GoKawiil
Tech News
Hiring Lab data shows software engineering job postings have risen 15% since 2025, with 71% of that growth concentrated in roles for experienced engineers who now also oversee AI-assisted development work. IEEE Computer Society warns that companies lacking structured training and career growth plans risk losing these in-demand professionals to competitors.
Google has introduced a new slash command, /boost, in Antigravity 2.0 and the Antigravity CLI that triggers a multi-agent reasoning pipeline for difficult coding problems like race conditions, complex refactors, and subtle bugs. The feature works through a three-phase process where an orchestrator agent breaks down the problem and dispatches specialized subagents to isolated workstreams, then verifies results across multiple rounds. The command is restricted to paid plan subscribers.
A software engineer describes how LLM coding assistants perform well on new, small projects but degrade sharply on older codebases burdened with technical debt and inconsistent naming conventions. The core problem, he argues, isn't the AI model itself but that legacy systems lack a clear, shared vocabulary for concepts, forcing the model to guess and often introduce duplicate or wrong abstractions. He proposes incrementally building clarity into codebases—using domain-driven design principles—so AI agents have unambiguous context to work from.
A veteran engineering manager with over 13 years in the industry argues that the tech world's obsession with AI tools is overshadowing a more fundamental driver of productivity: workplace culture. Drawing on personal experience with both toxic and healthy team environments, the author contends that AI adoption alone cannot fix dysfunction like interdepartmental blame or poor collaboration. The piece warns against narratives suggesting fewer engineers are needed simply because AI makes building software easier.
A software engineering commentary distinguishes between writing code—translating ideas into executable instructions, a task AI handles well—and building software, which involves deciding what instructions should exist, how systems interact, and which tradeoffs and constraints matter. The piece uses an example of processing incoming events to show that architectural decisions like consistency, retries, and ordering have little to do with syntax or programming language choice.
A new industry analysis argues that as AI coding agents rapidly improve and handle longer tasks asynchronously, the traditional sequential product development pipeline—idea, product, design, engineering, QA, production—is being restructured. Implementation, once the dominant time cost in this pipeline, is shrinking, shifting the bottleneck to verification and simulation instead.
A developer sets an AI coding assistant named Pol to work on a small proof-of-concept todo app overnight, expecting the usual rough first draft in the morning. Instead they find no working software at all, and discover the agent has consumed 100% of their weekly usage allowance in about 12 hours, with the quota not resetting for another week.