But, the $21.8 million franchise tag allows for Pittman’s representatives and the Colts to continue to talk and work toward a new contract for one of the best receivers in the franchise’s history. …

Colts use franchise tag on Michael Pittman Jr. as they work toward long-term deal

With free agency starting next week, Tuesday&#x27;s NFL franchise tag deadline reshaped the open market. But which teams and players made out best?

NFL franchise tag deadline winners, losers: Who emerged from 2024 deadline with advantage?

The Patriots have placed the transition tag on Dugger, giving the club the right to match any offer to Dugger in free agency.

Analysis: Patriots Place Transition Tag on Standout Safety Kyle Dugger

Barkley had long been seeking an extension to stay with the franchise.

Saquon Barkley does not receive franchise tag from Giants, will test free agent market - Yahoo Sports

Colts General Manager Chris Ballard said last week that wide receiver Michael Pittman Jr. will be on the team&#x27;s 2024 roster one way or another and the Colts moved to make sure that proves to be the case on Tuesday.

Colts use franchise tag on Michael Pittman Jr. - NBC Sports

Tuesday marked the official deadline for NFL teams to use a tag on players whose contracts are set to expire. NFL.com is tracking which players are getting the tag, and which ones are not, up to the 4 p.m. ET deadline.

2024 NFL franchise tag tracker: Latest tag news on deadline day

Taag

taag

Incomplete Meta Tags are Unprofessional TL;DR: Incomplete or null meta tags break functionality and user experience. Problems Tags appear in output
Email texts include placeholders between human-readable text
Missed placeholders confuse users
Websites are rendered with strange characters
Null values trigger errors
Potential security injection vulnerabilities Solutions Validate meta tags
Assert completeness early
Fail Fast
Avoid null values
Throw meaningful exceptions
Automate meta validation Context When you leave meta tags unfinished, such as {user_name} or {product_name}, they often sneak into your final output. Imagine sending an email that says, "Hi {user_name}, your order for {product_name} is ready." It screams unprofessionalism and confuses users. Null values worsen things by causing crashes or silent failures, leading to bad user experiences or broken processes. You can avoid this by asserting completeness before rendering or sending. When your code finds an incomplete meta tag or a null value, stop the process immediately and throw an exception. Sample Code Wrong <?php

$emailBody = "Hello {user_name}, 
your order for {product_name} is confirmed.";

// You forget to make the replacements
sendEmail($emailBody); Right <?php

$emailBody = "Hello {user_name},
your order for {product_name} is confirmed.";

if (strpos($emailBody, '{') !== false) {
    throw new Exception(
        "Incomplete meta tags found in email body.");
}
sendEmail($emailBody); Detection [x]Automatic You can detect this smell with automated tests or linters scanning unfinished placeholders ({} or similar patterns). Tags Fail Fast Level [x]Beginner Why the Bijection Is Important Your system must maintain a one-to-one mapping when representing user data with placeholders. You break this mapping if your {user_name} placeholder exists but lacks a corresponding real name. This causes errors, confusion, and a loss of trust in your application. Ensuring bijection compliance avoids these issues. AI Generation AI tools sometimes introduce this smell when generating templates with placeholders but fail to substitute real data. You must validate and complete all placeholders before using the output. AI Detection AI tools like linters or email rendering validators can detect unfinished meta tags if you configure them correctly. Use these tools to automate meta-tag detection and reduce human error. Try Them! Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Conclusion Incomplete meta tags are more than just sloppy—they're harmful. Validate tags, assert completeness, and throw exceptions when needed. Handling meta tags carefully prevents errors and ensures a professional experience. Relations https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iii-t7h3zkv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxviii https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xx-we-have-reached-100 More Info https://hackernoon.com/fail-fast-philosophy-explained-si963vk9?embedable=true https://hackernoon.com/null-the-billion-dollar-mistake-8t5z32d6?embedable=true Disclaimer Code Smells are my opinion. Credits Photo by Tomas Martinez on Unsplash The best error message is the one that never shows up. Thomas Fuchs https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series. https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true Incomplete Meta Tags are Unprofessional Incomplete Meta Tags are Unprofessional TL;DR: Incomplete or null meta tags break functionality and user experience. TL;DR: Incomplete or null meta tags break functionality and user experience. Problems Tags appear in output Email texts include placeholders between human-readable text Missed placeholders confuse users Websites are rendered with strange characters Null values trigger errors Potential security injection vulnerabilities Tags appear in output Email texts include placeholders between human-readable text Missed placeholders confuse users Websites are rendered with strange characters Null values trigger errors Null Potential security injection vulnerabilities security injection vulnerabilities Solutions Validate meta tags Assert completeness early Fail Fast Avoid null values Throw meaningful exceptions Automate meta validation Validate meta tags Assert completeness early Fail Fast Fail Fast Avoid null values null Throw meaningful exceptions Automate meta validation Context When you leave meta tags unfinished, such as {user_name} or {product_name} , they often sneak into your final output. Imagine sending an email that says, "Hi {user_name}, your order for {product_name} is ready." {user_name} {product_name} It screams unprofessionalism and confuses users. Null values worsen things by causing crashes or silent failures, leading to bad user experiences or broken processes. Null You can avoid this by asserting completeness before rendering or sending. When your code finds an incomplete meta tag or a null value, stop the process immediately and throw an exception. Sample Code Wrong <?php

$emailBody = "Hello {user_name}, 
your order for {product_name} is confirmed.";

// You forget to make the replacements
sendEmail($emailBody); <?php

$emailBody = "Hello {user_name}, 
your order for {product_name} is confirmed.";

// You forget to make the replacements
sendEmail($emailBody); Right <?php

$emailBody = "Hello {user_name},
your order for {product_name} is confirmed.";

if (strpos($emailBody, '{') !== false) {
    throw new Exception(
        "Incomplete meta tags found in email body.");
}
sendEmail($emailBody); <?php

$emailBody = "Hello {user_name},
your order for {product_name} is confirmed.";

if (strpos($emailBody, '{') !== false) {
    throw new Exception(
        "Incomplete meta tags found in email body.");
}
sendEmail($emailBody); Detection [x]Automatic [x] Automatic [x] You can detect this smell with automated tests or linters scanning unfinished placeholders ({} or similar patterns). Tags Fail Fast Fail Fast Level [x]Beginner [x] Beginner [x] Why the Bijection Is Important Your system must maintain a one-to-one mapping when representing user data with placeholders. You break this mapping if your {user_name} placeholder exists but lacks a corresponding real name. This causes errors, confusion, and a loss of trust in your application. Ensuring bijection compliance avoids these issues. bijection AI Generation AI tools sometimes introduce this smell when generating templates with placeholders but fail to substitute real data. You must validate and complete all placeholders before using the output. AI Detection AI tools like linters or email rendering validators can detect unfinished meta tags if you configure them correctly. Use these tools to automate meta-tag detection and reduce human error. Try Them! Remember: AI Assistants make lots of mistakes Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Conclusion Incomplete meta tags are more than just sloppy—they're harmful. Validate tags, assert completeness, and throw exceptions when needed. Handling meta tags carefully prevents errors and ensures a professional experience. Relations https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iii-t7h3zkv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iii-t7h3zkv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxviii https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxviii https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xx-we-have-reached-100 https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xx-we-have-reached-100 More Info https://hackernoon.com/fail-fast-philosophy-explained-si963vk9?embedable=true https://hackernoon.com/fail-fast-philosophy-explained-si963vk9?embedable=true https://hackernoon.com/null-the-billion-dollar-mistake-8t5z32d6?embedable=true https://hackernoon.com/null-the-billion-dollar-mistake-8t5z32d6?embedable=true Disclaimer Code Smells are my opinion . opinion Credits Photo by Tomas Martinez on Unsplash Tomas Martinez Unsplash The best error message is the one that never shows up. The best error message is the one that never shows up. Thomas Fuchs Thomas Fuchs https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series. https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true

Code Smell 283 - Unresolved Meta Tags

The HackerNoon Newsletter: DIY Tagged Cache (12/10/2024)

Developers often joke that programming has two main challenges: naming variables
cache invalidation This joke is not far from the truth: managing caches, especially their invalidation, can indeed become a serious task. In this article, I’ll explain how to easily implement tagged cache functionality based on an existing caching service. Imagine we have a system where users add articles. For each user, we display statistics about their articles in their personal dashboard: the number of articles, average word count, publication frequency, etc. To speed up the system, we cache this data. A unique cache key is created for each report. The question arises: how to invalidate such caches when data changes? One approach is to manually clear the cache for each event, for instance, when a new article is added: class InvalidateArticleReportCacheOnArticleCreated {
    public function handle(event: ArticleCreatedEvent): void {
        this->cacheService->deleteMultiple([
	          'user_article_report_count_' . event->userId,
	          'user_article_report_word_avg_' . event->userId,
	          'user_article_report_freq_avg_' . event->userId,
        ])
    }
} This method works but becomes cumbersome when dealing with a large number of reports and keys. This is where tagged caching comes in handy. Tagged caching allows data to be associated not only with a key but also with an array of tags. Subsequently, all records associated with a specific tag can be invalidated, significantly simplifying the process. Writing a value to the cache with tags: this->taggedCacheService->set(
    key: 'user_article_report_count_' . user->id,
    value: value,
    tagNames: [
        'user_article_cache_tag_' . user->id,
        'user_article_report_cache_tag_' . user->id,
        'user_article_report'
    ]
) Invalidating the cache by tags: class UpdateCacheTagsOnArticleCreated {
    public function handle(event: ArticleCreatedEvent): void {
        this->taggedCacheService->updateTagsVersions([
            'user_article_cache_tag_' . user->id,
        ])
    }
} Here, the tag 'user_article_cache_tag_' . $user->id represents changes in the user's articles. It can be used to invalidate any caches dependent on this data. A more specific tag 'user_article_report_cache_tag_' . $user->id allows only the user's reports to be cleared, while a general tag 'user_article_report' invalidates report caches for all users. If your caching library does not support tagging, you can implement it yourself. The main idea is to store the current version values of tags, as well as for each value tagged, to store the tag versions that were current at the time the value was written to the cache. Then, when retrieving a value from the cache, the current tag versions are also retrieved, and their validity is checked by comparing them. Creating a TaggedCache class class TaggedCache {
   private cacheService: cacheService
} Implementing the set method for writing to the cache with tags. In this method, we need to write the value to the cache, as well as retrieve the current versions of the tags provided and save them associated with the specific cache key. This is achieved by using an additional key with a prefix added to the provided key. class TaggedCache {
   private cacheService: cacheService

    public function set(
        key: string,
        value: mixed,
        tagNames: string[],
        ttl: int
    ): bool {
        if (empty(tagNames)) {
            return false
        }

        tagVersions = this->getTagsVersions(tagNames)

        tagsCacheKey = this->getTagsCacheKey(key)

	      return this->cacheService->setMultiple(
            [
                key => value,
                tagsCacheKey => tagVersions,
            ],
            ttl
        )
    }

    private function getTagsVersions(tagNames: string[]): array<string, string> {
        tagVersions = []

        tagVersionKeys = []

        foreach (tagNames as tagName) {
            tagVersionKeys[tagName] = this->getTagVersionKey(tagName)
        }

        if (empty(tagVersionKeys)) {
            return tagVersions
        }

        tagVersionsCache = this->cacheService->getMultiple(tagVersionKeys)

        foreach (tagVersionKeys as tagName => tagVersionKey) {
            if (empty(tagVersionsCache[tagVersionKey])) {
                tagVersionsCache[tagVersionKey] = this->updateTagVersion(tagName)
            }

            tagVersions[$tagName] = tagVersionsCache[tagVersionKey]
        }

        return tagVersions
    }

    private function getTagVersionKey(tagName: string): string {
        return 'tag_version_' . tagName
    }

    private function getTagsCacheKey(key: string): string {
        return 'cache_tags_tagskeys_' . key
    } Adding the get method to retrieve tagged values from the cache. Here, we retrieve the value using the key, as well as the tag versions associated with that key. Then we check the validity of the tags. If any tag is invalid, the value is deleted from the cache and null is returned. If all tags are valid, the cached value is returned. class TaggedCache {
    private cacheService: cacheService

    public function get(key: string): mixed {
        tagsCacheKey = this->getTagsCacheKey(key)

        values = this->cacheService->getMultiple([key, tagsCacheKey])

        if (empty(values[key]) || empty(values[tagsCacheKey])) {
            return null
        }

        value = values[key]

        tagVersions = values[tagsCacheKey]

        if (! this->isTagVersionsValid(tagVersions)) {
            this->cacheService->deleteMultiple([key, tagsCacheKey])

            return null
        }

        return value
    }

    private function isTagVersionsValid(tagVersions: array<string, string>): bool {
        tagNames = array_keys(tagVersions)

        actualTagVersions = this->getTagsVersions(tagNames)

        foreach (tagVersions as tagName => tagVersion) {
            if (empty(actualTagVersions[tagName])) {
                return false
            }

            if (actualTagVersions[tagName] !== tagVersion) {
                return false
            }
        }

        return true
    }
} Implementing the updateTagsVersions method to update tag versions. Here, we iterate over all the tags provided and update their versions using, for example, the current time as the version. class TaggedCache {
    private cacheService: cacheService

    public function updateTagsVersions(tagNames: string[]): void {
        foreach (tagNames as tagName) {
            this->updateTagVersion(tagName)
        }
    }

    private function updateTagVersion(tagName: string): string {
        tagKey = this->getTagVersionKey(tagName)

        tagVersion = this->generateTagVersion()

        return this->cacheService->set(tagKey, tagVersion) ? tagVersion : ''
    }

    private function generateTagVersion(): string {
        return (string) hrtime(true)
    }
} This approach is both convenient and universal. Tagged caching eliminates the need to manually specify all keys for invalidation, automating the process. However, it requires additional resources: storing tag version data and checking their validity with each request. If your caching service is fast and not heavily constrained in size, this approach will not significantly affect performance. To minimize the load, you can combine tagged caching with local caching mechanisms. In this way, tagged caching not only simplifies invalidation but also makes working with data more flexible and understandable, especially in complex systems with large amounts of interconnected data. Developers often joke that programming has two main challenges: naming variables cache invalidation naming variables cache invalidation This joke is not far from the truth: managing caches, especially their invalidation, can indeed become a serious task. In this article, I’ll explain how to easily implement tagged cache functionality based on an existing caching service. Imagine we have a system where users add articles. For each user, we display statistics about their articles in their personal dashboard: the number of articles, average word count, publication frequency, etc. To speed up the system, we cache this data. A unique cache key is created for each report. The question arises: how to invalidate such caches when data changes? One approach is to manually clear the cache for each event, for instance, when a new article is added: class InvalidateArticleReportCacheOnArticleCreated {
    public function handle(event: ArticleCreatedEvent): void {
        this->cacheService->deleteMultiple([
	          'user_article_report_count_' . event->userId,
	          'user_article_report_word_avg_' . event->userId,
	          'user_article_report_freq_avg_' . event->userId,
        ])
    }
} class InvalidateArticleReportCacheOnArticleCreated {
    public function handle(event: ArticleCreatedEvent): void {
        this->cacheService->deleteMultiple([
	          'user_article_report_count_' . event->userId,
	          'user_article_report_word_avg_' . event->userId,
	          'user_article_report_freq_avg_' . event->userId,
        ])
    }
} This method works but becomes cumbersome when dealing with a large number of reports and keys. This is where tagged caching comes in handy. Tagged caching allows data to be associated not only with a key but also with an array of tags. Subsequently, all records associated with a specific tag can be invalidated, significantly simplifying the process. Writing a value to the cache with tags: this->taggedCacheService->set(
    key: 'user_article_report_count_' . user->id,
    value: value,
    tagNames: [
        'user_article_cache_tag_' . user->id,
        'user_article_report_cache_tag_' . user->id,
        'user_article_report'
    ]
) this->taggedCacheService->set(
    key: 'user_article_report_count_' . user->id,
    value: value,
    tagNames: [
        'user_article_cache_tag_' . user->id,
        'user_article_report_cache_tag_' . user->id,
        'user_article_report'
    ]
) Invalidating the cache by tags: class UpdateCacheTagsOnArticleCreated {
    public function handle(event: ArticleCreatedEvent): void {
        this->taggedCacheService->updateTagsVersions([
            'user_article_cache_tag_' . user->id,
        ])
    }
} class UpdateCacheTagsOnArticleCreated {
    public function handle(event: ArticleCreatedEvent): void {
        this->taggedCacheService->updateTagsVersions([
            'user_article_cache_tag_' . user->id,
        ])
    }
} Here, the tag 'user_article_cache_tag_' . $user->id represents changes in the user's articles. It can be used to invalidate any caches dependent on this data. A more specific tag 'user_article_report_cache_tag_' . $user->id allows only the user's reports to be cleared, while a general tag 'user_article_report' invalidates report caches for all users. 'user_article_cache_tag_' . $user->id 'user_article_report_cache_tag_' . $user->id 'user_article_report' If your caching library does not support tagging, you can implement it yourself. The main idea is to store the current version values of tags, as well as for each value tagged, to store the tag versions that were current at the time the value was written to the cache. Then, when retrieving a value from the cache, the current tag versions are also retrieved, and their validity is checked by comparing them. Creating a TaggedCache class TaggedCache class TaggedCache {
   private cacheService: cacheService
} class TaggedCache {
   private cacheService: cacheService
} Implementing the set method for writing to the cache with tags. In this method, we need to write the value to the cache, as well as retrieve the current versions of the tags provided and save them associated with the specific cache key. This is achieved by using an additional key with a prefix added to the provided key. set class TaggedCache {
   private cacheService: cacheService

    public function set(
        key: string,
        value: mixed,
        tagNames: string[],
        ttl: int
    ): bool {
        if (empty(tagNames)) {
            return false
        }

        tagVersions = this->getTagsVersions(tagNames)

        tagsCacheKey = this->getTagsCacheKey(key)

	      return this->cacheService->setMultiple(
            [
                key => value,
                tagsCacheKey => tagVersions,
            ],
            ttl
        )
    }

    private function getTagsVersions(tagNames: string[]): array<string, string> {
        tagVersions = []

        tagVersionKeys = []

        foreach (tagNames as tagName) {
            tagVersionKeys[tagName] = this->getTagVersionKey(tagName)
        }

        if (empty(tagVersionKeys)) {
            return tagVersions
        }

        tagVersionsCache = this->cacheService->getMultiple(tagVersionKeys)

        foreach (tagVersionKeys as tagName => tagVersionKey) {
            if (empty(tagVersionsCache[tagVersionKey])) {
                tagVersionsCache[tagVersionKey] = this->updateTagVersion(tagName)
            }

            tagVersions[$tagName] = tagVersionsCache[tagVersionKey]
        }

        return tagVersions
    }

    private function getTagVersionKey(tagName: string): string {
        return 'tag_version_' . tagName
    }

    private function getTagsCacheKey(key: string): string {
        return 'cache_tags_tagskeys_' . key
    } class TaggedCache {
   private cacheService: cacheService

    public function set(
        key: string,
        value: mixed,
        tagNames: string[],
        ttl: int
    ): bool {
        if (empty(tagNames)) {
            return false
        }

        tagVersions = this->getTagsVersions(tagNames)

        tagsCacheKey = this->getTagsCacheKey(key)

	      return this->cacheService->setMultiple(
            [
                key => value,
                tagsCacheKey => tagVersions,
            ],
            ttl
        )
    }

    private function getTagsVersions(tagNames: string[]): array<string, string> {
        tagVersions = []

        tagVersionKeys = []

        foreach (tagNames as tagName) {
            tagVersionKeys[tagName] = this->getTagVersionKey(tagName)
        }

        if (empty(tagVersionKeys)) {
            return tagVersions
        }

        tagVersionsCache = this->cacheService->getMultiple(tagVersionKeys)

        foreach (tagVersionKeys as tagName => tagVersionKey) {
            if (empty(tagVersionsCache[tagVersionKey])) {
                tagVersionsCache[tagVersionKey] = this->updateTagVersion(tagName)
            }

            tagVersions[$tagName] = tagVersionsCache[tagVersionKey]
        }

        return tagVersions
    }

    private function getTagVersionKey(tagName: string): string {
        return 'tag_version_' . tagName
    }

    private function getTagsCacheKey(key: string): string {
        return 'cache_tags_tagskeys_' . key
    } Adding the get method to retrieve tagged values from the cache. Here, we retrieve the value using the key, as well as the tag versions associated with that key. Then we check the validity of the tags. If any tag is invalid, the value is deleted from the cache and null is returned. If all tags are valid, the cached value is returned. get null class TaggedCache {
    private cacheService: cacheService

    public function get(key: string): mixed {
        tagsCacheKey = this->getTagsCacheKey(key)

        values = this->cacheService->getMultiple([key, tagsCacheKey])

        if (empty(values[key]) || empty(values[tagsCacheKey])) {
            return null
        }

        value = values[key]

        tagVersions = values[tagsCacheKey]

        if (! this->isTagVersionsValid(tagVersions)) {
            this->cacheService->deleteMultiple([key, tagsCacheKey])

            return null
        }

        return value
    }

    private function isTagVersionsValid(tagVersions: array<string, string>): bool {
        tagNames = array_keys(tagVersions)

        actualTagVersions = this->getTagsVersions(tagNames)

        foreach (tagVersions as tagName => tagVersion) {
            if (empty(actualTagVersions[tagName])) {
                return false
            }

            if (actualTagVersions[tagName] !== tagVersion) {
                return false
            }
        }

        return true
    }
} class TaggedCache {
    private cacheService: cacheService

    public function get(key: string): mixed {
        tagsCacheKey = this->getTagsCacheKey(key)

        values = this->cacheService->getMultiple([key, tagsCacheKey])

        if (empty(values[key]) || empty(values[tagsCacheKey])) {
            return null
        }

        value = values[key]

        tagVersions = values[tagsCacheKey]

        if (! this->isTagVersionsValid(tagVersions)) {
            this->cacheService->deleteMultiple([key, tagsCacheKey])

            return null
        }

        return value
    }

    private function isTagVersionsValid(tagVersions: array<string, string>): bool {
        tagNames = array_keys(tagVersions)

        actualTagVersions = this->getTagsVersions(tagNames)

        foreach (tagVersions as tagName => tagVersion) {
            if (empty(actualTagVersions[tagName])) {
                return false
            }

            if (actualTagVersions[tagName] !== tagVersion) {
                return false
            }
        }

        return true
    }
} Implementing the updateTagsVersions method to update tag versions. Here, we iterate over all the tags provided and update their versions using, for example, the current time as the version. updateTagsVersions class TaggedCache {
    private cacheService: cacheService

    public function updateTagsVersions(tagNames: string[]): void {
        foreach (tagNames as tagName) {
            this->updateTagVersion(tagName)
        }
    }

    private function updateTagVersion(tagName: string): string {
        tagKey = this->getTagVersionKey(tagName)

        tagVersion = this->generateTagVersion()

        return this->cacheService->set(tagKey, tagVersion) ? tagVersion : ''
    }

    private function generateTagVersion(): string {
        return (string) hrtime(true)
    }
} class TaggedCache {
    private cacheService: cacheService

    public function updateTagsVersions(tagNames: string[]): void {
        foreach (tagNames as tagName) {
            this->updateTagVersion(tagName)
        }
    }

    private function updateTagVersion(tagName: string): string {
        tagKey = this->getTagVersionKey(tagName)

        tagVersion = this->generateTagVersion()

        return this->cacheService->set(tagKey, tagVersion) ? tagVersion : ''
    }

    private function generateTagVersion(): string {
        return (string) hrtime(true)
    }
} This approach is both convenient and universal. Tagged caching eliminates the need to manually specify all keys for invalidation, automating the process. However, it requires additional resources: storing tag version data and checking their validity with each request. If your caching service is fast and not heavily constrained in size, this approach will not significantly affect performance. To minimize the load, you can combine tagged caching with local caching mechanisms. In this way, tagged caching not only simplifies invalidation but also makes working with data more flexible and understandable, especially in complex systems with large amounts of interconnected data.

DIY Tagged Cache

DIY Etiquetado Caché ukax mä jach’a uñacht’äwiwa

Caché con etiquetas DIY

DIY برچسب کش

Caché etiquetado DIY

מטמון מתויג עשה זאת בעצמך

DIY タグ付きキャッシュ

DIY-getagde cache

Skrytka oznaczona DIY

Cache cu etichete DIY

Speculative decoding is an advanced AI inference technique that is gaining traction in natural language processing (NLP) and other sequence generation tasks. It addresses one of the most significant challenges in deploying large-scale models: balancing computational efficiency with the quality of generated outputs. Models like GPT-3 and GPT-4 have set new benchmarks in AI capability but are computationally expensive, especially for real-time applications such as chatbots, translation systems, and interactive assistants. Speculative decoding offers a clever solution by introducing a two-model system: a smaller, faster draft model and a larger, more accurate target model. This approach reduces latency without compromising the quality of the generated sequences. At its core, speculative decoding is a two-step process. First, a lightweight draft model generates a sequence of tokens quickly. This model is typically a distilled version of the target model or another smaller model trained for speed. The draft model's output is not final but serves as a proposal. The second step involves the larger, more accurate target model, which evaluates the draft model’s tokens. The target model either accepts the proposed tokens if they meet a confidence threshold or refines them if they do not. This process allows the system to leverage the efficiency of the draft model while maintaining the precision of the target model. The reason speculative decoding is particularly appealing lies in its ability to reduce the computational burden of the larger model. By delegating the initial token generation to the draft model, the target model operates selectively, focusing only on validation or correction rather than generating every token from scratch. This division of labor significantly reduces inference latency and resource consumption, making it a preferred technique for real-time applications. The technique works well in scenarios where speed is critical, and the quality of generated outputs cannot be compromised. For instance, in chat-based applications, users expect near-instantaneous responses. Speculative decoding ensures that the system generates text quickly while retaining the nuanced understanding of the larger model. Similarly, in real-time translation systems, where delays can disrupt communication, speculative decoding provides a practical way to meet latency constraints. To understand speculative decoding more concretely, let us consider a code implementation. Below is an example that demonstrates how speculative decoding could be implemented using PyTorch and Hugging Face's Transformers library. This implementation involves a draft model (a smaller, faster version like DistilGPT-2) and a target model (a larger, more accurate model like GPT-2). from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft (smaller) and target (larger) models

draft_model_name = "distilgpt2"  # Smaller, faster model 

target_model_name = "gpt2"      # Larger, more accurate model

draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name) draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)

target_tokenizer = AutoTokenizer.from_pretrained(target_model_name) target_model = AutoModelForCausalLM.from_pretrained(target_model_name)

def speculative_decode(prompt, max_length=50, threshold=0.9): """ Performs speculative decoding using a draft model and a target model.

Args:
    prompt (str): The input text to the model.
    max_length (int): The maximum length of the generated sequence.
    threshold (float): The confidence threshold for the target model to accept tokens.

Returns:
    str: The generated text sequence.
"""
# Step 1: Generate tokens using the draft model
draft_inputs = draft_tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
draft_outputs = draft_model.generate(draft_inputs.input_ids, max_length=max_length)  # Generate draft tokens
draft_tokens = draft_tokenizer.decode(draft_outputs[0], skip_special_tokens=True)  # Decode draft tokens to text

# Step 2: Verify tokens using the target model
target_inputs = target_tokenizer(draft_tokens, return_tensors="pt")  # Tokenize the draft output for the target model
target_logits = target_model(**target_inputs).logits  # Get logits (un-normalized probabilities) from the target model

# Step 3: Calculate confidence scores for each token
probs = target_logits.softmax(dim=-1)  # Convert logits to probabilities
confidence = probs.max(dim=-1).values.mean().item()  # Calculate the average confidence score across all tokens

# Step 4: Decide whether to accept or refine tokens
if confidence >= threshold:  # If confidence exceeds the threshold
    return draft_tokens  # Accept the draft tokens
else:  # Otherwise, refine the sequence using the target model
    refined_outputs = target_model.generate(target_inputs.input_ids, max_length=max_length)
    return target_tokenizer.decode(refined_outputs[0], skip_special_tokens=True)  # Return the refined sequence


prompt = "The future of AI is" output = speculative_decode(prompt, max_length=20) print(output) In this implementation, the speculative decoding process begins with the draft model generating a sequence of tokens. These tokens are then passed to the target model, which calculates the confidence for each token. If the average confidence exceeds a pre-defined threshold, the tokens are accepted as is. Otherwise, the target model refines the sequence to ensure output quality. This code includes detailed comments explaining each step, from tokenizing the input for the draft model to generating and validating tokens with the target model. The threshold parameter can be adjusted based on the application’s tolerance for speed versus accuracy trade-offs. One of the primary challenges in implementing speculative decoding is training the draft model. The draft model must align closely with the target model in terms of predictions, as significant discrepancies can reduce the efficiency of the process. For example, if the draft model frequently proposes incorrect tokens, the target model spends more time correcting them, negating the benefits of speculative decoding. Ensuring this alignment typically involves fine-tuning or distillation techniques that transfer knowledge from the target model to the draft model. Another critical aspect of speculative decoding is the confidence threshold used by the target model to accept or reject the draft model's tokens. If the threshold is too strict, the target model rejects too many tokens, increasing its workload and reducing efficiency. Conversely, if the threshold is too lenient, the system may accept suboptimal tokens, degrading output quality. Fine-tuning this parameter requires careful experimentation and may vary depending on the application. Speculative decoding can also integrate with parallel computation to further enhance efficiency. For example, while the target model processes one batch of tokens, the draft model can begin generating the next batch. This overlap reduces idle time for both models and maximizes throughput, making speculative decoding scalable for high-volume systems. While challenges like draft-target alignment and confidence threshold tuning exist, ongoing advancements in model training and inference techniques promise to make speculative decoding even more robust and accessible. This technique is poised to play a pivotal role in the future of AI deployment, particularly as demand for high-speed, high-quality systems continues to grow. The code above is publicly available in my github https://github.com/philhopkinsML Speculative decoding is an advanced AI inference technique that is gaining traction in natural language processing (NLP) and other sequence generation tasks. It addresses one of the most significant challenges in deploying large-scale models: balancing computational efficiency with the quality of generated outputs. Models like GPT-3 and GPT-4 have set new benchmarks in AI capability but are computationally expensive, especially for real-time applications such as chatbots, translation systems, and interactive assistants. Speculative decoding offers a clever solution by introducing a two-model system: a smaller, faster draft model and a larger, more accurate target model. This approach reduces latency without compromising the quality of the generated sequences. At its core, speculative decoding is a two-step process. First, a lightweight draft model generates a sequence of tokens quickly. This model is typically a distilled version of the target model or another smaller model trained for speed. The draft model's output is not final but serves as a proposal. The second step involves the larger, more accurate target model, which evaluates the draft model’s tokens. The target model either accepts the proposed tokens if they meet a confidence threshold or refines them if they do not. This process allows the system to leverage the efficiency of the draft model while maintaining the precision of the target model. The reason speculative decoding is particularly appealing lies in its ability to reduce the computational burden of the larger model. By delegating the initial token generation to the draft model, the target model operates selectively, focusing only on validation or correction rather than generating every token from scratch. This division of labor significantly reduces inference latency and resource consumption, making it a preferred technique for real-time applications. The technique works well in scenarios where speed is critical, and the quality of generated outputs cannot be compromised. For instance, in chat-based applications, users expect near-instantaneous responses. Speculative decoding ensures that the system generates text quickly while retaining the nuanced understanding of the larger model. Similarly, in real-time translation systems, where delays can disrupt communication, speculative decoding provides a practical way to meet latency constraints. To understand speculative decoding more concretely, let us consider a code implementation. Below is an example that demonstrates how speculative decoding could be implemented using PyTorch and Hugging Face's Transformers library. This implementation involves a draft model (a smaller, faster version like DistilGPT-2) and a target model (a larger, more accurate model like GPT-2). from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft (smaller) and target (larger) models

draft_model_name = "distilgpt2"  # Smaller, faster model 

target_model_name = "gpt2"      # Larger, more accurate model

draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name) draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)

target_tokenizer = AutoTokenizer.from_pretrained(target_model_name) target_model = AutoModelForCausalLM.from_pretrained(target_model_name)

def speculative_decode(prompt, max_length=50, threshold=0.9): """ Performs speculative decoding using a draft model and a target model.

Args:
    prompt (str): The input text to the model.
    max_length (int): The maximum length of the generated sequence.
    threshold (float): The confidence threshold for the target model to accept tokens.

Returns:
    str: The generated text sequence.
"""
# Step 1: Generate tokens using the draft model
draft_inputs = draft_tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
draft_outputs = draft_model.generate(draft_inputs.input_ids, max_length=max_length)  # Generate draft tokens
draft_tokens = draft_tokenizer.decode(draft_outputs[0], skip_special_tokens=True)  # Decode draft tokens to text

# Step 2: Verify tokens using the target model
target_inputs = target_tokenizer(draft_tokens, return_tensors="pt")  # Tokenize the draft output for the target model
target_logits = target_model(**target_inputs).logits  # Get logits (un-normalized probabilities) from the target model

# Step 3: Calculate confidence scores for each token
probs = target_logits.softmax(dim=-1)  # Convert logits to probabilities
confidence = probs.max(dim=-1).values.mean().item()  # Calculate the average confidence score across all tokens

# Step 4: Decide whether to accept or refine tokens
if confidence >= threshold:  # If confidence exceeds the threshold
    return draft_tokens  # Accept the draft tokens
else:  # Otherwise, refine the sequence using the target model
    refined_outputs = target_model.generate(target_inputs.input_ids, max_length=max_length)
    return target_tokenizer.decode(refined_outputs[0], skip_special_tokens=True)  # Return the refined sequence


prompt = "The future of AI is" output = speculative_decode(prompt, max_length=20) print(output) from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft (smaller) and target (larger) models

draft_model_name = "distilgpt2"  # Smaller, faster model 

target_model_name = "gpt2"      # Larger, more accurate model

draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name) draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)

target_tokenizer = AutoTokenizer.from_pretrained(target_model_name) target_model = AutoModelForCausalLM.from_pretrained(target_model_name)

def speculative_decode(prompt, max_length=50, threshold=0.9): """ Performs speculative decoding using a draft model and a target model.

Args:
    prompt (str): The input text to the model.
    max_length (int): The maximum length of the generated sequence.
    threshold (float): The confidence threshold for the target model to accept tokens.

Returns:
    str: The generated text sequence.
"""
# Step 1: Generate tokens using the draft model
draft_inputs = draft_tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
draft_outputs = draft_model.generate(draft_inputs.input_ids, max_length=max_length)  # Generate draft tokens
draft_tokens = draft_tokenizer.decode(draft_outputs[0], skip_special_tokens=True)  # Decode draft tokens to text

# Step 2: Verify tokens using the target model
target_inputs = target_tokenizer(draft_tokens, return_tensors="pt")  # Tokenize the draft output for the target model
target_logits = target_model(**target_inputs).logits  # Get logits (un-normalized probabilities) from the target model

# Step 3: Calculate confidence scores for each token
probs = target_logits.softmax(dim=-1)  # Convert logits to probabilities
confidence = probs.max(dim=-1).values.mean().item()  # Calculate the average confidence score across all tokens

# Step 4: Decide whether to accept or refine tokens
if confidence >= threshold:  # If confidence exceeds the threshold
    return draft_tokens  # Accept the draft tokens
else:  # Otherwise, refine the sequence using the target model
    refined_outputs = target_model.generate(target_inputs.input_ids, max_length=max_length)
    return target_tokenizer.decode(refined_outputs[0], skip_special_tokens=True)  # Return the refined sequence


prompt = "The future of AI is" output = speculative_decode(prompt, max_length=20) print(output) In this implementation, the speculative decoding process begins with the draft model generating a sequence of tokens. These tokens are then passed to the target model, which calculates the confidence for each token. If the average confidence exceeds a pre-defined threshold, the tokens are accepted as is. Otherwise, the target model refines the sequence to ensure output quality. This code includes detailed comments explaining each step, from tokenizing the input for the draft model to generating and validating tokens with the target model. The threshold parameter can be adjusted based on the application’s tolerance for speed versus accuracy trade-offs. One of the primary challenges in implementing speculative decoding is training the draft model. The draft model must align closely with the target model in terms of predictions, as significant discrepancies can reduce the efficiency of the process. For example, if the draft model frequently proposes incorrect tokens, the target model spends more time correcting them, negating the benefits of speculative decoding. Ensuring this alignment typically involves fine-tuning or distillation techniques that transfer knowledge from the target model to the draft model. Another critical aspect of speculative decoding is the confidence threshold used by the target model to accept or reject the draft model's tokens. If the threshold is too strict, the target model rejects too many tokens, increasing its workload and reducing efficiency. Conversely, if the threshold is too lenient, the system may accept suboptimal tokens, degrading output quality. Fine-tuning this parameter requires careful experimentation and may vary depending on the application. Speculative decoding can also integrate with parallel computation to further enhance efficiency. For example, while the target model processes one batch of tokens, the draft model can begin generating the next batch. This overlap reduces idle time for both models and maximizes throughput, making speculative decoding scalable for high-volume systems. While challenges like draft-target alignment and confidence threshold tuning exist, ongoing advancements in model training and inference techniques promise to make speculative decoding even more robust and accessible. This technique is poised to play a pivotal role in the future of AI deployment, particularly as demand for high-speed, high-quality systems continues to grow. The code above is publicly available in my github https://github.com/philhopkinsML https://github.com/philhopkinsML

Meet The AI Tag-Team Method That Reduces Latency in Your Model's Response

SYDNEY, Australia, November 21st, 2024/Chainwire/--The GMT DAO, an independent community organization of GMT holders, is excited to announce the launch of the GMT Burn Initiative—an event designed to place the future of GMT firmly in the hands of its community. Through this initiative, GMT holders will vote on the decision to burn up to 600 million GMT, equivalent to 100 million USD, marking this as one of the most ambitious token burns in the web3 ecosystem. The GMT Burn Initiative reflects the GMT DAO’s commitment to transparency, governance, and sustainable token management. This community-driven approach aligns with the DAO's goal to shape GMT’s future responsibly and collaboratively. How It Works: Lock and Vote: GMT holders can participate by visiting burngmt.com, where they can lock their tokens and cast their vote on various proposals, including the first of its kind “GMT Burn Initiative”. Participants will receive an exclusive soulbound ‘Make GMT Great Again’ Red Hat NFT as a token of acknowledgement for their involvement. This NFT will record metadata detailing the claimed and eligible airdrops, as well as the votes cast.
Earn Rewards: A total of 100 million GMT will be distributed at the end of the 60-day voting period as rewards to participants, contingent on achieving milestones.
On-Chain Transparency: The voting process is recorded on the blockchain, allowing anyone to verify results and ensuring the integrity and transparency of each decision.
Regular Voting Cycles: This burn initiative is just the beginning. Moving forward, GMT DAO will introduce new proposals and voting cycles every quarter, giving holders regular opportunities to shape the future of GMT via gmtdao.com. This initiative showcases a proactive effort to ensure GMT's sustainable future, emphasizing innovation and community engagement. The GMT DAO remains committed to true decentralization, empowering GMT holders to shape its direction. By enabling votes on key proposals, including a potential major token burn, the DAO highlights the power of collective input and its dedication to building a strong, thriving ecosystem. The GMT Burn Initiative marks a new chapter in GMT’s journey, giving every holder a voice. Voting, which starts at 100 GMT, is now open at burngmt.com, inviting all GMT holders to help shape the token's future. About GMT DAO The GMT DAO is an independent organization of GMT holders dedicated to overseeing GMT’s future through transparent and community-led governance. The DAO’s formation represents a commitment to empowering GMT holders with a voice in key decisions, from token management to ecosystem development. For more information on the GMT Burn Initiative and to participate, users can visit burngmt.com. About GMT GMT sits at the heart of the FSL ecosystem. Exclusively earned through STEPN, this token plays a crucial role across all FSL products. GMT operates on four blockchains—Solana, BNB Chain, Ethereum, and Polygon—and is pivotal in Gas Hero, where it's used to trade items and burn for NFT level-ups. On MOOAR, all items can be traded using GMT, and it’s also central to primary sale raffle mints with key partners and is used in the newly launched STEPN GO to buy Sneakers and perform various in app functions. Contact GMT DAO hello@gmtdao.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here SYDNEY, Australia, November 21st, 2024/Chainwire/--The GMT DAO, an independent community organization of GMT holders, is excited to announce the launch of the GMT Burn Initiative—an event designed to place the future of GMT firmly in the hands of its community. Through this initiative, GMT holders will vote on the decision to burn up to 600 million GMT, equivalent to 100 million USD, marking this as one of the most ambitious token burns in the web3 ecosystem. The GMT Burn Initiative reflects the GMT DAO’s commitment to transparency, governance, and sustainable token management. This community-driven approach aligns with the DAO's goal to shape GMT’s future responsibly and collaboratively. How It Works: Lock and Vote: GMT holders can participate by visiting burngmt.com, where they can lock their tokens and cast their vote on various proposals, including the first of its kind “GMT Burn Initiative”. Participants will receive an exclusive soulbound ‘Make GMT Great Again’ Red Hat NFT as a token of acknowledgement for their involvement. This NFT will record metadata detailing the claimed and eligible airdrops, as well as the votes cast. Earn Rewards: A total of 100 million GMT will be distributed at the end of the 60-day voting period as rewards to participants, contingent on achieving milestones. On-Chain Transparency: The voting process is recorded on the blockchain, allowing anyone to verify results and ensuring the integrity and transparency of each decision. Regular Voting Cycles: This burn initiative is just the beginning. Moving forward, GMT DAO will introduce new proposals and voting cycles every quarter, giving holders regular opportunities to shape the future of GMT via gmtdao.com. Lock and Vote: GMT holders can participate by visiting burngmt.com , where they can lock their tokens and cast their vote on various proposals, including the first of its kind “GMT Burn Initiative”. Participants will receive an exclusive soulbound ‘Make GMT Great Again’ Red Hat NFT as a token of acknowledgement for their involvement. This NFT will record metadata detailing the claimed and eligible airdrops, as well as the votes cast. burngmt.com burngmt.com Earn Rewards: A total of 100 million GMT will be distributed at the end of the 60-day voting period as rewards to participants, contingent on achieving milestones. On-Chain Transparency: The voting process is recorded on the blockchain, allowing anyone to verify results and ensuring the integrity and transparency of each decision. Regular Voting Cycles: This burn initiative is just the beginning. Moving forward, GMT DAO will introduce new proposals and voting cycles every quarter, giving holders regular opportunities to shape the future of GMT via gmtdao.com . gmtdao.com gmtdao.com This initiative showcases a proactive effort to ensure GMT's sustainable future, emphasizing innovation and community engagement. The GMT DAO remains committed to true decentralization, empowering GMT holders to shape its direction. By enabling votes on key proposals, including a potential major token burn, the DAO highlights the power of collective input and its dedication to building a strong, thriving ecosystem. The GMT Burn Initiative marks a new chapter in GMT’s journey, giving every holder a voice. Voting, which starts at 100 GMT, is now open at burngmt.com , inviting all GMT holders to help shape the token's future. burngmt.com burngmt.com About GMT DAO The GMT DAO is an independent organization of GMT holders dedicated to overseeing GMT’s future through transparent and community-led governance. The DAO’s formation represents a commitment to empowering GMT holders with a voice in key decisions, from token management to ecosystem development. For more information on the GMT Burn Initiative and to participate, users can visit burngmt.com . burngmt.com burngmt.com About GMT GMT sits at the heart of the FSL ecosystem. Exclusively earned through STEPN , this token plays a crucial role across all FSL products. STEPN STEPN GMT operates on four blockchains—Solana, BNB Chain, Ethereum, and Polygon—and is pivotal in Gas Hero , where it's used to trade items and burn for NFT level-ups. Gas Hero Gas Hero On MOOAR , all items can be traded using GMT, and it’s also central to primary sale raffle mints with key partners and is used in the newly launched STEPN GO to buy Sneakers and perform various in app functions. MOOAR MOOAR STEPN GO STEPN GO Contact GMT DAO hello@gmtdao.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

GMT DAO Launches Groundbreaking Burn Initiative With a Target Of Up To 600 Million GMT

GMT DAO стартира новаторска инициатива за изгаряне с цел до 600 милиона GMT

GMT DAO spouští průlomovou iniciativu Burn s cílem až 600 milionů GMT

GMT DAO lanza una innovadora iniciativa de quema con un objetivo de hasta 600 millones de GMT

GMT DAO Luncurkan Inisiatif Pembakaran Terobosan dengan Target Hingga 600 Juta GMT

GMT DAO、最大6億GMTを目標とした画期的なバーンイニシアチブを開始

GMT DAO pristato novatorišką deginimo iniciatyvą, kurios tikslas – iki 600 mln. GMT

GMT DAO лансира револуционерна иницијатива за изгореници со цел до 600 милиони GMT

GMT DAO Melancarkan Inisiatif Pembakaran Pecah Tanah Dengan Sasaran Sehingga 600 Juta GMT

GMT DAO GMT මිලියන 600 ක් දක්වා ඉලක්කයක් සමඟ බිම් කඩකිරීමේ පිළිස්සීමේ මුලපිරීම දියත් කරයි

GMT DAO lanserar banbrytande Burn-initiativ med ett mål på upp till 600 miljoner GMT

GMT DAO เปิดตัวโครงการ Burning สุดล้ำด้วยเป้าหมายสูงถึง 600 ล้าน GMT

GMT DAO, 600 Milyon GMT'ye Kadar Hedefle Çığır Açan Yakma Girişimini Başlattı

AI and machine learning have changed how people communicate, learn, create, and consume content. With AI entering more and more facets of everyday life, some worry about losing the human element; however, experts in the field of AI are working to not only make AI work for you but also to use it to connect people better than ever before. Jin Tang is a software engineer whose work has informed how AI functions in today's internet. With her extensive knowledge and background in machine learning, Tang has helped create AI-driven tools that have transformed the world's digital content, improving user and creator experience along the way. Jin Tang's First Inspiration for Better Life Through Tech Jin Tang's interest in tech was initially inspired by her father, who taught himself software development. Watching her father work exposed her to a world full of potential for creativity. Tang started her tech journey by interning at a leading tech company in China, where she worked on their smartphone assistant product (similar to Siri) and saw that AI could transform how digital content works for its users. Observing the digital assistant's capabilities, she realized her work was more than a fun and convenient product; it had the potential to help people with disabilities by better enabling them to connect with others through their smartphone assistant. For drivers, it could make navigation safer. It could even help children by serving as a portal to learning more about the world around them. Inspired by these revelations, Tang embarked on a more extensive journey in tech to build products that really benefit people. Tang attended Boston University, where she immersed herself in Mathematics and Computer Science. Ever curious about the possibilities of new technologies, she continued her education at Yale University and earned her master's degree. After graduating, Tang was hired at a major tech conglomerate, where her skills were used in developing new technologies that could reshape how people use social media and produce digital content. How AI-Driven Personalization Drives User Engagement Jin Tang has used AI to transform digital content by focusing on engagement. When she began her work in AI for social media, she focused on improving the user experience. The key to this proved to be personalization. Machine learning was also crucial for producing digital content tailored to each individual's preferences. Tang’s development of an interest-based, machine learning-driven feed significantly increased user engagement, leading to a feed session time increase of 1.3%. This demonstrated that AI resonated with users when applied in this format; rather than alienating users, the personalized spaces that AI could create made a better online experience. Empowering Digital Creators with Machine Learning Tools Jin Tang's work focused not only on those who consume content but also on those who create it. She found that AI and machine learning could positively transform the content creator experience. Initially, Tang needed help to identify features that would suit creators. She saw that creators have distinct needs as opposed to the users that followed their content. Through extensive research and close collaboration with experts, she developed machine learning tools that could empower digital creators. These tools focused on allowing creators to understand their audience better and produce content that would draw in more significant numbers. Tang built a personalized feed for influencers. The AI-powered tool led to a 10% increase in followers and a 2.2% increase in feed session time. Through machine learning, creators could more effectively connect with their audience and grow their digital presence. AI as a Tool for Enhancing Marketing Performance Beyond users and content creators, AI and machine learning tools can be scaled up to assist businesses and digital marketers. Analyzing data and personalizing strategies for success allows for marketing that reaches broader audiences, potentially transforming how companies operate. Jin Tang's development of professional tools for small businesses has helped empower 6 million entrepreneurs to understand and improve their marketing performance. During the pandemic, many small businesses saw their sales plummet. AI-driven strategies helped business owners to adapt and find new strategies for survival. Staying ahead of trends is essential in a digital marketplace that continues to grow and evolve. AI-driven tools can help businesses remain adaptable and competitive. The Future of Digital Content Through AI Innovation Jin Tang believes that the future of digital content lies in its ability to adapt to users' interests. Driven by ongoing advancements in AI and machine learning, technology has changed the way the world functions. As these technologies continue to develop, they have the potential to unlock better user experiences through more personalized and engaging content. With AI, social media can grow more responsive to its users' interests, creating a more dynamic experience. Jin Tang's Commitment to a Better Tech World Through AI Through her work in AI, Jin Tang hopes to continue the trend of better online experiences with personalization and user-focused content. She is also committed to further integrating generative AI into digital products. Tang has also empowered future innovators in tech. She has mentored four interns, using her personalized approach to tailor their projects to their interests and skills. This mentorship helped them sharpen their tech skills and deepen their understanding of the industry.As a result, Tang proudly saw all her mentees receive return offers after completing their internships. The Transformative Properties of AI and Machine Learning Jin Tang's expertise and work in the tech industry have demonstrated how AI and machine learning can transform today's digital content. By better connecting people to the world through their smartphones, creating more personalized, user-driven spaces, empowering content creators with tools that help them better understand their audiences, assisting businesses with their market performance, and developing social media experiences that are more responsive to users' interests, these new tools have paved the way for a better world through technology. Those wishing to see more of Jin Tang's work can follow her LinkedIn page. As she continues her work on AI-driven digital tools, she wants to remind users and tech experts of her favorite motto by Steve Jobs: "Stay foolish, stay hungry." AI and machine learning have changed how people communicate, learn, create, and consume content. With AI entering more and more facets of everyday life, some worry about losing the human element; however, experts in the field of AI are working to not only make AI work for you but also to use it to connect people better than ever before. Jin Tang is a software engineer whose work has informed how AI functions in today's internet. With her extensive knowledge and background in machine learning, Tang has helped create AI-driven tools that have transformed the world's digital content, improving user and creator experience along the way. Jin Tang's First Inspiration for Better Life Through Tech Jin Tang's interest in tech was initially inspired by her father, who taught himself software development. Watching her father work exposed her to a world full of potential for creativity. Tang started her tech journey by interning at a leading tech company in China, where she worked on their smartphone assistant product (similar to Siri) and saw that AI could transform how digital content works for its users. Observing the digital assistant's capabilities, she realized her work was more than a fun and convenient product; it had the potential to help people with disabilities by better enabling them to connect with others through their smartphone assistant. For drivers, it could make navigation safer. It could even help children by serving as a portal to learning more about the world around them. Inspired by these revelations, Tang embarked on a more extensive journey in tech to build products that really benefit people. Tang attended Boston University, where she immersed herself in Mathematics and Computer Science. Ever curious about the possibilities of new technologies, she continued her education at Yale University and earned her master's degree. After graduating, Tang was hired at a major tech conglomerate, where her skills were used in developing new technologies that could reshape how people use social media and produce digital content. How AI-Driven Personalization Drives User Engagement Jin Tang has used AI to transform digital content by focusing on engagement. When she began her work in AI for social media, she focused on improving the user experience. The key to this proved to be personalization. Machine learning was also crucial for producing digital content tailored to each individual's preferences. Tang’s development of an interest-based, machine learning-driven feed significantly increased user engagement, leading to a feed session time increase of 1.3%. This demonstrated that AI resonated with users when applied in this format; rather than alienating users, the personalized spaces that AI could create made a better online experience. Empowering Digital Creators with Machine Learning Tools Jin Tang's work focused not only on those who consume content but also on those who create it. She found that AI and machine learning could positively transform the content creator experience. Initially, Tang needed help to identify features that would suit creators. She saw that creators have distinct needs as opposed to the users that followed their content. Through extensive research and close collaboration with experts, she developed machine learning tools that could empower digital creators. These tools focused on allowing creators to understand their audience better and produce content that would draw in more significant numbers. Tang built a personalized feed for influencers. The AI-powered tool led to a 10% increase in followers and a 2.2% increase in feed session time. Through machine learning, creators could more effectively connect with their audience and grow their digital presence. AI as a Tool for Enhancing Marketing Performance Beyond users and content creators, AI and machine learning tools can be scaled up to assist businesses and digital marketers. Analyzing data and personalizing strategies for success allows for marketing that reaches broader audiences, potentially transforming how companies operate. Jin Tang's development of professional tools for small businesses has helped empower 6 million entrepreneurs to understand and improve their marketing performance. During the pandemic, many small businesses saw their sales plummet. AI-driven strategies helped business owners to adapt and find new strategies for survival. Staying ahead of trends is essential in a digital marketplace that continues to grow and evolve. AI-driven tools can help businesses remain adaptable and competitive. The Future of Digital Content Through AI Innovation Jin Tang believes that the future of digital content lies in its ability to adapt to users' interests. Driven by ongoing advancements in AI and machine learning, technology has changed the way the world functions. As these technologies continue to develop, they have the potential to unlock better user experiences through more personalized and engaging content. With AI, social media can grow more responsive to its users' interests, creating a more dynamic experience. Jin Tang's Commitment to a Better Tech World Through AI Jin Tang's Commitment to a Better Tech World Through AI Through her work in AI, Jin Tang hopes to continue the trend of better online experiences with personalization and user-focused content. She is also committed to further integrating generative AI into digital products. Tang has also empowered future innovators in tech. She has mentored four interns, using her personalized approach to tailor their projects to their interests and skills. This mentorship helped them sharpen their tech skills and deepen their understanding of the industry.As a result, Tang proudly saw all her mentees receive return offers after completing their internships. The Transformative Properties of AI and Machine Learning Jin Tang's expertise and work in the tech industry have demonstrated how AI and machine learning can transform today's digital content. By better connecting people to the world through their smartphones, creating more personalized, user-driven spaces, empowering content creators with tools that help them better understand their audiences, assisting businesses with their market performance, and developing social media experiences that are more responsive to users' interests, these new tools have paved the way for a better world through technology. Those wishing to see more of Jin Tang's work can follow her LinkedIn page. As she continues her work on AI-driven digital tools, she wants to remind users and tech experts of her favorite motto by Steve Jobs: "Stay foolish, stay hungry." LinkedIn LinkedIn

5 Ways AI and Machine Learning Are Transforming Digital Content, According to Jin Tang

5 τρόποι με τους οποίους η τεχνητή νοημοσύνη και η μηχανική μάθηση μεταμορφώνουν το ψηφιακό περιεχόμενο, σύμφωνα με τον Jin Tang

Cinco formas en las que la IA y el aprendizaje automático están transformando el contenido digital, según Jin Tang

जिन टैंग के अनुसार, 5 तरीके जिनसे AI और मशीन लर्निंग डिजिटल कंटेंट को बदल रहे हैं

AIと機械学習がデジタルコンテンツを変革する5つの方法（ジン・タン氏）

Джин Танның айтуынша, AI және машиналық оқыту цифрлық мазмұнды түрлендірудің 5 жолы

5 ວິທີ AI ແລະການຮຽນຮູ້ເຄື່ອງຈັກກໍາລັງຫັນປ່ຽນເນື້ອຫາດິຈິຕອນ, ອີງຕາມການ Jin Tang

5 sposobów, w jaki sztuczna inteligencja i uczenie maszynowe zmieniają treści cyfrowe, według Jin Tanga

5 spôsobov, ako AI a strojové učenie transformujú digitálny obsah, podľa Jin Tanga

5 Nzira AI uye Muchina Kudzidza Ari Kushandura Digital Zvemukati, Maererano naJin Tang

ஜின் டாங்கின் கூற்றுப்படி, AI மற்றும் இயந்திர கற்றல் டிஜிட்டல் உள்ளடக்கத்தை மாற்றும் 5 வழிகள்

Jin Tangyň pikiriçe, AI we maşyn öwrenmegiň sanly mazmuny üýtgetmeginiň 5 usuly

5 Paraan na Binabago ng AI at Machine Learning ang Digital Content, Ayon kay Jin Tang

Reputation, in its most traditional definition, is a fragile tapestry of interaction amongst humans: an elaborate interplay between social proof, intuition, and subjectivity. It's an unspoken calculus by which we decide whom to trust, whom to listen to, and whom best to avoid when operating in the physical world. But in the decentralized world, this fluid-and-frail version of reputation must become hardened into something concretely mathematical, something machine-readable, and yet still quintessentially human. Its realization might be the most ambitious challenge for any designer of decentralized infrastructures. It is not only a question of how to make trust secure but also how to render reputation measurable, reliable, useful, and actionable outside the realm of theory. We are standing in front of a new frontier in trust, an age that requires us to make the notion of reputation programmable and enforceable. While blockchain technology promises a "trustless" world in which centralized intermediaries have no relevance, this vision leaves one critical part of human interaction. Guess what? It is how we quantify such subjective factors as trust and reputation without sacrificing the nuances that make it meaningful and untouched. This is where I want to dig deeper. From Abstract to Measurable: How Reputation Evolved Let me map the key paradox before going into the specifics of the solutions: On decentralized systems, reputation can't exist as an abstract conceptual philosophical issue. The trustworthiness of a user can't be left to subjective judgment on a peer-to-peer network. It has to be quantified, audited, and then enforced. And to actually design this effectively, we really have to resort not just to technology automating trust but encoding quite literally what reputation means. Let me outline three pressing challenges that reputation faces in decentralized systems: Reputation portability. While in a centralized system, reputation might be confined to one particular platform or network; in decentralized ecosystems, reputation has to be portable. This portability is necessary because users span multiple dApps, platforms, and ecosystems, from DeFi protocols to social marketplaces. Reputation data should not be left in silos. In itself, however, portability creates risks: reputation needs to be adapted to context. How can one protocol make assessments about an established reputation elsewhere? How does the user guarantee that standing in a decentralized social network applies in, for instance, a decentralized lending platform? Verifiable trust without full disclosure. Decentralized infrastructures prize privacy and anonymity. Still, trust (at least in its traditional sense) demands transparency. A balance needs to be reached whereby one's reputation can be verifiable without sensitive information about one's identity or past interactions. How can you prove you are reputable without giving away private transactions or other private information? Dynamic reputation systems. Trust is never set in concrete. A brilliant reputation today may be blemished tomorrow, but in the decentralized world, reputation cannot afford to remain static. Yet, blockchain's immutable nature creates an issue. How can we ensure that reputation reflects the real-time evolution of a user's trustworthiness? Autonomy Matrix and Aut: A Unified Approach to Trust Āutonomy Matrix and Āut's unified system is active in the space of proposing reputation, not as a concept but rather, as a real and integral part of the solution within a formalized environment. The system is working toward the implementation of a global reputation economy by using reputation scores pegged to DIDs. The gaps remain, though, even with forward momentum in these systems. Of these many intriguing proposals in this matrix, one of the more exciting involves adding zero-knowledge proofs to reputation management. ZKPs allow a person to prove some statement (for instance, that she has behaved in a trustworthy manner) without actually needing to reveal the underlying actions that came to that conclusion. For example, being able to prove that you have a 90% trust score across a DeFi lending platform without revealing just who lent or paid back what. This technique provides an extra layer of privacy, while a person's reputation is portable and provable in this decentralized and trustless environment. Now, a creative question: what if reputation could be tokenized itself? Just picture tokens of reputation flowing in value as any given user behaves well or badly. These would be transferable across platforms and would represent a liquid form of one's digital reputation. This might create "Reputation Markets" - a place where individuals and organizations can trade, lend, or borrow reputation. Undoubtedly, this would embed a layer of gamification, and reputation could be a currency while opening opportunities for abuse and innovation. Nevertheless, the idea that reputation can be digitized into something that one owns and can invest in, trade, or loan stretches the limits of how we think about trust. Filling the Gaps in Decentralized Reputation Making reputation measurable and, above all, safe, transforms it from an abstraction into something tangible, to which several structural elements need to be developed and deployed. Trust Provenance Protocols Just as decentralized systems record the origin of assets, so too does reputation require provenance. Smart contract histories could be attached to profiles, and decentralized systems might then track the origins of trust: who vouched for whom, on what terms, and under what circumstances. This all needs to be a lot more sophisticated than simplistic upvote or downvote mechanisms, as now exist on centralized platforms such as Reddit or Yelp. The origin of trust needs to weigh algorithmically according to context, not just frequency. For instance, the Reciprocity Index can be a decentralized algorithm that monitors the degree to which a user contributes to others' trust in the system versus the degree to which they benefit from it. Scores would rise for those giving trust freely and authentically, while those who merely benefit from others would see diminishing returns on reputation. Context-Aware Reputation Engines Reputation is inherently context-sensitive: the great coding reputation a user has on GitHub doesn't necessarily hold as much standing in some decentralized financial exchange. The silos can only be bridged by having context-aware reputation engines within decentralized protocols that would assess not raw reputation scores but the relevance of the latter for particular domains, dynamically adapting the user trust profiles in accordance with the environment. Reputation systems should also embed models of time decay in which the trust disappears unless it is kept alive. Applying temporal decay ensures that the reputational value will always be current. For example, a person who frequently participated in decentralized governance would see an increased reputation, whereas non-participation would equate to a decrease in their reputation score. This dynamic ensures that the trust indicators reflect the most recent behavior and, therefore, a real-time evolving snapshot of the reputation. Concluding Thoughts By aligning decentralized infrastructure with dynamic, cross-contextual reputation systems, we can push reputation from the realm of philosophy into a space where it's concrete and programmable. The integration of zero-knowledge proofs, time-weighted trust mechanisms, and cross-platform identity verification systems is not just the next step in decentralization but an entirely different approach to trust. But here's the rub: this is infrastructure that is not just technical; it is cultural. The tools we design to handle decentralized reputation must be ones that perform and reflect the rich nuances in human behavior. Trust is nuanced, multilayered, and evolves over time. If decentralized systems can handle that both in code and in practice, we might just build a future where reputation becomes as liquid an asset as currency itself. Perhaps someday, trust will no longer be a question but an asset to be made clear: securely, privately, and with confidence. Reputation, in its most traditional definition, is a fragile tapestry of interaction amongst humans: an elaborate interplay between social proof, intuition, and subjectivity. It's an unspoken calculus by which we decide whom to trust, whom to listen to, and whom best to avoid when operating in the physical world. But in the decentralized world, this fluid-and-frail version of reputation must become hardened into something concretely mathematical, something machine-readable, and yet still quintessentially human. Its realization might be the most ambitious challenge for any designer of decentralized infrastructures. It is not only a question of how to make trust secure but also how to render reputation measurable, reliable, useful, and actionable outside the realm of theory. We are standing in front of a new frontier in trust, an age that requires us to make the notion of reputation programmable and enforceable. While blockchain technology promises a "trustless" world in which centralized intermediaries have no relevance, this vision leaves one critical part of human interaction. Guess what? It is how we quantify such subjective factors as trust and reputation without sacrificing the nuances that make it meaningful and untouched. This is where I want to dig deeper. From Abstract to Measurable: How Reputation Evolved Let me map the key paradox before going into the specifics of the solutions: On decentralized systems, reputation can't exist as an abstract conceptual philosophical issue. The trustworthiness of a user can't be left to subjective judgment on a peer-to-peer network. It has to be quantified, audited, and then enforced. And to actually design this effectively, we really have to resort not just to technology automating trust but encoding quite literally what reputation means. Let me outline three pressing challenges that reputation faces in decentralized systems: Reputation portability. While in a centralized system, reputation might be confined to one particular platform or network; in decentralized ecosystems, reputation has to be portable. This portability is necessary because users span multiple dApps, platforms, and ecosystems, from DeFi protocols to social marketplaces. Reputation data should not be left in silos. In itself, however, portability creates risks: reputation needs to be adapted to context. How can one protocol make assessments about an established reputation elsewhere? How does the user guarantee that standing in a decentralized social network applies in, for instance, a decentralized lending platform? Reputation portability. While in a centralized system, reputation might be confined to one particular platform or network; in decentralized ecosystems, reputation has to be portable. This portability is necessary because users span multiple dApps, platforms, and ecosystems, from DeFi protocols to social marketplaces. Reputation data should not be left in silos. In itself, however, portability creates risks: reputation needs to be adapted to context. How can one protocol make assessments about an established reputation elsewhere? How does the user guarantee that standing in a decentralized social network applies in, for instance, a decentralized lending platform? Verifiable trust without full disclosure. Decentralized infrastructures prize privacy and anonymity. Still, trust (at least in its traditional sense) demands transparency. A balance needs to be reached whereby one's reputation can be verifiable without sensitive information about one's identity or past interactions. How can you prove you are reputable without giving away private transactions or other private information? Verifiable trust without full disclosure. Decentralized infrastructures prize privacy and anonymity. Still, trust (at least in its traditional sense) demands transparency. A balance needs to be reached whereby one's reputation can be verifiable without sensitive information about one's identity or past interactions. How can you prove you are reputable without giving away private transactions or other private information? Dynamic reputation systems. Trust is never set in concrete. A brilliant reputation today may be blemished tomorrow, but in the decentralized world, reputation cannot afford to remain static. Yet, blockchain's immutable nature creates an issue. How can we ensure that reputation reflects the real-time evolution of a user's trustworthiness? Dynamic reputation systems. Trust is never set in concrete. A brilliant reputation today may be blemished tomorrow, but in the decentralized world, reputation cannot afford to remain static. Yet, blockchain's immutable nature creates an issue. How can we ensure that reputation reflects the real-time evolution of a user's trustworthiness? Autonomy Matrix and Aut: A Unified Approach to Trust Āutonomy Matrix and Āut's unified system is active in the space of proposing reputation, not as a concept but rather, as a real and integral part of the solution within a formalized environment. The system is working toward the implementation of a global reputation economy by using reputation scores pegged to DIDs. The gaps remain, though, even with forward momentum in these systems. Of these many intriguing proposals in this matrix, one of the more exciting involves adding zero-knowledge proofs to reputation management. ZKPs allow a person to prove some statement (for instance, that she has behaved in a trustworthy manner) without actually needing to reveal the underlying actions that came to that conclusion. For example, being able to prove that you have a 90% trust score across a DeFi lending platform without revealing just who lent or paid back what. This technique provides an extra layer of privacy, while a person's reputation is portable and provable in this decentralized and trustless environment. Now, a creative question: what if reputation could be tokenized itself? Just picture tokens of reputation flowing in value as any given user behaves well or badly. These would be transferable across platforms and would represent a liquid form of one's digital reputation. This might create "Reputation Markets" - a place where individuals and organizations can trade, lend, or borrow reputation. Undoubtedly, this would embed a layer of gamification, and reputation could be a currency while opening opportunities for abuse and innovation. Now, a creative question: what if reputation could be tokenized itself? Just picture tokens of reputation flowing in value as any given user behaves well or badly. These would be transferable across platforms and would represent a liquid form of one's digital reputation. This might create "Reputation Markets" - a place where individuals and organizations can trade, lend, or borrow reputation. Undoubtedly, this would embed a layer of gamification, and reputation could be a currency while opening opportunities for abuse and innovation. Nevertheless, the idea that reputation can be digitized into something that one owns and can invest in, trade, or loan stretches the limits of how we think about trust. Nevertheless, the idea that reputation can be digitized into something that one owns and can invest in, trade, or loan stretches the limits of how we think about trust. Filling the Gaps in Decentralized Reputation Making reputation measurable and, above all, safe, transforms it from an abstraction into something tangible, to which several structural elements need to be developed and deployed. Trust Provenance Protocols Just as decentralized systems record the origin of assets, so too does reputation require provenance. Smart contract histories could be attached to profiles, and decentralized systems might then track the origins of trust: who vouched for whom, on what terms, and under what circumstances. This all needs to be a lot more sophisticated than simplistic upvote or downvote mechanisms, as now exist on centralized platforms such as Reddit or Yelp. The origin of trust needs to weigh algorithmically according to context, not just frequency. For instance, the Reciprocity Index can be a decentralized algorithm that monitors the degree to which a user contributes to others' trust in the system versus the degree to which they benefit from it. Scores would rise for those giving trust freely and authentically, while those who merely benefit from others would see diminishing returns on reputation. Reciprocity Index Context-Aware Reputation Engines Reputation is inherently context-sensitive: the great coding reputation a user has on GitHub doesn't necessarily hold as much standing in some decentralized financial exchange. The silos can only be bridged by having context-aware reputation engines within decentralized protocols that would assess not raw reputation scores but the relevance of the latter for particular domains, dynamically adapting the user trust profiles in accordance with the environment. Reputation systems should also embed models of time decay in which the trust disappears unless it is kept alive. Applying temporal decay ensures that the reputational value will always be current. For example, a person who frequently participated in decentralized governance would see an increased reputation, whereas non-participation would equate to a decrease in their reputation score. This dynamic ensures that the trust indicators reflect the most recent behavior and, therefore, a real-time evolving snapshot of the reputation. Concluding Thoughts By aligning decentralized infrastructure with dynamic, cross-contextual reputation systems, we can push reputation from the realm of philosophy into a space where it's concrete and programmable. The integration of zero-knowledge proofs, time-weighted trust mechanisms, and cross-platform identity verification systems is not just the next step in decentralization but an entirely different approach to trust. But here's the rub: this is infrastructure that is not just technical; it is cultural. The tools we design to handle decentralized reputation must be ones that perform and reflect the rich nuances in human behavior. Trust is nuanced, multilayered, and evolves over time. If decentralized systems can handle that both in code and in practice, we might just build a future where reputation becomes as liquid an asset as currency itself. Perhaps someday, trust will no longer be a question but an asset to be made clear: securely, privately, and with confidence.

The Tangible Reputation: Engineering Trust in a Decentralized World

Hmatatelná pověst: Inženýrská důvěra v decentralizovaném světě

La reputación tangible: generando confianza en un mundo descentralizado

La réputation tangible : l'ingénierie de la confiance dans un monde décentralisé

A kézzelfogható hírnév: Mérnöki bizalom egy decentralizált világban

具体的な評判: 分散化された世界における信頼の構築

ხელშესახები რეპუტაცია: საინჟინრო ნდობა დეცენტრალიზებულ სამყაროში

Материалдық бедел: орталықтандырылмаған әлемдегі инженерлік сенім

실체적 평판: 분산화된 세계에서의 신뢰 엔지니어링

Taustāmā reputācija: inženieru uzticība decentralizētā pasaulē

Опипљива репутација: инжењерско поверење у децентрализованом свету

እቲ ዝጭበጥ ዝና፡ ምህንድስናዊ ምትእምማን ኣብ ዘይተማእከለ ዓለም

Ang Tangible na Reputasyon: Engineering Trust sa isang Desentralisadong Mundo

In Our Innovation Era: Seamless Messaging, 77 Language Translations, Tag Search, and More

In ons innovasie-era: naatlose boodskappe, 77 taalvertalings, merkersoektog en meer

በእኛ የኢኖቬሽን ዘመን፡ እንከን የለሽ መልእክት፣ 77 የቋንቋ ትርጉሞች፣ የመለያ ፍለጋ እና ሌሎችም

في عصر الابتكار: المراسلة السلسة، والترجمة إلى 77 لغة، والبحث عن العلامات، والمزيد

Jiwasan Machaq lurañ pachasanxa: Jan ch’axwañ yatiyawinak, 77 arut jaqukipäwinak, Etiqueta thaqhaña, ukat juk’ampinaka

İnnovasiyalar Dövrümüzdə: Sorunsuz Mesajlaşma, 77 Dil Tərcüməsi, Etiket Axtarışı və s.

У нашу эру інавацый: бясшвоўны абмен паведамленнямі, пераклады на 77 моў, пошук па тэгах і многае іншае

В нашата ера на иновациите: безпроблемно изпращане на съобщения, преводи на 77 езика, търсене в етикети и още

আমাদের উদ্ভাবন যুগে: বিরামহীন বার্তাপ্রেরণ, 77টি ভাষা অনুবাদ, ট্যাগ অনুসন্ধান এবং আরও অনেক কিছু

U našoj eri inovacija: besprijekorno slanje poruka, 77 jezičnih prijevoda, pretraživanje oznaka i još mnogo toga

En la nostra era de la innovació: missatgeria perfecta, traduccions de 77 idiomes, cerca d'etiquetes i molt més

V naší éře inovací: bezproblémové zasílání zpráv, 77 jazykových překladů, vyhledávání značek a další

I vores innovationsæra: problemfri meddelelser, 77 sprogoversættelser, tagsøgning og mere

In unserem Innovationszeitalter: Nahtlose Nachrichtenübermittlung, 77 Sprachübersetzungen, Tag-Suche und mehr

Στην εποχή της καινοτομίας μας: Απρόσκοπτη ανταλλαγή μηνυμάτων, 77 μεταφράσεις γλωσσών, αναζήτηση ετικετών και άλλα

En nuestra era de innovación: mensajería fluida, traducciones a 77 idiomas, búsqueda de etiquetas y más

Gure berrikuntzaren garaian: mezurik gabeko mezularitza, 77 hizkuntza-itzulpen, etiketa-bilaketa eta gehiago

در عصر نوآوری ما: پیام رسانی یکپارچه، 77 ترجمه زبان، جستجوی برچسب و موارد دیگر

Innovaatioaikanamme: saumaton viestintä, 77 kielen käännöstä, tunnistehaku ja paljon muuta

À l'ère de l'innovation : messagerie fluide, traductions en 77 langues, recherche par tags et bien plus encore

Na nosa era da innovación: mensaxes sen fisuras, traducións de 77 idiomas, busca de etiquetas e moito máis

בעידן החדשנות שלנו: העברת הודעות חלקה, 77 תרגומי שפות, חיפוש תגים ועוד

हमारे नवाचार युग में: निर्बाध संदेश, 77 भाषा अनुवाद, टैग खोज, और बहुत कुछ

U našoj eri inovacija: besprijekorno slanje poruka, prijevodi na 77 jezika, pretraživanje oznaka i više

Nan epòk inovasyon nou an: Mesaj san pwoblèm, 77 tradiksyon lang, rechèch tag, ak plis ankò

Innovációs korunkban: zökkenőmentes üzenetküldés, 77 nyelvi fordítás, címkekeresés és egyebek

Մեր նորարարության դարաշրջանում՝ անխափան հաղորդագրություններ, 77 լեզուների թարգմանություններ, պիտակների որոնում և ավելին

Di Era Inovasi Kita: Perpesanan yang Sempurna, 77 Terjemahan Bahasa, Pencarian Tag, dan Lainnya

Nella nostra era dell'innovazione: messaggistica senza soluzione di continuità, traduzioni in 77 lingue, ricerca per tag e altro ancora

イノベーションの時代: シームレスなメッセージング、77 言語の翻訳、タグ検索など

ჩვენი ინოვაციების ეპოქაში: უწყვეტი შეტყობინებები, 77 ენის თარგმანი, ტეგების ძებნა და სხვა

Біздің инновация дәуірінде: үздіксіз хабар алмасу, 77 тілдегі аударма, тегтерді іздеу және т.б.

នៅក្នុងយុគសម័យច្នៃប្រឌិតរបស់យើង៖ ការផ្ញើសារគ្មានថ្នេរ ការបកប្រែ 77 ភាសា ការស្វែងរកស្លាក និងច្រើនទៀត

혁신 시대: 매끄러운 메시징, 77개 언어 번역, 태그 검색 등

Биздин инновация доорунда: үзгүлтүксүз билдирүү, 77 тил котормосу, тэг издөө жана башкалар

Na eleko na biso ya bokeli ya sika: Nsango ya kozanga boyokani, 77 Mabongoli ya nkota, Boluki ya bilembo, mpe mingi mosusu

ໃນຍຸກນະວັດຕະກໍາຂອງພວກເຮົາ: ການສົ່ງຂໍ້ຄວາມແບບບໍ່ມີຮອຍຕໍ່, ການແປ 77 ພາສາ, ການຊອກຫາແທັກ ແລະອື່ນໆອີກ.

Mūsų naujovių eroje: vientisas susirašinėjimas, 77 kalbų vertimai, žymų paieška ir kt.

Mūsu inovāciju laikmetā: nevainojama ziņojumapmaiņa, 77 valodu tulkojumi, tagu meklēšana un daudz kas cits

Amin'ny vanim-potoanan'ny fanavaozana: Hafatra tsy misy dikany, Dikanteny amin'ny fiteny 77, Fikarohana Tag, ary maro hafa

Во нашата ера на иновации: беспрекорно испраќање пораки, 77 преводи на јазици, пребарување ознаки и повеќе

Бидний инновацийн эрин үед: Тохиромжгүй мессеж, 77 хэл дээрх орчуулга, шошго хайх гэх мэт.

Dalam Era Inovasi Kami: Pemesejan Lancar, 77 Terjemahan Bahasa, Carian Teg dan Banyak Lagi

हाम्रो नवप्रवर्तन युगमा: सिमलेस सन्देश, 77 भाषा अनुवाद, ट्याग खोज, र थप

In ons innovatietijdperk: naadloze berichtgeving, vertalingen in 77 talen, tag-zoekopdracht en meer

Mehleng ya Rena ya Boitlhamelo: Melaetša ye e se nago Seam, Diphetolelo tša Maleme tše 77, Patlo ya Dithegi, le tše dingwe

W naszej erze innowacji: bezproblemowe przesyłanie wiadomości, tłumaczenia w 77 językach, wyszukiwanie tagów i wiele więcej

زموږ د نوښت په دوره کې: بې سیمه پیغامونه، د 77 ژبې ژباړې، د ټاګ لټون، او نور ډیر څه

Em nossa era de inovação: mensagens contínuas, traduções em 77 idiomas, pesquisa de tags e muito mais

Musuqyachiy pachanchikpi: Mana llamkanayuq willakuy, 77 Simi tikray, Etiqueta maskay, hukkunapas

În epoca noastră de inovare: mesagerie fără întreruperi, traduceri în 77 de limbi, căutare de etichete și multe altele

В нашу эру инноваций: бесперебойный обмен сообщениями, переводы на 77 языков, поиск по тегам и многое другое

Mubihe Byacu byo Guhanga udushya: Ubutumwa butagira akagero, Ubuhinduzi bw'indimi 77, Gushakisha Tagi, nibindi byinshi

අපගේ නවෝත්පාදන යුගයේ: බාධාවකින් තොරව පණිවිඩ යැවීම, භාෂා පරිවර්තන 77, ටැග් සෙවීම, සහ තවත් දේ

V našej ére inovácií: bezproblémové zasielanie správ, 77 jazykových prekladov, vyhľadávanie značiek a ďalšie

Munguva Yedu Yekuvandudza: Kutumira Meseji, makumi manomwe nenomwe Dudziro dzeMitauro, Tag Tsvaga, uye Zvimwe

Waagii Hal-abuurkayaga: Farriinta aan kala go'a lahayn, 77 Turjumaada Luuqadaha, Raadinta Tag, iyo in ka badan

Në epokën tonë të inovacionit: Mesazhe pa probleme, 77 përkthime në gjuhë, kërkim të etiketave dhe më shumë

У нашој ери иновација: беспрекорна размена порука, 77 превода језика, претрага ознака и још много тога

I vår innovationsera: sömlösa meddelanden, 77 språköversättningar, taggsökning och mer

Katika Enzi Yetu ya Ubunifu: Ujumbe Mzito, Tafsiri za Lugha 77, Utafutaji wa Lebo, na Zaidi.

எங்கள் கண்டுபிடிப்பு காலத்தில்: தடையற்ற செய்தி அனுப்புதல், 77 மொழி மொழிபெயர்ப்புகள், குறிச்சொல் தேடல் மற்றும் பல

Дар давраи инноватсионии мо: паёмнависии бефосила, 77 тарҷумаи забон, ҷустуҷӯи барчаспҳо ва ғайра

ในยุคแห่งนวัตกรรมของเรา: การส่งข้อความแบบไร้รอยต่อ การแปลภาษา 77 ภาษา การค้นหาแท็ก และอื่นๆ อีกมากมาย

ኣብ ዘመነ ምህዞና፡ ስሉጥ መልእኽቲ፡ 77 ትርጉማት ቋንቋ፡ ምድላይ መለለዪን ካልእን

Innowasiýa döwrümizde: üznüksiz habarlaşma, 77 dil terjimesi, bellik gözlegi we başgalar

Sa Our Innovation Era: Seamless Messaging, 77 Language Translations, Tag Search, at Higit Pa

Yenilik Çağımızda: Kusursuz Mesajlaşma, 77 Dilde Çeviri, Etiket Arama ve Daha Fazlası

Eka Nguva Ya Hina Ya Vutshila: Mahungu Lama Nga Riki Na Swirhalanganyi, 77 wa Vuhundzuluxi bya Tindzimi, Ku Lavisisa Tithegi, na Swin’wana

У нашу еру інновацій: безперервний обмін повідомленнями, переклади 77 мовами, пошук за тегами тощо

ہمارے اختراعی دور میں: ہموار پیغام رسانی، 77 زبانوں کے ترجمے، ٹیگ تلاش، اور مزید

Innovatsiyalar davrimizda: uzluksiz xabar almashish, 77 ta til tarjimasi, teglarni qidirish va boshqalar

Trong Kỷ nguyên Đổi mới của Chúng ta: Nhắn tin Liền mạch, 77 Bản dịch Ngôn ngữ, Tìm kiếm Thẻ và Nhiều hơn nữa

KwiXesha lethu lokuYila: iMiyalezo engenamthungo, iinguqulelo zoLwimi ezingama-77, uPhendlo lwethegi, kunye nokunye

在我们的创新时代：无缝消息传递、77 种语言翻译、标签搜索等

Enkathini Yethu Yokusungula: Ukuthumela Imiyalezo Engenamthungo, Ukuhunyushwa Kwezilimi ezingama-77, Ukusesha Umaka, nokunye

Hello reader 👋 , I’m glad that you chose to read my blog. Let’s get started! During my college days, I did not envision that I would take the customer support responsibility among other roles in any company! But I got an offer that I couldn’t resist, and in hindsight, I don’t regret. I became a freelance customer support agent with my domain knowledge in Software Development to my advantage. And eventually, customer support is one of the several responsibilities in my current job. To be honest, at first, it took some me time to get used to customer support. This role grew on me. Having helped more than 1000+ customers, day after day, changed my perspective, and the way I view this role. By now, I have gained such a huge respect for customer support that I felt I had to share my journey. Here we go! Always Stay Grounded I learnt this lesson the hard way. Through constant self reflection over time and with deep analysis of my work. By reading behind the lines. Emotions must be set aside when dealing with Customer Support Emails. When customers are emotional like angry, dissatisfied, confused, curious, or demanding, it’s because they are stuck and they need help. Don’t take it personally. They are worried about the product/service they paid the price for. Avoid angry replies. There’s always a way to reply in a professional manner. Even when the customer is crossing the line, we don’t need to. We have to make sure that our job is to provide Support without bias. Principles The golden rules I started to follow. Respond within 24 hours. Always! Nothing beats immediate action. Stand in their shoes: Understand the customer’s perspective. Categorize the diverse set of support emails. (Covered in detail below). Inbox Labels Use labels to categorize similar support queries. Take advantage of Filters and Rules in Email Inboxes that can help clean up emails from other platforms and business-type emails. For example, if the email inbox you manage always gets an email from any Payment platform used by the business, you can easily apply filters to these emails and label them. So that they are securely saved under a label that can be easily accessed and don’t get lost. Additionally, these filters can help us archive such emails so that the email inbox is clean and we can make sure that most of the time we only receive customer support emails. And using labels is very efficient for accessing important emails quickly all in one place. FAQs Once you answer enough queries, 1000+ Emails in my case. Preparing a documentation to save Frequently Asked Questions is very helpful. I used simple Windows Notepad tool and Notion docs to save such commonly asked queries. Because most of the time, 80% of the Support queries raised by customers, will be 20% of the queries/concerns that are frequently asked. (Pareto Principle). Eventually, I also created an FAQ page for our company, and it has been beneficial. So much so that we attached the FAQ page link to our Auto-reply email so that customers can go through it until I can respond to the support query. How Does It Feel Working as an Agent 🕵‍♂️ (a Customer Support Agent)? Satisfaction: Once customers are satisfied, I’m also equally satisfied. Insightful: When we want to improve our products & services. Empathy: The more you help each customer with personalized care, the better you become in understanding the customer. Physical pain: Pretty much what you may experience in any other desk job. _Repetitive and Boring at time_s: FAQs idea will help ease this pain. Scope of Customer Support in the Future You may have heard that Customer Support is an evergreen field. I believe that it is true. Now, I know that in the Age of A.I., so many companies and businesses have introduced and/or even replaced this space with the help of chatbots and GPT-trained models or even implemented a blend of both bots and customer support agents. Bots do help with FAQs. In my opinion, human intervention along with AI bots works the best. You may have noticed that several companies are already implementing such systems. And I’m optimistic about this approach. My Request to Customers (Like You!) I became more compassionate in my interactions with other customer support agents. I greeted them with a simple “Thank you”, or “Have a nice day”, or an acknowledgement that my issue/concern is resolved. All of us are customers of several products and services, myself, the author and you the reader. All I request you is that at the end of each interaction with the customer support agent/team, if you can respond with a simple acknowledgement like “My issue is resolved” and with simple greeting like “Have a nice day” or just a “Thank you” would be awesome. Because that’s all I expect when I answer Support Emails. It makes the end of our day happy and filled with a sense of accomplishment and joy. PS: Thank you for reading my blog! Have a nice day! Photo by SEO Galaxy on Unsplash Hello reader 👋 , I’m glad that you chose to read my blog. Let’s get started! During my college days, I did not envision that I would take the customer support responsibility among other roles in any company! But I got an offer that I couldn’t resist, and in hindsight, I don’t regret. I became a freelance customer support agent with my domain knowledge in Software Development to my advantage. And eventually, customer support is one of the several responsibilities in my current job. To be honest, at first, it took some me time to get used to customer support. This role grew on me. Having helped more than 1000+ customers, day after day, changed my perspective, and the way I view this role. By now, I have gained such a huge respect for customer support that I felt I had to share my journey. Here we go! Always Stay Grounded I learnt this lesson the hard way. Through constant self reflection over time and with deep analysis of my work. By reading behind the lines. Emotions must be set aside when dealing with Customer Support Emails. When customers are emotional like angry, dissatisfied, confused, curious, or demanding, it’s because they are stuck and they need help. Don’t take it personally. They are worried about the product/service they paid the price for. Avoid angry replies. There’s always a way to reply in a professional manner. Even when the customer is crossing the line, we don’t need to. We have to make sure that our job is to provide Support without bias. Principles The golden rules I started to follow. Respond within 24 hours . Always! Nothing beats immediate action. Respond within 24 hours Stand in their shoes : Understand the customer’s perspective. Stand in their shoes Categorize the diverse set of support emails. (Covered in detail below). Categorize Inbox Labels Use labels to categorize similar support queries. Take advantage of Filters and Rules in Email Inboxes that can help clean up emails from other platforms and business-type emails. For example, if the email inbox you manage always gets an email from any Payment platform used by the business, you can easily apply filters to these emails and label them. So that they are securely saved under a label that can be easily accessed and don’t get lost. Additionally, these filters can help us archive such emails so that the email inbox is clean and we can make sure that most of the time we only receive customer support emails. And using labels is very efficient for accessing important emails quickly all in one place. FAQs Once you answer enough queries, 1000+ Emails in my case. Preparing a documentation to save Frequently Asked Questions is very helpful. I used simple Windows Notepad tool and Notion docs to save such commonly asked queries. Because most of the time, 80% of the Support queries raised by customers, will be 20% of the queries/concerns that are frequently asked. (Pareto Principle). Eventually, I also created an FAQ page for our company, and it has been beneficial. So much so that we attached the FAQ page link to our Auto-reply email so that customers can go through it until I can respond to the support query. How Does It Feel Working as an Agent 🕵‍♂️ (a Customer Support Agent)? Satisfaction : Once customers are satisfied, I’m also equally satisfied. Satisfaction Insightful : When we want to improve our products & services. Insightful Empathy : The more you help each customer with personalized care, the better you become in understanding the customer. Empathy Physical pain : Pretty much what you may experience in any other desk job. Physical pain _Repetitive and Boring at time_s: FAQs idea will help ease this pain. Scope of Customer Support in the Future You may have heard that Customer Support is an evergreen field. I believe that it is true. Now, I know that in the Age of A.I., so many companies and businesses have introduced and/or even replaced this space with the help of chatbots and GPT-trained models or even implemented a blend of both bots and customer support agents. Bots do help with FAQs. In my opinion, human intervention along with AI bots works the best. You may have noticed that several companies are already implementing such systems. And I’m optimistic about this approach. My Request to Customers (Like You!) I became more compassionate in my interactions with other customer support agents. I greeted them with a simple “Thank you”, or “Have a nice day”, or an acknowledgement that my issue/concern is resolved. All of us are customers of several products and services, myself, the author and you the reader. All I request you is that at the end of each interaction with the customer support agent/team, if you can respond with a simple acknowledgement like “My issue is resolved” and with simple greeting like “Have a nice day” or just a “Thank you” would be awesome. Because that’s all I expect when I answer Support Emails. It makes the end of our day happy and filled with a sense of accomplishment and joy. PS: Thank you for reading my blog! Have a nice day! Photo by SEO Galaxy on Unsplash Photo by SEO Galaxy on Unsplash SEO Galaxy Unsplash

Here's What 33 Months in Customer Support Taught Me

Ah, George Eley. a name destined to go down in history—not for any noble cause, mind you, but for his laughably misguided attempt to become the King of Lunch Box Links. Currently studying at the Graduate School of Business at the University of Cape Town, George seems to have missed the memo that being a business school student doesn't give you a license to annoy the entire internet. For those not yet blessed with the knowledge of George's antics, let me paint you a picture. Imagine a virtual vandal, armed not with spray paint but with a bot army, hell-bent on spamming every blog comment section, forum, and search box within reach. His ultimate goal? To boost the search engine rankings of his lunch box website, MagicBoxPro (because, obviously, lunch boxes need a guerrilla marketing campaign). Little did he know, his reign would be short-lived, thanks to a trail of breadcrumbs he unwittingly left behind. Let me tell you how I managed to track down this would-be spam king after he tried—and failed—to bombard my website. It all started with an alert from CleanTalk, the anti-spam plugin I use on my WordPress site. The plugin had flagged an unusual spike in suspicious activity. Normally, CleanTalk does a stellar job of keeping the spammers at bay, but this time something was different. The attempted spam wasn’t just your run-of-the-mill junk—this was a concerted effort, and it had George Eley’s fingerprints all over it. It didn’t take long to find a social media profile that matched the digital fingerprints. The profile name that caught my attention was George Eley, self-proclaimed king of the lunch boxes. The image below depicts an audit of the Lunch Box Kings website's backlink profile, conducted using the SEO tool Ahrefs. The audit reveals that the site is plagued with inbound link spam, indicating a problematic backlink profile with potentially harmful or irrelevant links. But George's escapades don't stop at merely annoying the internet at large. No, he’s taken it a step further by enlisting the help of servers hosted by Leaseweb.com to do his dirty work. That’s right—George has weaponized Leaseweb’s infrastructure to host his spam bots, turning what could be a perfectly respectable server into a breeding ground for spammy, worthless links. It's like using a luxury yacht to smuggle expired canned goods—both baffling and a complete waste of potential. Now, you might be thinking, "Surely, this can't be a terrible idea. The internet is a vast place, and George is just a small, insignificant part of it." But, oh, dear reader, that is where you'd be wrong. You see, George has no respect for your digital property. He and his bot buddies waltz right in, take a metaphorical dump on your website's front lawn, and strut away, leaving behind a trail of poorly disguised spam links and a bad aftertaste. All this in hopes that his lunch boxes will miraculously appear at the top of Google search results. But here's the kicker: 98% of all blog comment sections are automatically no-follow. That means George's "brilliant" strategy is about as effective as trying to sell ice to penguins in Antarctica. The search engines aren’t impressed, and the only thing George’s efforts have achieved is widespread annoyance and a burgeoning reputation as that guy. Yes, that guy—the one who thinks spamming is a valid marketing strategy in 2024. You have to wonder what George was thinking. Did he imagine that webmasters everywhere would fall to their knees, thanking him for blessing their sites with his magical lunch box links? Did he dream of being hailed as a marketing genius, the likes of which the world has never seen? Or was he simply so blinded by the allure of SEO supremacy that he forgot the basic principles of respect, decency, and common sense? The internet is a shared space, a digital community where millions of us coexist (mostly) peacefully. But then there are the spammers—the digital equivalent of the guy who leaves his trash in your front yard because "someone will clean it up eventually." Spammers like George don't just disrespect individual websites; they disrespect the entire online ecosystem. They're like digital parasites, contributing nothing of value and instead feeding off the hard work of others. So, George, if you're reading this—and I suspect you might be, given your obsession with SEO—please, for the love of all things sacred in digital marketing, stop. Take your bots, your lunch boxes, and your Leaseweb servers, and find a more honorable way to promote your products. Maybe try creating quality content? Building genuine relationships with customers? Or, here's a wild idea: respect the internet property of others. Because, at the end of the day, the only thing worse than a spammer is a spammer who doesn't even know how ineffective his tactics are. And trust me, George, nobody wants to buy a lunch box from that guy. Ah, George Eley . a name destined to go down in history—not for any noble cause, mind you, but for his laughably misguided attempt to become the King of Lunch Box Links. Currently studying at the Graduate School of Business at the University of Cape Town, George seems to have missed the memo that being a business school student doesn't give you a license to annoy the entire internet. George Eley For those not yet blessed with the knowledge of George's antics, let me paint you a picture. Imagine a virtual vandal, armed not with spray paint but with a bot army, hell-bent on spamming every blog comment section, forum, and search box within reach. His ultimate goal? To boost the search engine rankings of his lunch box website, MagicBoxPro (because, obviously, lunch boxes need a guerrilla marketing campaign). his MagicBoxPro Little did he know, his reign would be short-lived, thanks to a trail of breadcrumbs he unwittingly left behind. Let me tell you how I managed to track down this would-be spam king after he tried—and failed—to bombard my website. It all started with an alert from CleanTalk, the anti-spam plugin I use on my WordPress site. The plugin had flagged an unusual spike in suspicious activity. Normally, CleanTalk does a stellar job of keeping the spammers at bay, but this time something was different. The attempted spam wasn’t just your run-of-the-mill junk—this was a concerted effort, and it had George Eley’s fingerprints all over it. on my It didn’t take long to find a social media profile that matched the digital fingerprints. The profile name that caught my attention was George Eley, self-proclaimed king of the lunch boxes. The image below depicts an audit of the Lunch Box Kings website's backlink profile, conducted using the SEO tool Ahrefs . The audit reveals that the site is plagued with inbound link spam, indicating a problematic backlink profile with potentially harmful or irrelevant links. SEO tool Ahrefs But George's escapades don't stop at merely annoying the internet at large. No, he’s taken it a step further by enlisting the help of servers hosted by Leaseweb.com to do his dirty work. That’s right—George has weaponized Leaseweb’s infrastructure to host his spam bots, turning what could be a perfectly respectable server into a breeding ground for spammy, worthless links. It's like using a luxury yacht to smuggle expired canned goods—both baffling and a complete waste of potential. Now, you might be thinking, "Surely, this can't be a terrible idea. The internet is a vast place, and George is just a small, insignificant part of it." But, oh, dear reader, that is where you'd be wrong. You see, George has no respect for your digital property. He and his bot buddies waltz right in, take a metaphorical dump on your website's front lawn, and strut away, leaving behind a trail of poorly disguised spam links and a bad aftertaste. All this in hopes that his lunch boxes will miraculously appear at the top of Google search results. But here's the kicker: 98% of all blog comment sections are automatically no-follow. That means George's "brilliant" strategy is about as effective as trying to sell ice to penguins in Antarctica. The search engines aren’t impressed, and the only thing George’s efforts have achieved is widespread annoyance and a burgeoning reputation as that guy . Yes, that guy —the one who thinks spamming is a valid marketing strategy in 2024. that guy that guy You have to wonder what George was thinking. Did he imagine that webmasters everywhere would fall to their knees, thanking him for blessing their sites with his magical lunch box links? Did he dream of being hailed as a marketing genius, the likes of which the world has never seen? Or was he simply so blinded by the allure of SEO supremacy that he forgot the basic principles of respect, decency, and common sense? The internet is a shared space, a digital community where millions of us coexist (mostly) peacefully. But then there are the spammers—the digital equivalent of the guy who leaves his trash in your front yard because "someone will clean it up eventually." Spammers like George don't just disrespect individual websites; they disrespect the entire online ecosystem. They're like digital parasites, contributing nothing of value and instead feeding off the hard work of others. So, George, if you're reading this—and I suspect you might be, given your obsession with SEO—please, for the love of all things sacred in digital marketing, stop. Take your bots, your lunch boxes, and your Leaseweb servers, and find a more honorable way to promote your products. Maybe try creating quality content? Building genuine relationships with customers? Or, here's a wild idea: respect the internet property of others. Because, at the end of the day, the only thing worse than a spammer is a spammer who doesn't even know how ineffective his tactics are. And trust me, George, nobody wants to buy a lunch box from that guy . that guy

The Tragic Tale of the "Lunch Box Spammer": George Eley's Quest for Internet Infamy

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