ImagePickerKMP 1.0.42 upgrades to Kotlin 2.3.20 and Compose Multiplatform 1.10.3

·1 views

The ImagePickerKMP library has released version 1.0.42, upgrading its core dependencies to Kotlin 2.3.20, Compose Multiplatform 1.10.3, Ktor 3.4.1, and Android Gradle Plugin 8.13.2. The update brings compiler optimizations in Kotlin 2.3.20 that produce smaller and faster binaries for Android and iOS, while Compose Multiplatform 1.10.3 resolves rendering issues on iOS and Desktop. Developers should note that Kotlin 2.3.x introduces breaking ABI changes, meaning projects still on Kotlin versions below 2.3.x will encounter compilation errors if they upgrade to this release. Those not yet ready to migrate can continue using ImagePickerKMP 1.0.41, which retains compatibility with older Kotlin versions. Full documentation and installation guidance are available at imagepickerkmp.dev.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

A 4-level AI literacy framework for professionals, minus the fear-mongering

Aditya Kachave, co-founder of AI training platform Be10x, has outlined a four-level framework to help working professionals build AI literacy without succumbing to hype or panic. The levels progress from basic awareness of how AI tools work, to applying them on real tasks, integrating them into repeatable workflows, and finally building technical automations. Kachave argues that most professionals only need to reach Level 2 — using AI for tasks like drafting or summarizing — to capture the majority of practical benefits. He cautions against over-relying on AI for final decisions, recommending it instead for first drafts and routine work. Acknowledging his own commercial stake in AI training, he frames the core message simply: a few focused hours of deliberate practice at the right level outweighs expensive courses driven by fear.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How FIFA's Best Third Group Stage Draw Works, Explained Simply

The FIFA World Cup group stage uses a 'best thirds' rule to determine which third-place teams advance, making the draw process complex. A developer named Rahul Devaskar broke down this logic in a simple, relatable way by explaining it to an eight-year-old child. The explanation uses an analogy of eight kids and eight chairs to illustrate how the selection rule works. The piece, published on DEV Community on June 27, sits at the intersection of web development, mathematics, and football. It aims to make a confusing tournament format accessible to a general audience.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Student Essay on Silent Classroom Confusion Sparks Conversation About Learning Gaps

A student developer writing under the handle bjumani published a candid personal essay on DEV Community describing a common classroom experience: nodding along in class despite understanding nothing, to avoid embarrassment. The author argues this silent cycle of confusion leads students home with unresolved knowledge gaps, no clear starting point for self-study, and eventual poor test performance. Bjumani contends the problem is widespread, suggesting most classmates share the same confusion but rarely voice it openly. The post calls for a personalised learning tool that can identify a student's specific knowledge gap and provide a tailored, step-by-step path to understanding, rather than generic explanations. The essay is part of a build-in-public series and ends with a direct question to readers, signalling the author may be exploring this as a product idea to build.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Releases Three Open-Source MCP Servers for AI Agents on PyPI

A developer has built and published three production-ready Model Context Protocol (MCP) servers to PyPI and GitHub, designed to extend AI agent capabilities through a standardized interface. The three servers handle web search, automated code review, and document intelligence respectively, each offering multiple tools for tasks like fetching live search results, analyzing code diffs, and extracting text from PDFs. MCP is an open standard that allows AI clients such as Claude Desktop, Cursor, and VS Code to connect with external tools and data sources through a single unified protocol. All three servers share a common credit-based billing system, with a free tier offering 50 calls per day and paid tiers starting at $20 for 2,000 credits that never expire. The billing backend is also open-sourced on GitHub, and the servers are distributed across PyPI, HuggingFace, Gumroad, and MCP registries.

0 comments Read more at DEV Community