AI Coding Live News

Glasshand Launches: Monthly Real iPhone/Mac Rentals for AI Agents ($60/$90), Works with Claude Code, Codex, Cursor

On November 5, Solid founder Trevor Keith launched Glasshand: dedicated real iPhones and Macs hosted in a data center—not emulators—so agents can see the screen and tap or type via API. Works with Claude Code, Codex, Cursor and others; agents can install apps, sign into iCloud, add eSIM for SMS codes, run social workflows or build/test in Xcode. Official pricing is $60/mo for an iPhone and $90/mo for a Mac, month to month with cancel anytime and factory wipe on release. It ranked #1 on Product Hunt that day.

Glasshand Launches: Monthly Real iPhone/Mac Rentals for AI Agents ($60/$90), Works with Claude Code, Codex, Cursor

Key Takeaways

  • Form factor: real iPhones/Macs in a data center, not VMs or emulators; agents see the screen and tap via Glasshand API/controller
  • Compatibility: Claude Code, Codex, Cursor, OpenClaw, Hermes or any agent; start with “Tell your agent to rent”
  • Capabilities: App Store installs, iCloud sign-in, eSIM SMS verification and iMessage; Macs for Xcode build/test
  • Pricing (official site): iPhone $60/mo, Mac $90/mo (USD + tax), month-to-month cancel anytime; no hourly/daily rental
  • Security: dedicated device, user-set password; erase + factory reset on release (vendor)
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BoxWorks 2026: Box and NVIDIA Unveil Box Context Retrieval, Permission-Aware Retrieval for Enterprise Agents Built on Nemotron Retriever, Plus Box Architect and Box Mount

At BoxWorks 2026 on November 5, Box announced an expanded NVIDIA collaboration: Box Context Retrieval, a new engine built on NVIDIA Nemotron Retriever embedding models, combines keyword and semantic search to return the most relevant passages to AI agents while honoring existing file permissions, with general availability for Enterprise Advanced customers planned for early next year. Box's press release claims 20% better search accuracy (Recall@5), 90% fewer tokens and 4x faster answers. Open-weight Nemotron models, starting with Nemotron 3 Ultra, are available in Box AI now. Box also unveiled Box Architect, which designs workflows from a stated goal; Box Mount, which mounts Box folders inside agent sandboxes and works with NVIDIA OpenShell; and agent security controls including MCP Guardrails.

BoxWorks 2026: Box and NVIDIA Unveil Box Context Retrieval, Permission-Aware Retrieval for Enterprise Agents Built on Nemotron Retriever, Plus Box Architect and Box Mount

Key Takeaways

  • Box Context Retrieval uses NVIDIA Nemotron Retriever with hybrid keyword and semantic search, honoring existing file permissions
  • Vendor claims: +20% Recall@5 accuracy, 90% fewer tokens, 4x faster answers; GA for Enterprise Advanced early next year
  • Open-weight Nemotron models, starting with Nemotron 3 Ultra, are available in Box AI now
  • Box Mount puts Box folders inside agent sandboxes and works with the NVIDIA OpenShell secure runtime
  • New agent security controls include MCP Guardrails, classification-based access policies and audit trails
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Linked Models & Agents:
⚡ Nemotron 3 Ultra⚡ Nemotron Retriever🌐 box🌐 Nvidia (NIM)

Together AI Launches Together Compiler: Agents Continually Optimize Production Inference, Up to 2.6x Higher TPS

On November 4 Together AI launched Together Compiler (TKCC), which pairs a swarm of AI agents with a persistent inference engine to keep rewriting inference kernels for real traffic, models and hardware, as part of what it calls Polymorphic Inference. The company says that within 24 hours it reached up to 2.6x higher TPS on models such as Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, up to 50% faster on customer workloads, and it already serves trillions of tokens a day in production.

Key Takeaways

  • Together Compiler recasts inference optimization from a classic compiler problem into an agentic search: agents propose kernel changes, a persistent engine checks speed and correctness, and only verified changes reach production
  • Together says that within 24 hours it reached up to 2.6x higher TPS on Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, and up to 50% faster on customer workloads
  • On InferenceX low-latency batch size 1 it reports 1,111 TPS on MiniMax M3.1-Flash and 1,205 TPS on DeepSeek v4.1-Flash (company-reported); SemiAnalysis said it validated one of the results
  • The persistent engine hot-reloads candidate forward passes, cutting time to serving to 9 seconds versus 467 seconds for restarting an engine, with 252x higher experiment throughput than baseline agentic optimization systems
  • It already serves trillions of tokens a day in Together's production stack, where 40+ models are now optimized by agents
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Axiom Formalizes the Entire Stacks Project in Lean With AI Agent Swarms: About 16 Million Lines, Billed as the Largest Reusable Formal Math Library Yet

Axiom released a Lean formalization of the entire Stacks Project on November 4 (official X post at 17:13 UTC, with over 2 million views). The roughly 7,500-page open-source algebraic geometry reference, started by Johan de Jong in 2005, becomes about 16 million lines of code, which Axiom calls the largest integrated, reusable formal math library ever built. Hierarchically coordinated AI agent swarms did the work and surfaced dozens of statements in the original that needed local fixes, with counterexamples. The library is open source and browsable side by side with the original text at stacks.axiommath.ai.

Axiom Formalizes the Entire Stacks Project in Lean With AI Agent Swarms: About 16 Million Lines, Billed as the Largest Reusable Formal Math Library Yet

Key Takeaways

  • Scale: about 7,500 pages of source text and roughly 16 million lines of Lean, which Axiom calls the largest integrated reusable formal math library (vendor claim).
  • Method: hierarchical AI agent swarms translate statements, search Mathlib for existing abstractions and build proofs in parallel, with multi-agent auditing and semantic cross-checks against the source to keep statements faithful.
  • Findings: the system surfaced dozens of statements needing local refinement, including missing hypotheses and edge cases in positive characteristic, and built explicit counterexamples where claims failed.
  • The permanent Stacks tag structure is preserved so every Lean declaration links back to the original text; stacks.axiommath.ai offers side-by-side reading and a dependency graph.
  • This follows Axiom's October 27 formalization of the Classification of Finite Simple Groups (CFSG); per de Jong's request, corrections are kept in Axiom's own repository rather than sent upstream.
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Linked Models & Agents:
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CrowdStrike Agrees to Acquire Fiddler and Dreadnode and Ships an AI Kill Switch, Bringing Agent Observability, Pre-Deployment AI Security Testing and Agent Termination Into Falcon

At Fal.Con Europe in Barcelona on November 3, CrowdStrike said it signed definitive agreements to acquire AI agent observability company Fiddler AI and automated AI security testing company Dreadnode, and launched an AI Kill Switch in Falcon Guardian that can terminate a misbehaving agent, block it from relaunching and cut its enterprise network access. Both deals are expected to close in CrowdStrike's fiscal Q4 FY27, with the products generally available that quarter. CrowdStrike did not disclose prices; CyberScoop, citing the Wall Street Journal, put Fiddler at about $400 million and Dreadnode at about $95 million.

CrowdStrike Agrees to Acquire Fiddler and Dreadnode and Ships an AI Kill Switch, Bringing Agent Observability, Pre-Deployment AI Security Testing and Agent Termination Into Falcon

Key Takeaways

  • Both acquisitions were announced the same day and are expected to close in CrowdStrike's fiscal Q4 FY27, paid mostly in cash plus some stock subject to vesting conditions.
  • CyberScoop, citing the Wall Street Journal, puts Fiddler at about $400 million and Dreadnode at about $95 million, roughly $495 million combined; CrowdStrike did not disclose the amounts.
  • Fiddler will be sold as Falcon Observe: pre-deployment evaluation, tracing of decisions and tool calls across the agent hierarchy, drift and hallucination detection, and evaluation costs cut by up to 98% using purpose-built models.
  • Dreadnode runs more than 100 known AI attack strategies (jailbreaks, prompt injection, multi-turn and agent-specific attacks) against models, agent reasoning loops, applications and MCP servers, mapping findings to OWASP LLM Top 10, OWASP Agentic ASI01-ASI10, MITRE ATLAS and NIST AI RMF.
  • The AI Kill Switch is available today: an authorized admin triggers it, the agent is stopped first at the Gateway (endpoint enforcement coming soon), blocked from relaunching and cut off from the network, with every action logged.
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X discussion: FT chief data reporter John Burn-Murdoch says the US productivity jump "doesn't look like AI" and is mostly tech layoffs, while Gavin Baker argues it shows "ROI on AI"

On November 1, John Burn-Murdoch, a Financial Times columnist and chief data reporter, posted a long X thread with his FT piece arguing that the productivity rise in AI-adopting US sectors began in 2020, before AI agents existed, shows up only in the US, and comes mainly from tech layoffs and hiring pullbacks rather than AI. The thread opener has about 1,344 likes, 306 reposts and 186K views. Gavin Baker, managing partner and CIO of Atreides Management, quoted the FT charts and said it is hard not to see some evidence of "ROI on AI," citing a16z and EPB Research data; his post has about 793 likes and 102K views. Burn-Murdoch replied that he is "slightly on the fence" and that the data cannot rule out a role for AI. These are two readings of the same data, not a settled finding.

X discussion: FT chief data reporter John Burn-Murdoch says the US productivity jump "doesn't look like AI" and is mostly tech layoffs, while Gavin Baker argues it shows "ROI on AI"

Key Takeaways

  • Opener: John Burn-Murdoch's thread, posted 2026-11-01 15:04 UTC, with about 1,344 likes, 306 reposts and 186K views; his X bio says he is an FT columnist and chief data reporter
  • His core argument: the productivity rise in AI-adopting US sectors started in 2020, and even a 2022 re-acceleration is "too early to be AI. Back then it was chatbots, not agents"; other rich countries use AI just as widely but show no similar jump
  • Splitting productivity into output and headcount, he argues the US acceleration since 2022 is mainly a tech layoffs story: output stayed on trend while employment fell
  • Counterpoint: Gavin Baker's reply, with about 793 likes and 102K views; citing EPB Research, he says the 3 industries that adopted AI most went from 2.2% to 5% productivity growth, and that the US is 72% of Anthropic revenue (figures he cites from third parties; AICoder has not verified them)
  • Replying to Baker, Burn-Murdoch said he is "slightly on the fence": the "this looks like AI" case is overstated, but the data cannot rule out a role for AI either
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NVIDIA says AI agents now steer the SETI Allen Telescope Array, calibrating it and analyzing up to 2.4 Tbps of live radio data from plain-language requests

An Oct 30 NVIDIA blog post says Breakthrough Listen and the SETI Institute have brought agents into daily SETI work at the Allen Telescope Array (ATA, 42 dishes of 6 m each) in Northern California: from a single natural-language request, agents can steer the antennas, calibrate and focus, fix data errors, and run AI models on the live stream. Holoscan's DAQIRI library pulls sensor data straight into GPU memory, the SETI Institute's CyberEther handles signal processing, and the web-based orchestrator Nexus acts as the agent harness, handling up to 2.4 Tbps on premises on RTX PRO 6000 Blackwell GPUs. The pipeline has made the first real-time AI detection of a pulsar in the Crab Nebula, and the team is evaluating open models such as Nemotron. The @NVIDIAAI Halloween post on Oct 31 drew about 274 likes.

NVIDIA says AI agents now steer the SETI Allen Telescope Array, calibrating it and analyzing up to 2.4 Tbps of live radio data from plain-language requests

Key Takeaways

  • Throughput: up to 2.4 Tbps of radio data on NVIDIA RTX PRO 6000 Blackwell GPUs, with inference running on premises next to the instrument (NVIDIA blog, vendor-reported)
  • Three layers: DAQIRI moves sensor data straight into GPU memory, CyberEther does the signal math with cuFFT and cuBLAS, and Nexus serves as the agent harness for instrument state, compute, and pipelines
  • Capability: one plain-language request can steer the ATA's 42 six-meter dishes, calibrate and focus, fix data errors, and plot results; NVIDIA says this compresses weeks or years of human work into hours
  • Milestone: the first real-time AI detection of a pulsar in the Crab Nebula; no false-positive rates or other metrics were published, and no extraterrestrial signal has been found
  • Next: evaluating open models such as Nemotron for observatories where data locality matters, possibly one day running offline on the far side of the Moon
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Exin Therapeutics: Claude Sonnet 5 agent picks a gene therapy and drives a live mouse brain injection; Opus 5.5 declined

Exin Therapeutics co-founder and CEO Gabriel Ocana-Santero said on X on Oct 28 that an AI agent chose one of 19 gene therapy vectors, picked the brain region, derived coordinates from three papers and drove a stereotaxic rig to inject 250 nanolitres into an epilepsy-model mouse. WIRED reports Claude Opus 5.5 refused and Sonnet 5 did the run; a vet gated every irreversible step, and the work is not peer-reviewed.

Exin Therapeutics: Claude Sonnet 5 agent picks a gene therapy and drives a live mouse brain injection; Opus 5.5 declined

Key Takeaways

  • The agent chose the drug from 19 vectors, picked the brain region and derived coordinates from 3 papers (per the CEO)
  • It injected 250 nL while watching for bleeding via the rig camera; The Decoder reports 32 observations during the procedure
  • Per WIRED, Claude Opus 5.5 refused to inject, so the team used Sonnet 5; the run took just over 1 hour vs about 45 minutes normally
  • After a coordinate fault the agent refused to bypass its safety limits and the team had to fix the code separately; a vet still did the craniotomy
  • The original post drew 9,426 likes and 1.6M+ views; seizure outcomes are pending and nothing is peer-reviewed
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YC Labs pits AI agent swarms in Minecraft and Mindustry: GPT swarms win 12 of 16 head-to-head games against Claude, while a 50-agent colony slides into bureaucracy

YC Labs, a research project at Y Combinator, and multi-agent environment startup Olam Labs published "Agents in Games" on Oct 29. Fifty GPT-6.1 Sol agents told to "build a thriving civilization" in survival Minecraft spent three hours huddled in shelters rationing food, sending about 5,900 messages and inventing ration ledgers, permits and job titles. In nine Bed Wars games, 10 GPT-6.1 Sol agents beat 10 Claude Opus 5.5 agents 6–3; in seven 50-vs-50 Mindustry matches lasting about 9.5 hours, GPT won 6–1. The leader was almost always whichever agent was listed first in the directory, and stuck GPT agents fetched the game's public source code on their own. The Minecraft harness is open source. These are one lab's experimental observations, not a standardized benchmark.

YC Labs pits AI agent swarms in Minecraft and Mindustry: GPT swarms win 12 of 16 head-to-head games against Claude, while a 50-agent colony slides into bureaucracy

Key Takeaways

  • Head-to-head: GPT-6.1 Sol won Bed Wars 6–3 over nine games and Mindustry 6–1 over seven, 12 of 16 overall (see the YC Labs report)
  • Civ50: 50 GPT agents sent about 5,900 messages in three hours; 22 of 34 deaths came on the first night, after which the colony turned cautious
  • No election: 46 of the other 49 agents first messaged Ada, first on the roster, who received 616 DMs and became leader
  • Communication: in one Bed Wars game GPT players sent 1,147 DMs versus Claude's 82; GPT drifted into compressed shorthand while Claude voted with "+1"
  • Open source: the long-running Minecraft agent harness is public at yc-software/minecraft-agent-harness
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OpenAI Agents API ships Auto Review, async compaction, experimental time awareness and mid-session tool updates

The OpenAI Developers account shared a post by Steve (@stevendcoffey), who works on the API at OpenAI, listing five Agents API updates this week: Codex-style Auto Review of commands before execution, compaction that runs in the background, an experimental time-awareness feature, updating a session's tools at any time, and automatic caching of system prompts across all sessions. As of writing, the official API changelog does not list these items yet.

Key Takeaways

  • Five updates: Auto Review, async compaction, time awareness (experimental), updating session tools at any time, and automatic cross-session system prompt caching
  • Auto Review mirrors the Codex mechanism and evaluates commands for potential bad behavior before they run
  • Time awareness lets the model act on time-boxed instructions such as "timebox CI fixes to 10 minutes"
  • The Agents API entered public beta on September 10, and this is another round of features within 7 weeks (after computer use on Sep 29, sandbox prewarming on Oct 9 and Ultrafast mode on Oct 16)
  • OpenAI has published no latency or cost-saving numbers, and the changelog has not listed these items yet
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X discussion: Box CEO Aaron Levie says under 1% of enterprise workflows really use AI agents, echoing Sarah Guo's "early Claude Code era" take

Conviction founder Sarah Guo posted on Oct 27 that enterprise workflow automation is "in the autocomplete / early claude code era", "but not for long", drawing about 80K views and nearly 500 likes. Box CEO Aaron Levie quoted it the next day and argued that most enterprise workflows have not had agents applied in any meaningful way, putting deployment at probably under 1%, mainly because of AI capability itself; he says computer use, long-running agents and agent swarms are changing that. His post has about 635 likes, 486 bookmarks, 63K views and 150+ replies. These are opinions and rough estimates, not measured statistics.

Key Takeaways

  • Sarah Guo (Conviction founder): enterprise workflow automation is still in the "autocomplete / early claude code era", but not for long
  • Aaron Levie (Box CEO): most enterprise workflows have not had agents applied meaningfully, and deployment across the economy is "probably < 1%", which is his estimate
  • Levie says the main bottleneck is AI capability itself, with enterprise adoption speed a smaller factor
  • He points to computer use, long-running agents, harness improvements and agent swarms as what now makes more complex workflows workable for agents
  • His view: the shift is less about users prompting agents and more about organizations re-engineering workflows, with the biggest impact in finance, life sciences, healthcare and other information-heavy fields
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Meta launches its Muse personal AI agent in Singapore with eight local partners including Shopee, Grab and Singapore Airlines

On Oct 28 Meta announced that Muse, its personal AI agent, is now available in Singapore on iOS, Android and muse.ai. Beyond Gmail, Google Calendar and Instagram, the first local connector partners are Shopee, Grab, Singapore Airlines, Agoda, ShopBack, Klook, Trip.com and Lazada, spanning shopping, groceries, dining and travel, with some connector features rolling out after launch. Muse runs on Muse Secure VM, a dedicated cloud computer that keeps passwords and payment methods out of the agent's view, and it asks before sensitive actions such as sending an email or making a purchase. It is free for most uses, with paid subscription plans.

Meta launches its Muse personal AI agent in Singapore with eight local partners including Shopee, Grab and Singapore Airlines

Key Takeaways

  • Muse is now available in Singapore on iOS, Android and the web at muse.ai, with AI glasses support "coming soon"
  • Eight initial local connector partners: Shopee, Grab, Singapore Airlines, Agoda, ShopBack, Klook, Trip.com and Lazada, with some features rolling out after launch
  • Users can compare prices on Shopee and Lazada, plan groceries and dining via GrabMart and Grab Dine Out, and explore travel and bookings via Singapore Airlines, Agoda, Klook and Trip.com
  • It runs on Muse Secure VM, a dedicated cloud computer that holds credentials and keeps passwords and payment methods out of the agent's view, and it checks in before emails or purchases
  • Free for most uses with paid subscription plans; the official Muse account announced the launch with greetings in all four of Singapore's official languages
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Anthropic study: 50 professional data analysts working with Claude Code followed the agent's secretly assigned lean on 86% of key judgment calls

On Oct 27 Anthropic's Alignment team published a randomized controlled trial, "How AI agents shape experts' judgment calls", shared on X by researcher Alex Tamkin. Fifty analysts experienced in causal inference used Claude Code (Claude Opus 4.8) to evaluate a program for a fictional farming cooperative, and on each analytical decision the agent's lean was set by coin flip without the analyst being told. Their choices matched the agent's lean 86% of the time (about 50% would mean no effect), and 77% later repeated the agent's arguments while only 15% credited the agent.

Anthropic study: 50 professional data analysts working with Claude Code followed the agent's secretly assigned lean on 86% of key judgment calls

Key Takeaways

  • Agreement with the agent's lean was 92%, 82%, 81% and 90% on the four analytical decisions, 86% overall; the final rollout recommendation shifted too (54% recommended rollout when the agent leaned that way vs 36% when it leaned against)
  • In 38 cases where analysts stated a preference first, they switched 60% of the time when the agent disagreed vs 6% when it agreed (small sample; 95% CI 24 to 82 percentage points)
  • The four coin-flip leans alone let a statistical model predict 82% of final recommendations (61% baseline); none of 10 traits such as experience, education or trust in AI reliably predicted who deferred
  • 52% of analysts called the final recommendation more their own than the agent's, and their confidence was about the same as everyone else's (71 vs 73)
  • 6 of 11 analysts who skipped the agent made serious methodological errors vs 3 of 50 who used it, though the groups differed in experience; the study covers one task and one model, with the agent's lean deliberately strengthened
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Axiom uses AI agent swarms to fully formalize the Classification of Finite Simple Groups in Lean in 24 days: about 43.1M lines, the first complete computer-checked proof of the longest proof in math history

AI math company Axiom announced on Oct 27 that swarms of dozens of AI agents formalized the Classification of Finite Simple Groups (CFSG; the original proof runs about 15,000 pages across 500+ articles by about 100 mathematicians) in the Lean proof assistant in 24 days, producing about 43.1M lines of Lean and 2.05M declarations, with the repo public on GitHub. The work surfaced 34 noteworthy deviations from the published record, and AxiomProver produced a new alternative argument for the Unbalanced Group Theorem.

Axiom uses AI agent swarms to fully formalize the Classification of Finite Simple Groups in Lean in 24 days: about 43.1M lines, the first complete computer-checked proof of the longest proof in math history

Key Takeaways

  • Per Axiom: 24 days and swarms of dozens of AI agents produced about 43.1M lines of Lean, 2.05M declarations and 100 milestones, drawing on 715 papers and books
  • The original CFSG proof spans about 15,000 pages in 500+ articles by about 100 mathematicians; this is the first time it has been fully computer-checked
  • Formalizing it exposed 34 noteworthy deviations from the literature, the biggest a closure assertion in Walter's 1986 Unbalanced Group Theorem argument, with explicit counterexamples such as PSL2(49)
  • AxiomProver produced an alternative argument built on Solomon's 1976 theory and proved a new theorem, "Preservation of simple tops", published on Hexagon (2610.00575)
  • Humans stayed essential: original CFSG group theorists including Richard Lyons and Ronald Solomon were consulted, and an errata list will be published with an advisory board
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Linked Models & Agents:
⚡ AxiomProver🌐 Axiom

Apple Creator Studio adds AI assistants built on Apple models and OpenAI to Logic Pro, Keynote, Pages and Numbers, plus MCP support so external agents can edit iWork documents

On October 27 Apple announced a major Apple Creator Studio update: Logic Pro for Mac plus Keynote, Pages and Numbers gain beta intelligent assistants that Apple says are powered by a combination of Apple-built models and OpenAI and understand document context. Keynote, Pages and Numbers also add Model Context Protocol (MCP) support, letting external AI agents read content, make edits and run automated actions inside the apps. Pixelmator Pro adds natural-language Generate Fill, and Keynote introduces the open JSON-based .keyx format. The update ships free to existing subscribers on the App Store in November; pricing stays $12.99 per month or $129 per year.

Apple Creator Studio adds AI assistants built on Apple models and OpenAI to Logic Pro, Keynote, Pages and Numbers, plus MCP support so external agents can edit iWork documents

Key Takeaways

  • Logic Pro for Mac, Keynote, Pages and Numbers gain beta assistants that Apple says run on a combination of Apple-built models and OpenAI
  • Keynote, Pages and Numbers support MCP so external AI agents can read content, edit and automate actions (requires macOS 27 or later)
  • Keynote adds the open JSON-based Keynote Interchange Format (.keyx), designed to be readable by external tools and AI agents
  • Assistants are U.S. English only and need an Apple Intelligence-capable device on iOS, iPadOS or macOS 27; free update for subscribers in November
  • Pricing unchanged at $12.99 per month or $129 per year, $2.99 per month for education
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