We are in a golden era for buying and selling digital LPs. While I’ll use Bandcamp, sleek alternatives like Ampwall, Subvert, and Mirlo are equally great options. These online markets inherently incentivize artists to avoid filler or risk losing a sale, while the subscription streaming model requires artists to pad their catalog for pay per play. Streaming has revived the worst trope of the old music industry: the album that is just "two hits and a bunch of crap."

Spotify’s business model demands album filler because the platform pays out royalties based on "stream share" which trigger a payout the second a track hits the 30 second mark, incentivizing artists to maximize volume over value. This has fundamentally warped modern songwriting: albums are aggressively padded with short, two minute tracks and repetitive hooks designed specifically to feed the algorithm and inflate stream counts. On Spotify, a deep, cohesive artistic statement takes a back seat to sheer data output, turning what should be a focused LP into a bloated playlist of algorithmic bait.

Accidental hits happen way more often than you’d think. As it turns out, artists are notoriously bad at predicting their own success. When you buy a digital LP on a platform like Bandcamp, you are investing in a complete and curated piece of art where even the tracks the artist never expected to blow up exist naturally as part of a cohesive story. On subscription services like Spotify, those same happy accidents are treated like lottery tickets while surrounded by cynical, algorithm optimized filler designed just to farm streams. Buying the album ensures you are experiencing those unexpected gems as genuine creative discoveries, rather than digging through algorithmic bloat to find them.

Bandcamp serves the genre; streaming serves the algorithm. When producers target platforms like Spotify, artistic nuances like tempo variations and volume dynamics are sacrificed to strict LUFS loudness standards and predictable, club friendly danceability. This algorithmic pressure strips electronic and club music of its experimental edge, forcing tracks into a uniform, compressed sonic mold just to survive on a playlist. On Bandcamp, however, the music is freed from these rigid streaming constraints, allowing producers to prioritize raw genre authenticity and dynamic storytelling over sanitized, playlist ready optimization. Soundtrack and orchestral music have become major casualties of this shift, as their essential cinematic highs and quiet, emotional lows are flattened into a lifeless wall of sound just to meet streaming's volume requirements.

Just so we're clear, I'm not here to sell you my album. Go ahead and enjoy the whole thing ad free on my website.
thejoyo.com/#more

LoRa Communication

Image: en.wikipedia.org - LoRa

LoRa (long range) is a proprietary radio modulation technique based on Chirp Spread Spectrum (CSS), which encodes data on radio waves using frequency-sweeping chirp pulses. It operates on license-free sub-gigahertz bands: 868 MHz in Europe, 915 MHz in North America and Australia, 433 MHz globally.

The core tradeoff is range vs. data rate. Spreading factors (SF5–SF12) let you tune this: higher SF means longer range and better sensitivity, but slower throughput and more battery drain. Data rates run from 0.3 to 27 kbit/s, per Wikipedia. Typical range is 2–5 km urban, 5–15 km rural, and beyond 15 km line-of-sight, according to readthedocs.io.

LoRa is the physical radio layer only. LoRaWAN sits on top as the network protocol (MAC layer), defining how devices connect to gateways and the internet. The Things Network describes LoRaWAN devices as capable of running up to 10 years on a single coin cell battery.

Semtech owns the LoRa IP and makes the chipsets. The LoRa Alliance, a 500-member non-profit, maintains the LoRaWAN standard, which the ITU formally recognized in December 2021.

Common applications include smart agriculture, asset tracking, water leak detection, cold chain monitoring, and mesh networks like Meshtastic.

For a thorough technical grounding, The Things Network's LoRaWAN guide is the most practical starting point.

Sources: Wikipedia, The Things Network, Semtech, readthedocs.io

Samsung Electronics narrowly averted a walkout by nearly 48,000 workers this week, after executives agreed to a tentative deal over bonus payments. But the labor union’s demand for a bigger share of profits from the company’s semiconductor business has sparked questions — in South Korea and elsewhere — about who benefits from the AI industry, and whether its rewards should be shared more equitably.

Samsung, the world’s biggest memory chip maker, has reported record profits in recent months amid a global shortage of memory chips. The labor union had demanded the company allocate 15% of operating profit to bonuses for all workers, not just those at the memory chip division that supplies Tesla, Nvidia, and other big tech companies.

“As the AI industry drives record operating profits, union members are in a structure where they cannot receive the performance-based rewards they deserve,” Choi Seung-ho, head of Samsung’s union, told Rest of World. “We want to change that.”

Google's reCAPTCHA Now Requires Play Services on Android

Google has tied its next-generation reCAPTCHA system to Google Play Services, meaning users running GrapheneOS or any custom ROM without Google's software automatically fail verification challenges, per Reclaim The Net.

When reCAPTCHA flags suspicious activity, it skips the old image puzzles and instead demands a QR code scan. That scan requires Play Services version 25.41.30 or higher running in the background. No Play Services, no access.

Google announced the broader system, called Google Cloud Fraud Defense, at Cloud Next on April 23, framing it as a platform to handle AI agents and bots. The Play Services dependency was not highlighted. An Internet Archive snapshot from October 2025 shows the same requirement was already listed at version 25.39.30, meaning Google built this in quietly at least seven months before a Reddit user on r/degoogle flagged it, with PiunikaWeb and Android Authority picking it up.

The iOS comparison is telling: Apple devices on iOS 16.4 or later pass the same verification without any additional software. Only Android users without Play Services are locked out.

Image: Reclaim The Net - Google Broke reCAPTCHA for De-Googled Android Users

An Ars Technica forum user noted the practical problem bluntly: "I'm betting some sites or services that do use it are unavoidable." Commenters on LinkedIn have suggested hCaptcha as an alternative for web developers who don't want to exclude privacy-conscious users.

Sources: Reclaim The Net, Ars Technica OpenForum, PiunikaWeb

While the excess sales can partially be explained by converting CPU and bitcoin servers, and upgrading functional or burnt out older GPUs, there is finite replaceable powered capacity, in addition to small growth rate of datacenters under active construction that can hope for 2026 opening. "Grey market" diversion to China can be a hidden source of sales.

This is a refined estimate based on taking out networking/software from each of NVidia's sales channels.

Hyperscalers rarely buy commercial software licenses from NVIDIA (they build their own stacks), while Enterprise buyers are heavily dependent on software subscriptions like NVIDIA AI Enterprise ($4,500/GPU/year). Similarly, networking intensity follows a drastic gradient: a massive LLM training cluster requires a massive networking tax, whereas an Enterprise inference node does not.

To resolve this, we must break down NVIDIA's $75.2 billion total data center revenue by applying asymmetric networking and software multipliers to each specific customer segment.

Phase 1: Re-Allocating Networking and Software by Segment

NVIDIA's software layer consists of subscription revenue (which scales with the historical installed base, not just new capacity) and architecture licensing. Its networking segment consists of InfiniBand and Spectrum-X Ethernet switches, adapters, and cables.

Let's dissect how these costs actually apply to each of the three purchasing categories:

1. Hyperscalers ($38.0B Total Segment)

• Software Allocation (0.5%): Negligible. Hyperscalers rely on their own internal orchestrators and proprietary AI software layers. They only pay minimal foundational firmware fees.
• Networking Allocation (22%): Exceptionally high. Building multi-thousand GPU clusters for LLM training requires massive networking fabrics. Even with the integrated copper backplane of the GB200 NVL72, hyperscalers must purchase massive external Quantum-X800 InfiniBand or Spectrum-X800 switches to link multiple racks together into a single cluster.
• Net Compute Revenue: $29.45 Billion

2. AI Clouds & Sovereigns (~$21.2B of ACIE)

• Software Allocation (3%): Moderate. Specialized AI clouds lease a small portion of NVIDIA’s software stack to provide turnkey developer environments, but their core business is raw infrastructure provision. Sovereign clouds often pay a premium for localized security software layers.
• Networking Allocation (15%): High. They host large-scale foundational model clusters, requiring strong interconnect fabrics, though slightly less dense than the multi-tier topologies deployed by core hyperscalers.
• Net Compute Revenue: $17.38 Billion

3. Enterprise & Industrial (~$16.0B of ACIE)

• Software Allocation (20%): Very high. This is where NVIDIA's recurring subscription revenue lives. Enterprise clients cannot build their own software stacks; they pay heavily for NVIDIA AI Enterprise, NIM microservices, and Omniverse licenses. This revenue applies to both new shipments and their legacy installed base.
• Networking Allocation (5%): Very low. Most enterprise applications are small-scale clusters or isolated 8-GPU nodes executing localized inference or fine-tuning, requiring zero massive cluster switching.
• Net Compute Revenue: $12.00 Billion

Phase 2: Refined Segment-by-Segment Power Calculations

With the refined, asymmetric compute revenue isolated, we can run the physical power conversion using tailored Average Selling Prices (ASPs), system power demands, and facility Power Usage Effectiveness (PUE) metrics.

Category A: Hyperscalers ($29.45B Net Compute)

• Product Mix: 50% Blackwell NVL72 / 50% Hopper H200.
• Blended Compute ASP: ~$42,000 (reflecting a mix of raw chip pricing and heavy rack-integration premiums).
• Total GPUs Shipped:
  

  GPUs\=$29,450,000,000$42,000≈701,000 unitsGPUs equals the fraction with numerator $ 29 comma 450 comma 000 comma 000 and denominator $ 42 comma 000 end-fraction is approximately equal to 701 comma 000 units

  GPUs\=$29,450,000,000$42,000≈701,000 units

• Blended Power per GPU: 1,300W (Nominal system draw including Grace CPUs and cooling pumps).
• Hyperscaler Grid Footprint (1.15 PUE for ultra-efficient facilities):
  

  Grid Power\=(701,000×1,300 W)×1.15≈1.05 GWGrid Power equals open paren 701 comma 000 cross 1 comma 300 W close paren cross 1.15 is approximately equal to 1.05 GW

  Grid Power\=(701,000×1,300 W)×1.15≈𝟏.𝟎𝟓 GW

Category B: AI Clouds & Sovereigns ($17.38B Net Compute)

• Product Mix: 80% Hopper (H100/H200) / 20% standalone Blackwell (B200).
• Blended Compute ASP: ~$35,000 (standard market rate for high-end accelerator nodes without bulk hyperscaler discounts).
• Total GPUs Shipped:
  

  GPUs\=$17,380,000,000$35,000≈497,000 unitsGPUs equals the fraction with numerator $ 17 comma 380 comma 000 comma 000 and denominator $ 35 comma 000 end-fraction is approximately equal to 497 comma 000 units

  GPUs\=$17,380,000,000$35,000≈497,000 units

• Blended Power per GPU: 1,100W (Weighted heavily toward standard Hopper HGX server topologies).
• AI Cloud Grid Footprint (1.25 PUE for mixed commercial multi-tenant sites):
  

  Grid Power\=(497,000×1,100 W)×1.25≈0.68 GWGrid Power equals open paren 497 comma 000 cross 1 comma 100 W close paren cross 1.25 is approximately equal to 0.68 GW

  Grid Power\=(497,000×1,100 W)×1.25≈𝟎.𝟔𝟖 GW

Category C: Enterprise & Industrial ($12.00B Net Compute)

• Product Mix: 70% low-power inference cards (L40S, H100 NVL) / 30% mainstream H100s.
• Blended Compute ASP: ~$18,000 (strongly depressed by high-volume, lower-cost PCIe form factors).
• Total GPUs Shipped:
  

  GPUs\=$12,000,000,000$18,000≈667,000 unitsGPUs equals the fraction with numerator $ 12 comma 000 comma 000 comma 000 and denominator $ 18 comma 000 end-fraction is approximately equal to 667 comma 000 units

  GPUs\=$12,000,000,000$18,000≈667,000 units

• Blended Power per GPU: 450W (Reflecting the dramatically lower power draw of enterprise edge and inference cards).
• Enterprise Grid Footprint (1.25 PUE for on-premises or traditional enterprise cages):
  

  Grid Power\=(667,000×450 W)×1.25≈0.38 GWGrid Power equals open paren 667 comma 000 cross 450 W close paren cross 1.25 is approximately equal to 0.38 GW

  Grid Power\=(667,000×450 W)×1.25≈𝟎.𝟑𝟖 GW

Phase 3: Final Comparison: GW Sold vs. GW Deployed

Now, let's look at how this highly refined model maps against the 1.55 GW of net-new trackable data center capacity that physically came online across the globe during the quarter:

Key Takeaways from the Refined Model

1. The Grid Deficit Narrowed: By properly allocating NVIDIA's high software subscription margins out of the Enterprise sector and stripping heavy networking switch infrastructure out of the Hyperscale sector, the true global power footprint shipped by NVIDIA drops to 2.11 GW. The total global grid deficit sits at 560 Megawatts.
2. Where the Logjam Actually Sits: Notice that the Hyperscaler gap is remarkably tight—only 120 MW. This proves that hyperscalers are incredibly efficient at matching their massive utility contracts directly to their hardware delivery schedules.
3. The Hidden Crisis is in Tier-2 AI Clouds & Sovereigns: This segment represents a massive 260 MW deficit. Because these buyers lack the immense, multi-gigawatt land and power pipelines of the tech giants, they are receiving high-performance, high-power silicon far faster than their regional, third-party colocation data centers can actually deploy physical electricity to the racks.

This model confirms that the "homeless GPU" crisis is primarily concentrated outside of the core hyperscalers, driving smaller AI clouds to aggressively bid up any available third-party power capacity in the market today.

Game 7 in the NBA playoffs: a chance to kick back, enjoy the drama of a winner-takes-all shootout between basketball’s big beasts, and … switch over from your regular TV provider to Amazon Prime? The excitement drains from the occasion at the first touch of the remote. Amazon no doubt imagined it had landed a real coup when the Eastern Conference semi-final series between Detroit and Cleveland extended to its maximum length, thereby handing the retail giant’s streaming arm, Prime Video, the right to air a Game 7 in the first season of its partnership with the NBA. In the event, Sunday’s game was a dud: a blowout win for the Cavs, playing on the road, that had all the electricity and charm of a stint in the doctor’s waiting room. Fortunately for viewers, Prime Video did its best to match the moment by producing a broadcast that was every bit as dull and juiceless as events on the court.

The pre-tipoff highlight was an interview with Shai Gilgeous-Alexander, on the occasion of his coronation as this season’s MVP, in which the Oklahoma City star appeared to be speaking from a movie theater for some reason. Blake Griffin, the house beefcake on Prime Video’s studio set, chided ESPN insider Shams Charania for leaking this year’s MVP announcement hours earlier: “It’s Sunday, Shams – go to brunch, you nerd.” If Hillary had won and Shams had kept his trap shut, we’d all be at brunch! The game got under way, and things did not improve. During the half-time show, Dirk Nowitzki rambled Germanly about various topics, while fellow former MVP Steve Nash delivered lines like “That decisiveness in isolation is so important” with all the conviction of a hostage recording a ransom video. Host Taylor Rooks tried valiantly to compensate for the lack of chemistry on set by laughing at even the slightest hint of a joke from any of her panellists. Awkward laughter delivered over dead air on a platform it feels like a punishment to access: that’s the Prime Video NBA playoffs guarantee.

These have been a difficult debut playoffs for Prime as it muscles in on the broadcast territory once ruled by what the media analysts call “linear TV”. The feed dropped out for several minutes during overtime in the play-in game between the Hornets and the Heat; buffering, the nightmare we all thought we outlived in 2006, has plagued the stream in several games; and video has frequently been mistimed with audio, producing delays and mismatches. There’s primetime, which is when the bulk of these playoffs are taking place, and then there’s Prime Video time, which comes in around three seconds later. The audio itself in many games has often, in my experience at least, been strangely soft, requiring a trip all the way to the top of the volume scale to hear what the analysts and announcers are saying.

"Designers unveil mad spaceship concept that will definitely never fly" would be a more accurate headline, though it's a fun idea.

The concept pairs the winged, reusable booster stage from DLR’s long-running SpaceLiner project with an expendable upper stage designed to maximize payload. It burns liquid hydrogen and liquid oxygen, which is a more efficient combination than the methane and oxygen that power Starship’s Raptor engines, and its booster does not land the way Starship’s does.

Instead of descending tail-first on a column of rocket fire, the SpaceLiner booster glides back through the atmosphere on wings, before being captured in mid-air by a large subsonic aircraft. It is a recovery method that sounds almost science fictional, but one that DLR researchers argue has distinct advantages: the booster needs no fuel reserved for landing burns, which means more of every kilogram of propellant goes towards actually reaching orbit.

In comparison, Starship is more than three times heavier than the RLV C5 at launch. A significant portion of that mass is the cost of full reusability: heat shield tiles, landing fuel, structural reinforcements, the wings. Of every ton Starship sends to orbit, only around 40% is payload however the RLV C5, with its simpler partially reusable approach, manages to put 74% of its mass-to-orbit into useful payload. What it lacks in raw capacity, it gains in efficiency.

#Space
#Technology

scitechdaily.com/europe-just-u…

As Meta races to recenter itself around artificial intelligence, the tech giant is mandating that more than 7,000 workers must move to new teams, and it’s radically changing some employees’ jobs. The Guardian has also learned that some of these reassigned employees will shift to two new teams: one building AI cloud infrastructure and another that’s building an internal AI agent codenamed Hatch.

Late last week, Meta employees received a notice that engineers had been “selected” for reassignment and would begin reporting to the cloud infrastructure and Hatch teams by the end of this week. Meta made a similar move last month when it reshuffled at least 1,000 engineers on to a new data labeling team called Applied AI, or AAI – at first giving them the option to volunteer, but later telling workers: “Transfers aren’t optional.”

“Our work, infrastructure and our products are fundamentally changing as a result of the continued acceleration of AI,” wrote Peter Hoose, vice-president of production engineering at Meta, in an internal post about the two new teams viewed by the Guardian. “The pace of what we are building is unprecedented, and these are exactly the kind of challenges that define what we do best.”

A Meta employee referenced last month’s reshuffle in a comment on Hoose’s announcement, writing: “Does ‘selected’ imply this is an [Applied AI]-style draft rather than a voluntary move?”

Further proof the Meta doesn't know what the Zuck it's doing. The rebrand has gone swimmingly, given all the time we spend in the legless metaverse.

AI is, as it stands, not economically viable for anybody involved other than the construction firms, NVIDIA, and the surrounding hardware companies benefitting from the irrational exuberance of a data center buildout that doesn’t appear to be happening at the speed we believed.

Every AI startup loses millions or billions of dollars a year, and nobody appears to have worked out a way to stop hemorrhaging cash. Hyperscalers have invested over $800 billion in the last three years, with plans to add another $700 billion or so in 2026 and another $1 trillion in 2027, meaning that they need to make at least three trillion dollars in AI specific revenue just to break even, and $6 trillion or more for AI to be anything other than a wash. I went into detail about this (albeit at a lower, pre-2026/2027 capex number) in a premium piece last year.

To give you some context, Microsoft made $281 billion, Meta $200 billion, Amazon $716 billion, and Google $402.8 billion in revenue in their most-recent fiscal years for every single product combined, for a total of $1.599 trillion. None of them will talk about their actual AI revenues. Yes, yes, I know Microsoft said that it had $37 billion in AI revenue run rate ($3.08 billion a month or so) and Amazon had $15 billion, or around $1.25 billion a month, but both of these are snapshots of single months that are meant to make it sound like they’re going to make that much in a year but in the end, you don’t actually know anything about how much money they’ve made from AI.