“Exit the Cave”: reconnecting with the real world
“Exit the Cave” is an essay about the tension between living inside digital systems and staying connected to the real world. It uses a personal reflection to question how much attention, judgment, and experience we delegate to platforms and devices. Rather than a technology news item, it is a cultural analysis of technology’s human impact: what digital convenience gives us and what we may lose when we stop looking beyond it.
How sources behind AI recommendations are manufactured
An independent analysis of 380 software categories examines how AI-integrated search systems build recommendations. It sent 760 queries—one per category to Perplexity/sonar and one to Perplexity/sonar-pro through OpenRouter—and collected 7,534 citations. 59.8% of cited domains were outside the 100,000 most visited sites, while 23.4% did not appear in Tranco’s top million. The central finding is not that a few famous sites control answers: the ten most-cited sources represented only 17.3% of citations. The striking pattern lies at the periphery, where 751 of 2,055 cited domains were unranked and had more recent Wayback captures. Some appeared designed to be consumed by retrieval systems, with automated pages and descriptions explicitly oriented toward model grounding. The report identifies three apparently related domains that published 215,128 automated “best software” pages, and highlights guideflow.com, whose blog was cited 194 times across 96 categories despite not being a directory or operating in those markets. The authors compare sitemaps, templates, DNS, registration dates, and archived pages, while noting that shared infrastructure is circumstantial evidence, not proof of common ownership. The broader lesson is that AI answers can rely on recent, high-volume content designed for machines without making its authority obvious. The study measured only Perplexity—Google was excluded—so its findings should not automatically be generalized to every search engine or model.
Meta Unveils Muse Spark 1.3: New Open-Source Language Model
Meta has announced the release of Muse Spark 1.3, a new iteration of its open-source language model designed for advanced natural language generation and understanding tasks. This version introduces significant improvements in computational efficiency and output quality, positioning itself as a competitive alternative in the accessible language model ecosystem. Muse Spark 1.3 stands out for its ability to process extended contexts and generate coherent responses in multiple languages, including enhanced support for low-resource languages. The model's architecture has been optimized to reduce carbon footprint during training and inference, aligning with Meta's AI sustainability commitments. The model is available under an open-source license that permits commercial use and modification, fostering community innovation. Researchers and developers can access the model weights and detailed documentation through Meta AI's official repository, driving the development of custom applications and academic research.
FCC plans robocall scorecard to grade phone companies on spam call blocking
The Federal Communications Commission today said it will create a robocall mitigation scorecard to rate phone companies on how effectively they block illegal spam calls. The scorecards could include call-blocking statistics along with data on customer complaints and enforcement actions. The FCC said scorecards could grade providers on a number scale, with letter grades, or by classifying providers as low risk, medium risk, or high risk. "The Scorecard will empower consumers and encourage provide
I rented a car, and within hours, my driver's license was for sale
Not long ago, I rented an SUV from a well-known car rental company. Within hours of an employee scanning my driver's license, a high-resolution scan of my ID was available for sale on the dark web. An exposé published Tuesday by KrebsOnSecurity reports that my license was one of more than 153 million that were available through Nexus, the name of the new ID theft service. Like other driver's licenses available there—including some belonging to journalist Brian Krebs, his mother, an FBI assistant
TechCrunch Disrupt 2026’s new Real World AI Stage features Nvidia, robots, and extinct animals
TechCrunch Disrupt 2026 will once again place AI at the heart of its program, but with a new twist: a dedicated Real World AI Stage. The event, taking place October 13‑15 at San Francisco’s Moscone West, expands its AI offerings by introducing this fresh stage, which explores the intersection of digital and physical realms. It will highlight how autonomous hardware is moving beyond self‑driving cars to occupy public spaces, battlefields, homes, and even potentially aid extinct species in re‑entering Earth. The lineup features speakers from leading companies such as Shield AI, Colossal Biosciences, FieldAI, Foxglove, and Nvidia, among others still to be announced. The first session tackles the data gap that hampers general‑purpose robotic intelligence. While large language models had the internet and self‑driving cars have millions of hours of road data, robots lack that foundational data. This shortfall is the primary reason most experts believe broad robotic AI is still years away, despite breakthroughs elsewhere in AI. A new wave of startups is racing to close this gap by building data pipelines, simulation environments, and foundation models that could spark a capability explosion similar to that seen with LLMs. Les Karpas, Head of Physical AI at Nvidia, will lead a discussion on what the ChatGPT moment for physical AI truly requires and how close we really are. The second session focuses on safety and readiness when AI enters the physical world. A mistake can ground an aircraft, crash a vehicle, or jeopardize a mission. Leaders building autonomous vehicles, defense technologies, and industrial systems will address one of the toughest questions every hard‑tech founder faces: how to know when a system is safe to deploy. The panel will explore how founders can cultivate a safety culture, test and validate AI, navigate regulatory hurdles, and build trust in high‑stakes environments. Nate Michael, CTO of Shield AI, will participate in this discussion. The third session, a fireside chat, features Ben Lamm, CEO of Colossal Biosciences, a company known for turning de‑extinction from science fiction into a billion‑dollar business. Lamm will discuss the technologies used to revive extinct species, the role AI plays in modern biology, and the growing debate over whether engineering nature is a conservation breakthrough or a distraction from protecting existing wildlife. His talk highlights the ethical, technical, and commercial dimensions of attempting to bring back lost species. The fourth session brings together leaders in edge AI from defense, space, and industrial sectors. Dr. Ali Agha, CEO and founder of FieldAI; Michelle Lee, CEO and founder of Medra; and Aidan Madigan‑Curtis, partner at Eclipse Ventures, will share how they have made AI work where latency matters, connectivity is limited, and failure is not an option. Expect practical lessons on architectural principles, design decisions, and trade‑offs that enable AI‑centric systems to function in real‑world conditions. These insights have direct implications for companies deploying AI in challenging environments. Finally, the fifth session addresses the critical transition from prototype to production and scaling. Founders who have successfully crossed this gap in space hardware, humanoid robotics, and autonomous systems will share what they got wrong, what they would have done earlier, and what the prototype‑to‑production journey looks like when supply chains and manufacturing realities replace lab conditions. John Mackey will contribute his perspective on navigating this complex path. The panel underscores the practical challenges and strategies needed to move from a working prototype to a scalable, profitable business, offering actionable takeaways for deep‑tech startups.
Palo Alto Networks Buys Thrive‑Backed Console for $500M
Palo Alto Networks acquired Console, an AI IT service automation startup backed by Thrive Capital, for $500 million. The deal positions Palo Alto in the growing AI‑driven IT automation market, while leaving Sequoia‑backed Serval as the de‑facto startup leader in the space. The move signals strong investor confidence and could spur further consolidation in the sector.
Sequoia-X: quantitative stock selection for China
Sequoia-X V2 is a quantitative stock-selection system for China’s A-share market. It is rebuilt in Python with object-oriented architecture, vectorized calculations, and incremental data updates; it uses baostock for historical and daily data and SQLite for local storage. After market close it can run the selection process and send results to a Feishu group. The project provides two operating modes: a daily mode that updates data and runs strategies with parallel processing, and a backfill mode for the initial historical load. The README documents strategies including TurtleTrade, moving-average and volume breakouts, High Tight Flag, post-limit confirmation, and RPS Breakout, making it an extensible base for experimenting with selection rules. Its main strength is bringing data acquisition, persistence, strategies, and notifications into a reproducible local workflow. The README also spells out requirements—Python 3.10 or newer, environment-based configuration, and an initial historical backfill—which helps users study or modify the system. It is not a profitability guarantee or a system that should be used without validation: strategies depend on data quality, assumptions, and the Chinese market. Before making financial decisions, users should review the code, test out of sample, account for costs and biases, and evaluate the maintenance of baostock. Verdict: a useful educational quantitative starting point, with real risk if treated as advice.
Ponytail: less code for coding agents
Ponytail is a tool and skill for coding agents focused on reducing generated code in real development tasks. Its approach gives agents more precise operational context so they can produce smaller, faster, and cheaper changes; the README publishes comparative measurements against sessions without the skill. It is relevant for teams using agents on existing repositories and wanting to measure cost, speed, and change size. Its results should be validated in the target workflow before treating the reported figures as a general guarantee.
Google Avoids Breakup of Ad Tech Business in Major Ruling
Google has successfully avoided a forced breakup of its ad tech business, marking a significant legal victory for the tech giant amid increasing regulatory scrutiny. The ruling, issued by a federal court, preserves Google's current structure in digital advertising operations, allowing the company to maintain its dominant position in the online ad market. This outcome reinforces Google's control over a critical segment of the internet economy. The decision comes after prolonged regulatory battles concerning Google's market power and its effects on competition. Critics and regulators have long argued that Google's vertical integration across the advertising supply chain harms competitors and stifles innovation. However, the court determined that proposed remedies were disproportionate and unsupported by sufficient evidence, ultimately siding with Google's defense. This ruling carries far-reaching implications for the future of tech regulation in the U.S. and globally. As authorities worldwide intensify their scrutiny of major tech companies, the favorable outcome for Google may set a precedent affecting similar cases involving other dominant players in their respective markets.
Paint.net 5.2 alpha now runs on Linux
Paint.net 5.2 alpha now runs on Linux