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For a while, “just add evals” felt like the obvious answer to shipping LLM systems responsibly. That made sense. OpenAI describes evals as a way to make LLM applications more stable and resilient to code and model changes, while Anthropic frames evals as the mechanism for testing whether an AI system succeeds on a task at all. At the same time, tools like Promptfoo and DeepEval have pushed evaluations closer to the software engineering mainstream by explicitly supporting […]

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Lovable, the AI-powered app builder, hit $200M ARR in under a year — the fastest AI startup ever to cross $100M ARR, doing it in just eight months. You’d think a company growing that fast has PMF locked down. Elena Verna, their Head of Growth, calls PMF at Lovable a “perishable good” — something the team must re-earn every 90 days. The playbook of “find PMF, then scale” is a trap. This article is the operating manual for […]

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I studied law. Seven years later, I did an MBA. At no point during either degree or in my work in between did anyone pull me aside and say, “Hey, you should probably learn to write a ‘for loop’.” And yet here I am, running a legaltech startup, shipping production code, and mass-texting my cofounder screenshots of features at 2 am. I’ve been chewing on the idea of the “Humanities Engineer” since about 2018-2020, back when I first […]

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Let’s learn about Ai Agents via these 218 free blog posts. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read blog posts about any technology. ai-agent 1. AI Agent: Meet the Minds of Smart Machines In this article, we will learn and break down what AI agents are, how they think, and how they are shaping the tech world and becoming part of our daily lives. 2. Beep […]

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Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language models’ (LLMs) strength in natural language processing. In this work, we identify a primary bottleneck in informal theorem proving as a lack of insight, namely the difficulty of recognizing the core techniques required to solve complex problems. To address this, we propose a novel framework designed to cultivate this essential reasoning skill and enable LLMs to perform […]

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Existing multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited representation of cross-hazard dependence and uncertainty. To address these limitations, this study proposes a deep learning (DL) workflow for joint flood-landslide multi-hazard susceptibility mapping (FL-MHSM) that combines two-level spatial partitioning, probabilistic Early Fusion (EF), a tree-based Late Fusion (LF) baseline, and a soft-gating Mixture of Experts (MoE) model, with MoE serving as final predictive model. The proposed design preserves spatial […]

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