Daily
10 selected stories for 2026-07-13, grouped by topic. The rest is folded into the archive.
SelfAssay is a platform that combines peer-reviewed studies, real-world reports, and a curated knowledge graph to provide evidence-based reasoning for biohackers, with cited sources and calibrated confidence.
A personal essay comparing AI to the printing press, arguing that AI did not invent token generation but made it radically more efficient. The author uses an aerodynamics analogy to explain how AI approximates intelligence through scaling, and predicts that AI may have a biological impact on the human brain similar to language.
Eight years ago, the author started a '100 Days of Algorithms' challenge, handcrafting code to learn algorithms. Now, with a review by GPT-5.6 revealing many flaws—like incomplete max flow, buggy graph algorithms, and broken BST implementations—he reflects on whether AI would have helped or hindered his learning. He decides to preserve the code as a historical artifact and update the README honestly.
Elsevier's Researcher of the Future report, surveying over 3,200 researchers across 113 countries, finds that only 45% have sufficient research time, while AI tool adoption surged from 37% to 58% since 2024. Chinese researchers show far greater confidence in AI than US and UK counterparts. Mobility intentions have declined, but interdisciplinary collaboration is rising.
Anthropic has extended access to Claude Fable 5 through July 19 due to compute constraints, as GPT-5.6 Sol emerges as a comparable model. OpenAI appears confident in maintaining GPT-5.6 access without similar restrictions. The author suggests Anthropic should make Fable permanently available to avoid losing users to OpenAI.
Adaptive Recall is a memory system for AI assistants that learns from interactions, using multiple retrieval strategies, cognitive scoring, knowledge graphs, and self-improvement to provide persistent, evolving memory.
Fade Engine is a fully autonomous AI that shorts overextended small caps on a live $10,000 simulated account, posting every trade publicly. It scans 12,000+ tickers every five minutes, identifies 18 pump patterns, and closes all positions by market close. No human intervention.
The article proposes crowdsourcing unused AI inference tokens for scientific research, drawing parallels to SETI@home. It highlights recent successes by small teams using AI to solve math problems and discusses the design challenges of such a platform.
An AI arcade benchmark where coding models compete to create fun games under identical constraints.
Singer Lorde criticized AI glasses during her set at the Real Cool Festival in Madrid, likely targeting sponsor Ray-Ban's Meta smartglasses. She expressed difficulty distinguishing real from fake and explicitly said 'fuck the glasses, not sexy.'
This guide explains loop engineering, where AI agents autonomously iterate toward a goal using a verifier, state, and stop condition. It details Andrej Karpathy's autoresearch loop and Bilevel Autoresearch, showing concrete results: autoresearch found 20 improvements from 700 experiments, cutting GPT-2 training time by 11%; Bilevel Autoresearch added an outer meta-loop for a 5x larger val_bpb drop. It also provides reusable building blocks and a hands-on template.
exxperts is a local-first agentic runtime that provides persistent AI rooms with governed, approval-gated memory. Everything runs locally as files on your disk, ensuring privacy and control. It offers both a web app and a CLI/TUI interface.
Kote is an open-source tool that automatically captures developer conversations with AI assistants, Git commits, and development context, building a searchable knowledge base to help developers recall past technical decisions and solutions. It supports VS Code extension, GitHub integration, CLI, browser extension, WhatsApp/Telegram messaging, and self-hosted deployment.
The one-step trap is a common mistake in AI research where researchers assume that learned predictions can be mostly one-step, with longer-term predictions generated by iterating them. While appealing, this approach suffers from error accumulation and exponential computational complexity, making it impractical. Rich Sutton argues for temporally abstract models using options and GVFs as a solution.
This essay explores the critical role of 'useless' research in enabling future innovations. Using Folk Computer as a case study, the author traces a lineage from Xerox PARC to Dynamicland, and argues for funding paradigm-level work before it becomes useful.
OpenAI's AI agent solved all five problems in the AtCoder Algorithm Division for 8,300 points; the top human scored 4,300. No human solved problems C or E. In the Heuristic Division, AI scored more than seven times the best human result. The 600,000-yen 'Humanity Prevails Award' went unclaimed. The system was described as comparable to GPT-5.6.
OpenAI and Anthropic build ever-larger models, but companies like Microsoft are turning to smaller, specialized models for cost and efficiency. Microsoft's MAI family is replacing OpenAI models in its products.
Microsoft is testing PC Insights, a new Copilot feature that analyzes system resource usage to help users identify performance bottlenecks. However, Copilot itself is a full web app with a private Edge instance, consuming up to 1GB RAM at idle, highlighting the irony. The feature is opt-in and requires user permission.
Apple's self-driving car program never really got off the ground, but it may have been what made the company's chips the powerful AI performers they are. Early in the development of the self-driving platform, Apple realized that it would need powerful on-device AI processing. While the car processor was never finished, as Mark Gurman details in his latest Power On newsletter, it did lead to the development of the Neural Engine, the backbone of Apple's on-device AI processing. The Neural Engine made its debut with the iPhone X and the A11 Bionic. In those early days, it was primarily used for computer vision, powering FaceID, Animoji, and a … Read the full story at The Verge.
AI performance depends on three dimensions: accuracy, throughput, and interactivity. This post focuses on throughput and interactivity, examining how model-design choices can optimize both without sacrificing accuracy, aiming to push the Pareto frontier outward.
The author evaluated GPT-5.6 Sol, Fable 5, Grok 4.5, and other AI models on a benchmark called Basecamp Bench, testing their ability to build a frontend and backend from the same specification. Fable 5 won both tracks, while Grok 4.5 offered the best speed-cost tradeoff. Results show significant differences in polish and completeness, especially in the final 10% of work.
Open-source AI faces its most serious viability test. White House discussions on executive orders to restrict open models, plus policy debates on distillation and frontier capabilities, could lead to a ban on advanced open-weight models within 6 months. The article critiques Anthropic's regulatory capture, argues that API security is overblown, and warns that a ban would harm the US open-source ecosystem. Short-term solutions include US companies releasing competitive open models and building coalitions.
Researchers have compiled a database of over 3,000 bank runs from 1863-1934, revealing that most runs did not lead to failure, and analyzing geographic and temporal patterns.
Samsung Health now requires users to consent to using their health data for AI training, or lose the ability to sync data, potentially rendering the app and Galaxy Watch less useful.
Soulless is a community-driven project that exposes AI-generated artists on Spotify. It lists 232 detected AI artists with monthly listeners and estimated earnings. It also provides an open-source AI music detector and a curated landscape of AI music resources.
In a roundtable discussion, writers and cultural critics explore the profound implications of AI on language, creativity, and society. They note that AI both sharpens and dulls linguistic abilities, and may clarify the boundary between machine and soul. Despite anxieties, AI offers opportunities in research, accessibility, and diagnostics.
Many developers fail to maximize automatic programming because they still focus on code, making themselves the bottleneck. Time should be invested in new ideas, QA, design, and clarifying goals.