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Tech

The Need For Speed: Internet Speed Measurement (or DIY?)

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Car enthusiasts want to know how quickly they can make a quarter mile. Weightlifters are forever trying to add one more plate to the bar. Internet denizens have their own favorite number to brag about: the result from a speed test.

The ritual is familiar. Close a few browser tabs, click the big “Go” button, and watch the needle climb. Perhaps you pay for gigabit service and see 940 megabits per second, which produces a satisfied nod. Perhaps you see 299 megabits and begin obsessing over network hardware. But before you get too excited either way, try another test. There is a fair chance it will give you a different answer.

That does not necessarily mean one test is lying. “Internet speed” is not a single physical quantity waiting to be measured. A speed test measures the performance of a particular device, over a particular local connection, through a particular ISP route, to a particular server, at a particular time using a particular test method. Change any of those things and the answer can change too.

The Usual Suspects

Ookla on a WiFi connection to a 1Gbit Ethernet network. The limiting factor is the 802.11s WiFi link between the computer’s Ethernet port and the router’s.

Speedtest by Ookla is probably the best-known test. It selects a nearby server, although you can choose another. It attempts to saturate the connection with multiple simultaneous transfers. That makes it good at answering the question most consumers are asking: approximately how much aggregate bandwidth can this Internet connection deliver?

Running several connections matters. A single TCP connection must gradually increase its sending rate while reacting to round-trip time, packet loss, receive-window limits, and congestion-control behavior. On a high-bandwidth or high-latency path, one connection may not fill the available pipe. Several parallel connections can ramp up independently and make it easier to reach the link’s aggregate capacity. That number is valid, but it represents something like a busy household, a large segmented download, or several applications operating at once. It does not necessarily predict the speed of one file transfer from one distant server.

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Google’s built-in search speed test (search “speed test”) uses Measurement Lab’s Network Diagnostic Tool, or NDT. M-Lab describes NDT as a single-stream measurement of bulk-transport capacity. That makes it an interesting counterpoint to Ookla. A single flow may expose latency, loss, or TCP-window limitations that a multi-stream test can partially conceal. You can also use M-Lab’s own speed test directly.

While you may get similar numbers between the two approaches, you also may not get similar numbers, especially on high-latency connections where Ookla’s multiple streams will help hide latency.

Netflix’s Fast.com is deliberately simple. Open the page, and it immediately begins transferring data from Netflix infrastructure. By default it emphasizes download performance, since its original purpose was to answer a practical question: can this connection deliver Netflix video properly? Selecting “Show more info” adds upload speed and both unloaded and loaded latency.

Fast is barebones and measures speed to Netflix.

The use of Netflix servers is significant. Fast.com measures the route between you and Netflix’s content-delivery network, while Ookla may test against a server operated by your ISP only a few network hops away. A superb Ookla result and a poor Fast.com result do not prove deliberate throttling, but they do tell you that the destinations — or the routes to them — are behaving differently.

Cloudflare offers two related tests. Its Radar Network Quality Test provides a quick summary, while speed.cloudflare.com  gives an extremely detailed breakdown. The latter reports download and upload throughput, idle and loaded latency, jitter, packet loss, server location, and application-oriented quality estimates.

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Cloudflare provides a wealth of stats and graphs.

Loaded latency is especially useful. An otherwise fast connection can become miserable when a large upload or download fills an oversized queue in the modem or router. Your idle ping might be 12 milliseconds, but under load it may jump to several hundred milliseconds. That is the classic symptom usually called bufferbloat.

If you want more options, there is testmy.net, which allows you to test upload and download speeds separately, and speedof.me, which keeps a history for you, among others. It isn’t always obvious which ones are measuring a single connection vs multiple ones, so you may have to dig through whatever documentation you can find.

Your WiFi Is Part of the Test

A browser speed test cannot automatically tell you what’s hurting your speed. A laptop connected through marginal WiFi may report 180 megabits per second even though the router has a flawless gigabit Internet connection.

In fact, once incoming Internet service reaches several hundred megabits per second, WiFi is frequently the limiting factor. The link rate displayed by the operating system is not the same thing as usable throughput. Wireless protocols have framing overhead, acknowledgments, contention, retransmissions, and half-duplex operation. The advertised 866, 1200, or 2400 megabit link rate is therefore not a promise that application data will move at that rate.

The numbers printed on WiFi boxes add another layer of optimism. A router sold as “AC1800,” for example, does not provide an 1800-megabit connection to one device. The figure is normally the sum of the maximum advertised PHY rates on separate radios — perhaps 1300 Mb/s on 5 GHz plus 450 Mb/s on 2.4 GHz — with some rounding for marketing. A conventional WiFi client connects to one band at a time, so it cannot combine those rates. The total is better understood as the router’s theoretical aggregate capacity while serving multiple devices across both bands. Even then, protocol overhead, contention, signal quality, and client limitations make actual data throughput considerably lower. Newer WiFi 7 equipment can sometimes combine links using Multi-Link Operation, but that exception does not make the old ACxxxx arithmetic any less misleading.

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WiFi also uses shared airtime. Devices on the same channel — including neighboring access points that can hear one another — must contend for opportunities to transmit. A slow or distant client takes longer to send a given amount of data and can consume disproportionate airtime while doing so. Modern access points may provide airtime fairness and other mitigations. One old device does not invariably drag every client down to its rate, but it can still reduce the capacity available to the rest of the network. Interference has a similar effect. A weak signal, a crowded channel, microwave noise, or an overlapping neighboring network causes frames to be delayed or retransmitted. Those retries consume airtime without delivering additional data.

Repeaters and wireless mesh backhaul add another complication. A simple same-channel repeater must receive each packet and then transmit it again over the same shared medium. In the worst case, each repeated hop can roughly halve the available throughput. Modern tri-band mesh systems can avoid much of that penalty by using a dedicated backhaul radio, and Ethernet backhaul avoids it almost entirely.

This means it is entirely reasonable to buy gigabit Internet service and obtain only 300 or 500 megabits per second from a WiFi laptop. Whether that represents a problem depends on the client, radio band, channel width, signal level, backhaul, and local RF environment.

For a meaningful ISP test, begin with a computer connected directly to the router by Ethernet. Stop large transfers and temporarily disable any VPN. Record the chosen server, latency, upload speed, and download speed rather than preserving only the most flattering number. Then run the same tests over WiFi. The difference is an approximate measurement of what the wireless portion of the network is costing you.

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Remove the Internet From the Experiment

OpenSpeedTest running on an OpenWRT node.

Better still, remove the ISP from the test completely. OpenSpeedTest is a self-hostable, browser-based test. Run its server on a wired computer, NAS, or container, then visit it from laptops, phones, and tablets around the house. Because the traffic remains on your LAN, a slow result points toward WiFi, switching, cabling, or the client rather than the Internet connection.

It is possible to run this on the uhttpd server used with OpenWRT, although you’ll need to coax it to measure upload speeds since the server can’t handle the default method. The trick is to create a CGI script that accepts a large amount of data successfully and then configure uhttpd to run that.

A browser-based local test is convenient, but for serious diagnosis it is hard to beat iperf3, the client/server tool we recently used while testing mesh routers. On one machine (say, 192.168.1.100), start the server:

iperf3 -s

From another machine, run:

iperf3 -c 192.168.1.100

By default, iperf3 uses one TCP connection. Add -P 4 to try four parallel streams, or -R to reverse the direction so that the server sends and the client receives. Those variations can tell you something. If four streams are much faster than one, the network may have enough aggregate capacity but a single TCP flow is being limited by latency, loss, window growth, CPU performance, or offload behavior. If the reverse test is much faster, examine the weaker machine’s transmit path, drivers, antennas, or CPU.

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iperf3 can also generate UDP traffic at a specified rate and report packet loss and jitter. That is often more informative for evaluating a wireless link than merely chasing the largest TCP number.

Can Linux Make It Faster?

Linux offers an impressive array of network tuning knobs, which naturally tempts us to turn them. But first, you need to understand what needs tweaking.

Check the negotiated Ethernet rate and interface counters:

ethtool eth0
ip -s link show eth0

A gigabit adapter that has negotiated 100 megabits per second usually has a cabling, connector, or switch-port problem. Increasing TCP buffers will not repair it. Rising interface errors and drops point toward a physical, driver, or congestion problem. TCP retransmits (view with ss -ti) may indicate loss elsewhere on the path.

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You can inspect the active queue discipline with:

tc qdisc show

Linux supports queue disciplines such as fq_codel, which combines per-flow queueing with active queue management. It attempts to prevent one large transfer from building an enormous queue and delaying unrelated interactive packets. The kernel documentation specifically lists fq_codel as a sensible queue discipline that works without extensive configuration.

It can be selected as the default for newly created interfaces with:

sudo sysctl -w net.core.default_qdisc=fq_codel

That may improve queueing on traffic leaving the Linux machine. It does not, however, fix a large queue in the cable modem or Internet router. Queue management must be applied at the bottleneck. If the ISP link is limited to 20 megabits upstream, controlling a queue on a gigabit Ethernet interface after it has already handed packets to the router is too late.

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For a home connection, the most effective bufferbloat treatment is usually Smart Queue Management on the router. OpenWrt’s SQM system supports both fq_codel and CAKE. CAKE generally provides better performance. However, fq_codel requires less CPU overhead.

High-latency paths introduce a different problem. TCP must keep enough data in flight to fill the bandwidth-delay product. Modern Linux generally autotunes TCP buffers, so the old advice to assign enormous fixed values to tcp_rmem and tcp_wmem is less universally useful than it once was. Before changing them, use ss -ti during a transfer and look for retransmissions, round-trip time, congestion-window size, and whether the receiver window is actually limiting the connection.

Linux also supports selectable TCP congestion-control algorithms:

sysctl net.ipv4.tcp_available_congestion_control
sysctl net.ipv4.tcp_congestion_control

Algorithms such as BBR can improve throughput and queue behavior on some long-distance or lossy paths. But changing the algorithm affects connections sent by that Linux machine; it does not control the remote speed-test server, repair poor WiFi, or eliminate a queue in the router. Congestion-control tuning is therefore a useful experiment for a server, VPN endpoint, or long-haul transfer machine — not a universal solution to slow networking.

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Finally, inspect hardware offload features when a Linux system cannot keep up with a fast LAN:

ethtool -k eth0

Advanced network tuning is a bit beyond the scope of this post, but there are plenty of roadmaps down this rabbit hole.

The Lesson

The lesson here is that there is no universally correct speed-test result. Ookla tests how effectively multiple transfers can fill a route to one of its servers. M-Lab examines a single bulk flow. Fast.com tests the path to Netflix. Cloudflare pays unusual attention to latency under load and overall connection quality. OpenSpeedTest and iperf3 can determine whether the Internet connection is even the problem.

Run enough tests, and you will eventually obtain a number worth bragging about. Run the right tests, though, and you may find ways to truly increase real-world performance. If you want to chase that extra 1 kbit per second speed, be our guest — we know how it is. But the truth is that if the Internet is doing what you want it to do, then it is fast enough.

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Amazon Music Opens Alexa+ AI Playlists and Conversational Search to Every U.S. User

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Amazon Music has never lacked catalog depth or audio quality; the harder problem has been giving listeners a reason to choose it over Spotify, Apple Music or YouTube Music once the trial period ends and the monthly charge starts feeling less hypothetical.

Alexa+ is Amazon’s latest attempt to close that gap. The company has expanded the feature inside the Amazon Music mobile app to all U.S. customers, including listeners on free and paid tiers, with support on both iOS and Android at no additional charge.

Users can type or speak conversational requests, identify songs from partial clues, ask questions about artists and recordings, create playlists, refine them through follow-up instructions and save the results directly to their libraries. Amazon is not merely improving search; it is trying to replace it with a music assistant that can understand what listeners mean when they remember everything about a song except the title, artist and half the lyrics.

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Alexa+ Is More Than an AI Playlist Generator

Creating a playlist from a text prompt is no longer particularly novel. Spotify, Apple Music, YouTube Music, and Deezer already offer some variation of that feature.

Amazon is trying to make Alexa+ broader.

Listeners can ask it to identify a song from remembered lyrics, an artist, a film or television appearance, or some other incomplete clue. Amazon’s examples include finding the track used during the opening credits of The Sopranos or identifying a song from a partial lyric.

Alexa+ can also answer follow-up questions about:

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  • Genres and regional music scenes
  • Artist influences and discographies
  • Song meanings
  • Samples and their origins
  • Album and release history
  • Chart performance
  • Band membership changes
  • Festival lineups
  • Relationships between artists and musical styles

A listener could begin by requesting contemporary electronic music, ask for more information about one of the recommended artists, remove slower tracks, increase the tempo, and then save the finished selection.

That moves the feature closer to a conversational music guide than a conventional recommendation engine. Whether it is a knowledgeable guide or the person at the record store who confidently invents an answer rather than admitting they do not know will depend on accuracy.

Amazon has not published an accuracy rate for Alexa+ music responses, so claims about samples, credits, chart history, release dates, and song meanings should still be treated as useful starting points rather than digital liner notes carved into stone.

Alexa+ Can Build and Edit Playlists

Alexa+ can create playlists from specific combinations of mood, era, genre, geography, tempo, language, and activity.

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Amazon suggests prompts such as:

  • Music for driving along the Pacific Coast Highway at sunset
  • French-language songs that sound appropriate for a Parisian café
  • Ambient electronic music combined with light classical piano and no lyrics
  • Contemporary jazz that will not overpower a dinner party
  • 1990s pop featuring Madonna but excluding boy bands

The resulting playlists can be renamed, saved to an Amazon Music library, shared, and edited later by asking Alexa+ to add or remove specific tracks.

That last part is useful. Many generative playlist tools produce a list and then leave the listener to repair it manually when the algorithm decides that “quiet Sunday morning” requires Imagine Dragons.

Alexa+ allows the user to continue the conversation and adjust the result rather than starting again.

Available Across Every Amazon Music Tier

The most important competitive distinction is availability.

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Amazon says Alexa+ is available to all U.S.-based Amazon Music customers across every subscription tier, at no additional cost, through the iOS and Android apps.

That does not mean Alexa+ erases the differences between Amazon Music plans.

The underlying subscription still determines which songs can be played on demand, whether advertising is included, how skipping works, and which audio-quality formats are available. Amazon Music’s paid Unlimited and Standard plans provide access to HD, Ultra HD, Dolby Atmos, and Sony 360 Reality Audio where available; adding Alexa+ to a free account does not quietly turn it into a lossless subscription.

Amazon has also not announced when the Amazon Music integration will expand beyond the United States or reach desktop and web versions of the music service.

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How Amazon Music Alexa+ Compares

Music Service AI Music Feature Current Availability What It Does Main Limitation
Amazon Music Alexa+ All U.S. Amazon Music tiers on iOS and Android Conversational search, song identification, music questions, playlist creation, follow-up editing, playback and library actions U.S. mobile rollout only; plan restrictions still apply
Spotify Talk to Spotify Gradual beta for eligible Premium users aged 18 and older in the U.S., Ireland, and Sweden Typed or spoken conversation, playback control, music context, listening-history questions, podcast and audiobook discovery Premium only and limited beta availability
Apple Music Playlist Playground Beta for U.S. Apple Music subscribers aged 13 and older, in English Creates and refines playlists from moods, genres, activities, and eras Primarily a playlist tool rather than a broad music assistant
YouTube Music Ask Music YouTube Music Premium and YouTube Premium subscribers in select countries Generates personalized radio stations and playlists; now includes podcast recommendations Paid subscribers only and limited regional availability
Deezer Playlist with AI and Flow Tuner Playlist with AI began as a beta for 5% of paying subscribers; Flow Tuner is more broadly available Text-generated playlists and direct control over genres used by the recommendation system The conversational playlist beta has not been confirmed as a universal rollout
TIDAL My Mix Available to subscribers after sufficient listening history Produces up to six algorithmic mixes based on recent listening and saved music No comparable prompt-based conversational music assistant is publicly documented
Qobuz Discover and personalized mixes Available through Qobuz apps Combines DailyQ, WeeklyQ, personalized radio, recommendations, and extensive human editorial curation No conversational playlist assistant; Qobuz deliberately emphasizes human curation

Spotify Offers the Closest Direct Rival

Spotify’s new Talk to Spotify feature is the closest comparison to Alexa+.

Eligible Premium users can type or speak to Spotify from the Home and Now Playing screens, request unfamiliar artists, adjust the mood or tempo, add music to the queue, save tracks, follow artists, and ask questions about songs, albums, genres, podcasts, and audiobooks. Spotify can also answer questions about the user’s listening history, including when they first heard a song or which genres they have explored recently.

It is a deeper listener-history tool than Amazon has described so far, particularly because Spotify can examine years of personal playback behavior.

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The limitation is availability. Talk to Spotify is rolling out gradually in English to selected Premium users aged 18 and older in the United States, Ireland, and Sweden. Amazon is offering its music assistant to every U.S. customer, including people who are not paying for Amazon Music.

Spotify also warns that the beta remains a work in progress and that responses will not always be correct. That is unusually honest language from a technology company introducing AI and should probably be printed on the front of every device containing a microphone.

Apple Music Keeps Its AI Focus Narrower

Apple Music’s Playlist Playground creates playlists from text requests involving moods, genres, activities, and musical eras. Users can refine the results, rearrange songs, accept suggested additions, rename the playlist, and save it to their libraries.

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ios-26-iphone-17-pro-apple-music-playlist-playground

It is available in beta to Apple Music subscribers aged 13 and older in the United States and currently works only in English.

Playlist Playground competes directly with Alexa+ for playlist generation, but Apple does not currently describe it as a broader conversational music expert. It is designed to build playlists rather than explain musical relationships, identify tracks through extended dialogue, or answer detailed questions about what is playing.

Apple’s approach is cleaner and more focused. Amazon’s is more ambitious.

Ambition is useful, provided Alexa+ does not explain that John Coltrane joined Black Sabbath in 1973.

YouTube Music Uses AI for Radio and Podcast Discovery

YouTube Music’s Ask Music allows paid YouTube Music Premium and YouTube Premium subscribers in selected countries to describe what they want to hear and generate a personalized radio station or playlist.

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Google has expanded the feature to include podcast recommendations based on mood, genre, or programs the listener already enjoys.

YouTube Music has an enormous advantage when a request involves live performances, remixes, covers, rare recordings, and material that may not exist in a conventional streaming catalog.

Alexa+ appears to provide a more extensive conversational layer and more direct playlist editing, while Ask Music remains primarily focused on generating something to play.

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Deezer Gives Listeners More Control Over the Algorithm

Deezer began testing Playlist with AI globally in 2024, allowing selected paying subscribers to create playlists through text prompts involving activities, decades, genres, and moods. The initial test was limited to 5% of Deezer’s paying audience.

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More recently, Deezer introduced Flow Tuner, which allows users to activate, deactivate, or add genres and subgenres that influence the service’s recommendation algorithm. Deezer also excludes identified AI-generated recordings from Flow recommendations.

deezer-tune-flow

Flow Tuner may be less theatrical than asking a chatbot to build “music for driving through New Jersey while wondering why the Garden State Parkway is not moving,” but direct control over the recommendation engine is arguably more useful over time.

TIDAL and Qobuz Are Taking Different Paths

TIDAL continues to rely on My Mix, which creates as many as six personalized mixes from recent listening behavior and saved music. Those mixes change gradually as the service learns more about the listener.

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TIDAL does not currently document a consumer-facing conversational playlist assistant comparable to Alexa+, Spotify’s new chat feature, or Apple’s Playlist Playground.

Qobuz has moved even further in the opposite direction. Its redesigned Discover section combines personalized mixes, radio stations, listening history, and recommendations with extensive human editorial content, including reviews, interviews, Essential Discography selections, and curated playlists.

Qobuz uses technology to personalize discovery but continues to make human curation part of its identity. In a market racing to replace the record-store employee with a language model, Qobuz is betting that some listeners still want advice from people who have actually heard the album.

What Makes Amazon Music Alexa+ Different?

Alexa+ combines four capabilities that competitors usually separate:

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  1. Natural-language music search
  2. Contextual questions about artists and recordings
  3. Dynamic playlist generation and editing
  4. Direct playback and library control

The feature is also available across all U.S. subscription tiers rather than being reserved for paying customers or a small test group.

Amazon reports that Alexa+ users explore music three times as often as users of the original Alexa, while customers who request recommendations listen to almost 70% more music. Those are Amazon’s internal engagement figures, and the company has not disclosed the sample size, time period, or methodology behind them.

The numbers still explain why every major streaming company is investing heavily in conversational discovery. More questions lead to more recommendations, which lead to more listening, more data, and a lower chance that a subscriber remembers they are paying for four music services.

Who Is It For?

Alexa+ will be most useful for listeners who know what they want emotionally or stylistically but cannot express it through artist names and conventional search terms.

It should also appeal to people who frequently use Amazon Music through Echo products and already think of Alexa as the front door to their music library. The mobile integration allows those users to continue the same kind of interaction without standing within shouting distance of a smart speaker.

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It is less compelling for listeners who prefer choosing complete albums, already maintain carefully organized libraries, or view algorithmic playlists as the musical equivalent of allowing a supermarket to plan dinner.

Audiophiles should also understand that Alexa+ is a discovery and control feature, not an audio upgrade. It does not improve the mastering, increase the bit depth, repair a poor recording, or stop the Amazon Music app from occasionally making basic navigation feel like filing a tax return.

alexa-questions-amazon-music

The Bottom Line

Amazon Music Alexa+ is one of the more complete AI music assistants currently available because it goes beyond generating playlists. It can search conversationally, identify partially remembered songs, explain artists and recordings, refine selections through follow-up requests, and make direct changes to playback and the user’s library.

Making it available to every U.S. Amazon Music customer gives Amazon a wider launch audience than Spotify, Apple, YouTube Music, or Deezer currently offer for their closest comparable tools.

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That does not automatically make Alexa+ the best music-discovery system. Spotify knows more about the listening history of many users, YouTube Music has access to a wider universe of rare and unofficial material, Apple’s implementation is more focused, and Qobuz still understands that human editorial judgment has value.

The larger shift is impossible to miss. Music streaming services no longer want listeners to browse catalogs or build playlists by hand. They want us to describe a mood, surrender the queue, and keep listening while the assistant learns enough about our habits to become increasingly difficult to leave.

Convenient? Absolutely.

A little unsettling? Alexa already knows what is in the shopping cart. It may as well know which album we play while regretting the purchase.

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Careful What You Sue For: Trump’s BBC Case Just Forced His Financial Records Into Discovery

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from the this-time-we-could-wait-for-discovery dept

We’ve written in the past that people online often get way too excited about theoretical pending “discovery” in frivolous lawsuits filed by bad actors. Because while there are certainly a few cases where (1) a frivolous case even reaches discovery and (2) some elements of that discovery are revealed to the public, in the vast majority of cases, that doesn’t happen. The legal strategy for most defendants is to get a case thrown out before it reaches discovery because discovery is incredibly expensive. And, even then, most often what is handed over in discovery never goes public.

But… hey, sometimes, “can’t wait for discovery” turns out to be an accurate sentiment.

Last year we noted that Donald Trump had filed an obviously frivolous lawsuit against the BBC, asking for $10 billion. At issue was an edit in the documentary he didn’t like which might be considered mildly misleading (though Donald Trump repeatedly falsely claimed that the BBC used AI to fabricate quotes, the reality was they edited two separate parts of the same speech to sound like they were said together, when they were really many minutes apart). That’s not defamation, though.

Either way, the case has not been going well for Trump. Because he argued that this documentary (which was only shown once in the UK and not in the US) harmed Trump’s business interests in Florida (where he sued), the BBC asked for Trump’s financial records as part of their discovery requests. Given that Donald Trump made more money last year (around $2 billion) than ever before, even as he remains the President of the United States, it seems like a reasonable request.

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Trump and his (not very bright) lawyers tried to wriggle out of this by dropping some of the initial claims that were about how much harm the documentary did to his business, saying instead that it just harmed his reputation. The BBC said it still needed his financial records anyway. And now, Magistrate Judge Enjoliqué Lett has agreed, noting in court that the financial records would be relevant to the claims of reputational harm as well.

“All of President Trump’s brand, properties and businesses are impugned or said to have been impugned. Reputational, economic damages, all of that is now at issue in this case,” Lett said at the conclusion of a three-hour hearing.

Of course, Trump’s lawyers can (and almost certainly will) ask the Article III Judge (Roy Altman, who is a Trump appointee) to overrule the magistrate, but it might not work. After all, earlier in the case, Trump’s lawyers had sought to remove Lett from the case, claiming that she was biased against him, because before she became a Magistrate Judge, she had represented a client in a case against Trump. Judge Altman rejected that claim back in May, siding with his colleague, Magistrate Judge Lett:

The Plaintiff asks us to withdraw our referral of discovery matters from Magistrate Judge Lett and reassign them to a different Magistrate Judge. … He advances two arguments in support of this request: First, he cites our unrelated referral of discovery matters in Donald J. Trump Revocable Trust et al. v. Capital One…. Second, he argues that “Magistrate Judge Lett had appeared as counsel of record on behalf of a party directly adverse to President Trump in active federal litigation: Trump v. Clinton… The Plaintiff’s first argument is unavailing. “Effective April 19, 2026,” Magistrate Judge Hernandez replaced Magistrate Judge Lett as our “paired” Magistrate Judge for Miami-based cases…. We reassigned discovery in Capital One the next day based on case workload and the parties’ compressed discovery period…. Nothing about that decision mandates a withdrawal of the referral in the different circumstances of this case. The Plaintiff next argues that Magistrate Judge Lett previously “represent[ed] [a] defendant directly adverse to President Trump.” … Despite his claim to the contrary, the Plaintiff effectively seeks Magistrate Judge Lett’s recusal. … But 28 U.S.C. § 455 is clear that: “Any justice, judge, or magistrate judge of the United States shall disqualify himself in any proceeding in which his impartiality might reasonably be questioned.” Accordingly, we’ll leave any decision regarding Magistrate Judge Lett’s recusal to her sound judgment. Signed by Judge Roy K. Altman on 5/19/2026.

So, at this point, Judge Altman seems willing to trust Magistrate Judge Lett’s judgment on the recusal question — and that deference may well carry over to the financial-records dispute too.

Of course, even if discovery does move forward, Trump could still file for a protective order to keep most of the records secret, outside of whatever has to be used in court. Alternatively, he could try to dismiss the case to get out of having to provide discovery.

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Either way, this was a stupid, vexatious, obvious SLAPP suit designed to punish the BBC and waste its time and money. So it’s quite nice to see that backfiring on the censorial bully that is the President of the United States.

Filed Under: 1st amendment, defamation, documentary, donald trump, florida, panorama, slapp suit

Companies: bbc

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Tesla Can’t Avoid Recall Fix for Cars With Overly Bright Headlights

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Drivers who’ve been blinded by the lights, it’s not just your imagination. There are cars on the road with headlights that are so bright they’re illegal.

Two years after Tesla informed the National Highway Traffic Safety Administration that low-beam headlights on some Tesla EVs exceed brightness limits, the company’s move to make those headlights street-legal has failed. The NHTSA denied Tesla’s petition to avoid a recall, meaning the company will need to issue a fix for nearly 20,000 Model 3 and Model Y vehicles from 2017 to 2023 for a problem the company called “inconsequential.”

A representative for Tesla didn’t immediately respond to a request for comment.

Tesla argued in its petition that because the bright lighting in question falls outside of the field of vision of oncoming drivers, “noncompliance is inconsequential as it relates to motor vehicle safety.”

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In its response, NHTSA associate administrator for enforcement Eileen Sullivan wrote, “NHTSA disagrees with Tesla’s conclusion that there is no increased risk of glare for surrounding traffic or the driver of the subject vehicle in any driving conditions.”

The agency argued, citing public comment, that the headlights can still produce glare that could affect other drivers.

The group referenced a similar decision it made in 2022 involving GM Terrain vehicles.

For Tesla owners, the NHTSA ruling means the company must provide a free fix for the affected cars.

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Additional steps are possible

The NHTSA isn’t commenting on pending legislation, including its denial of Tesla’s petition, but in an email to CNET, a spokesperson said that the organization is assessing studies about LED-headlamp glare and may take additional steps based on what it finds.

“Some issues may arise from the inappropriate use of high beams, illegal aftermarket lamps, improperly maintained lamps or misaligned lamps,” the spokesperson wrote.

Extra-bright headlights are illegal, and devices that don’t comply can be outlawed from sale on platforms like Amazon, while drivers using overbright headlights can face escalating fines, the suspension of their driver’s license or civil liability.

“Although headlight technology has changed over the years, NHTSA’s lighting standard has remained constant in limiting the amount of glaring light directed toward traffic both coming and going,” the spokesperson said.

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SUVs, which sit taller on the road than sedans, have become the most popular vehicles in the US. That, combined with LEDs taking over as the prime technology for headlights, has led more people to notice that high-intensity low beams seem brighter and harder to avoid.

Some modern cars have benefited from adaptive lighting technologies that can adjust brightness, direction or lighting pattern as driving conditions change, but many cars on the road aren’t equipped with these systems.

Headlights in the congressional spotlight

Earlier this year, the US House of Representatives joined the discussion about bright headlights with the introduction of a bill from Rep. Marie Gluesenkamp Perez, a Democrat from Washington state.

The LIGHT Safety Act would set maximum allowable brightness limits for low-beam headlamps on vehicles in the US. So far, the proposal has only been sent to committee, meaning it’s been assigned to a House panel for initial review, with no further action taken on it since March.

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The bill seeks to revise existing federal standards by establishing a maximum allowable brightness for low beams.

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Treasury threatens sanctions after White House claims Moonshot distilled Anthropic’s Fable

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U.S. Treasury secretary Scott Bessent doubled down on his warnings to Chinese AI companies on Wednesday, saying that sanctions remain on the table after a White House official accused Moonshot of improperly distilling Anthropic’s Fable model. 

Model distillation is a common AI training technique in which a smaller model learns from the outputs of a larger one. While this process can infringe on intellectual property rights, it’s also widely used as a legitimate optimization method. 

“Open source is not open season on American IP,” Bessent posted on X. “When [Chinese] firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations will be on the table.”

Earlier this week, Bessent stated that the U.S. government would examine open source models from China for signs of intellectual property theft and impose sanctions if found. 

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Bessent’s latest remarks come hours after the White House’s science and technology policy chief Michael Kratsios accused the China-based Moonshot of conducting large-scale distillation against U.S. models. He alleged that Moonshot had acquired Nvidia’s “GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models,” raising questions about whether the firm violated U.S. export-control rules.

The GB300 servers are part of Nvidia’s Blackwell generation, which are banned from being sold to Chinese companies.  

Some experts dispute the idea that Kimi K3 could have been developed primarily through distillation from Fable, which has only been publicly available since July 1. Moonshot released K3 last week as an open-weight model, and its advanced capabilities have called into question the underlying business models of leading U.S. AI labs, casting doubt on whether they can continue to justify the enormous capital requirements underpinning the frontier AI race. 

The episode has also intensified a broader debate in Washington over the influx of Chinese open models. Some, including former White House AI adviser and current OpenAI Head of Strategic Futures, Dean Ball, have argued that the U.S. should restrict or effectively ban the use of Chinese open-weight models to preserve America’s technological advantage and mitigate potential national security risks. 

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TechCrunch has reached out to Moonshot and the Treasury for comment.

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‘Trap And Trace’ Lawsuit Claims Toyota Illegally Tracked Customers

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Toyota is the latest automaker accused of invading drivers’ privacy — and then selling personal information. The popular Japanese carmaker is facing a class action lawsuit filed in Los Angeles County Superior Court over “trap and trace” claims, a kind of privacy breach that’s becoming far more common. According to lead plaintiff Brittany Conner, Toyota is secretly tracking and selling personal information it gets on its official website, all without consent. 

When consumers head to Toyota’s website, they will see a consent banner where they can choose to “accept” or “decline” tracking cookies. Conner found that Toyota installed tracking technology on her device without her knowledge — despite her declining — allowing the company to collect her online activity and then send her marketing and advertising it believes are related to her interests and the websites she visited. This isn’t the first time that Toyota has been sued for collecting and selling driver data: In January 2026, a man claimed Toyota sold his driving data to insurance companies.

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Websites keep getting sued for trap-and-trace data collection

Website tracking has become a hot legal topic as of late, with more than 800 claims filed under the California Invasion of Privacy Act (CIPA) in 2025. This law includes a section on “trap and trace” tactics, stating that it’s illegal to install a trap-and-trace device — which captures information without someone’s knowledge — without first obtaining a court order. This originally referred to wiretaps and hidden recording equipment when it was written in 1967, but it’s since been used for website tracking in recent years. And it has worked. 

In 2025, Google had to pay over $425 million for collecting users’ phone data despite them opting out of the tracking. This lawsuit included 98 million Google accounts. But even those who didn’t use Google-branded apps or devices found they were getting tracked by Google’s software. In March 2026, the Los Angeles Times paid $3.85 million after violating California’s privacy laws by tracking users’ data without their knowledge. The lawsuit claimed the media outlet was using three website trackers. Forbes followed in May 2026, agreeing to a $10 million settlement after being accused of tracking, collecting, and selling private data from people who visited any websites owned by Forbes — without consent, of course.

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GM Is Quietly Becoming a Subscriptions Company

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“General Motors has been pulling a Tim Cook and boosting its software and subscription business,” reports Business Insider. During the automaker’s Tuesday earnings call, executives said they’re increasingly leaning on software subscriptions like OnStar and Super Cruise to generate high-margin recurring revenue long after customers buy their vehicles. GM says OnStar brought in about $800 million in the second quarter, while Super Cruise revenue grew about 70% year over year. From the report: GM says its software business keeps roughly 70 cents of every dollar it brings in. That’s a rare level of profitability in the auto industry, as many car sales generate just four to 10 cents per sales dollar. […] GM expects to add about 1 million OnStar subscribers this year, bringing the total close to 13 million. Super Cruise, GM’s hands-free, eyes-on driving system, is growing even faster. GM added about 70,000 subscribers during the quarter and expects to end the year with more than 850,000. Revenue from the service increased about 70% from a year earlier.

And a lot of drivers are sticking around after the free period ends. GM said between 30% and 40% of eligible owners continue paying after their included three-year Super Cruise subscription expires. [..] “We do think we have tremendous levers, multiple levers of growth,” Barra said on the call. “We definitely think there’s a lot of opportunity at GM to grow, improve margins, and become less cyclical.”

“Software and services are becoming increasingly important to how customers experience GM vehicles and how we deliver value beyond the initial purchase,” a spokesperson previously told Business Insider. “As vehicles become more software-defined, we can introduce new digital experiences through updates and optional services rather than hardware changes.”

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Startup Spotlight: MediaPact wants to reinvent digital ads for the AI era

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Lacie Thompson previously worked in marketing at Expedia, Blue Nile and New Engen, and is now putting those skills to work at MediaPact.

As AI changes how people discover products online, marketers are rethinking the traditional digital advertising playbook. With AI-generated answers reducing clicks on search results and display ads, brands are looking for new ways to reach customers.

Seattle startup MediaPact wants to capitalize on that shift.

Founded in 2026 by online marketing veteran Lacie Thompson, MediaPact makes finding and signing ad deals quicker, painless, and accountable for both publishers and companies. It has raised $200,000 in a small friends and family round, and recently added companies like BroBible, Gadget Review and Penske Media to the platform.

We caught up with Thompson for GeekWire’s Startup Spotlight to learn more about her one-person startup, how AI helped her build the business despite having no coding experience and what surprised her most about launching in a market she thought she already knew.

In 50 words or less, give us your startup’s elevator pitch?

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MediaPact is a marketplace and workflow for flat-fee direct media. Buyers discover publishers, newsletters, and creators, then negotiate terms, sign the IO (insertion order), and pay, all in one place. Seller inventory is standardized to list inventory in a searchable format. It is the direct media buy without the 40-email thread.

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What problem are you obsessed with solving?

Flat-fee media is a massive market that still runs on emails, PDFs, calls, bespoke IOs and a Google Sheet named “final_FINAL_v3.”

Nine out of ten publishers I have interviewed described their flat-fee workflow as exactly that: manual email threads, hand-built IOs, invoices they chase for 60 days. Meanwhile, the buyer on the other side of that thread is sitting on budget and cannot find them.

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Programmatic solved this for banner ads 15 years ago. Nobody has ever solved it for this type of media: sponsored articles, newsletters, or podcast reads. I am obsessed with making a direct media buy as easy as booking a flight.

What surprised you after talking to customers?

Two things:

Supply is not the problem. I have spent 15 years in this industry, so I can sign publishers all day. Demand is the hard part. Every marketplace founder reads The Cold Start Problem and still thinks they are the exception. I was not the exception.

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The buyers are much more broad than I thought. I come from affiliate and performance. Those teams live and die on click-based measurement. While they often purchase flat-fee media, they sometimes avoid the risk of guaranteed placement because of over-scrutinized click-based attribution (especially on a last click).

One hyper-performance-based agency told me flatly that this was not for them. Brand marketers who understand top of funnel growth get it. They are typically at a mid-stage consumer brand that has plateaued on Meta and Google and needs somewhere else to go. Shopper marketers are also very focused on working with partners that can reach their audience, even if they are influencing in-store behavior in ways that are difficult to measure. Said another way, MediaPact is for the marketer who uses art, the marketer who uses science and the marketer who uses both. 

How has AI changed the way you build your company?

Two ways, and the second is a strategic angle for the platform, not just an operational efficiency.

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The obvious one: I built and shipped (and am continuing to do so) the entire platform with Claude Code. React, TypeScript, Supabase, Stripe Connect, the whole thing. I have zero experience writing code, managing dev teams, or product management. And now I can ship features to production within less than a day. I don’t say this to boast, but rather to show that this is a structural change in who gets to start what kinds of companies.

AI is eating the click. When ChatGPT answers the question, nobody clicks. And the content is so trusted that conversion happens at 4.4 times the rate. So brands stop competing for rankings and start competing to be inside the source material that the models cite, which is high-authority editorial. That is not just SEO anymore. It is Answer Engine Optimization, and the only way in is to be in the content. MediaPact allows buyers to do this.

What’s one thing people misunderstand about your startup?

That it is for affiliate marketers. My résumé makes people assume rev-share, cookies, and last-click attribution.

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It is the opposite. Flat fee, guaranteed placement, signed IO, and automated payment. Sellers get paid for their audience and their authority, not for whatever the attribution model felt like giving them that month. Publishers have been shortchanged by last-click for decades and everyone in our industry knows it.

What’s the toughest decision you’ve made in the past year?

Launching the company and determining the real TAM. 

My network is affiliate. Those are warm calls, fast meetings, and lots of enthusiastic nodding. It would have been very comfortable to build for them. But the customer discovery data pointed toward brand marketers, shopper marketers, and media planning and buying teams—audiences who don’t know me. 

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Even though I know this challenge, I’m tackling it by figuring things out as I go, in the same way I did before: by building partnerships and relationships that grant me access to the right opportunities. 

What’s the one piece of advice you give to other entrepreneurs?

Ask for help. The key, though, is that you have to give help, you must be someone people want to help and that isn’t just granted—it’s earned over years. I naturally think of asking my network for help: my friends, my family, and my advisors. But now you can also ask Claude (or your preferred AI) for help. While it’s definitely not the same, knowing when to ask whom or what for help is probably the most powerful needle-mover. 

We’ll know our company has made it when… 

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I’m the most proud when I know the platform has benefited someone. Usually when that’s the case, they want to tell their friends about it. That part of the growth cycle is always the most fun for me because I have the luxury of getting out of hustle mode and into innovation mode, pushing beyond the beta, dreaming big and taking things beyond my current scope. 

When sellers tell brands “just send it through MediaPact” without me anywhere in the conversation, that will be a milestone. The day the marketplace works without the founder in the middle is the day it is actually a marketplace.

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After shocking quarter, IBM insists that AI isn’t killing the mainframe

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On Wednesday, IBM officially reported earnings and the news was as bad as everyone knew it would be.

While the 115-year-old company still generates boatloads of cash — $17.2 billion in revenue, $9.9 billion in gross profit, nearly 58% margins, and $2.2 billion in net earnings for the quarter — its results fell well short of Wall Street’s expectations.

It was such a bad miss that IBM CEO Arvind Krishna and the board took an unprecedented step of warning investors ahead of time that the earnings “was worse than our expectations,” offering everyone a sneak peek.

He published a “letter to investors,” last week sharing preliminary results. It warned of abysmal revenue in the company’s all-important “infrastructure” category and said that profit margins were also going to take a hit. The company’s stock instantly tanked 25%, it’s biggest single-day decline ever. Until then, the stock had performed well under Krishna’s six years of leadership, buoyed by the AI data center boom that had been lifting all boats.

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On Wednesday, IBM also lowered its full-year growth forecasts, meaning this horrible quarter would impact the rest of the year. The culprit? IBM’s cash-cow mainframe business was down 42%.

That’s a cascading problem, because as CFO Jim Kavanaugh explained on the quarterly call with investors, IBM earns $3 in software revenue for every $1 of mainframe hardware it sells.

However, the CEO and CFO spent the call insisting that this was a temporary blip and all would be well soon.

What happened, they said, was that “tens” of customers that were due to buy a new mainframe during the quarter opted not to do so. That may not sound like a lot of customers, but mainframes are systems that cost hundreds of thousands to millions of dollars, and with maintenance contracts and software, generate many millions more.

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The same AI boom that lifted IBM’s boat also sank it.

Instead of buying a new mainframe, these clients bought other hardware, Krishna explained. They were faced with astronomically high cost increases of 15% to 30% for data center gear and PCs.

“When they were faced with that issue, then they decided to move budget to those areas where they were having that extreme price,” Krishna said.

Enterprise hardware makers like Dell and HP have warned that rising costs on components like memory, caused by the AI build-out boom, have forced them to raise prices. Apple has said the same.

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But Krishna promised that those customers will still buy their new mainframes eventually — along with their new software contracts. In fact, he said some of them have already done so this quarter. “We see no evidence of clients moving off the mainframe,” he said.

We’ll have to wait and see. But the tech industry has predicted the death of the mainframe for many decades now. Maybe even AI won’t kill it.

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Nvidia’s Jensen Huang defends Chinese AI: “Open-source models that are excellent should be used”

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Big quote: Jensen Huang just spent a week watching his company’s valuation get hit by the threat of a Chinese open-weight model. His response was to go on the record defending it. Speaking to Axios in Fort Worth, Texas, at the opening of a new phase of the Wistron plant that builds Nvidia’s AI infrastructure, the Nvidia CEO said American companies should “absolutely” be free to run Chinese models. “These Chinese models are excellent,” he said. “Open-source models that are excellent should be used.” Asked whether China could displace American labs, he was blunt: “Zero possibility.”

The timing is what makes it interesting. Moonshot AI released Kimi K3 on July 16. Independent evaluators put it third on Artificial Analysis’ Intelligence Index, only behind Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol, and first on LMArena’s blind Frontend Code Arena board.

The market took it about as well as it took DeepSeek. The Philadelphia Semiconductor Index fell 12.5% in a week, its worst stretch in 15 months. Taiwan’s benchmark dropped more than 6%, Japan closed down 4%, and Chinese AI stocks actually got hit harder than American ones, with Zhipu down as much as 30% in Hong Kong and MiniMax off 16%.

“The market misunderstood the impact of DeepSeek the first time,” Huang told Axios, adding that Wall Street has “misunderstood the impact of Kimi again this time.”

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His pitch is one Nvidia has made before, delivered without much subtlety this time: “Free AI should be great for hardware. Free AI should be great for chips. Free AI should be great for data centers.” The logic is that as cheap, capable models get used more, not less, more usage means more chips running somewhere. He also argued open models don’t cannibalize OpenAI and Anthropic, but they give people a free first taste, and plenty of those people end up paying for something faster and more reliable.

Bloomberg’s analysis of K3 noted that where DeepSeek’s story was about cheaper training, Moonshot’s is about a much larger model that leans harder on memory infrastructure.

The security argument, flipped

Huang waved off the idea that a downloaded Chinese model is some kind of backdoor to Beijing, pointing out you can inspect the weights, tweak them, and run the whole thing sealed off from the internet if you want. His argument is that openness actually makes things safer, because more people are looking for problems. Lock everything into one closed system, he said, and “if everything just becomes one single model, one single point of attack, one single source of failure, I think the world is much, much more vulnerable.”

Then he turned that same logic on an American company. Huang said Anthropic should open up Claude Mythos, its cybersecurity model that’s currently restricted to a vetted group of partners, calling it something that “should be available as a service” and arguing “holding Anthropic back is not in the benefit of the United States.” His framing: “Just because Mythos is not available, open models are available anyhow.”

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It is worth noting that Nvidia is one of the partners with access to Mythos already, through Anthropic’s Project Glasswing program. Glasswing has grown from around 50 partners to roughly 200 across about 15 countries, and Anthropic has said models this capable will likely be widely available within 6 to 12 months regardless of what it decides.

Meanwhile, in Washington…

Huang’s comments landed hours after Treasury Secretary Scott Bessent told Fox Business the administration is looking into whether Chinese AI models were built on stolen US intellectual property.

“If we see … that overseas models are stealing from our great companies, we have the ability to sanction them because of this theft,” Bessent said, pointing to what he called “watermarks” of US models showing up inside Chinese ones, with action possible within days or weeks. He also floated whether US companies should have to disclose to customers when they’re running Chinese models.

Behind the scenes, Axios says this fight has been simmering for a while. US Commerce has been considering blacklisting Chinese AI labs since last year, and the White House had a draft executive order that would’ve made US companies liable for running Chinese models. All of it stalled, but Kimi’s release seems to have brought it back to life.

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Jensen Huang split the difference on the “they stole our work” question: “Distillation, learning from AI, learning from other sources of knowledge, is fundamental to intelligence.”

Read the incentives

Granted, all this commentary is not coming from a neutral party. Nvidia’s China revenue is basically zero right now, down from a business Huang once said could be worth $50 billion a year. Huang has spent two years arguing against export controls, so of course he’s going to say demand for AI is elastic and that restrictions can backfire. That said, he has also endorsed keeping Blackwell and Rubin out of China’s hands.

The good news is that his core claim could soon be verified. Every number on Kimi K3 so far comes from Moonshot itself or from early API testing. The full weights go public on July 27, and at that point the benchmarks either hold up under independent testing or they don’t.

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Anthropic has accused Moonshot of training on 3.4 million Claude conversations earlier this year, though analyst Nathan Lambert’s take is that even if some of that happened, it’s not enough to explain how good K3 actually is. Separately, Epoch AI estimates the gap between the best open and closed models is now down to around three months.

If that gap keeps shrinking, the real question stops being whether US companies should be allowed to use Chinese models, and starts being whether banning them would even do anything once the weights are already sitting on servers everywhere.

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A MacBook Neo refresh may already be in testing and could address its biggest performance bottlenecks

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Apple is already working on a refreshed version of the MacBook Neo, only a few months after launching its cheapest laptop.

According to Bloomberg’s Mark Gurman, Apple has been testing an updated MacBook Neo with an A19 Pro chip and more memory. The report backs earlier claims that a second-generation model could arrive in 2027, bringing better multitasking performance and improved long-term usability.

More memory could be the most important upgrade

The current MacBook Neo runs on a binned A18 Pro chip paired with 8GB of unified memory. It handles browsing, streaming, and everyday productivity reasonably well, but the limited memory can become noticeable once several apps and dozens of browser tabs are running together.

Bloomberg does not reveal the exact memory capacity of the new model. Previous reporting pointed to 12GB, which would give the Neo more breathing room for multitasking and future macOS features. Not to mention that 12GB memory is also a requirement to run advanced Apple Intelligence features locally.

The A19 Pro, which is onboard the iPhone 17 Pro and Pro Max models. should also bring improvements across CPU, graphics, and Neural Engine performance. Apple is reportedly planning to refresh the laptop periodically with new colors as well.

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More memory could also push the price higher

The bigger question is how much Apple will charge. The MacBook Neo launched at $599 in March, but its starting price climbed to $699 in June after rising memory costs forced Apple to increase prices across the Mac lineup. Adding more unified memory could make it difficult to hold the current price, although the Neo would still sit comfortably below the MacBook Air.

There is no word yet on whether Apple will address some of the other compromises found on the first model, including the non-backlit keyboard, mechanical trackpad, and inconsistent USB speeds. The refresh is expected to arrive at the 2027 spring launch event, along with the standard iPhone 18 and the iPhone 18e.

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