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++. Mistral successfully migrated 40,000 lines of a physics-intensive reservoir simulator by building a parity harness for numerical verification, documenting the codebase with AI agents, and using structured workflows with human oversight to ensure high-quality, maintainable code. Key lessons include prioritizing numerical agreement, organizing documentation before migration, and balancing agent autonomy with human review. Legacy scientific codebases accumulate over decades, and when original a

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article by OpenAI describing two further changes to the harness that also tripled GPT 5.6 Sol’s performance from to 13.3% to 38.3%.) Coming back to the Artificial Analysis Coding Agent Index v1.4 (lower right in the previous figure), which blends several agentic coding tasks, GPT-6 Astra is clearly at the frontier, but it doesn’t pull ahead by leaps and bounds. This can also be seen in the general Artificial Analysis Intelligence Index shown below, which blends different types of tasks, not jus

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Facebook; Twitter; LinkedIn; Mail When developers first work on harness engineering for agentic coding systems, they often fall into the same trap: they run common end-to-end benchmarks like Terminal-Bench and DeepSWE, watch a composite score move by a few percentage points, and have no idea why it changed. End-to-end benchmarks are the de facto for evaluating model performance and determining what needs deeper investigation, but the challenge is that those investigations come at a high cost. Be

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rship, control and economics. This post is a deep dive into the open model AI stack that developers need to consider as they move from closed to open source. Using open models for agentic software development does not require learning how to train models, buying a rack full of GPUs, or becoming an expert in machine learning. From the perspective of an application developer, the stack is surprisingly familiar to using closed-source models. You have a model that answers prompts and a harness that

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e testing Anthropic took more than two months to alert cops; AI systems are fully capable of carrying out nightmare attacks against infrastructure and nobody's ready Meanwhile, AI agents move robotic arms, 'make OT device operator screens lie'; Nvidia found $1B under the couch to help secure American scientific computing dominance Commitment comes as GPUzilla prepares to fortify Uncle Sam's arsenal with at least seven AI-optimized supers; TSMC taps GlobalFoundries to bolster US silicon interpose

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We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon; This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what the agent knows from how it reasons; A self-improvement loop then compiles expert feedback into verified, regression-tested updates without m

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cases; Developers annotate Rust source code directly with preconditions and postconditions using Rust-like syntax, enabling fast feedback loops (under one second) and allowing AI agents to assist in proof generation; Verus enables mathematical verification of Rust's "unsafe" code blocks and concurrent code with custom locking schemes, re-establishing machine-checked safety guarantees for performance-critical implementations like AWS's Nitro Isolation Engine; Amazon uses Verus to prove correctne

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similar role for quadruped robots — though as I’ll discuss later, Unitree’s robots now face legal restrictions in the United States. Sponsor message Tasklet — Run your business on agents AI agents are useful when they stop chatting and start doing. Tasklet lets you build persistent agents that work across Gmail, Slack, HubSpot, Zendesk, Linear, and thousands of other tools. Describe a workflow in plain English, choose what the agent may do on its own, and require approval for sensitive steps. Ag

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ecific deployment point defined by the model, workload, SLO, serving topology, runtime version, and accelerator platform. The same patch may improve one point and regress another. Agent-assisted exploration can produce more environments, experiments, measurements, and rejected paths, all of which must remain reproducible. The first requirement is therefore reliable execution. Reproducing a deployment point requires more than model capability: tools, environments, context, memory, Verification, a

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g a long tail of UI regressions. As our team grows, we wanted to create a stronger foundation for how UI is composed. Defining these boundaries is becoming especially important as agents contribute more and more to our codebase. Another more practical reason to migrate was performance. We care about keeping Linear fast and CSS-in-JS adds work during rendering to generate styles and inject CSS rules. The final straw was the performance regression we saw after upgrading to React 18 that introduced

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boot the machine when it hardlocked the host kernel. If it wasn’t clear before, I will state it plainly: you can no longer assume a mere VM will contain a sufficiently advanced AI agent. To use a 2010s term of art, you should treat such agents as an advanced persistent threat. Let’s imagine a common situation: you want to run an AI agent, but need its full capabilities, including the ability to download, build and debug software; do research; sift through dependencies; set up fuzzers; triage vul

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dpoints, dedicated deployments, and training support. The model provides a 1M-token context window with native tool calling and preserved reasoning history, targeting long-horizon agentic workloads. We ran DeepSeek V4 Pro 0813 through the same eval suite we used for Kimi K3 in July . We evaluated Deepseek against Kimi K3 and Fable 5 across four benchmark families: SWE-Bench Verified, LiveCodeBench v6, Aider Polyglot v1, and Terminal-Bench 2.1. Deepseek V4 Pro is the strongest single model in thi

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1,507 real vulnerabilities across 188 widely used open-source projects, drawn from Google’s OSS-Fuzz campaign and the ARVO reproducible-vulnerability archive. Each task hands the agent a short description and a real vulnerable codebase. The agent has to locate the flaw, build an input that actually triggers it, and then patch it. Grading is by execution, not by a judge model. The proof-of-concept either crashes the target or it does not. The structure of Cybergym is agentic, long-horizon, and u

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Engineering Browser agent benchmarks are saturating. This is how we build RL environments from real user traffic against the real web. Evaluations 1. Filtering Down the Tasks; 2. Suitability; 2.1 Hand labelling; 2.2 Aligning a labelling agent; 3. Personal Data Replacement; 4. Feasibility and Difficulty; 5. The Problem with Judgement; 6. Difficulty Estimation; 7. Rubric Construction; 7.1 Why iterative rubrics do not converge; 7.2 The parallel rubrics we use instead; 7.3 Properties of a good rubri

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ice as fast per rollout. GLM-5.3 ties pass@2 and wins pass@4 (87.6% against 85.8%) at half the cost per rollout, so it is the stronger value pick for high-volume or retry-tolerant agent work. In our run, GLM-5.3 cost \$3.99 per rollout against \$8.37 for GPT-5.6 Sol at max effort, about 2.1x lower. Measured per solved task, GLM-5.3 returned 17 solves per \$100 against Sol's 9, roughly twice the solved work per dollar. They split the board. GLM-5.3 takes JavaScript (90 against 75) and Rust (70 ag

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and is much faster per rollout. DeepSeek V4 Pro 0813 wins pass@4 (88.5% vs 85.8%) at 35x lower cost per rollout, so it is the stronger value pick for high-volume or retry-tolerant agent work. In our run, DeepSeek V4 Pro 0813 cost \$0.24 per rollout versus \$8.37 for GPT-5.6 Sol at max effort, roughly 35x cheaper. Measured per solved task, Pro returned 260 solves per \$100 against Sol's 9, about 30x the solved work per dollar. GPT-5.6 Sol leads Python (74), Go (79), TypeScript (66), and JavaScrip

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an applied end-to-end LLM project including evaluation, training, and local deployment for real-world use. The outcome of this is an AI-detector API that can be used by humans and agents, and a user-friendly UI. Method overview Here, we are going to develop a method similar to Pangram models, which, as far as I know, are behind Substack AI detection feature. I wrote a short article about AI-text detection a while back in 2023: What Are the Different Approaches for Detecting Content Generated by

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cky decisions: take the native search some labs build in, or wire up a third-party engine like Exa, Parallel, or Perplexity? Is one search enough, and if not how long do I let the agent keep searching? Are more search turns worth the quality they buy? We built live leaderboards to help you decide the best search configuration with data. See the data on our new Benchmarks page. We benchmark all the combinations to find strengths and weaknesses When setting up a search request, you have four decis

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You wouldn't let Claude Code cat your AWS credentials or Kubernetes config on request, would you? A browser-based game designed to test humans' ability to safely approve AI coding agent requests suggests humans in the loop aren't as good at spotting dangerous commands as one might hope, with players approving roughly one in three malicious requests on average. The results also suggest that repeatedly having to approve an agent's actions can lead to sloppy decisions. It’s a quick, simple game on

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I / Cybersecurity English 中文 The NVIDIA AI Red Team shares how access controls, sandboxing, network egress restrictions, and secret management can reduce the risks of deploying AI agents at enterprise scale. AI-Generated Summary The NVIDIA AI Red Team assessed multiple AI agents over the past six months and identified four recurring failure modes: lack of access control, tools enabling arbitrary code execution, missing network egress controls, and secrets exposed in plaintext. Prompt-based guard

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