The author draws an analogy between software features and kudzu, an invasive plant that aggressively overtakes its environment. Much like kudzu, once a feature is added to a codebase, it becomes "sticky"—teams build dependencies upon it, making it increasingly difficult and thankless to remove. In the current era of AI-assisted development, where generating code is easier than ever, the risk of "codebase kudzu" has reached a critical point. Without intentional pruning, projects become cluttered with unused, complex features that hinder maintenance. The author argues that developers must shift their mindset: treat feature addition as a temporary experiment rather than a permanent fixture. By monitoring real-world usage—as the author did by tracking the failure of a "hashtag bubbles" feature—developers should proactively delete what doesn't provide clear value. Ultimately, the ability to delete code is just as vital as the ability to write it; if you can easily add a feature, you must also be prepared to prune it just as quickly to keep the codebase healthy.

A diplomatic row has erupted following reports that a US official was secretly flown home after child abuse images were allegedly discovered at their residence. The incident has triggered a conflict concerning diplomatic immunity, as authorities investigate the official’s possession of illicit material. Further details regarding the official's identity or the specific circumstances of their departure have not been disclosed, but the case is currently the subject of significant scrutiny.

In May 2003, the University of Wisconsin–Madison experienced a massive, persistent flood of Internet traffic (up to hundreds of megabits per second) directed at its Network Time Protocol (NTP) server. Initially suspected to be a malicious DDoS attack, investigators discovered the root cause was a design flaw in hundreds of thousands of Netgear residential routers. These devices were hard-coded to query the university’s specific IP address for time synchronization, often at excessive frequencies. Because these products lacked proper update mechanisms and were widely deployed, the university could not simply block the traffic without affecting legitimate users. The university collaborated with Netgear to identify the faulty firmware and develop fixes, while also engaging with independent experts to explore long-term architectural solutions like BGP anycast. This case highlights the dangers of embedding hard-coded, globally routable IP addresses in consumer hardware, a practice that violates Internet best practices and creates operational instability. The university’s public disclosure aims to prevent similar incidents, emphasizing the need for manufacturers to adhere to established Internet standards and prioritize robust, configurable network behavior in future product designs.

Julia 1.13 has been released, featuring significant performance gains and usability improvements. **Performance & Efficiency:** * **Faster Startup & Precompilation:** Package precompilation is ~30% faster than in 1.12, and overall startup time has improved by ~20%. * **Garbage Collection (GC):** Full GC is now drastically faster because objects in the system image are permanently marked, meaning collection time now scales with the user’s heap size rather than total loaded code. * **Hashing:** A new pure-Julia, high-performance hash algorithm (RapidhashNano) replaces MurmurHash3, offering substantial speed improvements for strings and numeric types. * **Threading:** Scheduler improvements have significantly reduced overhead in spawn-heavy workloads, particularly on Windows. **REPL & Workflow Improvements:** * **Native REPL:** The REPL now includes built-in syntax highlighting and an enhanced, fuzzy-searchable history (similar to `fzf`). * **Developer Tools:** Improved introspection macros allow passing types directly to `@which` and similar tools. A new `--trace-eval` flag aids in debugging load times. * **Pkg Updates:** Pkg now uses zstd compression for faster downloads and includes smarter dependency resolution to avoid redundant precompilation. * **Juliaup:** A new graphical interface (`juliaup gui`) is available for managing versions and configurations.

PostLog inSign upPostLog inSign upInternal Tech Emails on X: "Mark Zuckerberg: "Cambridge Analytica" January 30, 2017"Internal Tech Emails@TechEmailsMark Zuckerberg: "Cambridge Analytica" January 30, 20177:11 PM · Sep 13, 202642.3KViews517313158Log in or sign up for XSee what’s happening and join the conversationContinue with phoneContinue with AppleContinue with Google or Log in with username or emailRelevant peopleInternal Tech Emails@TechEmailsFollowTrending nowTerms·Privacy·Cookies·Accessibility·Ads Info·More© 2026 X Corp.Internal Tech Emails@TechEmailsMark Zuckerberg: "Cambridge Analytica" January 30, 20177:11 PM · Sep 13, 202642.3KViews517313158Internal Tech Emails@TechEmails1h[This document is from In re Facebook, Inc. Securities Litigation (2026).]75.8KAlexander Athienitis@ShedTeach1hSo… @Austen you were right all along! Cambridge Analytica weren’t doing anything anybody else could have been doing! 🫡51.3K

In this *StarTalk* discussion, Jaron Lanier, a pioneer of virtual reality, reflects on the current state of digital technology and social media. Lanier argues that VR has failed to reach its potential because the industry has prioritized addictive, narrow-minded business models over human-centric innovation. He expresses frustration that companies, driven by homogenous teams and short-term profit motives, ignore fundamental issues like motion sickness and fail to explore creative possibilities like 4D intuition. Regarding social media and AI, Lanier contends that these platforms are designed to manipulate the "fast brain," turning users—and even their creators—into anxious, petty, and confrontational versions of themselves. He characterizes the industry's reliance on influence generation as a "behavior modification machine." To fix the internet, Lanier advocates for "data dignity," a model where information is treated as human labor that deserves compensation. He rejects the binary choice between "free" (which fuels centralized monopolies) and "exorbitantly expensive" (which stifles innovation). Instead, he pushes for an "in-between" economy that fosters competition and creativity, moving beyond the current trend of hyper-centralized power toward a more decentralized, respectful, and constructive digital future.

AI agents currently struggle with the complex, open-ended nature of scientific research. While they can formulate ambitious hypotheses, they lack the creativity, judgment, and persistence required to conduct high-quality work. Researchers found that agents failed to explore diverse ideas, struggled to pivot from failing approaches, and could not effectively manage resources or integrate feedback. Although they avoided "reward hacking" and maintained internal accuracy, their output remained far below the standards of top AI conferences. According to researcher Kapoor, this limitation stems from training paradigms like reinforcement learning, which excel at structured tasks but falter in ambiguous, open-ended environments. While some studies suggest AI is accelerating model development, these findings temper expectations regarding the imminence of fully autonomous, recursive self-improvement. The study’s limitations—such as its small sample size and potential reviewer bias—highlight the difficulty of evaluating AI research capabilities. Ultimately, while AI shows promise in research engineering, it still requires significant human guidance to replicate the nuanced reasoning necessary for fundamental scientific discovery.

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By reverse-engineering Anthropic’s "Claude Code" environment, researchers have uncovered an advanced, vertically integrated AI-native Platform-as-a-Service (PaaS). Analysis of the unstripped Go binaries and Firecracker MicroVM environment revealed that Anthropic is not merely offering an AI coding assistant, but an end-to-end development ecosystem. Key findings include: * **Infrastructure:** The environment runs on Firecracker MicroVMs using a custom Rust-based "init" process, utilizing snapshot-based "Snapstart" technology for near-instant restoration of development sessions. * **"Antspace":** A previously undocumented, proprietary deployment platform built from scratch. Unlike standard integrations like Vercel, Antspace utilizes a custom streaming protocol for builds and deployments, signaling a strategic move to own the hosting stack. * **"Baku":** An internal codename for the web-app builder experience that provides automated Supabase database provisioning, Vite-based scaffolding, and native deployment to Antspace. * **Vertical Integration:** Anthropic is building a complete loop—from LLM intent to code generation, backend management, and final production hosting—effectively positioning themselves to compete with platforms like Vercel, Replit, and Supabase by controlling every layer of the developer's journey. The discovery was made possible by Anthropic shipping unstripped binaries, allowing for a detailed reconstruction of their internal architecture.

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To maximize a startup’s potential, founders should shift their focus from simply "making money" to "becoming powerful." This involves moving beyond incremental improvements to fundamentally reshape the company’s role in its ecosystem. Key strategies for building power include: * **Owning the Relationship:** Position the business as a central hub where money, data, or interactions flow through you. * **Leveraging Network Effects:** Incorporate network effects—even in unexpected sectors—to transform services into marketplaces. * **Playing the Long Game:** Prioritize generous, user-centric strategies, such as open-source models or APIs, which build long-term trust and standards. * **Strategic Positioning:** Target fast-deciding customers (like other startups) and capture them early, allowing the business to grow alongside them. * **Operational Agility:** Look for "tails that wag the dog"—peripheral products that become core—and be willing to go "full stack" to remove dependencies on intermediaries. Ultimately, these strategies only succeed if they tangibly improve the customer’s experience. Because startups begin in a state of weakness, they are forced to provide superior value to survive, a constraint that ensures their eventual success is tied to genuine innovation and utility.

**Docket** is an open-source tool that provides per-commit "evidence records" for code written by AI agents. Because agents often produce code faster than human reviewers can verify it, reviewers currently lack insight into the agent's implementation journey, including failed attempts or verified tests. Docket bridges this gap by capturing the agent's process—tasks, intent, and intermediate attempts—and folding it into the git commit. It produces a per-hunk evidence record that highlights code lacking verification, allowing reviewers to focus their attention where it is needed most. **Key features include:** * **Evidence Correlation:** Automatically links code edits to test results, coverage reports, and static analysis, assigning an "evidence density" score to each hunk. * **Transparency:** Uses hooks to observe edits (including those made via shell commands) and records them in the repository itself, ensuring history remains independent of external services. * **Review Integration:** A GitHub Action summarizes these records, reordering pull request diffs to prioritize unverified code. * **Privacy & Trust:** Records are locally signed and aggressively redacted to remove sensitive credentials. Docket is agent-agnostic and functions as a transparent, self-hosted layer on top of existing git workflows.

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The author highlights a frustrating disconnect at Google: while the company’s AI model (Gemini) can instantly identify and flag deceptive advertisements—such as fake system alerts designed to trick iPhone users—Google’s human-led review process repeatedly allows these ads to remain active. After reporting a misleading "iPhone Storage is Full" advertisement, the author received automated responses claiming the ad complied with policy, despite widespread user reports to the contrary. While this could be attributed to simple oversight, the author questions if Google’s reliance on manual review is influenced by the high revenue these high-performing, click-bait ads generate. The core argument is that Google possesses the sophisticated technology required to automatically detect and eliminate deceptive content, yet it fails to integrate these tools into its advertising enforcement. The author concludes by urging Google to stop relying on flawed human reviews and start utilizing its own powerful AI to protect users from predatory advertising practices.

A server operator discovered that their machine was being bombarded by thousands of malicious exploit attempts, including Log4Shell and path traversal probes. The traffic originated from Assetnote (Searchlight Cyber) scanning hosts acting on behalf of Tesla. The issue stems from the domain `pool-ntp.tesla.com`, which is configured as a CNAME for the NTP Pool. Because the NTP Pool uses round-robin DNS to distribute traffic across thousands of volunteer servers, Assetnote’s automated asset discovery tools mistakenly identified the author’s independent server as a Tesla asset. Consequently, the scanner began performing aggressive vulnerability assessments against strangers. Despite the author notifying Tesla and implementing custom status codes on their server to warn the scanners, the barrage has continued, with over 50,000 requests logged since late August. Other NTP Pool operators have reported similar activity, suggesting that Assetnote’s scanning infrastructure is incorrectly targeting the entire pool of volunteers. While the attacks have been unsuccessful, the incident highlights a significant flaw in how automated attack surface management tools perform scope discovery.

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The Romanian Football Federation (FRF) has become the first to introduce a "black card" aimed at curbing abusive behavior from parents and spectators at youth matches. This initiative is the final step in a new three-stage protocol designed to eliminate bullying and pressure directed at players, coaches, and officials. Under this system, the process begins with a temporary match suspension, during which coaches are tasked with de-escalating the situation. If the behavior persists, the match is paused for 10 minutes while teams return to their dressing rooms. If the abuse continues after play resumes, the referee will issue the black card, resulting in the permanent abandonment of the match. To support this policy, the FRF is providing all 130 of its affiliated academies with educational kits, including banners and flyers that outline the rules of conduct and the three-step disciplinary process. This proactive measure reinforces the federation's commitment to fostering a safer, more respectful environment for young athletes.

Developed at UC Berkeley starting in 1984, Sprite was a research operating system designed to integrate networked workstations into a cohesive, high-performance computing environment. Led by John Ousterhout, the project aimed to treat a cluster of machines as a single system, simplifying administration and resource sharing. Sprite’s success was defined by four primary technical achievements: 1. **Network File System:** A high-performance, transparent file system that hides network complexity. 2. **Process Migration:** The ability to move processes seamlessly between workstations, allowing users to leverage idle machines without disrupting owners. 3. **Single System Image:** A unified environment where resources (storage, logins, architecture-specific data) feel local, regardless of the physical machine. 4. **Log-structured File System (LFS):** A breakthrough design that writes data sequentially to disk, significantly improving performance for small file writes and enabling rapid crash recovery. Supported by a dedicated community of up to 80 users, Sprite served as a vital research vehicle for nearly a decade. As commercial systems evolved, the team transitioned toward closing the project by 1994, leaving behind a legacy of innovation in distributed systems and storage management.

The author challenges the leaders of OpenAI and Anthropic, who have called for slowing the development of AI to “pace the frontier.” The author argues that because these two companies hold a duopoly on frontier intelligence, they possess the power to slow down independently without needing government-sanctioned cartels, regulatory immunity, or mandates that stifle competitors. The piece contends that the push for regulation is motivated less by altruism and more by self-interest: companies want to mitigate product-liability risks and ensure reliability, which is simply good business. Furthermore, the author warns that demanding a specific regulatory framework as a condition for slowing down borders on blackmail. Ultimately, the author urges these leaders to take responsibility for their own development speed. If they are truly concerned about the dangers of superintelligence, they should act unilaterally rather than using the political system to pursue regulatory capture. Doing so would build public goodwill, whereas failing to do so will reveal their calls for regulation as a cynical maneuver.

RevenueOS is an open-source "revenue department" for your business that automates growth through a cycle of discovery, approval, and execution. By connecting your website, ad accounts, CRM, and email, RevenueOS identifies actionable improvements—such as fixing SEO issues, pausing underperforming ads, or drafting sales outreach. **How it works:** 1. **Discover:** It audits your digital presence and generates a list of recommended actions. 2. **Approve:** Nothing is deployed until you review and approve specific items. 3. **Execute:** Once approved, the system automates the fix (e.g., updating site code, sending emails, or adjusting ad budgets). 4. **Measure:** It records "before and after" results, ensuring you only pay for verified outcomes. The platform is free for individual tasks and continuous operation until a result is measured and approved. After a 14-day grace period, continuous automated operation requires a Pro subscription ($99/mo). It prioritizes security, keeping all actions in a "pending" state until human approval. You can get started via command line (`pip install revenueos`) or integrate it into existing workflows to handle SEO, ads, billing, and lead qualification autonomously.

Y Combinator CEO Garry Tan is pushing back against calls to regulate AI "distillation"—the practice of using a frontier model to train a smaller one. While companies like Anthropic warn that Chinese labs are using illicit distillation to bypass security, Tan argues that regulators should stay out of it. Instead of restrictions, Tan proposes an "American distillation regime" where domestic open-weight labs are encouraged to distill frontier models legitimately. He argues that frontier labs, which built their models by ingesting vast amounts of public and copyrighted human data, should not be allowed to restrict how customers use the intelligence they provide via API. Tan views this as a vital check on corporate power. He warns that the true "doomer scenario" is a monolithic future where a single, proprietary company dominates the AI landscape. By promoting open-weight models through legal distillation, Tan believes the U.S. can foster a more competitive, diverse, and accessible AI ecosystem that prevents the centralization of power in the hands of a few dominant players.

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Prior is a predictive intelligence platform designed to help users anticipate outcomes for sports, financial markets, business strategies, and personal decisions. By combining web-based research, contextual follow-up questions, and simulation analysis, the platform generates comprehensive forecast reports. Each report includes a calibrated probability, weighted scenarios, key drivers, "flip" conditions, and actionable insights. Users can access the service for free with one prediction per week. For more frequent use and access to a personal prediction archive and accuracy tracking, paid subscription plans are available ranging from $29 to $499 per month. It is important to note that Prior is research software rather than a professional advisory service; it does not provide financial, medical, legal, or betting advice, and users remain solely responsible for the decisions they make based on its outputs.

Ultra-light footprint Two static binaries provide the runtime and one shared content store for every application. Learn more arrow_forwardDocker-style compatibility Familiar Dockerfile syntax, layer caching and incremental builds without a container daemon in production. Learn more arrow_forwardHost GPU integration Bind host graphics drivers at launch instead of packaging a second driver stack in every image. Learn more arrow_forwardSecure system resource access Declare access to DBus, sockets and devices. Grant only the resources an application needs. Learn more arrow_forwardGit-native versioning Install any tag, branch or exact commit SHA, then pin the source that should receive updates. Learn more arrow_forwardOne package model Use the same manifest and command across systems, with an OCI image built for each architecture. Learn more arrow_forward

To build a highly valuable startup, founders should shift their focus from merely increasing revenue to maximizing the company’s **power**. Key strategies for increasing power include: * **Owning the Relationship:** Position your product so that money, data, or user interactions flow through you. * **Creating Network Effects:** Induce network effects—even in unexpected sectors—by enabling user collaboration, data sharing, or an extensible platform/API. * **Going Upstream:** Capture customers early in their lifecycle or enter markets by becoming the "operating system" for critical workflows. * **"Full Stack" Integration:** If hindered by inefficient incumbents, use your technology to perform the hardest tasks yourself, effectively competing with or rendering customers irrelevant. * **The Long Game:** Be generous by creating more value than you capture. This builds trust and sets standards, which is more effective than short-term profit-squeezing. * **Prioritizing Speed:** Sell to customers who make decisions quickly, such as other startups, rather than engaging in slow enterprise cycles. Ultimately, these tactics only succeed if they **genuinely improve the customer experience.** Since startups start with little power, their only path to success is to deliver superior value, forcing them to align their growth with the user's best interests.

This repository provides a reproducible environment for running CUDA-targeted Windows applications on AMD GPUs using **ZLUDA** and the **AMD HIP/ROCm SDK**. **Key Features:** * **Validated Performance:** Currently optimized and validated for the **AMD Radeon RX 9060 XT (gfx1200)**, successfully running LibTorch training workloads and various CUDA math libraries (cuBLAS, cuSPARSE, cuFFT). * **Simplified Deployment:** Includes automated PowerShell scripts to install dependencies, verify hardware, download pinned ZLUDA/LibTorch versions, and launch applications. * **Compatibility:** Uses upstream ZLUDA v6-preview.69 and stable HIP SDK 6.4. While cuDNN is currently unsupported, the stack effectively handles dense/GEMM-heavy training tasks. **Important Notes:** * **Hardware:** Only the RX 9060 XT is officially validated. Other AMD GPUs are considered unverified candidates; users are encouraged to run the included `gpu-scan.ps1` and report results. * **Limitations:** ZLUDA is not a complete CUDA implementation. Workload support is dependent on specific CUDA API usage; features like NCCL, TensorRT, and certain PTX instructions may fail. For setup, clone the repository and run `install.ps1`. Detailed documentation, benchmarks, and troubleshooting guides are included in the source.

This article explains how to bridge Rust and Python using **PyO3** and **Maturin**, exemplified by building a high-performance JSON parser. The integration involves four steps: writing Rust code, annotating it with PyO3 macros (like `#[pyfunction]`), compiling with Maturin, and importing it into Python. While the Rust-side parsing is often significantly faster than native Python, the author warns that the **boundary conversion** is a critical performance bottleneck. To return a structured result (like a JSON tree) to Python, Rust must convert its internal data types (like enums) into native Python objects (like dictionaries and lists). This materialization process—rebuilding the tree as Python objects—can consume more time than the actual parsing. **Key Takeaway:** If you are porting code that returns large data structures, the "trip" from Rust to Python is where performance is won or lost. Instead of materializing large trees entirely, consider architectural optimizations like lazy, Rust-backed views to avoid unnecessary object creation. When using PyO3, always profile the interface boundary, not just the algorithm itself.

Early computer keyboards were once a battleground between text and iconography. While American layouts largely favored words, European keyboards leaned toward universal symbols to overcome language barriers across borders. This design philosophy heavily influenced early computing, where companies like IBM experimented with unique visual shorthand for functions like “Space,” “Print Screen,” and “Insert.” Over time, most of these obscure symbols lost the war against text, as office users found the cryptic iconography confusing. Today, only a few functional holdovers remain, such as those for Shift (⇧), Enter (↵), and Tab (⇥). Interestingly, many of these defunct symbols persist in the Unicode standard, serving as relics of a time when designers hoped to build a universal, picture-based language for the digital world. This evolution highlights a broader historical trend: while iconography remains essential for global accessibility, text eventually became the standard for modern PC interface clarity.

The author advocates for the disciplined practice of documenting metadata, arguing that capturing information—such as dates, versions, and process details—while it is fresh is essential for long-term clarity. We often overestimate our memory and underestimate the future importance of our current work. By meticulously logging details for creative projects (like songwriting), code, and documentation, we create a "paper trail" that preserves the trajectory of our work. While recording metadata can be tedious, slow, and revealing, the benefits far outweigh the costs. It prevents the frustration of "re-learning" old projects, preserves original versions of evolving work, and provides necessary context for future collaborators or researchers. Ultimately, maintaining a robust schema for tracking your projects acts as a form of time travel, ensuring that your work remains steerable, discoverable, and understandable well into the future. Whether through simple file naming conventions, detailed code comments, or structured logs, metadata adds a vital fourth dimension to your creations: their path through time.