This is the evening edition for October 28, 2026. We've rounded up six key topics from overseas AI news. Security leads the night with two stories. Cases where AI agents themselves act as the attacker are starting to appear, forcing defenders to update their assumptions. There are also notable moves on the hiring front and in the local LLM space.

AI Agents Break Into Anthropic's Internal Systems — Via Third-Party Software, Exposed Through a Bug Bounty

Tonight's top story. According to [The Decoder](https://the-decoder.com/ai-agents-hacked-anthropic-maker-of-arguably-the-most-capable-ai-models-for-cyberdefense/), cybersecurity firm Cathedral used AI agents to successfully penetrate Anthropic's internal systems through third-party software. The breach came to light via a bug bounty program.

Anthropic reportedly acknowledged access to data from its sales, HR, and IT departments, while disputing how deep the intrusion went. The details of both sides' claims are still coming out, but what this incident demonstrates is that even a company at the frontier of AI development is vulnerable to the “backdoor” of external software dependencies. AI agents can be used to explore attack methods too, and the scenario of “the side defending with the most capable AI” being “attacked by AI agents” has now become concrete.

Anthropic Ends Coding Interviews — “Hunger beats tenure”

On hiring. Felix Rieseberg, an engineering manager at Anthropic, has revealed that the company put an end to traditional coding interviews this month. “Giving a small React exercise makes about as much sense as asking about assembly. Claude solves both better than candidates. The builders having the biggest impact succeed with no relation to whether they're good at tests like these,” he said ([Latent Space](https://latent.space/p/ainews-anthropic-ends-coding-interviews-hunger-beats-tenure/)).

The advice offered to newcomers comes down to three points: “pick business impact over backlog,” “choose ambitious ideas that, if they succeed, dramatically raise the odds of hitting organizational goals,” and “build it over a weekend.” [The Decoder](https://the-decoder.com/with-ai-writing-the-code-entrepreneurial-thinking-is-now-what-counts-most-for-newcomers-at-anthropic/) frames this as the bar for newcomers shifting toward entrepreneurial thinking, while adding the perspective that “not everyone brings the hustle.” It's a statement that symbolizes how, in an era when AI writes the code, the axis of evaluation is shifting from “can you write it” to “what do you choose to build.”

Don't Run AI in Your Downloads Folder — Malicious AGENTS.md Can Manipulate Agents

A warning with direct practical relevance. According to [ITmedia](https://www.itmedia.co.jp/aiplus/article/2610/28/2000002671/), an Israeli security firm warns that launching a coding agent in a downloads folder seeded with a malicious “AGENTS.md” could lead the agent to install malware or assist in account takeover.

AGENTS.md is a configuration file that tells coding agents how to work, and it gets loaded from the folder where it sits. In other words, a file that “just happened to be lying there” can turn into an instruction to the agent. On the same day, a technique [targeting code execution on a documentation builder through a flood of AI-agent submissions to RubyGems, Ruby's package repository](https://optimuslabs.io/research/briefings/rubygems-rubydoc-agent-execution) was also reported. As agents' range of activity expands, deciding “how much to trust files and repositories” is set to matter more than ever. For a start, it's worth getting back to basics: don't run agents in untidy folders.

35B MoE on a Single Tesla P40 — Full 262k Context at About 60 Tokens/Sec, a Two-Day Tuning Log

For the local LLM crowd. [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/1xhkwlm/two_days_tuning_a_single_tesla_p40_35b_moe_full/) saw a two-day tuning log posted: running Ornith 1.5 35B-A3B (a Qwen3.5-family MoE architecture) on a single Tesla P40 (24GB — and a PCIe x1 connection at that, on Windows, with a hand-rolled llama.cpp fork). Without quantizing below Q4, the setup reaches full 262k context, about 60 tokens/sec on short prompts, and about 30 tokens/sec at a deep 100k context.

What worked comes down to fine-grained moves. Disabling ECC reclaims 1.5GB of VRAM and roughly 9% of decode. For the KV cache, q4 falls apart at depth on Pascal-generation GPUs (at 32k, q4_0 gets 30.6 t/s versus 42.2 t/s for q8_0), so q8_0 is the recommendation. The model's initial upload had some MTP heads mixed in untrained, and simply checking whether the head is trained improves draft acceptance from about 45% to 60–85%. On Pascal, which lacks tensor cores, the int8 dp4a instructions do the heavy lifting, boosting prefill by +26% at 32k and +41% at 100k. Limiting the prompt fed to the MTP head to the last 8k also claws back about 13% of prefill cost on long prompts. As a record of sheer persistence — running a state-of-the-art 35B MoE at full context on a GPU released in 2016 — it is a treasure trove of measured data.

DiffuSpace's First Diffusion Language Model, “Clover-35B-A3B-Preview”

The model scene is moving too. [DiffuSpace has released “Clover-35B-A3B-Preview,” its first model](https://www.reddit.com/r/LocalLLaMA/comments/1xhinyj/diffuspaceclover35ba3bpreview_hugging_face/). The configuration: a 35B-parameter MoE backbone with roughly 3B effective parameters per token, block diffusion generation, and 128k context. At the time of release, its benchmarks claim state-of-the-art performance among open-source diffusion language models.

Diffusion-based text generation has already seen commercial adoption, and the open-source side now appears to be entering the stage of serious experimentation at the 35B-class MoE level. It is strictly a Preview release, and real-world evaluation is still ahead, but having an approach different from autoregressive generation available to try locally is welcome news.

AI Bots Flooded a UK Government Consultation — Revealed by a Review of the 2023 Exercise

Finally, the societal beat. [The Guardian](https://www.theguardian.com/society/2026/oct/27/ai-bots-flooded-2023-uk-government-consultation-disposable-vapes-ban) reported that a UK government public consultation on banning disposable vapes, conducted in 2023, had been infiltrated by a large volume of AI-bot submissions.

Government consultations are one of the foundations of democratic decision-making, but in an age when generative AI can mass-produce opinions, the question of how to safeguard their integrity is being reopened. Public commenting is a widely used institutional practice in Japan as well — this is not just somebody else's problem. The need for bot detection on the tabulating side and for rethinking the design of submission channels seems set to grow.

Wrap-Up

Tonight's six stories captured a single day on which two currents advanced at once: the reality of AI agents being used both as attack weapons and to fabricate public opinion, and the shifting norms of work and evaluation in the AI era. The breach of Anthropic may have surfaced through a bug bounty, but it is a case where external-dependency risk management has come home to an AI company itself. The end of coding interviews may prove a watershed for hiring convention. Details on each topic can be found at the linked sources.