#future of development

We thought AI would help us write code faster. Instead, it's changing what coding actually is.
We started with prompts, then copilots, then agents. Each step felt like a leap forward — until you try to build something real at scale.Because prompts don’t remember.
Agents don’t coordinate.
And models still hallucinate and miss context.
What’s emerging instead is a different approach: not writing code line by line, but designing systems that produce, validate, and evolve code.
Instead of a single assistant, we orchestrate multi-agent workflows — planning, implementing, reviewing, and testing — with shared context and feedback loops.In this talk, we’ll cover:
- why prompt-based and single-agent approaches break down
- how multi-agent systems reshape development workflows
- practical patterns for planning, execution, validation, and control loops
- where things fail — and how to make systems reliable

We’ll show how structured orchestration makes agent-based systems actually work in practice — especially when moving beyond isolated, task-level automation.
The shift isn’t from coding to prompting — it’s from coding to designing systems that write code.

Alex discusses spec-driven development as the best approach for building software with AI, emphasizing the importance of planning ahead and tools evolution. Reflecting on coding tools evolution towards instructing AI agents and changing roles. Challenges in articulating coding intent and the need for Spec-Driven Development. Importance of changes, goals, and OpenSpec in development. Up-front planning, collaborative nature, and challenges in spec-driven development highlighted. Managing AI limitations, spec updates, and collaborating with non-engineering on specs. Optimizing prompts and spec organization, context importance, and OpenSpec skills.

AI dashboard utilization challenges include lack of clear metrics for effectiveness and low adoption rates. Companies struggle with AI implementation leading to delivery improvements and financial gains. AI transformation hurdles stem from a focus on fluency over workflow redesign. Achieving true AI integration requires deep integration for transformative change. Organizational challenges in AI involve balancing code production with product outcomes. Key steps for AI transformation include measuring real changes in production and prioritizing killing ineffective pilots. Managing cloud costs and addressing unused resources are key concerns. Measuring AI impact on teams, business, people growth, and skill development is crucial for successful implementation.

AI-powered software development is rapidly evolving, leading to pressure for more AI implementation. Developers are transitioning from code completion to orchestrating change, managing increased output. Challenges arise from neglecting code relevance and adapting to accelerated workflows. Internal developer platforms reduce cognitive load and system complexity, emphasizing adaptable strategies and communication. Organizational learning and intentional system strategies are crucial for acceleration. Engineering leaders must consider deterministic controls and generative AI impact. AI integration for non-developer contributions requires scalability and security considerations. Standardization in development pipelines is crucial, balancing with flexibility for experimentation and evaluation.

The speaker emphasizes the importance of engineers becoming multipliers in the AI era, highlighting the evolution of tools and methodologies in software development. The shift towards engineering leadership necessitates essential skills like feedback, delegation, and project leadership. The changing landscape of engineering roles reflects a rise in tech leads and product engineers. Adaptability and the ability to learn fast are crucial in the evolving demands of the industry. Strategies for career growth include showcasing skills, embracing AI adoption, and fostering a culture of continuous learning and adaptation.

Ishai, CTO at LearnrB, discusses the evolution and impact of AI code reviews in software development. The transition from experimentation to maturity with autonomous AI agents in code reviews is highlighted. Challenges in AI-driven code creation and review processes, as well as the benefits of AI code reviews for productivity and efficiency, are discussed. Common mistakes and challenges in AI code review adoption, along with the importance of adapting processes for AI advancements, are emphasized. The importance of enhancing AI code review processes, measuring their impact, and leveraging AI for productivity metrics is also explored.

Discussing the importance of adopting AI in legacy modernizations and the risks associated with outdated systems, challenges in legacy ecology, reliability rupture, and migration mutiny. Exploring Lean Tech at Theodos for maintaining agility and customer value, improving system architecture through visualizing endpoints and high-quality coding. Optimizing accuracy with agent coders, industrializing migration with LeanTech principles for 10x acceleration. Leveraging AI for product building, AI model evaluation, and the preference for Claude over GPT in migration.

Today's Talk covers native features in browsers, including upcoming ones and the Interop Project. Native features offer speed, compatibility, and improved debugging. Notable examples include dynamic viewport units, flexbox/grid gap, container queries, and motion path. The Interop Project ensures consistent web platform features across different browsers. Upcoming features like native CSS nesting and entry/exit animations are on the horizon.

During the Talk, the speaker discusses the power of streaming and its impact on music consumption and production. They also explore the challenges and benefits of incorporating streaming into software development, using examples from Spotify. The future of code development is envisioned as a streaming world, where code is always live and changes are instantly available to everyone. The speaker emphasizes the importance of treating components as complete products, prioritizing component reviews, and enhancing the workflow for forking and contributing to components.

This Talk explores controlling apps with the mind and the future of UI and UX. It discusses the integration of VR and AR into UI and UX, the understanding of neurons and EEG headsets, connecting to Muse via Bluetooth, measuring brain waves and blink detection, feeding data to machine learning, and mind control with AR. The speaker emphasizes the importance of learning React Native, AR, React, Bluetooth, and drones for those interested in exploring these topics.