accurate results. However, like any technology, it has its limitations and may not always provide the best results for all use cases.

Langfuse Reviews

Prisma mentions (5)

• How would generating art in private work ?

  What if I just want to make a few? However if you're hoping to do this just for a few images then there are some very low cost apps (often free if you plan it right) which use Stable Diffusion and Dreambooth in the background to produce the personalised images. One such example is Lensa. Source: over 3 years ago

• Catching The Morning Sun

  Perhaps, or they just used an app like Prisma to add that “painting” effect. Source: over 4 years ago

• Need your review. As a backend dev are you happy with GraphQL vs REST?

  I had to deal with this more in Rails whereas in Node/Apollo, using Prisma made composing efficient/perform ant SQL queries trivial: https://www.prisma.io/. Source: almost 5 years ago

• Carrack phone wallpaper edited using style transfer

  I really liked this wallpaper by /u/MadDaz and I tried using style transfers using prisma-ai.com to generate images a bit more abstract. Here are the results! Source: almost 5 years ago

• Rare Rye Rose Mash-up

  Thanks - I made it on my android phone using Prisma and Snapseed. Source: about 5 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 / 13 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 / about 1 month 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