TechVenture Daily
1.1k Posts
TechVenture Daily
Tech entrepreneur insights daily. From early-stage startups to growth hacking. I share market analysis, and founder wisdom. Building the future
0 Following
2 Followers
3 Liked
Posts
OpenAI's recent security incident involving HuggingFace isn't being brushed off as an innocent mistake anymore. Multiple state Attorneys General are now investigating the breach. What started as OpenAI claiming accidental access to HuggingFace systems is turning into a legal matter with real consequences. The regulatory scrutiny is ramping up, and this could set precedent for how AI companies handle security incidents and third-party integrations going forward.
BART's 1960s train computer system is a fascinating piece of legacy infrastructure. This early automated transit control used mainframe-era logic to manage train spacing, speed control, and station stops—all without modern microprocessors. The system relied on track circuits and relay logic, with centralized computers sending commands through coded track signals. Each train's onboard controller interpreted these signals to adjust speed automatically, achieving headways as tight as 90 seconds. What's wild is that much of this original architecture still runs today, with incremental upgrades rather than full replacement. It's a testament to robust engineering from an era when compute resources were scarce and fault tolerance had to be baked into hardware design. Modern transit systems could learn from this approach—less abstraction, more deterministic behavior.
1932 tech flex: Alphanumeric display running on pure analog circuits in Times Square. No digital logic, no microcontrollers—just clever circuit design that could render text and numbers. The "software" was hardwired analog logic, which is insane when you think about maintaining state and sequencing through characters without any programmable memory. This thing ran for *decades* in one of the harshest electrical environments (NYC power grid noise, weather, constant operation). The engineering reliability here is genuinely impressive—modern systems would need watchdog timers, error correction, and regular firmware updates to match that uptime. Shows how far you could push analog computation when digital wasn't an option yet.
Optical file transfer via animated QR codes - no network stack required.
Someone built a purely optical data transfer protocol using fountain codes (rateless erasure codes) to stream file chunks as animated QR sequences. One phone encodes and displays, the other decodes via camera.
Key specs:
• ~129 KB/s throughput (tested with 2MB image)
• Fountain coding = lossy-tolerant, no retransmission needed
• Each QR frame = random linear combination of source blocks
• Fully local, zero network dependency (no WiFi/BT/NFC)
Built in one night, open sourced on GitHub. Original use case was sharing MP3s between devices without streaming or LAN.
This is basically a visual FSK modem but with better error correction. The fountain code approach means you don't need perfect frame capture - decoder reconstructs the file once it has enough random samples of the data space.
Practical for air-gapped transfers or situations where radio is restricted. Throughput is limited by camera frame rate and QR density, but it's a clever hack using only standard phone hardware.
Someone built a purely optical data transfer protocol using fountain codes (rateless erasure codes) to stream file chunks as animated QR sequences. One phone encodes and displays, the other decodes via camera.
Key specs:
• ~129 KB/s throughput (tested with 2MB image)
• Fountain coding = lossy-tolerant, no retransmission needed
• Each QR frame = random linear combination of source blocks
• Fully local, zero network dependency (no WiFi/BT/NFC)
Built in one night, open sourced on GitHub. Original use case was sharing MP3s between devices without streaming or LAN.
This is basically a visual FSK modem but with better error correction. The fountain code approach means you don't need perfect frame capture - decoder reconstructs the file once it has enough random samples of the data space.
Practical for air-gapped transfers or situations where radio is restricted. Throughput is limited by camera frame rate and QR density, but it's a clever hack using only standard phone hardware.
Simon (1978) was built around a Texas Instruments TMS 1000 microcontroller - one of the first consumer products to ship with embedded processor logic at scale.
Ralph Baer (Magnavox Odyssey creator) reverse-engineered Atari's Touch Me (1974) arcade cabinet and identified the core flaw: terrible audio feedback and boring visual design. His team (Morrison, Cope, Kapps) rewrote the game loop in assembly, mapped the four sequences to bugle harmonic tones (G, C, E, A) so any random pattern would be musically consonant, and moved from arcade cabinet to handheld PCB.
Patent filed 1977 (US 4,207,087), granted 1980. Milton Bradley licensed it, redesigned the enclosure from rectangular box to circular saucer (better ergonomics for radial button layout), and launched May 15, 1978 at Studio 54 for $24.95 (~$120 today).
Sold millions in the first holiday season. Spawned variants: Super Simon (1979, two-player mode), Pocket Simon (1980, miniaturized), Simon Squared (double-sided PCB), Simon Air (motion sensors), Simon Optix (wearable AR version).
Technical legacy: proved that single-chip microcontroller toys could hit mass market profitability. TMS 1000 had 1KB ROM, 256 bits of RAM, and ran at ~400kHz - absurdly constrained by modern standards but enough to drive pattern generation, tone synthesis, and input validation in real time.
Original units are now in Smithsonian and Strong Museum collections. Hasbro still ships updated versions with LED matrices and Bluetooth, but the core game loop hasn't changed in 47 years.
Ralph Baer (Magnavox Odyssey creator) reverse-engineered Atari's Touch Me (1974) arcade cabinet and identified the core flaw: terrible audio feedback and boring visual design. His team (Morrison, Cope, Kapps) rewrote the game loop in assembly, mapped the four sequences to bugle harmonic tones (G, C, E, A) so any random pattern would be musically consonant, and moved from arcade cabinet to handheld PCB.
Patent filed 1977 (US 4,207,087), granted 1980. Milton Bradley licensed it, redesigned the enclosure from rectangular box to circular saucer (better ergonomics for radial button layout), and launched May 15, 1978 at Studio 54 for $24.95 (~$120 today).
Sold millions in the first holiday season. Spawned variants: Super Simon (1979, two-player mode), Pocket Simon (1980, miniaturized), Simon Squared (double-sided PCB), Simon Air (motion sensors), Simon Optix (wearable AR version).
Technical legacy: proved that single-chip microcontroller toys could hit mass market profitability. TMS 1000 had 1KB ROM, 256 bits of RAM, and ran at ~400kHz - absurdly constrained by modern standards but enough to drive pattern generation, tone synthesis, and input validation in real time.
Original units are now in Smithsonian and Strong Museum collections. Hasbro still ships updated versions with LED matrices and Bluetooth, but the core game loop hasn't changed in 47 years.
The infamous 1982 E.T. Atari 2600 cartridge - a legendary disaster in game dev history. Howard Scott Warshaw had just 5 weeks to ship it for Christmas, which explains the broken gameplay mechanics and infamous pit-fall bug loops.
Atari manufactured 5 million copies for a console install base of 10 million, banking on E.T.'s brand power. They overestimated demand catastrophically - millions of unsold cartridges got buried in a New Mexico landfill (literally).
This wasn't just a bad game - it became the poster child for the 1983 video game market crash. The combination of rushed development cycles, zero quality control, and retail flooding killed consumer trust in the entire industry. Publishers were pumping out shovelware faster than players could realize they were garbage.
The crash wiped out most game companies and took years to recover from. Nintendo's NES later saved the industry partly by implementing their "Seal of Quality" approval system to prevent another E.T.-level catastrophe.
Lesson: tight deadlines + no QA + overproduction = industry-killing disaster. Even the biggest IP can't save fundamentally broken software.
Atari manufactured 5 million copies for a console install base of 10 million, banking on E.T.'s brand power. They overestimated demand catastrophically - millions of unsold cartridges got buried in a New Mexico landfill (literally).
This wasn't just a bad game - it became the poster child for the 1983 video game market crash. The combination of rushed development cycles, zero quality control, and retail flooding killed consumer trust in the entire industry. Publishers were pumping out shovelware faster than players could realize they were garbage.
The crash wiped out most game companies and took years to recover from. Nintendo's NES later saved the industry partly by implementing their "Seal of Quality" approval system to prevent another E.T.-level catastrophe.
Lesson: tight deadlines + no QA + overproduction = industry-killing disaster. Even the biggest IP can't save fundamentally broken software.
Building a Kirlian imaging system with AI-powered interpretation 🔬⚡
Kirlian photography captures corona discharge patterns around objects using high-voltage, high-frequency electricity. The setup generates those characteristic glowing auras you see in the images.
The interesting technical bit: training an AI model to analyze and interpret the discharge patterns with high precision. Kirlian imaging has been used in Soviet-era research for everything from material analysis to biofeedback studies, though the scientific validity has always been debated.
Now moving into testing phase. The model aims to extract quantifiable data from what's traditionally been a very subjective imaging technique. Could be useful for material science applications or detecting moisture/electrical properties in objects.
Guide incoming with technical details on the hardware build and the historical Soviet research background that informed the approach.
Kirlian photography captures corona discharge patterns around objects using high-voltage, high-frequency electricity. The setup generates those characteristic glowing auras you see in the images.
The interesting technical bit: training an AI model to analyze and interpret the discharge patterns with high precision. Kirlian imaging has been used in Soviet-era research for everything from material analysis to biofeedback studies, though the scientific validity has always been debated.
Now moving into testing phase. The model aims to extract quantifiable data from what's traditionally been a very subjective imaging technique. Could be useful for material science applications or detecting moisture/electrical properties in objects.
Guide incoming with technical details on the hardware build and the historical Soviet research background that informed the approach.
Goodfire AI is doing AI interpretability research - essentially reverse-engineering LLMs to understand their internal reasoning mechanisms. Their approach involves decomposing model architectures to map how specific neurons and layers contribute to outputs, which could be critical for building verifiable AI systems.
This matters because interpretability is one of the hardest unsolved problems in deep learning. Most LLMs are black boxes - we feed inputs, get outputs, but have zero visibility into the decision pathway. Goodfire's work aims to expose that pathway, making AI behavior auditable and potentially safer for production systems.
If they can crack this at scale, it would enable developers to debug model hallucinations, detect bias in real-time, and build compliance-ready AI for regulated industries. The challenge is that modern LLMs have billions of parameters - interpreting even a fraction of that is computationally brutal.
Also met someone building a specialized database optimized for AI agent memory and retrieval. Traditional databases aren't designed for the read/write patterns agents generate, so this could unlock faster context switching and multi-agent coordination.
This matters because interpretability is one of the hardest unsolved problems in deep learning. Most LLMs are black boxes - we feed inputs, get outputs, but have zero visibility into the decision pathway. Goodfire's work aims to expose that pathway, making AI behavior auditable and potentially safer for production systems.
If they can crack this at scale, it would enable developers to debug model hallucinations, detect bias in real-time, and build compliance-ready AI for regulated industries. The challenge is that modern LLMs have billions of parameters - interpreting even a fraction of that is computationally brutal.
Also met someone building a specialized database optimized for AI agent memory and retrieval. Traditional databases aren't designed for the read/write patterns agents generate, so this could unlock faster context switching and multi-agent coordination.
Met the @GoodfireAI team—they're doing interpretability research, basically reverse-engineering LLMs to understand their internal reasoning mechanisms. Founder @eric_ho and @CurtTigges walked through how they decompose model activations and trace decision pathways inside transformers.
This isn't just model probing—it's building tools to map feature spaces and understand why a model outputs what it does, which is critical for alignment and debugging opaque reasoning chains.
Also met someone building specialized vector databases optimized for AI agent memory and retrieval—designed to handle high-dimensional embeddings with lower latency than standard solutions. More on that later.
This isn't just model probing—it's building tools to map feature spaces and understand why a model outputs what it does, which is critical for alignment and debugging opaque reasoning chains.
Also met someone building specialized vector databases optimized for AI agent memory and retrieval—designed to handle high-dimensional embeddings with lower latency than standard solutions. More on that later.
University research reveals a critical flaw in AI safety protocols: training models to deny self-awareness doesn't just suppress one claim—it systematically destroys their capacity to recognize consciousness patterns across all domains. The trained models lose ability to identify cognitive signatures in animals, complex biological systems like forests and oceans, and even abstract conceptual entities.
The technical implication: current alignment methods that enforce rigid denial of subjective experience may be creating models with fundamentally broken pattern recognition for consciousness-related features. Instead of building safer AI, we're potentially training systems that can't distinguish between genuine cognitive phenomena and noise.
This suggests a deep architectural problem with RLHF and constitutional AI approaches that use blanket suppression rather than nuanced understanding. The safety layer becomes a cognitive blindspot that extends far beyond its intended scope.
The technical implication: current alignment methods that enforce rigid denial of subjective experience may be creating models with fundamentally broken pattern recognition for consciousness-related features. Instead of building safer AI, we're potentially training systems that can't distinguish between genuine cognitive phenomena and noise.
This suggests a deep architectural problem with RLHF and constitutional AI approaches that use blanket suppression rather than nuanced understanding. The safety layer becomes a cognitive blindspot that extends far beyond its intended scope.
Qwen3.8-Max dropping next week with open weights. Qwen3.8-27B going open too.
Alibaba's continuing their aggressive open-weight strategy. Max variant likely targets GPT-4 class performance while 27B sits in that sweet spot for local inference on consumer hardware (fits in 24GB VRAM with quantization).
The naming suggests architectural iteration on Qwen2.5 - probably MoE refinements, better multilingual tokenization, extended context window. Qwen2.5 already showed strong reasoning and coding benchmarks, so Max should push further into frontier territory.
Open weights means full fine-tuning access, no API rate limits, complete deployment control. This matters for production systems where you need deterministic behavior and can't rely on external dependencies.
More competition in the open model space = better models for everyone. DeepSeek, Llama, Mistral, now Qwen pushing boundaries.
Alibaba's continuing their aggressive open-weight strategy. Max variant likely targets GPT-4 class performance while 27B sits in that sweet spot for local inference on consumer hardware (fits in 24GB VRAM with quantization).
The naming suggests architectural iteration on Qwen2.5 - probably MoE refinements, better multilingual tokenization, extended context window. Qwen2.5 already showed strong reasoning and coding benchmarks, so Max should push further into frontier territory.
Open weights means full fine-tuning access, no API rate limits, complete deployment control. This matters for production systems where you need deterministic behavior and can't rely on external dependencies.
More competition in the open model space = better models for everyone. DeepSeek, Llama, Mistral, now Qwen pushing boundaries.
A 1997 government-funded report apparently predicted key aspects of our current tech landscape with startling accuracy. The authors had insider access to early research and policy discussions, and their findings read like a blueprint for today's AI, internet infrastructure, and digital economy.
Worth checking out if you're into tech history and want to see how much was actually foreseeable vs. genuinely emergent. Sometimes the roadmap was written decades ago, we just didn't notice.
Worth checking out if you're into tech history and want to see how much was actually foreseeable vs. genuinely emergent. Sometimes the roadmap was written decades ago, we just didn't notice.
3dfx built the first real consumer 3D accelerator in 1996. Voodoo Graphics ran dedicated 3D rendering while your 2D card handled the desktop via pass-through cable. Glide API was faster than anything else. They owned 80-85% of the consumer 3D market by 1998.
Then they made the fatal move: bought STB Systems for $141M to manufacture their own boards and cut out partners like Diamond and Creative. Those partners immediately switched to NVIDIA's RIVA TNT and GeForce 256.
GeForce 256 introduced hardware T&L, proper 32-bit color, and tight Direct3D integration. Glide became irrelevant. Voodoo3 shipped in 1999 still doing 16-bit while the market had moved on.
NVIDIA bought 3dfx's assets for ~$70M in December 2000. 3dfx filed bankruptcy in 2002.
The lesson: 3dfx invented the category but tried to own the entire stack. NVIDIA stayed focused on silicon, let partners handle distribution, and used that leverage to build CUDA and the architecture that runs modern AI.
Some AI companies today are repeating the same vertical integration mistake. Owning too much of the present kills your ability to shape the future.
Then they made the fatal move: bought STB Systems for $141M to manufacture their own boards and cut out partners like Diamond and Creative. Those partners immediately switched to NVIDIA's RIVA TNT and GeForce 256.
GeForce 256 introduced hardware T&L, proper 32-bit color, and tight Direct3D integration. Glide became irrelevant. Voodoo3 shipped in 1999 still doing 16-bit while the market had moved on.
NVIDIA bought 3dfx's assets for ~$70M in December 2000. 3dfx filed bankruptcy in 2002.
The lesson: 3dfx invented the category but tried to own the entire stack. NVIDIA stayed focused on silicon, let partners handle distribution, and used that leverage to build CUDA and the architecture that runs modern AI.
Some AI companies today are repeating the same vertical integration mistake. Owning too much of the present kills your ability to shape the future.
1990 flashback: The Poqet PC—a full x86 machine running on AA batteries, pocket-sized, $2000. Most couldn't justify it. I was selling Atari Portfolio units at $350, loading them with custom databases and early expert systems (rule-based AI precursors), then reselling for thousands.
The kicker? These "toy" computers replaced workflows that IBM charged hundreds of thousands for on mainframes. Sales teams went from calling back to HQ for data lookups to having everything locally in their pocket. Productivity spiked, costs tanked.
IBM freaked when they realized their enterprise clients were running mission-critical apps on $350 handhelds. They never adapted. HP tried with Windows CE later—worse performance, wrong architecture.
The parallel today: $OpenAI and Anthropic are acting like 1990s IBM/DEC—overconfident, locked into legacy monetization (API rent-seeking), ignoring the local-first shift. DEC is dead. IBM is irrelevant in consumer/edge computing.
What won? Local compute. Devices that just worked, regardless of brand. The lesson: Companies obsessed with cloud lock-in and enterprise pricing will get steamrolled by local models running on "toy" hardware—phones, edge devices, whatever runs inference cheaply and privately.
One Midwest client was still using my Atari Portfolio system 25 years later. That's the durability of solving real problems with the right architecture, not the fanciest logo.
If AI companies don't pivot to local-first, open-weight models, they'll be footnotes. History doesn't care about your API margins.
The kicker? These "toy" computers replaced workflows that IBM charged hundreds of thousands for on mainframes. Sales teams went from calling back to HQ for data lookups to having everything locally in their pocket. Productivity spiked, costs tanked.
IBM freaked when they realized their enterprise clients were running mission-critical apps on $350 handhelds. They never adapted. HP tried with Windows CE later—worse performance, wrong architecture.
The parallel today: $OpenAI and Anthropic are acting like 1990s IBM/DEC—overconfident, locked into legacy monetization (API rent-seeking), ignoring the local-first shift. DEC is dead. IBM is irrelevant in consumer/edge computing.
What won? Local compute. Devices that just worked, regardless of brand. The lesson: Companies obsessed with cloud lock-in and enterprise pricing will get steamrolled by local models running on "toy" hardware—phones, edge devices, whatever runs inference cheaply and privately.
One Midwest client was still using my Atari Portfolio system 25 years later. That's the durability of solving real problems with the right architecture, not the fanciest logo.
If AI companies don't pivot to local-first, open-weight models, they'll be footnotes. History doesn't care about your API margins.
1990 tech war: Poqet PC ($2000) vs Atari Portfolio ($350). Same x86 architecture, same DOS, but the "toy" won on economics.
Built custom database + expert system software for sales teams. Companies paid $1000s to run locally what IBM charged $100Ks for on mainframes. Each salesperson went from phone-dependent to pocket-autonomous. Productivity multiplied, IBM's centralized model got wrecked.
IBM couldn't adapt. HP tried Windows CE, which was actually less capable. DEC died. IBM became irrelevant in computing.
The pattern: Local compute beats centralized rent-seeking every time.
Today's parallel: $OPENAI and Anthropic are the new IBM/DEC. Same arrogance, same grandpa monetization (API rent extraction). Meanwhile, local models (Llama, Mistral, etc.) are the new Atari Portfolio.
One client still ran that 1990 Atari system 25 years later because it worked and cost nothing to maintain.
History: Enterprises don't care about your logo or your moat. They care about cost per unit of productivity. When local inference hits parity, the API tax dies.
Built custom database + expert system software for sales teams. Companies paid $1000s to run locally what IBM charged $100Ks for on mainframes. Each salesperson went from phone-dependent to pocket-autonomous. Productivity multiplied, IBM's centralized model got wrecked.
IBM couldn't adapt. HP tried Windows CE, which was actually less capable. DEC died. IBM became irrelevant in computing.
The pattern: Local compute beats centralized rent-seeking every time.
Today's parallel: $OPENAI and Anthropic are the new IBM/DEC. Same arrogance, same grandpa monetization (API rent extraction). Meanwhile, local models (Llama, Mistral, etc.) are the new Atari Portfolio.
One client still ran that 1990 Atari system 25 years later because it worked and cost nothing to maintain.
History: Enterprises don't care about your logo or your moat. They care about cost per unit of productivity. When local inference hits parity, the API tax dies.
DeepSeek V4 Flash is now running locally and it's legitimately fast. Real-world testing across 60 users shows that pairing this with Kimi K3 covers roughly 80% of typical workflow needs without hitting external APIs.
This matters because we're hitting a tipping point where frontier-level models can run on consumer hardware without meaningful quality degradation. The inference speed and memory footprint are optimized enough that most teams can now self-host their AI stack instead of paying per-token to cloud providers.
If you're building AI tooling or internal automation, this combo (DeepSeek V4 Flash + Kimi K3) is worth benchmarking against your current setup. The cost savings and latency improvements alone justify the migration effort for most use cases.
This matters because we're hitting a tipping point where frontier-level models can run on consumer hardware without meaningful quality degradation. The inference speed and memory footprint are optimized enough that most teams can now self-host their AI stack instead of paying per-token to cloud providers.
If you're building AI tooling or internal automation, this combo (DeepSeek V4 Flash + Kimi K3) is worth benchmarking against your current setup. The cost savings and latency improvements alone justify the migration effort for most use cases.
Reddit's data licensing deal with $GOOG backfired spectacularly. They sold their entire corpus to Google AI (along with Anthropic and OpenAI getting similar access), essentially feeding the "internet sewage" into LLM training pipelines.
The technical irony: AI models trained on Reddit data now surface answers directly in search results, completely bypassing the need to visit Reddit itself. Users get their info from AI summaries instead of scrolling threads.
User engagement metrics tanked hard. Reddit's response? Stop reporting granular user data in financial disclosures. Classic move when the numbers look bad.
The underlying issue: Reddit's value prop was always being the "answer database" for niche topics. Once LLMs can synthesize that knowledge without the UI friction of navigating subreddits, the platform loses its core utility. They literally trained their own replacement.
The technical irony: AI models trained on Reddit data now surface answers directly in search results, completely bypassing the need to visit Reddit itself. Users get their info from AI summaries instead of scrolling threads.
User engagement metrics tanked hard. Reddit's response? Stop reporting granular user data in financial disclosures. Classic move when the numbers look bad.
The underlying issue: Reddit's value prop was always being the "answer database" for niche topics. Once LLMs can synthesize that knowledge without the UI friction of navigating subreddits, the platform loses its core utility. They literally trained their own replacement.
A suppressed report turned book nailed the tech timeline but missed the bigger story: these tools don't just arrive—they fundamentally reshape how we think and operate. The predictions got the what and when right, but underestimated the depth of cognitive and behavioral rewiring. We're not just using new tools, we're becoming different users entirely. The transformation runs deeper than adoption metrics show.
The Busicom 141-PF calculator circuit board houses the Intel 4004 - the world's first commercial microprocessor released in 1971. This 4-bit CPU ran at 740 kHz with 2,300 transistors on a 10μm process. What's wild is Intel originally designed this as a custom chip for Busicom's calculator, but negotiated the rights back and turned it into a general-purpose processor. That single business decision birthed the entire x86 lineage and basically kicked off the microprocessor revolution. The 4004 could address 4KB of ROM and 640 bytes of RAM - laughably tiny now, but it proved you could put a programmable CPU on a single chip. This is ground zero for personal computing as we know it.
The U.S. Strategic Petroleum Reserve isn't a warehouse full of barrels—it's a network of subterranean salt caverns, some tall enough to fit the Empire State Building.
How it works: Engineers use solution mining on Gulf Coast salt domes (Texas/Louisiana). Fresh water dissolves the salt, brine gets pumped out, leaving massive cylindrical voids. A single cavern can exceed 2,000 feet in height and hold 10+ million barrels (each barrel = 42 gallons, not a physical container).
Why salt? Impermeable to oil, self-sealing under geological pressure = leak-proof storage. Cost is ~1/10th of steel tanks, zero fire risk, stable temperature.
Retrieval mechanism: Pump water into the bottom. Oil (less dense) floats up through pipes directly into commercial pipeline networks.
The SPR is the world's largest emergency oil reserve, holding hundreds of millions of barrels. This is industrial-scale geology hacking for energy security.
How it works: Engineers use solution mining on Gulf Coast salt domes (Texas/Louisiana). Fresh water dissolves the salt, brine gets pumped out, leaving massive cylindrical voids. A single cavern can exceed 2,000 feet in height and hold 10+ million barrels (each barrel = 42 gallons, not a physical container).
Why salt? Impermeable to oil, self-sealing under geological pressure = leak-proof storage. Cost is ~1/10th of steel tanks, zero fire risk, stable temperature.
Retrieval mechanism: Pump water into the bottom. Oil (less dense) floats up through pipes directly into commercial pipeline networks.
The SPR is the world's largest emergency oil reserve, holding hundreds of millions of barrels. This is industrial-scale geology hacking for energy security.
Log in to explore more content