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DeepSeek

30 stories from 10 sources across 11 topics.

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30

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10

Topics

11

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24 stories in this edition match your reader profile.

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3

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0

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24

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116

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Topics

Agents3 storiesBig Tech3 storiesFunding3 storiesHot Takes3 storiesModels3 storiesMusk AI3 stories

Entities

DeepSeek30 storiesAnthropic12 storiesClaude12 storiesOpenAI12 storiesQwen10 storiesAlibaba8 stories

Sources

Hacker News AI8 storiesLatent Space6 storiesThe Decoder4 storiesAWS Machine Learning Blog3 storiesBloomberg AI2 storiesGitHub Trending AI2 stories

The Decoder / 1:50 PM

How hackers used Claude for missiles, drone swarms, and surveillance, while Chinese labs mined it for training data

Anthropic's new threat intelligence report documents eight months of Claude abuse. Chinese AI labs like Alibaba's Qwen team, DeepSeek, and Moonshot AI relayed requests en masse or extracted training data, with Qwen alone accounting for more than 151 million exchanges. Actors also used Claude for missile software, autonomous kamikaze drones, and nationwide surveillance systems. The article How hackers used Claude for missiles, drone swarms, and surveillance, while Chinese labs mined it for training data appeared first on The Decoder .

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The Decoder / 12:40 PM

New Deepseek model V4.1-Flash cuts memory needs for AI agents

Deepseek releases V4.1-Flash, a multimodal model with 552 billion parameters that cuts KV cache memory to a quarter of its predecessor. On the DeepSWE coding benchmark, it narrowly beats Opus 5 and GPT-5.6 Sol, even though only 16 billion parameters are active per token. The model ships under the MIT license and targets much cheaper AI agents. The article New Deepseek model V4.1-Flash cuts memory needs for AI agents appeared first on The Decoder .

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Simon Willison LLMs / 10:12 PM

Just a rumour of a bug is enough to find a security exploit these days

Just a rumour of a bug is enough to find a security exploit these days Anil Madhavapeddy is a professor of computer science at Cambridge and a core maintainer of the OCaml compiler. In this somewhat alarming post he reports that security issues in OCaml projects are seeing evidence of attempted exploits within minutes of patches being shared for discussion: This normally takes a few days and a release within a week or two is reasonable. Within about ten minutes (!) this website was fielding probes for percent-encoded traversal sequences, indicating that automated watchers are keeping an eye on public repositories. Modern coding agents have become so effective at finding flaws that the slightest hint at a new bug can be enough information for them to find it, something Anil has been able to demonstrate using his own agents, switching to DeepSeek V4 Pro⁠ when Claude Fable refused the task. Anil points out that this rate of discovery appears incompatible with existing open source embargo practices for new issues. If an issue can become an exploit this fast, we need to figure out new processes for keeping our communities safe. rclone maintainer Nick Craig-Wood confirms in the Hacker News comments that his project is seeing this problem: In the first 10 years of the rclone project we received about 20 security disclosures through GitHub. We had to deal with over 40 in the last month! That has taken a huge amount of my time, even using AI tools to triage and come up with fixes for review. The hit rate for those security disclosures is pretty good - about 75% of them have a nugget of something which needs looking at. [...] GitHub assigns CVEs for the advisories. Before the AI apocalypse they took 2-3 days for an assignment but now it they are running at 3-4 weeks so I have to send the point releases out with CVE-PENDING in the changelog which isn't ideal. Via Hacker News Tags: open-source , security , ai , generative-ai , llms , coding-agents , ocaml , ai-security-research

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Simon Willison LLMs / 11:58 PM

Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index

Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.6 Luna (max), and just one point behind GLM-5.2 (max) and DeepSeek V4 Pro 0813 (max) - that GLM is 753B and that DeepSeek is 1.7T parameters , and Luna is size unknown but presumably a whole lot bigger than 27B. Qwen 3.8 27B is a truly astonishing model . Via Hacker News Tags: ai , generative-ai , llms , qwen , ai-in-china , artificial-analysis

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The Decoder / 2:40 PM

Alibaba releases Qwen3.8-Flash-Next, targeting "ultimate cost efficiency"

Alibaba's Qwen team is previewing the Qwen4 architecture with Qwen3.8-Flash-Next, a mixture-of-experts model that activates just 6 out of 125 billion parameters per token. At one-ninth the training cost, it beats much larger competitors like DeepSeek-V4-Flash and Claude Opus 4.6 on coding and office benchmarks, adding more pricing pressure on OpenAI and Anthropic. The article Alibaba releases Qwen3.8-Flash-Next, targeting "ultimate cost efficiency" appeared first on The Decoder .

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The Decoder / 8:40 AM

Taiwanese cybersecurity firm warns that AI tools have more than doubled Chinese state-backed cyberattacks

Chinese state-backed hacking groups have more than doubled their attacks since they started using AI models like DeepSeek to write exploit code and scan networks, according to Taiwanese security firm TeamT5. Hackers also used ChatGPT and Anthropic's Claude Code. A UK study shows the cyber capabilities of open models are catching up fast. The article Taiwanese cybersecurity firm warns that AI tools have more than doubled Chinese state-backed cyberattacks appeared first on The Decoder .

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Latest story in this edition: 5:59 AM

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