HTTP Toolkit Reviews

Langfuse Reviews

HTTP Toolkit mentions (30)

• GrapheneOS – Break Free from Android and iOS

  I can add certificates on my unrooted android. That how HTTPToolkit [0] works, it only requires adb, which (thankfully) doesn't trip banking apps. Banking apps can (and do iirc) pin certificates, so a rooted phone adds no risk whatsoever. Also in my experience a rooted phone experience is by far more secure than the OEM androids. Security is supposed to assess risk objectively, yet "running on a Xiaomi phone with... - Source: Hacker News / 5 months ago

• Charles Proxy

  For my rather simple needs I've been using https://httptoolkit.com free edition, I like that it launches a independent Firefox window on its own for the intercepting so I don't have to touch my working browser or deal with configuring a proxy anywhere. - Source: Hacker News / 7 months ago

• Charles Proxy

  This one is truly a gem: https://httptoolkit.com It even bypasses SSL pinning on Android using 1 click. - Source: Hacker News / 7 months ago

• APKLab: Android Reverse-Engineering Workbench for VS Code

  Https://httptoolkit.com also worth a look if you're interested in this space: has some neat automated setup for Android MITM that can be much simpler _and_ more effective than the manual config route (with automated Frida setup on rooted devices, so it handles unpinning too!). More UI & less CLI focused, so depends which way your preferences go there. - Source: Hacker News / about 1 year ago

• Launch HN: Integuru (YC W24): Reverse-Engineer Internal APIs Using LLMs

  Just setup httptoolkit [0], it just works. [0] - https://httptoolkit.com/. - Source: Hacker News / over 1 year 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 / 16 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 2 months 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