Andreas Ellervee

was at OpenAI hq yesterday and got to talk with their head of product about the new agentkit 🤯 a lot of people think this kills zapier (i even thought so at first). it doesn’t. agentkit is for devs, not no-coders. it’s about putting ai inside products, not automating workflows. this makes it easier for teams to build agents that live inside their own apps, not connect tools together. so yeah, this isn’t about replacing zapier. it’s about replacing ui. #ai #openai #chatgpt #agents #startups #developers

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It’s clear that almost everything about software development has fundamentally changed for good with AI agents. Hard to imagine another area of knowledge work that has had its underlying process get transformed so substantially in such a short period of time. If you went back even 2 years and told the average developer what software engineering would look like right now, it would not be comprehensible. We’ve already seen 3-4 major step functions changing workflows just in that time period alone. The ultimate outcome is clearly that software engineering will become heavily dependent on how good you are at prompting, how good you are at reviewing code, and the interplay between the two. Once the workflow becomes about kicking off agents to do work in the background, your success is dependent on the work that gets kicked off and how you review it. Importantly, both require a deep understanding of how to code, systems, and what you want to build; the greater the underlying skill of the engineer, the greater the leverage with AI agents. And while we’re seeing this workflow get defined first in software, a similar pattern will eventually emerge across all areas of knowledge work. We’re only in the earliest stages of seeing what this will look like. Crazy times.

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👏 JUST IN: OpenAI announced a few important updates during their Dev Day an hour ago. Here is everything you need to know: - A new no-code platform for everyone to create agentic workflows. And AgentKit for developers - A way to create “Apps” within ChatGPT and an “App store” to come later this year - A list of Apps already available eg Hubspot and Canva, Coursera - GPT-5 pro and a small voice model are now available in the API - New video tool Sora 2 is also available in the API - A major partnership with AMD to purchase chips One interesting trend : want to guess which public company share price will go up? Just watch OpenAI partnership announcements. Last week OpenAI sent Shopify soaring and today: AMD (29%), Hubspot (7%) and Coursera (3%). (Not a financial advice of course) ----- 👉 Follow Will Liang for more and hit the 🔔 to stay ahead. 👉 DM me to chat through how AI apply to your business.

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Still stuck with coding bottlenecks and repetitive dev tasks? Most developers spend countless hours debugging, refactoring, and stitching code together—time that slows innovation and adds friction to real software development. Meet Qoder – an agentic coding platform that doesn’t just autocomplete code, but understands your entire codebase and acts like a true AI engineering teammate. With Qoder, you can co-build features, delegate complex engineering tasks, and deliver production-ready code—faster and smarter. Here’s how it helps you think deeper, build better: ✅ Conversational Pair Programming – Chat with Qoder like a professional engineer and co-build across multiple files, with intelligent completions that anticipate your next move. ✅ Quest Mode (Autonomous Programming) – Delegate multi-step engineering tasks. Qoder analyzes, plans, codes, and tests—while keeping you in control. ✅ Repo Wiki & Architecture Insights – Uncover hidden dependencies and patterns in your codebase, turning tribal knowledge into actionable intelligence. ✅ Self-Evolving Memory – Qoder learns your preferences, past solutions, and coding style—becoming more effective with every interaction. ✅ Developer Toolbox – Equipped with built-in tools and MCPs, Qoder can observe, plan, and execute tasks like a real engineer. No more juggling between tools or chasing bugs. Just delegate, collaborate, and ship with confidence. 👉 Use Claude4 for free: https://lnkd.in/dKHkX5nx #QoderAI #ThinkDeeperBuildBetter #AgenticCoding #RealSoftware #AIEngineer #CodeWithQoder

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Interesting visual of a head-of-line blocking behavior in the Moderne CLI before and after a fix. Each block here represents a different repository with an #OpenRewrite recipe running on it in a large-scale multi-repository run. Knut Wannheden found the bug here yesterday and fixed. Concurrency is hard and the visuals here were produced by Claude Code looking at Moderne trace data we co-designed with our friends at American Express and introduced in 3.45.0. Next, we're exploring different prioritization methods where we're trying to balance between two apparently competing objectives: 1. Runs that should be fast are prioritized early to show incremental results as quickly as possible. After all, if a recipe result is incorrect on the first few repositories, developers like to cancel them quickly and iterate further on the recipe. 2. Runs on repositories that are going to take the longest shouldn't be prioritized last or we have the long tail effect on a run that you can observe even after the fix to the head-of-line blocking bug. We're thinking that Highest Response Ratio Next (HRRN) is a good balance between the two with a simple formula shown below. Over the years we've iterated on a "weight" measure for repositories based roughly on the density of information (including type attribution) of a repository present in the LST. Regardless of which method we wind up choosing, we'll be using this weight measure as one that run time is roughly proportional to. I know it may be a little outside of your typical problem space Geoffrey De Smet, but do you have any suggestions? I imagine our problem area is quite a bit simpler than yours typically. 😉

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I caught up with a friend who works at a mid-size Swedish tech company. Over the last 4 months, their shipping velocity has almost doubled – not because they hired more engineers, adopted some new agile framework, or worked late nights. It came down to a single change in how they build products: they started using Lovable to prototype features instead of writing traditional spec docs. Before Lovable, it usually went like this: PMs drafted long PRDs, trying to anticipate every detail. Multiple stakeholders reviewed these documents, leaving comments and raising concerns. The document grew with each iteration. Alignment meetings were frequent but often resulting in ambiguity. Engineers often began implementation while details were still debated. Inevitably, confusion emerged about trade-offs, timelines got pushed, and features shipped incomplete or scaled back. Now, PMs build interactive prototypes directly in Lovable. These aren’t wireframes or rough mockups – they’re fully clickable, end-to-end experiences that feel like the real product. Engineers don’t have to guess what the flow should be. Designers don’t have to explain interactions. Everyone sees the same thing, from day one. The end result is fewer meetings, fewer misunderstandings, fewer rewrites. What used to take weeks of coordination now happens in a single day. This is what has provided the most value for enterprises using Lovable so far. Over time, the increase of clarity and velocity saves the companies millions of $ in wasted effort.

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