Hyperprobe

Product Introduction

1. Definition: HyperProbe is an AI-native production debugging and observability platform. It is a software-as-a-service (SaaS) tool that enables developers to capture the live runtime state of their applications without requiring code changes, redeployments, or service restarts.
2. Core Value Proposition: HyperProbe exists to eliminate the inefficient "log, redeploy, wait" cycle in production debugging. Its primary value is enabling engineering teams to achieve faster mean time to resolution (MTTR) by allowing AI coding agents to place non-invasive, read-only probes directly into running services to capture variable states that are missing from traditional logs and traces.

Main Features

1. AI-Powered Probe Placement: HyperProbe integrates with AI coding assistants like Cursor, Claude Code, and Codex to automatically analyze logs and distributed traces from incidents. The AI identifies the suspected file and line of code responsible for a failure and plans a debugging flow. This feature automates the initial, time-consuming scouting phase of an incident investigation.
2. Read-Only, Non-Blocking Probes: The core technology is a virtual, read-only breakpoint (called a "probe") that can be injected into a live service. When the specific line of code is executed by real user traffic, the probe captures a snapshot of all variable states at that moment. Crucially, it operates asynchronously with zero thread pause, ensuring no performance impact (claimed <1% overhead at 3,000 RPS) or user-visible latency.
3. Secure, In-Infrastructure Execution: Probes execute entirely within the customer's infrastructure. It supports deployment models including managed cloud, self-hosted, or within a private VPC. Data never leaves the environment, and built-in PII redaction occurs at the agent level before any capture. All probe activity is logged to an immutable audit trail, with approval gates available for enterprise security compliance.

Problems Solved

1. Pain Point: Lengthy and inefficient production debugging cycles that pull senior engineers away from product development. The core problem is the lack of specific runtime data (variable states) at the moment of failure, which forces teams to rely on guesswork, add log statements, and wait for slow redeployments.
2. Target Audience: The primary user personas are Site Reliability Engineers (SREs), DevOps engineers, and senior backend developers who are on-call and responsible for diagnosing production incidents in complex, distributed systems built with JavaScript, TypeScript, Java, Python, or Ruby.
3. Use Cases: The product is essential for diagnosing silent failures (200 OK with wrong data), exceptions whose stack trace is far from the root cause, race conditions and duplicate processing, third-party API contract drift, and business logic errors where no exception is thrown (e.g., dropping payment success rates).

Unique Advantages

1. Differentiation: Unlike traditional Application Performance Monitoring (APM) or log analytics tools (e.g., Datadog, New Relic) that only reason over existing telemetry data, HyperProbe actively captures new, missing data from live memory. Unlike debuggers that require service pauses or local reproduction, HyperProbe's probes are non-blocking and work directly in production.
2. Key Innovation: The combination of AI-driven incident analysis with safe, runtime state capture in production. The specific innovation is the "virtual breakpoint" technology that allows for read-only introspection of a live application's heap and stack without stopping the process, coupled with an agent that automates the placement of these probes based on alert context.

Frequently Asked Questions (FAQ)

1. How does HyperProbe work without restarting my service? HyperProbe uses a language-specific SDK that integrates with your application's runtime. It leverages just-in-time instrumentation techniques to dynamically insert safe, read-only hooks at specific lines of code. This allows it to capture variable state from live requests without requiring a code deployment, container restart, or process interruption.
2. Is HyperProbe safe for use in production with sensitive data? Yes, security is a core design principle. The probe agent runs inside your infrastructure (cloud VPC or self-hosted). All data capture is read-only, and PII redaction rules are applied before data is persisted. An immutable audit log tracks all probe activity, and enterprise plans offer role-based access control (RBAC) and approval workflows for probe placement.
3. What kinds of production bugs is HyperProbe best at diagnosing? HyperProbe is particularly effective for bugs that are hard to reproduce locally or lack sufficient logging. This includes silent data corruption, race conditions, logic errors with no exceptions, third-party API integration issues, and state-related failures where the problematic variable value was never logged.
4. How does HyperProbe integrate with existing alerting and AI tools? The platform integrates natively with alerting systems like PagerDuty, Datadog, and Slack to automatically trigger investigations. It is designed to work with AI coding agents (Cursor, Claude Code, etc.) which act as the interface to analyze traces and place probes, making it an "AI-native" debugging workflow.
5. What is the performance overhead of using HyperProbe's probes? According to the vendor, the overhead is minimal. The asynchronous, non-blocking probe design claims to add less than 1% latency overhead even at high load (3,000 requests per second). The probes are ephemeral and removed after capturing the required data, minimizing continuous impact.