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ature parity with Facebook Groups in eight weeks, with AI handling much of the routine implementation so engineers could focus on architecture, product decisions, and quality. Leverage that lasted: After launch, agent-driven investigation helped engineers triage and root-cause user feedback. The AI did more than generate code. It tightened our quality loop, helping a small team deliver a high-quality app with less ongoing rework. Acknowledgements We thank Neeharika Sinha, Jane Kou, Gang Yang, Gu

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LX on Mac with Apple silicon lets AI researchers and developers experiment locally with the latest models and methods in a private environment. The open-source array framework leverages the unified memory architecture of Apple silicon, enabling anyone to run new inference and fine-tuning methods on their own hardware and explore other AI techniques. In a previous post, we highlighted the advantages of the M5 architecture and its GPU Neural Accelerators. In this post, we show how to run the lates

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eviously passing metric regresses. This prevents developers from accidentally trading a critical capability for a minor edge-case improvement. Shared dashboards and centralized storage: Configure your eval processes to export run artifacts—including raw trace JSONL, prompt hashes, model versions, latency profiles, and scorecard summaries—into a central data warehouse like BigQuery or Cloud Storage. Publishing these metrics to a shared team dashboard gives everyone visibility into accuracy, laten

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EngineeringHow the Claude Code team does code review How do you review code at the volume agents produce it? On the Claude Code team, once we had high coverage and deep reviews, we started optimizing for the minimum amount of human review time that doesn't raise reverts or incidents.AUTHORAddy Osmani, Morgan Westlee Lunt, and Qing WangPUBLISHEDOct 15, 2026READING TIME14 min We've said before that Claude writes about 90 percent of Claude Code's own code. So how do we review code at the volume we

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dles that remain. How could AI help people make bioweapons? It depends on who wants to make them. One risk is the rogue, unskilled bad actor or terrorist group that works from a garage laboratory. AI chatbots could help them skill up, by compiling relevant information, planning experiments and troubleshooting problems. It’s likely the work would focus on using existing pathogens and toxins. The other risk comes from well-resourced organisations and nation states that have plenty of skilled scien

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2.6 and GPT-Live-1 (Sol, low) at 80.1. It is up on Grok Voice 2.0 at 81.3 (+5.6 points) and Grok Voice 1.0 at 73.7 (+13.2 points), released just under six months ago. The Index averages Speech Reasoning (Big Bench Audio), Agentic Performance (Tau Voice), Arena Preference, and Task Success Rate ➤ Speech Agent Arena: Grok Voice 3.0 ranks 3 in preference at Elo 1070, behind Gemini 3.1 Flash Live Minimal (1100) and Gemini 3.8 Live (1088) and ahead of GPT-Live-1 (Sol, low) at 1057, and 1 on Task Succ

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anted to see what Argon would do when given only the outcome the team cared about. In round one, we tested Argon with zero custom repository instructions. We gave it this single-paragraph prompt: Make ADK add no noticeable overhead to any agent: at startup, in long sessions, in multi-agent and workflow setups. Keep what the model receives and the public API unchanged, and back each change with a test that would catch a regression. Round one ran for about two hours. Argon wrote a benchmark script

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The V2.5 model family, including MiMo-V2.5 and MiMo-V2.5-Pro, combines several architectural design choices: Hybrid Sliding Window Attention (Hybrid SWA) compresses KVCache storage to roughly 1/7 that of Full Attention; sparse MoE activation cuts per-token compute while preserving model capacity; and multimodal encoders enable cross-modal understanding across vision, audio, and video. Together, these features give the MiMo-V2.5 series significant performance and efficiency potential in long-cont

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m exhaustively evaluates every candidate subtree to guarantee the closest match. Each distance is normalized by the size of the LLM snippet, yielding a ratio in [0, 1]. We then average these ratios across all snippets in the inference, and compute the reward is then computed as 1 - average ratio. A perfect match receives a score of 1, while lower scores indicate poorer performance. Formally, say that an LLM inference outputs nnn code snippets with corresponding concrete syntax trees I={t1,…,tn}I

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Fine-Tuning for o4-mini on terminal-benchThe script below demonstrates how to fine-tune o4-mini on terminal-bench using TensorZero’s programmatic API; Deploy with confidence: Leverage the experimentation features to gradually route traffic to your fine-tuned models, starting with a small allocation and scaling based on performance metrics So, is reinforcement fine-tuning worth it? For most use cases, not yet. At 100-700x the cost of supervised fine-tuning, we only observed RFT to deliver clear

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L; T; F; R; E AI-Generated Summary NVIDIA cuDF plugin for Apache Spark leverages GPU parallelism to accelerate data analytics workloads and improve total cost of ownership; User-defined functions in Spark break the declarative abstraction, forcing GPU-accelerated pipelines to fall back to CPU execution and incur data transfer overhead; UDFs can be rewritten using Spark SQL expressions, the RapidsUDF interface with the Java API for cuDF, or custom CUDA kernels via JNI to eliminate fallback and ac

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rn context and slow work down. Pointing to some docs can still be helpful, however, so long as it is contextual. Be sure to keep your docs updated too! Previous models needed encouragement to run tests and check their work. GPT-6 Astra does that on its own, so the same instructions can lead to unnecessary testing. GPT-6 Astra is thorough, but it can be more tentative about how far to take a task. Sometimes it needs a little push to keep going. You can use AGENTS.md to give it permission for a sp

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. Atmosphere changes with physical altitude, and clouds move over the planet’s geography. They help connect the orbital view to the landscape. The renderer only removes coarse coverage where finer terrain is ready. Mountain silhouettes can still change as finer geometry appears. Landing adds a stricter requirement. The visible ground, collision triangles, and liquid hazards for a contact patch must all come from the same generation and become ready together. Walking uses Rapier in a small local

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Fine-Tuning for o4-mini on terminal-benchThe script below demonstrates how to fine-tune o4-mini on terminal-bench using TensorZero’s programmatic API; Deploy with confidence: Leverage the experimentation features to gradually route traffic to your fine-tuned models, starting with a small allocation and scaling based on performance metrics So, is reinforcement fine-tuning worth it? For most use cases, not yet. At 100-700x the cost of supervised fine-tuning, we only observed RFT to deliver clear

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are, there exists a problem. And that problem is cost. Running these models continuously can cost an upwards of 6k per day! This makes these models completely unusable for the average consumer. The goal So the question I asked is how far can these models be pushed? What if you had an agent search the internet for every optimization, every speed up, every cost savings, and definitely every dirty hack that sacrificed quality for speed. And then what if you put it all together into a single custom

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s from recent runs and recommend harness changes. Figure 6 shows how a trace reveals the first wrong decision. 7. Evaluate multiple attempts, but watch the cost KGMON improved coverage and reliability through repeated attempts and answer selection. It grouped attempts by answer values rather than column names and could give contested tasks additional runs. Multiple attempts helped distinguish stable answers from one-off mistakes under the leaderboard’s exact value-level scoring. Important caveat

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router was never built for, so we add 150 out-of-scope CLINC queries and 150 queries from other CLINC domains. The calibrated confidence separates them sharply: banking queries average 0.912, the others about 0.25, and the 5 percent gate stops 89.3 percent of other-domain queries and 93.3 percent of out-of-scope ones while abstaining on 7.8 percent of banking queries. The alternative needs no labeled data: a sixteenth option, not a banking request, catches 80.0 and 90.0 percent of them, at the c

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nd network levels, see the cloud environments docs. LOCAL OR CLOUD? Cloud sessions don't replace local ones, and most people use both. The table shows where they differ, and the paragraphs after it say when each one fits. Stay local when the task needs something only your machine has. That covers a database with real local data, a service you reach over VPN, a GPU, a phone simulator, or hardware on your desk. Stay local too for tight visual loops where you want to see each change in your own bro

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ng 100% utilization and 92% API gross margins, maxing out Opus 5.5 vs Fable 5.1 usage corresponds to -369% and 1% gross margins respectively. If we were to use a more realistic average utilization of 20%, then gross margins rise to 6% and 80% respectively. In other words, Anthropic would likely already have software like margins on their subscription plans if everyone only used Fable. Opus class models will continue to become cheaper to serve over time as Anthropic figures out how to make the mo

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s Python and Bash in a sandbox Anthropic manages, on the Messages API. The documented environment is a Linux x86 64 container with Python 3.11, 5 GiB of RAM, 5 GiB of workspace storage, and one CPU. Internet access is disabled and no outbound connections are permitted, so Claude works with the preinstalled libraries and can’t install a package during a run. Three tool versions exist, and every supported model accepts all three. code execution 20250825 supports Bash commands and file operations.

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