wallet-guard v0.1.0 adds loop detection and budget guardrails for AI agents

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Developer Pedro Sordo Martínez has released wallet-guard v0.1.0, an open-source Python library designed to prevent AI agents from getting stuck in unproductive retry loops or exceeding spending limits. The package exposes two core components: LoopGuard, which halts execution after a configurable number of retries with no measurable progress, and a budget enforcer that respects predefined spending or configuration limits. A notable fix was included after an audit revealed that user confirmation requests were incorrectly being counted as irreversible failures. The library ships with eight passing tests and clean linting via ruff, and was audited on a fresh clone at commit 372b2a9. wallet-guard is available on GitHub under the AGPL-3.0-or-later license.

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YC-backed Bloomy Launches AI Mastery-Learning Platform for K-12 Students

Bloomy, a Y Combinator S26 startup founded by former Teach For America teacher Alex Southmayd, has launched an AI-powered mastery-learning platform for K-12 students covering Math, English Language Arts, and Writing. The platform diagnoses individual skill gaps, places students on personalized learning paths, and uses a Socratic AI tutor called BloomyBot that guides reasoning rather than simply providing answers. Students must demonstrate at least 90% mastery on an independent assessment before advancing to the next skill, with the curriculum structured around a three-stage model of concept introduction, guided practice, and independent testing. The product is aimed at homeschool families, microschools, hybrid schools, and traditional classrooms, and its knowledge graph of skill prerequisites was built in collaboration with the Chan Zuckerberg Initiative. Bloomy draws inspiration from Benjamin Bloom's two-sigma research, which found that one-on-one tutoring produces significantly better learning outcomes than conventional classroom instruction.

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ProgrammingDEV Community ·

AceKit Lets Coding Agents Access Image, Video, and Search APIs via One Installer

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ProgrammingDEV Community ·

Mumbai Woman Defied Family Pressure to Build a Global Tech Career at AWS

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ProgrammingDEV Community ·

Why AI Agents Have Made the Classic Test Pyramid Obsolete

The software testing 'pyramid' — which recommends many unit tests and few end-to-end tests — was originally a cost-structure argument, not a quality principle. It reflected the economics of human labor: unit tests were cheap and fast to write, while end-to-end tests were slow, flaky, and expensive to maintain. AI coding agents have disrupted this logic by generating thousands of tests at negligible cost, eliminating the labor scarcity that justified the pyramid's shape. With test authorship no longer a bottleneck, the key constraint shifts from writing effort to test truthfulness — how accurately tests reflect real-world behavior. This means the optimal distribution of tests across unit, integration, and end-to-end levels must now be rethought under an entirely different set of trade-offs.

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