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China Is Already Beating the US in Quantum Computing

I asked someone recently, “What’s the most remarkable thing about the Solana ecosystem?” Here’s what he said: Oh meme coins and PumpFun, right? No, don’t smirk… if I asked you what you knew about Solana, your answer would most definitely be “meme coins,” “PumpFun,” or maybe based on more recent events, “Pengu airdrop.” No, it’s not your fault, it’s more like that’s what the prevailing narrative has been about the ecosystem. This sentiment is more than backed up by Messari’s State of Solana report for Q3, 2024 which shows that Solana’s average daily DEX volume has continued to rise impressively since Q4, 2023. The most impressive increase by far was the $0.4B to $1.2B jump between Q4 2023 and Q1 2024. Why is this particular timing significant? Because it ties into PumpFun’s launch on January 19, 2024 —a direct indication of the impact PumpFun has had on the volume of transactions in the Solana ecosystem. That’s not the only indication. According to Dexu.AI, the top 10 tokens in the Solana ecosystem by MarketCap are almost all meme coins (excluding stables, of course). And all of these tokens have enjoyed a massive price surge over the meme coin super cycle of last here. See here for context👇🏼 Source: https://www.coingecko.com/en/chains/solana And it is also important to point out that Solana has been a hub for meme coin trading since way before PumpFun came into existence. OG tokens like $POPCAT and $BONK come to mind. Based on all this it’s easy to see why Solana has gained a reputation for being “The blockchain for meme coins.” But is that all there is to the blockchain? That would be a huge NO! Peeking Under The Hood Solana is one of the most stacked blockchains currently existing performance-wise. To start with Solana combines a unique consensus mechanism known as “Proof of History” with Proof of Stake (PoS) to deliver incredible performance largely unmatched in the current Web3 space. Speed To start with, Solana can theoretically handle up to 65,000 transactions per second (TPS), benchmarking it as one of the fastest L1s around. Just so you understand, this is way faster than Avalanche, Ethereum, BSC, Algorand, and Cardano combined! In addition to this, Solana offers near-instant finality, making it an excellent choice for projects that require quick confirmation of transactions. A huge reason why the blockchain is so fast is its unique consensus framework that allows for the hyper-efficient rapid processing of large transaction volumes. Resilience But it's not just that Solana is fast, the blockchain’s network is also very resilient, making it capable of handling significant load. $DOGS and $NOTairdrops caused outages in the TON blockchain because of the sheer number of demand placed on it by airdrop claimers. The number of people who received the airdrop was about 45 million and 35 million respectively. $BONK airdropped to over 37 million users, making it one of the largest-ever airdrop events on Solana. Yet there was NO network outage recorded during the event. This clearly demonstrates that Solana is capable of handling the huge demand required by sectors like GameFi, AI and even RWA. We’ll get to that in a moment. Reach It’s also important to point out that despite being powered by SVM, Solana’s reach is near-boundless. This is thanks to its integration of interoperability protocols like The Rome Protocol, Wormhole, and LayerZero– a particularly significant development that connects Solana to over 70 other chains. Correcting The Misconception Now ask yourself: Is it possible that the only thing all this artillery is good for is launching and trading meme coins? Sounds absurd, doesn’t it? Now you’re beginning to understand just how incredible Solana as a product really is. Memes may have hugely outshadowed utility plays on Solana but don’t make the popular mistake of assuming they are absent. Solana is still home to tons of innovative projects. What I find particularly interesting is there are tons of innovative solutions already built on Solana and they cut across all of the major DeFi sectors. Here’s a walkthrough of each sector 👇🏽 #1. DeFAI DeFAI is a newly-emerged sector in the DeFi ecosystem that focuses on integrating AI with core DeFi components in order to supercharge on-chain activities and operations. By this, I mean integrating AI with traditional DeFi tools and processes in order to arrive at a much more efficient product. For example, trading bots have long been a staple in the degen trading landscape with tools like Bonkbot, Bananagunbot and so on. Now although these tools offer much better position entry and exit accuracy + speed, they still have a lot of limitations. They can’t research tokens and spot gems on their own
They can’t set entry and exit targets themselves
They rely on human input to carry out their functions. A DeFAI improvement on this like Griffain’s Agent Dora is able to 100% autonomously scrape for data, develop key actionable insights, and take appropriate trading action. And guess what? Griffain is built on Solana! I should also mention that Griffain actually has a vast collection of specialist AI agents capable of performing different tasks from Whale watching to yield farming and even training other AI agents. Other examples of projects in this category native to Solana include: MOBY


NEUR


BUZZ #2. DePIN DePIN is short for Decentralized Physical Infrastructure Network. This is a DeFi sector that involves using decentralized/blockchain systems to deploy, manage, and operate physical infrastructure. The way this works is pretty simple: Protocols here usually offer incentives to users in exchange for them performing a specific action like running a node. Rewards are allocated based on participation in the network in order to drive the message that active participation will be rewarded.\ DePIN has actually proved to be a revolutionary tool in improving existing Web2 systems and even breaking ground where Tradfi has failed. Some sectors where DePIN projects have made a name for themselves include the following:\ Data storage (Filecoin)
Telecommunication (Helium)
Energy (Power Ledger)
IoT (IOTEX)
Computing, and many more. It will interest you to know that Solana is actually a treasure trove of DePIN projects. Almost all the biggest names in this sector are on the Solana blockchain. Examples include: IONET
Grass
Render
GenesysGo An excellent use case example of Solana DePIN projects doing crazily well is Helium Beacons using Starlink to successfully cell service for over 20 thousand Hurricane Helene victims in NC. Check it out here. #3. DeSCI DeSCI stands for Decentralized Science, and it’s one of the fastest-growing sectors in Web3. DeSCI projects work to apply blockchain technology to scientific research, with a focus on how scientific research is funded, conducted, published, shared, and accessed. DeSCI is so important because it introduces the much-needed transparency layer into the field of scientific research, ensuring that all data from funding to peer review processes are transparent, immutable, and verifiable by anyone. Examples of DeSCI projects on Solana include: Pump.science


Molecule #4. RWA According to Artemis.xyz, RWA was the best-performing sector in 2024. This becomes even more astonishing when you consider that the meme coin supercycle that led to the birth of icons like $GOAT, $SUNDOG, $HIPPO, and $PNUT happened last year too! Institutional players like Blackrock and Franklin Templeton have all identified real-world assets as the next logical evolution of TradFi and DeFi and they’ve been pouring billions of dollars into the sector. So this is definitely a sector worth paying attention to. And it’s interesting to point out that a number of solid RWA projects are also present in the Solana ecosystem. Some examples include: Parcl
Homebase
Collector Crypt Other Notable Mentions There are literally lots of incredible products in the Solana ecosystem so I definitely won’t be able to give you a walkthrough of every one of them. The best I can do is provide you with 10 more project recommendations I think you should take a look at… you’ll be surprised: Vodra - vodra.io
Moonwalk Fitness - moonwalk.fit
SendAI - solanaaihackathon.com
Top Hat AI - tophat.one
Solana Mobile - solanamobile.com
EnsoFi - __a__pp.ensofi.xyz
Sponge - app.spongehq.xyz
Bullet - projectbullet.xyz
Hivemapper - docs.hivemapper.com
Save - save.finance Taking all that I’ve said thus far into account, this is my thesis: Solana has too much quality to be relegated to a “meme coin chain” narrative. We’ve already seen several incredible products, some of which I’ve highlighted already. There will be more. You have no idea what’s coming. I asked someone recently, “What’s the most remarkable thing about the Solana ecosystem?” Here’s what he said: I asked someone recently, “What’s the most remarkable thing about the Solana ecosystem?” Here’s what he said: Oh meme coins and PumpFun, right? Oh meme coins and PumpFun, right? Oh meme coins and PumpFun, right? No, don’t smirk… if I asked you what you knew about Solana, your answer would most definitely be “meme coins,” “PumpFun,” or maybe based on more recent events, “Pengu airdrop.” No, it’s not your fault, it’s more like that’s what the prevailing narrative has been about the ecosystem. This sentiment is more than backed up by Messari’s State of Solana report for Q3, 2024 which shows that Solana’s average daily DEX volume has continued to rise impressively since Q4, 2023. The most impressive increase by far was the $0.4B to $1.2B jump between Q4 2023 and Q1 2024. Why is this particular timing significant? Because it ties into PumpFun’s launch on January 19, 2024 —a direct indication of the impact PumpFun has had on the volume of transactions in the Solana ecosystem. That’s not the only indication. According to Dexu.AI, the top 10 tokens in the Solana ecosystem by MarketCap are almost all meme coins (excluding stables, of course). And all of these tokens have enjoyed a massive price surge over the meme coin super cycle of last here. See here for context👇🏼 Source: https://www.coingecko.com/en/chains/solana https://www.coingecko.com/en/chains/solana https://www.coingecko.com/en/chains/solana And it is also important to point out that Solana has been a hub for meme coin trading since way before PumpFun came into existence. OG tokens like $POPCAT and $BONK come to mind. Based on all this it’s easy to see why Solana has gained a reputation for being “The blockchain for meme coins.” But is that all there is to the blockchain? That would be a huge NO! Peeking Under The Hood Solana is one of the most stacked blockchains currently existing performance-wise. To start with Solana combines a unique consensus mechanism known as “Proof of History” with Proof of Stake (PoS) to deliver incredible performance largely unmatched in the current Web3 space. Speed To start with, Solana can theoretically handle up to 65,000 transactions per second (TPS), benchmarking it as one of the fastest L1s around. Just so you understand, this is way faster than Avalanche, Ethereum, BSC, Algorand, and Cardano combined! In addition to this, Solana offers near-instant finality, making it an excellent choice for projects that require quick confirmation of transactions. A huge reason why the blockchain is so fast is its unique consensus framework that allows for the hyper-efficient rapid processing of large transaction volumes. Resilience But it's not just that Solana is fast, the blockchain’s network is also very resilient, making it capable of handling significant load. $DOGS and $NOTairdrops caused outages in the TON blockchain because of the sheer number of demand placed on it by airdrop claimers. The number of people who received the airdrop was about 45 million and 35 million respectively. $BONK airdropped to over 37 million users, making it one of the largest-ever airdrop events on Solana. Yet there was NO network outage recorded during the event. This clearly demonstrates that Solana is capable of handling the huge demand required by sectors like GameFi, AI and even RWA. We’ll get to that in a moment. Reach It’s also important to point out that despite being powered by SVM, Solana’s reach is near-boundless. This is thanks to its integration of interoperability protocols like The Rome Protocol, Wormhole, and LayerZero– a particularly significant development that connects Solana to over 70 other chains. Correcting The Misconception Now ask yourself: Is it possible that the only thing all this artillery is good for is launching and trading meme coins? Sounds absurd, doesn’t it? Now you’re beginning to understand just how incredible Solana as a product really is. Memes may have hugely outshadowed utility plays on Solana but don’t make the popular mistake of assuming they are absent. Solana is still home to tons of innovative projects. What I find particularly interesting is there are tons of innovative solutions already built on Solana and they cut across all of the major DeFi sectors. Here’s a walkthrough of each sector 👇🏽 #1. DeFAI DeFAI is a newly-emerged sector in the DeFi ecosystem that focuses on integrating AI with core DeFi components in order to supercharge on-chain activities and operations. By this, I mean integrating AI with traditional DeFi tools and processes in order to arrive at a much more efficient product. For example, trading bots have long been a staple in the degen trading landscape with tools like Bonkbot, Bananagunbot and so on. Now although these tools offer much better position entry and exit accuracy + speed, they still have a lot of limitations. They can’t research tokens and spot gems on their own They can’t set entry and exit targets themselves They rely on human input to carry out their functions. They can’t research tokens and spot gems on their own They can’t set entry and exit targets themselves They rely on human input to carry out their functions. A DeFAI improvement on this like Griffain’s Agent Dora is able to 100% autonomously scrape for data, develop key actionable insights, and take appropriate trading action. And guess what? Griffain is built on Solana! I should also mention that Griffain actually has a vast collection of specialist AI agents capable of performing different tasks from Whale watching to yield farming and even training other AI agents. Griffain Griffain Other examples of projects in this category native to Solana include: MOBY NEUR BUZZ MOBY MOBY MOBY MOBY NEUR NEUR NEUR NEUR BUZZ BUZZ BUZZ BUZZ #2. DePIN DePIN is short for Decentralized Physical Infrastructure Network. This is a DeFi sector that involves using decentralized/blockchain systems to deploy, manage, and operate physical infrastructure. The way this works is pretty simple: Protocols here usually offer incentives to users in exchange for them performing a specific action like running a node. Rewards are allocated based on participation in the network in order to drive the message that active participation will be rewarded.\ DePIN has actually proved to be a revolutionary tool in improving existing Web2 systems and even breaking ground where Tradfi has failed. Some sectors where DePIN projects have made a name for themselves include the following:\ Data storage (Filecoin) Telecommunication (Helium) Energy (Power Ledger) IoT (IOTEX) Computing, and many more. Data storage (Filecoin) Telecommunication (Helium) Energy (Power Ledger) IoT (IOTEX) Computing, and many more. It will interest you to know that Solana is actually a treasure trove of DePIN projects. Almost all the biggest names in this sector are on the Solana blockchain. Examples include: IONET Grass Render GenesysGo IONET IONET IONET Grass Grass Grass Render Render Render GenesysGo GenesysGo GenesysGo An excellent use case example of Solana DePIN projects doing crazily well is Helium Beacons using Starlink to successfully cell service for over 20 thousand Hurricane Helene victims in NC. Check it out here . here here here #3. DeSCI DeSCI stands for Decentralized Science, and it’s one of the fastest-growing sectors in Web3. DeSCI projects work to apply blockchain technology to scientific research, with a focus on how scientific research is funded, conducted, published, shared, and accessed. DeSCI is so important because it introduces the much-needed transparency layer into the field of scientific research, ensuring that all data from funding to peer review processes are transparent, immutable, and verifiable by anyone. Examples of DeSCI projects on Solana include: Pump.science Molecule Pump.science Pump.science Pump.science Pump.science Molecule Molecule Molecule Molecule #4. RWA According to Artemis.xyz, RWA was the best-performing sector in 2024. This becomes even more astonishing when you consider that the meme coin supercycle that led to the birth of icons like $GOAT, $SUNDOG, $HIPPO, and $PNUT happened last year too! Institutional players like Blackrock and Franklin Templeton have all identified real-world assets as the next logical evolution of TradFi and DeFi and they’ve been pouring billions of dollars into the sector. So this is definitely a sector worth paying attention to. And it’s interesting to point out that a number of solid RWA projects are also present in the Solana ecosystem. Some examples include: Parcl Homebase Collector Crypt Parcl Parcl Parcl Homebase Homebase Homebase Collector Crypt Collector Crypt Collector Crypt Other Notable Mentions There are literally lots of incredible products in the Solana ecosystem so I definitely won’t be able to give you a walkthrough of every one of them. The best I can do is provide you with 10 more project recommendations I think you should take a look at… you’ll be surprised: Vodra - vodra.io Moonwalk Fitness - moonwalk.fit SendAI - solanaaihackathon.com Top Hat AI - tophat.one Solana Mobile - solanamobile.com EnsoFi - __a__pp.ensofi.xyz Sponge - app.spongehq.xyz Bullet - projectbullet.xyz Hivemapper - docs.hivemapper.com Save - save.finance Vodra - vodra.io Moonwalk Fitness - moonwalk.fit SendAI - solanaaihackathon.com Top Hat AI - tophat.one Solana Mobile - solanamobile.com EnsoFi - __a__pp.ensofi.xyz Sponge - app.spongehq.xyz Bullet - projectbullet.xyz Hivemapper - docs.hivemapper.com Save - save.finance Taking all that I’ve said thus far into account, this is my thesis: Solana has too much quality to be relegated to a “meme coin chain” narrative. We’ve already seen several incredible products, some of which I’ve highlighted already. There will be more. You have no idea what’s coming.

Beating the "Meme Coins" Allegations: Explore the Solana Ecosystem

I’ve had an iPhone for ten years, and I love it. Unlike some people, I really enjoy Siri and use it frequently. But after ten years, Siri hasn't figured out that when it transcribes my texts, it should know my wife's name is not Aaron, it's Erin. Now that Apple Intelligence has been released, these sorts of issues are more rare, but many of us are still using older iPhones. In this article I propose an easy fix for mis-transcriptions that could be rolled out to older phones. Speech-to-text systems often struggle with homophones—words that sound the same but have different spellings and meanings. These errors can be frustrating, especially when they affect personal names or commonly used terms. The key to fixing this problem lies not in overhauling the speech recognition engine but in a lightweight, post-transcription text processing layer that adapts to user corrections over time. Here’s the PyTorch-based code I designed to address this. This isn’t a manual fix to a problem that should be addressed solely with transformer-scale learning, it creates a feature that could be used as an input to any model, including LLMs. And this doesn’t have to wait for a new phone release to be deployed. It would make life better for me in the next update Apple releases for my iPhone. The Core Idea This approach focuses on three main elements: Correction History: Stores previous user corrections, prioritizing words the user has explicitly fixed before.
Frequent Contacts: Tracks frequently used words or names, assigning a higher likelihood to those more commonly used.
Contextual Analysis: Uses Natural Language Processing (NLP) to analyze the surrounding text for clues that help disambiguate homophones. The system calculates a likelihood score for each homophone candidate based on these three factors and selects the most likely correction. Below is the Python implementation broken into sections with explanations. Loading the Homophones Database The first step is loading a database of homophones, and updating it smartly. These are word pairs (or groups) that are likely to be confused during transcription. # Homophones database
homophones_db = {
    "Aaron": ["Erin"],
    "bare": ["bear"],
    "phase": ["faze"],
    "affect": ["effect"],
} This is a simple dictionary where the key is the incorrectly transcribed word, and the value is a list of homophone alternatives. For example, "phase" can be confused with "faze". Later, this database will be queried when an ambiguous word is encountered. Tracking Correction History The code tracks user corrections in a dictionary where each key is a tuple of (original_word, corrected_word) and the value is the count of times the user corrected that error. Correction history tracker # Correction history tracker
correction_history = {
    ("phase", "Faye's"): 3,
    ("bear", "bare"): 2,
} If the user corrects "phase" to "Faye’s" three times, the system prioritizes this correction for future transcriptions. Frequent Contacts Another factor influencing homophone selection is how often a particular word is used. This could be personal names or terms the user frequently types. # Frequent contact tracker
frequent_contacts = {
    "faye": 15,
    "phase": 5,
    "erin": 10,
    "aaron": 2,
} The system gives more weight to frequently used words when disambiguating homophones. For instance, if "faye" appears 15 times but "phase" appears only 5 times, "faye" will be preferred. Contextual Analysis Context clues are extracted from the surrounding sentence to further refine the selection. For example, if the sentence contains the pronoun "she", the system might favor "Erin" over "Aaron". from transformers import pipeline Load an NLP model for context analysis from transformers import pipeline

# Load an NLP model for context analysis
context_analyzer = pipeline("fill-mask", model="bert-base-uncased")

def detect_context(sentence):
    """Detect context-specific clues in the sentence."""
    pronouns = ["he", "she", "his", "her", "their"]
    tokens = sentence.lower().split()
    return [word for word in tokens if word in pronouns] This function scans the sentence for gender-specific pronouns or other clues that might indicate the intended meaning of the word. Calculating Likelihood Scores Each homophone candidate is assigned a likelihood score based on: Past Corrections: Higher weight (e.g., 3x).
Frequent Usage: Medium weight (e.g., 2x).
Context Matching: Lower weight (e.g., 1x). def calculate_likelihood(word, candidate, sentence):
    """Calculate a likelihood score for a homophone candidate."""
    correction_score = correction_history.get((word, candidate), 0) * 3
    frequency_score = frequent_contacts.get(candidate, 0) * 2
    context = detect_context(sentence)
    context_clues = homophones_db.get(candidate, [])
    context_score = sum(1 for clue in context if clue in context_clues)
    return correction_score + frequency_score + context_score This score combines the three factors to determine the most likely homophone. Disambiguating Homophones With the likelihood scores calculated, the system selects the homophone with the highest score. def prioritize_homophones(word, candidates, sentence):
    """Prioritize homophones based on their likelihood scores."""
    likelihoods = {
        candidate: calculate_likelihood(word, candidate, sentence) for candidate in candidates
    }
    return max(likelihoods, key=likelihoods.get)

def disambiguate_homophone(word, sentence):
    """Disambiguate homophones using likelihood scores."""
    candidates = homophones_db.get(word, [])
    if not candidates:
        return word
    return prioritize_homophones(word, candidates, sentence) This process ensures the most appropriate word is chosen based on history, frequency, and context. Processing Full Transcriptions The system processes an entire sentence, applying the disambiguation logic to each word. def process_transcription(transcription):
    """Process the transcription to correct homophones."""
    words = transcription.split()
    corrected_words = [disambiguate_homophone(word, transcription) for word in words]
    return " ".join(corrected_words) Full Example Workflow # Example transcription and correction
raw_transcription = "This is phase one plan."
corrected_transcription = process_transcription(raw_transcription)

print("Original Transcription:", raw_transcription)
print("Corrected Transcription:", corrected_transcription)

# Simulate user feedback
update_correction_history("phase", "faye")
print("Updated Correction History:", correction_history)
print("Updated Frequent Contacts:", frequent_contacts) Updating Feedback When the user corrects a mistake, the correction history and frequent contacts are updated to improve future predictions. def update_correction_history(original, corrected):
    """Update correction history and frequent contacts."""
    correction_history[(original, corrected)] = correction_history.get((original, corrected), 0) + 1
    frequent_contacts[corrected] = frequent_contacts.get(corrected, 0) + 1
    frequent_contacts[original] = max(0, frequent_contacts.get(original, 0) - 1) Example transcription and correction Original Transcription: This is phase one plan.
Corrected Transcription: This is Faye's one plan.
Updated Correction History: {('phase', 'Faye's'): 4}
Updated Frequent Contacts: {'Faye's': 16, 'phase': 4} Conclusion This lightweight text-processing layer enhances the accuracy of speech-to-text applications by learning from user corrections, leveraging frequent usage, and analyzing context. It’s compact enough to run on mobile devices and adaptable to individual user needs, offering a smarter alternative to traditional static models. With minimal effort, Apple—or any other company—could integrate this functionality to make virtual assistants like Siri more responsive and personalized. I’ve had an iPhone for ten years, and I love it. Unlike some people, I really enjoy Siri and use it frequently. But after ten years, Siri hasn't figured out that when it transcribes my texts, it should know my wife's name is not Aaron, it's Erin. Now that Apple Intelligence has been released, these sorts of issues are more rare, but many of us are still using older iPhones. In this article I propose an easy fix for mis-transcriptions that could be rolled out to older phones. Speech-to-text systems often struggle with homophones—words that sound the same but have different spellings and meanings. These errors can be frustrating, especially when they affect personal names or commonly used terms. The key to fixing this problem lies not in overhauling the speech recognition engine but in a lightweight, post-transcription text processing layer that adapts to user corrections over time. Here’s the PyTorch-based code I designed to address this. This isn’t a manual fix to a problem that should be addressed solely with transformer-scale learning, it creates a feature that could be used as an input to any model, including LLMs. And this doesn’t have to wait for a new phone release to be deployed. It would make life better for me in the next update Apple releases for my iPhone. The Core Idea The Core Idea This approach focuses on three main elements: Correction History: Stores previous user corrections, prioritizing words the user has explicitly fixed before. Frequent Contacts: Tracks frequently used words or names, assigning a higher likelihood to those more commonly used. Contextual Analysis: Uses Natural Language Processing (NLP) to analyze the surrounding text for clues that help disambiguate homophones. Correction History: Stores previous user corrections, prioritizing words the user has explicitly fixed before. Correction History: Frequent Contacts: Tracks frequently used words or names, assigning a higher likelihood to those more commonly used. Frequent Contacts: Contextual Analysis: Uses Natural Language Processing (NLP) to analyze the surrounding text for clues that help disambiguate homophones. Contextual Analysis: The system calculates a likelihood score for each homophone candidate based on these three factors and selects the most likely correction. Below is the Python implementation broken into sections with explanations. Loading the Homophones Database Loading the Homophones Database The first step is loading a database of homophones, and updating it smartly. These are word pairs (or groups) that are likely to be confused during transcription. # Homophones database
homophones_db = {
    "Aaron": ["Erin"],
    "bare": ["bear"],
    "phase": ["faze"],
    "affect": ["effect"],
} # Homophones database
homophones_db = {
    "Aaron": ["Erin"],
    "bare": ["bear"],
    "phase": ["faze"],
    "affect": ["effect"],
} This is a simple dictionary where the key is the incorrectly transcribed word, and the value is a list of homophone alternatives. For example, "phase" can be confused with "faze". Later, this database will be queried when an ambiguous word is encountered. Tracking Correction History Tracking Correction History The code tracks user corrections in a dictionary where each key is a tuple of (original_word, corrected_word) and the value is the count of times the user corrected that error. Correction history tracker # Correction history tracker
correction_history = {
    ("phase", "Faye's"): 3,
    ("bear", "bare"): 2,
} # Correction history tracker
correction_history = {
    ("phase", "Faye's"): 3,
    ("bear", "bare"): 2,
} If the user corrects "phase" to "Faye’s" three times, the system prioritizes this correction for future transcriptions. Frequent Contacts Frequent Contacts Another factor influencing homophone selection is how often a particular word is used. This could be personal names or terms the user frequently types. # Frequent contact tracker
frequent_contacts = {
    "faye": 15,
    "phase": 5,
    "erin": 10,
    "aaron": 2,
} # Frequent contact tracker
frequent_contacts = {
    "faye": 15,
    "phase": 5,
    "erin": 10,
    "aaron": 2,
} The system gives more weight to frequently used words when disambiguating homophones. For instance, if "faye" appears 15 times but "phase" appears only 5 times, "faye" will be preferred. Contextual Analysis Contextual Analysis Context clues are extracted from the surrounding sentence to further refine the selection. For example, if the sentence contains the pronoun "she", the system might favor "Erin" over "Aaron". from transformers import pipeline Load an NLP model for context analysis from transformers import pipeline

# Load an NLP model for context analysis
context_analyzer = pipeline("fill-mask", model="bert-base-uncased")

def detect_context(sentence):
    """Detect context-specific clues in the sentence."""
    pronouns = ["he", "she", "his", "her", "their"]
    tokens = sentence.lower().split()
    return [word for word in tokens if word in pronouns] from transformers import pipeline

# Load an NLP model for context analysis
context_analyzer = pipeline("fill-mask", model="bert-base-uncased")

def detect_context(sentence):
    """Detect context-specific clues in the sentence."""
    pronouns = ["he", "she", "his", "her", "their"]
    tokens = sentence.lower().split()
    return [word for word in tokens if word in pronouns] This function scans the sentence for gender-specific pronouns or other clues that might indicate the intended meaning of the word. Calculating Likelihood Scores Each homophone candidate is assigned a likelihood score based on: Past Corrections: Higher weight (e.g., 3x). Frequent Usage: Medium weight (e.g., 2x). Context Matching: Lower weight (e.g., 1x). Past Corrections : Higher weight (e.g., 3x). Past Corrections Frequent Usage : Medium weight (e.g., 2x). Frequent Usage Context Matching : Lower weight (e.g., 1x). Context Matching def calculate_likelihood(word, candidate, sentence):
    """Calculate a likelihood score for a homophone candidate."""
    correction_score = correction_history.get((word, candidate), 0) * 3
    frequency_score = frequent_contacts.get(candidate, 0) * 2
    context = detect_context(sentence)
    context_clues = homophones_db.get(candidate, [])
    context_score = sum(1 for clue in context if clue in context_clues)
    return correction_score + frequency_score + context_score def calculate_likelihood(word, candidate, sentence):
    """Calculate a likelihood score for a homophone candidate."""
    correction_score = correction_history.get((word, candidate), 0) * 3
    frequency_score = frequent_contacts.get(candidate, 0) * 2
    context = detect_context(sentence)
    context_clues = homophones_db.get(candidate, [])
    context_score = sum(1 for clue in context if clue in context_clues)
    return correction_score + frequency_score + context_score This score combines the three factors to determine the most likely homophone. Disambiguating Homophones With the likelihood scores calculated, the system selects the homophone with the highest score. def prioritize_homophones(word, candidates, sentence):
    """Prioritize homophones based on their likelihood scores."""
    likelihoods = {
        candidate: calculate_likelihood(word, candidate, sentence) for candidate in candidates
    }
    return max(likelihoods, key=likelihoods.get)

def disambiguate_homophone(word, sentence):
    """Disambiguate homophones using likelihood scores."""
    candidates = homophones_db.get(word, [])
    if not candidates:
        return word
    return prioritize_homophones(word, candidates, sentence) def prioritize_homophones(word, candidates, sentence):
    """Prioritize homophones based on their likelihood scores."""
    likelihoods = {
        candidate: calculate_likelihood(word, candidate, sentence) for candidate in candidates
    }
    return max(likelihoods, key=likelihoods.get)

def disambiguate_homophone(word, sentence):
    """Disambiguate homophones using likelihood scores."""
    candidates = homophones_db.get(word, [])
    if not candidates:
        return word
    return prioritize_homophones(word, candidates, sentence) This process ensures the most appropriate word is chosen based on history, frequency, and context. Processing Full Transcriptions Processing Full Transcriptions The system processes an entire sentence, applying the disambiguation logic to each word. def process_transcription(transcription):
    """Process the transcription to correct homophones."""
    words = transcription.split()
    corrected_words = [disambiguate_homophone(word, transcription) for word in words]
    return " ".join(corrected_words) def process_transcription(transcription):
    """Process the transcription to correct homophones."""
    words = transcription.split()
    corrected_words = [disambiguate_homophone(word, transcription) for word in words]
    return " ".join(corrected_words) Full Example Workflow # Example transcription and correction
raw_transcription = "This is phase one plan."
corrected_transcription = process_transcription(raw_transcription)

print("Original Transcription:", raw_transcription)
print("Corrected Transcription:", corrected_transcription)

# Simulate user feedback
update_correction_history("phase", "faye")
print("Updated Correction History:", correction_history)
print("Updated Frequent Contacts:", frequent_contacts) # Example transcription and correction
raw_transcription = "This is phase one plan."
corrected_transcription = process_transcription(raw_transcription)

print("Original Transcription:", raw_transcription)
print("Corrected Transcription:", corrected_transcription)

# Simulate user feedback
update_correction_history("phase", "faye")
print("Updated Correction History:", correction_history)
print("Updated Frequent Contacts:", frequent_contacts) Updating Feedback When the user corrects a mistake, the correction history and frequent contacts are updated to improve future predictions. def update_correction_history(original, corrected):
    """Update correction history and frequent contacts."""
    correction_history[(original, corrected)] = correction_history.get((original, corrected), 0) + 1
    frequent_contacts[corrected] = frequent_contacts.get(corrected, 0) + 1
    frequent_contacts[original] = max(0, frequent_contacts.get(original, 0) - 1) def update_correction_history(original, corrected):
    """Update correction history and frequent contacts."""
    correction_history[(original, corrected)] = correction_history.get((original, corrected), 0) + 1
    frequent_contacts[corrected] = frequent_contacts.get(corrected, 0) + 1
    frequent_contacts[original] = max(0, frequent_contacts.get(original, 0) - 1) Example transcription and correction Original Transcription: This is phase one plan.
Corrected Transcription: This is Faye's one plan.
Updated Correction History: {('phase', 'Faye's'): 4}
Updated Frequent Contacts: {'Faye's': 16, 'phase': 4} Original Transcription: This is phase one plan.
Corrected Transcription: This is Faye's one plan.
Updated Correction History: {('phase', 'Faye's'): 4}
Updated Frequent Contacts: {'Faye's': 16, 'phase': 4} Conclusion Conclusion This lightweight text-processing layer enhances the accuracy of speech-to-text applications by learning from user corrections, leveraging frequent usage, and analyzing context. It’s compact enough to run on mobile devices and adaptable to individual user needs, offering a smarter alternative to traditional static models. With minimal effort, Apple—or any other company—could integrate this functionality to make virtual assistants like Siri more responsive and personalized.

Beating Siri at its Text Game

Омръзна ли ви Siri да бърка името ви? Ето едно просто решение

¿Cansado de que Siri se equivoque con tu nombre? Aquí tienes una solución sencilla

Fatigué que Siri perturbe votre nom ? Voici une solution simple

נמאס לך שסירי מבלבלת את השם שלך? הנה תיקון פשוט

क्या आप Siri द्वारा आपके नाम में गड़बड़ी से परेशान हैं? तो यहाँ है इसका आसान समाधान

Fatige Siri dega non ou? Isit la nan yon ranje senp

Siriがあなたの名前を間違えるのにうんざりしていませんか？簡単な解決法があります

დაიღალეთ Siri თქვენი სახელის არევით? აქ არის მარტივი გამოსწორება

සිරි ඔයාගේ නම අවුල් කරලා එපා වෙලාද? මෙන්න සරල විසඳුමක්

Jeni të lodhur nga Siri duke ju ngatërruar emrin? Këtu është një rregullim i thjeshtë

U karhele Hi Siri Ku Pfilunganya Vito Ra Wena? Hi leyi Ndzulamiso wo Olova

Ngaba udiniwe nguSiri ngokubhala igama lakho? Nantsi Ulungiso oluLula

Yes, bad mental health days happen even for hackers. I had one last week. And I'll tell you how I turned it around in about 45 minutes. I had been stressed about more than a few things. To name a few... Business has not been great lately.
I hadn't slept great the night before (it happens, even to sleep hackers sometimes).
There are some exciting but intimidating pivots I'm in the midst of making with my business (which involves a lot of work and learning some new things).
And the looming threat of nuclear war (in the part of the world where my little family lives!) Then, my wife made a perfectly understandable, concerned comment while rushing out the door to attend to matters in Sofia, which set me off. So, I dove into work with ferocious intensity. But I was less productive than I wanted to be. My MacBook was dragging a bit (it needed an optimization which it received), a Brazilian guy messaged me who was excited to read my book for men but my e-commerce store was broken because of a really annoying issue with my new hosting company (so I sent him the book for free - and the store is now fixed, by the way), and some new AI toolsI'm experimenting with were frustratingly not delivering the creative vision I had in my head.When my wife came home a few hours later, it was time to deal with one of my least favorite things in the world: customs paperwork for importing supplements. Getting a package of Biohacking goodness in the EU is often like negotiating with a very litigious demon for the release of your soul from purgatory! My wife is great; she handles most of the unpleasant mess of paperwork this entails, but she had a lot of questions to ask me about the package, interrupting what I was trying to do. Standing in front of my laptop (facing way too many open browser tabs), seething at the world, I realized I was about to blow up at my wife and her small barky dog (my little frenemy here in our abode). I had promised her and myself earlier this year that I'd handle my anger better and that blow-up episodes were behind me, along with explaining to her that sometimes, I needed her to stop talking and give me space. So that's what I did, and it was really time to close my laptop. I sat down in the chair where I meditate, slipped on my headphones, put a calming Brain.FM binaural beat on in my headphones, and I just breathed. Slow in the through the nose filling my belly, slow out the mouth. After 10-15 minutes, I returned to my senses. It was time for a little positive self-talk, so I reviewed the last few days; they had been stressful, but actually, I got a lot of important things done, almost all the important things. All things considered, I was doing pretty well as a husband and entrepreneur this week. And there was a lot of reason for optimism about this year (except for the nuclear war concern, which is a good thing I got a stash of "Nuke Pills" - Potassium Iodide). So, I borrowed a bit from Dr. Joe Dispenza's transformative meditation and began future-casting gratitude. In faith, I raised my hands and quietly praised my Creator (in advance) for the beautiful life waiting for me, where my efforts would come to fruition. After about 30 minutes of that, I was back to cool cat Jonathan. Then, it was time to do a coaching call with one of my favorite clients. The client was also struggling with life a bit; I listened attentively, and I think I was able to provide some insight, inspiration, clarity, and out-of-the-box solutions. It ended up being a downright decent day. To summarize, what works when compounding stress and to-dos push you to the boiling point? Know when to get off the damn computer or your "smart" device.
Establish a rule with your loved ones or domestic partners - sometimes, you need silence and privacy.
Have a designated private space for relaxation - go there.
Binaural beats are a game-changer for mood - have some just a few clicks or swipes away at any given time.
Take 5-10 minutes just to breathe - I know it's boring, but it works!
Positive self-talk - praise yourself for what you've done right recently.
Then, future-cast gratitude - be thankful for the life you'll have. Hopefully, you can borrow from my tool kit when you inevitably find yourself on the verge of a blow-up, meltdown, or panic attack. Yes, bad mental health days happen even for hackers. I had one last week. And I'll tell you how I turned it around in about 45 minutes. I had been stressed about more than a few things. To name a few... Business has not been great lately. I hadn't slept great the night before (it happens, even to sleep hackers sometimes). There are some exciting but intimidating pivots I'm in the midst of making with my business (which involves a lot of work and learning some new things). And the looming threat of nuclear war (in the part of the world where my little family lives!) Business has not been great lately. I hadn't slept great the night before (it happens, even to sleep hackers sometimes). There are some exciting but intimidating pivots I'm in the midst of making with my business (which involves a lot of work and learning some new things). And the looming threat of nuclear war (in the part of the world where my little family lives!) Then, my wife made a perfectly understandable, concerned comment while rushing out the door to attend to matters in Sofia, which set me off. So, I dove into work with ferocious intensity. But I was less productive than I wanted to be. My MacBook was dragging a bit (it needed an optimization which it received), a Brazilian guy messaged me who was excited to read my book for men but my e-commerce store was broken because of a really annoying issue with my new hosting company (so I sent him the book for free - and the store is now fixed, by the way ), and some new AI tools I'm experimenting with were frustratingly not delivering the creative vision I had in my head. When my wife came home a few hours later, it was time to deal with one of my least favorite things in the world: customs paperwork for importing supplements. Getting a package of Biohacking goodness in the EU is often like negotiating with a very litigious demon for the release of your soul from purgatory! My wife is great; she handles most of the unpleasant mess of paperwork this entails, but she had a lot of questions to ask me about the package, interrupting what I was trying to do. by the way new AI tools Standing in front of my laptop (facing way too many open browser tabs), seething at the world, I realized I was about to blow up at my wife and her small barky dog ( my little frenemy here in our abode). I had promised her and myself earlier this year that I'd handle my anger better and that blow-up episodes were behind me, along with explaining to her that sometimes, I needed her to stop talking and give me space. So that's what I did, and it was really time to close my laptop. barky my little frenemy I sat down in the chair where I meditate, slipped on my headphones, put a calming Brain.FM binaural beat on in my headphones, and I just breathed. Slow in the through the nose filling my belly, slow out the mouth. After 10-15 minutes, I returned to my senses. It was time for a little positive self-talk, so I reviewed the last few days; they had been stressful, but actually, I got a lot of important things done, almost all the important things. All things considered, I was doing pretty well as a husband and entrepreneur this week. And there was a lot of reason for optimism about this year (except for the nuclear war concern, which is a good thing I got a stash of "Nuke Pills" - Potassium Iodide). So, I borrowed a bit from Dr. Joe Dispenza's transformative meditation and began future-casting gratitude. In faith, I raised my hands and quietly praised my Creator (in advance) for the beautiful life waiting for me, where my efforts would come to fruition. After about 30 minutes of that, I was back to cool cat Jonathan. cool cat Then, it was time to do a coaching call with one of my favorite clients. The client was also struggling with life a bit; I listened attentively, and I think I was able to provide some insight, inspiration, clarity, and out-of-the-box solutions. It ended up being a downright decent day. To summarize, what works when compounding stress and to-dos push you to the boiling point? To summarize, what works when compounding stress and to-dos push you to the boiling point? Know when to get off the damn computer or your "smart" device. Establish a rule with your loved ones or domestic partners - sometimes, you need silence and privacy. Have a designated private space for relaxation - go there. Binaural beats are a game-changer for mood - have some just a few clicks or swipes away at any given time. Take 5-10 minutes just to breathe - I know it's boring, but it works! Positive self-talk - praise yourself for what you've done right recently. Then, future-cast gratitude - be thankful for the life you'll have. Know when to get off the damn computer or your "smart" device. Establish a rule with your loved ones or domestic partners - sometimes, you need silence and privacy. Have a designated private space for relaxation - go there. Binaural beats are a game-changer for mood - have some just a few clicks or swipes away at any given time. Take 5-10 minutes just to breathe - I know it's boring, but it works! Positive self-talk - praise yourself for what you've done right recently. Then, future-cast gratitude - be thankful for the life you'll have. Hopefully, you can borrow from my tool kit when you inevitably find yourself on the verge of a blow-up, meltdown, or panic attack.

7 Lifehacks for Beating Back a Bad "Mental Health Day"

With a beating heart and fearful, I knew not why

Passive Income 2.0 Making money as you sleep is the ultimate goal for many of us. Like it or not, it’s something that we have come to accept in this space we call blockchain gaming. Yes, for those of us in the business of building and development, some (like myself) insist that the focus is on gaming, and that’s a big reason why my gaming team at recently released the pre-alpha as a pure playable game without any sort of token model built in. But we also know that blockchain gamers want the benefits of blockchain-enabled models that make them part of the revenue generation – something they’ve never been able to do in the traditional gaming world easily. Cradles Throughout the years, investors have been seeking ways to earn a steady income while being actively involved with other types of businesses. Using the money that one already has to make more money by being patient for a certain amount of time became a popular way to side hustle. Whether we call it , it is a smart way to extend your wealth. investing, asset building, or asset sharing Digital innovations changed the way we live, interact and conduct business on a daily basis. Bending on new trends in the realm of digital technologies, we found a way to make the next best investment, whether actively or passively. There are so many things you could actively do with digital assets, NFTs in particular, yet NFT staking refers specifically to an NFT owner locking up their digital asset for a certain amount of time to earn passive income in the form of cryptocurrencies. The tech behind NFT staking isn’t hard to comprehend. Namely, staking per se is a crucial trait of many divergent blockchains. And we can observe many gaming projects involved in staking cryptocurrencies and NFTs. The most basic manner to earn interest in crypto was to stake coins for a predetermined lock-up period in time. It was like earning interest in the sense of traditional banking. Then, thanks in a large part to gaming Dapps, we got the opportunity to stake NFTs on compatible platforms. The genuine features of NFTs make them an ideal option for wait-and-HODL strategies even though it may take some time before such long-term investments come to their expected fruition. Such staking opens many possibilities for collectors to monetize their collections. Hence, we have that lock up their NFTs in decentralized finance platforms and even gaming platforms to receive monetary rewards with the passage of time without the need to sell or give up ownership in any other manner. Not at all dissimilar to the concept of DeFi yield farming since both are investment strategies. HODLers Profit Talks Nevertheless, NFT staking is a concept still in its infancy. The NFT craze carried most people away with headlines about sellers who earned insane amounts of money for a piece of digital art. We won’t forget easily about the who earned $400,000 by creating and selling digital pics of whales while Beeple managed to acquire $69 million in an auction for his art, turning him into one of the most financially successful living artists. 12-year-old kid At the same time incredible and borderline strange, the rate of success is proportionate to the degree of market demand and the ability to sell. Otherwise, you are just stuck with a pic. Despite the attraction of NFT staking, there are some particular drawbacks such as liquidity problems due to the infancy of the ecosystem and the practice of buying NFTs for long-term investments. At the moment, the concept is not as notorious as cryptocurrency staking, yet the forecast is somehow sunny with the predictions of ETH2’s Merge with staking taking over mining. Furthermore, most investors have been attracted to the NFT world because of the hype surrounding the concept while thinking of the solidity of its foundations and profitability calculations. NFT staking could be a good business venture following the right conditions and taking certain matters into account. As the story goes with every type of investment and earning passive income, one has to understand the system. For instance, the Annual Percentage Yield (APY) refers to the rate reflecting the rewards that may be earned over a year, based on the rewards rate of the current year. A good degree of knowledge of staking platforms and return rates helps tremendously. Furthermore, understanding price volatility and price fluctuations, with the exception of the reserve price of NFT collections, is the step that makes the difference. Let’s think of a puppy NFT locked up on a fictional platform called RisingStar and the puppy produces 5$ tokens per day, currently worth $5 on CoinMarketCap. The NFT is now earning $25 per day just from staking. Let’s assume that the puppy NFT costs you $15,000. The math goes like this: $25 per day for 365 days amounts to $9,125, and we get the APY number by dividing the cost of the NFT by $9,125. Hence, APY amounts to 61%. Not that bad, right? Only, it doesn’t always work that way when the crypto earned from staking keeps going down in value in a crypto winter, stripping away at the APY… NFT Staking and Gaming: A Whole New Level with Staking-into-NFTs So NFT staking in its original concept may be in its death throes because it’s not sustainable and cannot only hope to produce profit in a short-lived bull market. The concept may encompass a number of problems due to the infancy of the system. As it goes with everything, beginnings are always hard, but we overcome obstacles with a little help from our friends. For us, we can see how NFT staking goes hand-in-hand with blockchain-based games, if done in creative ways that are more sustainable, and more meaningful. My team love to come up with new solutions to keep you entertained, focused, and satisfied with numerous options that don’t sacrifice gameplay, yet also seek to meet the earning needs of less-active players. We know many others that want to do the same, and I’ll be the first to say I hope one of us succeeds. That’s a big part of why we made up a new tokenomics model, under which we are introducing a subscription-based game with 0% transaction fees. And for those less interested in actual gaming, but who still want to earn, we have the new Staking-into-NFT (SIN) mechanism that also involves gameplay, but passively! In this system, passive players with game tokens can aid active players by staking crypto INTO the players’ NFTs, making them more powerful. Stake crypto into Player A’s sword and it deals more damage, for example. This player is then able to perform better and earn more from gameplay. In return for the “staking boost”, rewards earned by Player A are also shared proportionately to those who staked into his sword NFT. It’s a win-win situation as both parties earn more in-game coins along with broader rewards in the game itself. Additionally, live streamers are going to be rewarded with large sums of tokens and the possibility to call on their audience to Staking-into-NFTs on their behalf, further expanding on potential growth opportunities within the game. NFT staking may be dying, but long live Staking into NFTs! *I am the founder of blockchain MMORPG Cradles, and this is my introduction. I plan to write about the industry, much more about insight, much less about my company.

Staking NFTs – A Beating Heart Among Stillness or Clinically Dead?

By now, we can hopefully all agree that producing sustainable energy from renewable sources is something humanity should strive for. Though it’s taken us way too long to get started, great leaps have been made in that direction over the last decade. However, the main energy challenge of the 21st century is not making it sustainably. That part is easy. that energy . This is where come in. As we try to “beat” climate change, they will become ever-more important by helping to move cars, trucks and the power sector away from oil, coal and natural gas. Storing is hard and expensive new and improved batteries New battery technologies take many forms, and the world has not yet decided which technology will be the winner. Below is short a primer on the most promising ones. How do batteries work? It’s impossible to understand the below without basic engineering knowledge. Electricity is produced by electrons traveling from point A to point B. This movement creates an electrical current which can then be harnessed to power everyday objects. In batteries, point A is a “negative electrode” called the anode, and point B is a “positive electrode” called a cathode. Because batteries are not directly connected to a power source, they need a way to create electrons so they can travel. This is done thanks to “electrolytes”. This substance interacts with the anode, producing electrons and ions (you need both for balance). The electrons travel and produce power, while the ions are released into the electrolyte to balance the charge at both ends of the battery. When we recharge a battery, the chemical reaction that occurred during use is reversed. Pretty simple, right? Lithium-ion Batteries is the technology used in the majority of electric cars, PCs and mobiles today. In these batteries, the anode is generally made of either (lower costs), carbon or ( ), while the cathode is typically made of a lithium-cobalt or compound. These batteries have a high , and low , which is why they’re so widespread. A lot of Lithium-ion batteries varieties exist. Lithium iron phosphate (LFP) batteries, for example, offer worse performances, but are cheaper and safer. Lithium-ion graphite silicon the supposed best option lithium-nickel energy density self-discharge Despite their benefits, most lithium-ion batteries are safety hazard as they contain . If they are damaged or incorrectly charged, this can lead to explosions and fires. They are also expensive: today, these batteries can make up a quarter to a third of the cost of electric cars. flammable electrolytes Safety and costs are not the only downside to this technology : components from batteries come from mining, which is extremely harmful to the environment. There are also sociological and political aspects to the issue. Cobalt, for example, comes from , where it is mined by or by freelancers who sometimes employ children. Lithium mining harms Argentinian and American natives. The nickel industry, meanwhile, relies heavily on Russian suppliers. . the Democratic Republic of Congo Chinese-financed companies Other options need to be explored Lithium-sulfur Batteries As the name indicates, Lithium-Sulfur batteries still use lithium, but at least do away with cobalt. They instead use sulfur to make up the batteries’ cathodes. As such, these batteries lower environmental impacts and manufacturing costs while reducing the battery’s weight and providing high energy density. In fact, Li–S batteries offer twice the amount of energy per kilogram than Li-i batteries use. For all these reasons, Lithium–sulfur (Li-S) batteries may commercially replace lithium-ion cells in the future. Metal-Air Batteries A uses an anode made from pure metal and an external cathode of ambient air. As the battery is discharging, the anode is going through oxidation (losing electrons), while oxygen is in the cathode to induce a current flow. metal–air battery reduced The specific capacity and energy density of metal–air batteries is higher than that of lithium-ion batteries, making them a prime candidate for use in electric vehicles. are the most promising metal-air batteries. They have the highest energy densities of all batteries. They are however not widely used because of high costs. They also tend to be single-use (non-rechargeable). This has restricted their use to mainly military applications. However, a car with aluminium batteries has the potential for up to eight times the range of a lithium-ion battery with a significantly lower total weigh. As such, investments in research seem to be worth it. Aluminium–air and Lithium-air batteries Sodium-Ion Batteries The ( or ) is (very) similar to the lithium-ion battery, but uses sodium ions as the charge carriers instead of lithium. sodium-ion battery NIB SIB In fact, lithium, cobalt, copper and nickel are not strictly needed to make sodium-ion batteries. As such, this type of battery has received a lot of interest over the past decade because of the uneven geographic distribution, high environmental impact and high cost of these elements (as mentioned earlier). The largest advantage of sodium-ion batteries is the high natural abundance of sodium (salt is cheap). This would make commercial production of sodium-ion batteries far less costly than lithium-ion batteries. There are however many challenges to mass adoption : these batteries have low energy density, and a limited number of . Nevertheless, they may yet have a future as lithium-ion complements for lower-quality products. charge-discharge cycles Nanowire Batteries Regardless of the chemical make-up of future batteries, a nanowire battery would increase the surface area of the electrodes in contact with the electrolyte, thereby increasing the anode’s power density and allowing for faster charging and higher delivery. Nanowires are usually made of silicon, which tends to expend and damage the battery. In 2016, however, researchers at the University of California, Irvine announced the invention of a nanowire made of capable of over 200,000 charge cycles without any breakage. The technology could lead to batteries that never need to be replaced in most applications. gold, Solid-State Batteries The batteries above use liquid electrolytes, which cannot be compressed. The Holy Grail, as such, is — batteries with solid electrolytes. They would first and foremost improve safety levels: solid electrolytes are non-flammable and will work it harsher environments, unlike their liquid counterparts. Secondly, it permits the use of (such as carbon or ), enabling denser, lighter, 3D batteries with better shelf-life. Moreover, at system level, it will bring additional advantages such as simplified mechanics as well as thermal and safety management. As the batteries can exhibit a high power-to-weight ratio, they may be ideal for use in electric vehicles. solid-state batteries new materials glass Challenges however abound, especially if mass adoption is the goal. These batteries are difficult to mass-produce, and are very expensive. This isn’t surprising, as this is the trend for all new technologies. Many of these technologies are not new at all. Lithium-ion and Lithium-sulfur batteries, in fact, were first developed in the 60s. But as our needs and manufacturing capabilities evolves, so do our interests in various battery technology. Whatever the best technology, NextGen batteries will need to be cheaply produced at a large scale, with manufacturing capacities we do not have. They will need to have a long life-span to avoid them ending in landfills after a couple of charges. They will also be dependent on the chargers we use, a technology the still needs to evolve. We’ve got our work cut out for us. Good luck out there. Also Published . Here

New Battery Technologies and the Race to Beating Climate Change

The Ultimate AHREFs Guide: Keyword Research and Beating Your Competition

Digital Transformation articles tell us about Amazon putting bricks-and-mortar bookstores out of business or about Netflix killing video rental stores. Big game-changing moves. But business moves can't all be game-changers. What should companies which aren't tech giants be aiming at with Digital Transformation? What businesses should aim for is to become more adaptable so that they can avoid getting outpaced by a new competitor. The point is to avoid ending up like the now departed bookstores or the video rental stores. The authors of offer a succinct definition of what the goal of adaptability looks like - they call it : The Technology Fallacy Digital Maturity “Aligning an organisation’s people, culture, structure, and tasks to compete effectively by taking advantage of opportunities enabled by technological infrastructure, both inside and outside the organisation.” Digital Transformation is, or should be, about reaching Digital Maturity. But what does Digital Maturity mean in detail? And why is it such a mammoth task that companies are spending huge sums on? How Disruption Happens: The Complacency Trap Why was Blockbuster video on the wrong side of history when Netflix came along? It’s not that Blockbuster didn’t have the opportunity to do what Netflix did. Blockbuster famously even . declined on the chance to buy Netflix (Image from .) runningremote.com Blockbuster underestimated the threat posed by video streaming. But this was more than just an isolated case of an error in judgement. It is an example of what is called . 'The Complacency Trap' Companies tend to get large because their initial idea has proven successful. They find a market of customers and need to get big order to service more of that market. Most companies try to do this efficiently and so they keep operations focused around the core product. A specialised focus on the core idea/s of the company helps it maximise the competitive advantage. But specialisation also means that alternative paths become less visible to the company. A classic Complacency Trap example is how Wal-Mart overtook Kmart to become the largest chain of low-cost retail stores. Kmart had operated on a model where stores largely sourced their own supplies and set their own prices. This was part of Kmart’s philosophy which prized the instincts of local store managers and allowed stores to be suited to their local markets. Wal-Mart radically changed this by creating linked chains of stores supplied by a shared network. That made it viable to open stores in areas of lower population density (since supply costs were shared across the network) and access new economies of scale. “Kmart saw Wal-Mart like Goliath saw David - smaller and less experienced in the big leagues. But Wal-Mart’s advantages were not inherent in its history or size. They grew out of a subtle shift in how to think about discount retailing. Tradition saw discounting as tied to urban densities, whereas Sam Walton saw a way to build efficiency by embedding each store in a network of computing and logistics. Today we call this supply-chain management, but in 1984 it was an unexpected viewpoint.” Richard Rumelt, Good Strategy / Bad Strategy Technology was certainly part of Wal-Mart’s success. But KMart could in principle have leveraged the same technology. They didn’t because KMart had become specialised towards a particular model of operating and that led them to underestimate the potential of alternative approaches. The Complacency Trap is a key dynamic in business and is played out many times over many industries. Other classic examples are or how . What is unusual about the current period in history is how much change is happening and how frequently - companies that enter the S&P 500 Index (image from ). Kodak’s underestimation of digital photography Xerox underestimated small-scale printing technology do not stay there as long as they used to peerinsight Technology is becoming more widely available and advancing more rapidly. This is creating new business situations and new players are sometimes able to disrupt established markets very quickly. So how can companies become more adaptable and give themselves a better chance of surviving in times of change? Culture, Priorities and the Complacency Trap As a company becomes specialised around a particular operating model, its whole way of working becomes tailored to that approach. It will affect the way the company hires, the kinds of skills and perspectives that it sees as important, the way the organisation’s hierarchy is structured, the culture, the executive team and who it is that has most influence over the executive team. So the company self-selects for the skills and way of thinking that it prizes. Becoming more adaptable has to mean widening perspectives. It may mean more training, bringing in more tech-savvy younger staff, hiring more wizz-kid engineers and looking at how much voice they have in the organisation. But the way in which perspectives are expanded might need a specific focus. This is because organisations (and individuals) can struggle to learn new things that they don’t have adequate background for. call this : It is not simply about fostering general creativity - the organisation needs to target the kinds of knowledge and skills it wants to grow its capabilities for. Cohen and Levinthal Absorptive Capacity Ideas in nuclear physics are unlikely to be accessible to anyone who has not first studied foundational physics. This kind of learning barrier can apply in lots of ways. Some fields of knowledge are accessible at a basic level to the uninitiated but require deep background in order to apply them in new ways. Software engineering techniques are an example - more experienced developers able to do a wider range of more creative things using techniques that less experienced developers are aware of but aren't able to use so creatively. The absorptive capacity of an organisation is not simply the sum of the knowledge of the individuals. An individual may arrive at a brilliant new insight and find themselves unable to convince others at the organisation of the insight’s value. For example, and arrived at innovations such as GUIs and the mouse (images from ): Xerox was a pioneer of early personal computer research wikipedia But Xerox shut down that line of work rather than put a focus on the computing market, missing a huge opportunity. Xerox had done some brilliant computing product R&D. But . Xerox was a printer company, not a computing company, and never aligned enough around the venture to make it a commercial success Organisations with higher absorptive capacity will be better placed to spot new opportunities as they open up. But no organisation can master every skill and domain of knowledge. Companies need to be clear about their key domains of knowledge and what kinds of spaces to be watching for opportunities. Each Company is Different Not every organisation needs to become a tech company. Specific industries have their own opportunities and threats from technological change. Across many industries a key change is that consumer expectations for commercial interactions have been reshaped by digital media and e-commerce. People have grown accustomed to seeing searchable purchasing information clearly available to them, to being able to purchase with a click and to be managing future interactions through easy to use software. Not every industry has fully caught up to these expectations yet. That presents opportunities for those who can master it and risks for those who don’t master it faster enough. In other industries there are anticipated technologies in development with great potential for change. For example, driverless technologies look set to revolutionise the car industry, . according to the Boston Consulting Group It is not yet clear exactly how or who will be the main beneficiaries. This could mean making a list of the key changes they see or anticipate, what the potential impacts of those changes are. Not every technological change can be relevant or concrete enough to justify dedicating an initiative to. Each company needs to choose which areas are most important to them and how to reorganise to be best positioned. Companies need to tailor their digital transformation priorities to their own situation. Digital Maturity If Digital Transformation is about achieving Digital Maturity, it does not have to be a process of upheaval at every company. It can be a gradual process of evolution. It may involve running small pilot projects on particular concepts before working changes into the core business model. Becoming more responsive to changes in the business environment means becoming more experimental. To avoid that trap, companies need to look at their situation, their organisational structure and culture and how they gather and respond to customer feedback. depicts the interaction of these elements in the following organisation dynamics diagram (my rendition): Having a well-planned strategy and executing on it can be highly profitable but on its own it can also lead into the complacency trap. The Technology Fallacy Culture, people and structure will be set up to facilitate work in accordance with a particular strategy. As the company learns more from its customers and as the commercial environment changes, that strategy may need to change also. If it does then this can mean changing the culture, people and structure to fit the new strategy and the new work that it entails. Change may not necessarily come all at once or in a wholesale way. And further change later might mean going through another rethink and more restructuring. Reaching digital maturity means always being on the lookout for the next cycle of change - creating room for experimentation and treating change itself as a normal part of the company’s operation. We can now better understand the definition of Digital Maturity offered at the beginning. Digital Maturity is: “Aligning an organisation’s people, culture, structure, and tasks to compete effectively by taking advantage of opportunities enabled by technological infrastructure, both inside and outside the organisation.” Kane, Copulsky et al., The Technology Fallacy Successful tech companies exhibit digital maturity. They each have a clear strategy with a core set of products and services. They also experiment and adjust to market conditions - they improve existing offerings, add new ones and retire old ones. Tech companies are able to do this faster because much of their operation is around software rather than physical structures and arrangements. The pattern is broad and the particulars are unique to a particular company’s strategy and circumstances. This is part of what makes the challenge of Digital Transformation . Each company must confront the difficult details of its situation. Digital maturity is this broad pattern of using technology to be more adaptive to opportunities and challenges. so difficult Despite the hype, there’s no copy-paste formula for success. Title Image by from . Dmitry Abramov Pixabay If you liked this you can vote for me for Hacker Noon Contributor of the Year - Project Management

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