If you want to build something instead, I suggest taking a look at indie hackers to see what other people are doing or using a tool like glimpse to find trends before they pop-off and build a solution to those things. Source: almost 3 years ago

The most valuable and impressive thing you can do is build a business. Hands down. Especially if it makes money. That will show you're not just a cog in the wheel but able to critically think and have valuable practical skillsets. I would experiment with something that has tailwinds. Like an AI business, or a VR business once the new apple VR app store comes out. you'd be shocked at how much you can make... Source: about 3 years ago

For example; trends.co is not very good because the people that write for them are journalists, not business owners so although the writing is good, the ideas are poorly researched. On the other hand, meetglimpse.com is pretty good, they have nuanced and unique business ideas that you can take advantage of but the market research behind it is a little lacking, their chrome extension tool is great tho. And then... Source: about 3 years ago

a lot of things. I've built 3 online businesses that were profitable with under $1000. it's really just a hustle once you get product market fit. Starting something online shouldn't take that much money if you know how to test it. Check out like trends.vc, explodingideas.co, meetglimpse.com etc. They may be able to spark your creativity for ideas that could be good opportunities for the price point. Source: about 3 years ago

Great idea. You should scrape ideas from meetglimpse.com, explodingideas.co, trends.co and the other sites that post ideas. Would be an easy way to bulk up the document. Source: about 3 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 / 9 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 / 28 days 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 1 month 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 1 month ago

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