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. https://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

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

https://archive.ph/ReAtU

Despite mounting scrutiny over Palantir’s alleged links to human rights abuses and Israeli war crimes, several major media organisations have still partnered with the company – including German publishing giant Axel Springer, the new owner of the British newspaper The Telegraph.

Axel Springer – which also owns Politico, Business Insider, Bild, and Welt – uses Palantir’s Foundry software across its media operations.

Palantir has said that Axel Springer used Foundry to integrate data from its various publications and revenue streams, helping to build what the company described as "a more agile, data-driven publishing organisation" capable of responding more effectively to shifts in consumer behaviour and audience interests.

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: 

| Customer Segment | NVIDIA GW Sold (Refined Power Footprint) | Actual New GW Deployed (Capacity Online) | Net Capacity Gap (Deficit) | |

|

|

|

| | Hyperscalers | 1.05 GW | 0.93 GW | +0.12 GW (120 MW Deficit) | | AI Clouds & Sovereigns | 0.68 GW | 0.42 GW | +0.26 GW (260 MW Deficit) | | Enterprise & Industrial | 0.38 GW | 0.20 GW (Est. legacy footprint) | +0.18 GW (180 MW Deficit) | | Total Global Market | 2.11 GW | 1.55 GW | +0.56 GW (560 MW Deficit) |

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.

I use Calibre with special plugins to strip DRM from ebooks I've purchased . . . hoping this will still work after the changeover (and yes, I know, Anna's Archive will still be there . . . Hopefully . . . )