FM Speaker Highlight: @bnl5110 A founder who designs new ways for humans to interact — blending play, architecture, and technology to build tools and communities that feel both experimental and deeply human. She is the founder of Loopy, a tool for animating realities with over 50 million organic impressions, and previously designed new interactions at Y Combinator Research. Really excited to have her joining us at FM26. FUTUREMODE 2026 📍 Taipei Expo Dome | Sep 4–6, 2026 🌐 🏟 Early Bird Till 7/1 Local - International -

🔥 RAPBEAT 2026 Final Lineup Officially Announced! ✨ New surprise additions including The Cohort, H1GHR MUSIC, NCT TAEYONG, Loopy, Shyboii, Wuuslime, Marv, Rich Iggy, GGM Kimbo — and more! 🎤 Diverse flows clash freely, hard-hitting beats strike straight to the soul, melodic rap warms the entire venue. Come embrace an ultimate hip-hop feast for all rap fans! 📍 Seoul | Oil Tank Culture Park 📅 6.20 & 6.21 🎫 Available Ticket Tiers · ROCKET PASS (1 DAY – SAT / SUN) · 3RD TICKET (1 DAY – SAT) · 4TH TICKET (1 DAY – SUN) 👉 Click secure your limited spot now. The full final lineup is assembled. Let’s enjoy the purest hip-hop carnival together! #RAPBEAT2026# #RAPBEAT# #MusicFestival# #RAPBEATFESTIVAL# #rap# #HipHop# #ZICO# #JayPark# #LNGSHOT# #RIIZE# #Wuuslime# #Marv# #Loopy# #YangHongWon# #TAEYONG#

MADLY MEDLEY 2026 is officially announced! 🔥 The first round lineup is already heating up! ✨ AKMU, MONSTA X, IMJM with Swings, The Rose, Tiger JK & yoonmirae, BIBI, Paul KIM, LOOPY… More artists to be announced soon! 📍 Seoul | OIL TANK CULTURE PARK 📅 September 5~6, 2026 ⏰️First-Round Ticket Sales: July 9, 2026, 18:00（UTC+9） 🎫 First-Round Ticket Options: - 1ST TICKET (1DAY-SAT) - 1ST TICKET (1DAY-SUN) One stage, a thousand rhythms. Madly Medley—where all sounds come together💫 Click to secure your tickets in advance! #madlymedley# #musicfestival# #Yedong# #monstax# #swings# #BIBI# #loopy#

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system. External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent. With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both! I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering). [Original text: The Batch]

Loopring, the oldest zkRollup from 2019, will end. I'm a little shocked because I still use it occasionally, but I feel like I can't help it with this congestion and this market. As a DEX, I've seen it since 2017, and it's a project that I have a lot of memories of. Thanks to Loopring, I stepped into zkRollup. While I was developing Plasma and giving up once, I was skeptical about zkRollup, but they proved it at the very beginning. When I saw it, I was really moved. Thank you so much!!

LoopCoder-V2 is here, a 7B PLT coding model that reuses shared Transformer blocks for one extra round of test-time reasoning. License: Apache 2.0. 👉 Try it now: ✨ 2-loop sweet spot: stronger than the 1-loop baseline across coding, software engineering, terminal, and tool-use benchmarks 🛠️ Repo-level gains: biggest improvements show up on SWE-bench Verified and Multi-SWE 🌍 Code-first training: trained from scratch on 18T tokens with 100+ programming languages