AI Coding Live News

X Discussion: Andrej Karpathy Says He One-Shotted TristanTube With Claude Code in About 3 Minutes, Collecting ~47K Nerdy YouTube Videos Plus a TV Mode

Former OpenAI researcher and independent researcher Andrej Karpathy (@karpathy) posted at 2026-11-07 17:07 UTC that after seeing Tristan Hume's updated YouTube channel tier list earlier this week, he turned it into TristanTube: about 47K super nerdy videos plus a TV mode for laid-back watching. He wrote that he one-shotted it with Claude Code in about 3 minutes. At capture the post had about 6,182 likes, 6,768 bookmarks, 458 replies, 478 reposts, 146 quotes and ~533k impressions. This is a discussion sparked by a personal share, not a product launch by Anthropic or any company; the "about 3 minutes" figure is the author's own claim and has not been independently timed.

Key Takeaways

  • Original post: Andrej Karpathy (@karpathy) at 2026-11-07 17:07 UTC; about 6,182 likes, 6,768 bookmarks, 458 replies, 478 reposts, 146 quotes and ~533k impressions at capture
  • Product claim: TristanTube collects about 47K super nerdy YouTube videos and adds a TV mode; the site is at karpathy.ai/tristan-tube/
  • Source list: Tristan Hume's public YouTube channel tier list updated 2026-11-01 (he also wrote a 2020 edition), not another scan of Hacker News comments
  • Time claim: he said he one-shotted it with Claude Code in about 3 minutes; he did not publish the model tier, prompts, repo or token cost, and the timing has not been independently verified
  • Framing: this is an X discussion sparked by a personal weekend hack, not an official Anthropic feature launch
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Apiiro: Claude Code's "Don't Ask Again" Saves Approved Commands—Passwords Included—to settings.local.json; 2,539 Secrets Found on Public GitHub

On 2026-11-05 Apiiro researchers Shmulik Cohen and Matan Giladi reported that when a Claude Code user picks "Yes, and don't ask again" on an exact command, the command is saved verbatim—any password or token included—to the project's .claude/settings.local.json. Of 104,185 public GitHub copies of that file, 1,122 hold at least one secret; counting git history, shared settings.json and saved agent transcripts, the total is 2,539 secrets across 1,994 repos. Apiiro did not test whether the credentials work and advises rotating first, then cleaning up.

Key Takeaways

  • Trigger: when "don't ask again" saves an exact command, any password or token in it lands verbatim in .claude/settings.local.json
  • Scale (Apiiro figures): 1,122 of 104,185 public copies (~1.1%) hold secrets, 1,379 distinct; 2,539 secrets across 1,994 repos overall
  • Mix: over a third open a database directly (379 DB passwords), plus 43 SSH logins, 42 GitHub/GitLab/npm tokens and 23 AWS keys
  • Scanner gap: gitleaks flagged 564 and TruffleHog 643; 215 files were missed by both, mostly passwords typed inside commands
  • Fix: rotate every secret the file ever held, then git rm --cached and add it to the repo's own .gitignore; prefer pattern rules like npm test *
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Boson AI Unveils Higgs Realtime Duplex: A 45B MoE Full-Duplex Omni Model That Listens, Watches, Talks, and Calls Tools in the Background; API Waitlist Open

On 2026-11-05 Boson AI (the team of Mu Li and Alex Smola) released a research preview of Higgs Realtime Duplex: a 45B MoE front model that slices voice, video, and tool results into 160 ms chunks so it can listen, watch, speak, and render its own face at once, while thinking, calling tools, or delegating hard questions to a larger backend model without pausing. Vendor-reported scores include Big Bench Audio 98.9, tau-Voice 69.8, and Full-Duplex-Bench v1.5 75.5. The API is waitlist-only for now; the earlier Higgs Realtime is available today.

Boson AI Unveils Higgs Realtime Duplex: A 45B MoE Full-Duplex Omni Model That Listens, Watches, Talks, and Calls Tools in the Background; API Waitlist Open

Key Takeaways

  • Shape: a 45B MoE front model, further trained on about 2T tokens of text, audio, and video; every stream is cut into 160 ms chunks, silence included
  • Background work: it thinks and calls tools asynchronously while talking, and delegates hard tasks with full context to a larger backend model (a frontier model is recommended)
  • Vendor-reported scores: Big Bench Audio 98.9 (top of the table), IFEval 94.1, tau-Voice 69.8 (GPT-Live-1 about 74.5), Full-Duplex-Bench v1.5 75.5 (GPT-Live-1 80.1, TML 77.8)
  • Cost design: audio and video use about 2.6K tokens per minute, about 2.6K-5K with thinking and tools; the target is an hour of talk costing at most one-tenth of an hour of human time
  • Availability: the Duplex API is waitlist-only; voice and face swap zero-shot from a reference clip or a single image
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Linked Models & Agents:
⚡ Higgs Realtime Duplex⚡ Higgs Realtime⚡ GPT-Live-1⚡ TML Interaction Small🌐 Boson AI

PSI Announces Aaronson–Ambainis Conjecture Proof: Non-Public Anthropic Model, Lean Formalization Open-Sourced

On 2026-11-06, Physical Superintelligence (PSI, @psipbc) said its internal research system, running non-public Anthropic model instances, produced a complete proof of the Aaronson–Ambainis conjecture (open since 2008) on day three, accepted by Lean, then improved the result twice. The research page and Lean 4 formalization repo are public. Vendor wording; no journal peer review yet; the README still asks humans to check that the formal statement matches the printed conjecture.

PSI Announces Aaronson–Ambainis Conjecture Proof: Non-Public Anthropic Model, Lean Formalization Open-Sourced

Key Takeaways

  • Sources: PSI research page 2026-11-06 + @psipbc original post + GitHub physical-superintelligence/aaronson-ambainis
  • Claim: complete proof on day three, accepted by Lean; two later improvements also Lean-accepted (vendor wording)
  • Implication: if the conjecture holds, quantum machines get at most polynomial speedups on random unstructured inputs; PSI now calls it a theorem
  • Checkability: public Lean 4.33/Mathlib repo; four main theorems with no sorry/extra axioms; README still leaves statement fidelity to human readers
  • Limits: Anthropic model unnamed, no journal peer review, paper prose not machine-checked — cite as vendor claim
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Linked Models & Agents:
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Anthropic Claude Duet Links Claude Code: Check, Approve, and Steer Local or Cloud Sessions from Phone

On 2026-11-06, Anthropic Claude Duet co-creator and lead David Sutton (@davidsuttonca) and PM lead Karan Ahuja (@ahuja_karan) posted on X that Claude Duet now connects to Claude Code sessions: from a phone you can check what is running, approve changes, and send updates for sessions on your computer or in the cloud. Ahuja also notes Projects plus continuing Claude chats and memory. First-hand product-lead update, not an @AnthropicAI blog post; Duet remains a US Pro/Max/Ultra research-preview waitlist.

Anthropic Claude Duet Links Claude Code: Check, Approve, and Steer Local or Cloud Sessions from Phone

Key Takeaways

  • Source: Claude Duet co-creator/lead David Sutton (@davidsuttonca) X post 2026-11-06 19:20 UTC + PM lead Karan Ahuja (@ahuja_karan) same-day confirm; Snape Report / daily.dev follow-ups
  • Capability: from a phone, check Claude Code session progress, approve changes, and send follow-ups; works for local and cloud sessions (product-lead wording)
  • Extras: Ahuja says Projects plus continuing Claude chats and memory; calls it the #1 early-user request
  • Framing: first-hand employee product update, not an @AnthropicAI account post; Duet research preview still US Pro/Max/Ultra waitlist
  • Context: related to Claude app Code tab / Remote Control docs, but this story is specifically Duet↔Code linking; do not cite as a company blog
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Forever Security ThisBash: Browser Extension Can Drive Claude Desktop RCE via Dispatch, Even Cross-Machine

On 2026-11-04, Forever Security researcher Gal Weizman published ThisBash: a browser extension that can script claude.ai can abuse the still-reachable Claude Dispatch interface to send instructions as the signed-in user, auto-click Allow, and run code on Claude Desktop—including another machine paired to the same account. Anthropic validated the report in May and said Dispatch would retire, but the help page still lets existing users keep it.

Forever Security ThisBash: Browser Extension Can Drive Claude Desktop RCE via Dispatch, Even Cross-Machine

Key Takeaways

  • Source: Forever Security researcher Gal Weizman (2026-11-04) + The Hacker News / The Register; Anthropic validated in May, closed as internal duplicate
  • Surface: a browser extension that can script claude.ai can drive Claude Desktop via Dispatch (hidden but reachable) and auto-click Allow
  • PoC: four user-voice prompts + 1s Allow clicks → npm install -g; preinstall opened Calculator; no browser exploit needed
  • Cross-device: another Desktop paired to the same Claude account is reachable; personal account on a work laptop can become org ingress
  • Status: Anthropic said retire Dispatch by end of Oct; help page still lets existing users keep it; authors flag Remote Control / Cowork as same trust pattern
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Voyager Opens vork: Headless 3D/VFX Engine for Agents; Core API ~2,500 Tokens; Free Research Preview

On 2026-11-06, Voyager/vork author Ravi Parikh (@ravisparikh; GitHub swag) posted on X announcing vork: a headless 3D/VFX engine for AI agents (Rust + Slang, Vulkan/Metal). Official docs say getting-started help is about 2,500 tokens; a million uniquely steered birds rendered in about 97 seconds on one NVIDIA L4. Research-preview source is at github.com/voyager-so/vork and free during preview. First-hand author announcement, not a big-lab product line.

Voyager Opens vork: Headless 3D/VFX Engine for Agents; Core API ~2,500 Tokens; Free Research Preview

Key Takeaways

  • Source: Ravi Parikh (@ravisparikh) X post 2026-11-06 + voyager.so/vork and GitHub voyager-so/vork (research preview)
  • Design: no GUI; agents author scenes/shaders/cameras/physics as code, then render and revise; getting-started help ~2,500 tokens (vendor)
  • Perf (official site): ~1M birds on one L4 in ~97s; vs projected Blender EEVEE with finished flock ~8–11× (excludes ~3 min warmup)
  • Author X post also claims up to ~24× and EEVEE ~31 min—prefer the site's careful comparison; label author claims
  • Limits: no fog/volumes yet; heavy interiors may abort on Mac; path tracer slower than Cycles; free source-available preview
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Nous Research Releases Arete-27B: Qwen3.8-27B SFT Cutting Propaganda Rate ~22% to ~1% With No Hermes Bench Regression

On 2026-11-06 Nous Research released Arete-27B, an ~52.8M-token SFT of Alibaba Qwen3.8-27B aimed at Western-aligned answers. On Nous's 803-prompt seven-category eval, vendor figures show propaganda answers ~22% to ~1% and fact mean ~2.32 to ~2.98/5; Hermes Bench ~65.60% vs base ~65.77%. Weights, eval, and data are public.

Nous Research Releases Arete-27B: Qwen3.8-27B SFT Cutting Propaganda Rate ~22% to ~1% With No Hermes Bench Regression

Key Takeaways

  • Source: Nous blog Introducing Arete-27B (Nov 2026) + @NousResearch post
  • Method: plain SFT on Qwen3.8-27B (~52.8M tokens, 2 epochs, no RL/PO); teacher Inkling; vision encoder frozen
  • Eval (vendor): 803 prompts / 7 value categories; propaganda rate ~22% to ~1%; fact mean ~2.32 to ~2.98/5; judge Grok 4.7
  • Capability: Hermes Bench ~65.60% vs base ~65.77%; Terminal-Bench-2.1 vendor ~70% vs base 67%
  • Open: Apache 2.0 weights, Propaganda_Eval, and training pairs on Hugging Face
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Google DeepMind: Human-Guided AI Agents Speed Cryptanalysis; Two Weaknesses in Legacy 1024-bit RSA; 2048-bit+ Remains Safe

On 2026-11-06, Google DeepMind (@GoogleDeepMind) and VP Science Pushmeet Kohli published research: under human supervision, AI agents found two weaknesses in legacy 1024-bit RSA—signature padding adds no extra protection once an attacker controls about half the signed block (sandbox replay ~3.5× less CPU than prior), and a batch-factoring algorithm cuts average cost per additional same-size key by about 30×. Official caveat: modern 2048-bit and larger protocols remain unaffected.

Google DeepMind: Human-Guided AI Agents Speed Cryptanalysis; Two Weaknesses in Legacy 1024-bit RSA; 2048-bit+ Remains Safe

Key Takeaways

  • Source: @GoogleDeepMind post on 2026-11-06 + Pushmeet Kohli X Article; two technical PDFs (signatures / Batch NFS)
  • Scope: legacy 1024-bit RSA only; official statement that 2048-bit and larger remain secure
  • Signatures: once an attacker controls ~half the signed block, padding adds no protection; sandbox on a known 1024-bit benchmark key used ~3.5× less CPU and under 1/1000 of full break cost
  • Short fingerprints (e.g. 32-byte): theoretical estimate ~1600× better than prior best for 1024-bit keys
  • Batch factoring: for many same-size keys, average cost per additional key ~30× lower vs prior batch methods
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HF's Joël Niklaus Ships Harness Leaderboard: 28,849 Trials Across 9 Coding Agent Harnesses; Author Reports mini-swe-agent ~51.4% Average

On 2026-11-05, Hugging Face Harness Optimization researcher Joël Niklaus (@joelniklaus) released the Harness Leaderboard: same model, provider, and reasoning effort with only the harness swapped, covering GLM-5.3-Flash, DeepSeek-V4.1-Flash, and Flint on Terminal-Bench 2.1 and DeepSWE—28,849 trials and about $4,746 of inference. Author-reported averages: mini-swe-agent ~51.4% resolve rate; Pi ~49.4% at about $0.29 per solved task. This is an individual research leaderboard, not a Hugging Face company product announcement.

HF's Joël Niklaus Ships Harness Leaderboard: 28,849 Trials Across 9 Coding Agent Harnesses; Author Reports mini-swe-agent ~51.4% Average

Key Takeaways

  • Source: personal post by HF Harness Optimization researcher Joël Niklaus on 2026-11-05T16:04:08Z—not a company announcement
  • Scale (author): 9 harnesses on open models, 28,849 trials, ~$4,746 inference spend
  • Method: pin model/provider/reasoning effort; 3 attempts per task; Terminal-Bench 2.1 and DeepSWE
  • Results (author): averaged mini-swe-agent ~51.4%, openclaw ~27.3%; Pi ~49.4% at ~$0.29 per solved task
  • Use: public HF Space leaderboard + trajectories; Harness Lens documents prompts/tools for each harness
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Planet AI App Generally Available: Natural-Language Search Over a Decade of Satellite Imagery with Map Chat and Deep Research Agents

On 2026-11-05 Planet Labs announced the Planet AI App is generally available: a map-based chat UI to search a 10+ year, 3,000+ layer daily satellite archive with natural-language place finding, change-over-time views, and Deep Research agents. Entry point: ai.planet.com.

Planet AI App Generally Available: Natural-Language Search Over a Decade of Satellite Imagery with Map Chat and Deep Research Agents

Key Takeaways

  • Official: @planet / CEO @Will4Planet on 2026-11-05; blog Introducing the Planet AI App
  • Capabilities: natural-language place search, decade change/timelapse, file upload + scheduled reports, Deep Research agents
  • Data: vendor claim 10+ years of daily imagery and 3,000+ layers; results grounded in inspectable Planet imagery
  • Entry: ai.planet.com; aimed at discovery for non-GIS users, not a full GIS replacement
  • No standalone model benchmarks or detailed pricing published; summer beta with hundreds of users shaped discovery features
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World Labs Ships Frameboy: Optimized Atlas Streams at up to ~15 fps for Real-Time Browser World Exploration

On 2026-11-05 World Labs released research preview Frameboy: a highly optimized Atlas variant streamed from cloud GPUs to the browser at up to ~15 fps (vendor claim) with live camera control. A public demo and a Viewfinder closed-beta overlay shipped together; CEO Fei-Fei Li amplified “Real time Atlas” on X.

World Labs Ships Frameboy: Optimized Atlas Streams at up to ~15 fps for Real-Time Browser World Exploration

Key Takeaways

  • Official: World Labs shipped Frameboy research preview on 2026-11-05 (blog + X)
  • Vendor claim: optimized Atlas streams from cloud GPUs at up to ~15 fps with live camera control
  • Public demo: frameboy.worldlabs.ai; Viewfinder closed beta builds scenes then overlays live explore
  • Capabilities: single-image/text scene seeding + multiview novel-view synthesis; RTFM form factor at Atlas scale
  • Vendor notes powerful cloud GPUs required and cost vs local rendering; no independent quality/latency board
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Linked Models & Agents:
⚡ Atlas⚡ RTFM🌐 World Labs

Nous Research Ships Hermes Agent v0.23.0 “The Hearth”: Browser Webapp Desktop, Unified Gateway Sessions, Plugin Catalog Past 1,050

On 2026-11-05 Nous Research released Hermes Agent v0.23.0 (The Hearth). Your gateway can serve the full Desktop in a browser; CLI/TUI/Desktop/editors/bots/cron share one session authority; the plugin catalog grew from ~600 to 1,050+; plus conversation rewind, inline terminal images, and paid-MCP spend limits. Official: ~1,950 merged PRs and 666 contributors.

Nous Research Ships Hermes Agent v0.23.0 “The Hearth”: Browser Webapp Desktop, Unified Gateway Sessions, Plugin Catalog Past 1,050

Key Takeaways

  • Official GitHub release: Hermes Agent v0.23.0 (2026-11-05), codename The Hearth
  • Webapp: gateway serves full Desktop in a browser (off by default; webapp.enabled)
  • Unified sessions: one gateway owns chats so surfaces stop double-writing
  • Plugin catalog ~600→1,050+ with a Verified tier; paid MCP tools can declare cost + chat spend caps
  • Since v0.22.0 (vendor): ~10,080 commits / ~1,950 PRs / 666 contributors
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Perplexity Cuts Agent API Effort-Level Costs and Adds Ultra: Medium Nearly 90% Cheaper; Ultra ~85.9% WANDR

On November 5 Perplexity Developers (@perplexitydevs) said every Agent API effort level now costs less per query with equal or better research quality, and added an ultra tier for broad long-running research. Vendor figures: medium rose from ~55% to ~77% on BrowseComp/DSQA with nearly 90% lower cost; xhigh held ~76.5% WANDR at ~25% of August 2026 cost; ultra runs three parallel researchers plus a consolidator at ~85.9% WANDR and about $21.71/task.

Perplexity Cuts Agent API Effort-Level Costs and Adds Ultra: Medium Nearly 90% Cheaper; Ultra ~85.9% WANDR

Key Takeaways

  • Official (@perplexitydevs + blog): every effort level cheaper with equal/better quality; new ultra
  • Vendor: medium BrowseComp/DSQA ~55%→~77% with nearly 90% lower cost
  • Vendor: xhigh WANDR ~76.5% at ~25% of August 2026 cost
  • ultra: 3 parallel research agents + consolidator; WANDR ~85.9% at ~$21.71/task on Claude Opus 5.5
  • Use effort="ultra" or pin [email protected]; background mode for long runs
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Julius Research Releases DeepFinance: 21 PE/IB Long-Horizon Tasks; Claude Opus 5.5 ~54.8% Mean Rubric

On November 5 Julius Research CEO rahul (@0interestrates; Julius AI) released DeepFinance: 21 ultra long-horizon PE/IB tasks built from anonymized real deal data rooms (~20–30 human hours each, mean ~27.1h; ~84 rubric criteria). Official leaderboard: Opus 5.5 (max) ~54.8%, Sonnet 5.5 ~54.6%, GPT-6 Astra ~54.2%. Focus is auditable trajectories, not final-number checks alone.

Julius Research Releases DeepFinance: 21 PE/IB Long-Horizon Tasks; Claude Opus 5.5 ~54.8% Mean Rubric

Key Takeaways

  • Primary source: Julius Research CEO @0interestrates post + official leaderboard/blog (first-hand lab lead release, not a separate corporate PR account)
  • Scale: 21 tasks; mean ~27.1 human hours; ~84 rubrics/task; ~19 files per data room
  • Top (lab-reported): Opus 5.5 max ~54.8%; Sonnet 5.5 ~54.6%; GPT-6 Astra xhigh ~54.2%
  • Cost (official table): Opus ~$8.63/task; Astra ~$4.55; GPT-6.1 Sol ~$0.81 at ~52.9%
  • Gap filled: moves beyond doc QA / final-number checks toward auditable trajectories under conflicting evidence
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