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The HackerNoon Newsletter: How to Make A Clicker Game on the Desmos Graphing Calculator (6/13/2026)

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How are you, hacker? 🪐 What’s happening in tech today, June 13, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Pioneer 10 Crossed the Orbit of Neptune in 1983, Leonard Kleinrock Was Born in 1934, Microsoft Partnered With Time-Warner and Tele-Communications to Develop Interactive TV in 1993, Scottish Physicist James Clerk Maxwell Was Born in 1831, Norcross Received the First US Diving Suit Patent in 1834, and we present you with […]

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AI Coding Tip 024 – Force a Criteria Check Before the Task Ends

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Don’t let the AI grade its own homework. TL;DR: Spawn a fresh subagent after every task to check your rules, because the AI that did the work can’t audit itself. Common Mistake ❌ You write a detailed AGENTS.md with strict mandatory rules. The AI reads the file, completes the task, and reports: “Done. I followed all the rules.” You trust the report. It didn’t check. It assumed. That’s hallucinated compliance, and it’s the default behavior of every AI agent that […]

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I Built a Telegram Bot That Tells Me When My Stocks Are “Cheap” — Built on 50-Year-Old Math

I Built a Telegram Bot That Tells Me When My Stocks Are “Cheap” — Built on 50-Year-Old Math

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Bollinger Bands, RSI, and a few hundred lines of Python — a walkthrough of building a personal market monitor. Source: Image by Torie Hilley on Torie Hilley Fine Art Table of contents The problem TL;DR Why these four indicators, and not thirty others The architecture, in one diagram Setting it up (≈10 minutes) Living with the bot What a real alert looks like What this bot isn’t What I’d add next Why I built this for myself References The problem I open my brokerage app maybe twelve times a day. Most […]

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Mastering the ML Lifecycle

Mastering the ML Lifecycle

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If you are building machine learning models or deploying complex AI agents today, your biggest enemy isn’t the math — it’s the chaos. In the early days of data science, managing code, datasets, hyperparameter sweeps, and weights felt like trying to organize a library where everyone throws books wherever they want. Tracking who trained what, with which data, and why a model suddenly failed in production was a logistical nightmare. Enter MLflow. Originally created by Databricks, MLflow has matured into the […]

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Local Ai model training

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I’m fine-tuning Qwen2.5-7B on my own dataset. It answers simple questions but hallucinates on complex questions. What is the best approach to improve accuracy and reasoning over my data? Should I use fine-tuning, RAG, agents, knowledge graphs, or another method? My hardware is RTX 5070 Ti (16GB), 64GB RAM, 20-core CPU. submitted by /u/Asleep_Fold5405 [link] [comments]

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Building a Custom AI Agent with SAP Joule Studio: The Complete Guide Nobody Wrote

Building a Custom AI Agent with SAP Joule Studio: The Complete Guide Nobody Wrote

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The Undocumented Journey of Connecting External REST APIs to SAP’s AI Agent Framework For developers tired of battling the ‘black box’ of SAP Joule integration – this is the guide I wish I had two weeks ago. A practical engineering guide compiled from weeks of trial-and-error, failed deployments, undocumented limitations, and lessons that never made it into the official documentation. Source: Image by the Author Why This Article Exists If you’ve ever tried to build a custom AI agent in SAP […]

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Anomaly Detection vs Classification for Visually Similar Cancer vs Mimics? [P]

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I’m working on a paper and would love some input on model choice. Suppose you’re trying to detect a specific type of cancer, but the negative samples are visually and morphologically very similar (i.e., “mimics” of the cancer). In this setting, would it make more sense to approach the problem as: Anomaly detection (treating the cancer as the target distribution and everything else as out-of-distribution), or Supervised classification (explicitly learning to distinguish cancer vs. mimics)? submitted by /u/DryHat3296 […]

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Threads of underground fungal networks are long enough to reach beyond the Solar System

Threads of underground fungal networks are long enough to reach beyond the Solar System

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Hidden underground around the world lie 110 quadrillion kilometers of arbuscular mycorrhizal fungal networks—webs of ultra-thin threads that, if connected in a single line, would stretch almost a billion times the distance between the Earth and the sun, according to new research published in Science on Thursday. These fungal communities form intimate relationships with the roots of plants, which they provide with nutrients like phosphorus and nitrogen in exchange for carbon, 1 billion tons of which the networks […]

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Anthropic Disables Claude Fable 5 and Mythos 5 After US Government Order

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Anthropic has disabled its two most capable models for every customer. The shutdown followed a US government export control directive. The order arrived on June 12, 2026. It named Claude Fable 5 and Claude Mythos 5 specifically. Both models had launched only three days earlier, on June 9. The directive cited national security authorities, according to Anthropic. It suspended access by any foreign national, inside or outside the United States. That scope included Anthropic’s own foreign national employees. […]

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Anthropic, Mythos y la innovación en un país que ya no sabe distinguir entre tecnología y amenaza

Anthropic, Mythos y la innovación en un país que ya no sabe distinguir entre tecnología y amenaza

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Anthropic acaba de protagonizar uno de esos episodios que, vistos dentro de unos años, probablemente utilizaremos para explicar hasta qué punto el desarrollo de la inteligencia artificial de frontera se ha convertido en una actividad políticamente explosiva. La compañía lanzó Claude Fable 5 y Claude Mythos 5, dos modelos construidos sobre la misma base, pero con una diferencia fundamental: Fable 5 está pensado para un acceso amplio, con salvaguardas reforzadas en ámbitos como ciberseguridad, biología o química, mientras […]

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