February 2026

I’ve been thinking about an approach for fine-tuning/RL on limited data and I’m not sure it’s the right one , curious if anyone has done something similar. i need a model that generates document templates from structured input + a nl comment. The only data I have are existing compiled templates, no input/output pairs. The idea is to bootstrap with reverse engineering, feed each template to a strong LLM, extract the parameters that could have generated it, use […]

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How are you, hacker? 🪐 What’s happening in tech today, February 20, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From I Migrated My Blog From Jekyll to Hugo – Or At Least, I Almost Did to Living With the Lethal Trifecta: A Guide to Personal AI Agent Security, let’s dive right in. Developer Hackathon by {{Company}} HackerNoon? By @hackmarketing [ 2 Min […]

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Welcome to the third edition of HackerNoon Projects of the Week, where we spotlight standout projects from the Proof of Usefulness Hackathon, HackerNoon’s competition designed to measure what actually matters: real utility over hype. n n Each week, we’ll highlight projects that demonstrate clear usefulness, technical execution, and real-world impact – backed by data, not buzzwords. This week, we’re excited to share three projects that have proven their utility by solving concrete problems for real users: QuantumLayer, Ekstra AI, […]

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We know that engaging developers isn’t easy. They despise flashy ads and can spot “marketing” from a mile away.  But there’s one thing developers consistently show up for: Hackathons. They’re hands-on. They’re competitive. They’re creative. They let builders actually use your tech, instead of just hearing about it, and create something useful from it.  Incentivization + gamification + awareness = win-win-win! :::tip Learn more about HackerNoon Hackathons ::: HackerNoon Remote Technology Hackathons: Built for Longevity (Not Weekend Noise) […]

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I gave my OpenClaw AI agent the name Aris, access to my health data, family Telegram chat, calendar, and GitHub. OpenClaw is an open-source agent framework for building and running personal AI assistants that can interact with various apps and data sources. Simon Willison would call this insane, and he is probably right. Here’s what a Tuesday morning looks like. At 7:30, Aris sends my morning briefing: sleep score from Apple Watch, resting heart rate trending up, recovery […]

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Hi everyone, I’m currently working on my thesis and I’m specifically looking for research papers or resources on solving emergency transportation or emergency dispatch problems (such as ambulance routing, dynamic fleet management, or emergency logistics) using Quantum Multi-Agent Reinforcement Learning (QMARL). My focus is on integrating quantum computing techniques (e.g., variational quantum circuits, quantum-enhanced policy/value functions, hybrid quantum-classical models) within a multi-agent RL framework to handle dynamic, stochastic, and decentralized decision-making settings. Despite extensive searching, I haven’t found […]

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It’s refreshingly honest There is no shortage of AI bots on Telegram. Perplexity AI’s bot will answer your questions with sourced references. TelegramGPT wraps ChatGPT in a convenient chat interface. Dozens of others offer variations on the same theme: you type a prompt, you get a response, you move on. The Portal One+ Telegram bot assistant, built on the OpenClaw runtime, aims to be fundamentally different. It doesn’t just answer questions – it lives on your machine, remembers […]

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Hi all, I’ve been studying causal inference (mainly through Elias Bareinboim’s lectures) and understand the theoretical side — structural causal models (SCMs), do-calculus, identifiability, backdoor/frontdoor criteria, etc. However, I’m struggling with the implementation side of Causal RL. Most material I’ve found focuses on: – Theorems about identifiability – Action space pruning – Counterfactual reasoning concepts But I’m not finding concrete examples of: How to incorporate a known causal graph into an RL training loop How to parameterize the […]

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Data-efficient neural decoding is a central challenge for speech brain-computer interfaces. We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production. We pre-train a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants. Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%. Not only does pre-training improve performance […]

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