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All digests tagged AI Development
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Learn how to build a voice agent by integrating Speech-to-Text, LLMs, and Text-to-Speech. We demonstrate using AssemblyAI and LiveKit, including advanced features like tool calling and voice cloning.
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Agentic finance is powered by the HTTP 402 standard (x402), enabling machine-to-machine commerce. The shift from ad-based revenue to microtransactions is redefining online payments.
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OpenAI unveils Codex Cloud, allowing persistent work in customizable cloud environments. New Codex CLI features include agent management and conversation forking. The episode also showcases building a retro ramen simulator game using Ultrafast.
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AI agents are accelerating commit volume, creating bottlenecks in CI and review queues designed for human speed. Solutions require systemic changes: incremental builds, caching, and robust governance.
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Building internal tools with AI agents requires more than just prompting. Achieving a polished, usable product demands implementing advanced patterns like optimistic updates, autosave, and rigorous end-to-end verification.
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Gabriel Martinez argues that AI accelerates 'slop'—work lacking judgment. He stresses that generation is cheap, but evaluation is expensive, advocating for diagrams and conventions over massive PRs.
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As AI agents level the implementation playing field, the differentiator becomes building the right thing. Learn to use Jobs to be Done and the Kano Model to guide product judgment and technical architecture.
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Learn how to set up an NVIDIA Jetson for remote AI development. This guide covers secure SSH setup, Wi-Fi configuration, Codex CLI integration, and validating CUDA GPU access via Docker containers.
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Antigravity 2.0 introduces agent teams, powered by dynamic subagents, sidecars, and generative UI. This architecture allows complex tasks, like building an OS kernel, to be automated by swarms of specialized agents.
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Marc Brooker argues that the hardest part of software is specification, not code. He details how agentic AI is shifting the focus to rigorous testing, agentic policy, and learning from historical postmortems.
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AI accelerates code generation, but security requires a 'shift-left' approach. Learn five key principles for securing AI-assisted development, focusing on outcome validation and dependency integrity.
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A deep dive into Claude's new `plugin eval` tool. While promising for automated skill testing, the speakers highlight that the process is currently bottlenecked by manual data collection and lacks an 'in-the-loop' annotation interface.
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Coding agents are great at generating code, but verification is the hard part. Learn how to use test oracles, anti test slop, and ADRs to ensure AI-generated code is reliable.
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Mike Krieger discusses how to be 'unreasonable' with AI, detailing workflows like porting Python to TypeScript over a weekend using LLMs. He also shares lessons on scaling infrastructure and structuring research labs.
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To make AI apps look professional, developers must focus on UX polish and avoid common 'slop' patterns (like purple gradients). Use visual references, break tasks into small prompts, and iterate with smaller open-source models.
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Code review must evolve beyond checking code diffs. The focus needs to shift from semantic accuracy (bugs) to preserving alignment, mentorship, and architectural intent by capturing session decisions.
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To manage the risks of AI-driven code, a zero-trust, multi-layered verification regime is critical. This involves running automated checks in both inner agentic loops and outer CI/CD pipelines.
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Discover how to build agent-powered web apps with Angular using the new Copilot Genkit integration. Learn about context-aware AI, SmartText areas, and Generative UI.
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A live demonstration shows how running `tessl review run` on a Snyk skill improved its score from 87% to 90%. The session highlights progressive disclosure and using Agent Scan to catch security vulnerabilities.
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As agents write 95% of our code, traditional review fails. Learn about 'harness engineering': defining invariants, using analytics on agent logs, and implementing risk policies to keep quality high.
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