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mostlysignssomeportents · 1 year ago

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"Open" "AI" isn’t

Tomorrow (19 Aug), I'm appearing at the San Diego Union-Tribune Festival of Books. I'm on a 2:30PM panel called "Return From Retirement," followed by a signing:

https://www.sandiegouniontribune.com/festivalofbooks

The crybabies who freak out about The Communist Manifesto appearing on university curriculum clearly never read it – chapter one is basically a long hymn to capitalism's flexibility and inventiveness, its ability to change form and adapt itself to everything the world throws at it and come out on top:

https://www.marxists.org/archive/marx/works/1848/communist-manifesto/ch01.htm#007

Today, leftists signal this protean capacity of capital with the -washing suffix: greenwashing, genderwashing, queerwashing, wokewashing – all the ways capital cloaks itself in liberatory, progressive values, while still serving as a force for extraction, exploitation, and political corruption.

A smart capitalist is someone who, sensing the outrage at a world run by 150 old white guys in boardrooms, proposes replacing half of them with women, queers, and people of color. This is a superficial maneuver, sure, but it's an incredibly effective one.

In "Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI," a new working paper, Meredith Whittaker, David Gray Widder and Sarah B Myers document a new kind of -washing: openwashing:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4543807

Openwashing is the trick that large "AI" companies use to evade regulation and neutralizing critics, by casting themselves as forces of ethical capitalism, committed to the virtue of openness. No one should be surprised to learn that the products of the "open" wing of an industry whose products are neither "artificial," nor "intelligent," are also not "open." Every word AI huxters say is a lie; including "and," and "the."

So what work does the "open" in "open AI" do? "Open" here is supposed to invoke the "open" in "open source," a movement that emphasizes a software development methodology that promotes code transparency, reusability and extensibility, which are three important virtues.

But "open source" itself is an offshoot of a more foundational movement, the Free Software movement, whose goal is to promote freedom, and whose method is openness. The point of software freedom was technological self-determination, the right of technology users to decide not just what their technology does, but who it does it to and who it does it for:

https://locusmag.com/2022/01/cory-doctorow-science-fiction-is-a-luddite-literature/

The open source split from free software was ostensibly driven by the need to reassure investors and businesspeople so they would join the movement. The "free" in free software is (deliberately) ambiguous, a bit of wordplay that sometimes misleads people into thinking it means "Free as in Beer" when really it means "Free as in Speech" (in Romance languages, these distinctions are captured by translating "free" as "libre" rather than "gratis").

The idea behind open source was to rebrand free software in a less ambiguous – and more instrumental – package that stressed cost-savings and software quality, as well as "ecosystem benefits" from a co-operative form of development that recruited tinkerers, independents, and rivals to contribute to a robust infrastructural commons.

But "open" doesn't merely resolve the linguistic ambiguity of libre vs gratis – it does so by removing the "liberty" from "libre," the "freedom" from "free." "Open" changes the pole-star that movement participants follow as they set their course. Rather than asking "Which course of action makes us more free?" they ask, "Which course of action makes our software better?"

Thus, by dribs and drabs, the freedom leeches out of openness. Today's tech giants have mobilized "open" to create a two-tier system: the largest tech firms enjoy broad freedom themselves – they alone get to decide how their software stack is configured. But for all of us who rely on that (increasingly unavoidable) software stack, all we have is "open": the ability to peer inside that software and see how it works, and perhaps suggest improvements to it:

https://www.youtube.com/watch?v=vBknF2yUZZ8

In the Big Tech internet, it's freedom for them, openness for us. "Openness" – transparency, reusability and extensibility – is valuable, but it shouldn't be mistaken for technological self-determination. As the tech sector becomes ever-more concentrated, the limits of openness become more apparent.

But even by those standards, the openness of "open AI" is thin gruel indeed (that goes triple for the company that calls itself "OpenAI," which is a particularly egregious openwasher).

The paper's authors start by suggesting that the "open" in "open AI" is meant to imply that an "open AI" can be scratch-built by competitors (or even hobbyists), but that this isn't true. Not only is the material that "open AI" companies publish insufficient for reproducing their products, even if those gaps were plugged, the resource burden required to do so is so intense that only the largest companies could do so.

Beyond this, the "open" parts of "open AI" are insufficient for achieving the other claimed benefits of "open AI": they don't promote auditing, or safety, or competition. Indeed, they often cut against these goals.

"Open AI" is a wordgame that exploits the malleability of "open," but also the ambiguity of the term "AI": "a grab bag of approaches, not… a technical term of art, but more … marketing and a signifier of aspirations." Hitching this vague term to "open" creates all kinds of bait-and-switch opportunities.

That's how you get Meta claiming that LLaMa2 is "open source," despite being licensed in a way that is absolutely incompatible with any widely accepted definition of the term:

https://blog.opensource.org/metas-llama-2-license-is-not-open-source/

LLaMa-2 is a particularly egregious openwashing example, but there are plenty of other ways that "open" is misleadingly applied to AI: sometimes it means you can see the source code, sometimes that you can see the training data, and sometimes that you can tune a model, all to different degrees, alone and in combination.

But even the most "open" systems can't be independently replicated, due to raw computing requirements. This isn't the fault of the AI industry – the computational intensity is a fact, not a choice – but when the AI industry claims that "open" will "democratize" AI, they are hiding the ball. People who hear these "democratization" claims (especially policymakers) are thinking about entrepreneurial kids in garages, but unless these kids have access to multi-billion-dollar data centers, they can't be "disruptors" who topple tech giants with cool new ideas. At best, they can hope to pay rent to those giants for access to their compute grids, in order to create products and services at the margin that rely on existing products, rather than displacing them.

The "open" story, with its claims of democratization, is an especially important one in the context of regulation. In Europe, where a variety of AI regulations have been proposed, the AI industry has co-opted the open source movement's hard-won narrative battles about the harms of ill-considered regulation.

For open source (and free software) advocates, many tech regulations aimed at taming large, abusive companies – such as requirements to surveil and control users to extinguish toxic behavior – wreak collateral damage on the free, open, user-centric systems that we see as superior alternatives to Big Tech. This leads to the paradoxical effect of passing regulation to "punish" Big Tech that end up simply shaving an infinitesimal percentage off the giants' profits, while destroying the small co-ops, nonprofits and startups before they can grow to be a viable alternative.

The years-long fight to get regulators to understand this risk has been waged by principled actors working for subsistence nonprofit wages or for free, and now the AI industry is capitalizing on lawmakers' hard-won consideration for collateral damage by claiming to be "open AI" and thus vulnerable to overbroad regulation.

But the "open" projects that lawmakers have been coached to value are precious because they deliver a level playing field, competition, innovation and democratization – all things that "open AI" fails to deliver. The regulations the AI industry is fighting also don't necessarily implicate the speech implications that are core to protecting free software:

https://www.eff.org/deeplinks/2015/04/remembering-case-established-code-speech

Just think about LLaMa-2. You can download it for free, along with the model weights it relies on – but not detailed specs for the data that was used in its training. And the source-code is licensed under a homebrewed license cooked up by Meta's lawyers, a license that only glancingly resembles anything from the Open Source Definition:

https://opensource.org/osd/

Core to Big Tech companies' "open AI" offerings are tools, like Meta's PyTorch and Google's TensorFlow. These tools are indeed "open source," licensed under real OSS terms. But they are designed and maintained by the companies that sponsor them, and optimize for the proprietary back-ends each company offers in its own cloud. When programmers train themselves to develop in these environments, they are gaining expertise in adding value to a monopolist's ecosystem, locking themselves in with their own expertise. This a classic example of software freedom for tech giants and open source for the rest of us.

One way to understand how "open" can produce a lock-in that "free" might prevent is to think of Android: Android is an open platform in the sense that its sourcecode is freely licensed, but the existence of Android doesn't make it any easier to challenge the mobile OS duopoly with a new mobile OS; nor does it make it easier to switch from Android to iOS and vice versa.

Another example: MongoDB, a free/open database tool that was adopted by Amazon, which subsequently forked the codebase and tuning it to work on their proprietary cloud infrastructure.

The value of open tooling as a stickytrap for creating a pool of developers who end up as sharecroppers who are glued to a specific company's closed infrastructure is well-understood and openly acknowledged by "open AI" companies. Zuckerberg boasts about how PyTorch ropes developers into Meta's stack, "when there are opportunities to make integrations with products, [so] it’s much easier to make sure that developers and other folks are compatible with the things that we need in the way that our systems work."

Tooling is a relatively obscure issue, primarily debated by developers. A much broader debate has raged over training data – how it is acquired, labeled, sorted and used. Many of the biggest "open AI" companies are totally opaque when it comes to training data. Google and OpenAI won't even say how many pieces of data went into their models' training – let alone which data they used.

Other "open AI" companies use publicly available datasets like the Pile and CommonCrawl. But you can't replicate their models by shoveling these datasets into an algorithm. Each one has to be groomed – labeled, sorted, de-duplicated, and otherwise filtered. Many "open" models merge these datasets with other, proprietary sets, in varying (and secret) proportions.

Quality filtering and labeling for training data is incredibly expensive and labor-intensive, and involves some of the most exploitative and traumatizing clickwork in the world, as poorly paid workers in the Global South make pennies for reviewing data that includes graphic violence, rape, and gore.

Not only is the product of this "data pipeline" kept a secret by "open" companies, the very nature of the pipeline is likewise cloaked in mystery, in order to obscure the exploitative labor relations it embodies (the joke that "AI" stands for "absent Indians" comes out of the South Asian clickwork industry).

The most common "open" in "open AI" is a model that arrives built and trained, which is "open" in the sense that end-users can "fine-tune" it – usually while running it on the manufacturer's own proprietary cloud hardware, under that company's supervision and surveillance. These tunable models are undocumented blobs, not the rigorously peer-reviewed transparent tools celebrated by the open source movement.

If "open" was a way to transform "free software" from an ethical proposition to an efficient methodology for developing high-quality software; then "open AI" is a way to transform "open source" into a rent-extracting black box.

Some "open AI" has slipped out of the corporate silo. Meta's LLaMa was leaked by early testers, republished on 4chan, and is now in the wild. Some exciting stuff has emerged from this, but despite this work happening outside of Meta's control, it is not without benefits to Meta. As an infamous leaked Google memo explains:

Paradoxically, the one clear winner in all of this is Meta. Because the leaked model was theirs, they have effectively garnered an entire planet's worth of free labor. Since most open source innovation is happening on top of their architecture, there is nothing stopping them from directly incorporating it into their products.

https://www.searchenginejournal.com/leaked-google-memo-admits-defeat-by-open-source-ai/486290/

Thus, "open AI" is best understood as "as free product development" for large, well-capitalized AI companies, conducted by tinkerers who will not be able to escape these giants' proprietary compute silos and opaque training corpuses, and whose work product is guaranteed to be compatible with the giants' own systems.

The instrumental story about the virtues of "open" often invoke auditability: the fact that anyone can look at the source code makes it easier for bugs to be identified. But as open source projects have learned the hard way, the fact that anyone can audit your widely used, high-stakes code doesn't mean that anyone will.

The Heartbleed vulnerability in OpenSSL was a wake-up call for the open source movement – a bug that endangered every secure webserver connection in the world, which had hidden in plain sight for years. The result was an admirable and successful effort to build institutions whose job it is to actually make use of open source transparency to conduct regular, deep, systemic audits.

In other words, "open" is a necessary, but insufficient, precondition for auditing. But when the "open AI" movement touts its "safety" thanks to its "auditability," it fails to describe any steps it is taking to replicate these auditing institutions – how they'll be constituted, funded and directed. The story starts and ends with "transparency" and then makes the unjustifiable leap to "safety," without any intermediate steps about how the one will turn into the other.

It's a Magic Underpants Gnome story, in other words:

Step One: Transparency

Step Two: ??

Step Three: Safety

https://www.youtube.com/watch?v=a5ih_TQWqCA

Meanwhile, OpenAI itself has gone on record as objecting to "burdensome mechanisms like licenses or audits" as an impediment to "innovation" – all the while arguing that these "burdensome mechanisms" should be mandatory for rival offerings that are more advanced than its own. To call this a "transparent ruse" is to do violence to good, hardworking transparent ruses all the world over:

https://openai.com/blog/governance-of-superintelligence

Some "open AI" is much more open than the industry dominating offerings. There's EleutherAI, a donor-supported nonprofit whose model comes with documentation and code, licensed Apache 2.0. There are also some smaller academic offerings: Vicuna (UCSD/CMU/Berkeley); Koala (Berkeley) and Alpaca (Stanford).

These are indeed more open (though Alpaca – which ran on a laptop – had to be withdrawn because it "hallucinated" so profusely). But to the extent that the "open AI" movement invokes (or cares about) these projects, it is in order to brandish them before hostile policymakers and say, "Won't someone please think of the academics?" These are the poster children for proposals like exempting AI from antitrust enforcement, but they're not significant players in the "open AI" industry, nor are they likely to be for so long as the largest companies are running the show:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4493900

I'm kickstarting the audiobook for "The Internet Con: How To Seize the Means of Computation," a Big Tech disassembly manual to disenshittify the web and make a new, good internet to succeed the old, good internet. It's a DRM-free book, which means Audible won't carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:

http://seizethemeansofcomputation.org

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

#pluralistic #llama-2 #meta #openwashing #floss #free software #open ai #open source #osi #open source initiative #osd #open source definition #code is speech

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siliconec · 2 years ago

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datascraping001 · 2 days ago

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sahraeyll · 15 days ago

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trueenewshub · 3 months ago

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Trump welcomed Indian ‘bahu’

IMC WEB DESK NEW DELHI: ‘As I began to think deeply about my identity, I fell hard for a classmate of mine named Usha’ JD Vance, Ohio Senator and Trump’s pick for Vice President reminisced about his Indian-origin wife.

INSIDE PAGE CONTENT

The intercultural marriage of US Senator

Born on August 2, 1984 James David Vance was the surprise pick as the running mate for by Donald Trump. The American politician, lawyer, author, and Marine veteran has served since 2023 as thejunior United states senator from Ohio. Republican candidate for vice president in the 2024 United States president election.

It is not that Trump and Vance has always been in paradise but at the end of the day the comfort level between the two won the day.

Trump’s running mate has an Indian-Origin mate in real life! James David Vance met his wife Usha Chilukuri at Yale Law School in the year of 2010 while discussing over a group project named social decline in white America.

In 2014, they finally got married in Kentucky after dating for four years by both Hindu and Catholic customs and rituals in the presence of their families.

Who is Usha Chilukuri?

Daughter of Indian immigrants, Usha Chilukuri Vance hails from a distinguished family of academicians in Vishakhapatnam.

The San Francisco Litigator’s parents Chilikuri Radhakrishna and Lakshmi moved to USA in 1980. Usha was raised in Sandiago suburbs. Usha’s grandfather and father have studied from the prestigious IIT.

Her father currently teaching engineering and her mother takes classes on molecular biology.

Her younger sister is a mechanical engineer with a semiconductor company in San Diego and an aunt a medical professional in the southern Indian city of Chennai.

Ms. Chilikuri has a great-aunt in southern India, aged 96, celebrated in local media as the country's oldest active professor.

The Indian-origin wife of Trump’s pick for Vice President had graduated in BA in History from Yale University 2008 and went on to pursue MPhil from University of Cambridge as Gates Cambridge Scholar in 2009.

According to The New York Times, during their wedding in Kentucky in 2014, the pair was blessed by a Hindu Pundit in a separate ceremony.

The Chilikuri family is known to uphold the Hindu ideology despite residing in USA for the longest time.

Foreign is not foreign to Indians

USA has always been inviting to Indians. It is noteworthy that Indian Americans hold over 60 notable positions in the federal government in 2013 and increasing to more than 150 by 2023.

The political radar is surely topped by Kamala Harris, the Indian Vice President of USA. She was the nation’s first Indian American senator and California’s first female and South Asian attorney general. Harris is the first woman to become vice president, as well as the first Black or Asian American person to hold the office.

The corporate world has always been open to super intelligent brains from India. Sundar Pichai is currently the CEO of Alphabet Inc. and its subsidiary company Google. He studied material engineering. Pichai joined Google in 2004, where he led the product management and innovation efforts for a suite of Google's client software products, including Google crome and ChromeOs, as well as being largely responsible for Google Drive.

Another Indian-American Corporate Stalwart, Satya Narayana Nadella was born 19 August 1967. He is the executive chairman and CEO of Microsoft, succeeding Steve Balmer in 2014 as CEO and Jihn W Thompson in 2021 as chairman. Before becoming CEO, he was the executive vice president of Microsoft's cloud and enterprise group, responsible for building and running the company's computing platforms.

One of the top CEOs of the world is an Indian-American, Indra Nooyi Krishnamurthy who was was the chairman and chief executive officer (CEO) of PepsiCo from 2006 to 2018. Born on October 28, 1955, Nooyi has been consistently ranked among the world’s 100 most powerful woman. In 2014, she was ranked at number 13 on the Forbest list, and the second most powerful woman on the Fortune list in 2015 and 2017. She sits on the board of Amazon and the International Cricket Council , among other organizations.

#news #india #CEO

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bytesfarms · 7 months ago

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Top 10 Tech Companies in San Diego to Watch in 2024

San Diego was home to several notable tech companies across various industries. Here's a list of some of the top tech companies in San Diego at that time that could still be worth watching in 2024:

DeltaInfotek: Founded in the vibrant city of San Diego, Delta Infotek is a trailblazer in the realm of Low Code technology. With a team of seasoned experts, we bring years of experience in guiding clients through the evolution from BPA to BPM to DPA. Our mission is to empower organizations to scale and thrive in the digital age.

Illumina: A leader in genomic sequencing and genetic analysis, Illumina continues to play a significant role in advancing healthcare and life sciences.

ViaSat: ViaSat specializes in satellite communication and internet services, providing high-speed internet access to consumers, businesses, and government agencies.

Teradata: A provider of database and analytics-related software, Teradata helps businesses manage and analyze large volumes of data to derive insights and drive decision-making.

Dexcom: Dexcom focuses on continuous glucose monitoring systems for people with diabetes, offering innovative solutions to improve health outcomes.

ServiceNow: Known for its cloud-based IT service management software, ServiceNow helps organizations streamline their operations and improve service delivery.

Brain Corporation: Brain Corp develops AI-powered technologies for autonomous robots used in various industries, including retail, healthcare, and cleaning.

TuSimple: TuSimple is an autonomous trucking technology company, working to develop self-driving technology for long-haul trucking fleets.

Lytx: Lytx provides video telematics and fleet management solutions, helping companies improve driver safety, efficiency, and compliance.

Seismic: Seismic offers sales enablement and marketing orchestration software, empowering businesses to create engaging content and deliver personalized experiences to customers.

Keep in mind that the tech landscape is dynamic, and new companies may emerge while others evolve or shift focus over time. It's essential to stay updated on the latest developments in the San Diego tech scene to identify emerging players and promising startups.

#software development

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datahorizzon-research1 · 7 months ago

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The Neuromorphic Computing Market size was valued at USD 6.8 Billion in 2023

The neuromorphic computing market size was valued at USD 6.8 Billion in 2023 and is expected to reach a market size of USD 82.1 Billion by 2032 with a CAGR of 31.8%.

The neuromorphic computing market has been steadily expanding, driven by the demand for efficient and powerful computing solutions inspired by the human brain’s architecture. Neuromorphic computing mimics the parallel processing and energy efficiency of biological neural networks, offering promising capabilities for tasks like pattern recognition, sensor data processing, and machine learning. This technology has found applications in various fields, including robotics, healthcare, automotive, and cybersecurity.

Recent developments in the neuromorphic computing market include:

Advancements in Hardware: Companies are continually developing neuromorphic chips with increased complexity and performance, enabling faster and more energy-efficient processing of neural networks.

Software Innovations: There’s a growing focus on developing specialized algorithms and software frameworks optimized for neuromorphic hardware, enhancing the usability and effectiveness of these systems.

Expansion of Applications: Neuromorphic computing is being applied to a broader range of use cases, including real-time data analysis, autonomous systems, and edge computing, driving its adoption across industries.

Research Collaborations: Academic institutions, research organizations, and industry players are collaborating to advance the understanding and implementation of neuromorphic computing, fostering innovation and knowledge exchange.

Market Growth and Investment: The neuromorphic computing market is experiencing significant growth, with increased investments from both established technology companies and startups, indicating growing confidence in its potential and future prospects.

Top Companies are:

· Intel Corporation

· SK Hynix Inc.

· IBM Corporation

· Samsung Electronics Co. Ltd

· GrAI Matter Labs

· HP Enterprise

· Nepes Corporation

· General Vision Inc.

· Qualcomm Technologies

· Gyrfalcon Technology Inc.

· BrainChip Holdings Ltd

· Vicarious FPC Inc.

Market Segmentations:

By Component (2024–2032)

· Hardware

· Software

· Services

By Deployment (2024–2032)

· Cloud Deployment

· Edge Deployment

By Application (2024–2032)

· Signal Processing

· Image Processing

· Data Processing

· Object Detection

· Others

By End-use (2024–2032)

· Military & Defense

· Automotive

· Consumer Electronics

· Industrial

· Medical

· IT & Telecommunication

· Others

Regional Analysis:

Neuromorphic Computing market in North America demonstrated a remarkable stronghold, attributed to the region’s robust adoption of artificial intelligence technologies and the presence of key industry players like General Vision Inc., IBM Corporation, Intel Corporation, and HRL Laboratories LLC. This dominance was further fueled by substantial government investments aimed at fostering research in neuromorphic computing.

Notably, in September 2022, the Department of Energy renewed its funding of USD 12.6 million to the University of California San Diego’s Quantum Materials for Energy Efficient Neuromorphic Computing center. This funding injection is geared towards advancing the development of materials crucial for enhancing neuromorphic computing capabilities, underscoring North America’s pivotal role in shaping the trajectory of this cutting-edge technology.

Key highlights of the report include:

1. The report delivers thorough Market analysis, furnishing valuable insights to guide strategic decision-making.

2. The comprehensive research outlined in the study enhances the depth of your presentations and marketing strategies.

3. By offering crucial insights into key market competitors, the study empowers businesses with a strategic edge.

4. It delivers a precise assessment of evolving market dynamics, ensuring readers stay abreast of the latest industry trends.

5. With meticulous breakdowns of various market niches, the report facilitates informed decision-making processes.

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mostlysignssomeportents · 9 months ago

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This day in history

I'm on tour with my new novel The Bezzle! Catch me TONIGHT in SALT LAKE CITY (Feb 21, Weller Book Works) and TOMORROW in SAN DIEGO (Feb 22, Mysterious Galaxy). After that, it's LA, Seattle, Portland, Phoenix and more!

#15yrsago Parent of gamer asks his son to honor the Geneva Conventions https://memex.craphound.com/2009/02/21/parent-of-gamer-asks-his-son-to-honor-the-geneva-conventions/

#15yrsago UAE plans ban on negative economic reporting https://www.nytimes.com/2009/02/12/world/middleeast/12dubai.html

#15yrsago UK’s top snoop gets finked out by her neighbours https://www.theguardian.com/commentisfree/2009/feb/22/jacqui-smith-expenses-inquiry

#15yrsago Stimulus bill requires RSS feeds of how the money is spent http://www.aaronsw.com/weblog/rssstimulus

#10yrsago Conservative western bloggers: Ukraine strongman’s pay-for-play useful idiots https://www.buzzfeednews.com/article/rosiegray/exclusive-how-ukraine-wooed-conservative-websites

#10yrsago I am a Ukrainian: powerful, viral video about Euromaidan https://www.youtube.com/watch?v=Hvds2AIiWLA

#10yrsago Kansas lawmaker introduces bill to permit teachers to hit children hard enough to bruise https://slate.com/human-interest/2014/02/kansas-spanking-bill-new-legislation-allows-parents-and-teachers-to-hit-kids-harder.html

#10yrsago Canadian court rules on copyright trolls: letters can go ahead, under strict supervision https://www.michaelgeist.ca/2015/03/defending-privacy-doesnt-pay-federal-court-issues-ruling-in-voltage-teksavvy-costs/

#10yrsago Mall cops freak out over steampunk meetup, call the real cops https://www.kpbs.org/news/2014/feb/19/steampunk-carousel-outing-cut-short-security-guard/

#10yrsago Openknit: a Reprap-inspired open source knitting machine http://openknit.org

#5yrsago Beyond “more copyright”: how do we improve artists’ lives and livelihoods through policy? https://www.youtube.com/watch?v=0294Y6Lv3Eo

#5yrsago Iowa’s electricity monopolist Midamerican Energy has written a bill to let it “monopolize the sun” https://www.bleedingheartland.com/2019/02/20/new-bill-is-clear-attempt-by-midamerican-to-monopolize-the-sun-in-iowa/

#5yrsago Tucker Carlson thought anti-elite historian would be an easy interview, but ended up telling him “go fuck yourself” https://www.theguardian.com/society/2019/feb/20/historian-who-confronted-davos-billionaires-leaks-tucker-carlson-rant

#5yrsago As sports company abandons support for “smart” basketball, Nike pushes a software update that bricks its self-tying shoes https://mashable.com/article/nike-app-connected-shoe-bricked#duGbFcvYdsqa

#5yrsago The TRUE Fees Act: legislative proposal to force cable/ISP companies to advertise the true cost of their services, inclusive of surcharges https://www.vice.com/en/article/j57ddb/new-bill-would-stop-internet-service-providers-from-screwing-you-with-hidden-fees

#1yrago Matt Ruff's "Destroyer of Worlds" https://pluralistic.net/2023/02/21/the-horror-of-white-magic/#anti-lovecraftian

#pluralistic

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siliconec · 2 years ago

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Reinforcement Detailing Services in USA

Silicon Engineering Consultant Pvt. Ltd. provides top-notch quality Reinforcement Detailing Outsourcing Services. Our Reinforcement Detailing CAD Services to involve the use of computer-aided design (CAD) software to create detailed drawings and models of reinforcement structures. Reinforcement Outsourcing Services involve the outsourcing of reinforcement detailing and design work to a third-party company or service provider. Reinforcement Design and Drafting Services involve the creation of detailed design and drafting documentation for reinforced concrete structures. Connected with the US for your next Reinforcement Detailing Consultant Services.

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datascraping001 · 1 month ago

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G2 Product Information Extraction