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[AINews] Anthropic ends Coding Interviews: "Hunger beats tenure"

Latent Space's AINews episode covers several AI developments. Anthropic hiring manager Felix Reiseberg announced he has stopped asking candidates to do coding interviews, arguing Claude now outperforms people at React and assembly tasks; he advises junior engineers to prioritize business impact over backlog, pursue ambitious ideas, build working prototypes over a weekend, and lean into their fresh user perspective ('Hunger beats tenure'). Google DeepMind introduced Genie 4, a real-time world model with native audio, physics, and Gemini-based planning, positioned toward testing agents and robots. In AI for science, Axiom announced a Lean auto-formalization of the Classification of Finite Simple Groups (~15,000 pages across ~500 papers, ~50M lines of Lean produced by agent swarms in 24 days), displacing the four-color theorem as the largest computer-verified proof, while OpenAI and Anthropic reported independent claims related to disproving the Exponential Time Hypothesis. Independent evals were updated: Artificial Analysis Intelligence Index v5, FrontierCode 2.0, and results for a new GPT-6.1 model.

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Open-weight model GLM 5.3 built full Firefox exploit chains with zero human input in under 48 hours

Researchers at Epoch AI report that the open-weight Chinese model GLM 5.3 autonomously built full Firefox exploit chains with no human input, exploiting the 'patch gap' between when security fixes appear in code and when they reach users. The model identified disguised security fixes, chained vulnerabilities with a previously unpublished flaw to bypass browser sandbox protections, and could read local files when a user visited a webpage. Attacks cost about $1,000 in API fees per Firefox version across six versions released Sept 17–Oct 26, taking 12 hours for the fastest and up to 48 hours. No API providers tested triggered safety guardrails. Mozilla patched the flaw in Firefox 159.0 (Oct 27). The same setup spent roughly $100,000 on Google Chrome without producing an equivalent exploit. Researchers warn the patch-gap risk spans the software industry and note untested benchmark data suggesting Kimi K3.1 Pro could outperform GLM 5.3 on offensive cyber tasks.

ArticleLatent Space

[AINews] Building AIE CODE: The top Coding Agent Conference

AINews covers three AI developments: Mistral Large 4 ('Le Chonk') shipped as an open-weights release built for agentic coding and cybersecurity, with 1.05T total and 52B active parameters, 1M context, text+image input, and pricing near $0.70/$2.10 per 1M input/output tokens; OpenAI now monitors all frontier RL runs, evaluations, and internal Codex use using activation probes, reviewer models over reasoning and tool use, and investigator agents; and Anthropic says Claude now produces a preliminary misuse assessment immediately after each response, which it says has surfaced misuse its classifiers were not designed to catch.

VideoNate Herk

I Told Claude to "Make it better" 100 Times (It Went Too Far)

In a YouTube video, creator Nate Herk tested Claude Code by giving it a deliberately ugly coffee shop website and instructing it to 'make it better' 100 times in a row, running the experiment twice—once with Claude Sonnet 5.5 and once with Claude Opus 5.5, both at medium effort. Each run was in a clean room (new session, no skills, plugins, or memory), so each model relied only on its own knowledge and built-in tools. With Sonnet, the model made small, careful changes across 100 rounds, generating 11 images and 2 videos at a cost of about $29.93 in API billing, with code lines growing linearly while the actual quality gains were unclear. Milestones included the first AI-generated image and mobile-ready site (round 1), the first scroll animation (round 24), and the first online-ordering form (round 32). With Opus, the model kept adding features until the site reached 21,000 lines and started breaking itself. Neither model used web search even though it could. Herk's takeaway is that AI is not naturally good at thinking outside the box, and setting vague goals leads to getting stuck around the 80th improvement, so prompting must be specific and actively drive innovation to push past the 90-100 mark.

Latent Space

ArticleLatent Space

Can Frontier Models See Past a Decoy? Elorian Tests Visual Reasoning

Elorian, which emerged from stealth in April with $55 million in seed funding co-led by Striker Venture Partners, Menlo Ventures and Altimeter Capital, launched its first visual reasoning model, Glimpse, described as a 'visual thinking model' that reasons about the physical world from what it sees. In partnership with AI data company Mercor, Elorian co-released DecoyCount, a benchmark testing whether models genuinely count objects or rely on visual shortcuts and substitution heuristics. According to a paper accompanying the release, frontier models scored below 50% accuracy, with GPT-6 Astra highest at 43.17%. CEO Andrew Dai said Glimpse scored 50.48% on 1,660 DecoyCount items in an internal test, making it the only model to clear 50%, though he noted the result came from a single run versus the frontier models' three-run averages. Dai, a former Google DeepMind researcher on GLaM MoE, PaLM 2 and Gemini, said the model uses visual thinking—generating and editing images as part of the reasoning trace—and distinguished Elorian's 'indirect action' approach from world models like World Labs' Atlas and Google's Genie 4.