February 2026

Every product I have worked on has features that cost more to keep than they are worth. Not broken features. Not controversial ones. Features that work fine, get used by almost nobody, and sit there quietly taking up space. I have kept features like that alive for years. Not because they added value, but because removing them felt like more work than leaving them in place. They worked. They were not hurting anyone. Why bother? That reasoning felt […]

Ver mais

In Part 1, we built the engine. A small set of EODHD-backed tools, an agent with strict rules, and a single run_brief() function that returns two outputs: the markdown brief and the structured artifacts. This part is about turning that into something you can actually demo. We’ll build a simple Streamlit app that feels like a real feature: a query box on the left, a brief in the main view, and the tool-backed numbers rendered next to it. […]

Ver mais

Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execute extended sequences of actions before receiving meaningful feedback. Most existing reinforcement learning (RL) approaches model LLM agents as flat policies operating at a single time scale, selecting one action at each turn. In sparse-reward settings, such flat policies must propagate credit across the entire trajectory without explicit temporal abstraction, which often leads to unstable optimization […]

Ver mais

In this article you’ll learn how CPython lists actually work under the hood: how a static array model explains fast indexing and “no holes” behavior why contiguity matters, and how indexing reduces to one computation what really happens when the list grows: allocate → copy → repoint how size vs capacity drives CPython’s over-allocation strategy why append() is amortized O(1), even though resizes are sometimes O(N) why insert() / delete-from-middle are O(N) due to shifting how the list is structured (metadata “head” […]

Ver mais

Try signing up for any SaaS product right now. You’ll click buttons, fill forms, verify your email, maybe solve a CAPTCHA. Takes 30 seconds, no big deal. Now imagine you’re an AI agent. You don’t have a browser. You don’t have fingers. You don’t have an email inbox. That 30-second flow? It’s a wall. This is the state of the agentic internet in 2026. Agents can discover tools via MCP. They can pay for things via x402. They […]

Ver mais

How Can I Use Claude Code Effectively?​ There’s a lot of hype around vibe coding with Claude Code. The good news is that it’s warranted. Claude Code can take care of some surprisingly complex coding tasks. The bad news is that it’s over-hyped, with claims of amazing apps being vibe coded in a couple hours, or complex workflows that use 10 parallel sub-agents running in a loop to replace the work of 5 software engineers. If you’re not […]

Ver mais

Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 7. Conclusion In this paper, we study graph clustering without predefined cluster […]

Ver mais

Introduction Even today, browsers still can’t provide the same smooth calling experience as native mobile apps. Because of that, many popular services simply block calls from mobile browsers and force users to install a native app instead. But sometimes a native app isn’t an option: the user didn’t install it, has no storage left, or just prefers using the web. So the question is: how close can we get to a native call experience using only the browser? […]

Ver mais

Learning with shared representation is widely recognized as an effective way to separate commonalities from heterogeneity across various heterogeneous sources. Most existing work includes all related data sources via simultaneously training a common feature extractor and source-specific heads. It is well understood that data sources with low relevance or poor quality may hinder representation learning. In this paper, we further dive into the question of which data sources should be learned jointly by focusing on the traditionally deemed […]

Ver mais