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The Google Pixel 10a is not even out but and Verizon is already making an attempt to offer me one for FREE, no trade-in required

Technology

February 20, 2026

The Google Pixel 10a is not even out but and Verizon is already making an attempt to offer me one for FREE, no trade-in required

The Google Pixel 10a was simply introduced just a few days in the past, however we have already seen variety of preorder offers that might make a budget Android telephone even cheaper (or free) if you realize what you are doing. Verizon’s supply is especially spectacular, gifting away the Pixel 10a for FREE while you add a line with an eligible limitless plan on its website.

You are not restricted to the most costly limitless plans if you wish to seize the deal, both. Verizon’s Limitless Welcome, Limitless Plus, and Limitless Final plans will ALL land you the free Pixel 10a, plus there’s potential for a $200 reward card if you happen to’re switching from one among Verizon’s rivals. It is a Google Pixel 10a deal that simply retains on giving, so act quick if you happen to’re .

low cost Android telephone with a refined show, excellent battery life and charging speeds, and a flagship-level digital camera with Gemini AI software program options. You additionally get seven years of OS and safety upgrades assured.

The largest downside of the Pixel 10a is absolutely extra of a comparability downside than a problem with the telephone itself. Certainly, as a result of the 10a caught with the last-gen Tensor G4 chipset, it feels extra like a souped-up model of the Google Pixel 9a than a totally new smartphone.

The intense aspect is that 2025’s Pixel 9a was an amazing telephone in its personal proper. Take that critically-acclaimed basis and slap on a more-durable glass show and a few flagship-level battery life and also you’re an superior general bundle.

In fact, if you happen to want the simplicity of unlocked telephones, preordering the Pixel 10a by means of Finest Purchase or Amazon is the sensible transfer. However if you happen to’re already a Verizon person (or keen to change), this deal is price some critical consideration.

What Are Multi-Agent Techniques? Structure, Advantages, and Actual-World Examples

Dr. Mike

February 20, 2026

What Are Multi-Agent Techniques? Structure, Advantages, and Actual-World Examples

Work isn’t linear anymore, and that modifications the whole lot! It brings Multi-Agent Techniques into context like by no means earlier than.

Give it some thought. A buyer order triggers procurement. Procurement works its impact on suppliers. Logistics is approach past supply, affecting money stream, buyer expertise, and model belief. One resolution hardly ever stays remoted, and by the point people coordinate all of it, the second has handed.

That’s precisely why Multi-Agent Techniques (MAS) matter now.

Conventional automation follows scripts. AI instruments usually deal with single duties or predictions. However trendy enterprises want one thing extra dynamic: Techniques that may suppose regionally, act independently, and nonetheless work towards a shared enterprise end result.

Like a workforce of specialists, each is aware of its function. Each makes choices in actual time, and none of them wants to attend for fixed managerial approval.

When provide chains begin performing up, prospects don’t at all times keep put. Pricing turns into a shifting goal. MAS stops feeling futuristic; it begins feeling crucial.

What Are Multi-Agent Techniques (MAS)?

In observe, MAS takes big, advanced enterprise issues and chops them up into smaller choices made independently however directed towards the identical goal. As an alternative of a single AI making an attempt to do the whole lot, you may have a number of brokers sharing the load. Totally different roles however the identical aim.

Earlier than moving into advantages or use circumstances, there’s worth in pausing right here. MAS doesn’t make choices the way in which conventional automation or standalone AI instruments do.

At its core, a Multi-Agent System is only a set of software program brokers that act on their very own, speak to one another, and react to their surroundings to achieve a aim. If this nonetheless sounds abstruse, don’t fear. Let’s decompose it:

One workforce watches demand indicators
One other displays stock
A 3rd negotiates provider choices
A fourth handles buyer commitments

Now think about all of them working concurrently, sharing context, resolving conflicts, and optimizing outcomes—with out ready for conferences or electronic mail chains.
That’s MAS!

Step Into The World of Multi-Agent Techniques. Let Us Assist You Navigate Easily with the Greatest Practices & Roadmaps.

The Key Elements of a Multi-Agent System

The effectiveness of Multi-Agent Techniques relies upon much less on intelligence and extra on construction. Clear roles, managed interactions, and shared context decide whether or not brokers cut back complexity or multiply it.

1. Brokers (The Resolution Makers)

Brokers are impartial software program entities. Every agent:

Has a selected function or duty
Can understand its surroundings
Makes choices based mostly on guidelines, knowledge, or studying fashions
Acts with out direct human intervention

In enterprise phrases, suppose autonomous digital workers with clearly outlined KPIs.

2. Setting (The Enterprise Actuality)

It spans ERP and CRM. Additionally, it reacts to markets and prospects, and stays inside budgets, SLAs, and rules. Nothing stays static. Brokers need to adapt because it modifications.

3. Communication & Coordination Mechanisms

Right here’s the place issues get attention-grabbing. Brokers don’t work in silos. They share context. They negotiate priorities. And so they coordinate actions so one good resolution doesn’t by accident create three dangerous ones someplace else.

That is what prevents “native optimization” from hurting the larger image.

4. Resolution Logic & Insurance policies

Every agent operates inside:

Enterprise guidelines
Governance insurance policies
Danger thresholds
Moral and compliance boundaries

That is the place management intent is embedded into the system.

5. Studying & Adaptation

Superior MAS can be taught from outcomes. What labored. What failed. What value greater than anticipated? Over time, the system doesn’t simply execute choices—it improves them.

What Are the Advantages of Multi-Agent Techniques?

The actual worth of Multi-Agent Techniques isn’t uncooked intelligence. It’s how shortly choices transfer, how properly techniques recuperate, and the way simply they scale. In observe, what they ship to companies is the power to run choices in parallel with out fixed human coordination.

The worth turns into notably very express below excessive situations on the system—basically when there are spikes in demand or disruption that require choices sooner than people can coordinate.

This isn’t a tooling subject. It’s a choice bottleneck. That is the place Multi-Agent Techniques quietly shine.

1. Sooner, Parallel Resolution-Making

Conventional automation waits its flip. Multi-Agent Techniques brokers suppose, determine, and act concurrently. Outcome? Bottlenecks disappear. Response time shrinks.

2. Higher Resilience in Unsure Environments

Markets change, suppliers fail. Clients behave unpredictably. With Multi-Agent Techniques, choices don’t collapse when one part fails. Different brokers adapt, reroute, or compensate. Suppose shock absorbers, not brittle pipelines.

3. Scalability With out Linear Headcount Development

As operations develop, coordination prices explode. Extra conferences. Extra approvals. Extra delays. Multi-Agent Techniques scale decision-making with out scaling individuals. That’s operational leverage.

4. Native Intelligence, World Alignment

Every agent optimizes its personal area—pricing, stock, logistics, compliance—whereas staying aligned to shared enterprise targets. No tunnel imaginative and prescient. No chaos.

5. Steady Optimization

With learning-enabled brokers, techniques don’t simply execute choices. They be taught from what occurs and enhance as they go, which static automation merely can’t do.

Multi-Agent Techniques in Follow: Actual-World Enterprise Use Circumstances

You don’t need to look far to search out Multi-Agent Techniques in motion. They’re already at work in provide chains, pricing engines, IT operations, and danger administration immediately. These techniques don’t simply analyze knowledge; they act on it in actual time. One of the simplest ways to know Multi-Agent Techniques is to see how they function in manufacturing environments immediately.

1. Enterprise-Scale Provide Chain

Brokers don’t react late. They repeatedly monitor demand and provider reliability. This they do even throughout pricing shifts and logistics constraints. When disruption hits, they modify orders and discover alternate options, no escalation emails required.

2. Dynamic Pricing & Income Administration

One agent tracks market indicators, one other displays competitor pricing. A 3rd enforces margin guidelines. Collectively, they modify costs in actual time with out sacrificing margins.

3. Buyer Expertise Association

Brokers deal with personalization, assist prioritization, churn prediction, and retention provides, coordinating actions throughout channels as a substitute of reacting in isolation.

4. IT Operations & Incident Administration

In IT operations, monitoring brokers can assist detect anomalies, whereas prognosis brokers isolate root causes, and remediation brokers execute fixes. Human groups step in solely when wanted.

5. Fraud Detection and Danger Administration

A number of brokers can concurrently analyze the transaction, behavioral sample, and contextual danger. This flags points not solely sooner however extra precisely in comparison with rule-based techniques.

Challenges and Issues of Multi-Agent Techniques

Multi-Agent Techniques introduce autonomy, and with out self-discipline, that autonomy shortly turns into danger. If not managed correctly, complexity will construct up moderately than be decreased. That is the half that issues earlier than pilots flip into manufacturing at scale.

1. Architectural Complexity

Designing agent roles, interplay guidelines, and escalation paths takes severe thought. Poor design results in noise, not intelligence.

2. Governance & Management

Autonomy with out guardrails is a danger.
Enterprises should outline:

Resolution boundaries
Approval thresholds
Auditability and explainability

With out governance, MAS can drift from enterprise intent.

3. Safety & Belief

Brokers work together throughout techniques and generally with exterior companions. That expands the assault floor. Robust id, entry management, and monitoring aren’t non-obligatory.

4. Value & ROI Readability

This isn’t the most affordable path upfront. The worth comes later, via scale, velocity, and resilience. Sensible enterprises begin small. Then broaden.

Multi-Agent Techniques in AI Defined and Why Companies Ought to Care

Learn Extra!

Often Requested Questions (FAQ)

When executives assess multi-agent techniques, the questions are normally predictable. These are wise questions, and clear solutions matter.

1. What are multi-agent techniques in AI?

Multi-agent techniques in AI are constructed round the concept multiple clever agent, working collectively and reacting to alter, usually makes higher choices than one performing alone.

2. How do multi-agent techniques work?

Every agent watches what’s altering, shares context with others, decides its subsequent transfer, and acts with out dropping sight of the broader enterprise targets.

3. What’s multi-agent system structure?

A multi-agent system structure outlines knowledge flows, communication protocols, governance pointers, agent roles, and enterprise system integration.

Why Multi-Agent Techniques Are Foundational to Agentic AI?

Agentic AI isn’t a couple of single super-intelligent system. It’s about many clever brokers working collectively responsibly. That’s why Multi-Agent Techniques sit on the basis of agentic AI. They create construction to autonomy and self-discipline to intelligence.

Enterprises that succeed don’t begin massive. Begin with one area, outline clear boundaries. Then measure outcomes and broaden steadily. The aim isn’t changing human judgment, nevertheless it’s amplifying it.

How Can Fingent Assist Enterprises Begin Small and Scale Safely?

Designing Multi-Agent Techniques is as a lot a enterprise resolution as a technical one. Fingent helps enterprises architect, construct, and govern Multi-Agent Techniques that align with actual outcomes—not experiments.

Join with our specialists immediately and uncover methods you possibly can leverage the newest applied sciences for your small business. Speak to us now!

First experiments with TensorFlow mixed-precision coaching

Artificial Intelligence

Dr. Mike

February 20, 2026

First experiments with TensorFlow mixed-precision coaching

Ranging from its – very – current 2.1 launch, TensorFlow helps what is known as mixed-precision coaching (within the following: MPT) for Keras. On this publish, we experiment with MPT and supply some background. Said upfront: On a Tesla V100 GPU, our CNN-based experiment didn’t reveal substantial reductions in execution time. In a case like this, it’s exhausting to determine whether or not to truly write a publish or not. You might argue that similar to in science, null outcomes are outcomes. Or, extra virtually: They open up a dialogue which will result in bug discovery, clarification of utilization directions, and additional experimentation, amongst others.

As well as, the subject itself is fascinating sufficient to deserve some background explanations – even when the outcomes are usually not fairly there but.

So to begin, let’s hear some context on MPT.

This isn’t nearly saving reminiscence

One method to describe MPT in TensorFlow might go like this: MPT allows you to prepare fashions the place the weights are of kind float32 or float64, as common (for causes of numeric stability), however the knowledge – the tensors pushed between operations – have decrease precision, specifically, 16bit (float16).

This sentence would most likely do tremendous as a TLDR;
for the new-ish MPT documentation web page, additionally out there for R on the TensorFlow for R web site. And primarily based on this sentence, you may be result in assume “oh certain, so that is about saving reminiscence”. Much less reminiscence utilization would then indicate you can run bigger batch sizes with out getting out-of-memory errors.

That is in fact appropriate, and also you’ll see it occurring within the experimentation outcomes.
However it’s solely a part of the story. The opposite half is expounded to GPU structure and parallel (not simply parallel on-GPU, as we’ll see) computing.

AVX & co.

GPUs are all about parallelization. However for CPUs as effectively, the final ten years have seen necessary developments in structure and instruction units. SIMD (Single Instruction A number of Information) operations carry out one instruction over a bunch of knowledge directly. For instance, two 128-bit operands might maintain two 64-bit integers every, and these could possibly be added pairwise. Conceptually, this reminds of vector addition in R (it’s simply an analogue although!):

# image these as 64-bit integers
c(1, 2) + c(3, 4)

Or, these operands might comprise 4 32-bit integers every, through which case we might symbolically write

# image these as 32-bit integers
c(1, 2, 3, 4) + c(5, 6, 7, 8)

With 16-bit integers, we might once more double the variety of parts operated upon:

# image these as 16-bit integers
c(1, 2, 3, 4, 5, 6, 7, 8) + c(9, 10, 11, 12, 13, 14, 15, 16)

Over the past decade, the key SIMD-related X-86 meeting language extensions have been AVX (Superior Vector Extensions), AVX2, AVX-512, and FMA (extra on FMA quickly).
Do any of those ring a bell?

Your CPU helps directions that this TensorFlow binary was not compiled to make use of:
AVX2 FMA

It is a line you’re prone to see if you’re utilizing a pre-built TensorFlow binary, versus compiling from supply. (Later, when reporting experimentation outcomes, we will even point out on-CPU execution instances, to supply some context for the GPU execution instances we’re thinking about – and only for enjoyable, we’ll additionally do a – very superficial – comparability between a TensorFlow binary put in from PyPi and one which was compiled manually.)

Whereas all these AVXes are (principally) about an extension of vector processing to bigger and bigger knowledge varieties, FMA is completely different, and it’s an fascinating factor to find out about in itself – for anybody doing sign processing or utilizing neural networks.

Fused Multiply-Add (FMA)

Fused Multiply-Add is a sort of multiply-accumulate operation. In multiply-accumulate, operands are multiplied after which added to accumulator retaining monitor of the operating sum. If “fused”, the entire multiply-then-add operation is carried out with a single rounding on the finish (versus rounding as soon as after the multiplication, after which once more after the addition). Normally, this leads to larger accuracy.

For CPUs, FMA was launched concurrently with AVX2. FMA may be carried out on scalars or on vectors, “packed” in the best way described within the earlier paragraph.

Why did we are saying this was so fascinating to knowledge scientists? Properly, a whole lot of operations – dot merchandise, matrix multiplications, convolutions – contain multiplications adopted by additions. “Matrix multiplication” right here truly has us depart the realm of CPUs and soar to GPUs as a substitute, as a result of what MPT does is make use of the new-ish NVidia Tensor Cores that stretch FMA from scalars/vectors to matrices.

Tensor Cores

As documented, MPT requires GPUs with compute functionality >= 7.0. The respective GPUs, along with the same old Cuda Cores, have so referred to as “Tensor Cores” that carry out FMA on matrices:

The operation takes place on 4×4 matrices; multiplications occur on 16-bit operands whereas the ultimate outcome could possibly be 16-bit or 32-bit.

We are able to see how that is instantly related to the operations concerned in deep studying; the small print, nevertheless, are not essentially clear.

Leaving these internals to the consultants, we now proceed to the precise experiment.

Experiments

Dataset

With their 28x28px / 32x32px sized photos, neither MNIST nor CIFAR appeared notably suited to problem the GPU. As a substitute, we selected Imagenette, the “little ImageNet” created by the quick.ai of us, consisting of 10 lessons: tench, English springer, cassette participant, chain noticed, church, French horn, rubbish truck, gasoline pump, golf ball, and parachute. Listed below are a couple of examples, taken from the 320px model:

Determine 3: Examples of the ten lessons of Imagenette.

These photos have been resized – retaining the side ratio – such that the bigger dimension has size 320px. As a part of preprocessing, we’ll additional resize to 256x256px, to work with a pleasant energy of two.

The dataset might conveniently be obtained by way of utilizing tfds, the R interface to TensorFlow Datasets.

library(keras)
# wants model 2.1
library(tensorflow)
library(tfdatasets)
# out there from github: devtools::install_github("rstudio/tfds")
library(tfds)

# to make use of TensorFlow Datasets, we'd like the Python backend
# usually, simply use tfds::install_tfds for this
# as of this writing although, we'd like a nightly construct of TensorFlow Datasets
# envname ought to confer with no matter surroundings you run TensorFlow in
reticulate::py_install("tfds-nightly", envname = "r-reticulate") 

# on first execution, this downloads the dataset
imagenette <- tfds_load("imagenette/320px")

# extract prepare and check components
prepare <- imagenette$prepare
check <- imagenette$validation

# batch dimension for the preliminary run
batch_size <- 32
# 12895 is the variety of objects within the coaching set
buffer_size <- 12895/batch_size

# coaching dataset is resized, scaled to between 0 and 1,
# cached, shuffled, and divided into batches
train_dataset <- prepare %>%
  dataset_map(perform(report) {
    report$picture <- report$picture %>%
      tf$picture$resize(dimension = c(256L, 256L)) %>%
      tf$truediv(255)
    report
  }) %>%
  dataset_cache() %>%
  dataset_shuffle(buffer_size) %>%
  dataset_batch(batch_size) %>%
  dataset_map(unname)

# check dataset is resized, scaled to between 0 and 1, and divided into batches
test_dataset <- check %>% 
  dataset_map(perform(report) {
    report$picture <- report$picture %>% 
      tf$picture$resize(dimension = c(256L, 256L)) %>%
      tf$truediv(255)
    report}) %>%
  dataset_batch(batch_size) %>% 
  dataset_map(unname)

Within the above code, we cache the dataset after the resize and scale operations, as we need to reduce preprocessing time spent on the CPU.

Configuring MPT

Our experiment makes use of Keras match – versus a customized coaching loop –, and given these preconditions, operating MPT is generally a matter of including three strains of code. (There’s a small change to the mannequin, as we’ll see in a second.)

We inform Keras to make use of the mixed_float16 Coverage, and confirm that the tensors have kind float16 whereas the Variables (weights) nonetheless are of kind float32:

# in the event you learn this at a later time and get an error right here,
# take a look at whether or not the situation within the codebase has modified
mixed_precision <- tf$keras$mixed_precision$experimental

coverage <- mixed_precision$Coverage('mixed_float16')
mixed_precision$set_policy(coverage)

# float16
coverage$compute_dtype
# float32
coverage$variable_dtype

The mannequin is an easy convnet, with numbers of filters being multiples of 8, as specified within the documentation. There may be one factor to notice although: For causes of numerical stability, the precise output tensor of the mannequin needs to be of kind float32.

mannequin <- keras_model_sequential() %>% 
  layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, padding = "identical", input_shape = c(256, 256, 3), activation = "relu") %>%
  layer_batch_normalization() %>%
  layer_conv_2d(filters = 64, kernel_size = 7, strides = 2, padding = "identical", activation = "relu") %>%
  layer_batch_normalization() %>%
  layer_conv_2d(filters = 128, kernel_size = 11, strides = 2, padding = "identical", activation = "relu") %>%
  layer_batch_normalization() %>%
  layer_global_average_pooling_2d() %>%
  # separate logits from activations so precise outputs may be float32
  layer_dense(items = 10) %>%
  layer_activation("softmax", dtype = "float32")

mannequin %>% compile(
  loss = "sparse_categorical_crossentropy",
  optimizer = "adam",
  metrics = "accuracy")

mannequin %>% 
  match(train_dataset, validation_data = test_dataset, epochs = 20)

Outcomes

The primary experiment was finished on a Tesla V100 with 16G of reminiscence. Only for curiosity, we ran that very same mannequin beneath 4 different situations, none of which fulfill the prerequisite of getting a compute functionality equal to no less than 7.0. We’ll shortly point out these after the principle outcomes.

With the above mannequin, closing accuracy (closing as in: after 20 epochs) fluctuated about 0.78:

Epoch 16/20
403/403 [==============================] - 12s 29ms/step - loss: 0.3365 -
accuracy: 0.8982 - val_loss: 0.7325 - val_accuracy: 0.8060
Epoch 17/20
403/403 [==============================] - 12s 29ms/step - loss: 0.3051 -
accuracy: 0.9084 - val_loss: 0.6683 - val_accuracy: 0.7820
Epoch 18/20
403/403 [==============================] - 11s 28ms/step - loss: 0.2693 -
accuracy: 0.9208 - val_loss: 0.8588 - val_accuracy: 0.7840
Epoch 19/20
403/403 [==============================] - 11s 28ms/step - loss: 0.2274 -
accuracy: 0.9358 - val_loss: 0.8692 - val_accuracy: 0.7700
Epoch 20/20
403/403 [==============================] - 11s 28ms/step - loss: 0.2082 -
accuracy: 0.9410 - val_loss: 0.8473 - val_accuracy: 0.7460

The numbers reported beneath are milliseconds per step, step being a go over a single batch. Thus basically, doubling the batch dimension we might count on execution time to double as effectively.

Listed below are execution instances, taken from epoch 20, for 5 completely different batch sizes, evaluating MPT with a default Coverage that makes use of float32 all through. (We should always add that other than the very first epoch, execution instances per step fluctuated by at most one millisecond in each situation.)

32	28	30
64	52	56
128	97	106
256	188	206
512	377	415

Persistently, MPT was sooner, indicating that the supposed code path was used.
However the speedup just isn’t that large.

We additionally watched GPU utilization throughout the runs. These ranged from round 72% for batch_size 32 over ~ 78% for batch_size 128 to hightly fluctuating values, repeatedly reaching 100%, for batch_size 512.

As alluded to above, simply to anchor these values we ran the identical mannequin in 4 different situations, the place no speedup was to be anticipated. Though these execution instances are usually not strictly a part of the experiments, we report them, in case the reader is as interested in some context as we have been.

Firstly, right here is the equal desk for a Titan XP with 12G of reminiscence and compute functionality 6.1.

32	44	38
64	70	70
128	142	136
256	270	270
512	518	539

As anticipated, there isn’t any constant superiority of MPT; as an apart, trying on the values general (particularly as in comparison with CPU execution instances to return!) you may conclude that fortunately, one doesn’t all the time want the most recent and best GPU to coach neural networks!

Subsequent, we take one additional step down the {hardware} ladder. Listed below are execution instances from a Quadro M2200 (4G, compute functionality 5.2). (The three runs that don’t have a quantity crashed with out of reminiscence.)

32	186	197
64	352	375
128	687	746
256	1000	–
512	–	–

This time, we truly see how the pure memory-usage side performs a task: With MPT, we will run batches of dimension 256; with out, we get an out-of-memory error.

Now, we additionally in contrast with runtime on CPU (Intel Core I7, clock velocity 2.9Ghz). To be sincere, we stopped after a single epoch although. With a batch_size of 32 and operating an ordinary pre-built set up of TensorFlow, a single step now took 321 – not milliseconds, however seconds. Only for enjoyable, we in comparison with a manually constructed TensorFlow that may make use of AVX2 and FMA directions (this subject may actually deserve a devoted experiment): Execution time per step was lowered to 304 seconds/step.

Conclusion

Summing up, our experiment didn’t present necessary reductions in execution instances – for causes as but unclear. We’d be completely satisfied to encourage a dialogue within the feedback!

Experimental outcomes however, we hope you’ve loved getting some background info on a not-too-frequently mentioned subject. Thanks for studying!

This camera-focused Extremely flagship is all set to launch earlier than MWC

Technology

Dr. Mike

February 20, 2026

This camera-focused Extremely flagship is all set to launch earlier than MWC

TL;DR

Xiaomi has confirmed a world launch occasion for the Xiaomi 17 Extremely on February 28, 2026, in Barcelona.
The occasion can be anticipated to incorporate the Xiaomi 17, the Xiaomi Pad 8, and the Xiaomi Pad 8 Professional.
The Xiaomi 17 Extremely could function a 6,000mAh battery for international markets, with costs beginning at €1,499.

MWC 2026 is inching nearer, and we’ve affirmation of 1 extremely anticipated launch. Confirming earlier leaks, Xiaomi has introduced that the Xiaomi 17 Extremely will launch globally this month.

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Xiaomi has formally confirmed that it’s going to maintain a launch for “The brand new wave of images” on February 28, 2026, in Barcelona, marking it as a pre-MWC occasion. The teaser picture on their international touchdown web page is for the Xiaomi 17 Extremely, leaving no doubts in regards to the star of the present.

Leaks recommend that not solely will the corporate launch its Extremely flagship on the occasion, but in addition use it to launch the Xiaomi 17 and Xiaomi Pad 8 and Pad 8 Professional for the worldwide markets.

The Xiaomi 17 is predicted to begin at €999 (~$1,184) for its 12GB/256GB variant, roughly the identical because the Xiaomi 15 earlier than it. In the meantime, the Xiaomi 17 Extremely is predicted to begin at €1,499 (~$1,765) for its 16GB/512GB variant, however with a decrease 6,000mAh battery than the 6,800mAh battery within the Chinese language counterpart (almost certainly to adjust to worldwide battery laws).

We’ve not but heard whether or not the highest-end mannequin of the Xiaomi 17 Extremely, probably known as the Xiaomi 17 Extremely Leica Version, can even be launched on the occasion, because the teaser picture is for the usual variant. The Leica Version is much more costly than the usual version in China. It comes with a mechanical rotating zoom ring across the digital camera, a definite two-tone design, and software program options similar to Leica movie filters.

Both method, we’ll solely have to attend for a few week to study extra about Xiaomi’s plans for international markets. I’m notably excited in regards to the Xiaomi 17 Extremely, as Chinese language OEMs have been pushing the boundaries of smartphone pictures with their Extremely flagships greater than Samsung and Google have with theirs.

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The Search Engine for OnlyFans Fashions Who Look Like Your Crush

Science

Dr. Mike

February 20, 2026

The Search Engine for OnlyFans Fashions Who Look Like Your Crush

For 3 days in February, porn star Alix Lynx flew to Miami for her first unique creator gathering the place she was in full grind mode: capturing Reels and speaking technique with different creators. “It was sort of like SoHo Home for OnlyFans ladies,” she says of the expertise, which is named The Circle and drew greater than a dozen intercourse staff, together with Remy LaCroix and Forrest Smith.

Lynx, who’s a former webcam mannequin turned OnlyFans starlet, has a mixed 2 million followers throughout Instagram, TikTok, and X. She joined OnlyFans in 2017 with “the luxurious of getting my very own following,” she says, however these numbers haven’t all the time translated to subscriptions. It’s why she was in Miami.

“I don’t suppose individuals perceive. I do a shitload of selling,” Lynx says.“That’s the large false impression with OnlyFans—when creators be part of they suppose it’s going to be simple. However except you’re a genius at advertising and marketing on social media, which is few and much between, it’s genuinely onerous to get discovered and acquire a following.”

Lots of OnlyFans’ 4 million creators have mentioned the identical factor: native discovery on the platform sucks. “There’s only a frustration,” Lynx says. “In an ideal world, there can be that searchability characteristic as a result of it makes it an excellent taking part in discipline for creators.” (In keeping with OnlyFans, the platform limits its search characteristic as a security precaution so customers don’t by accident stumble throughout content material they didn’t intend to see.)

It’s an issue that Presearch—a free, personal, decentralized search engine—needs to repair with the launch of its image-based discovery device Doppelgänger.

Doppelgänger is the most recent addition to Presearch’s Spicy Mode, a NSFW characteristic for looking out grownup content material. Customers can add a picture of a star—or any individual they suppose is scorching—to seek out OnlyFans creators that resemble them. The expertise matches the consumer with related creators who need an viewers moderately than to deepfake platforms which can be nonconsensual and unlawful. Ever puzzled who Sabrina Carpenter’s or Pedro Pascal’s porn doppelgängers have been? Surprise no extra.

In keeping with the corporate, Doppelgänger is constructed with particular guardrails—no monitoring what customers search, express age-gating—and runs on Presearch’s decentralized index, “which surfaces content material conventional engines like google and business AI suppress,” says Brenden Tacon, head of product and enterprise growth at Presearch.

“We’re making an attempt to supply a spot the place you may serendipitously develop into found,” Tacon tells WIRED. “You gained’t on OnlyFans. In the event you’re hustling your self on Instagram, Reddit, and all these locations, it’s so onerous to interrupt by means of the noise.”

With 300,000 each day lively searches on Presearch—in accordance with the corporate—Doppelgänger is among the first instruments in the marketplace pushing for moral discovery of grownup creators at scale. In contrast to conventional reverse picture instruments that scan throughout the open internet to find the place a photograph seems or try to hint somebody’s id, Doppelgänger doesn’t search the broader web, doesn’t floor private data, and doesn’t try to determine an individual. “It merely returns visually related public profiles primarily based on picture options, making it structurally extra restricted and, in some ways, extra privacy-protective than a typical reverse picture search,” Tacon says.

Nonetheless, the accuracy of Doppelgänger may use some enchancment. In a number of assessments run by WIRED, the AI appears higher tailor-made to seek out matches for girls than males. I had no downside discovering look-alikes for Cardi B and Sydney Sweeney. However when trying to find Michael B. Jordan look-alikes, it instructed feminine creators Chanell Coronary heart (the quantity 3 match) and Chamile Symone, along with Uncut Jock NYC, a white-presenting Brazilian intercourse employee. In truth, 5 girls have been instructed amongst Jordan’s high 40. Curious if this was a glitch, I dragged a photograph of actor Jeff Goldblum—a perennial “hottie,” in accordance with the subreddit r/VintageLadyBoners—into the picture finder, and the highest search outcome was for Jean B, a self-described “twink content material creator,” adopted by 38 ideas of large-breasted girls. (A second seek for Goldblum—who, for what it’s price, is extra zaddy than twink—with a special picture, didn’t fare any higher; the lone male “look-alike” was for YCC, who’s Chinese language.)

Google Gemini 3.1 Professional boosts complicated problem-solving

Dr. Mike

February 20, 2026

Google Gemini 3.1 Professional boosts complicated problem-solving

Google has launched a preview of Gemini 3.1 Professional, described as a better mannequin for essentially the most complicated problem-solving duties and a step ahead in core reasoning.

Introduced February 19, Gemini 3.1 Professional is designed for duties the place a easy reply just isn’t sufficient, taking superior reasoning and making it helpful for the toughest challenges, in accordance with the Google Gemini staff. The improved intelligence may help in sensible purposes equivalent to offering a visible rationalization of a fancy matter, synthesizing disparate information right into a single view, and fixing challenges that require deep context and planning. The mannequin is in preview for the builders through the Gemini API in Google AI Studio, Gemini CLI, Google Antigravity, and Android Studio. For enterprises, the mannequin is in Vertex and Gemini Enterprise. Customers can entry Gemini 3.1 Professional through the Gemini app and NotebookLM.

Gemini 3.1 Professional follows the Gemini 3.1 launch from November 2025. The Gemini staff mentioned the core intelligence in Gemini 3.1 Professional additionally was leveraged in final week’s replace to Gemini 3 Deep Assume to resolve challenges throughout science, analysis, and engineering. The staff additionally famous that, on the ARC-AGI-2 benchmark, which evaluates a mannequin’s skill to resolve new logic patterns, Gemini 3.1 Professional achieved a verified rating of 77.1%, greater than double the reasoning efficiency of Gemini 3 Professional.

NVIDIA Releases Dynamo v0.9.0: A Huge Infrastructure Overhaul That includes FlashIndexer, Multi-Modal Help, and Eliminated NATS and ETCD

Artificial Intelligence

Dr. Mike

February 20, 2026

NVIDIA Releases Dynamo v0.9.0: A Huge Infrastructure Overhaul That includes FlashIndexer, Multi-Modal Help, and Eliminated NATS and ETCD

NVIDIA has simply launched Dynamo v0.9.0. That is probably the most vital infrastructure improve for the distributed inference framework up to now. This replace simplifies how large-scale fashions are deployed and managed. The discharge focuses on eradicating heavy dependencies and bettering how GPUs deal with multi-modal information.

The Nice Simplification: Eradicating NATS and etcd

The largest change in v0.9.0 is the removing of NATS and ETCD. In earlier variations, these instruments dealt with service discovery and messaging. Nonetheless, they added ‘operational tax’ by requiring builders to handle additional clusters.

NVIDIA changed these with a brand new Occasion Airplane and a Discovery Airplane. The system now makes use of ZMQ (ZeroMQ) for high-performance transport and MessagePack for information serialization. For groups utilizing Kubernetes, Dynamo now helps Kubernetes-native service discovery. This alteration makes the infrastructure leaner and simpler to take care of in manufacturing environments.

Dynamo v0.9.0 expands multi-modal assist throughout 3 essential backends: vLLM, SGLang, and TensorRT-LLM. This enables fashions to course of textual content, photographs, and video extra effectively.

A key function on this replace is the E/P/D (Encode/Prefill/Decode) break up. In customary setups, a single GPU usually handles all 3 phases. This will trigger bottlenecks throughout heavy video or picture processing. v0.9.0 introduces Encoder Disaggregation. Now you can run the Encoder on a separate set of GPUs from the Prefill and Decode employees. This lets you scale your {hardware} primarily based on the particular wants of your mannequin.

Sneak Preview: FlashIndexer

This launch features a sneak preview of FlashIndexer. This part is designed to resolve latency points in distributed KV cache administration.

When working with giant context home windows, transferring Key-Worth (KV) information between GPUs is a gradual course of. FlashIndexer improves how the system indexes and retrieves these cached tokens. This ends in a decrease Time to First Token (TTFT). Whereas nonetheless a preview, it represents a significant step towards making distributed inference really feel as quick as native inference.

Good Routing and Load Estimation

Managing visitors throughout 100s of GPUs is tough. Dynamo v0.9.0 introduces a better Planner that makes use of predictive load estimation.

The system makes use of a Kalman filter to foretell the long run load of a request primarily based on previous efficiency. It additionally helps routing hints from the Kubernetes Gateway API Inference Extension (GAIE). This enables the community layer to speak straight with the inference engine. If a particular GPU group is overloaded, the system can route new requests to idle employees with larger precision.

The Technical Stack at a Look

The v0.9.0 launch updates a number of core parts to their newest secure variations. Right here is the breakdown of the supported backends and libraries:

Element	Model
vLLM	v0.14.1
SGLang	v0.5.8
TensorRT-LLM	v1.3.0rc1
NIXL	v0.9.0
Rust Core	dynamo-tokens crate

The inclusion of the dynamo-tokens crate, written in Rust, ensures that token dealing with stays high-speed. For information switch between GPUs, Dynamo continues to leverage NIXL (NVIDIA Inference Switch Library) for RDMA-based communication.

Key Takeaways

Infrastructure Decoupling (Goodbye NATS and ETCD): The discharge completes the modernization of the communication structure. By changing NATS and ETCD with a brand new Occasion Airplane (utilizing ZMQ and MessagePack) and Kubernetes-native service discovery, the system removes the ‘operational tax’ of managing exterior clusters.
Full Multi-Modal Disaggregation (E/P/D Break up): Dynamo now helps a whole Encode/Prefill/Decode (E/P/D) break up throughout all 3 backends (vLLM, SGLang, and TRT-LLM). This lets you run imaginative and prescient or video encoders on separate GPUs, stopping compute-heavy encoding duties from bottlenecking the textual content technology course of.
FlashIndexer Preview for Decrease Latency :The ‘sneak preview’ of FlashIndexer introduces a specialised part to optimize distributed KV cache administration. It’s designed to make the indexing and retrieval of dialog ‘reminiscence’ considerably quicker, aimed toward additional lowering the Time to First Token (TTFT).
Smarter Scheduling with Kalman Filters: The system now makes use of predictive load estimation powered by Kalman filters. This enables the Planner to forecast GPU load extra precisely and deal with visitors spikes proactively, supported by routing hints from the Kubernetes Gateway API Inference Extension (GAIE).

Take a look at the GitHub Launch right here. Additionally, be happy to observe us on Twitter and don’t neglect to hitch our 100k+ ML SubReddit and Subscribe to our Publication. Wait! are you on telegram? now you’ll be able to be part of us on telegram as properly.

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How we chosen the very best headphones for journey

The most effective headphones for journey: Evaluations & Suggestions

Finest total: Bowers & Wilkins Px7 S3

Professionals

Cons

Specs

Finest ANC: Sony WH-1000XM6

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Finest for audiophiles: Sennheiser HDB 630

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Finest splurge: Focal Bathys MG

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Finest for airplanes: JBL Tour One M3

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Finest for iPhone customers: AirPods Max

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Finest for avid gamers: SteelSeries Arctis Nova Elite

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Finest on-ear: beyerdynamic AVENTHO 100

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Finest worth: Soundcore House One

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Finest finances: JLab JBuds Lux ANC

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Finest true wi-fi earbuds: Bose QuietComfort Extremely (2nd Gen.)

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Finest IEM: Sennheiser IE 600

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Finest accent: Twelve South AirFly Professional 2

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Issues to contemplate when selecting the very best headphones for journey

Lively vs. passive noise cancellation

Battery life

Packability

Transparency mode

IP ranking

FAQs

Last ideas on the very best headphones for journey

What Are Multi-Agent Techniques (MAS)?

Step Into The World of Multi-Agent Techniques. Let Us Assist You Navigate Easily with the Greatest Practices & Roadmaps.

The Key Elements of a Multi-Agent System

1. Brokers (The Resolution Makers)

2. Setting (The Enterprise Actuality)

3. Communication & Coordination Mechanisms

4. Resolution Logic & Insurance policies

5. Studying & Adaptation

What Are the Advantages of Multi-Agent Techniques?

1. Sooner, Parallel Resolution-Making

2. Higher Resilience in Unsure Environments

3. Scalability With out Linear Headcount Development

4. Native Intelligence, World Alignment

5. Steady Optimization

Multi-Agent Techniques in Follow: Actual-World Enterprise Use Circumstances

1. Enterprise-Scale Provide Chain

2. Dynamic Pricing & Income Administration

3. Buyer Expertise Association

4. IT Operations & Incident Administration