artificial-intelligence

IntroductionTo assess and compare the accuracy, readability, and overall performance of large language models (LLMs) in answering questions about functional hypothalamic amenorrhea (FHA) for patients and healthcare professionals.MethodsA total of 11 patient-level and 15 clinician-level FHA-related questions were entered separately into four LLMs: ChatGPT 3.5 (free version), ChatGPT 4.0 (updated, …

The rapid evolution of artificial intelligence in financial technologies has fundamentally reshaped risk management within complex socio-economic systems. However understanding how users build transactional trust within high-density mobile economies remains a critical challenge. This study integrates technology fit and reassurance confirmation theories to investigate user security perceptions in …

Neural modeling and large language models (LLMs) has led to a significant improvement in the quality of machine translation (MT) output. While MT increasingly rivals human output, “translationese”—systematic linguistic fingerprints left by the translation process—has long been studied qualitatively, yet its quantitative boundaries remain unclear. We present an interpretable machine-learning frame…

Do you know Pulled Pork recipes and snakes games are being blocked by Claude Fable’s safety features? We will discuss this later in the article. Claude Fable 5 is the most capable AI model made till date, and it is generally ranked top by nearly every benchmark. The company Avidclan Technologies has a blog already covering the full Claude Fable 5 timeline from Project Glasswing to launch day, if …

This study investigates the role of generative artificial intelligence AI as an ethical and pedagogical mediator in the initial training of primary education teachers, particularly in strengthening the family-school relationship. A quasi-experimental intervention was conducted with a single group of 103 students from two Spanish universities, structured into four phases: theoretical instruction, …

IntroductionThe technologies such as artificial intelligence (AI) and immersive virtual reality (VR) are increasingly entering mathematics classrooms, yet their pedagogical role remains largely confined to enhancing visualization and feedback. This study addresses this limitation by reconceptualizing technology integration through an ecological–embodied perspective, examining how different techno…

When we shipped the first version of AI-generated replies for HelperX , each reply cost us about $0.011 in API spend. That sounds tiny until you multiply by 30 replies per slot per day times 200 active slots: roughly $66 per day, or ~$2,000 per month. Not catastrophic, but enough to eat into margins on the smaller plans. A year later, we're spending $0.0009 per reply — a 12x reduction. Same model…

A Korean research team has developed the "intelligent core" of a 6G network capable of autonomous control through artificial intelligence (AI). This achievement marks the first realization of an "intelligent 6G core" in Korea and represents a significant milestone in advancing next-generation network infrastructure.

A demo is a story. Production is a stress test. I’ve seen AI apps that feel like magic on a laptop… then crash the moment 10 users show up. Why? Latency kills the experience LLM outputs become unpredictable at scale No fallback when the API rate limits hit Prompt engineering works once, not for every edge case I learned this the hard way: Reliability > cleverness. If your AI stops working at 2 AM…

If you use LLMs long enough, you hit the same wall. The frontier model is impressive, but it is not always the best model for your job. It may be too expensive. It may be too slow. It may be too general. And once you start asking it to follow your company’s rules, tone, domain language, and task structure, the gap between “smart” and “useful” gets obvious fast. That is where post-training comes i…

A systematic review of 161 peer-reviewed publications found that AI research in food safety has expanded rapidly, rising from one study in 2012 to 46 in 2023. The review shows that machine learning and deep learning are being studied for pathogen detection, chemical contamination prediction, food fraud screening, outbreak surveillance, and regulatory decision-making, but data gaps, class imbalanc…

NSE, Treasury Bonds, Unit Trusts, Diaspora Investment pip install faida-mcp Server #21 in the AI-KungFU East Africa Coordination Infrastructure. GitHub

When I shipped Trooper , a privacy-aware LLM proxy written in Go, I didn't have a marketing plan. I had GitHub traffic analytics and a habit of checking them obsessively. Seven weeks later, I have something more useful than a viral moment: a ranked table of every traffic spike, what caused each one, and proof that the exact same playbook that worked at launch still works when you have something n…

On June 9, Anthropic shipped Claude Fable 5 — the most capable coding model the industry had ever seen. Three days later, the U.S. government ordered it offline for every user on Earth . No warning. No transition period. One directive, and the frontier vanished overnight. 📖 Read the full version with charts and embedded sources on ComputeLeap → The same week, Z.ai (Zhipu AI) released GLM-5.2 — a …

We open sourced Centaur last month—a Slack agent we built for our own investing and engineering work. Over the past few months it's grown to 100-150 daily power users across a few organizations, handling both judgment-heavy tasks like investment research and raw horsepower work like searching massive codebases. The interesting part isn't just our internal use. We've been running a small Slack Con…

Hermes-Crew Hybrid: A Hybrid Architecture for Secure Multi-Agent AI Workflows I built a hybrid system that combines a central orchestrator (Hermes) with temporary CrewAI micro-crews, protected by 3 layers of security. Here's what it does and why it matters. The Problem Multi-agent AI systems are powerful but dangerous. When you chain multiple agents together, a single compromised agent can poison…

We faced a recurring issue in our content generation pipeline: the LLM frequently outputted malformed Markdown. Unclosed code blocks, broken list levels—you name it. Relying solely on Prompt engineering became a game of whack-a-mole that we couldn't win. The core problem? Asking an LLM to generate Markdown is a probabilistic process. A Prompt is a "soft constraint." No matter how well you phrase …

If you've ever pointed an AI coding agent at a pull request and watched it confidently merge based on a stale title, a truncated API response, or prose that looked like instructions, you've hit a boundary problem. Git hosts know a lot. Agents need small, typed, trustworthy facts , not raw forge dumps and not your team's SDLC vocabulary mixed into SCM output. Remogram is a generic SCM/forge bounda…

Last month I needed to scrape product details from 30 different e-commerce sites. Each site used its own HTML structure, class names changed weekly, and some were just plain inconsistent. I had two options: write a mountain of brittle CSS selectors or try something I’d been avoiding—letting an LLM figure out the extraction. Here’s what I learned the hard way, including the code that actually work…

I started using GitHub Agentic Workflows a couple months ago: small Claude/Copilot agents that run inside your CI for code review, daily doc updates, malicious-code scans, and PR fixes. You author them in markdown, they compile to GitHub Actions, and an AI agent does the work on each run. Got the first few repos working and hit the question: how am I actually tracking what's deployed on each of t…