Earlier this year, Toyota revealed its first three-row electric SUV in the Highlander EV. Now, it’s Lexus’ turn to put its spin on this segment with the upcoming TZ, which boasts a more luxurious design, seating for up to six and a top range of around 300 miles.

Like its cousins the Highlander EV and Subaru Getaway, the TZ is based on Toyota’s e-TNGA platform and will be available with two battery sizes (76.9kWh or 95.8kWh) and an upgraded Direct4 AWD system. While Lexus has yet to provide specific info about power, based on the output available from other models sharing this platform, we’re expecting around 400 horsepower (or more) depending on the exact configuration. It’s a similar situation when it comes to range, because while we’re still waiting on an official figure from the EPA, Lexus estimates a TZ with the larger 95kWh power pack will go for around 300 miles between charges. 

Meanwhile, at 200.8 inches, the TZ is actually slightly longer than the Highlander EV, while sporting a similarly brawny exterior with lots of hard lines along and Lexus’ signature spindle-shaped grille. Other features include Dynamic Rear Steering (up to four degrees) that should provide better maneuverability at low speeds and increased stability at high speeds. Unfortunately, the TZ’s 400-volt architecture doesn’t look very impressive, with charging speeds that top out at just 150kW that should deliver 10 to 80 percent charging times of around 35 minutes. Thankfully, the car does come with a native NACS port and, for times when you need to charge your other gadgets, Lexus is making a dedicated accessory adapter that plugs into an AC inlet in the cargo area.

On the inside, the TZ’s infotainment is centered around a 14-inch main display with a secondary 12.3-inch digital instrument cluster for the driver. Lexus says the TZ will also support a Smart Digital Key+ that allows you to unlock the car with your phone or smartwatch, and will continue to work even if the gadget runs out of battery. Also, aside from the base infotainment system, the TZ supports both Android Auto and Apple CarPlay.

Lexus

The TZ’s platform and exterior are quite similar to the Highlander EV and Subaru Getaway, so Lexus seems to have really leaned into the EV’s interior as a way to distinguish itself from its rivals. The company claims the TZ has the quietest cabin of any of its SUVs (both EV and ICE) and that quest for muted peace and relaxation seems to have been a core design goal for the vehicle, as Lexus uses the word quiet eight separate times in its official press release. The TZ also features a number of sustainable materials scattered throughout the car including forged bamboo panels, a plant-based UltraSuede and recycled aluminum for components like its roof rails and tonneau cover frame.

Unfortunately, we’re still waiting for official info regarding the TZ’s pricing and availability, configurations and trim levels, which Lexus plans to release closer to the EV’s on sale date sometime later this year.

Samsung has started rolling out One UI 8.5, and it’s a pretty big one.

The update is landing on more than 40 Galaxy smartphones and tablets. It brings a refreshed interface and a new wave of AI features to devices that, in some cases, are years old.

The rollout began in Korea today, with other regions expected to follow from mid-May. As usual with Samsung updates, it won’t arrive everywhere at once. Even though the list of supported devices is already extensive, some users will still have to wait.

At the front of the queue are Samsung’s newest flagships, including the Galaxy S25, S25+, and S25 Ultra, along with the Galaxy S25 FE and S25 Edge. But the update doesn’t stop there; Samsung is also pushing One UI 8.5 to older generations like the Galaxy S24 and S23 series. This includes their Ultra and FE models.

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Foldables are included too, with support for the Galaxy Z Fold 5 through Fold 7 and Galaxy Z Flip 5 through Flip 7. Additionally, a wide range of tablets including the Galaxy Tab S9, S10, and S11 series will be supported. Even mid-range and entry-level devices like the Galaxy A05 are on the list.

That breadth is the main story here. Rather than limiting new software to premium devices, Samsung is once again pushing its latest One UI version across almost its entire ecosystem.

So what’s actually new? One UI 8.5 brings a visual refresh in parts of the interface, including updated menus and navigation elements. But the bigger focus is AI. Samsung is expanding tools like Photo Assist, which helps refine and adjust AI-generated images, alongside improvements to its Bixby assistant, which is becoming more context-aware and responsive.

It’s not a radical redesign, but it does continue Samsung’s steady shift toward AI-driven features across both hardware tiers and older devices.

As with most major Android updates, availability will vary depending on region and model. It may take months before every eligible Galaxy device receives it. Still, for a rollout that includes everything from flagship phones to budget A-series models, this is one of Samsung’s broader software updates in recent memory. In effect, it is a free upgrade sitting in the settings menu for millions of users.

Project Hail Mary introduced fans to an unforgettable alien named Rocky. Many who finished the book wanted more time with the character and his quirky way of speaking. One maker decided to satisfy that craving by constructing a physical robot that captures the essence of Rocky in every joint and word.

The engineer behind Leviathan Engineering worked tirelessly on this project for months, bringing Rocky to life. He began with digital models of the character purchased from 3D Totems, a store that does an excellent job of creating 3D models that are precisely perfect. Next, software programs such as Fusion 360 and Tinkercad were used to ensure that the parts were not only printable, but also strong enough to endure some hard treatment.

Months later, the printed components came together to create the body of a four-legged monster with arms that appear to lunge out at you in all the right ways. Ten metal-geared servos provide the driving force behind the movements, cleverly placed to allow the robot’s expressiveness to show. The shoulders each receive an additional servo for arm swings, while the knees of the legs receive one each for low crouches. Movement, gestures, and body language pull the alien’s exuberant personality straight out of the novel. He even gets to offer you a full fist bump, or make a wild arm gesture, just like in the novel.

The robot is powered by a Raspberry Pi 5 that is connected to a circuit board known as the PCA9685 HAT, which operates the servos and controls all of their motions. Power comes from an external supply because the motors draw plenty of current during lively movements. Software brings everything to life. It has speech recognition integrated with Vosk, so you can tell it what to do without experiencing internet lag. Then there’s Piper for the robot’s voice, which has that really unique, staccato talking style that we all love about Rocky. For talks, however, it uses Google’s Gemini model, which essentially determines what the robot says and even what gestures to make based on the situation.

The maker wrote all the code using assistance from Claude through its command line interface. No fixed animation scripts exist. Instead the language model chooses movements based on context through a process called tool calling. Ask for a fist bump and the arm extends while the robot says something like “fist bump yes much happy.”

Of course, like any good maker project, this one had some challenges along the way. The engineer experimented with pulleys and linear actuators before settling on servos since they provided him more control over the entire mess. To ensure that printed joints did not break under any force, he had to do some trial and error before getting it perfect. Then there was the enjoyable task of putting things together, a little hot glue here and little super glue there to keep everything from falling apart. Wires route neatly inside the body thanks to extension cables. The final assembly stands about the size of a small tabletop model yet moves with enough grace to feel like a living creature from the pages of the novel.
[Source]

If you talk to the FDA, there’s only one permanent method of hair removal—electrolysis. This involves sticking a needle into a hair follicle, getting it very hot or running a current through it, and then letting heat and/or the lye generated kill the root of the hair dead. Normally, you’d pay someone with a commercial machine to do this for you at great expense. Or, you could do it yourself with a home-built machine, as [n3tcat] did.

Based on the available information out in the wild, [n3tcat] decided to build a galvanic electrolysis machine. This specifically passes current through a needle in the hair follicle to generate lye at the hair bulb, which kills it. The amount of lye generated depends on the amount of current and the time over which it is applied. More lye is more likely to kill a follicle permanently, though there are limits with regards to avoiding scarring, other skin damage, and excessive pain.

[n3tcat]’s guide explains the basic theory behind galvanic electrolysis, as well as how the rig was built. An early attempt simply involved hooking up a 12-volt car battery to a standard electrolysis needle, sticking it in a hair, with the other electrode being an aluminium can held by the person being treated. The fun thing was that this allowed varying the current depending on how much contact and how stiffly the person grabbed the can.

After a few successful hair removals this way, [n3tcat] decided to build a better rig. An RP2040 microcontroller was enlisted to run the show, powered by a 3.7-volt lithium rechargeable battery. An OLED screen and a rotary encoder were selected to serve as the interface, while a foot pedal was added for firing off current. A boost converter was used to push the battery voltage up to the vicinity of 15 volts for delivery to the needle, set up to avoid excessive current delivery for safety. A DAC was paired with an LM358 op-amp feeding into a MOSFET to control the current passed to the needle for accurate, controlled treatment, with the RP2040 monitoring the current level via a dedicated ADC. The needle itself got a D-printed pen-like handle for better ergonomics, easing the process of slotting the needle into a hair follicle. Everything was then assembled on a cute PCB, and wrapped up in a nice 3D printed housing. The files are available for the curious.

Electrolysis is a process that can cost many thousands of dollars depending on how much hair you hope to remove. Thus, it’s easy to see the appeal in having a rig that lets you do it at home. It’s just one of those things where you have to take the proper precautions to ensure you’re not unduly hurting yourself.  Stay safe out there, hackers!

Even as leading AI providers like OpenAI and Anthropic battle over the compute to train and release ever larger, more powerful models, other labs are going in a different direction — pursuing the development of smaller, more efficient models and often open sourcing them.

The latest worth paying attention to comes from the lesser-known Palo Alto startup Zyphra, which this week released its new reasoning, mixture-of-experts (MoE) language model, ZAYA1-8B, with just over 8 billion parameters and only 760 million active — far fewer than the trillions estimated for the likes of the big labs. Yet, ZAYA1-8B retains competitive performance on third-party benchmarks against GPT-5-High and DeepSeek-V3.2.

It can be downloaded from Hugging Face now free of charge under a permissive, standard, enterprise-friendly Apache 2.0 license — and enterprises and indie developers can begin using and customizing it immediately to suit their needs. Individual users can also test it themselves here free at Zyphra Cloud, the startup’s inference solution.

But the real headline is what ZAYA1-8B was trained on: a full stack of AMD Instinct MI300 graphics processing units (GPUs), the rival to Nvidia GPUs released by AMD nearly three years ago, and which shows that this platform is capable of producing useful models and is a viable alternative to the preferential position Nvidia has maintained in recent years among AI model developers.

How ZAYA1-8B was trained

The “intelligence density” touted by Zyphra is the result of what they describe as a “full-stack innovation” approach, spanning architecture, pretraining, and reinforcement learning (RL).

ZAYA1-8B is built on Zyphra’s proprietary MoE++ architecture, described in a technical report released by the lab. This architecture introduces three fundamental changes to the standard Transformer architecture that gave rise to large language models (LLMs) and the entire generative AI era:

1. Compressed Convolutional Attention (CCA): Unlike standard attention mechanisms that struggle with memory as context windows grow, CCA performs sequence mixing in a compressed latent space. This results in an 8x reduction in KV-cache size compared to full multi-head attention, enabling more efficient long-context reasoning.

2. The ZAYA1 MLP Router: Most MoE models use a linear router to decide which “experts” handle a specific token. Zyphra replaced this with a more expressive multi-layer MLP-based design. To maintain stability during training—a common hurdle for MoEs—they implemented a bias-balancing scheme inspired by PID controllers from classical control theory.

3. Learned Residual Scaling: This controls the growth of the “residual norm” as data flows deeper into the model’s 40 layers, preventing gradient vanishing or explosion with negligible computational overhead.

Reasoning-First Pretraining

A critical differentiator for ZAYA1-8B is that reasoning was integrated from the start of pretraining, rather than being “bolted on” during post-training.

To handle long chain-of-thought (CoT) traces that would otherwise exceed the initial 4K pretraining context, Zyphra developed Answer-Preserving (AP) Trimming.

Think of AP-trimming like a film editor cutting a long scene: instead of cutting the ending (the solution) or dropping the scene entirely, the editor removes the “middle” of the character’s monologue while keeping the beginning (the problem setup) and the final reveal (the answer).

This ensures the model learns the relationship between complex problems and their solutions even when the full internal logic doesn’t yet fit into memory.

It seemed to work well on my test query about countertop stain removal to ZAYA1-8B running on Zyphra Cloud.

Markovian RSA: redefining test-time compute

The model’s most significant performance leap comes from Markovian RSA, a novel test-time compute (TTC) methodology.

Traditionally, if you want a model to “think harder,” you let it generate a longer chain of thought. However, this often leads to “context bloat,” where the model loses focus as the history grows too long.

Markovian RSA solves this by decoupling “thinking depth” from “context size”. It functions like a recursive scientific peer-review process:

• The model generates multiple parallel reasoning traces (candidates).

• It then extracts only the “tails” (the last few thousand tokens) of these traces.

• These tails are subsampled and presented to the model in a new “aggregation prompt,” asking it to reconcile the different approaches into a better solution.

By carrying forward only the tails (typically a 4K-token budget), the model can reason indefinitely without the context window ever overflowing. In practice, this allows the 700M active parameter ZAYA1-8B to achieve a 91.9% score on AIME ’25, closing the gap with models that have 30 to 50 times its active parameter count.

Because ZAYA1-8B maintains a small total parameter footprint (8.4B), it is uniquely positioned for on-device deployment and local LLM applications. For enterprises, this enables the deployment of high-tier reasoning capabilities—traditionally reserved for massive cloud-based models—directly onto local hardware or edge devices. This “local-first” reasoning approach addresses common enterprise hurdles regarding data residency, latency, and the high cost of persistent API dependencies.

Benchmarks show a remarkably performant small model that punches above its weight class

Zyphra is positioning ZAYA1-8B as a “punch above its weight” model for developers who need high-tier reasoning without the latency or cost of massive frontier models. After all, its active parameter count is much lower than other similarly-sized models, making it much cheaper and less compute-intensive to run in inference.

• Instruction Following: ZAYA1-8B scores 85.58 on IFEval, remaining competitive with much larger models like Intellect-3 (106B).

• Agentic Capabilities: On the τ² benchmark, the model reaches 43.12, and 39.22 on BFCL-v4, providing a baseline for its ability to handle tool-calling and multi-turn tasks.

In single-rollout evaluations (without the extra “thinking” time), ZAYA1-8B already outperforms its weight class. It beats Qwen3.5-4B and Gemma-4-E4B on math and code benchmarks.

When Markovian RSA is enabled, the results are startling:

• HMMT ’25 (Math): ZAYA1-8B hits 89.6%, surpassing Claude 4.5 Sonnet (79.2%) and GPT-5-High (88.3%).

• LiveCodeBench (Coding): The model achieves 69.2%, outperforming DeepSeek-R1-0528.

Zyphra notes that while the model is a specialist in algorithmic reasoning, it lags slightly behind larger models on “knowledge-heavy” tasks like broad factual retrieval (MMLU-Pro), which suggests that while reasoning can be compressed into smaller cores, factual memory still benefits from raw parameter count.

Apache 2.0 open licensed for research and commercial usage

Zyphra has released ZAYA1-8B under the Apache-2.0 license. This is a critical choice for the developer community. Unlike “copyleft” licenses like the GPL, which require any derived work to also be open-source, Apache-2.0 is highly permissive.

For developers and enterprises, this means they can use, modify, and distribute ZAYA1-8B—even within proprietary, commercial applications—without being forced to open-source their own codebases.

It also includes an explicit grant of patent rights from contributors, providing a layer of legal safety for startups building on top of Zyphra’s architecture. By opting for Apache-2.0 over more restrictive “research-only” licenses often seen from frontier labs, Zyphra is signaling a commitment to the open-weight ecosystem.

To deploy ZAYA1-8B, developers must use specific branches from Zyphra’s forks of core libraries, as the architecture requires specialized handling:

• Custom Forks: Users should install the zaya1 branch from Zyphra’s versions of the vllm and transformers libraries.

• Deployment Flags: When starting a vLLM server, specific flags are required to handle the reasoning parser and tool-calling (e.g., --reasoning-parser qwen3 and --tool-call-parser zaya_xml).

• Parallelism Strategy: For multi-GPU environments, Zyphra recommends using Data Parallelism (DP) combined with Expert Parallelism (EP). Notably, Tensor Parallelism (TP) for the model’s CCA mechanism is not currently supported, making DP+EP the optimal path for scaling inference throughput.

Background on Zyphra

Zyphra: A New Paradigm for Intelligence Density

Founded in 2021 and headquartered in Palo Alto, California, Zyphra Technologies is a full-stack artificial intelligence laboratory dedicated to building human-aligned artificial general intelligence (AGI) — that which outperforms people at most tasks — through a decentralized, open-source framework.

According to the company’s official mission statement, Zyphra seeks to challenge the “centralized” dominance of monolithic cloud models by focusing on “intelligence density”—a core guiding principle that aims to maximize the reasoning and logic extracted per parameter and per FLOP.

Zyphra CEO and Co-Founder Krithik Puthalath explained previously to VentureBeat that this strategy is essential for enabling high-performance AI to run locally on hardware such as tablets, wearable glasses, and enterprise servers, thereby ensuring user privacy and reducing reliance on third-party cloud infrastructure.

The company’s technical identity is deeply informed by computational neuroscience, led by Co-Founder and Chief Scientist Beren Millidge.

According to Millidge’s personal website, he currently serves as a Postdoctoral Researcher at the University of Oxford’s Nuffield Department of Clinical Neurosciences, where his research focuses on deep credit assignment and mathematical models of the brain.

Millidge, who earned his PhD from the University of Edinburgh, has pioneered research into active inference and the “free-energy principle,” concepts that directly influence Zyphra’s pursuit of multimodal architectures capable of long-term memory and continual learning.

This neuroscientific influence was central to the design of Zyphra’s prior Zamba model, released in 2024, which mimics the cortex-hippocampus interaction to share information across sequential layers. A recent TED Talk video provides insight into Millidge’s perspective on the intersection of biological neuroscience and AI, which serves as the theoretical foundation for Zyphra’s model architectures.

Zyphra has achieved significant technical milestones through a deep integration with the AMD hardware ecosystem, as detailed in the company’s research documentation.

Financial data from PitchBook indicates that Zyphra is currently a venture-backed company that attained “Unicorn” status in June 2025 following a $110 million Series A funding round. According to PitchBook and company press releases, Zyphra is supported by a group of strategic investors including Advanced Micro Devices (AMD), IBM, Bison Ventures, and BC VC. With a team of approximately 31 employees as of 2026, the company continues to expand its footprint through the Zyphra Inference Cloud and Maia, an intelligent assistant platform designed to bring advanced search and productivity tools to enterprise teams.

Community reactions and industry context

The announcement has resonated strongly within the AI community, garnering nearly 1 million views on X/Twitter within 24 hours. The excitement largely centers on two factors: the viability of the AMD stack and the efficiency of the reasoning “cascade.”

Technologists have noted that Zyphra’s post-training process—a 4-stage RL cascade—is unusually disciplined. Most labs use a single round of RL, but Zyphra’s pipeline includes a “reasoning warmup” followed by a curriculum of 400 adaptive puzzle-like environments (RLVE-Gym) before finally moving to behavioral polishing.

One of the most praised “under-the-hood” details is Router Replay. In MoE models, training can become unstable if the “trainer” engine and the “inference” engine make slightly different decisions about which expert to use for a token due to floating-point noise. Zyphra’s system records the exact expert choices made during generation and forces the trainer to use them, effectively “pinning” the computation path and ensuring higher learning stability.

As the industry faces a potential plateau in the benefits of simply adding more parameters, ZAYA1-8B provides a compelling counter-narrative: that the next frontier of AI isn’t just about bigger clusters, but about smarter “thinking” algorithms that can do more with less.

Elon Musk’s legal effort to dismantle OpenAI may hinge on how its for-profit subsidiary enhances or detracts from the frontier lab’s founding mission of ensuring that humanity benefits from artificial general intelligence.

On Thursday, a federal court in Oakland heard a former employee and board member say the company’s efforts to push AI products into the marketplace compromised its commitment to AI safety.

Rosie Campbell joined the company’s AGI readiness team in 2021, and left OpenAI in 2024 after her team was disbanded. Another safety-focused team, the Super Alignment team, was shut down in the same time period.

“When I joined it was very research-focused and common for people to talk about AGI and safety issues,” she testified. “Over time it became more like a product-focused organization.”

Under cross-examination, Campbell acknowledged that significant funding was likely necessary for the lab’s goal of building AGI, but said creating a super-intelligent computer model without the right safety measures in place wouldn’t fit with the mission of the organization she originally joined.

Campbell pointed to an incident where Microsoft deployed a version of the company’s GPT-4 model in India through its Bing search engine before the model had been evaluated by the company’s Deployment Safety Board (DSB). The model itself did not present a huge risk, she said, but the company needed “to set strong precedents as the technology gets more powerful. We want to have good safety processes in place we know are being followed reliably.”

OpenAI’s attorneys also had Campbell admit that in her “speculative opinion,” OpenAI’s safety approach is superior to that at xAI, the AI company that Musk founded that was acquired by SpaceX earlier this year.

OpenAI it releases evaluations of its models and shares a safety framework publicly, but the company declined to comment on its current approach to AGI alignment. Dylan Scandinaro, its current head of Preparedness, was hired from Anthropic in February. Altman said the hire would let him “sleep better tonight.”

The deployment of GPT-4 in India, however, was one of the red flags that led OpenAI’s non-profit board to briefly fire CEO Sam Altman in 2023. That incident took place after employees including then-chief scientist Ilya Sutskever and then-CTO Mira Murati complained about Altman’s conflict-averse mangement style. Tasha McCauley, a member of the board at the time, testified about concerns that Altman was not forthcoming enough with the board for its unusual structure to function.

McCauley also discussed a widely-reported pattern of Altman misleading the board. Notably, Altman lied to another board member about McCauley’s intention to remove Helen Toner, a third board member who published a white paper that included some implied criticism of OpenAI’s safety policy. Altman also failed to inform the board about the decision to launch ChatGPT publicly, and members were concerned about his lack of disclosure of potential conflicts of interest.

“We are a non-profit board and our mandate was to be able to oversee the for-profit underneath us,” McCauley told the court. “Our primary way to do that was being called into question. We did not have a high degree of confidence at all to trust that the information being conveyed to us allowed us to make decisions in an informed way.”

However, the decision to boot Altman came at the same time as a tender offer to the company’s employees. McCauley said that when OpenAI’s staff started to side with Altman and Microsoft worked to restore the status quo, the board ultimately reversed course, with the members opposed to Altman stepping down.

The apparent failure of the non-profit board to influence the for-profit organization goes directly to Musk’s case that the transformation of OpenAI from research organization into one of the largest private companies in the world broke the implicit agreement of the organization’s founders.

David Schizer, a former Dean of Columbia Law School who is being paid by Musk’s team to act as an expert witness, echoed McCauley’s concerns.

“OpenAI has emphasized that a key part of its mission is safety and they are going to prioritze safety over profits,” Schizer said. “Part of that is taking safety rules seriously, if something needs to be subject to safety review, it needs to happen. What matters is the process issue.”

With AI already deeply embedded in for-profit companies, the issue goes far beyond a single lab. McCauley said the failures of internal governance at OpenAI should be a reason to embrace stronger government regulation of advanced AI—”[if] it all comes down to one CEO making those decisions, and we have the public good at stake, that’s very suboptimal.”