**Microsoft Research Unveils Skala: A Neural Exchange-Correlation Functional for Efficient, Accurate DFT Calculations**

Microsoft Research has introduced Skala, a pioneering neural exchange-correlation (XC) functional for Kohn-Sham Density Functional Theory (DFT), designed to deliver hybrid-level accuracy at the computational cost of semi-local functionals. This innovative approach, detailed in a recent arXiv paper, targets rigorous main-group thermochemistry, with plans to extend its capabilities to transition metals and periodic systems in the future.

**Understanding Skala’s Approach**

Skala replaces traditional hand-crafted XC forms with a neural functional evaluated on standard meta-GGA grid features. It explicitly avoids learning dispersion in its initial release, opting instead for a fixed D3 correction (D3(BJ) by default). The goal is to achieve robust main-group thermochemistry performance at a computational cost comparable to meta-GGA functionals, rather than aiming to be a universal functional from day one.

**Impressive Benchmark Results**

Skala’s performance is compelling. On the W4-17 atomization energies benchmark, it achieves a mean absolute error (MAE) of 1.06 kcal/mol on the full set and an impressive 0.85 kcal/mol on the single-reference subset. On the GMTKN55 dataset, Skala attains a weighted mean absolute deviation (WTMAD-2) of 3.89 kcal/mol, placing it competitively alongside top hybrid functionals. These results were obtained using consistent dispersion settings (D3(BJ) unless otherwise noted).

**Architecture and Training**

Skala evaluates meta-GGA features on the standard numerical integration grid and aggregates information via a finite-range, non-local neural operator. The model adheres to key exact constraints, including size-consistency and coordinate-scaling. Training proceeds in two phases: initial pre-training on B3LYP densities with XC labels extracted from high-level wavefunction energies, followed by SCF-in-the-loop fine-tuning using Skala’s own densities.

The model is trained on a large, curated corpus dominated by approximately 80,000 high-accuracy total atomization energies (MSR-ACC/TAE) and additional reactions/properties. Notably, the W4-17 and GMTKN55 datasets were excluded from training to prevent data leakage.

**Efficient and Accessible Implementation**

Skala maintains semi-local cost scaling and is engineered for GPU execution via GauXC. Its public repository offers a PyTorch implementation and a microsoft-skala PyPI package with PySCF/ASE hooks, along with a GauXC add-on for integration into other DFT stacks. With around 276,000 parameters, Skala is ready for practical use in main-group molecular workflows.

**Practical Applications and Availability**

In practice, Skala slots into workflows where semi-local cost and hybrid-level accuracy are crucial. It enables high-throughput reaction energetics, conformer/radical stability ranking, and geometry/dipole predictions feeding QSAR/lead-optimization loops. Teams can run batched SCF jobs and screen candidates at near meta-GGA runtime, reserving hybrids/CC for final checks.

Skala is available for testing and use via Azure AI Foundry Labs and as an open-source project on GitHub and PyPI, complete with code, tutorials, and notebooks. The technical paper, GitHub page, and this blog provide detailed information and resources for getting started.

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*The post Microsoft Research Releases Skala: A Deep-Learning Exchange-Correlation Functional Targeting Hybrid-Level Accuracy at Semi-Local Cost first appeared on MarkTechPost.*

**Rephrased Blog Content**

**TL;DR:** Computer-use agents, or GUI agents, are vision-language models that mimic human users on unmodified software. Initial benchmarks on OSWorld showed human performance at 72.36% and the best model at 12.24%; Anthropic’s Claude Sonnet 4.5 now reports 61.4%. Gemini 2.5 Computer Use leads several web benchmarks but isn’t yet optimized for operating systems. Future work focuses on OS-level robustness, sub-second action loops, and enhanced safety policies, with open community recipes for training and evaluation.

**Definition:** Computer-use agents, also known as GUI agents, are AI models that observe the screen, identify UI elements, and execute bounded UI actions (click, type, scroll, key-combos) to complete tasks in unmodified applications and browsers. Key implementations include Anthropic’s Computer Use, Google’s Gemini 2.5 Computer Use, and OpenAI’s Computer-Using Agent powering Operator.

**Control Loop:** The typical runtime loop involves capturing screenshots and state, planning the next action with spatial and semantic grounding, executing the action via a constrained action schema, and verifying and retrying on failure. Vendors document standardized action sets and guardrails, while audited harnesses normalize comparisons.

**Benchmark Landscape:**

– **OSWorld (HKU, Apr 2024):** This benchmark covers 369 real desktop/web tasks, spanning OS file I/O and multi-app workflows. At launch, humans achieved 72.36%, with the best model at 12.24%.
– **State of play (2025):** Anthropic’s Claude Sonnet 4.5 reports 61.4% on OSWorld, a significant jump from its previous 42.2%.
– **Live-web benchmarks:** Google’s Gemini 2.5 Computer Use reports 69.0% on Online-Mind2Web, 88.9% on WebVoyager, and 69.7% on AndroidWorld. Notably, the current model is browser-optimized and not yet optimized for OS-level control.

**Architecture Components:**

1. **Perception & Grounding:** Periodic screenshots, OCR/text extraction, element localization, and coordinate inference.
2. **Planning:** Multi-step policy with recovery, often post-trained or RL-tuned for UI control.
3. **Action Schema:** Bounded verbs (click_at, type, key_combo, open_app), with benchmark-specific exclusions to prevent tool shortcuts.
4. **Evaluation Harness:** Live-web/VM sandboxes with third-party auditing and reproducible execution scripts.

**Enterprise Snapshot:**

– **Anthropic:** Offers a Computer Use API with Sonnet 4.5 at 61.4% on OSWorld, emphasizing pixel-accurate grounding, retries, and safety confirmations.
– **Google DeepMind:** Provides a Gemini 2.5 Computer Use API with model card, reporting Online-Mind2Web 69.0%, WebVoyager 88.9%, and AndroidWorld 69.7%, along with latency measurements and safety mitigations.
– **OpenAI:** Offers Operator, a research preview for U.S. Pro users, powered by a Computer-Using Agent, with separate system card and developer surface via the Responses API, but limited availability.

**Where They’re Headed: Web → OS**

– **Few-/one-shot workflow cloning:** Near-term focus is on robust task imitation from a single demonstration (screen capture + narration).
– **Latency budgets for collaboration:** To preserve direct manipulation, actions should land within 0.1–1 s HCI thresholds, requiring engineering on incremental vision, cache-aware OCR, and action batching.
– **OS-level breadth:** File dialogs, multi-window focus, non-DOM UIs, and system policies add failure modes absent from browser-only agents, making OS-level breadth the next step.
– **Safety:** Prompt-injection from web content, dangerous actions, and data exfiltration are key concerns, with model cards describing allow/deny lists, confirmations, and blocked domains, and typed action contracts and “consent gates” for irreversible steps expected.

**Practical Build Notes:**

– Start with a browser-first agent using a documented action schema and a verified harness (e.g., Online-Mind2Web).
– Add recoverability: explicit post-conditions, on-screen verification, and rollback plans for long workflows.
– Treat metrics with skepticism: prefer audited leaderboards or third-party harnesses over self-reported scripts; OSWorld uses execution-based evaluation for reproducibility.

**Open Research & Tooling:**

– Hugging Face’s Smol2Operator provides an open post-training recipe that upgrades a small VLM into a GUI-grounded operator, useful for labs/startups prioritizing reproducible training over leaderboard records.

**Key Takeaways:**

– Computer-use (GUI) agents are VLM-driven systems that perceive screens and emit bounded UI actions (click/type/scroll) to operate unmodified apps, with key implementations including Anthropic Computer Use, Google Gemini 2.5 Computer Use, and OpenAI’s Computer-Using Agent.
– OSWorld benchmarks 369 real desktop/web tasks, with humans achieving 72.36% and the best model at 12.24%, highlighting grounding and procedural gaps.
– Anthropic Claude Sonnet 4.5 reports 61.4% on OSWorld, a significant jump from prior Sonnet 4 results.
– Gemini 2.5 Computer Use leads several live-web benchmarks but isn’t yet optimized for OS-level control.
– OpenAI Operator is a research preview powered by the Computer-Using Agent (CUA) model, using screenshots to interact with GUIs, with limited availability.
– Open-source trajectory: Hugging Face’s Smol2Operator provides a reproducible post-training pipeline that turns a small VLM into a GUI-grounded operator, standardizing action schemas and datasets.

**Google Simplifies LLM Agent Integration with Google Ads API via Open-Sourced Model Context Protocol (MCP) Server**

Google has recently made a significant stride in facilitating the interaction between Large Language Model (LLM) agents and external systems by open-sourcing a Model Context Protocol (MCP) server. This server provides read-only access to the Google Ads API, catering specifically to agentic and LLM applications. The project, labeled as “Experimental,” is implemented in Python and available on the Google Ads GitHub repository (googleads/google-ads-mcp).

**Why It Matters**

The introduction of this MCP server is a game-changer for LLM agents that require campaign telemetry, budget pacing, and performance diagnostics. By offering a reference server for the Ads API, Google has significantly lowered the integration cost, eliminating the need for bespoke SDK glue. This move aligns with the broader adoption of MCP across various vendors and open-source clients, further cementing MCP as a practical path to agent-to-SaaS interoperability.

For Pay-Per-Click (PPC) and growth teams exploring agentic workflows, this server serves as a low-friction method to validate LLM-assisted Quality Assurance (QA), anomaly triage, and weekly reporting. It allows such teams to leverage the power of LLMs without granting write privileges to the Google Ads API.

**How It Works: A Developer’s Perspective**

The MCP standardizes “tools” that models can invoke with typed parameters and responses. The Ads MCP server advertises tools mapped to Google Ads API operations, which MCP clients like Gemini CLI/Code Assist can discover and call during a session.

To set up the server, you need to enable the Google Ads API in a Cloud project, obtain a developer token, and configure Application Default Credentials or the Ads Python client. For manager-account hierarchies, you should set a login customer ID. The required OAuth2 scope is `https://www.googleapis.com/auth/adwords`.

For client wiring, add an entry pointing to the MCP server invocation in your `~/.gemini/settings.json` file and pass credentials via environment variables. You can then query the server via the `/mcp` endpoint in Gemini or by prompting for specific Ads account information.

**Key Implementation Details**

The open-sourced Google Ads API MCP server showcases two tools: search (GAQL queries over Ads accounts) and `list_accessible_customers` (enumeration of customer resources). The project is licensed under Apache-2.0 and is marked as experimental. It can be installed and run using pipx and requires configuration of OAuth2 with the `https://www.googleapis.com/auth/adwords` scope, along with a developer token and an optional login-customer ID.

**Conclusion**

In essence, Google’s open-sourced Google Ads API MCP server provides a standards-based, read-only path for LLM agents to run GAQL queries against Ads accounts without the need for bespoke SDK wiring. While the project is marked as experimental, it exposes two useful tools – search and `list_accessible_customers` – and integrates with MCP-compatible clients like Gemini CLI/Code Assist. However, production use should consider proper OAuth scope management, secure handling of developer tokens, and the data-exposure caveats outlined in the project’s README file.

For those interested in exploring this project further, the GitHub page and technical blog offer valuable resources. You can also find tutorials, codes, and notebooks on the project’s GitHub page. Additionally, you can follow the project on Twitter, join the 100k+ ML SubReddit, subscribe to the newsletter, and even join the project’s Telegram group for real-time updates and discussions.

**Revolutionizing Language Models: The ACE Framework**

In a groundbreaking development, a collaborative effort between Stanford University, SambaNova Systems, and UC Berkeley has introduced the ACE (Agentic Context Engineering) framework. This innovative approach enhances the performance of Large Language Models (LLMs) by editing and expanding input context, rather than relying on traditional model weight updates. The context is treated as a dynamic “playbook” maintained by three distinct roles: the Generator, Reflector, and Curator. This method allows for incremental merging of small delta items, preventing brevity bias and context collapse.

The ACE framework positions “context engineering” as a primary alternative to parameter updates. Instead of compressing instructions into brief prompts, ACE accumulates and organizes domain-specific tactics over time. This approach argues that higher context density improves agentic tasks, where tools, multi-turn state, and failure modes are crucial.

**Methodology: A Three-Phase Process**

The ACE method involves a three-phase process:

1. **Generator**: This phase executes tasks and produces trajectories, exposing helpful and harmful moves.
2. **Reflector**: The Reflector distills concrete lessons from these traces, identifying patterns and insights.
3. **Curator**: The Curator converts these lessons into typed delta items, complete with helpful and harmful counters. These items are then merged deterministically, with de-duplication and pruning to keep the playbook targeted and efficient.

Two key design choices—incremental delta updates and grow-and-refine—preserve useful history and prevent “context collapse” from monolithic rewrites. To isolate context effects, the research team uses the same base LLM (non-thinking DeepSeek-V3.1) across all three roles.

**Benchmarks: ACE’s Performance**

ACE’s performance has been tested on two key benchmarks:

– **AppWorld (agents)**: Built on the official ReAct baseline, ReAct+ACE outperforms strong baselines like ICL, GEPA, and Dynamic Cheatsheet. It achieves an average improvement of +10.6% over selected baselines and ~+7.6% over Dynamic Cheatsheet in online adaptation. Notably, on the Sept 20, 2025 leaderboard, ReAct+ACE scores 59.4%, closely following IBM CUGA’s 60.3% (GPT-4.1). ACE even surpasses CUGA on the harder test-challenge split, using a smaller, open-source base model.
– **Finance (XBRL)**: On FiNER token tagging and XBRL Formula numerical reasoning, ACE reports an average improvement of +8.6% over baselines with ground-truth labels for offline adaptation. It also works with execution-only feedback, although the quality of signals matters.

**Cost and Latency: ACE’s Efficiency**

ACE’s non-LLM merges and localized updates significantly reduce adaptation overhead:

– **Offline (AppWorld)**: ACE reduces latency by -82.3% and rollouts by -75.1% compared to GEPA.
– **Online (FiNER)**: ACE reduces latency by -91.5% and token cost by -83.6% compared to Dynamic Cheatsheet.

**Key Takeaways**

ACE, a context-first adaptation method, improves LLMs by incrementally editing an evolving “playbook” (delta items) curated by the Generator, Reflector, and Curator. Using the same base LLM (non-thinking DeepSeek-V3.1) isolates context effects and prevents collapse from monolithic rewrites. Measured gains include +10.6% on AppWorld and 59.4% vs IBM CUGA’s 60.3% on the Sept 20, 2025 leaderboard, with finance benchmarks showing +8.6% average over baselines. ACE also reduces adaptation latency by ~82–92% and rollouts/token cost by ~75–84%, contrasting with reflective-rewrite baselines.

**Conclusion**

ACE positions context engineering as a first-class alternative to weight updates. By maintaining a persistent, curated playbook that accumulates task-specific tactics, ACE yields measurable gains on AppWorld and finance reasoning while cutting adaptation latency and token rollouts versus reflective-rewrite baselines. The approach is practical, with deterministic merges, delta items, and long-context–aware serving, and its limits are clear: outcomes track feedback quality and task complexity. If adopted, agent stacks may “self-tune” primarily through evolving context rather than new checkpoints.

**MetaSuperintelligence Labs Introduces MetaEmbed: Revolutionizing Multimodal Retrieval with Test-Time Scaling**

Imagine tuning multimodal retrieval in real-time, balancing accuracy, latency, and index size simply by adjusting the number of learnable Meta Tokens. MetaSuperintelligence Labs has introduced MetaEmbed, a novel late-interaction recipe for multimodal retrieval that offers a single control surface at serving time: the number of compact “Meta Tokens” to use on the query and candidate sides. Unlike existing methods that collapse each item into one vector (CLIP-style) or explode into hundreds of patch/token vectors (ColBERT-style), MetaEmbed appends a fixed, learnable set of Meta Tokens during training and reuses their final hidden states as multi-vector embeddings at inference. This approach enables test-time scaling, allowing operators to trade accuracy for latency and index size by selecting a retrieval budget without retraining.

**How MetaEmbed Works**

MetaEmbed trains using Matryoshka Multi-Vector Retrieval (MMR), organizing Meta Tokens into prefix-nested groups to ensure each prefix is independently discriminative. At inference, the retrieval budget is a tuple ((r_q, r_c)) specifying how many query-side and candidate-side Meta Tokens to use. Scoring employs a ColBERT-like MaxSim late-interaction over L2-normalized Meta Token embeddings, preserving fine-grained cross-modal detail while keeping the vector set small.

**Benchmarks and Efficiency**

MetaEmbed has been evaluated on the MMEB (Massive Multimodal Embedding Benchmark) and ViDoRe v2 (Visual Document Retrieval) benchmarks, designed to stress retrieval under diverse modalities and realistic document queries. On MMEB, MetaEmbed with Qwen2.5-VL backbones achieved overall scores at the largest budget ((16,64)): 3B = 69.1, 7B = 76.6, 32B = 78.7. Gains were monotonic with increasing budget and widened with model scale. On ViDoRe v2, the method improved average nDCG@5 versus single-vector and a naive fixed-length multi-vector baseline under identical training, with the gap growing at higher budgets.

Ablation studies confirmed that MMR delivers the test-time scaling property without sacrificing full-budget quality. When MMR was disabled (NoMMR), performance at low budgets collapsed; with MMR enabled, MetaEmbed tracked or exceeded single-vector baselines across budgets and model sizes.

In terms of efficiency and memory, with 100k candidates per query and a scoring batch size of 1,000, the research reported scoring cost and index memory on an A100. As the budget grew from ((1,1)) to ((16,64)), scoring FLOPs increased from 0.71 GFLOPs to 733.89 GFLOPs, scoring latency from 1.67 ms to 6.25 ms, and bfloat16 index memory from 0.68 GiB to 42.72 GiB. Crucially, query encoding dominated end-to-end latency: encoding an image query with 1,024 tokens was 42.72 TFLOPs and 788 ms, several orders larger than scoring for small candidate sets. Operators should therefore focus on encoder throughput and manage index growth by choosing balanced budgets or offloading indexes to CPU when necessary.

**Comparisons and Takeaways**

Compared to single-vector (CLIP-style) methods, MetaEmbed improves precision by using a small, contextual multi-vector set while preserving independent encoding. Unlike naive multi-vector (ColBERT-style) methods on multimodal data, MetaEmbed reduces vectors by orders of magnitude and allows budgeted MaxSim.

Key takeaways include:
– Train once, choose ((r_q, r_c)) at serve time for recall vs. cost.
– The encoder is the bottleneck; optimize image tokenization and VLM throughput.
– Memory scales linearly with budget; plan index placement and sharding (GPU vs. CPU) around the chosen ((r_q, r_c)).

**Editorial Notes**

MetaEmbed contributes a serving-time control surface for multimodal retrieval, offering nested, coarse-to-fine Meta Tokens trained with MMR that yield compact multi-vector embeddings adjustable after training. The results show consistent accuracy gains over single-vector and naive multi-vector baselines on MMEB and ViDoRe v2, while clarifying the practical cost profile—encoder-bound latency, budget-dependent index size, and millisecond-scale scoring on commodity accelerators. For teams building retrieval stacks that must unify fast recall and precise re-ranking across image–text and visual-document scenarios, the recipe is directly actionable without architectural rewrites.

**Google’s Veo 3.1: A New Contender in Generative Video AI**

The tech world is abuzz with anticipation for Google’s Veo 3.1, a significant update in the rapidly evolving landscape of generative video AI, particularly with OpenAI’s Sora 2 now accessible to the public. Veo 3.1 is poised to build upon the robust reputation established by its predecessor, Veo 3, promising advancements in video generation capabilities. The eager audience for this update includes content creators, marketers, and AI enthusiasts, especially those seeking alternatives to Sora or curious about Google’s offerings in video AI.

Rumors and leaks have hinted at the impending arrival of Veo 3.1. Traces of the new model have been spotted in Vertex AI quotas and, more concretely, within Google Vids. However, some platforms have prematurely claimed to offer Veo 3.1, only to be corrected by Google’s official stance. As the launch inches closer, excitement and speculation continue to grow.

Veo 3.1, when accessed through Google Vids, offers text-to-video generation functionality, producing 8-second, 720p video clips complete with audio. While this resolution may not be the highest, it’s a promising start, and users can expect improved quality on other Google products like Flow. The update brings substantial improvements in visual fidelity, prompt adherence, and, notably, audio tracks, which now feature more convincing music.

Comparative tests using prompts like “cyberpunk hacker robot” or “dynamic volcano scenes” reveal that Veo 3.1 generates more creative, detailed, and accurate outputs than its predecessor. While Veo 3 was consistent, it sometimes produced repetitive results. Veo 3.1, however, demonstrates a better understanding of nuance, generating videos that closely match the intent of the prompt and add richer visual details, such as flowing lava or more lifelike dinosaurs. Early tests also suggest that Veo 3.1 has addressed some of the proportion issues seen in its predecessor.

Google’s strategy has been to iterate quickly on foundation models and introduce them to select services like Vids and Vertex AI before a wider rollout. This phased approach allows Google to gather technical feedback and build marketing momentum against competitors like OpenAI. When Veo 3.1 is broadly available, it will likely spark direct comparisons with Sora 2, not just in terms of raw output but also in integration within Google’s broader workspace tools.

Industry insiders expect an official release in the coming weeks, so those in the AI video space should stay tuned for updates from Google. As more samples and user experiences become public, a clearer picture of Veo 3.1’s capabilities and impact on the generative video AI landscape will emerge. The stage is set for an exciting showdown between Google and OpenAI, with creators and users set to reap the benefits of their technological rivalry.

**Missed Amazon’s Prime Day? Here’s Your Second Chance with These Still-Live Deals**

If you’ve woken up to the realization that you’ve missed Amazon’s October Prime Day sale, don’t despair. Amazon has graciously kept some of its best-selling deals alive on its site, giving you another shot at scoring record-low prices on a wide array of products. From tech gadgets like TVs, smart home devices, and AirPods to kitchen appliances such as air fryers, vacuums, and coffee makers, there’s still plenty to snap up.

As TechRadar’s deals editor, I’ve spent the past two days scouring Amazon’s Prime Day sale and have compiled a list of 25 top deals that are still available. These offers are currently selling at the same Prime Day prices we saw on Tuesday and Wednesday, so act fast before they disappear.

**Still-Available Prime Day Deals**

Some of the hottest Prime Day deals that are still up for grabs include:

– Apple’s AirPods 4, now $89 (save $10)
– The best-selling Fire HD 10 tablet, now $69.99 (save $30)
– Bissell’s viral Little Green portable carpet cleaner, now $78.97 (save $11)

While these post-Prime Day deals are still live, I wouldn’t bet on them sticking around for long. If you see a price you like, I’d recommend grabbing it now to avoid disappointment or having to wait until Black Friday for another chance.

**Top 25 Post-Prime Day Deals**

Looking to upgrade your smart home? Amazon has you covered with some fantastic deals on Echo devices:

1. **All-new Echo Pop** – Amazon’s most affordable Echo device is now just $24.99, offering a compact smart speaker with Alexa built-in for hands-free music playback, questions, and weather checks.

2. **Echo Dot (5th Gen)** – For a more robust sound, the Echo Dot is now $34.99, featuring improved audio, a new temperature sensor, and all the handy Alexa features you know and love.

3. **Echo Spot (2nd Gen)** – This brand-new Echo Spot is now $74.99 (save $35), pairing a 2.83-inch touch screen with a 1.73-inch front-firing speaker for an ideal smart alarm clock.

If you’re in the market for a new tablet, Amazon has some fantastic deals on Fire tablets:

4. **Fire HD 10** – Amazon’s latest Fire HD 10 tablet is now at its lowest price yet, just $69.99 (save $30). This tablet features a large, bright 10-inch Full HD display, 3GB of RAM, and an octa-core processor for enhanced performance, along with 12 hours of battery life and Amazon Alexa support.

For coffee lovers, Amazon has some unbeatable deals on coffee makers:

5. **Keurig K-Mini** – This compact coffee maker is now just $54.99 (save $15), perfect for small kitchens and brewing a cup in minutes.

6. **Breville Barista Express** – This high-quality coffee machine is now $399.95 (save $200), featuring a grind size dial, powerful steam wand, and digital temperature control for the perfect cup every time.

Air fryers are another popular Prime Day category, and Amazon has some fantastic deals on these kitchen must-haves:

7. **Ninja Pro Air Fryer** – This top-rated air fryer is now $79.99 (save $20), featuring a 5-quart capacity, presets for air frying, roasting, reheating, and dehydrating, making it perfect for families.

8. **Cosori Air Fryer Max XL** – This best-selling air fryer is now $69.99 (save $30), offering a large 5.8-quart capacity, 11 presets, and a user-friendly control panel.

Vacuums are another category that always sees great Prime Day deals, and Amazon has some fantastic offers on these cleaning essentials:

9. **Shark Navigator Lift-Away** – This best-selling upright vacuum is now $149.99 (save $50), featuring lift-away technology for easy cleaning of hard-to-reach areas and over 100,000 positive reviews on Amazon.

10. **Dyson V8 Plus** – This highly-rated Dyson vacuum is now $329.99 (save $100), offering powerful suction, a cordless design, and the ability to transform into a handheld vac for quick clean-ups.

For Apple fans, Amazon has some fantastic deals on AirTags and AirPods:

11. **Apple AirTags (4-pack)** – You can now get a four-pack of Apple AirTags for just $64.99 (save $35), an instant savings and just cents away from a record-low price.

12. **AirPods 4** – Apple’s most affordable AirPods are now $89.99 (save $10), featuring a new design for all-day comfort, Apple’s H2 chip for personalized spatial audio and voice isolation, and a redesigned case with 30 hours of battery life and USB-C support for wireless charging.

13. **AirPods 4 with Active Noise Cancellation** – These AirPods are now $118.99 (save $31), matching the lowest price we’ve seen before and offering the same features as the standard AirPods 4 with the added benefit of active noise cancellation.

Amazon also has some fantastic deals on iPads:

14. **iPad A16 (11-inch)** – You can now get the latest iPad A16 for $279 (save $50), featuring a sharp 11-inch Liquid Retina display, the latest A16 chip for faster performance, double the storage of 128GB as standard, and solid 12MP front and back cameras.

15. **iPad Air (M3)** – The newest iPad Air is now $449 (save $150), featuring the more powerful M3 chip, a crisp Liquid Retina display, 128GB of storage as standard, 12MP front and rear cameras, and support for Apple’s AI features.

For those in the market for a new laptop, Amazon has some fantastic deals on MacBooks:

16. **MacBook Air (M4)** – This 13-inch MacBook is now $999 (save $200), featuring the latest M4 chip, unbeatable battery life, and a gorgeous design, making it a fantastic choice for those seeking a reliable, lightweight laptop for daily use.

17. **MacBook Air (M3)** – This MacBook Air is now $1,149 (save $150), featuring the more powerful M3 chip, a crisp Liquid Retina display, 128GB of storage as standard, 12MP front and rear cameras, and support for Apple’s AI features.

18. **MacBook Pro (M4 Pro)** – This powerful MacBook is now $1,799 (save $200), featuring a 14-core CPU and a 20-core GPU for seamless productivity and gaming, along with 24GB of unified memory.

Amazon also has some fantastic deals on smart TVs:

19. **Amazon Fire TV Omni QLED Series (65-inch)** – This premium smart TV is now $799.99 (save $10), featuring a QLED display, full-array local dimming, Dolby Vision IQ, and HDR10+ Adaptive support for a high-quality picture.

20. **Amazon Fire TV 4-Series (50-inch)** – This mid-sized smart TV is now $339.99 (save $10), featuring a 4K display with HDR 10 support, offering a sharp, clear, and vibrant image for TV shows, movies, and sports, along with support for all major streaming apps and voice control through Alexa.

21. **Insignia QF Series (55-inch)** – This QLED TV is now $209.99 (save $100), featuring 4K Ultra HD resolution with Quantum Dot technology for bold, bright colors and life-like images, along with Dolby Atmos Audio, smart capabilities with the Fire operating system, and a voice remote with hands-free Alexa.

22. **Amazon Fire TV Omni Series (75-inch)** – This large smart TV is now $719.99 (save $120), featuring 4K resolution support, Dolby Vision, HDR 10, and hands-free TV with Alexa for voice control.

23. **Insignia F50 Series (70-inch)** – This big-screen budget TV is now $329 (save $70), featuring 4K HD resolution, DTS Studio Sound, smart capabilities with the Fire OS, and a voice remote with hands-free Alexa.

For those looking to upgrade their home decor, Amazon has some fantastic deals on The Frame TV:

24. **Samsung The Frame TV (55-inch)** – This dream Prime Day purchase is now at its lowest price yet, just $897.99 (save $300), featuring Pantone-validated colors for lifelike images and the display now comes with Streams, a complimentary set of artwork streamed from the Samsung Art Store.

Finally, for gamers and movie buffs alike, Amazon has some fantastic deals on LG’s C4 OLED TV:

25. **LG C4 OLED TV (65-inch)** – This highly-rated TV is now $1,296.99 (save $1,300), featuring exceptional brightness, LG’s latest Alpha 9 AI chip for enhanced performance, and impressive gaming features, including four HDMI 2.1 ports with 4K 120Hz, VRR, and ALLM support, as well as 144Hz certification from NVIDIA.

**More of Today’s Best Amazon Deals**

If you’re looking for more deals, Amazon has plenty more to offer, including:

– **Amazon Devices**: Echo & Fire tablets from $18
– **Apple**: MacBooks, AirPods & iPads from $89
– **Cheap TVs**: smart TVs from $79.99
– **Cheap appliances**: air fryers, coffee makers from $29.99
– **Christmas**: decor, PJs & gift ideas from $9
– **Deals under $25**: tech, home, toys & more
– **Gift ideas**: holiday gifts for the whole family
– **Halloween**: candy, costumes & decor from $6
– **Headphones**: up to 50% off Beats & Samsung
– **Laptops**: from just $289
– **Vacuums**: Dyson, Shark & Bissell from $53.99

So, if you’ve missed out on Amazon’s Prime Day sale, don’t worry – there are still plenty of fantastic deals to be had. Act fast, though, as these post-Prime Day deals won’t last forever. Happy shopping!

**Revolutionizing Domain Management: AI and the Model Context Protocol**

For years, acquiring a domain name has been the initial step for anyone venturing into the online realm. However, the subsequent processes of configuration, integration, and ongoing management have often been disjointed and unnecessarily complex. Until now, that is. Artificial Intelligence (AI) is transforming this landscape, and emerging standards like the Model Context Protocol (MCP) are liberating domain management from clunky dashboards, integrating it into the everyday tools that builders, founders, and teams already rely on.

Historically, managing domains meant navigating static interfaces and Application Programming Interfaces (APIs). Tasks like renewals, Domain Name System (DNS) updates, SSL setup, and platform redirects required manual effort and constant context switching. This outdated process no longer aligns with today’s dynamic workflows, where builders are rapidly prototyping, scaling side projects, and integrating across multiple platforms.

The Model Context Protocol is changing this narrative. AI agents and developer tools can now communicate directly with certain domain platforms, eliminating the need for dashboard navigation. Founders and builders can simply type natural language commands within tools like Raycast, Cursor, Claude, or ChatGPT to execute tasks. For instance, a builder can effortlessly command “point my new domain to my Vercel app” or “renew all domains expiring this month,” and the task will be executed automatically, without requiring a dashboard.

One of MCP’s most powerful aspects is its ability to embed complex functionality like domain control within environments people already use daily. For example, a solo builder using Cursor can launch a project and map it to a live domain in a single step. Non-technical founders can ask AI assistants like ChatGPT or Claude to “search for a domain and register it,” and the integration handles the process end-to-end. As MCP support extends into more productivity apps, a startup team using Google Workspace with Gemini could register and configure a domain within the same environment where they’re drafting pitches and managing calendars. By reducing tool-switching friction, domains become a seamless part of the overall creation process.

This shift is particularly beneficial for today’s small businesses, entrepreneurs, and solo developers who want to focus on launching products, building communities, and growing revenue, rather than spending time on tool-switching, domain renewals, or troubleshooting DNS records. According to name.com’s recent research, 91% of customers expect AI agents to handle at least some domain management within the next two years, and 88% want natural language control for these tasks, reflecting a significant shift in expectations.

In this digital world where anyone can launch with just an idea and a prompt, the domain remains the anchor of identity and credibility. However, with AI-driven, MCP-powered workflows, domains are no longer a barrier but a strategic advantage, becoming easier to set up, configure, and maintain directly from the tools people are already using to build. This shift represents both a convenience and a rethinking of digital ownership, making digital identity immediate, accessible, and deeply integrated into the creative process.

As the industry embraces MCP and opens up domain workflows to AI agents and modern tools, a new class of builders is emerging. This class values speed, simplicity, and integration over complexity and silos. The internet is finally meeting creators where they are, and with AI-driven, MCP-powered domains, the online ecosystem is entering a new era.

**Revolutionizing Wireless Networks: Wi-Fi 7 Advanced Unveils New Era of Speed, Sensing, and Security**

The wireless networking landscape is set to transform with the advent of Wi-Fi 7 Advanced, a groundbreaking technology that promises unprecedented speeds, seamless integration with IoT and sensing, and robust security features. Huawei, in collaboration with the Institute of Electrical and Electronics Engineers (IEEE) and select industry partners, has released a white paper outlining the vision for this next-generation wireless networking standard.

Wi-Fi 7 Advanced, building upon the existing Wi-Fi 7, aims to deliver not just faster speeds, but also a more intelligent and secure networking experience. Dr. Edward Au, IEEE 802.11be Technical Editor, envisions Wi-Fi 7 Advanced creating “intelligent campuses” where communication, sensing, and IoT converge, enhancing user experience, bolstering security, and boosting digital productivity.

One of Wi-Fi 7 Advanced’s standout features is its intelligent Coordinated Scheduling and Spatial Reuse technology. This innovation enables large-scale 80MHz networking and doubles the single-user data rate, paving the way for 10Gbps enterprise networks. Coupled with Huawei’s VIP FastPass, the system ensures low-latency connections for priority users, making it ideal for AR-assisted workflows and AI-driven collaboration.

However, Wi-Fi 7 Advanced extends beyond raw speed. By integrating Wi-Fi, IoT, and sensing, it enables what Huawei terms “intelligent spaces.” Applications range from energy-saving building management through human presence detection to healthcare monitoring with mmWave sensors for continuous vital-sign tracking.

Security is another key focus. Wi-Fi 7 Advanced incorporates Wi-Fi Shield, an AI-powered scrambling system that prevents data leakage, and Wi-Fi Channel State Information sensing, which detects intrusions in real-time. Additionally, it includes tools for identifying hidden cameras via full-band scanning.

Shawn Zhao, President of the Campus Network Domain, Huawei’s Data Communication Product Line, believes that “Powered by Wi-Fi 7 Advanced, Huawei Xinghe AI Campus pushes communication efficiency to new limits with 10Gbps connectivity.”

The emergence of Wi-Fi 7 Advanced, however, raises intriguing questions about the future of Wi-Fi 8. Traditionally, each new Wi-Fi standard has focused on increasing peak speeds. Wi-Fi 8, on the other hand, is being positioned as a standard that prioritizes stability, latency, and performance under heavy load. The arrival of Wi-Fi 7 Advanced, with its 10Gbps office networks and AI-driven security, brings forward features that were previously expected of Wi-Fi 8, potentially reshaping our understanding of what future wireless standards might offer.

In conclusion, Wi-Fi 7 Advanced represents a significant leap forward in wireless networking. By combining high speeds, intelligent sensing, and robust security, it promises to create more efficient, secure, and intelligent networks. As we look towards the future of Wi-Fi, it’s clear that Wi-Fi 7 Advanced is setting a new benchmark for what’s possible in wireless connectivity.

**Attention, Banana Ball Fans! Here’s How to Catch the 2026 Savannah Bananas Selection Show for Free**

Mark your calendars, sports enthusiasts! The Savannah Bananas are gearing up for another thrilling season, and tonight is the night we’ve all been waiting for. The Banana Ball Selection Show is set to air at 6:30 PM ET (11:30 PM BST), revealing the tour dates, locations, and the two new teams joining the existing lineup of the Bananas, Party Animals, Firefighters, and Texas Tailgaters. But the big question on everyone’s mind is: how can you watch this exciting event for free? We’ve got you covered with a simple, step-by-step guide.

**Why YouTube is Your Best Bet for Free Streaming**

While there are plenty of streams available across the US, YouTube stands out as the only platform offering the 2026 Banana Ball Selection Show for free. Here’s why you should tune in on YouTube:

🟩 **Free Access**: No need to shell out any cash to watch the action. Simply click on the link above, and you’re good to go.

🟩 **Wide Reach**: YouTube’s global presence ensures that you can access the stream from virtually anywhere in the world.

However, there’s a small catch. If you’re trying to access the stream from outside the US, you might encounter geo-restrictions. That’s where a reliable VPN like NordVPN comes in handy.

**Unlock the Free Stream with NordVPN**

A VPN, or Virtual Private Network, is an essential tool for bypassing geo-restrictions and protecting your online privacy. NordVPN is our top pick for unblocking YouTube and streaming the Banana Ball Selection Show, thanks to its robust security features, user-friendly interface, and impressive global server network.

🟩 **NordVPN – The World’s Best VPN**: Not having a VPN is like leaving your front door wide open in a busy city – anyone can walk right in and take a peek. TechRadar regularly reviews the biggest and best VPN providers, and NordVPN consistently tops our list.

🟩 **Exclusive Deal**: Enjoy 70% off today, along with 3 extra months free, when you sign up for NordVPN. This limited-time offer also includes access to YouTube, making it the perfect choice for streaming the Banana Ball Selection Show.

**How to Watch the Banana Ball Selection Show for Free from Anywhere**

Unlocking the free YouTube stream with NordVPN is a breeze. Here’s a simple, three-step guide to help you catch all the action:

1. **Install the VPN of your choice**: As we’ve mentioned, NordVPN is our top recommendation for unblocking YouTube and streaming the Banana Ball Selection Show.

2. **Choose your location**: Once you’ve installed the VPN, open the app and select the location you wish to connect to. For instance, if you’re visiting the UK and want to watch your free stream, you’d select ‘US’.

3. **Enjoy the action**: With your VPN connected, head over to YouTube and watch the Banana Ball Selection Show unfold live.

**A Word on VPN Usage**

At TechRadar, we test and review VPN services in the context of legal recreational uses, such as accessing geo-restricted content and protecting your online privacy when abroad. We do not support or condone the illegal or malicious use of VPN services, including consuming pirated content that is paid-for. Always ensure that your VPN usage aligns with the terms and conditions of the services you’re accessing.

So, there you have it – a comprehensive guide to watching the 2026 Savannah Bananas Selection Show for free on YouTube, with the help of NordVPN. Don’t miss out on this exciting event, and enjoy the show!