A few months before Elon Musk left OpenAI’s board of directors in February 2018, he tried to recruit Sam Altman to join a “world-class AI lab” within Tesla. Musk went as far as offering the OpenAI CEO a Tesla board seat, according to emails and testimony presented in federal court on Wednesday during the Musk v. Altman trial. The emails were shown to a jury during the cross examination of Shivon Zilis, a former OpenAI adviser and board member who is also the mother of four of Musk’s children.

Musk’s core claim in this lawsuit is that Altman and OpenAI president Greg Brockman effectively stole a nonprofit, using the $38 million Musk invested to create a private company worth more than $800 billion today. On Wednesday, lawyers for Musk showed video depositions of former OpenAI CTO Mira Murati and former OpenAI board member Helen Toner, to raise concerns over Altman’s alleged history of deceit.

OpenAI’s legal team has responded to Musk’s claims by questioning his true motives, arguing that the Tesla CEO has had “sour grapes” ever since he failed to assume control of OpenAI in 2017. He has since started a rival, for-profit AI lab. OpenAI’s lawyers used Zilis’ cross-examination on Wednesday to bring up evidence about Musk’s alleged plans to subvert OpenAI, and tried to suggest Zilis was privy to those plans. As it pertains to this case, one of Zilis’ most important roles at OpenAI was acting as a conduit between Musk and Altman.

In a text from February 2018 presented as evidence, Zilis—then an OpenAI adviser, as well as a Neuralink and Tesla executive—asked Altman, “Did you think through a B Corp subsidiary of Tesla?”

“There was documentary evidence that, at several points, Mr. Musk had contemplated seeking to join Sam Altman to the board and offered that option,” said OpenAI lawyer William Savitt outside the courthouse on Wednesday. “It was part of Mr. Musk’s effort to corrupt OpenAI and absorb it into Tesla … he was trying to get Altman to abandon the mission and be part of Tesla.”

In an email to Tesla’s VP of communications, Sarah O’Brien, from November 2017, Zilis shared a draft of an FAQ page about an event Tesla was planning to hold at the NeurIPS AI conference. “The purpose of this event is to share that Tesla is building a world leading AI lab(?) which will rival the likes of Google / DeepMind and Facebook AI Research,” the drafted FAQ read. The document continues, “One major issue for Tesla is when people think of Elon and AI, they think of OpenAI.”

Another part of the FAQ labeled “Who?” lists several Tesla executives who were planned to lead the unit, including Musk and Andrej Karpathy, a former OpenAI researcher. Altman’s name is listed next to Musk’s with two question marks beside it.

The FAQ is marked up with notes including that Altman could be a moderator for the NeurIPS event, which “could be a forcing function for Sam to commit to TeslaAI.” Another note reads that Tesla AI’s “strategy had yet to be defined and some of it may be deeply proprietary.”

Zilis testified on Wednesday that Altman never ended up joining Tesla, and the AI lab and the NeurIPS launch event never came to fruition. She also testified that Musk reached out to Karpathy about recruiting him to Tesla. Savitt told reporters that Zilis’ testimony on Karpathy is “directly contrary to what Mr. Musk told the jury just a few days ago.” Earlier in this trial, Musk testified that Karpathy left OpenAI of his own volition.

AI

For AI agents, seeing is expensive

Businesses deploying AI agents to automate computer usage may be spending far more money than necessary if those agents try to emulate human visual interaction.

Reflex, an enterprise application platform, recently set out to compare vision agents with API agents.

A vision agent in this context refers to an AI agent that mimics human interaction by relying on image processing and optical character recognition to operate an application. In this instance, that’s Claude Sonnet navigating a web app user interface via browser-use 0.12, a tool for automated web browser operation.

An API agent here refers to Claude Sonnet interacting with a web app via tools and APIs. The agent calls the same handling mechanisms that the UI calls and receives structured data in response, rather than a web page screenshot that must be analyzed.

“Two agents target the same running app: one drives the UI via screenshots and clicks, the other calls the app’s HTTP endpoints directly,” explained Palash Awasthi, head of growth at Reflex, in a blog post. “Same Claude Sonnet, same pinned dataset, same task. The interface is the only variable.”

The following task was presented to each agent: “A customer named Smith has complained about a recent order. Find the Smith with the most orders, accept all their pending reviews, and mark their most-recent ordered order as delivered.”

According to Awasthi, the API agent completed the task in just eight calls. It listed pending customer reviews, accepted them, and marked the order delivered. 

The vision agent, however, found only one of four pending reviews because it failed to scroll the page where it would have seen the three other reviews hidden off-screen.

Analyzing and interpreting a web page visually is fundamentally more challenging for an AI model than interacting with API calls and tools.

When the prompt was revised to help the vision model perform better, the vision agent still took ~17 minutes, significantly longer than the API agent at ~20 seconds. The vision agent also consumed a lot more tokens – ~45x more.

The company made its test available as a benchmark for those interested in trying to reproduce the results.

Awasthi said that the cost difference between the two approaches reflects the architecture – vision agents need to see and seeing is costly – each screenshot demands thousands of input tokens to process.

Anthropic estimates that processing a 1000×1000-pixel image with Claude Sonnet 4.6 uses about 1,334 tokens. 

The vision agent expended around 500,000 input tokens and around 38,000 output tokens to complete its task. The API agent used around 12,150 input tokens and around 934 output tokens.

For Awasthi, the lesson is that while vision agents may be necessary for interacting with apps you don’t control, inwardly focused agents should target APIs. ®

OpenAI’s rumoured smartphone might be arriving earlier than anyone expected.

According to analyst Ming-Chi Kuo, the company is now aiming to mass-produce its first “AI agent phone” in the first half of 2027. This is a significant shift from earlier timelines, with earlier estimates pointing closer to 2028.

The accelerated push suggests OpenAI is getting more serious about hardware, and quickly. Kuo says the move is partly tied to a potential IPO. A flagship device could help strengthen its pitch to investors. Additionally, there is growing competition in the emerging “AI phone” space.

Details are still thin, but the early picture is interesting. The device will run on a custom chip based on MediaTek’s Dimensity 9600, potentially built on TSMC’s next-gen process. It could also feature dual AI processors. These processors would handle things like vision and language tasks simultaneously. This is a hint at how OpenAI wants this to function less like a traditional smartphone and more like an always-on assistant.

One of the enduring myths of audiophilia is the concept of the “end-game” system. No matter the quality of the system you have, there always seems to be a missing piece.

“If I can only add a (insert next-level piece of equipment here) to my system I will be forever content and can die a happy (wo)man.”

I have achieved what I thought was end-game several times – for a few days or several weeks – but each time a new siren song emerges. Klipsch Forté, Lenco L70 and Sansui AU-777 are calls of the past now silenced. Current objects calling with varying urgency include blue-baffle JBLs, concentric-driver Tannoys, Thorens TD-124 or 125, and the Sansui AU-111.

With a planned move back to Japan in two or three years, the question of whether any of these will be achieved is on hold. I am in an enforced end-game state, knowing I will sell everything I have before I move, and will start again when I land on the other side of the Pacific.

Upgrading my Cleaning Game

This continual desire to upgrade and improve the system is about more than just equipment. It applies also to furniture, storage, cables, accessories, and record cleaning.

Four years ago, as a fairly new vinyl collector with a few hundred records in my collection, I wrote about budget cleaning solutions that did the job and kept the wallet (and Mrs. Audiolove) happy.

Today, with nearly 1,800 albums, I’ve become pickier about cleaning; I won’t cut corners and am more willing to drop some coin on quality. I’ve replaced records that didn’t cut the mustard (including grey-market EU “Public Domain” reissues) with modern audiophile or early pressings, and I want to show these the respect they deserve so they play clean and clear for my remaining decades.

And so ladies and gentlemen I present my 2026 cleaning arsenal, with medium-of-choice dependent on dirt levels and apparent vinyl condition.

Dust and Static – Ramar Berlin Record Brush (Red)

Ramar record brushes are made with a combination of carbon fibre (six double rows) and two rows of goat hair to penetrate every groove and remove fine dust and larger dirt particles while dissipating electrostatic charges.

The body and protective case of my brush are made with cherry wood, fashioned from a single block. The case protects the brush fibres from damage and dirt. A range of handle and case styles are available, including other wood variants and metal finishes.

Brushes come with a natural felt cleaning pad for removing any dust or dirt caught between the fibres during use, and Ramar offers after-market renewal and repair services.

The Ramar brush replaced my $20 Audio-Technica anti-static brush, and the difference was obvious. It feels far more substantial and better made, and it delivers noticeably better dust and static removal. At €360, it had better be excellent, and it is. For most records, this is the only cleaning solution I need, which makes the expense easier to justify.

Minor Dirt – GrooveWasher Hardwood Record Cleaning Kit

GrooveWasher makes a variety of cleaning accessories and kits, including record and stylus fluids and brushes. They also make anti-static record sleeves.

This was my first cleaner “upgrade,” replacing the cheapie DiscWasher. The look and feel of the two cleaners are similar, but the heft of the GrooveWasher’s wooden handle and the cleaning performance of the black Terry microfibre pad are a step up in quality.

The Hardwood Kit costs around $50 and comes with a 4 oz spray bottle of G2 high tech record cleaning fluid. This combination effectively removes minor grime like errant fingerprints or other sticky dirt that the Ramar can’t tackle. I use this brush for a first clean of used records that look to be in very good condition, and every few plays for records I’ve had in the collection for some time.

Over time the plush terry cloth pad does wear down and flatten out, and I’m currently eyeing up a replacement pad. The cleaning pads are easily removable, and replacements adhere solidly to the wood handle by way of Velcro fasteners.

Embedded Dirt and Persistent Crackle – HumminGuru HG01 Ultrasonic

The HumminGuru HG01 ultrasonic replaced a Spin Clean Mk. II a couple of years ago. The Spin Clean manual water-and-brush system worked well initially, but for some reason had begun jamming, even after replacing the brushes. While it still did a good job of cleaning, using it became frustrating and an upgrade was called for.

I investigated various vacuum and ultrasonic systems and the HumminGuru seemed to offer a good balance between results and financial outlay (Yes, I’m willing to drop some coin, but my pockets are not bottomless).

Ultrasonic cleaning uses high-frequency sound waves to create cavitation bubbles in water, requiring zero physical contact with the record. The HumminGuru automates the process of both cleaning and drying. All that’s required of the user is to add distilled water to the bath area on top, insert the record vertically into the cleaning slot, and hit a few buttons to set cleaning and drying time and start the cleaning process.

The record spins in the bath for several minutes while the ultrasonics remove dirt, the bath auto-drains into a removable water receptacle at the bottom of the machine, and then dual warm air fans dry the record. After 7-10 minutes, remove the record and it’s ready to store or play.

Water can be re-used to clean multiple records, and HumminGuru recommends using a few drops of alcohol-free cleaning formula to reduce surface tension and facilitate better penetration into the record grooves and to enhance drying in humid environments. Adaptors are available for 7” and 10” records.

I’ve been very impressed with results from the HumminGuru, with big improvements in grading quality post cleaning. I also noted improvements for records already cleaned with the old Spin Clean (which was no slouch, even with the jamming issues I experienced).

At time of writing the HumminGuru HG01 costs around $400 direct from the manufacturer, which is down significantly from my original purchase price.

Since I purchased my HG01, Humminguru has introduced an advanced model (the Nova) which features quieter cleaning, faster drying and automatic adjustment for different record sizes. The Nova runs about $700.

HumminGuru also introduced an Automatic Water Dispenser ($159.99 at Amazon) unit which eliminates my one gripe with the HG01 (and Nova), that being the somewhat inelegant process of removing the water receptacle to refill the water bath. The water dispenser costs around $160, and is very definitely on my to-buy list (and an exception to enforced end-game status).

The Bottom Line

And there we have the three arrows in my cleaning quiver, with all needs and bases covered. A final mention goes to the Ramar brush, which elicits frequent comments on Instagram regarding what many see as an exorbitant price (about the same as the HumminGuru).

No, there are no moving parts. Yes, it’s just a brush. But what a brush! As mentioned, this is my main cleaner and so on a per-use basis the cost is not so high. Factor in craftsmanship and precision – hand crafted, grain-matched wooden handle and holder, exquisitely layered brush fibres – and an obviously time-intensive build process, and it all makes sense. In my mind the juice is worth the squeeze.

What do you think? Leave your thoughts in the comments, or message me on the ‘Gram at @audioloveyyc.

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Lachy Groom, one of Silicon Valley’s most closely watched solo investors, decided to back Indian startup Pronto just 20 minutes into his first meeting with its 24-year-old founder.

The meeting, which took place in February through a mutual connection, led to Groom investing $20 million in Pronto as an extension of its Series B round, valuing the startup at $200 million after the investment — double its valuation just over two months earlier, as TechCrunch had previously reported. The deal came together within weeks, bringing the solo investor on board as the Bengaluru-based startup expands to meet growing demand for on-demand home services in India.

Groom said he was drawn to Pronto’s ambition to build what he called the world’s largest platform for organizing domestic labor, starting with India’s vast and largely unstructured workforce. “The work underneath that is genuinely hard, and most attempts in adjacent categories have struggled with the operational discipline,” he said, adding that Pronto founder Anjali Sardana (pictured above) and her team were operating “at a level I haven’t seen elsewhere in this space.”

Before founding Pronto in 2025, Sardana worked at Bain Capital and venture firm 8VC, where she gained early exposure to investing and high-growth startups. The startup connects households with workers for everyday tasks such as cleaning and basic home services.

The introduction was arranged through Paul Hudson, founder of Glade Brook Capital, who connected Groom and Sardana during her trip to San Francisco earlier this year. Glade Brook has backed startups founded by both: Pronto, which Sardana leads, and Physical Intelligence, where Groom is a co-founder. Hudson and Groom have also backed Indian quick-commerce startup Zepto.

Sardana said Groom’s investment approach is heavily founder-driven. “He indexes two things. One is the founder, and that’s 95% of it. If he loves the founder, then he will invest,” she told TechCrunch, adding that the rest comes down to the scale and potential of the business.

Groom’s bet comes as a clutch of startups in India race to build instant home services platforms, a category that is seeing rapid adoption among urban households as more consumers turn to on-demand help for everyday tasks.

The opportunity is significant. A recent Bank of America note, reviewed by TechCrunch, estimates the instant home services market in India could grow into a $15 billion to $18 billion industry by the end of the decade, as companies including Pronto, Snabbit, and Urban Company’s InstaHelp compete for share in the fast-growing category.

Competition is intensifying, with heavy capital inflows and aggressive pricing, particularly to attract first-time users. Bank of America estimates that Snabbit and Urban Company’s InstaHelp each account for about 40% of the market, while Pronto has around a 20% share, even as it scales rapidly. The category is expected to remain “burn-heavy” over the next two to three years.

Despite trailing larger rivals, Pronto has been scaling rapidly, growing from around 18,000 bookings a day to 26,000 in just over a month. The startup is focused on driving repeat usage, betting that turning occasional demand into frequent, habit-driven usage will be key to winning the category, with its top 10% of users accounting for about 40% of bookings.

This growth has also brought challenges, particularly in building out supply. Pronto has expanded its network of service workers to 6,500, up from 1,440 in January. But Sardana said demand continues to outpace supply, making forecasting and capacity management key challenges as the startup grows.