Blogroll

There are times in our lives when we are consumed by the media. While it’s not always the case, a big thing media lovers tend to have fond memories of are those summer blockbusters—you know the ones. Whether it's high-stakes action, funny comedies, or fantasies beyond your wildest imagination, these movies made us smile, laugh, and create fond memories with loved ones during those summer days.

If you have DIY projects around the house that need attention, now is the perfect time to buy some new tools. Ryobi fans will want to run to a nearby Home Depot for its spring sale and grab its latest deal that gets you six power tools for under $300.

Long before everyone got used to plugging things into a familiar rectangular port (and flipping the cable around three times before getting it right), there was another connector quietly doing most of the heavy lifting. The serial port wasn't pretty, it wasn't fast, and it definitely wasn't friendly, but for decades, it was the way you connected almost anything that wasn't already inside your computer case.

Microsoft releases a lightweight foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings in grid analysis. At a glance

• Microsoft introduces GridSFM, a small foundation model that approximates AC optimal power flow in milliseconds, unlocking decisions that can directly impact up to $20B/year in congestion losses and 3.4 TWh of renewable curtailment.
• Beyond estimating generator dispatch and costs, GridSFM produces full AC system states, giving operators direct visibility into congestion, stability, and overall system health.
• It provides a foundation for the community to build advanced power grid simulators and planning tools without recreating data or models from scratch.

Microsoft introduces GridSFM, a small foundation model for solving AC optimal power flow (AC-OPF) problems in transmission power grids. This follows our earlier release of a U.S.-based open transmission-topology dataset that powers GridSFM.

Power grids face increasing strain from surging demand, the need to integrate renewable energy sources, transportation electrification, and extreme weather events. Across all these challenges, the core question is the same: what are the optimal operating points that keep the grid functioning under each new condition?

Answering this requires solving AC optimal power flow (AC‑OPF), a complex, non-convex optimization problem that computes the cheapest generator dispatch (how much each generator produces) that meets demands while respecting power flow physics, voltage limits, thermal constraints, and stability requirements, and underpins core power system operations including reliability, real-time dispatch, market clearing, and contingency analysis. These decisions directly govern outcomes at the scale of up $20 billion per year in congestion costs (opens in new tab) and multi‑terawatt‑hour renewable curtailment (opens in new tab) (lost renewable energy due to congestion), making both economic efficiency and grid reliability highly sensitive to how well these operating points are found. However, AC‑OPF is computationally expensive: power utility scale grid can take up to hours solve, forcing a trade-off between solving a small number of carefully selected scenarios or relying on approximations that ignore critical physics, which can misestimate power flows and binding constraints and lead to suboptimal dispatch and degraded reliability under stressed conditions.

Spotlight: Microsoft research newsletter

Microsoft Research Newsletter

Stay connected to the research community at Microsoft.

Subscribe today Opens in a new tab

To address this limitation, we introduce GridSFM, a single neural network that approximates AC‑OPF in milliseconds across grids ranging from 500 to 80,000 buses. It takes standard AC‑OPF inputs (grid topology, generator and load specifications, transmission line constraints) and produces an operating point and a feasibility verdict (whether the system satisfies all physical and operational constraints). By removing the compute bottleneck, GridSFM makes it possible to evaluate orders of magnitude more scenarios in real time, enabling more informed decisions and shifting grid operations from reactive response to proactive optimization.

In this initial release we offer two tiers:

• GridSFM-Open for research-scale grids up to 4,000 buses.
• GridSFM-Premier for production-scale systems up to 80,000 buses.

The model is built as a block-structured discrete neural operator (Figure 1), representing each grid as a directed graph, with buses (connection points in the grid) and generators as vertices, and transmission and AC lines as edges. It is trained using both solver supervision, where reference solutions are generated using the AC-OPF solver (IPOPT in PowerModels.jl (opens in new tab)), and physics-based constraints that penalize violations of fundamental physical laws such as Kirchhoff’s voltage and current laws, as well as operating constraints like thermal limits. This enables the model to learn from both feasible and infeasible regimes. Most learning-based AC-OPF surrogates train one model per grid on a narrow distribution (opens in new tab). GridSFM takes the opposite approach: in this release a single model trained across 150+ base grid topologies (network structures) and roughly half a million scenarios spanning varying load profiles, multi-element outages, line-rating derates, voltage-bound tightening, and different generator cost coefficients, so the model is forced to generalize rather than memorize. Across the 54-grid mix test scenarios for GridSFM-Open, our model achieves a median cost gap of 2.23% vs solver ground truth labels (mean 3.41%; <5% gap on 83 % of scenarios). When more precision is needed, GridSFM’s prediction also serves as a warm start seed for traditional numerical solvers, GridSFM-seeded-warm beats cold solve by 1.66× geometric mean across the same test scenarios and beats the industry-standard DC-OPF warm-start by 1.59× geomean (per-grid breakdown and full white-paper analysis to follow).  Geometric mean, otherwise known as the multiplicative average, is used here since it is more robust to outliers. Our model also demonstrates the ability to adapt to new grids with just a handful of fine-tune scenarios.

Figure 1. GridSFM architecture. Bus, generator, and branch features are embedded into a shared latent space, then refined by a stack of attention blocks operating directly on the grid topology. Output heads decode the latent state into (i) a full AC-OPF operating point, bus voltages and angles, generator dispatch, branch flows, and (ii) a per-scenario feasibility score. What it enables

A common pattern in grid operations and planning is having to choose between solving a small, hand-picked set of scenarios accurately with full AC-OPF or running thousands of scenarios through a faster approximation that drops parts of the physics. For example, a commonly used tool is the DC-OPF approximation, a linearized version that assumes flat voltage magnitudes and small angle differences and ignores reactive power and losses. DC-approximation solves in seconds what takes full AC minutes to hours, which is why most contingency screens, market-clearing pre-stages, and planning sweeps run on DC-approximation today. The cost is real: DC-approximation ignores voltage and reactive constraints entirely, and its dispatch cost can run >10% off the AC optimum on stressed scenarios (with worst-case grids out past 20% in our test benchmark).

GridSFM is designed as a drop-in alternative to DC-approximation in that fast approximation slot, and unlike most existing AC-OPF neural surrogates, which require a fresh training run for every new topology, GridSFM generalizes across grids in its supported size range without per-topology retraining, so it slots in as universally as DC-approximation. Especially when compared with DC-OPF, GridSFM has three concrete advantages:

• Same accuracy class as DC-approximation on standalone dispatch cost. GridSFM and DC fall within the same per-scenario cost-gap distribution (§2 / Figure 6), with complementary failure modes: DC fails on grids where its no-loss / no-reactive linearization is structurally wrong; GridSFM fails on grids outside its training distribution. The two limitations close along orthogonal axes. DC’s ceiling is fixed by the linearization, whereas GridSFM’s tail closes with more training data.
• 1,000× faster than a full AC solver and approximately 100× faster than DC-approximation at the inference step, fast enough to sweep thousands of contingencies (e.g., line or generator outages) in minutes on a single commodity GPU.
• A real AC operating point, not a linear approximation. GridSFM produces voltages and reactive power, so the same prediction can be handed to a traditional numerical solver as an AC warm-start, opening a workflow DC-approximation cannot.

1. Feasibility screening: stress-score triage

A scenario is infeasible when no dispatch satisfies all constraints simultaneously: the requested load cannot be served within voltage bounds, thermal limits or generator capacities. Operationally, infeasibility is the most consequential failure signal: the requested operating condition cannot be served at all, and the response is intervention (load shedding, redispatch, relaxing thermal limits). It is also the most expensive class of scenario to screen, because the solver only learns a scenario is infeasible after iterating to non-convergence: each infeasible case costs a full solver run, often longer than a feasible one. Sweeping thousands of contingencies or stress cases to identify the infeasible ones is therefore one of the worst-case budgets in any planning workflow.

GridSFM addresses this with a per-scenario stress score trained jointly with the dispatch head. We evaluate the score on three classes of scenarios on each grid: real-feas are scenarios the AC-OPF solver successfully converged on (i.e., genuinely feasible operating points), real-infeas are scenarios the solver failed to converge on (genuinely infeasible operating points), and synth-infeas are feasible base points we deliberately perturbed to violate a specific constraint (voltage squeeze, thermal bottleneck, angle tightening, or DC-thermal congestion). Across the 54-grid test scenarios, the stress score’s per-grid binary accuracy is broadly uniform across classes: real-feas (green) mean 94.5%, real-infeas (red) mean 96.1%, synth-infeas (orange) mean 90.4%. Most grids cluster within a few points of the means; outliers below 80% are the same hard grids that show up in cost-gap analysis below.

Figure 2. GridSM per-grid feasibility prediction accuracy across the 54-grid test scenarios, broken out by class (real-feas, real-infeas, synth_infesible). Filled KDE + per-grid dots, with mean (–) and median (:) light dashed lines. The three distributions overlap heavily, the model’s quality is broadly uniform across classes, with a small failing tail of structurally hard grids.

Drilling into a case study. Let’s zoom into a single representative grid, the Texas2k summer-peak grid (opens in new tab), to show how the learned representation separates feasibility and ROC for predicting.

Representation. Figure 3 visualizes the model’s learned representation of each Texas2k scenario. We project the per-graph representation (128-dimensional) onto two axes (LD1, LD2) chosen to maximally separate the scenario classes: real-feasible, real-infeasible, and synthetic-infeasible. Squeezing 128 dimensions into 2 inevitably loses information, so this view exaggerates apparent overlap: classes that look mixed here may still be cleanly separable in the full 128-dimensional space the model uses. The shaded cloud shows where graphs of each class concentrate, and the cross at the center of each cloud marks the class centroid, the average position of all graphs of that class. Centroids that sit far apart mean the model treats those classes as clearly distinguishable. Where two shaded clouds overlap, the model is producing similar embeddings for graphs with different labels.

Figure 3. Linear discriminant projection of grid embeddings on the Texas2k scenarios. Real feasibles (green), real infeasibles (red), and synthetic infeasibles (orange), projected onto two axes (LD1, LD2) chosen to maximize between-class separation. Crosses mark class centroids; shaded clouds show where each class concentrates. Overlap between clouds means the model produces similar embeddings for graphs in those classes; in the full 128-dimensional space the model may still separate them along directions not shown.

Operation and ROC. The score itself is continuous and ranking-calibrated. Figure 4 shows the ROC over its test mix: AUC = 0.986. At the natural operating point the same score, thresholded as a binary classifier, yields 95.5% accuracy. Per-mode detection at that threshold is 99–100% on the three perturbation modes that drive a constraint cleanly past its limit.

Figure 4. ROC curve of the GridSFM stress score for feasibility on the Texas2k summer-peak test mix (real feasibles + solver-labeled infeasibles + synthetic perturbation modes that drive a constraint past its limit). Area under the curve = 0.986, binary accuracy 95.5% at the natural operating point. The score is calibrated for ranking; where to draw the binary cutoff is an operator choice. 

Triage cutoff. For routing scenarios into action buckets, Figure 5 shows the stress-score distribution per population. Operators pick the cutoff that matches their workflow: very-confident feasibles pass through to indicative dispatch; very-confident-stressed scenarios are flagged for engineering review; the borderline middle band is sent to the solver for verification. The cutoff sets the balance between solver budget and screening miss-rate.

Figure 5. Distribution of the model’s feasibility logit on the same Texas2k test scenarios, split by population: real-feasibles (green), real-infeasibles (red), and synth-infeasibles (orange). The dashed vertical line is the decision boundary where logit=0. Samples to the right are predicted feasible. At this operating threshold, real-feasible pass through at 99.5%, real-infeas are correctly flagged at 90.4%, and the synthetic perturbation are caught at 88-100%. 2. GridSFM as a fast approximation

GridSFM’s prediction can be used in two ways without producing an exact AC-OPF solution from scratch: as a standalone dispatch and cost estimate, or as the initial guess (warm-start) for an exact numerical solver. We compare both against the same two reference points throughout: full AC-OPF (the ground-truth optimum) and DC-approximation (the established fast baseline). All numbers below come from the same test set of 54 grids scenarios GridSFM-Open, with solver solve_time measured per scenario under single-core CPU pinning.

Standalone cost estimate

When an exact solver round-trip is not required, GridSFM’s predicted dispatch can be costed directly. In our test set, GridSFM-Open and DC-approximation fall in the same accuracy class: comparable means (DC 2.80%, GridSFM 3.41%), comparable medians (DC 1.81% vs GridSFM 2.23%), and overlapping per-scenario distributions across two decades of cost gap (Figure 6). They have complementary failure modes rather than one dominating the other.

Figure 6. Per-scenario cost-gap distribution from AC-OPF ground truth: DC-approximation (blue) and GridSFM (green) across the 54-grid GridSFM-Open benchmark. Filled KDE + per-scenario dots underneath; light dashed lines mark mean (–) and median (:). DC: mean 2.8%, median 1.81%, <5% gap on 90% of scenarios. GridSFM: mean 3.41%, median 2.23%, <5% gap on 90% of scenarios. The two distributions overlap heavily in the body — methods are in the same accuracy class with complementary failure modes. Reference dashed line at 5%.

Both distributions look the same in shape: a single peak in the 2–3% gap range, with the bulk of scenarios under 5% and a small tail of outliers extending out into the >25% range. The outlier tails come from different sources: DC fails on grids where its no-reactive linearization is structurally wrong (case1803_snem and a handful of meshed transmission grids); GridSFM’s outliers are concentrated on a few of our open sourced grids whose AC-OPF reference itself required additional constraint relaxation to become feasible (opens in new tab), so the ground-truth target on those grids is noisier and the gap partly reflects reference-side instability. The two limitations close along orthogonal axes: DC’s ceiling is fixed by the linearization and does not improve with more data or compute; GridSFM’s tail closes with cleaner reference labels and more training data on those grid families.

The differentiating value of GridSFM is therefore not the standalone cost number, but that GridSFM produces a full AC operating point including voltages and reactive power. This allows operators to directly assess the state of the grid. This is important since the feasibility and security of a system is often determined by the voltage and reactive power limits, but neither are considered in DC-OPF.  At the same time, the operating point also enables the warm-start workflow, as we describe next.

Warm-start handoff

An AC-OPF solver works by iteratively refining an initial guess of the operating point until the optimality conditions are satisfied, and the number of refinement iterations it needs depends directly on how close the initial guess starts to the true optimum: a poor starting point can require thousands of iterations, a near-optimal one only a couple. A cold start (also known as a flat start) sets voltage magnitude to 1.0 per unit and angle to zero  on every bus, so the solver does the full amount of work. A warm start replaces that generic value with a closer estimate to make the solver converge faster. DC-approximation warm-start solves the linearized DC-OPF version of the problem first and seeds the AC solver with that solution. Whereas, GridSFM warm-start runs a single forward pass through the model and seeds the solver with its predicted voltage angles and active dispatch. The absolute ceiling on how much any warm-start can help is what we call the GT (ground-truth) ceiling: we run the full AC-OPF solve once at high precision to find the true optimum, then re-run the solver with that exact solution as the warm start seed. This is the practical limit on solving time and therefore the ceiling on speedup. 

Figure 7. Warm-start speedup over AC-OPF cold start, across the 54-grid test set (log-scale x axis). GridSFM (green, sits cleanly right of the cold-start reference) achieves a geomean speedup of 1.66×, and outperforms cold start on 41 of 54 grids ; DC-approximation (blue) achieves a geomean speedup of 1.04× and improves performance on 34 of 54 grids; the GT ceiling (gold,  geomean 2.72×) is the upper bound on warm-start headroom. Each method’s ratio is computed within the same Julia process to remove cross-run timing noise. 

Our profile showed that GridSFM warm-start is 1.66× faster than cold start and 1.59× faster than DC-approximation warm-start (geometric means across the 54 grids test scenarios) and is faster than both baselines on 41 of 54 grids. The largest per-grid speedups exceed 7× over cold on the meshed transmission grids (Texas2k summer-peak, case2742_goc). DC-approximation warm-start, by contrast, is a wash on average across this broader grid mix (geomean 1.04× vs cold), DC saves on AC iterations on some grids and spends them rebuilding voltage/reactive on others.

The gap between the GridSFM distribution in Figure 7 and the GT-ceiling distribution (2.72× geomean) can be closed by improving GridSFM’s residual reactive-power and voltage prediction error, both targeted by the next release.

Generalization

We tested whether GridSFM-Open acts like a true foundation model by running it on a grid it had never seen before: the 6,470-bus case6470_rte from OPFData (opens in new tab), about 1.4× larger than any grid in training.

In a zero-shot setting, performance drops as expected. Cost error increases from 3.35% in-sample to about 14% on the new grid. Voltage predictions capture only about 27% of the true variation and appear nearly flat. The feasibility classifier flags every scenario as infeasible. Even so, the model still preserves the correct ordering of costs across scenarios.

With light fine-tuning, performance recovers quickly. After 10 epochs on 1,000 scenarios, cost error falls to 1.12%, voltage variation reaches 91% of the true signal, and feasibility detection becomes nearly perfect. An N-1 contingency split that was fully held out during fine-tuning matches the full-topology results within 0.2 percentage points on all metrics, showing that adaptation transfers across contingencies.

The model adapts even with very limited data. With just 10 scenarios, cost errors are 1.76% and feasibility detection exceeds 90%, with strong results already on cost and active power dispatch. Voltage magnitude is slower to recover and needs closer to 1,000 scenarios (see Table 1).

This test showed that GridSFM-Open already captures AC-OPF physics during pre-training. Adapting to a new grid is mostly a matter of calibration rather than relearning. The released checkpoint can therefore serve as a practical starting point for users to fine-tune on their own topology and tasks.

Fine-tune scenariosCost errorFeasibility Detection0 (0-shot)14%0 (Collapsed)101.76%92%1000.88%97%10001.12%99%Table 1: Few-shot fine-tuning of GridSFM-Open on case6470_rte (held-out test split, 10 epochs per row): even ~10 scenarios already give useful cost and feasibility predictions. Looking ahead

Active directions for the next release:

• Generalization. Tighter accuracy on grids and operating conditions outside the training mix. The current out-of-distribution analysis is in the white paper.
• Continued accuracy improvements across all prediction channels, narrowing the residual gap between Figure 7’s GridSFM distribution and the gold GT-ceiling.
• Multi-snapshot extensions. Unit commitment (discrete on/off generator decisions across time), weather-conditioned scenario generation, dynamic-stability surrogates.

We previously released the GridSFM_US _Powergrid_dataset (opens in new tab). This release adds the first open AC-OPF model that supports multiple grid topologies, completing a stack of open topology data, open code, and open weights for ML-driven grid simulation and planning. We see it as a starting point for the community to build richer simulators, planning workflows, and decision-support tools without re-creating the data or the model from scratch. The applications we expect to see most leverage from are the ones where the cost of a single solve has historically forced cherry-picking: contingency screening, transmission expansion planning, demand-siting analysis, and resilience studies under extreme weather. 

Everything in the GridSFM-Open tier is released for research use today:

GitHub Hugging Face White Paper Project Page

A note on GridSFM-Premier. The larger production-scale tier is not part of this open release. If you are interested in evaluating it, collaborating with us, or otherwise getting access, please contact us at gridFM@microsoft.com.

Opens in a new tab

The post GridSFM: A new, small foundation model for the electric grid appeared first on Microsoft Research.

Instagram has a new feature that lets you share an unedited photo dump with your pals that disappears once viewed. Yes, we have questions.

The Meta-owned social media platform announced Instants on Wednesday, a tool that lets you send a batch of snaps to Close Friends or mutual followers. You can't edit the photos, which might throw some folks off, but you can add captions.

Mashable 101 Fan Fave: Vote for your favourite creator today.

Notably, Instants will disappear after being viewed or after 24 hours. In this capacity, the feature sounds exactly like Instagram's existing disappearing messages, as well as similar options on Snapchat, Signal, Telegram, and WhatsApp. Plus, there's an undo button if you want to take back a sent photo.

Credit: Instagram / Mashable

Instants will appear in your Instagram DMs inbox appearing like a pile of photos. Once you've sent a glut of Instants, the recipients can react with emoji, reply like a regular DM, or send their own collection of Instants, as you both throw mountains of unedited snaps back and forth until the comet finally hits. Recipients cannot screenshot the photos (more on that below), and your Instants will be saved in a folder only you can access — you can repost them from here to Stories.

SEE ALSO: Instagram users claim they're getting censored. Here's what Meta says. Featured Video For You Is U.S. TikTok censoring its users? Now, for the important caveats.

Firstly, if you use a tool like disappearing messages, I cannot stress this enough but your recipient can always record the screen or take a photo with another device. Though Instagram's Instants cannot be screenshotted, that doesn't mean the person you're sending the image to can't capture it.

Secondly, Instagram monitors Close Friends content even if you think it's some kind of incognito mode. It is not.

Next, all posts will need to comply with Instagram's nudity guidelines, which run a fine line between protection and censorship. Something tells me this won't be the last we talk about this when it comes to Instants.

Finally, Instagram has officially pulled end-to-end encryption on DMs, so the security of your Instants is under standard encryption.

Mashable has reached out to Instagram for comment.

SAVE $7.04: As of May 13, the Lego Botanicals Flowering Cactus is on sale for $27.95 at Amazon with an on-page coupon. That's 20% off its list price of $34.99 and its lowest price to date.

Opens in a new window Credit: Lego Lego Botanicals Flowering Cactus $27.95 at Amazon
$34.99 Save $7.04 with on-page coupon Get Deal

If your thumb is a few shades away from green, Lego's Botanicals sets offer the perfect way to brighten up your home without worrying about impending plant death. If succulents are your cup of tea, the recently released Flowering Cactus is a fun pick and just dropped to its best price yet.

As of May 13, the Lego Botanicals Flowering Cactus is just $27.95 at Amazon with the help of an on-page coupon. That's 20% off the $34.99 list price and the lowest price we've seen since its January launch.

With a piece count of 482, the Flowering Cactus set is recommended for anyone aged 9 and up. So both kids who can't quite take care of a plant yet and kids at heart will enjoy this build. Placed in a pastel blue pot, the set builds out to two faux cacti: one larger cactus in full bloom with a pink and yellow flower at the top and a smaller cactus with tiny pink buds. The building instructions are available in the Lego Builder app, so you can follow along with step-by-step digital instructions and track your progress if you desire. And the best part, of course, is that the result is a gorgeous, kill-proof plant that doubles as decor.

Add some greenery to your home in the form of tiny Lego bricks shaped like a succulent. It'll only cost you $27.95 if you add the Flowering Cactus set to your cart ASAP (and remember to use the coupon code).

Samsung is the dominant Android brand for good reason. Beyond its impressive lineup that spans everything from budget devices to ultra-premium flagships and foldables, Samsung keeps both hardware and software polished to a consistent standard.

SAVE $5: As of May 13, get a $50 Shell gift card at Best Buy for $45. That's a discount of 11%.

Opens in a new window Credit: Amazon $50 Shell gift card $45 at Best Buy
$50 Save $5   Get Deal

With soaring gas prices taking us on a constant roller coaster ride, of course we all want to save more money at the pump. While you can use discounts like those that Amazon Prime offer to shave some cash off of your purchases, saving on gift cards is another option you might want to consider. We found one that you'll want to jump on if you live near a Shell gas station.

As of May 13, get a $50 Shell gift card at Best Buy for $45. That's $5 off and a discount of 11%.

SEE ALSO: Don't pass up this perk: How to score an Amazon Prime gas discount

This gift card can be used for gas at any of the over 14,000 Shell stations across the United States. You can use it at the pump or inside the Shell station, so no need to fret if you have to run in and take care of some errands first. And though it might seem like a small amount, $5 is $5. You never know when it might come in handy.

Beyond gas, you can use this gift card to pay for just about anything Shell offers, including car washes, food and snacks, vehicle supplies, and more. So if you don't use a lot of gas but still take advantage of Shell stations, it's well worth stocking up on a couple of gift cards and saving some cash either way. And if you know someone who does need a bit of a discount, well, it's a gift card after all.

This is a limited time deal at Best Buy, so make sure you get yours within the next 14 hours before it goes away.

Samsung's next product showcase could be a really big one.

That's the vibe coming out of a new report from Seoul Economic Daily, anyway. According to the Korean news publication, Samsung's next Galaxy Unpacked event will take place on July 22 in London, a date and venue that have both been previously reported. However, what we didn't know is that, according to Seoul Economic Daily, this event will also feature a new pair of smart glasses, as well as a new Galaxy Watch and some new foldable devices.

SEE ALSO: Samsung Wide Fold design revealed in leaked images

The so-called "Galaxy Glasses" will use Google's Android XR platform, but be warned that these glasses will not feature a display of any kind. They have a microphone, some speakers, and a camera, all of which could theoretically be used to interface with an AI assistant.

Unfortunately, the report contained no real information about the Galaxy Watch 9, but based on precedent, it would not be surprising at all to see that show up at the showcase.

Aside from the glasses and the watch, it's widely expected that Samsung will launch three new foldable devices: a Galaxy Fold 8, a Flip 8, and a new "Wide Fold" phone that could compete with the rumored iPhone Fold. We'll all find out together in July.

Mashable reached out to Samsung representatives for comment and will update this story if we receive more information.

Competing humanoid robot-makers Figure and 1X revealed new footage from inside their factories this week, showcasing the similarities and differences in their approaches to bringing this technology to life.

SAVE 21%: As of May 13, you can get a 16-inch Asus V16 gaming laptop (Intel Core i7, NVIDIA GeForce RTX 5060, 32GB RAM, 1TB) for $1,189.99, down from $1,499.99. That's a 21% discount or $310 savings.

16-inch Asus V16 gaming laptop (Intel Core 7, NVIDIA GeForce RTX 5060, 32GB RAM, 1TB) $1,189.99 at Amazon
$1,499.99 Save $310   Get Deal at Amazon

Buying a laptop with enough memory and storage to run modern games smoothly (without deleting files to make room for new downloads) is almost impossible on a tight budget. But if you're patient (or just refuse to pay full price) and hold out for the right Amazon sale, you can get a heavy-duty setup without draining your savings.

Right now, you can get the 16-inch Asus V16 gaming laptop (Intel Core i7, NVIDIA GeForce RTX 5060, 32GB RAM, 1TB) for $1,189.99, down from $1,499.99. That's a 21% discount or $310 savings. It's also the lowest price we've tracked on this model to date.

SEE ALSO: The best gaming laptops of 2026: Check out our top picks

For that price, you'll get 32GB of memory, a 1TB PCIe 4.0 SSD, and a 16-inch display with a 144Hz refresh rate. It runs on an Intel Core 7 240H processor and an NVIDIA GeForce RTX 5060 GPU with 8GB of GDDR7, so it should handle everything from intense gaming to video editing without freezing. Bonus: the battery lasts up to 15 hours and has a fast-charge feature that'll take you from zero to 100 percent in just an hour and 20 minutes.

Multicolor 3D printing is such a neat idea, but it's hard to justify because of how much waste it generates. I was able to go hands-on with a 12-color no-waste 3D printer a few months ago, and it completely changed the multicolor 3D printing game.

SAVE $130: The DJI Flip with RC 2 is on sale at Amazon for $509, down from the standard price of $639. That's a 20% discount that matches the lowest we've ever seen at Amazon.

Opens in a new window Credit: DJI DJI Flip 2 with RC 2 $509 at Amazon
$639 Save $130   Get Deal

Now that we're not in freezing temperature anymore, you might be spending more time outside. If you've been searching for a new outdoor hobby, you might have contemplated become a drone pilot. Capture the world around you through a new view, either to keep for yourself or to share with others. If that's appealing, check out this DJI deal.

As of May 13, the DJI Flip with RC 2 is on sale at Amazon for $509, marked down from the normal price of $639. That's a 20% discount or a savings of $130. It also matches the lowest we've ever seen at Amazon.

From content creators to real estate developers and farmers, drones have plenty of uses. DJI has long been a favorite drone brand with creatives, and the Flip drone proves its worth. It can handle video transmission from 13 kilometers away (about 8 miles from us in the U.S.), and it comes with DJI's excellent subject tracking that keeps moving subjects in focus.

SEE ALSO: Low price alert: The DJI Osmo 360 Essential Combo is on sale at Amazon for over $200 off

The overall design is travel-friendly with folding propellers and carbon-fiber propeller guards. It weighs under 249 grams which is excellent for portability, and its weight means you don't need an FAA registration or a remote ID when flying in the U.S.

The DJI Flip will get up to 31 minutes of flight time before it'll need some time to recharge, and the included RC 2 remote adds a new layer of fun to this deal. The RC 2 is DJI's remote control that houses a 5.5-inch FHD display and 32GB of built-in storage. On its own, the RC 2 sells for $369 which makes today's deal of $509 that includes the Flip drone seem that much more attractive.

Before this record-low price sells out, become the best drone pilot on the block with the DJI Flip and RC 2. It's a beginner-friendly model with top-tier performance. Amazon has this sale listed as a limited-time only deal so act quickly.

Who'd have thought that we'd still be talking about printers in the 2020s? Wasn't it all supposed to be paperless by now? The reality is that we're about as likely to give up paper as we are to get flying cars, which means we have to deal with all the things wrong with the printing industry.

SAVE $700.01: As of May 13, get the Dreame X50 Ultra robot vacuum and mop for $899.98 at Amazon, down from its usual price of $1,599.99. That's a discount of 44%.

Opens in a new window Credit: Amazon Dreame X50 Ultra robot vacuum and mop $899.98 at Amazon
$1,599.99 Save $700.01   Get Deal

Looking for a faster and easier way to get clean floors? A robot vacuum is your best bet. Just program it with what you want it to tackle and send it out into your home to do the rest. If you're looking for a great model that can handle carpets as well as hard flooring, this Dreame combo unit is on sale now for an excellent price.

As of May 13, get the Dreame X50 Ultra robot vacuum and mop for $899.98 at Amazon, down from its usual price of $1,599.99. That's $700.01 off and a discount of 44%.

SEE ALSO: These are the top 4 robot vacuums for carpet after passing the cat hair test on my rugs

This powerful robot vacuum and mop combo can handle a wide berth of cleaning tasks. It boasts up to 20,000Pa of suction thanks to Dreame's TurboForce motor and can pick up a variety of different types of debris, from cat litter to ground-in dirt you've had issues with in the past. It can easily power through tangled hair with its DuoBrush system and cut through older messes with ease.

If your home is difficult to navigate, this unit can handle that issue as well thanks to its robotic retractable legs to get over door tracks, obstacles, and other hard-to-reach areas. It can lower its height to reach under door frames and furniture without you manually having to pick it up and move it, for example. It can also tackle corners with a brush for extended reach. When the time comes to get some mopping done, it can seamlessly swap between modes as well.

If you're ready to leave cleaning to the robots, this luxury robot vacuum and mop is where it's at, especially near 50% off as it is right now. Get yours before it goes out of stock.

Streaming TV shows and movies used to be affordable when there were only one or two major streaming platforms. These days, there are almost too many to count, and the prices continue to rise. I've ditched several of my streaming subscriptions, but here are the four I've kept.

Having a Windows and Linux PC doesn’t mean you need entirely different toolsets for each platform. Many apps offer seamless cross-platform experiences, ensuring productivity without the hassle of relearning software. Here are seven apps that work smoothly on both Windows and Linux.

I used to wish that I had an awesome supercomputer so I could run my own AI, but then I found out that I didn't have to. Just like with other DIY projects, you can make a server with what you already have; you just need to think outside the box. If you own a spare PC, can find one, or can buy one cheaply, you're one big step closer to an AI server. What's even better is that LM Studio was built to make this easier for you.

Drills and impact drivers are great for driving lag bolts or self-tapping screws, but they are also the fastest way to ruin a DIY project if you don't respect their power. If you're constantly using your impact driver on 3 (or the fastest setting) and stripping heads, snapping screws, or overdriving, you're using it wrong.

Windows 11 Pro costs more than the Home Edition of Windows, but many people who own Pro aren't making use of some of its most interesting features. They're not flashy, but they provide a very noticeable improvement over Home editions, especially if you like to experiment with applications or customize your PC.