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Artículos fetched (66)

• Anthropic opens Singapore office and signs MOU with the Government of Singapore
  anthropic-news· 14-oct

  Oct 14, 2026Announcements

• An update on independent evaluators
  anthropic-news· 15-oct

  Oct 15, 2026Announcements

• Preparing for the 2026 US midterms
  anthropic-news· 14-oct

  Oct 14, 2026Policy

• IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
  arxiv-ai· 16-oct

  arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We introduce IneqAgent, an agentic framework combining LLM reasoning with symbolic computation for inequality proving in Lean4. Lemma-level Monte Carlo Tree Search coordinates lemma construction, goal reformulation, and symbolic backend selection. Complementary sum-of-squares, Bernstein, and successive difference-substitution backends produce exact certificates for local Lean proof generation. The agent integrates these proofs with LLM-generated steps and uses Lean feedback to construct complete proofs checked against the original theorem statements. On four benchmarks, IneqAgent with DeepSeek-…

• L2G-SR: Learning to Guide LLM-based Symbolic Regression
  arxiv-ai· 16-oct

  arXiv:2610.20685v1 Announce Type: new Abstract: Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor an accurate surrogate over a candidate with the right structure. We propose L2G-SR, a framework for LLM-based symbolic regression that guides search with a learned structural verifier. We construct structural supervision signal using tree edit distance (TED), accounting for commutative operators and alternative expression forms. A verifier first aligns numerical observations with the formulas, then learns to score a candidate's structural similarity to the target formula. At search time, the target formula is hidden and the verifier complements numerical error in parent selection. An analyst t…

• When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
  arxiv-ai· 16-oct

  arXiv:2610.20764v1 Announce Type: new Abstract: This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system can no longer reliably perceive, reason, and act? We formulate this as a theory- and model-agnostic challenge in which systems must infer reliable actions for increasingly distant future states across a growing observation--reality gap. We operationalize this challenge through the effective Speed of Coherent Cognition (\(S_C^{\mathrm{eff}}\)), defined as the highest tested Speed of Reality (\(S_R\)) through which a system continuously satisfies a predefined behavioural reliability criterion from baseline. As a proof of concept, reinforcement-learn…

• ARIA: Runtime Inspection and Amendment for Multi-Agent Systems
  arxiv-ai· 16-oct

  arXiv:2610.20780v1 Announce Type: new Abstract: In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs or repeatedly re-executing them to search for corrections does not provide a learned policy for intervening while execution is still in progress. We present Agent Runtime Inspection and Amendment (ARIA), a framework for learning when and how to repair ongoing multi-agent executions. Our key intuition is to turn execution-verified repairs into reusable supervision, moving repair search into data construction and training so that a learned model can intervene at runtime without repeatedly testing alternatives. First, we collect over 24{,}000 successful and failed trajectori…

• The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
  arxiv-ai· 16-oct

  arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with 180 tasks across six game types, to evaluate agents while games continue running during inference. Paired evaluations show that GPT-6.1-sol outperforms the faster Jev when games are paused, but falls behind when they keep running. We propose Two-Timescale Offline Recursive Self-Improvement (TORSI): a slow teacher studies failures and tests corrections in paused games between attempts, then writes reusable instructions for a fast student. The student acts without teacher calls during gameplay, and validation scores…

• When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
  arxiv-ai· 16-oct

  arXiv:2610.20676v1 Announce Type: new Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12 Verus/Rust tasks across four model families under matched call budgets. Independent no-feedback discovery more than doubles certified-witness yield relative to IntentShield's corrective-feedback loop (59.0% versus 27.1%). A format-matched iterative control places the larger share of this gap in feedback content, not independent restarts. The advantage holds in every model family and survives parser repair. A separate phase-separated execution matches the aggregate yield without reusing responses; most certified wi…

• Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
  arxiv-ai· 16-oct

  arXiv:2610.20679v1 Announce Type: new Abstract: Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it should be trusted. We introduce Counterfactual Neighborhood Risk Fields (CNRF), an evidence-grounded framework for selective VLM safety. CNRF constructs context-matched benign-harmful counterfactual pairs, uses their representation differences as capability-related risk directions, and estimates neighborhood support separately from risk. This separation prevents unfamiliarity from being conflated with harmfulness and enables selective intervention rather than blanket rejection. A victim-adaptive interface accommodates different safety observations…

• Evaluating Model Capabilities After Benchmark Saturation
  arxiv-ai· 16-oct

  arXiv:2610.20667v1 Announce Type: new Abstract: Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and with it its utility for informing AI safety cases and frontier policies. Yet benchmark items produce far more information than the scores we typically extract from them: a full transcript of how each model solved the task. We investigate whether these transcripts can be used to assess model capabilities even after benchmark scores have saturated. We simulate saturation retrospectively using historical benchmark and model releases, and test whether protocols that access only correctly solved transcripts from saturated benchmarks can predict capabilities measured on he…

• Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
  arxiv-ai· 16-oct

  arXiv:2610.20669v1 Announce Type: new Abstract: Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computationally expensive. Active guidance steps require additional reward evaluations and, for gradient-based proposals, value-gradient computations. Sparse guidance reduces this cost by guiding only a subset of timesteps, but commonly used schedules are fixed heuristics whose effectiveness can vary substantially across tasks. We propose VISTA, a training-free method that automatically selects guided timesteps under a fixed budget. VISTA formulates sparse guidance as approximation of the ideal full-guidance probability path and derives an estimable upper bound on \(\ell_1\) pa…

• AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
  arxiv-ai· 16-oct

  arXiv:2610.20675v1 Announce Type: new Abstract: Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance requires linking execution conditions to outcomes, behavior, and resource use. We introduce AlphaDiana, a measurement system with modular interfaces for datasets, agents, environments, and scorers. It preserves harness-native control loops while controlling file reuse and memory retention separately. AlphaDiana supports macro analyses of complete agents and micro interventions on agent components such as tools, skills, and memory under matched conditions. We study three open-source models and three open-source harnesses on five answer-based benchmarks, with two models als…

• The Terminator Problem: How Teaching an LLM One Bad Habit Can Change Its Behavior
  bytebytego· 14-oct

  To understand why this happens, we need to connect three ideas: how models learn, how they generalize beyond their examples, and how they exhibit…Oct 14 • ByteByteGo18933

• How the Math Used in 19th-Century Heat Physics Helps LLMs Understand Sentences
  bytebytego· 13-oct

  In this article, we are going to understand how this connection works and why a model needs help understanding the order of the words even when the…Oct 13 • ByteByteGo22158

• How to Build Models Without Language, with Logical Intelligence
  bytebytego· 12-oct

  Something unusual happened in a public Sudoku experiment early this year.Oct 12 • ByteByteGo22674

• How to Design for Failures
  bytebytego· 15-oct

  In this article, we’ll look at the various strategies that can help developers deal with failures in their systems.16 hrs ago • ByteByteGo643

• Deploy with Void
  cloudflare· 15-oct

  Introducing Void: an open-source SDK, CLI, and self-hosted platform for building full-stack Vite apps and deploying them to Cloudflare.

• Inside Hyperdrive: How we solved a rare Hyperdrive MySQL caching bug
  cloudflare· 14-oct

  While adding MySQL support to Hyperdrive, we found a subtle protocol bug when caching query results. Here is how we tracked down a 0.001% rate error and made Hyperdrive MySQL generally available.

• A fully programmable secure web gateway
  cloudflare· 15-oct

  Cloudflare Gateway and Data Loss Prevention have inherited the flexibility and power of Cloudflare Workers through our full-stack integration with the Cloudflare Developer platform.

• Rewinding the exponential: How we keep hardware usage down
  cloudflare· 13-oct

  Our strategies to optimize our data pipeline throughput without compromising data quality.

• Filter, reshape, and redact logs before export with Transformers
  cloudflare· 12-oct

  Logpush now supports Transformers, allowing teams to filter, redact, and reshape log streams using SQL before export. Powered by Apache DataFusion and Apache Arrow, Transformers apply those changes directly to log data before it reaches the destination.

• Introducing on-demand CPU and memory profiling with flamegraphs for Workers and Durable Objects
  cloudflare· 09-oct

  On-demand CPU and memory profiling for Cloudflare Workers and Durable Objects is now available. Now you can generate interactive flamegraphs directly in production to quickly track down memory leaks and performance bottlenecks.

• Yandex outage: Data shows that instead of going dark, malicious bots went on the move
  cloudflare· 14-oct

  When strikes hit Yandex data centers, many assumed Russian bot operations went dark. Cloudflare data reveals that while search crawling stopped, malicious bots simply migrated to other networks.

• Introducing Clef-omni with full multimodality, plus a faster Clef and a cheaper Clef-flash
  cloudflare· 09-oct

  We are expanding the Clef decision model family with Clef-omni, natively processing audio, video, images, and text in a single pipeline. We’ve also lowered Clef-flash pricing and boosted Clef inference speeds by up to 2.0x.

• Cloudflare and OpenAI: Making AI discovery better for everyone
  cloudflare· 15-oct

  How Cloudflare and OpenAI are working together to help AI find useful content, keep up with changes, and make fewer wasted requests — making the experience better for users and website owners alike.

• Under the hood, off the hot path: How D1 scales to millions of databases
  cloudflare· 13-oct

  A deep dive into how D1 architected its control plane to scale and manage millions of databases, while keeping data plane query operations at the edge. We share guiding principles on how to build on the Developer Platform effectively.

• JuliusBrussee/caveman
  github-trending

  🪨 why use many token when few token do trick. Viral skill + proxy for coding agents that cuts 65% of tokens by talking like a caveman. https://docs.caveman.so/caveman why many token when few do trick Caveman make your AI agent say less and read less. Code stay exact. Brain still big. 33.2% fewer input tokens, whole sessions 54 Claude Code runs, 18 of 18 answers right 54% cheaper with Auto, same score 85 coding tasks vs Opus 5.5 at xhigh, interim 397 → 76 tokens for one test run Blocks vs | tail -20 1.4 to 2.4× cheaper with caveman-style output Adobe Research, eight models Cited by Adobe Research · A/B tested by JetBrains · Remade for Elasticsearch on Elasticsearch Labs #1 on Hacker News · #1 on GitHub Trending · "No way this actually works." ThePrimeagen Install · Six rocks · Proof · Say…

• K-Dense-AI/scientific-agent-skills
  github-trending

  Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. https://arxiv.org/abs/2609.00065 Scientific Agent Skills 🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude. New: K-Dense BYOK — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills. Bring your own API keys, pick from 40+ models, and get a full research workspace with web sear…

• NationalSecurityAgency/ghidra
  github-trending

  Ghidra is a software reverse engineering (SRE) framework Ghidra Software Reverse Engineering Framework Ghidra is a software reverse engineering (SRE) framework created and maintained by the National Security Agency Research Directorate. This framework includes a suite of full-featured, high-end software analysis tools that enable users to analyze compiled code on a variety of platforms including Windows, macOS, and Linux. Capabilities include disassembly, assembly, decompilation, graphing, and scripting, along with hundreds of other features. Ghidra supports a wide variety of processor instruction sets and executable formats and can be run in both user-interactive and automated modes. Users may also develop their own Ghidra extension components and/or scripts using Java or Python. In supp…

• obra/superpowers
  github-trending

  An agentic skills framework & software development methodology that works. Superpowers Superpowers is a complete software development methodology for your coding agents, built on top of a set of composable skills and some initial instructions that make sure your agent uses them. Table of Contents How it works Commercial Services Getting Started Claude Code Antigravity Codex App Codex CLI Cursor Devin CLI Factory Droid Gemini CLI GitHub Copilot CLI Grok Build CLI Kimi Code OpenCode Pi Qwen Code Hermes Agent Muse The Basic Workflow When Something Goes Wrong Community What's Inside Philosophy Contributing Updating License Visual companion telemetry How it works It starts from the moment you fire up your coding agent. As soon as it sees that you're building something, it doesn't just jump int…

• ruvnet/ruflo
  github-trending

  🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated https://Cognitum.One An agent meta-harness for Claude Code and Codex. Get started with Ruflo Start in Claude Code with core tools, the visual console and runtime mods. Requires Claude Code 2.1.287 or later. Run inside Claude Code: /plugin marketplace add ruvnet/ruflo /plugin install ruflo-core@ruflo /plugin install ruflo-console@ruflo /plugin install ruflo-mods@ruflo /reload-plugins /ruflo Core provides foundation tools. Console opens the agent cockpit. Mods add routing and policy enforcement insid…

• affaan-m/ECC
  github-trending

  The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. https://ecc.tools Language: English | العربية | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español | Українська | Polski | বাংলা Warning Official sources only. Install ECC only from verified channels: the GitHub repository github.com/affaan-m/ECC, the npm packages ecc-universal and ecc-agentshield, the GitHub App, the plugin slug ecc@ecc, and the project website ecc.tools. Third-party re-uploads and unofficial mirrors are not maintained or reviewed by the project and may contain malware. Install with Claude Code Use the guided setup or native plugin commands. Both inst…

• anthropics/claude-plugins-official
  github-trending

  Official, Anthropic-managed directory of high quality Claude Code Plugins. https://code.claude.com/docs/en/plugins Claude Code Plugins Directory A curated directory of high-quality plugins for Claude Code. ⚠️ Important: Make sure you trust a plugin before installing, updating, or using it. Anthropic does not control what MCP servers, files, or other software are included in plugins and cannot verify that they will work as intended or that they won't change. See each plugin's homepage for more information. Structure /plugins - Internal plugins developed and maintained by Anthropic /external_plugins - Third-party plugins from partners and the community Installation Plugins can be installed directly from this marketplace via Claude Code's plugin system. To install, run /plugin install {plugi…

• AI has killed my passion for coding [video]
  hn-ai· 16-oct

  Article URL: https://www.youtube.com/watch?v=Htdy1dsuSLY Comments URL: https://news.ycombinator.com/item?id=50114936 Points: 2 # Comments: 0

• Show HN: CiteProof – Catch fake AI legal citations
  hn-ai· 16-oct

  Article URL: https://getciteproof.com/ Comments URL: https://news.ycombinator.com/item?id=50114653 Points: 2 # Comments: 0

• Claude Code Features and Settings Reference
  hn-ai· 16-oct

  Article URL: https://hidekazu-konishi.com/entry/claude_code_features_settings_reference_2026.html Comments URL: https://news.ycombinator.com/item?id=50115096 Points: 1 # Comments: 0

• How AI proof is my business?
  hn-ai· 16-oct

  Article URL: https://keithbrown.com/tools/ai-proof Comments URL: https://news.ycombinator.com/item?id=50114999 Points: 2 # Comments: 1

• Ask HN: Has anyone decompiled Claude Code yet?
  hn-ai· 16-oct

  I’d be interested in examining it. How about codex? Comments URL: https://news.ycombinator.com/item?id=50115119 Points: 1 # Comments: 2

• AI as a part of capitalism – the right way to go
  hn-ai· 16-oct

  The way things go now are obsiously flawed. 1. AI companies objective is to get rid of a very large part of jobs, thus converting a large part of consumers into jobless people - who are very bad at consuming. 2. They also take other people's and companie's content for free, turning them into own profits, thus removing any incentives to create such content in the future. Proposed solution. 1. All data models are created anew. 2. All of the model content should be marked and paid for to its creators in a proportional amount to each specific training data usage. 3. No payment = no use in the model, unless a creator provides a free license to LLM's. 4. Any commercial data needs an explicit permission to be used in training. 5. Profits made on data with no explicit owner (say, Shakespeare) are…

• AI Prompt Hub – 150 categories of ready-to-use AI prompts and custom generator
  hn-ai· 16-oct

  Article URL: https://play.google.com/store/apps/details?id=com.ai_prompt_hub.setuvio&hl=en_IN Comments URL: https://news.ycombinator.com/item?id=50114944 Points: 1 # Comments: 0

• Investors dump data centre bonds as AI borrowers face 'Darwin moment'
  hn-ai· 16-oct

  Article URL: https://www.ft.com/content/678bcd31-6c2a-447b-a68c-ce6dec220bbc Comments URL: https://news.ycombinator.com/item?id=50114723 Points: 5 # Comments: 1

• Dwarkesh Patel on what the public doesn't know about AI
  hn-ai· 16-oct

  Article URL: https://www.nytimes.com/2026/10/15/opinion/interesting-times-dwarkes-patel-tyler-cowen.html Comments URL: https://news.ycombinator.com/item?id=50114675 Points: 1 # Comments: 1

• AI is driving a boom in modestly successful software projects
  hn-ai· 16-oct

  Article URL: https://www.seangoedecke.com/ai-is-driving-a-boom-in-modestly-successful-software/ Comments URL: https://news.ycombinator.com/item?id=50115251 Points: 1 # Comments: 1

• [AINews] Agents disprove Polysemanticity; greatly improve Interpretability & Alignment
  latentspace· 16-oct

  Nick Cammarata out of nowhere with some major claims!

• [AINews] Modal’s $1B ARR, $16B Series D makes it NYC’s newest AI Infra Decacorn, 3x usage and 3x valuation in 5 months, 15x in 1 yr
  latentspace· 15-oct

  Sandboxes are all you need.

• Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents
  latentspace· 14-oct

  At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.

• Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio
  latentspace· 14-oct

  How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists.

• Are Harnesses Worth the Cost?
  martin-fowler· 13-oct

  Naresh Jain compares coding agents building the same API service with and without a behavioral harness. A frontier model, GPT-6 Sol, passed every external evaluation either way. But a cost-optimized model, GPT-6 Luna, succeeded in only 3 of 10 unharnessed runs and all 10 with the harness. While adding the harness increased token usage and runtime for Luna, the harnessed Luna runs achieved the same 10-for-10 reliability as GPT-6 Sol while costing 33% less per run ($0.273 vs $0.406). This experiment suggests a behavioral harness might be a practical way to get dependable results from smaller, cost-optimized models. more…

• Build your slide template
  martin-fowler· 14-oct

  Building a slide template starts with three foundational choices — colours, fonts and layouts. Sumeet Gayathri Moghe covers the basics of color theory and how to build a reusable palette, the essentials of typography, including serif, sans-serif, handwritten and monospace fonts, plus the finer points of kerning and leading, and why a near-blank layout might serve you better than the prescribed layouts that ship with your slideware. more…

• The Craft of Practitioner Voice
  martin-fowler· 13-oct

  A couple of months ago Jim Highsmith explained how practitioners writing for each other forms a distinct style of writing. He now adds a section to the article with more detail on the craft of how we write in this style. How the writer shows up in the prose, its four principles, the role of story, how to sensibly use AI, three levels of editing, and clarity of the audience. more…

• When Software Meets Hardware
  martin-fowler· 15-oct

  When software meets hardware, engineering principles remain largely the same, but the sensible defaults often change. Kuldeep Singh describes our experience modernizing a large IoT fleet of battery-powered sensors. Our familiar principles remained important; what changed was how we applied them. These practices emerged through experiments, failures, and operational experience—each challenge helping us replace a reasonable assumption with a more sensible default. more…

• Netflix Stratum Media Processing: Automated Container Right-Sizing
  netflix-tech· 14-oct
• Running Apache Spark experiments in my sleep (and on a plane)
  netflix-tech· 14-oct
• The Lifecycle of LLM-as-a-Judge: Building, Aligning, and Monitoring at scale
  netflix-tech· 14-oct
• TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads
  netflix-tech· 14-oct
• Integrating Netflix’s Foundation Model into Personalization applications
  netflix-tech· 14-oct
• Measuring the Impact of Personalized Recommendations
  netflix-tech· 14-oct
• Netflix Conductor: The Next Chapter
  netflix-tech· 14-oct
• Reliable GPU compute for large training & inference jobs
  netflix-tech· 14-oct
• Behind the Scenes: Evolving Netflix’s Ads Event Pipeline for Live — Part II
  netflix-tech· 14-oct
• DataJunction as Netflix’s answer to the missing piece of the modern data stack
  netflix-tech· 14-oct
• Chirp Pong
  simonw· 15-oct

  <p><strong>Tool:</strong> <a href="https://tools.simonwillison.net/chirp-pong">Chirp Pong</a></p> <p>Another game experiment - I wanted to see if it was feasible to have a two player game on two separate phones running in static HTML where the networking code uses chirps sent via the microphone. It seems to work!</p> <blockquote> <p><code>Build a two player competitive pong like game where two players use their phones close to each other and the game state is sent via audio noises which the web microphone API can interpret - but add graphical and gameplay twists to make it a lot more fun and intertwine than pong</code></p> </blockquote> <p>Claude Opus 5.5 Extra <a href="https://claude.ai/code/session_019awUnNB7NoYd7noBNAsNdd">in Claude Code</a>.</p> <p>Tags: <a href="https://simonwillison…

• Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO
  simonw· 15-oct

  <p><strong><a href="https://www.ft.com/content/11632d03-358c-454b-af95-d06d4072932b?syn-25a6b1a6=1">Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO</a></strong></p> The phrase "told investors" appears four times in this FT article, which reports on Anthropic's day of hosting investors on Wednesday. Lots of details in here from "<a href="https://simonwillison.net/2023/Nov/22/deciphering-clues/#hints-to-look-out-for">people with knowledge of the matter</a>".</p> <p>Key things I learned from the article:</p> <ul> <li>Q1 revenue (Jan/Feb/Mar) was "about $4.8bn"</li> <li>Q2 (Apr/May/Jun) was $11.5bn</li> <li>Q3 (Jul/Aug/Sep) was $16.8bn</li> <li>In Q3: $1.70 in revenue for every $1 spent on "computing power"</li> <li>Adjusted operating margins (excluding stock…

• Tree Appreciator
  simonw· 14-oct

  <p><strong>Tool:</strong> <a href="https://tools.simonwillison.net/tree-appreciator">Tree Appreciator</a></p> <p>Another game experiment with Claude. This one used the default Claude iPhone app (chat mode, not Claude Code mode), where I prompted Claude Opus 5.5 Max with:</p> <blockquote> <p><code>Build a three js game called "Tree appreciator" - it shows you two algorithmically generated trees and the game mechanics is about appreciating the differences between them. Think hard about how to make sure it is both fun and beautiful.</code></p> </blockquote> <p>Somewhat embarrassingly given <a href="https://simonwillison.net/2026/Sep/27/2026-in-llms-so-far/#simon-willison-2026-in-llms.059.webp">my recent talk</a>, I've found that telling Opus 5.5 "make sure it is fun" works uncomfortably well…

• AI is mass decompiling games. That's both awesome and scary.
  simonw· 14-oct

  <p><strong><a href="https://popcar.bearblog.dev/ai-is-mass-decompiling-games-thats-both-awesome-and-scary/">AI is mass decompiling games. That&#x27;s both awesome and scary.</a></strong></p> Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and producing a boom in game preservation projects to get old games working on new hardware (often in a browser using WebAssembly).</p> <p>I like how this piece captures both the positives and the negatives of this new development:</p> <blockquote> <p>Most people already realized the scary part by now. If AI can decompile games and software, then the entire tech industry is about to explode. Anybody can decompile other software and use it any way they'd …