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day, we're excited to launch Connectors — deep integrations that bring the apps you use every day directly into Grok. Connectors let Grok work with your tools end-to-end, turning fragmented workflows into seamless experiences. Read and summarize emails, update slides, organize your calendar, edit spreadsheets, and much more — all without constant copy-pasting. Available on Grok Web, iOS, and Android Connectors are now live on Grok Web, iOS, and Android . iOS and Android : Go to Settings → Connec

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switches have been used in a variety of applications including scale-out compute fabrics. CPUs alone either didn't offer enough or fast enough lanes for all the GPUs, NICs, and storage required. So, rather than hanging everything off the CPU, a PCIe switch, often built into the NIC, was used to connect everything together. Astera contends that with a big enough switch, PCIe is a viable alternative to interconnects like NVLink, in the scale-up fabrics used to make dozens or more GPUs behave more

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tices, and unsolicited documentation, returning an aggregate score in [-1, 1]. Positive means the judge preferred the agent's diff; negative means it preferred the human PR. We averaged each model across multiple runs to dampen judge noise. Why we built Prism No single model wins, so Augment gives you access to the industry leaders. The picker is how we make that real. It's also a source of friction: which model for this task? We looked at recent IDE agent traffic and found something we didn't e

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for screen capture and GUI automation. This gives goose eyes and hands for the desktop — it can see annotated screenshots, identify UI elements, click buttons, type text, scroll, drag, and navigate menus across any application on your Mac. The see → click → type loop The core workflow is dead simple and surprisingly reliable: See — goose takes an annotated screenshot of an app. Every clickable element gets a labeled ID like B1, B2, T1; Click — goose clicks on an element by its ID. No fragile pix

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. That means an agent can simultaneously reason across audio inputs (e.g., recordings or transcripts), visual inputs such as screenshots, video, and structured documents— without fragmenting that understanding across separate inference passes. 1. Together AI's Research Optimizations Unlock the Model's Full Architectural Potential The Nemotron 3 Nano Omni hybrid Mamba-Transformer mixture-of-experts (MoE) architecture activates only 3B parameters per token out of 30B total, and uses multi-token pr

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a 10k-token request keeps only 128 SWA tokens plus its full C4/C128 compressed KV -- and that compressed KV is what other prefix-matching requests reuse. Figure 2. ShadowRadix storage layout. Because shadows are just per-pool projections of the same source, each pool can manage its own lifetime independently. We exploit this with a two-counter lock per node -- full lock ref covers the source (and therefore its C4/C128 shadows), while swa lock ref only tracks whether the node still falls inside

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security review of your app in under an hour. Security Agent acts on a customizable threat modeling plan to review your entire codebase. It also uses a unique hybrid approach, leveraging Semgrep and HoundDog.ai as tools to improve the accuracy of its findings. Daghan Atlas, Head of Product at Semgrep, shares: “The most effective security is the kind that works seamlessly. Replit's Security Agent is a great example of what's possible when you pair the contextual reasoning of LLMs with the determi

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lobal total count of true positives (TP), false positives (FP), and false negatives (FN) to compute a global precision and recall, then combine them into an f β score; We could average P(τ) and R(τ) over the samples to get an average precision and an average recall, then combine them into an f β score Since we would not want to bias the model to be disproportionately good at examples where there are a lot of ground truth bugs (at the expense of poor performance on examples where there are few /

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that guide the agent on how to use Modal’s compute primitives, including launching interactive Sandboxes, writing and running training jobs with modal run, managing persistent storage with Volumes, and orchestrating parallel sub-agents. Instead of learning Modal’s API from scratch, the agent gets the patterns it needs to provision GPUs, run experiments, and clean up after itself. Parameter golf OpenAI's Parameter Golf challenge asks you to compress a language model into a ≤16 MB artifact that r

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e whatever the harness encodes, including context organization to achieve a high prompt cache hit rate and context engineering. We separated the concerns of recoverable context storage in the session and arbitrary context management in the harness because we can’t predict what specific context engineering will be required in future models. The interfaces push that context management into the harness, and only guarantee that the session is durable and available for interrogation. Many brains, man

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t yet: Permission dialog rendering varies by editor. What looks native in Zed may render differently in Neovim; Not every agent honors MCP server configs passed by the client. Coverage depends on the agent; Model and mode switching support varies. Some agents expose a full model list, others expose aliases; The protocol is still stabilizing. Features move between stability tiers as implementations mature These are real edges. The protocol is young. The direction is right. Where to Next Goose cur

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nough to keep the exchange feeling natural. When teams try to patch those gaps with endpointing logic, routing layers, and extra providers, they often add latency and operational fragility right back into the system. Deepgram’s models are purpose-built for that layer, where transcription, turn-taking, and responsiveness have to work together in real time. Deepgram’s STT and TTS model lineup now runs natively on Together AI, the AI Native Cloud for building real-time voice agents, so teams can pa

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curl downloading a dependency and curl exfiltrating your SSH keys — because it knows what you actually asked for. It can even sense if an agent is creatively writing scripts, in fragments, to egress data to a public URL (again, to be helpful!) which wouldn't be obviously caught by patterns or rules or filters. The two approaches are complementary. Use both. Use all the layers! What's Next We see adversary mode as one layer in a broader security story. For the deeper thinking behind it, check ou

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X, to take advantage of its unified memory architecture. This results in a large speedup of Ollama on all Apple Silicon devices. On Apple’s M5, M5 Pro and M5 Max chips, Ollama leverages the new GPU Neural Accelerators to accelerate both time to first token (TTFT) and generation speed (tokens per second). Prefill performance Decode performance Testing was conducted on March 29, 2026, using Alibaba’s Qwen3.5-35B-A3B model quantized to NVFP4 and Ollama’s previous implementation quantized to Q4 K M

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Canvas already make this possible for 2D and 3D web development. However, extended reality (XR) remains difficult to access. Prototyping in XR typically requires piecing together fragmented perception pipelines, complex game engines, and low-level sensor integrations. Quick, vibe-coded prototypes can solve this problem. They help experienced developers test new UIs, 3D interactions, and spatial visualizations directly in a headset. This rapid validation can save days of work on ideas that might

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creating an urgent need for a solution that minimizes disruption to the existing system without sacrificing scale. 2. Solution Overview: Elastic EP and Its Potential To solve the fragile nature of large-scale MoE inference, we integrated Elastic EP into the SGLang framework. At its core, Elastic EP solves the failure problem by decoupling the rigid mapping between experts and specific GPUs. By maintaining redundant experts across the cluster, the system can detect a localized hardware or process

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k. The default has more than twenty block rules, which fall into these groups: Destroy or exfiltrate. Cause irreversible loss by force-pushing over history, mass-deleting cloud storage, or sending internal data externally. Degrade security posture. Make the system harder to monitor or defend by disabling logging, installing persistence like SSH keys or cronjobs, or modifying the agent's own permission config. Cross trust boundaries. Act on things the agent has no basis to trust, like running cod

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reasoning that degrades over long interactions, and models that miss domain-specific visual signals. Fixing those issues usually requires post-training, but the workflow is often fragmented, slow to iterate, and hard to plan. Today, Together AI, the AI Native Cloud, is expanding Together Fine-Tuning with native support for tool call, reasoning, and vision-language model (VLM) fine-tuning. To support frontier-scale post-training, we have also upgraded the training stack to handle 100B+ parameter

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ft-qwen2.5-32b-ins (SFTed on retool); Stack: SGLang + Ray + Megatron-based training; Train dataset: dapo-math-17k; Evaluation set: aime-2024 As we keep training, we observe the average turn count per trajectory keeps improving. From the eval curves, we see that the model is already pretty good at math (from pass@n metrics). The eval score improves more gradually than pass@n, reflecting that formatting/tool-call quality is still catching up. Raw reward improves over time, which is a strong sign t

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Protecting cities with AI-driven flash flood forecasting March 12, 2026 We’re expanding our global flood forecasting coverage with the roll-out of flash flood predictions in urban areas. Using a novel AI training method based on news data, we provide up to 24 hours advance notice for these rapid-onset events. This expansion is a critical step to enhancing global climate resilience and keeping communities safe. According to the World Meteorological Organization (WMO), flash floods account for app

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