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Let's learn about via these 55 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Proof Of Work /Learn Repo 1. Why Selfish Mining is Detrimental to a Blockchain Network Selfish mining is a scenario where a miner mines a new block but fails to publish it for other miners to see and add it to the chain of blocks on their ends. 2. Ethereum's Deflationary Impact: What Investors Need to Know What exactly does becoming a deflationary cryptocurrency mean for Ethereum? And why is this important? 3. The Future of PoW Mining After The ETH 2.0 Merge: An Overview Bitcoin will most likely continue to use PoW. Post ETH 2.0, there is elevated risk of a much lower profitability for current ETH miners going forward. 4. The Pros And Cons Of Proof Of Stake Technology There is no doubt that proof of stake (PoS) has become a popular consensus algorithm in the cryptocurrency world. 5. ETH-flexive Either the merge happens, or it doesn’t. That is the future event we are trading. The merge itself is unaffected by the price of ETH 6. My 5 Favorite Proof of Work Cryptocurrencies Proof of Work is an important aspect of the cryptocurrency solution. Here are my favorite Proof of Work cryptocurrencies. 7. Why Proof-of-work Will Always be an Essential Part of Ethereum Justin Sun highlights why proof-of-work is an essential part of Ethereum, and lends his expert insights regarding the matter. 8. Bitcoin White Paper: 4. Proof-of-Work To implement a distributed timestamp server on a peer-to-peer basis, we will need to use a proof-of-work system  similar to Adam  Back's  Hash cash  [6],  rather than  newspaper  or  Usenet  posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits.  The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. 9. Proof of Work is the Solution Not a Problem Proof of Work is the most secure Internet protocol ever created 10. Proof of Work is a Tried and True Solution Gold is the original Proof of Work to back a currency. 11. What is up with ETC in Context of the Imminent ETH's "The Merge" An analysis on what may happen to the price of ETC in the context of upcoming the Merge of ETH moving from PoW to PoS 12. Coming to Consensus: Proof-of-Work vs Proof-of-Stake An article that both explains and compares Proof-of-Work (used by Bitcoin and Ethereum) and Proof-of-Stake (used by many other blockchains) 13. Proof of Stake is an Old Solution To call Proof of Stake a savior is a stretch. To call Proof a Stake a stopgap…perhaps. To call Proof of Stake something new is simply not true. 14. What is the Difference Between Miner Extractable Value and Maximal Extractable Value Miner extractable value vs Maximal extractable value 15. What Ethereum's Move To Proof Of Stake Means Two common PoS implementations are BFT and Ouroboros. Blockchains turn to PoS due to factions such as TPS, decentralization, tokenomics, and fast finality. 16. Algorand and the Blockchain Trilemma Through a more clever mix of game theory and cryptography, Silvio Micali and his team might have created tomorrow’s infrastructure. Fast, secure and scalable. 17. One Hour of "Effective" Freelance Work is Worth 3 Hours in a 9-5 Job One effective freelance working hour is worth two or three regular 9-to-5 working hours. 18. Carbon Footprint: Proof-of-Work vs. Proof-of-Stake Many tech experts argue about Blockchain's carbon footprints consensus mechanisms. Learn the pros and cons of proof-of-stake vs. proof-of-work. 19. Web3 is not About Blockchain or Decentralization Web3 is not about blockchain or decentralization. It is about the democratization of the web 20. How Cosmos Proof-Of-Stake Helps Create a More Sustainable Blockchain Environment At the heart of the mission was the need to address the throughput, scalability, and importantly, environmental issues associated with Proof of Work blockchain. 21. 10 Shared Beliefs of a Bitcoin Maximalist While some maximalists assert that altcoins are just unregistered securities, most consider them technically and morally questionable, if not outright scams. 22. 51% Attacks Pose the Biggest Threat to Smaller Mining-Based Cryptocurrencies A 51% attack is where an actor controlled over half of the mining power in order to reverse transactions and compromise the security of the network. 23. Reducing The Environmental Impact of NFTs and Proof-Of-Work Why are NFTs bad for the environment? These are all examples built on blockchains that do not use Proof-of-Work or any other type of energy-intensive methods. 24. The Proof of Stake Trojan Horse The Proof of Stake versus Proof of Work debate will affect how cryptocurrencies move forward. 25. Ethereum Enters New Phase of Adoption Prices of ETH have gone bearish following the Merge's successful execution, which upgraded the Ethereum network from PoW to PoS in September. 26. What You Need to Know About Bitcoin's Energy Usage Learn the truth about Bitcoin’s energy consumption and understand why its environmental impact isn’t as bad you think. 27. What Is Hashing in Blockchain: A Beginner-Friendly Guide The implementation of a cryptographic hash function is beneficial to prevent fraudulent transactions, double spends in blockchain, and store passwords. 28. The Approach and the Solution to the Blockchain Trilemma Kaspa has made the blockchain trilemma a thing of the past with it's approach and the solution to the blockchain trilemma... and it's an actual solution. 29. Perils of Induction: Doubting Thomases, Rigor, NFTs & Descartes On this day, Second Sunday of Easter, also known as the Octave Day of Easter, White Sunday, Quasimodo Sunday, and Low Sunday, traditional Catholics following the traditional liturgical calendar in the Roman Rite, ponder the Gospel of St. John (here taken from the 1945 St. Andrew Missal): 30. Proof-of-Stake vs Proof-of-Work vs Proof-of-Time: What’s the Difference? We'll look at the difference between these three algorithms - Proof-of-Stake vs Proof-of-Work vs Proof-of-Time and how they compare. 31. The Next Evolution in Consensus: Proof of Burn! Koinos Group reveals the next generation of consensus algorithms with greater decentralization that proof-of-work, and greater efficiency than proof-of-stake. 32. Resisting Plutocracies In The World of Blockchain And Web3 Finding a human-centric solution to the problems that most blockchains face when it comes to fair voting in its currently plutocratic and unequal form. 33. The Art & Science of Node Monitoring One of the hardest parts of designing a monitoring system is predicting everything that “might” go wrong. That only comes from experience, not just over time, b 34. What is 'Sharding' and UTXO Scaling for Blockchain? We propose an improved algorithm called proof-of-less-work. 35. Differences Between Proof of Work and Proof of Stake Network When  Buying Ethereum Since the recent launch of Ethereum's new network called proof of stake, there have been multiple questions on how it differentiates from the already existing proof of work mining network and how it will affect people that buy Ethereum. It has become a major concern among investors, traders, cryptocurrency enthusiasts, and those who buy Ethereum. 36. Proof of Stake vs Proof of Work: The Case of Transaction Finality In this article, we look at the two most popular consensus mechanisms - PoW and PoS - and show why PoW stands out in terms of transaction finality. 37. Undying Mining Dispute: Be PoW-conformist or Try Alternatives? While traditional blockchains focus their rewarding on transaction processing, Proof-of-Data-chains are focused on data stored in decentralized filesystem. 38. Proof of Work (PoW) vs. Proof of Stake (PoS): Sharding Edition The blockchain scalability problem is currently the main limitation for the mass adoption of blockchain technology. In the standard permissionless p2p blockchain design introduced by Satoshi Nakamoto, every node has to process all the data in the network. 39. The Ethereum Merge, Explained Ethereum emits nearly the same amount of carbon dioxide as Singapore at this time and is the second-largest blockchain, after only bitcoin. If the Merge is successful, it will significantly reduce ethereum’s high electricity usage by almost 99%.Key Highlights 40. Layer 1 vs Layer 2: Where’s the Next Block Party At? Layer 1 versus Layer 2 blockchain solutions. Layer 2 blockchain protocols have posted stellar performance in 2021. So what are they to a layer 1 solution? 41. The Essential Guide to Proof of Work (PoW) in Blockchain Proof of Work (commonly abbreviated to PoW) is a consensus algorithm used for preventing the 51% attack or double-spends. 42. Is There a Security Vs. Coin Creation Trade-Off in Blockchains? Recent years have sparked significant interest in blockchain networks. This has resulted in significant progress in the theory of such distributed networks. In particular, followers gathered more data about possible blockchain designs, their limitations, and their trade-offs. There's called The trade-off between security and coin creation 43. Of Blockchain Technology, Sustainability, and Applicability INTRODUCTIONThe blockchain technology and the opportunities it created began to enter our lives about 10 years ago. The technology that we are most familiar with is Bitcoin, a cryptocurrency that uses the blockchain technology. After the invention of Bitcoin many other cryptocurrencies have followed suit. Bitcoin is still at the top and was the first to introduce the Proof of Work algorithm, which we'll talk about in a moment, however, this system is now quite outdated. In this article we are going to discuss the technical aspects of the Blockchain technology, Bitcoin and the algorithms it uses and applications for the Blockchain technology. 44. Rising Gas Fees in the Run-up to Ethereum 2.0 Upgrade Ethereum gas fees have been on the rise since the beginning of the year. In the latest developments, miners on the world’s second-largest blockchain protocol voted to increase the gas limit from 10 to 12.5 million Gwei. This increase of 25 percent in miner fees contributes to the more than 75 percent rise year-to-date. 45. Proof of Work vs. Proof of Stake: How Different Are They Really? Proof of work and proof of stake are the two most popular transaction validation methods on blockchains. Both have compelling arguments as to why they are more efficient, cheaper, more scalable or more secure, so let’s tackle what makes each different and why it matters. 46. Is Proof-of-Stake the Future of Consensus? Blockscale Doesn’t Think So! Intel's new pro-mining ASIC chipset—Blockscale—isn't just efficient. It has the future of crypto adoption in mind and Intel is in the space for the long haul. 47. The Origins of Proof-of-Less-Work and More A Founder interview with Cheng Wang, founder of Alephium and creator of Proof-of-Less-Work, an alternative third road in the world of blockchain staking 48. PoW-Based NFTs in 2021: A Comparison There are three currently known PoW based NFTs for readers to compare and consider which would be the next hotspot and opportunity in the NFT market. 49. Crypto Energy Consumption: Clean & Green Could Be the Solution For advocates of the widescale adoption of cryptocurrency, there is one major challenge that stands in the way of their dream coming true. Namely, the fact that mining cryptocurrencies require exorbitant — and seemingly wasteful — amounts of energy. 50. Looking Into Ethereum’s Merge And How KIRA Circumvents PoS’s Biggest Weakness Ethereum's Merge switched the blockchain from Proof of Work to Proof of Stake. While it addressed some of the old problems, it created a new set to deal with. 51. Halving Is No Longer Magic, but Not Yet a Messiah Halving is an event when the amount of payment for cryptocurrency mining is halved compared to what it was before. 52. Why PoW NFTs are the Next Blue-Chip NFT Collections PoW NFTs are still under the radar and this article will guide you through learning about PoW NFTs and understanding the values. 53. A Technical Q&A on Network Partition Attacks Cryptocurrencies are prone to many different types of attack. As Bitcoin is over 11 years on the track, we know and were facing many different attempts on breaking its security. One of them is network partition attack. But what about the Proof-of-Stake algorithms? Have they solved some of the vulnerabilities of Bitcoin and its Proof-of-Work algorithm? 54. Learn the blockchain basics - Part 2 : "Mining", "Miners", and the Proof of Work Algorithm The second episode of this series describes the Proof of Work algorithm.
After reading this article, you should have a basic understanding of the PoW algorithm. 55. Code A Full Node for A Proof Of Work Blockchain - From Scratch Learn the basics of PoW Blockchain and create your own Coin Thank you for checking out the 55 most read stories about Proof Of Work on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

55 Stories To Learn About Proof Of Work

THE FIRST MONTH

On this day, , also known as the , , , and , traditional Catholics following the traditional liturgical calendar in the Roman Rite, ponder the Gospel of St. John (here taken from the 1945 St. Andrew Missal): Second Sunday of Easter Octave Day of Easter White Sunday Quasimodo Sunday Low Sunday “Now Thomas, one of the 12, who is called Didymus was not with them when Jesus came. The other disciple therefore said to him: We have seen the Lord. But he said to them: Except I shall see in His hands the print of the nails, and put my finger into the place of the nails, and put my hand into His side, I will not believe. And after eight days, again His disciples were within, and Thomas with them. Jesus cometh, the doors being shut, and stood in the midst, and said: Peace be to you. Then He saith to Thomas: Put in thy finger hither thy hand, and put it into my side; and be not faithless, but believing. Thomas answered and said to Him: My Lord and my God. Jesus saith to him: Because thou has seen Me, Thomas, thou hast believed: blessed are they that have not seen, and have believed.” It’s an interesting grounding for a critique on academia, the world of the peer-review, what we call science, industry, and now it seems even the everyday. I’ve subjected myself to what academe calls ‘rigor’ in order to get published. But what exactly is this ‘rigor’? A simple google search for a definition yields, “the quality of being thorough, exhaustive, or accurate”, “severity or strictness”, “demanding, difficult, or extreme conditions”. In the world of the scientific peer-review, your work is evaluated by the editor of an academic journal, who decides whether your paper will be sent out to be critiqued (via a blind review) by three or four other academics. Your paper is sent back to the editor, who evaluates the critique and ultimately decides whether the journal will proceed to accept your paper for publication (oft subject to revisions) or not. This is essentially what rigor is—one is subjected to ‘strict’ rules of ones peers, who consider one another ‘experts’ in evaluating and critiquing a piece of work. The system is inductive, where general principles (also decisions, impressions, and critiques) are derived from specific observations. Just from an everyday common-sense perspective, one should see the highly subjective nature of the process. At the same time, from a logical perspective, it is a fallacy—because it assumes everyone in the whole must have the same characteristics as the parts. It should prove disturbing to anyone how the industry of knowledge isn’t instead premised on reason; and that science as we know it isn’t primarily logical or reasonable. What is logical is empirical. What isn't logical can’t be empirical. A prime test of reality and truth is whether it passes the logic test, where human folly, impressions, and biases can’t figure into determining whether something is or isn’t. Inductive ‘logic’, however, isn’t confined to academia. A majority rules or peer system governs both board rooms and labor unions. They used to call this sophistry. And as this global health issue has shown, the survey has become the primary arbiter of decisions and choices. Indeed, numbers are part and parcel of Math and arithmetic is logic; but haven’t the numbers been robbed of the essence of logic itself? Have not the graphs, charts, percentage points, various quantities cited from and by ‘expert’ upon ‘expert' culled so ‘rigorously’, which have come to be the plaque and emblem of trust been stripped of the essence of logic down to mere consensus, impression, and rule of the majority, or in some cases the majority of the elite? But rigor is not reason; and what isn’t logical can’t be empirical. Christ was/ is Logos Incarnate; and the civilization, which built what we have today (from science to architecture, engineering, law, education, and commerce) was (is) a Catholic Christian one, which is inevitably premised on this idea of ‘Logos Incarnate’. It should be paradoxically both refreshing and daunting to all (whether Catholic or not) that the very basis of civilization and its consequences came off this--'truth' for Catholics, 'concept' for non-Catholics. And that we should turn our world around (upside down, really) and premise it on inductive (vs. deductive logic) is a defiance of both reason and sense. This is why experience has been touted the best teacher, which contradicts Logos Incarnate and ‘what is logical is empirical’. Truth is, experience isn’t the default and inherent best teacher. It is the inefficient teacher. Logic oft acquired through much research and reading is the best teacher. Indeed, it does require practice (thus, experience); but the ‘experience’ touted today is not this type experience but an immersion with what is referred to as real-world situations (which is good in itself). Problem is, if this fondness and bias for ‘experience’ comes at the expense of logic. What if one is 'learning' in an environment that is anti-logic? Logic has become so difficult for many and in fact, most. Thus, while ‘what is logical is empirical’, the pain of achieving and maintaining logic (true rigor in the acquisition of habits of the mind towards consistency in thought and fidelity to the flow of these thoughts) is not an appealing proposition. Acting before thinking has trumped the more prudent prudence. In reality, however, ‘experience’ is an inefficient and slow teacher as one has to go through various events of pain, oft stretching an entire lifetime to learn and gain. You don’t have to jump off a cliff or touch fire to know how that’s going to feel or turn out. Logic saves you the trouble. But we’ve come to prefer the more circuitous and long route. This is not an affront on practice, praxis, and experience. I do not discount the necessity. I am merely arguing for the superiority of deductive logic. In fact, the affront on logic might be more serious. Let’s take the case of what the invention of bitcoin and its blockchain has enabled—the superiority of code; in other words, logic. Verification (on so public a ledger) vs. ‘trust’ (especially in comparison to legal tender known as ‘fiat’) is certainly an interesting and wonderful proposition. Anyone who has followed and looked into the history of bitcoin, knows while it is a product of human design (the anonymous Satoshi Nakamoto), it's human design that came as a consequence of central planning and the manipulation of ‘money’. One can argue, it was thus, organic and natural. The last straw seemed to have been the 2008 financial crisis. Moreover, this product of human design, which remains to have been created by someone who is either dead or prefers to be permanently invisible, is open source (thus, anyone can work on it), public (anyone and everyone can use and see its activities and transactions), and is built on economic incentives for its growth and survival, has made it practically fool-proof for any sort of compromise and infiltration. Its very foundation, grass roots development, and tokenomics (as the people in the space like to call it), ensure its fidelity to logic not just in its code (which non-coders can’t even understand) but its function and utility as a system and store of value. It’s a real case for ‘what is logical is empirical’. But while bitcoin and other cryptocurrencies and blockchains are based on code (math and logic), ‘what is logical is empirical’ isn’t yet quite clear with other blockchains and alt-coins. Qualitatively, when translated to experience and culture, the consequences and effects can be driven by anti-logic. This is most apparent in the space of non-fungible tokens (NFTs). I sometimes wonder if writers of code are based on logic or Cartesian ‘logic’—which is logical within its realm (and why Descartes lived in his head and the logic he invented was never and can never be empirical)—giving birth to bubbles as the norm and foundation of civilization and society. We might just be devolving from mere Doubting Thomases who need to see in order to believe to disciples of ‘rigor’ (rule by the majority, elite, or both) who don’t need to see because there are others (‘experts’ and ‘chosen ones’), who will do the job of seeing to an almost complete withdrawal from reality, choosing to be in a bubble of hype, hysteria, emotion, and noise now tokenized with possible value or ‘value’. The verdict is still out on this one though. I, myself dabble in NFTs. But I prefer to guard and ensure my inclination to logic and thus remain critical by default. It also helps to do so in determining, which are Cartesian (bubbles) and which are not. Ironically, it has become necessary to become a Doubting Thomas these days of what looks like a third level withdrawal from logic and doubt. Going back to Thomas though, and to be true to ‘what is logical is empirical’, to err is human (the stain of original sin has remained). His folly might just have been a case for this as well as a case against the premium we’ve placed on inductive logic as the backbone of civilization. St. Thomas, however, almost immediately acknowledged his folly, perhaps being first to directly acknowledge and proclaim the Divinity of Christ, with “My Lord and my God”. He went on to create converts to Christianity, preaching from Babylon to Persia and finally to Malabar, India (present-day Madras), where he was reportedly thrown into a pit and pierced with a Brahmin’s spear. There are, in fact, St. Thomas Christians (Mar Thomas Nazranis or Syrian Christians). Indeed, it might seem, our current more circuitous and inductive route of rigor vs. logic, might just be going through a test. Question is, is it a unicorn (bubble), empiricism, or will logic be incarnate?

Perils of Induction: Doubting Thomases, Rigor, NFTs & Descartes

Tutorial: Swift and SwiftUI for Data Science iOS Development

This is a guest post by a guy named When I requested that he post his amazing story on Medium, he told me he didn’t have an account, but he gave me permission to post it on his behalf. @trafalgar on Steemit.com. Here’s his permission comment from Steemit: His story is a crazy one and inspiring, too. It’s one of those stories that just needs to somehow get out to more people. So, here it is: Story written by @trafalgar from Steemit.com: It feels like it was only yesterday when I made my on Steemit, but it’s actually been almost 90 yesterdays. first post Sure takes a while to make a million bucks on this site doesn’t it? My journey on Steemit has undoubtedly been a surreal experience, and I wish to give a long account of it here depending on how long this post ends up being after I finish writing it. Disclaimer Those of you who are unfortunate enough to have gotten to know me would be aware that I generally dislike posting about Steemit on here. Maybe it’s entirely due to me being trapped in my own little social-political bubble, but my Facebook feed isn’t filled with posts about Facebook, my Reddit homepage isn’t cluttered with talk about Reddit and 80% of the Uber rides I request don’t just automatically take me to Uber headquarters as their only destination. So I’m generally fighting an uphill battle of trying to get more mainstream topics trending here as I think they’re better for the long term health of the platform, but I think this milestone justifies an exception. I am also acutely aware that this isn’t exactly going to be an inspirational rag to riches Disney fairy tale so I’m not going to patronize my readers by disguising it as one. This will be more like if Quasimodo won the lottery and used the money to to get cosmetic surgery only to still be rejected by Esmeralda, so he resorted to using roofies. I’m not a fan of motivational writing and essentially consider most of these self-help gurus to be akin to psychics who are deluded enough to actually believe they can communicate with the dead. If you’ve ever given life advice through an acronym, then you’re either a moron or a scammer, or I.Y.E.G.L.A.T.A.A.T.Y.E.A.M.O.A.S. is how I like to remember it. So this isn’t investment, money management or general life advice. What this is, is hopefully a somewhat entertaining description of how I got here, and a tribute to this wonderful community that has helped me along the way. Buying My First Bitcoin I first seriously considered investing in Bitcoin around February this year after listening to an EconTalk podcast recorded back in 2014. I had heard of Bitcoins before of course, but dismissed it as something like buying Gold in Candy Crush Saga, except without the actual game. It wasn’t until listening to the guest in this podcast that I realized the genius behind a secure and decentralized online ledger that is able to reach consensus in a trustless environment. It’ll mean men everywhere will be saved from the indignity of being kicked out of the house because their wives found out about the escort services showing up on their credit card receipts, services that perhaps wouldn’t have been required had they not have been kicked out of the house in the first place. The host of the podcast was not entirely convinced by his guest’s view on bitcoins. He voiced his concern about the possibility of Bitcoins, which stood at a whopping $200 at the time, being overvalued. As I was listening to this 3 years after the conversation was recorded, I took out my phone and looked up the then current price of Bitcoins. It was $1200. I didn’t really need any more convincing. I opened an account on a trading platform and began slowly moving money I’d been saving for a deposit on a house and escort services into Bitcoins. This process was quite daunting at first; getting an address that looked like my cat had ran over the keyboard was really off putting. But eventually I got it all working and had a decent chunk of Bitcoins in my newly installed wallet. Discovering Steemit As I was researching more into Bitcoin on YouTube, videos by a girl named Amanda B. Johnson started to creep into my recommended list. She kept going on and on about a next generation cryptocurrency, Bitcoin 2.0, and all this other stuff I didn’t understand, which made wanking difficult, but I finally figured out that she meant there were other coins like Bitcoin out there. Dash Digital Cash I started searching for a list of these ‘crytpocurrencies’ and found . : there were over 600 of these things, and any one of them could rise up and replace Bitcoins as the dominant currency of choice in the future. They all boasted a wonderful set of features, the more modest ones merely self styled as a ‘Worldwide Supercomputer’, while others promised to automatically take the bins out for you every week. Coinmarketcap I was immediately paralyzed Researching into these I was lucky enough to run into a video of a guy _FUD_ing the shit out of this cryptocurrency platform called . Despite his assessment, I was fascinated by the concept of a decentralized social media platform that rewarded authors based on the likes they receive on their posts. Coming from a Twitter background where I spent most of my free time thinking up stuff like , I couldn’t wait to try it! Steemit that is, not the chewing gum substitute. Steemit ‘Flavored condoms are also pretty good for when you run out of chewing gum’ The Power of Steem I set up my Steemit account in March and tested the platform out. I was stunned: the darn thing just…worked. I could vote, post, comment and it was all so seamless, there was no queue, no transaction fees; it simply just worked. Well there was that error message telling me not to vote more than once every 3 seconds which reminded me of that old Microsoft paperclip that use to pop out and say , but it’s not anywhere near as bad as the 3 hour waits that I was getting used to with Bitcoin. I loved it. ‘Hi, it looks like you’re trying to write a ransom note, perhaps I could help?’ I wanted to buy Steem. At the time, most people thought it was short for a Steeming pile of shit I went to check its graph on Coinmarketcap, and holy dick cheese was it ugly! From August last year, it looked like a trail of piss that hit the pavement by October and continued to dribble slowly down an incline for the next 6 months. It resembled a cardiograph of the last moments of a guy who’d just died of a heart attack. On an unrelated note, I never got that job as a technical analyst on the Business Channel for some reason. I didn’t know at the time, but this was when Ned and Dan had that conscious uncoupling thing going on. And the market didn’t react well to the fact that our two dads were going to start seeing other people. Screw it, I thought, I’m going all in on Steem, which turned out to be about 500,000 Steem for 20c each at the time. See, this is why you should take the ‘don’t try this at home’ part of the disclaimer seriously. If it isn’t yet apparent, I’m somewhat of a nutter, and just because I got lucky with a very irresponsible approach to crypto investment doesn’t mean you should do the same. A lot of traders and investors believe that there is insufficient reason to hold Steem Power, but from a content creator’s perspective, I think they couldn’t be more wrong. Not only does it allow you to reward the authors whose content you value, it also serves as a very direct way to invest in yourself on the platform. If you really believe you have what it takes to be an exceptional content creator and want hit the ground running, well on Steem you can do just that buy purchasing these influence tokens! Like an 80 year-old hedge fund manager being surrounded by young models, I’m somewhat aware it isn’t entirely my brilliant writing and dashing good looks that bring the boys to the yard. But I’d like to believe that having a sense of humor in my posts certainly helped with growth. Investing directly in Steem can fast track your career and is certainly a viable option for those truly considering writing full-time on the platform. To The Moon I had a lot of fun googling ‘moon’ but ended up going with this PG image. To make up for it, I’ll use the word fuck here In the first month, I also started researching into potential competition I had invested all my savings into Steem. This was due to a combination of laziness and good old-fashioned stupidity on my part. I found Akasha, Synereo, Yours and BelaCoin which, by dint of a small miracle, were all garbage. (For the record, this is the case at the time of writing. If you’re reading this a few years from now and Coinmarketcap reads BelaCoin Dominance: 94%, then please don’t bother giving me shit about it because, if that were the case, this article would have also served as a suicide note). after Every day I would check Steemit’s Alexa rank, and every day it would climb a little. The price remained low for a while, probably because the latest changes at the time reset the rewards pool, but I was as confident as Quasimodo with a bottle of roofies in his pocket that things were going to change for the better. Spoiler alert: god damn, was I right. Some time in early May the market woke up from hibernation and Steem price had a pulse again. It suddenly shot up sixfold within the span of a week. It was like somebody had fed it Viagra. To earn more rewards, people started breaking up their posts into multiple parts like movie adaptions of popular young adult fiction franchises. All of a sudden Steemit was seeing more action than the underside of a teenager’s computer desk. Alas, we saw the ‘C’ word finally appear. Market correction. The value of Steem plummeted by about 50% in a single day. To think that less than a fortnight ago I wrote a encouraging people to post more frequently and not be disheartened by the price drop. Yet 12 days later, here we are: a price of over $2, making me a . piece Steem millionaire Conclusion I write this partly to share an interesting story, but mostly to brag. Fingers crossed my exes will see it and drunk text me for a change. Putting jokes aside, I do want to thank all my followers as well as the entire Steemit community. Judging from the comments, I know at least 3 or 4 of you read my articles, and you have no idea how much I appreciate having people with a similar sense of humor to share a laugh on here. Steem at $2? Do you see me flinching? by @trafalgar from Steemit.com

Three Months, One Thousand Followers And One Million Dollars Later

Introduction As LLM technologies gain more mainstream adoption and the ecosystem begins to mature, organizations are beginning to recognize the limits and costs of using LLM technologies. Many enterprises, originally enthusiastic about the application of , have abandoned centralized initiatives, pursuing instead a strategy of encouraging decentralized efforts to incorporate services like ChatGPT and Claude into their workflows. LLM technologies There are several reasons for this phenomenon. Lack of LLM expertise, MLOps requirements, and reliance on specialized GPU infrastructure are all barriers to implementing large-scale AI initiatives. Of these, however, the thorniest issue is the reliance on GPUs. In this article, we will discuss the specific difficulties posed by GPU reliance, explore a potential solution, and look at an exciting example from one of the trailblazing companies working in this area. GPU Availability as Limitations to LLMs Most publicly available and highly performant models, such as GPT-4, Llama 2, and Claude, all rely on highly specialized GPU infrastructure. GPT-4, one of the largest models commercially available, famously runs on a cluster of 8 A100 GPUs. Llama 2’s 70B model, which is much smaller, still requires at least an A40 GPU to run at a reasonable speed. This level of GPU requirement practically forecloses the possibility of running these models locally - a A100 GPU, assuming you can find a seller, costs close to $25,000. Once you obtain the GPUs, you need specialized skills to set up and maintain the servers. Very few organizations would be willing to incur such an outlay to experiment with LLM technologies. To solve this problem, several startups and cloud providers have developed extensive PaaS offerings. Some services like Replicate, which I have used in past articles and projects, allow users to rent GPU servers and pay for compute time used. Other providers, like OpenAI and Anthropic, offer their models as a per-token API, further abstracting away infrastructural complexities. However, these services require data to be sent to an external network, which makes using these services a non-starter for privacy-conscious organizations. Additionally, many of these services suffer from shortages during demand spikes as GPU usage overtakes availability, making them unreliable options for production-critical workloads. Additionally, GPU time, no matter how they are charged for, is expensive for large compute tasks - the companies that own and operate these GPUs need a return on their investment, after all. While these costs are nearly negligible for experimental use cases, commercial use cases often require embedding large contexts, fine-tuning, or multi-shot examples. These costs represent a significant barrier to adoption, especially for organizations with large datasets or those that lack the financial resources of large U.S. firms. In a previous article, we explored parameter compression as one strategy to reduce dependence on GPUs. In today’s article, we will explore another exciting technique called quantization. Before we dive into the exploration, however, we might want to learn a little bit about quantization first. Quantization (Optional Reading) In this section, we will briefly go over the basics of quantization. However, if you’re simply looking for a way to run powerful LLMs locally on your computer, you can feel free to skip this section for now and come back later. LLMWare, the company whose technology we will be using today, has built some amazing tools that let you get started with quantized models without having to get into the nitty-gritty behind it all. C/C++ implementations What is Quantization? Quantization is a technique that seeks to reduce the computational and memory requirements of running an LLM by using lower-precision numerical types. Many popular open-source models, such as Llama, Falcon, and Alpaca, use PyTorch as the underlying framework. By default, PyTorch models use 32-bit floating points, meaning a single parameter takes up 32 “bits” in the GPU memory. Quantization aims to replace these parameters with 16-bit floating points, 8-bit integers, or even 4-bit integers. Successful quantization leads to dramatic improvements in computational speed and reductions in memory usage, meaning large models become runnable on lower-end GPUs, embedded graphic chips, or even CPUs. This idea has been around for a while - PyTorch itself has added support for 16-bit floating points and model compilations as the technology matured, but progress has been slow due to early design decisions in the PyTorch framework. Does Quantization Degrade Performance? At this point, it’s natural to wonder, wouldn’t this severely degrade the accuracy of the model? The short answer is yes, but only if you do it carelessly. Every optimization comes with inherent trade-offs, but with some specialized techniques, researchers have been able to squeeze incredibly stable performance out of highly quantized models. While we won’t go into extreme technical detail, let’s go over the broad strokes of the most common strategies being used right now. If you want to learn more, you can find out more about it in a from HuggingFace. guide Calibrated Quantization During the quantization process, a calibration dataset is run through the model. The value of each parameter is recorded, and the range is used to determine how the parameters get quantized. Assuming the calibration dataset is representative of the inputs that the model will encounter, this results in improved accuracy of the resulting model. Quantization-Aware Whereas Calibrated Quantization happens after training, Quantization-Aware Training tries to optimize the model during training. While the model is training, the activations are put through a “fake quantization,” simulating errors that will likely be introduced by the quantization process. The model is then able to adapt to the errors, resulting in a more robust model that can specifically adapt to the potential distortions. Llama.cpp and GGUF While PyTorch quantization and optimizations have been long blocked by framework design, two recent open-source technologies broke through these barriers and made quantization technologies much more accessible to the general public. Let’s briefly cover them below. Llama.cpp Llama.cpp was a project by Georgi Gerganov to port the Llama model into C/C++. This got rid of the complexity introduced by PyTorch, and the native implementation allowed quantization to be implemented directly. Therefore, the resulting model could run with up to 4-bit integer quantization, allowing high-parameter-count Llama models to be run without a specialized GPU. The project has since been extended by the community to include a roster of open-source models, including popular ones like Falcon and Mistral. GGUF GGUF is Llama.cpp’s file format for storing and transferring model information. Quantized models are stored in this format so that they can be loaded and run by the end-user. GGUF is the successor format to GGML and aims to improve on GGML by providing more extensibility, backward compatibility, and stability while still allowing for rapid development. The development of a universal file format opened the door for the open-source community to extend the Llama.cpp to optimize other models, and innovators like TheBloke and LLMWare have been working over the last several months to miniaturize popular open-source models. LLMWare’s Quantized Dragon Model In today’s example, we will be using open-source libraries and quantized models provided by LLMWare, which provides convenient tools for quickly building specialized RAG workflows. Who is LLMWare? LLMWare, a generative AI company specializing in the legal and financial industries, has been actively involved in the quantization community. As I’ve written before, their focus on privacy-conscious sectors makes them a natural candidate for experimenting with and innovating in miniaturization technologies. Previously, I wrote about their RAG-optimized BLING models that squeeze incredible performance out of 1 to 3 billion parameter models for specialized tasks like contract review and financial analysis. While most open-source models with such parameter counts tend to be only useful for toy problems, LLMWare is able to generate production-ready performance out of these models by training them for narrowly targeted tasks. These miniaturized models are then able to run without an external GPU, allowing for increased privacy and scalability. What is Dragon? Dragon is a collection of LLMs that can be thought of as more powerful versions of their BLING cousins. The original intent of Dragon was to train a higher parameter model using the same instruction fine-tuning techniques, providing an option to users that need more performance and have access to lower-end GPUs. The added parameter count resulted in more powerful models that could leverage larger context windows and generate more complex outputs, but required the user to have more specialized hardware, such as a GPU-embedded laptop or a cloud compute container with a GPU attached. However, they still represented an improvement over the extremely large models, which would require waiting for access to scarce A40 or A100 GPUs. Quantized Dragon, the Best of Both Worlds Given the above, it is easy to see why quantization gave a significant boost to LLMWare’s suite of AI tools. With quantization, a user could run Dragon-tier models on the same environment as BLING models, allowing for much more powerful analysis on commodity computers. Over the course of the last month, LLMWare has published quantized versions of several Dragon models. Today, we will evaluate LLMWare’s Dragon model built on top of Llama with a legal analysis RAG problem and compare it with a similar BLING model. Interested reachers can also explore other models - a Mistral-based model and a Yi-Based model are available from LLMWare at the time of this writing. Additionally, has made running inferences on Llama.cpp models a breeze with their tight integrations with the ctransformers library, which allows gguf models to be swapped seamlessly with PyTorch based models. LLMWare We will use a Macbook Air with M1 Chip for this experiment, meaning we will only be using widely available hardware for this exercise. Testing Out Quantized Dragon Remember that in my previous article, we built a RAG application focused on legislation search. We used vector search to quickly search through several large legislations, found sections relevant to our question about Qualified Opportunity Zone Partnership Interest, and ran the question through a BLING model. In today’s article, we will run the same question through LLMWare’s quantized Dragon model and determine if it performs better than BLING models. In order to focus on model comparison and to reduce the amount of prior knowledge required, we will do a lot of the PDF parsing and vector search manually. This has the added benefit of making the problem artificially harder for the model - LLMWare’s default embedding search chunks the source material to about 1000 tokens, but handling the parsing manually allows us to bump the context up to around 3000 tokens. This will help us clearly demonstrate the difference between the Dragon and BLING models. However, you should be able to easily integrate with the rest of the LLMWare’s ecosystem if you want to leverage their tools by following the setup steps from my last article on LLMWare. In fact, if you simply replace the name of the BLING models with the quantized Dragon model from this article, everything should run seamlessly. Without further ado, let’s get started! First, let’s import the required dependencies: import sklearn
import sklearn.metrics # for cosine similarity
from llmware.prompts import Prompt
import time
import os
from openai import OpenAI
from PyPDF2 import PdfReader 

client = OpenAI() # the library now loads the key automatically as an environment variable. We can now load the PDF. In the previous example, we loaded several large legislations, but for today, we will only focus on the PDF version of the Tax Cuts and Jobs Act of 2017. reader = PdfReader([path to PDF of tax cuts and jobs act]) Now we can generate the embeddings for each page: embeddings = []
for pg in reader.pages:
    text = pg.extract_text() 
    embeddings.append(client.embeddings.create(
        input=text,
        model="text-embedding-ada-002"
    ).data[0].embedding) Let’s also generate the embeddings for the question we’re going to ask: question = 'What is a qualified opportunity zone partnership interest?'
q_embed = client.embeddings.create(
        input=question,
        model="text-embedding-ada-002"
    ).data[0].embedding With the embedding in hand, we can perform a vector search. Because our search space is small, we can just do this manually. cos_sim = [(idx, sklearn.metrics.pairwise.cosine_similarity([e], [q_embed])[0][0]) for idx, e in enumerate(embeddings)] Now we can take the most relevant page (which is index 132 or page 133 if you want to verify the results): most_relevant = sorted(cos_sim, key=lambda x: x[1], reverse=True)[0][0] And with that, we have come to the most crucial step. We will instantiate an LLMWare Prompter object with the quantized Llama Dragon model. The Prompter class is key here because it handles the prompt engineering for us and makes sure that our prompt is consistent with the structure of Dragon’s training data. The prompt class also automatically handles the llamacpp binding, so you can use the quantized Dragon model exactly like other models. model_name = "llmware/dragon-llama-7b-gguf"
prompter = Prompt().load_model(model_name)

response = prompter.prompt_main(question, context='\n\n'.join([reader.pages[132].extract_text()]), 
                                prompt_name="default_with_context", temperature=0.3) Wait a little while, and you should see the function call return. Now print the results: print(response['llm_response']) And you should see something like the following: • A capital or profits interest acquired by the qualified opportunity fund after December 31, 2017, from the partnership solely in exchange for cash;
•As of the time such interest was acquired, the partnership was a qualified opportunity zone business (or, in the case of a new partnership, it was being organized for purposes of being a qualified opportunity zone business);
•During substantially all of the qualified opportunity fund's holding period for such interest, the partnership qualified as a qualified opportunity zone business. This is quite a good answer! For comparison, let’s see how a BLING model would perform on the same problem. One of the issues we can expect is that the large context size can “overwhelm” a lower-parameter model and lead to a less informative answer. In my previous experiments, the sheared llama 2.7b was one of the best performers for this problem, so I decided to use that as the representative of the BLING models. model_name_2 = "llmware/bling-sheared-llama-2.7b-0.1"
prompter2 = Prompt().load_model(model_name_2)

response = prompter2.prompt_main(question, context='\n\n'.join([reader.pages[132].extract_text()]), 
                                prompt_name="default_with_context", temperature=0.3) After some processing, you should see something like this. A qualified opportunity zone partnership interest is a capital or profits interest in a domestic partnership if such interest is acquired by the qualified opportunity fund after December 31, 2017, from the partnership solely in exchange for cash. The response is still good but misses some of the details captured by the Dragon model. Specifically, the answer misses the holding period requirement and the new business case. This conforms to our expectations about the lower-parameter models’ difficulty with processing larger contexts. Interested readers can extend this experiment by using even lower parameter models or increasing the size of the given context. You should see the effect become increasingly more pronounced, after which the model will give a short, garbled answer. From this experiment, it should be clear that quantized Dragon models are able to outperform lower-parameter models for their intended use cases without noticeably compromising the model’s accuracy. And with that, we have used a quantized model to solve a real-world use case and learned about its performance characteristics in the process! Conclusion Today, we explored the exciting field of LLM quantization and looked at how companies like LLMWare are taking advantage of these developments to enhance their specialized language models. As I’ve argued before, miniaturization represents one of the most promising paths to the widespread adoption of AI technologies. By combining specialization, fine-tuning, and quantization, innovators in the AI space can create scalable and performant models that solve real-world problems. You can find LLMWare’s RAG framework on and their DRAGON and BLING models on LLMWare’s . Github Hugging Face repository By the way, I am working on an exciting project that seeks to use language AI and miniaturization to revolutionize education in the developing world. We’re working with incredible activists and educators worldwide, and we’re working to bridge the global digital divide. If you’d like to learn more about my project or simply want to talk about exciting developments in the LLM space, please do not hesitate to reach out to me on either or . Github LinkedIn

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