A new developer productivity system claims to be game proof by design. A leading researcher on metric failure isn’t so sure... https://leaddev.com/reporting/is-microsofts-engthrive-framework-immune-to-goodharts-law
Open-source abandonware isn’t new. Repositories have always accumulated half-finished projects and good intentions. But there’s growing evidence that AI-generated code is making the problem significantly worse. https://leaddev.com/software-quality/ai-generated-abandonware-is-hollowing-out-open-source
After rolling out an AI coding assistant, most teams see the same pattern: PRs get bigger, cycle times drop, sprint records fall. Feels great. Then a few months in, the on-call rotation gets brutal.
This isn't coincidence. The DORA 2024 report confirmed it across the industry: teams with significantly higher AI adoption also showed higher change failure rates.
Three failure patterns explain most of it, and none of them are new problems — they're old ones running faster:
1. Polished code fools reviewers. AI-generated code looks right. It follows conventions, reads cleanly, gets approved faster. But a model can produce a wrong implementation with the same fluency as a correct one. Reviewers pattern-match to familiar structure and skip the hard reasoning.
2. The model grades its own homework. When the same model writes the code and the tests, it tests its own assumptions — not your requirements. Coverage goes green. Edge cases nobody described stay untested.
3. AI can't see the whole system. The model only knows the code it's shown. It has no awareness of the shared retry queue, the upstream producer, the implicit guarantee held together by a three-year-old design decision. Clean-looking refactors quietly remove something critical.
The fix isn't slowing down AI adoption. It's redesigning the delivery process so it's worth amplifying:
Teams that had strong practices before AI got faster. Teams that didn't started getting paged at midnight.
Full write-up with a FinTech case study (wrong field placement silently dropped disbursements during peak load, every unit test green): https://leaddev.com/ai/ai-coding-made-us-faster-why-did-incidents-increase
Model Context Protocol (MCP) was introduced by Anthropic in November 2024, and it has undergone tremendous development since then. The rapid evolution of Large Language Model (LLM) based agents has pushed MCP from experimental curiosity to the center of production at a speed faster than most developers anticipated.
As of Q1 2026, many big tech companies who own enterprise Application Programming Interfaces (APIs), like Google and Microsoft, have launched official MCP servers.
Transitioning from enterprise API to MCP is more than just prototype swapping or shallow interface wrappers. MCP demands the same rigor as enterprise APIs as both are essentially production infrastructure. MCP developers should also take careful considerations of MCP interface design.
Read the full article on LeadDev: https://leaddev.com/ai/an-engineers-guide-to-model-context-protocol-mcp
CTOs, engineering managers, and staff engineers are rushing to deploy autonomous AI agents across their businesses – either through their own volition or because of the clamor of demand from rank-and-file workers. However, they should think twice, a new study shows.
Enterprise large language model (LLM) agents are likely leaking company secrets, and throwing more compute at the problem is only making it worse, the study finds.
In part, that’s because of the AI’s ability to retrieve and synthesize vast amounts of internal data, from Slack messages to board transcripts, to automate tasks. By gathering that information, they also create issues with contextual integrity.
When retrieving dense corporate data, these agents routinely fail to disentangle essential task data from sensitive, contextually inappropriate information. Higher task completion rates often directly correlate with increased privacy violations.
Read the full story: https://leaddev.com/ai/frontier-ai-models-haemorrhage-sensitive-data
Are the pure manager’s days numbered? US-based cryptocurrency exchange Coinbase plans to cut 14% of its global workforce as it flattens its management structure and leans heavily into an AI-first strategy.
In an email to employees which was later shared on X, Brian Armstrong, CEO of Coinbase, said the layoffs are partly driven by the crypto downturn.
The other goal is to flatten the organization, enabling faster execution with AI at the core of how teams operate. Armstrong stated that AI is speeding up work dramatically – teams can now build in days what used to take weeks, and even non-engineers are shipping production code.
In response, the company is shifting to a leaner, faster, AI-first approach to regain startup-level speed and focus.
“To get there, we are not just reducing headcount and cutting costs, we’re fundamentally changing how we operate: rebuilding Coinbase as an intelligence, with humans around the edge aligning it,” he added.
Coinbase is the latest company linking layoffs and changes to organizational structures with the rise of AI, following recent cuts announced by Snap, Meta, Block, Amazon, and Oracle.
https://leaddev.com/ai/coinbase-flattens-management-and-trims-workforce-in-ai-driven-restructure
The technical interview is evolving as AI-assisted coding becomes the norm.
Over the past decade, a burgeoning industry formed around the promise of helping software developers pass technical interviews and nail exhaustive multi-round interviews at desirable, but elusive, tech firms.
Now with AI reshaping the entire software development industry, the traditional technical interview – heavy on LeetCode style tests and algorithmic questions which test developers’ coding skills and practical knowledge – is becoming redundant. However, the coaching firms who built their reputation helping developers pass these tests aren’t feeling the heat.
https://leaddev.com/hiring/think-the-technical-interview-is-dead-think-again
Your DORA metrics look great. Your systems are quietly becoming unmanageable.
AI coding tools are creating a dangerous gap between delivery speed and system comprehension — and DORA metrics are hiding it.
The pattern: deployment frequency up, change failure rate stable, MTTR looking healthy. Meanwhile nobody on the team can explain the critical path end-to-end. The dashboards are green. The operators are nervous.
The core problem isn't AI. It's that DORA measures the pipe, not whether anyone understands what's in it. AI just made that gap orders of magnitude bigger — more code shipping, less of it truly owned.
A few things worth taking seriously:
Full article here: https://leaddev.com/reporting/dora-metrics-are-lying-to-you-and-ai-is-making-it-worse
The shift into a senior engineering manager (EM) role does not come with a title change. It comes with a moment. Effective senior engineering managers shape the environment execution happens in.
The most important work is invisible: building trust across boundaries and moving decisions forward without formal authority. Make the system work, not yourself.
We’ve come a long way from in-line AI code suggestions. Now, developers are using AI-coding agents to reverse-engineer entire software components and even major frameworks.
The latest case comes from Cloudflare. In early 2026, an engineering director rebuilt Next.js, the popular React framework, in a matter of days using Claude in OpenCode.
“One weekend, an engineering director decided to point AI at it to see how far he could get with purely agentic coding,” Dane Knecht, CTO of Cloudflare, tells LeadDev. “After the first weekend, it worked pretty well.”
Full story here: https://leaddev.com/ai/how-cloudflare-rebuilt-next-js-in-a-weekend
Buzzwords are the industry’s ever-evolving shorthand for big ideas, trends, and sometimes… just marketing hype. They often crop up in meetings, job descriptions, and blog posts to signal insider knowledge, expertise, or alignment with current practices.
At their best, they compress complex ideas into catchy phrases that teams can rally around. At their worst, they become vague, overused, and quietly corrosive to good decision-making.
Explore six terms that software engineers are quietly rolling their eyes at in 2026, either because the concepts behind them have become a problem, the words stopped being precise, or both. https://leaddev.com/leadership/6-software-engineering-buzzwords-you-need-to-stop-using
AI-coding tools and harnesses have changed the role of software engineering – and fast. Devs have started tokenmaxxing, with some companies, including Meta, launching leaderboards to measure their workers’ ability to burn through AI usage.
Agentic coding tools have radically changed AI usage, acting semi-independently through command lines, files, internet access, and tool calls. This also means they burn far more tokens than older chat-based tools, says Sebastian Baltes, professor of software engineering at the University of Heidelberg. “I’m a daily user of these tools myself, and it’s just astonishing how fast the field moves,” he says.
As software engineering evolves, the suppliers of that AI inference, including the biggest providers within the tech sector, have pulled the rug from beneath coders and the companies they work for.
Price hikes, tighter usage limits, and the shutting down of some providers to new signups has upended the new way of work software engineers were just getting used to.
Read the full article: https://leaddev.com/ai/your-ai-coding-budget-just-got-a-lot-more-complicated
If you’ve been grumbling that Claude Code hasn’t been pulling its weight lately, you aren’t imagining it.
Anthropic has confirmed that a string of engineering missteps led to a noticeable drop in performance in its Claude Code tool. It’s a slide that has frustrated users and sparked a backlash over the past month.
In a blog post, the company said it reviewed user complaints about the quality of Claude Code and identified three issues likely contributing to a poorer user experience.
Read the full story here: https://leaddev.com/ai/how-anthropics-silence-fueled-a-claude-code-trust-crisis
Generative AI enables teams to build clean, modern applications from scratch. However, most engineering leaders face an aging monolithic legacy system full of interdependent codes written years ago by developers who no longer exist in the system.
When you use a modern AI tool on top of these legacy codes, it doesn’t just struggle to work , it completely breaks.
Token usage is the lines-of-code metric of the AI era. The industry knows it. It just hasn't agreed on what comes next....
Meta built a leaderboard ranking engineers by how many AI tokens they consumed. It has since been taken down, but the impulse behind it hasn't gone away.
Across engineering organizations, there is enormous pressure to prove that the millions being spent on AI tooling are paying off. When that pressure mounts, leaders reach for the easiest number available.
Token usage is objective, automated, and scalable. It's also easy to game and almost entirely disconnected from whether AI is actually making engineers more productive.
As one engineering leader put it: "I wouldn't be surprised if we see the opposite trend next year, aiming for efficient usage of tokens as opposed to celebrating burning them at expensive rates."
So what should organizations be measuring instead?
Full article available here: https://leaddev.com/ai/tokenmaxxing-and-the-search-for-ai-metrics-that-matter