Author: Carter

Even with the crypto market’s inherent instability, many investors still consider XRP a long-term holding that could either fund their retirement or serve as a hedge against inflation and currency devaluation. But does that narrative hold up with actual math on its side? Some market forecasters have mapped out trajectories toward $1 million by 2035, while other experts caution that XRP continues to grapple with wild price swings and lingering questions about its real-world utility in decentralized finance and institutional settings. XRP’s Historical Price Track Record. Source: BeInCrypto Markets How Much XRP Would You Need to Retire by 2035? XRP…

Introduction Most enterprise security teams rely on 40 or more security tools, providing extensive visibility into internal telemetry and asset data. However, these tools often operate in isolation, producing overlapping alerts and fragmented data. Despite this abundance of information, breach dwell times remain stubbornly high—around 43 days—response windows close before teams can take action, and analysts are overwhelmed by the sheer volume of noise rather than focusing on actual threats. The issue isn’t a lack of effort—it’s a flawed architecture. Security programs were designed for an era when threats evolved slowly enough for humans to coordinate responses manually. That era…

This continuation of the document parsing series within Enterprise Document Intelligence focuses on building an enterprise RAG system from four foundational components. It expands upon Article 5 (document parsing) by examining a single table: toc_df, which captures the document’s organizational structure. Article 5 populates this table using the PDF’s built-in outline (via PyMuPDF’s doc.get_toc) when available. This installment addresses the scenario where no built-in outline exists, showing how to reconstruct that structure from the visible content on the page. The placement of this companion piece: it builds on Article 5 (document parsing), within Part II (the four components), recreating the…

import random from collections import Counter, defaultdict from datetime import date, timedelta from prefab_ui.actions import AppendState, OpenLink, PopState, SetState, ShowToast, ToggleState from prefab_ui.app import PrefabApp from prefab_ui.components import ( Alert, AlertDescription, AlertTitle, Badge, Button, Card, CardContent, CardDescription, CardFooter, CardHeader, CardTitle, Code, Column, DataTable, DataTableColumn, Form, Grid, H2, Input, Markdown, Mermaid, Metric, Muted, Progress, Ring, Row, Slider, Small, Switch, Tab, Tabs, Text ) from prefab_ui.components.charts import ( BarChart, ChartSeries, LineChart, PieChart, RadarChart, ScatterChart, Sparkline ) from prefab_ui.components.control_flow import Else, ForEach, If from prefab_ui.rx import EVENT, STATE random.seed(42) TODAY = date.today() DATES = [TODAY – timedelta(days=29 – i) for i in range(30)]…

Charles Hoskinson is building a more compelling argument for artificial intelligence as development on Midnight City advances. The Cardano founder now views AI agents as the foundation of how the network communicates and grows. Here’s a summary of his recent comments, along with a deeper look at what Midnight City is aiming to demonstrate. How Hoskinson Views AI’s Role in Cardano’s Next Chapter AI agents operate on their own, handling trades, posting content, and coordinating tasks without a person at the controls. Hoskinson has embraced this concept after facing criticism over recent experiments connected to Cardano’s official social channels. The…

From cloud computing to worldwide food supply chains, no industry is safe from the constantly changing nature of cyber threats. The 2025 sector cyber threat reports recently published by IT-ISAC and Food and Ag-ISAC highlight a clear truth: organizations in every field are up against determined adversaries, including both state-backed hacking groups and organized cybercriminal networks. These reports also shed light on how adversaries are shifting their techniques and how rising global tensions are making the threat landscape more complicated for all organizations, no matter the industry. Understanding Risk Through the PASS Framework To get a clearer picture of the…

In my previous post, I discussed how to obtain structured, machine-readable outputs from an LLM using JSON Mode, function calling, and structured outputs. In that discussion, we briefly explored function calling as a way to retrieve structured responses. However, function calling is much more than just a way to get organized data back from a model — it is, in fact, the foundation of agentic AI workflows. So, in this post, we’re going to dive deeper into this subject. In every example we’ve discussed up to now, the LLM has been treated merely as a passive respondent — it receives…

I recently revisited my matrix recurrent units algorithm (the MRU), a novel linear-time sequence architecture I created as an alternative to attention. I explain it in depth at the repo, but the gist is the MRU works by transforming the embedding into an input state matrix, cumulatively multiplying the matrices across the sequence dimension to get the output state matrix, and then transforming the matrices back into a vector. In order to make the MRU efficient on DL hardware, I created a parallel scan by utilizing the operation's associativity. About a year ago, I shared my project on Reddit (I've…

In brief Mercury 2, developed by Inception Labs, produces around 1,000 tokens per second and earned a score of 90 on the AIME 2026 benchmark. Google’s DiffusionGemma reaches comparable speeds but falls short on performance benchmarks. DiffusionGemma is freely available as an open-weight model on Hugging Face, whereas Mercury 2 is a closed-weight, paid API offering. On Thursday, Inception Labs unveiled Mercury 2, branding it as the planet’s quickest reasoning language model. According to the company’s release, it churns out approximately 1,000 tokens per second—the fragments of text an AI system processes and generates—compared to roughly 89 tokens per second…

`Ravie Lakshmanan`Jun 19, 2026Threat Intelligence / Firewall Security On Thursday, the U.S. Cybersecurity and Infrastructure Security Agency (CISA) advised Fortinet clients using FortiGate devices to implement security measures. This comes in response to a large-scale attack targeting thousands of devices accessible over the internet. Known by the codename FortiBleed, this extensive campaign is being linked to Russian-speaking hackers. As of June 19, 2026, reports indicate that 86,644 devices have been compromised. SOCRadar’s data shows that a majority of the stolen credentials are generic admin accounts (35%) and built-in Fortinet system accounts (28.3%). Specific organizational accounts make up the remaining 36.7%…