adi-shield v0.1.0 launches open-source prompt injection detection across 5 data vectors

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Developer Pedro Sordo Martínez has released adi-shield v0.1.0, an open-source Python library designed to detect prompt injection attacks (CWE-1427) in AI agent pipelines. The tool exposes an InjectionShield class with an evaluate() method that checks whether untrusted data from five sources — email, web, tickets, third-party repositories, and calendar invites — contains embedded instructions. Rather than using a probabilistic classifier, adi-shield performs deterministic detection of instruction-like content within data explicitly marked as untrusted. The library passed all 10 of its test cases on the main branch at commit c125db4 and 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

AceKit is a lightweight installer that connects Ace Data Cloud capabilities to popular coding agents such as Claude Code, Codex CLI, Cursor, Gemini, and OpenCode. It bundles a set of skills covering tasks like image generation, video creation, music production, web search, and URL shortening, which coding agents cannot handle natively. Each skill includes a SKILL.md instruction file so agents understand authentication, parameters, and expected outputs without guesswork. The tool requires Node.js 18 or later and authenticates through a single Ace Data Cloud API token, which can be set as an environment variable before installation. Developers can run the installer with a single npx acekit command, after which agents can immediately begin using the installed skill set.

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ProgrammingDEV Community ·

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

A woman from Mumbai who originally pursued theatre and business studies pivoted to IT after discovering a passion for computers, eventually joining AWS in Cape Town in 2013 as a Cloud Support Engineer despite cultural pressure to marry before moving abroad alone. Over the following decade, she advanced through multiple roles across continents, becoming a Solutions Architect and later a Developer Advocate at AWS. Beyond her formal roles, she voluntarily built AWS User Groups across South Asia, spoke at major tech events, and created educational content for developers. After returning from maternity leave and feeling out of step with the rapid rise of AI, she launched a series called 'Learning AI Out Loud' to document her own relearning process in public. Her career story highlights how unconventional paths, community involvement, and persistence through personal and professional setbacks can shape a meaningful journey in the tech industry.

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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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