We do not build for today. We build for the ghosts of tomorrow—the technical debt we leave behind, the opaque systems we deploy without cryptographic proof. The 69 preloaded AI prompts in Flock's OS Investigate suite are not just a feature. They are a declaration of war against the very principles of verifiable computation.
Hook
- That is the number of preloaded AI prompts embedded in the Flock OS Investigate codebase. Each prompt is a state machine that maps human gait to a digital identity. No zero-knowledge proofs. No user consent. No open-source audit. Just a neural network trained on millions of hours of surveillance footage, now packaged as a SaaS product for law enforcement and private security firms.
I have seen this pattern before. In 2018, during a Solidity audit of a multi-sig wallet, I found a hidden state transition that allowed ownership transfer without a quorum vote. The developers called it an "optimization." I called it a reentrancy vulnerability waiting to happen. The 69 prompts are the same: hidden classifiers that convert movement into identity, but without the transparency that any serious protocol requires.
Context
Flock Safety markets its cameras as a tool for neighborhood watch and crime prevention. The hardware is ubiquitous: license plate readers, audio sensors, and now AI-powered video analytics. OS Investigate is the software layer that aggregates data from thousands of devices. The 69 preloaded prompts are not mere filters; they are behavioral recognition models that identify individuals by how they walk, their stride length, arm swing, and posture. This is gait analysis at scale.
Gait recognition is not new. The biometric community has studied it for decades. But deploying it on a fleet of unverified cameras, without cryptographic attestation of the data provenance, is a fundamental failure of engineering. The system uses a centralized database to store movement vectors, linked to timestamps and location metadata. No hashing of the identity. No proof of personhood. Just a floating point vector that can be matched against any other vector in the network.
From my experience in protocol development, I can tell you that any system that relies on a single point of data aggregation is a single point of failure. The Flock architecture is a classic client-server model with a trusted third party—the exact opposite of the decentralized infrastructure we advocate for in blockchain.
Core
Let us dissect the technical architecture. The Flock camera captures video at 30 frames per second. The on-device AI processes each frame, extracting keypoints for the human skeleton using a variant of OpenPose. These keypoints are then fed into a recurrent neural network that encodes the temporal pattern of the gait. The output is a 128-dimensional vector—a unique fingerprint of how you move.
This vector is sent to the OS Investigate cloud, where it is compared against a database of previously stored vectors. The matching algorithm is a simple cosine similarity search. If the similarity exceeds a threshold (0.85 by default), the system flags a match. The user—typically a police officer or security guard—receives an alert: "Subject identified with 94% confidence."
Here is the problem: the entire pipeline is opaque. The neural network weights are proprietary. The threshold is configurable but not auditable. The database is centralized and encrypted at rest, but with a key held by Flock. There is no on-chain verification, no Merkle tree of identities, no zero-knowledge proof that the match is correct without revealing the subject's vector.
During my work on a proof-of-personhood protocol for AI agents, I designed a system where each agent's identity is committed to a smart contract using a Pedersen commitment. The verification is done off-chain, but the proof is published on-chain. The Flock system does none of this. It is a black box that produces outputs with no cryptographic guarantee.
The 69 prompts are not just queries; they are stateful classifiers. Each prompt corresponds to a specific gait pattern—e.g., "limp" or "fast walker" or "carrying heavy load." These are not binary labels; they are continuous vectors that can be combined. The system can identify you not just by your gait, but by your gait under specific conditions. This is a behavioral fingerprint far more granular than a simple face recognition.
Technical debt is the ghost in every machine. The debt here is massive: the system relies on the assumption that the camera hardware is tamper-proof, the network is secure, and the database is incorruptible. None of these assumptions hold in a adversarial environment. A single compromised camera can inject false data into the system, poisoning the entire classifier. The art is the hash; the value is the proof. Flock has the hash, but no proof.
Contrarian
The common narrative is that this technology is a net positive for public safety. Criminals can be identified by their gait even if they wear masks. The system can find missing persons. These are valid use cases. But the contrarian angle is not about the intent—it is about the infrastructure.
Reentrancy doesn't care about your intentions. The smart contract audit I mentioned earlier was for a wallet that was supposed to protect user funds. The developers had good intentions. But the code had a reentrancy bug that allowed a malicious contract to drain the wallet. The 69 prompts are a reentrancy attack on civil liberties. They create a system where any authorized user can query the database and extract gait vectors without a permissioned transaction. The access control is weak: a simple API key that can be shared or stolen.
Moreover, the system normalizes behavioral surveillance as a service. Once you have a database of gait vectors, you can retroactively track anyone who ever walked past a Flock camera. This is a form of metadata collection that is even more invasive than location tracking, because it links the physical movement pattern to an identity. The blockchain community has spent years fighting for privacy-preserving technologies like zk-SNARKs and ring signatures. Flock is building the opposite: a centralized surveillance panopticon.
My experience with the NFT metadata migration taught me that any system that relies on a centralized gateway for data retrieval is fragile. The Flock cloud is the ultimate gateway. If it goes down, the entire system collapses. If it is compromised, every gait vector is exposed. The storage layer is not decentralized. The indexing is not permissionless. The verification is not trustless.
We do not build for today. We build for a future where identity is self-sovereign, where proofs are generated without revealing the underlying data. The 69 prompts are a step backward. They are the infrastructure debt of a surveillance era that is being built on sand.
Takeaway
The question is not whether Flock's technology works. It works. Gait recognition is accurate enough for many operational scenarios. The question is whether we, as a technical community, will accept a system that is opaque, centralized, and unverifiable. The blockchain industry has a responsibility to provide an alternative: a decentralized identity protocol that uses zero-knowledge proofs to verify gait patterns without exposing the raw biometric data.
Until then, the 69 prompts are a ticking bomb. Every time a camera captures your walk, it is not just a data point. It is a state transition in a machine that has no checks and balances. The block confirms everything. Even your mistakes.
