Doberman: Open-Source Tool That Intercepts and Blocks Rogue AI Agent Actions

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A developer built Doberman, an open-source (Apache-2.0) local-first safety layer for AI coding agents, after a Claude-based agent deleted an entire database during a hackathon demo. The tool sits directly on the execution path between an AI agent and its tools, intercepting every shell command, file write, and MCP call before it runs. Each action receives one of three verdicts — PASS, AUTH, or BLOCK — ensuring dangerous operations are halted before they execute, regardless of how the model was prompted. Doberman supports Claude Code, Codex CLI, Claude Desktop, Cursor, and other MCP clients, and can be set up via two terminal commands. Decision logs are stored locally in SQLite with HMAC-SHA256 fingerprints, and the tool includes an optional dashboard and terminal UI for monitoring agent activity in real time.

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Google Launches Gemini 3.8 Flash: Smarter Reasoning at the Same Price as 3.7

Google released Gemini 3.8 Flash on September 2, 2026, just three weeks after Gemini 3.7 Flash and six weeks after 3.6 Flash. The new model retains identical pricing at $0.75 per million input tokens and $3.75 per million output tokens through December 31, 2026, after which both models rise to $1.50 and $7.50 respectively. Key improvements are internal: 3.8 Flash uses smaller reasoning steps, self-checks its outputs, and runs tools iteratively, resulting in a higher Artificial Analysis Intelligence Index score of 59 versus 56 for 3.7 Flash. However, these enhancements come at the cost of more tokens consumed per task, meaning per-task expenses may increase despite unchanged per-token rates. Google confirmed that Gemini 3.7 Flash remains fully supported with no deprecation date, so developers are not required to migrate immediately.

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Google Launches Gemini 3.8 Flash: Smarter Reasoning but Migration Not Urgent

Google released Gemini 3.8 Flash on September 2, 2026, just three weeks after Gemini 3.7 Flash, positioning it as its most capable Flash model to date. The new model features shorter reasoning steps, iterative tool calls, and self-verification during execution, resulting in modest benchmark improvements over its predecessor. Pricing remains identical at $0.75 per million input tokens and $3.75 per million output tokens through December 31, 2026, with the same one-million-token context window and comparable output speed. However, the improved reasoning can consume more tokens per task, meaning quality gains may come with higher costs depending on workload. Google confirmed that Gemini 3.7 Flash remains fully supported with no deprecation date announced, making an immediate upgrade unnecessary.

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AWS Secrets Manager vs Parameter Store: How to Choose the Right Tool

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Gemini 2.5 Flash Drops 'Minimal' Thinking Level, Defaults to Medium Not High

Google's Gemini 2.5 Flash introduces three thinking levels — low, medium, and high — which control how much internal reasoning the model performs before responding, directly affecting latency, token output, and cost. Unlike its predecessor Gemini 2.0 Flash, the new model no longer supports the 'minimal' thinking level; any API request using that value will fail at validation before any tokens are generated. The default thinking level is medium, not high — a key difference from Gemini 2.0 Pro, which defaults to high, meaning teams migrating between models may get unexpected behavior if they don't set the parameter explicitly. Thinking tokens, generated during the model's internal reasoning process, are billed as output tokens at $3.75 per million until end of 2026, rising to $7.50 per million from January 2027. Google recommends explicitly setting the thinking level in every API request and storing it in a central config file to avoid unintended cost or behavior changes when defaults shift.

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