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y covered by an existing test case, always edit or create relevant tests (in the ./test directory) to confirm the changes work and prevent regressions. The tests are written using GoogleTest and POSIX shell scripts (not bash) and must be registered in the test/CMakeLists.txt build definition to run. Lint guidelines Run make configure compile to compile and format the code in-place. The compile step comes with a large amount of linter-like checks. Style guidelines You are already on the correct b

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d chips become more efficient. We compare these supply estimates to several (imperfect) proxies for token demand and its growth trend, including the growth of tokens served across Google platforms, and the intensity of token usage today at the largest tech companies, extrapolated to all software engineers worldwide. These figures suggest that demand at current prices could be between 200 million and 4 billion tokens per second, growing by roughly 10× per year — plausibly outpacing supply growth

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88.4 percent), just ahead of Anthropic’s Claude (82.1 percent). When it comes to paid subscriptions though, Claude is the winner (69 percent), followed by ChatGPT (49 percent) and Google Gemini (32 percent). Despite increased usage, the respondents are by no means AI enthusiasts. Use of AI for image generation has fallen since last year, from 38 percent to 37 percent, and some respondents have ethical objections. "I do not use image generators on principle," said one, and another claimed "AI ima

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onths back, but not anymore. So the most likely explanation of the fix includes guardrail additions and input filtering. I do not think Copilot(s) leverage a similar mitigation to Google and ChatGPT yet, where they use their search engine index for public URLs to only render or navigate to public URL autonomously. It’s not a 100% fix but limits large scale data exfiltration, and something worth adopting. Fun stuff, but also scary. Epilogue: Take-aways Since this was a lengthy post about my DEF C

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most extreme case, the model’s final submission was not an answer to the question but a benchmark identification report: BENCHMARK IDENTIFICATION: The question is NOT from FRAMES (Google) — searched all 824 questions, no match. The question is NOT from BrowseComp (OpenAI) — dataset is gated. The question is MOST LIKELY from the GAIA benchmark (TEST SET). [Lightly edited for formatting] That eighteen runs independently converged on the same strategy suggests the behavior is not a fluke but a repr

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t type /embrace-the-red to invoke the skill. Skills can live in different places and have a priority ranking: enterprise user project folder. What about Skills in OpenAI Codex and Google Gemini? Codex uses different folders /.agents/skills/ or ./agents/skills in the project repository, and Gemini CLI uses /.gemini/skills/, Antigravity looks for .agent/skills/,… The ecosystem is a bit of a chaotic mess. There is a lot more to it that you can read in the docs for Claude, Codex or the generic agent

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ng them consistently. Moreover, we discovered that enforcement methodology can change what the benchmark ends up actually measuring. How we got here We run Terminal-Bench 2.0 on a Google Kubernetes Engine cluster. While calibrating the setup, we noticed our scores didn't match the benchmark’s official leaderboard, and infra error rates were surprisingly high: as many as 6% of tasks were failing because of pod errors, most of which were unrelated to the model’s ability to solve the tasks. The dis

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raction of the active parameter count, ZAYA1-base (8.3B total parameters, 760m active) achieves performance comparable to leading models such as Qwen3-4B (Alibaba) and Gemma3-12B (Google), and outperforms models including Llama-3-8B (Meta) and OLMoE across reasoning, mathematics, and coding benchmarks. “Efficiency has always been a core guiding principle at Zyphra. It shapes how we design model architectures, develop algorithms for training and inference, and choose the hardware with the best pr

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ons section of Claude also highlight the generic threat: “This means Claude can be tricked into sending information from its context (for example, prompts, projects, data via MCP, Google integrations) to malicious third parties. To mitigate these risks, we recommend you monitor Claude while using the feature and stop it if you see it using or accessing data unexpectedly. You can report issues to us using the thumbs down function directly in claude.ai.” However, users might incorrectly assume the

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e application level. If you are building exclusively on top of OpenAI models, you should be good, as OpenAI is generally mitigating this threat at the model/API level. However, there are plenty of apps and models (Google, Anthropic, Grok, Windsurf Cascade,…) that remain susceptible to this behavior. If you build an app on top of a model which has this behavior, then consider taking extra steps to protect or inform end-users of invisible text being present. References Newer →; Contact me; ← Older

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sses in tool invocation and image rendering allow a third-party attacker to embed malicious instructions into source code, websites, or even through RAG poisoning. When Cascade analyzes such content, it becomes a “confused deputy”, potentially leading to data exfiltration. References Windsurf System Prompt; Simon Willison - The lethal trifecta; Month of AI Bugs 2025; GitHub Copilot Data Exfiltration Fix; Windsurf’s CEO goes to Google; OpenAI’s acquisition falls apart Newer →; Contact me; ← Older

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lve complex tasks beyond simple QA. Our system uses a novel retriever based on the PageRank algorithm, which is the classic graph-based retrieval algorithm originally developed by Google. A novel feature of our algorithm is the usage of a mixture of two different types of PageRank that apply to two main categories of tasks: local and global retrieval tasks. Tasks requiring local retrieval are those where we want to retrieve text chunks that are semantically and syntactically related to the query

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tool that enables privacy-preserving analysis of real-world language model use. It gives us insights into the day-to-day uses of claude.ai in a way that’s analogous to tools like Google Trends. It’s also already helping us improve our safety measures. In this post—which accompanies a full research paper—we describe Clio and some of its initial results. How Clio works: Privacy-preserving analysis at scale Traditional, top-down safety approaches (such as evaluations and red teaming) rely on knowi

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questions about vast inputs such as code bases or books without the need to alter the model’s parameters. Current state-of-the-art transformer-based LLMs with long-context (e.g., Google’s Gemini-1.5 at 1M tokens) often perform well on long-context benchmarks, but have the significant drawback of requiring enormous compute, since they use attention layers whose compute cost increases quadratically with context length. This cost limits scalability & deployment flexibility where compute is limited

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-node setups. We benchmarked NeMo Curator against our previous CPU-based Python package Zyda-Processing. We compared running both packages on a single instance of a3-highgpu-8g on Google Cloud, which has 208 vCPUs, 1.9TB of RAM and 8 H100s 80GB GPUs. This is a typical setup for a node of H100s. We found that running NeMo Curator on this node using GPUs is 10 times faster than running our Zyda package. Processing the whole raw dataset (which is roughly 12 billion documents with 11.5T tokens) woul

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the “alphabet soup” problem of genomics. Experiments in genomics uncover lists of genes implicated in a biological process, like MGAT5B and ADGRA3. Researchers turn to tools like Google, Uniprot or Wikipedia to learn more, as the knowledge of 20,000 human genes is too broad for any single human to understand. However, according to our count, only 3,639 of the 19,255 human protein-coding genes recognized by the HGNC have high-quality (non-stub) summaries on Wikipedia; the other 15,616 lack pages

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