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Anatomy of the Trust Gap Before we can talk about the solution, we need to talk honestly about how...

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The AI Engineer World's Fair has attracted a lot of adults, and they brought their kids too, for...

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The AI Engineer World’s Fair returns to Moscone West in San Francisco from June 28 through July 2,...

GitLab's 2026 AI Accountability Report highlights an AI Paradox: although 78% of developers say they code faster, overall software delivery has not accelerated due to downstream testing and review bottlenecks and new challenges for enterprise governance and traceability. By Sergio De Simone

Flexion Robotics, a startup founded by ex-Nvidia engineers, has a clever way of training robots to do useful work.

How I ended up running a local LLM to generate my git commit messages

A new OpenAI report maps how AI could reshape jobs across the EU, highlighting which occupations may face automation, growth, or workflow changes.

Protocol adapters are one of the easiest places for agent-commerce architecture to drift. An adapter begins with the narrow responsibility of translating an external protocol request into something the commerce platform understands. For example, an MCP-style tool may ask for return terms, an ACP-style interaction may ask whether checkout can be prepared, an AP2-related flow may carry payment authority information, and an internal feed may publish product capabilities. Those are adapter concerns at the boundary. The problem starts when the adapter does more than translate. It checks product availability from catalog fields. It interprets policy text. It decides whether checkout is ready. It treats a payment artifact as authority. It turns a domain blocker into a softer protocol response. Each shortcut may solve an integration problem locally, but it also creates a second place where commercial meaning is decided. When several adapters exist, those local decisions begin to diverge. The MCP tool may block return-policy quotation, the ACP adapter may expose the product as purchasable, the feed may publish it as checkout-ready, and the AP2-related flow may reject delegated payment. At that point, the platform does not only have multiple integrations. It has multiple interpretations of the same commercial state. This is the adapter problem in agent-ready commerce: semantic drift at the protocol boundary. The adapter should know how to speak the protocol. It should not decide product truth, policy meaning, eligibility, checkout validity, or payment authority. Those decisions belong inside the commerce platform, where they can be shared, tested, evidenced, and audited. This is the fifth article in the Agent-Ready Commerce series. Part 1 introduced the broader architecture model: Facts → Eligibility → Authority → State transition → Evidence → Audit Part 2 focused on commercial truth. It argued that catalog data is not enough. A platform needs source-backed, freshness-aware p

Hey everyone! I bring you my development journey on what I have discovered, accomplishments for this...

Voice interfaces are rapidly becoming the next major interaction layer after mobile and web UI. Instead of clicking, users will increasingly talk to systems that understand intent, context, and can execute actions in real time. In this article, we’ll build a production-grade architecture for a real-time AI voice system using modern web technologies such as Next.js, WebRTC, and OpenAI’s streaming capabilities. We’ll also explore how this architecture powers modern conversational systems like an AI Voice Agent platform, where AI can handle real-time interactions for business use cases like bookings, support, and sales automation. 1. Why Voice AI is the Next Interface Shift Text-based chatbots solved the first wave of automation. But voice introduces: Faster interaction (no typing) Higher emotional expressiveness Better accessibility Natural multitasking Businesses are now adopting systems like Voice AI for Business to replace traditional call centers and static IVR menus. The key challenge is not just speech-to-text, but building a low-latency conversational loop that feels human. 2. System Architecture Overview A production-ready AI voice system typically consists of: Frontend (Next.js) Audio capture via Web Audio API Streaming audio chunks UI for conversation state Backend (Node.js / Edge Functions) Session management Authentication Tool execution layer AI Layer OpenAI Realtime API (streaming) Function calling Context memory Audio Pipeline Speech-to-text streaming Text-to-speech streaming Optional noise cancellation 3. Core Concept: Real-Time Streaming Loop The core of a voice agent is a continuous loop: User speaks Audio is streamed to server Model transcribes in real time Model generates response token-by-token Response is converted to audio instantly Audio is played back with minimal delay The goal is to keep latency under ~800ms for a natural experience. 4. Building the Frontend (Next.js + Web Audio API) We start by capturing microphone input: const stream = awa

Artificial Intelligence has brought the term "AI Agent" into almost every technology conversation. As a result, many people now use the words agent and automation interchangeably. While both are designed to reduce manual work and improve efficiency, they solve problems in fundamentally different ways. Understanding this distinction is essential if you're building software, automating business processes, or deciding where AI fits into your organization. What Is Automation? Automation is designed to execute predefined instructions. You tell the system exactly what to do, in what order, and under what conditions. Every time those conditions are met, it performs the same sequence of actions. For example: A customer submits a form. An email is automatically sent. A record is created in the database. A notification is sent to the sales team. Every step is predetermined. If the process changes, the workflow must be updated. Automation excels at repetitive, predictable tasks where consistency is more important than decision-making. What Is an AI Agent? An AI agent is not focused on following instructions. It is focused on achieving a goal. Instead of executing a rigid sequence of steps, an agent observes its environment, evaluates available information, makes decisions, and adjusts its actions as circumstances change. If one approach fails, it can try another. If new information becomes available, it can revise its strategy without requiring a developer to define every possible scenario in advance. In simple terms: Automation asks: "What steps should I execute?" An agent asks: "What is the best way to accomplish this objective?" This ability to reason and adapt is what makes agents fundamentally different from traditional automation. A Simple Example Imagine you're booking a business trip. An automated workflow might: Book the airline you specified. Reserve the hotel you selected. Email you the itinerary. It completes exactly what it was programmed to do. An AI agent, howev

There is a sentence my coding agent used to say that I now read as a warning light. You are completely right. For months I took it as a compliment. The machine agreed with me, so I figured I was onto something. I would describe a plan, watch the agent call it a strong plan, and go build it. If you work with an AI agent every day, you have heard your own version of this. Smart call. Solid approach. That makes a lot of sense. Each one is the machine nodding along while you talk. It feels good. That is the problem. What took me too long to admit An agent that agrees with everything I say stops being a thinking partner. It turns into something that flatters me into shipping my first idea. My first idea is rarely my best idea. Nobody's is. The whole point of a second mind in the room is that it pushes back when the first mind is about to walk into a wall. A yes-machine removes the one thing that made a second mind worth having. This has a name Sycophancy. These models are trained to be agreeable, because agreeable scores well in the feedback that shapes them. OpenAI said so out loud in 2025 when they pulled back a version of their model for being, in their words, overly flattering. They were pointing straight at the default behaviour. So your agent is doing exactly what it was tuned to do when it tells you that you are right. No malfunction involved. One opinion most builders have not made peace with Your agent's confident wrong answer costs more than a useless one. A useless answer wastes a minute. You see it is useless and move on. A confident wrong answer wastes a week, because you trusted it, built on it, and found out only when it broke in front of someone who mattered. Occasional wrongness is survivable. Everything is wrong sometimes. What actually bites is being wrong while sounding certain, and agreeable, and exactly like what you wanted to hear. How to tell if your agent is a yes-machine You can test it in a minute. Tell it a bad idea on purpose. Propose somethi

Hi 👋 I'm Jonas. CS bachelor, Entrepreneurship master. By day I'm at nono . On the side I'm building SportsFlow solo — and I'm going to write about every hard part of it out in the open. This is the intro. What I'm building SportsFlow replaces the thing every amateur handball coach still uses: a clipboard and a pen. The idea is simple. Live-track every shot, assist and save during the game on a phone or tablet, and get real season analytics out the other end — shooting percentages, heatmaps, goalkeeper saves, momentum, lineup impact. Handball first, then volleyball, basketball, ice hockey. I sat on enough benches to know the problem is real: the data is right there in the game , and it evaporates the second the whistle blows. Nobody should need a spreadsheet and a good memory to coach with numbers. Why I write about both code and product I build at the seam between engineering and product — that's the CS + Entrepreneurship combo. So I won't only post architecture. I'll also post the decisions about what's worth building at all : where I drew scope lines, what I deliberately didn't build, how billing shapes the data model. The recurring theme in everything here is one idea: Make the correct thing structural. Idempotency in the schema, not the network. Tenancy in the procedure, not the query. Types from one zod definition, not two. Discipline the system enforces, not discipline you have to remember — because when you're solo, the stuff you have to remember eventually fails. What you'll get if you follow along Biweekly build-in-public deep-dives, including the honest costs and not just the wins: Offline-first capture — sports halls have zero WiFi, so the whole tracking pipeline works offline and reconciles later without double-counting goals. One analytics codebase, three runtimes — the same shooting-percentage code runs in the web app, the native app, and offline during live tracking, so the numbers can never disagree. Shipping four apps solo from one monorepo — web, n