HoundDog.ai - Code Context Engine
Stop wasting AI tokens rediscovering your code
530 followers
Stop wasting AI tokens rediscovering your code
530 followers
HoundDog.ai is the deterministic code context engine. Rust scanners map services, APIs, and data flows across monorepos and microservices on every commit.
ProtoMap: maps gRPC service, API, and field names across all call sites and multiple hops. Coding agents get it over MCP, with 13× fewer tokens and 7.7× faster answers in our benchmarks.
Privacy Code Scanner: GDPR data mapping and code-level evidence of sensitive data flows across logs, storage, APIs, third parties, and AI integrations.
This is the 2nd launch from HoundDog.ai - Code Context Engine. View more
ProtoMap
Launched this week
ProtoMap helps engineers and AI coding agents understand how services, APIs, and data fields depend on each other across large codebases. It builds a deterministic dependency map for gRPC and Protobuf systems, updated on every commit and accessible through a web app or MCP. Instead of wasting time and tokens searching code across repositories, agents query ProtoMap directly to understand change impact. One enterprise team reduced change-planning time and tokens by 8×.
Free Options
Launch Team / Built With
Render Sandboxes: a computer for every agentRun untrusted code safely. Idle sandboxes cost nothing
Promoted
When it says a change is safe, can I see which callers the agent actually opened and which ones it skipped? That list is what I'd check before trusting the verdict.
@0x47xavier Great question! ProtoMap maps services, APIs, fields, and their dependencies across multiple hops and call sites. Your agent can query this graph to identify all affected callers without having to discover them by opening files. In our detailed benchmark (https://hounddog.ai/protomap-benchmark/), ProtoMap achieved 100% accuracy across all 20 prompts, while Claude Code with Opus 5.5 at High Effort missed some dependencies. That said, tracking which files the agent actually opened or skipped is separate from identifying all the dependencies.
Fair, those are two different problems. The graph tells you who should get touched. In review I'd still want the gap between that list and what the agent actually edited, so a missed caller shows up before merge.
Returning actual caller locations alongside the dependency graph is a useful detail. It gives an engineer something concrete to inspect before deciding who needs to be involved in a change.
@oleksii_sekundant Thanks, Oleksii! That's exactly why we built ProtoMap. With AI coding agents, the amount of code is about to explode, and dependencies are becoming far more complex, way beyond what humans can keep up with. I really appreciate your support!