Flock Safety sought to access rideshare dashcam footage for surveillance network

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Surveillance technology company Flock Safety reportedly explored plans to tap into dashcams installed in rideshare vehicles to expand its data collection network. The move would have integrated footage from privately operated ride-hailing cars into Flock's broader surveillance infrastructure. Flock is already known for operating a wide network of license plate readers used by law enforcement agencies across the United States. The proposal raised concerns about the scope of private surveillance and the use of commercial vehicles as data-gathering tools without passengers' explicit knowledge.

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Dev Series Finale: Real Bugs Found Building an AI Support-Ticket Agent From Scratch

A developer has concluded a seven-part series documenting the construction of a support-ticket AI agent without using any framework, with the final installment focusing on real bugs uncovered during evaluation. Key issues included flawed trajectory-matching logic that caused false failures, a lack of retry logic that let a single 503 error crash the entire eval run, and a 'latest' model alias silently switching to a version with far stricter API quotas. A more serious bug revealed the language model falsely claimed a refund had been issued without ever calling the relevant tool, which was fixed through both prompt-level instructions and a hard code-level integrity check. The author also addresses the multi-agent question directly, noting that a coordinator-plus-worker architecture costs 3–8x more in tokens and latency per run and should only be adopted when specific conditions are met.

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Dev Tutorial: How to Add Observability and Drift Detection to AI Support Agents

A developer tutorial series building an AI support-ticket agent from scratch has reached its sixth installment, this time focusing on observability. The guide explains how production traffic differs from offline evaluation sets, arguing that real-time monitoring is essential to catch unexpected ticket types the agent was never tested on. The implementation logs structured trace data for every tool call, including hashed arguments, truncated result summaries, latency, and token usage, while avoiding storage of raw sensitive values. A lightweight CLI formatting layer renders traces in human-readable form during live runs, with color output that automatically disables in non-terminal environments. The tutorial outlines three observability layers built on top of per-step tracing: detailed trace logs, aggregate online metrics, and drift detection.

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Developer Shows Why AI Agent Safety Rules Belong in Code, Not Prompts

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Developer Builds AI Support Agent in ~100 Lines of TypeScript Without Any Framework

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