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Databricks built a RAG agent it says can handle every kind of enterprise search

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Most enterprise RAG pipelines are optimized for one search behavior. They fail silently on the others. A model trained to synthesize cross-document reports handles constraint-driven entity search poorly. A model tuned for simple lookup tasks falls apart on multi-step reasoning over internal notes. Most teams find out when something breaks.

Databricks set out to fix that with KARL, short for Knowledge Agents via Reinforcement Learning. The company trained an agent across six distinct enterprise search behaviors simultaneously using a new reinforcement learning algorithm. The result, the company claims, is a model that matches Claude Opus 4.6 on a purpose-built benchmark at 33% lower cost per query and 47% lower latency, trained entirely on synthetic data the agent generated itself with no human labeling required. That comparison is based on KARLBench, which Databricks built to evaluate enterprise search behaviors.

“A lot of the big reinforcement learning wins that we’ve seen in the community in the past year have been on verifiable tasks where there is a right and a wrong answer,” Jonathan Frankle, Chief AI Scientist at Databricks, told VentureBeat in an exclusive interview. “The tasks that we’re working on for KARL, and that are just normal for most enterprises, are not strictly verifiable in that same way.”

Those tasks include synthesizing intelligence across product manager meeting notes, reconstructing competitive deal outcomes from fragmented customer records, answering questions about account history where no single document has the full answer and generating battle cards from unstructured internal data. None of those has a single correct answer that a system can check automatically.

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“Doing reinforcement learning in a world where you don’t have a strict right and wrong answer, and figuring out how to guide the process and make sure reward hacking doesn’t happen — that’s really non-trivial,” Frankle said. “Very little of what companies do day to day on knowledge tasks are verifiable.”

The generalization trap in enterprise RAG

Standard RAG breaks down on ambiguous, multi-step queries drawing on fragmented internal data that was never designed to be queried.

To evaluate KARL, Databricks built the KARLBench benchmark to measure performance across six enterprise search behaviors: constraint-driven entity search, cross-document report synthesis, long-document traversal with tabular numerical reasoning, exhaustive entity retrieval, procedural reasoning over technical documentation and fact aggregation over internal company notes. That last task is PMBench, built from Databricks’ own product manager meeting notes — fragmented, ambiguous and unstructured in ways that frontier models handle poorly.

Training on any single task and testing on the others produces poor results. The KARL paper shows that multi-task RL generalizes in ways single-task training does not. The team trained KARL on synthetic data for two of the six tasks and found it performed well on all four it had never seen.

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To build a competitive battle card for a financial services customer, for example, the agent has to identify relevant accounts, filter for recency, reconstruct past competitive deals and infer outcomes — none of which is labeled anywhere in the data.

Frankle calls what KARL does “grounded reasoning”: running a difficult reasoning chain while anchoring every step in retrieved facts. “You can think of this as RAG,” he said, “but like RAG plus plus plus plus plus plus, all the way up to 200 vector database calls.”

The RL engine: why OAPL matters

KARL’s training is powered by OAPL, short for Optimal Advantage-based Policy Optimization with Lagged Inference policy. It’s a new approach, developed jointly by researchers from Cornell, Databricks and Harvard and published in a separate paper the week before KARL.

Standard LLM reinforcement learning uses on-policy algorithms like GRPO (Group Relative Policy Optimization), which assume the model generating training data and the model being updated are in sync. In distributed training, they never are. Prior approaches corrected for this with importance sampling, introducing variance and instability. OAPL embraces the off-policy nature of distributed training instead, using a regression objective that stays stable with policy lags of more than 400 gradient steps, 100 times more off-policy than prior approaches handled. In code generation experiments, it matched a GRPO-trained model using roughly three times fewer training samples.

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OAPL’s sample efficiency is what keeps the training budget accessible. Reusing previously collected rollouts rather than requiring fresh on-policy data for every update meant the full KARL training run stayed within a few thousand GPU hours. That is the difference between a research project and something an enterprise team can realistically attempt.

Agents, memory and the context stack

There has been a lot of discussion in the industry in recent months about how RAG can be replaced with contextual memory, also sometimes referred to as agentic memory.

For Frankle, it’s not an either/or discussion, rather he sees it as a layered stack. A vector database with millions of entries sits at the base, which is too large for context. The LLM context window sits at the top. Between them, compression and caching layers are emerging that determine how much of what an agent has already learned it can carry forward.

For KARL, this is not abstract. Some KARLBench tasks required 200 sequential vector database queries, with the agent refining searches, verifying details and cross-referencing documents before committing to an answer, exhausting the context window many times over. Rather than training a separate summarization model, the team let KARL learn compression end-to-end through RL: when context grows too large, the agent compresses it and continues, with the only training signal being the reward at the end of the task. Removing that learned compression dropped accuracy on one benchmark from 57% to 39%.

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“We just let the model figure out how to compress its own context,” Frankle said. “And this worked phenomenally well.”

Where KARL falls short

Frankle was candid about the failure modes. KARL struggles most on questions with significant ambiguity, where multiple valid answers exist and the model can’t determine whether the question is genuinely open-ended or just hard to answer. That judgment call is still an unsolved problem.

The model also exhibits what Frankle described as giving up early on some queries — stopping before producing a final answer. He pushed back on framing this as a failure, noting that the most expensive queries are typically the ones the model gets wrong anyway. Stopping is often the right call.

KARL was also trained and evaluated exclusively on vector search. Tasks requiring SQL queries, file search, or Python-based calculation are not yet in scope. Frankle said those capabilities are next on the roadmap, but they are not in the current system.

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What this means for enterprise data teams

KARL surfaces three decisions worth revisiting for teams evaluating their retrieval infrastructure.

The first is pipeline architecture. If your RAG agent is optimized for one search behavior, the KARL results suggest it is failing on others. Multi-task training across diverse retrieval behaviors produces models that generalize. Narrow pipelines do not.

The second is why RL matters here — and it’s not just a training detail. Databricks tested the alternative: distilling from expert models via supervised fine-tuning. That approach improved in-distribution performance but produced negligible gains on tasks the model had never seen. RL developed general search behaviors that transferred. For enterprise teams facing heterogeneous data and unpredictable query types, that distinction is the whole game.

The third is what RL efficiency actually means in practice. A model trained to search better completes tasks in fewer steps, stops earlier on queries it cannot answer, diversifies its search rather than repeating failed queries, and compresses its own context rather than running out of room. The argument for training purpose-built search agents rather than routing everything through general-purpose frontier APIs is not primarily about cost. It is about building a model that knows how to do the job.

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NYT Strands hints and answers for Monday, April 20 (game #778)

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Looking for a different day?

A new NYT Strands puzzle appears at midnight each day for your time zone – which means that some people are always playing ‘today’s game’ while others are playing ‘yesterday’s’. If you’re looking for Sunday’s puzzle instead then click here: NYT Strands hints and answers for Sunday, April 19 (game #777).

Strands is the NYT’s latest word game after the likes of Wordle, Spelling Bee and Connections – and it’s great fun. It can be difficult, though, so read on for my Strands hints.

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‘No more excuses’ as EU launches free age verification app

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European Commission President Ursula von der Leyen says the app is technically ready and will be available to citizens soon.

The European Commission yesterday (15 April) unveiled a digital age verification app aimed at shielding children from harmful content online, with European Commission president Ursula von der Leyen declaring there are “no more excuses” for platforms that fail to act.

Announcing the tool in Brussels on Wednesday (15 April), von der Leyen painted a stark picture of the risks children face in the digital world. “One child in six is bullied online. One child in eight is bullying another child online,” she said, warning that social media platforms use “highly addictive designs” that damage young minds and leave children vulnerable to predators.

Users set up the app using a passport or ID card, after which they can confirm their age anonymously. The free app, which the Commission says is technically ready and will soon be available to citizens, allows users to verify their age when accessing online platforms “without revealing any other personal data”, according to von der Leyen. “Users cannot be tracked,” von der Leyen stressed, adding that the app is fully open source and compatible with any device.

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Drawing a comparison with the EU’s Covid certificate – adopted in record time and used across 78 countries – von der Leyen said the age verification tool follows “the same principles, the same model.” Seven member states, including France, Italy, Spain and Ireland, are already planning to integrate the app into their national digital wallets.

The announcement comes ahead of the second meeting of the Commission’s Special Panel on Children’s Safety Online, which is due to deliver its recommendations by summer. Von der Leyen was unambiguous about the Commission’s direction of travel on enforcement. “Children’s rights in the European Union come before commercial interest. And we will make sure they do.”

Platforms were put on notice that voluntary compliance alone will not suffice. “We will have zero tolerance for companies that do not respect our children’s rights,” she said, adding that the Commission is “moving ahead with full speed and determination on the enforcement of our European rules”.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

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The Mac Mini is no longer a niche product, it's local AI infrastructure

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Consumer Intelligence Research Partners estimates the Mac Mini accounted for roughly 3% of Apple’s US Mac unit sales last year. That position has shifted quickly.
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Blue Origin’s New Glenn put a customer satellite in the wrong orbit during its third launch

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Jeff Bezos’ space company Blue Origin successfully re-used one of its New Glenn rockets for the first time ever on Sunday, but the company failed at its primary mission: delivering a communications satellite to orbit for customer AST SpaceMobile.

AST SpaceMobile issued a statement Sunday afternoon that the upper stage of the New Glenn rocket placed BlueBird 7 satellite into an orbit that was “lower than planned.” The satellite successfully separated from the rocket and powered on, the company said, but the altitude is too low “to sustain operations” and will now have to be de-orbited — left to burn up in the atmosphere of Earth.

The cost of the loss of the satellite is covered by AST SpaceMobile’s insurance policy, according the company, and there are successive BlueBird satellites that will be completed in around a month. AST SpaceMobile has contracts with more than just Blue Origin, and the company said it expects to be able to launch 45 more to space by the end of 2026.

But this represents the first major failure for Blue Origin’s New Glenn program, which only made its first flight in January 2025 after more than a decade in development. This was the second mission where New Glenn carried a customer payload to space, after launching twin spacecraft bound for Mars on behalf of NASA last November. The company did not immediately respond to a request for comment.

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The apparent failure of New Glenn’s second stage could have wider implications beyond Blue Origin’s near-term commercial ambitions. The company is pushing hard to become one of the main launch providers for NASA’s Artemis missions to the moon and beyond. The space agency — and the Trump administration — has put pressure on Blue Origin and SpaceX to be able to put landers on the moon by the end of President Donald Trump’s second term, before advancing to returning humans to the lunar surface.

Blue Origin CEO Dave Limp has even said his company “will move heaven and Earth” to help NASA get back to the moon faster.

Blue Origin recently completed testing its first version of its own lunar lander, which the company is expected to try and launch at some point this year (without any crew). Blue Origin had suggested last year that it was considering launching this lander on New Glenn’s third mission, but ultimately decided to launch the AST SpaceMobile satellite instead.

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The third New Glenn launch seemed to start just fine on Sunday, with the the mega-rocket lifting off at 7:35 a.m. local time from Cape Canaveral, Florida. It was the first time Blue Origin re-used a previously-flown New Glenn booster — the same one that flew during New Glenn’s second mission. Roughly 10 minutes after liftoff, the booster came back down and landed on a drone ship in the ocean, just like it had last November. Jeff Bezos even shared drone footage of the booster’s landing on X, the social media site owned by his rival Elon Musk. (Musk offered congratulations.)

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Roughly two hours after the launch, though, Blue Origin announced in its own post that the New Glenn upper stage placed AST SpaceMobile satellite in an “off-nominal orbit.” The company has not released any more information since that post.

Blue Origin spent a long time developing New Glenn, and it has been taken as a sign of confidence in that process that the company decided to start launching commercial payloads during these early missions. By comparison, SpaceX has spent the last few years flying test versions of its massive Starship, but has stuck with using dummy payloads as it works out the rocket’s kinks.

SpaceX did lose payloads deeper into its Falcon 9 program. In 2015, on the 19th Falcon 9 mission, the rocket blew up mid-flight and lost an entire International Space Station cargo spacecraft. In 2016, a Falcon 9 exploded on the launch pad during testing, causing the loss of an internet satellite for Meta.

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NYT Connections hints and answers for Monday, April 20 (game #1044)

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Looking for a different day?

A new NYT Connections puzzle appears at midnight each day for your time zone – which means that some people are always playing ‘today’s game’ while others are playing ‘yesterday’s’. If you’re looking for Sunday’s puzzle instead then click here: NYT Connections hints and answers for Sunday, April 19 (game #1043).

Good morning! Let’s play Connections, the NYT’s clever word game that challenges you to group answers in various categories. It can be tough, so read on if you need Connections hints.

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What Is The ‘Green Wave’ When It Comes To Traffic Lights?

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There are many drivers who often bemoan the very existence of traffic lights. Despite incurring the daily ire of commuters who are running late for work, even those haters have to acknowledge the traffic signal’s invaluable function in helping to keep our roadways safe.

Traffic signals have, of course, evolved considerably since they were first pressed into use in the late-1860s, with the first electric lights coming into play sometime around 1912. It wasn’t long until those signals started using colored lights, and have since evolved into the red, yellow, and green modes we are all too familiar with today. Even as safety remains the primary purpose of the hundreds of thousands of traffic lights currently employed throughout the United States, some theorize that the life-saving devices may one day cease to exist

Until that fateful day, getting stuck at red lights when you’re in a rush will remain a constant source of commuter frustration. On some occasions, however, a stream of greens opens up on the road ahead like the parting of the Red Sea. That stream of green has a name, with researchers dubbing it the “Green Wave.” While they may seem rare, the “Green Wave” is a common occurrence in certain parts of the world, and it serves a very important purpose.

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What is the purpose of a traffic light Green Wave?

While it might seem like a weird sort of karmic intervention, that “Green Wave” of traffic lights was actually programmed for a specific purpose by whatever government organization is in charge of maintaining the traffic signals in your city, state or township. They are, however, far more commonly utilized on high-volume roads in urban areas. The purpose of a “Green Wave” is to improve the flow of traffic in those areas, particularly during times with increased traffic volume. 

At its core, the concept is very simple. The idea is to keep traffic flowing during peak volume times by simply reducing the number of stops at concurrent traffic signals. To enact a “Green Wave,” planners and engineers simply synchronize the traffic lights in congested areas to all turn green at the same time and stay that way for a specified period that ensures a steady flow of traffic in one direction. The method is, naturally, easier to manage on one-way streets with no turning lanes, though some cities have attempted to aid traffic flow further by simply outlawing left turns in metropolitan areas. Some have even taken to banning right turns too

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In any case, on top of aiding the flow of traffic in congested areas, “Green Wave” traffic patterns are also believed to have a positive effect on the environment. After all, the reduction in stop-and-go traffic also reduces a vehicle’s idling time, which, in turn, leads to reduced greenhouse gas emissions.



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Digit Humanoid Nails a 65-Pound Deadlift and Reveals How Agility Trains Its Robots

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Agility Digit Humanoid Robot Deadlifting Weightlifting
Digit is seen performing deadlifts with a 65-pound weight in the center of a lab. Agility Robotics shared the video a few days ago, and to be honest, the robot maintains a fairly steady balance and completes the task from beginning to end. Someone mentions that the new version can lift significantly more weight than the previous one, while another laughs about how it can run all day without stopping.



The engineers designed the test so that Digit had to work harder than usual. Every additional pound it must lift causes the robot to modify its entire body at simultaneously, including its arms, legs, torso, and everything else. The system must keep the weight centered and avoid tipping over, therefore the legs, arms, and rest of the robot must all function together. These actuators and joints can withstand repeated load without breaking down. Digit’s video simply shows the robot grasping the weight, rising up, then effortlessly placing it down repeatedly in a standard indoor location built for people.


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  • High Flexibility & Safe Movement: Boasting 23 joint degrees of freedom (6 per leg, 5 per arm), it offers an extensive range of motion. For safety, it…
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Simulation is where all of the training takes place, because before it touches a real weight, an engineer creates a digital copy of the same thing in a virtual world. Then they anticipate what will happen when the weight shifts. The grip pressure remains constant, with no slipping or lowering. Any changes to the robot’s equilibrium are registered extremely instantly. The policy learns the perfect lift in the simulated environment with no complications before being transmitted directly to the real robot. When you see the real robot perform it, it looks fairly natural because it has already handled every potential variable thousands of times in the simulation.

Agility Digit Humanoid Robot Deadlifting Weightlifting
Engineers chose deadlifts for the test because the movement requires complete body control. A simple arm raise would not put the hardware under the same level of stress. By incorporating weight into the simulation loop, the team is able to handle balancing changes that a pre-programmed script cannot handle alone. As a result, Digit lifts consistently, with no wobbling or resets. This method is easily adaptable to other objects or larger loads in future tests.

Digit was built by Agility to manage long, repetitive jobs that wear people out, such as working in factories or warehouses where you must squeeze into tight spaces, pick up oddly shaped goods, and continue without taking a break. This deadlift test demonstrates Digit’s ability to lift weight on ordinary floors while remaining steady, which is ideal for picking up boxes, carrying tools, and stacking things in human-designed places.

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It also illustrates how far they’ve come in teaching robots to perform physical tasks. Whole-body synchronization was originally a nightmare, with hand-tuned code for each joint angle. But now they can simply train a policy in simulation that adapts on the go. Digit detects weight using its sensors, corrects itself in real time, and completes the lift without assistance, while the hardware can keep up because the training has already taught the actuators and joints to be more durable.
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Is the Iran War Driving a Surge of Interest in Electric Cars?

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In October and through November, America’s EV sales reached their lowest point since 2022 after government subsidies expired, remembers Time. “But first-quarter data for 2026 shows that used EV sales were 12% higher than the same time last year and 17% higher than the previous quarter.

“One factor likely helping push buyers toward these cars is high gas prices, which recently topped $4.00 a gallon for the first time in four years,” they write — but it’s not just in the U.S. Instead, they argue the conflict “is driving a global surge of interest in electric vehicles…”


In the U.K., electric car sales reached a record high, with 86,120 vehicles sold in March… The French online used-car retailer Aramisauto reported its share of EV sales nearly doubled from February 16 to March 9, rising to 12.7% from 6.5%, while sales of fueled models dropped to 28% of sales from 34%, and sales of diesel models dropped to 10% from 14%. Germany’s largest online car market, mobile.de, told Reuters that the share of EV searches on its website has tripled since the start of March — from 12% to 36%, with car dealers receiving 66% more enquiries for used EVs than in February.

South Korea reported that registrations for electric vehicles more than doubled in March compared to the prior year, due in part to rising fuel prices and government subsidies… In New Zealand, more than 1,000 EVs were registered in the week that ended on March 22, close to double the week before, making it the country’s biggest week for electric vehicle registrations since the end of 2023, according to the country’s Transport Minister, Chris Bishop.

In America, Bloomberg also reports 605 high-speed EV charging stations switched on in just the first three months of 2025, “a 34% increase over the year-earlier period,” according to their analysis of federal data. A data platform focused on EV infrastructure tells Bloomberg that speedier and more reliable chargers are convincing more drivers to go electric and use public plugs.

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La Dolce Audio Current Drive Tube Amplifiers Have a Different Take on Valve Amplification: AXPONA 2026

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Most loudspeaker designers don’t spend much time debating open versus closed the way headphone enthusiasts do. Cabinets are part of the equation for a reason, offering control, efficiency, and predictable performance. That’s the accepted playbook. But like any good rule in audio, someone is always trying to break it.

At AXPONA 2026La Dolce Audio showed what happens when you ignore that playbook and lean into experimentation. Founder Terry Gesualdo isn’t approaching amplification or speaker design from a traditional standpoint, he’s part of a growing group of builders exploring open designs and current drive amplification as an alternative to the usual voltage driven norm.

I met Gesualdo on the shuttle ride over to the show, which feels about right. This isn’t a polished, corporate origin story, it’s the familiar path of someone who started by modifying gear, then building his own tube amps for himself, then for friends and family. The difference here is that he didn’t stop at tweaking circuits. He kept pushing until the results looked and sounded like something entirely his own.

Current Drive Tube Amplification: Why La Dolce Audio Isn’t Following the Script

Having built a few tube amps, I’m always curious to see what others are doing, and Terry Gesualdo is not following the usual path. Most of his designs are single ended pentode circuits, not triodes, and not push pull designs chasing more voltage swing. That choice alone puts him in a different lane than a lot of tube builders.

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Where things really diverge is the move to current drive. Most amplifiers are voltage driven. That’s the standard approach across both solid state and tube designs. Current drive shows up more often inside DACs where signal levels are extremely small, and occasionally in headphone amplifiers, but rarely in loudspeaker systems where current demands are far higher.

la-dolce-amps-rack-axpona-2026

The idea behind current drive is fairly straightforward. By controlling current instead of voltage, the amplifier reduces the impact of back EMF from the driver. That back EMF is the voice coil behaving like a generator as it moves through the magnetic field, feeding energy back into the amplifier. Reduce that interaction and, in theory, you reduce distortion and improve control over the driver.

It’s not a new concept, but it’s one that almost nobody is applying to loudspeakers in this way, especially with tube amplification. That’s what makes what La Dolce Audio is doing worth paying attention to.

Control Over Harmonics Instead of Chasing Purity

Circling back to that idea of ignoring the usual playbook, another aspect that reinforces how La Dolce Audio is taking a different path is the near exclusive use of pentode tubes instead of the more common triodes. Triodes are the simplest form of amplification with three active elements, anode, cathode, and grid. Fewer parts in the signal path is why many listeners and designers gravitate toward them. The assumption is less complexity means lower distortion and fewer unwanted artifacts.

But that’s only part of the story. Harmonic distortion doesn’t disappear just because the circuit is simpler. It just changes character. And not all harmonics are a problem. A lot of what people describe as tube warmth comes from second and third order harmonics, which many listeners actually prefer.

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Terry Gesualdo leans into that reality rather than trying to avoid it. By using pentodes, which add additional control elements beyond what a triode offers, he can shape those harmonic structures instead of accepting whatever the circuit gives him. That includes adjusting the balance between second and third order harmonics and even their phase relationships.

It’s a different mindset. Instead of chasing the lowest possible distortion number, the goal is control over how that distortion presents itself, and giving the listener a way to fine tune the result.

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Some will find that approach a bit sacrilegious. There’s a large part of the hobby focused on removing as much of this behavior as possible, chasing lower distortion numbers and cleaner measurements. That’s not the goal here.

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La Dolce Audio leans into a different philosophy. “If it sounds good, do it” is more than a slogan. It reflects the idea that listening is subjective and that not every system needs to be locked into a single interpretation of neutrality. By giving users control over harmonic structure, the design puts some of that decision making back in the listener’s hands.

UA2.5 and UA2.5M: Modular Power and User Tunability

ua-25m-angle
La Dolce Audio UA2.5M monoblock

La Dolce Audio offers two amplifier paths built around the same core ideas but with different roles. The UA2.5 is a dual channel amplifier rated at roughly 3 to 5 watts depending on tube selection, and it’s where most of the flexibility lives. With 24 possible sound signatures, it gives the user direct control over how the amplifier presents harmonic content and overall character.

The UA2.5M monoblocks step things up in output, delivering around 9 watts per channel, but they take a more focused approach. They are designed to be paired with the UA2.5, which handles preamp duties and sound shaping. As a result, the monoblocks do not include the same tuning controls, focusing instead on providing additional power while maintaining the same underlying design philosophy.

HPA2.3 Headphone Adapter

hpa-23-ua-25-front
La Dolce Audio UA2.5 Tube Amplifier (top) with HPA2.3 Headphone Adapter (bottom)

Alongside its amplifiers, La Dolce Audio offers the HPA2.3 headphone “amplifier,” although that label needs a bit of clarification. It’s not an amplifier in the traditional sense. The HPA2.3 is a passive device designed to work with the UA2.5, relying on it for signal processing and gain. In practice, it converts the UA2.5 into a headphone amplifier rather than operating as one on its own.

That means the HPA2.3 can drive a wide range of headphones depending on how the UA2.5 is configured, but it cannot function independently. No preamp, no sound.

Pricing reflects that modular approach. The UA2.5, which serves as the foundation of the system, runs between $1,799 and $2,499 depending on configuration and tube selection. The UA2.5M monoblocks are $1,999 each, and the HPA2.3 adds another $599. A full system lands in the $3,500 range, depending on how far you go down the rabbit hole.

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The Bottom Line

La Dolce Audio isn’t trying to fit into the usual mold, and that’s the point. In a category where a lot of designs feel like small variations on the same theme, this is a reminder that there are still different ways to approach amplification and system building.

Beyond the amplifiers, the partnership with ABX Audiophiles on Discord to offer open baffle speaker kits adds another layer. It invites listeners to get involved, not just as buyers but as participants, with a community that shares ideas, solves problems, and pushes designs forward together. We’ll have more on that ABX side of things in a forthcoming article.

It won’t be for everyone. If you want plug and play simplicity, this isn’t it. But if you’re the type who likes to understand what your system is doing and shape it to your preferences, La Dolce offers something most companies don’t. A system you can actually interact with, not just listen to.

For more information: ladolceaudio.com

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Hisense U7SG TV Review (2026): Better Design, Great Value

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Unlike previous years in what TV nerds like me call the “brightness wars,” the U7SG doesn’t outblast its predecessor, but it’s not a problem. It gets around three times as bright as anything you can stream (which is naturally capped due to compression), and has enough firepower for all but the flashiest 4K HDR Blu-rays. Its color processing shows a little more restraint than in previous models. It’s not quite what I’d call “accurate to the director’s intent,” like the best TVs I test, but it does keep itself from blasting your eyeballs most of the time.

The high brightness is matched by deep black levels, without much of the “blooming” or “haloing” around bright objects that can dilute the contrast of many budget-friendly TVs. It’s not as striking as OLED TVs, which can control each of their millions of pixels on demand, but it’ll wow you in deep space scenes just the same. I was pleased that the TV’s odd local dimming issue didn’t crop up in real-world content, but the picture does tend to flatten shadows in dark scenes more than expected, even as the matte-like screen does a good job keeping reflections at bay.

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Photograph: Ryan Waniata

There are some other notable flaws. Moving off to the TV’s side in my easy chair led to dimmer colors, washed-out contrast between the brightest and darkest images, and uneven backlighting, aka the “dirty-screen effect.” That stood out most in the green backdrop of the Masters on Sunday as Rory McIlroy held on for the win. It wasn’t an issue when viewing head-on, but even then, I noticed some dingy yellow lines along the screen’s left and right sides with light backgrounds. (I may not have noticed them much if I hadn’t been bombarding this TV with test content first.)

The U7SG still doesn’t feel quite like a premium model. But it’s a very clear, bright TV, and will feel more like it’s worth the money once RGB shows up on other Hisense models and the price on this one drops. If you want something brighter than a similarly priced OLED like the LG B5, the U7 is a great buy and has a few good upgrades over last year’s U75QG.

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We’ll know more about the 2026 TV landscape once the new RGB TVs have landed, but if you need a powerful, classy-looking TV before then, the U7SG should be on your list.

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