Raindrop
Monitor your AI Agent the right way. Get alerted when your agent fails in production, trace exactly what went wrong, and prove your fix worked.
Raindrop launched on April 29, 2025 with 241 votes, #135 of 7,818 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Raindrop is a monitoring and observability platform designed specifically for AI products. It helps teams track, debug, and optimize the performance of their AI systems in production environments. The tool provides real-time insights into model behavior, error tracking, and system health, enabling developers and product teams to quickly identify and resolve issues before they impact users. Raindrop is built for engineering teams deploying machine learning models and AI applications that require reliable, continuous monitoring.
written from the facts on this page · September 2026
From the sources
AI engineers use Raindrop to get alerts about hidden issues and successes in their AI products. Raindrop sends alerts when your AI misbehaves and links straight to events, so you can dig into conversations or traces, understand the root cause, and fix it, fast
Introducing rd-signal-2: Frontier accuracy, production scale Read the post Detect silent agent failures in real-time. Trace every run. Auto-fix silent failures. Ship confidently. Every production run leaves a trail of messages, tool calls, retries, and errors. Raindrop captures it all in one place. Raindrop reads the trace and sends the real failures to Slack, from hallucinations and loops to broken tools. Just like a teammate, Raindrop lives in Slack. Ask questions, triage issues, create signals, and more. Every production run leaves a trail of messages, tool calls, retries, and errors. Raindrop captures it all in one place. Raindrop reads the trace and sends the real failures to Slack,…from raindrop.ai
Does a similar job
all alternatives →
AI Observability by OpenObserveAug 2026 · openobserve.ai · ▲317OpenTelemetry-native observability for agents and LLMs
Is Your Site Agent-Ready? by CloudflareApr 2026 · isitagentready.com · ▲264Scan your website to see how ready it is for AI agents.
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
RaptorCIApr 2026 · getraptorci.com · ▲99Hello! With more and more AI coding agents popping up and companies expecting higher output have you noticed more issues in production or other bottlenecks? I’m a principal engineer at a leading cybersecurity company and we have a very lean engineering team and the expectations have shifted to us using AI to increase productivity instead of hiring. The result has been production becoming the most unstable it has ever been and bottlenecks around testing and releasing as we don’t have the resources to cope. I spent a few weeks of thinking of ways that AI could help our full SDLC and one thing…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 28d ago · world.pixverse.video
Source: Product Hunt launch ↗
Launched alongside, April 2025
the whole month →
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
The Unsure Calculator is an online tool that lets you calculate with numbers you’re not sure about.
Life & fun · 2025 · filiph.github.io
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com
