SEO
Bulk Loading Performance Tests With PageSpeed Insights API & Python

Google offers PageSpeed Insights API to help SEO pros and developers by mixing real-world data with simulation data, providing load performance timing data related to web pages.
The difference between the Google PageSpeed Insights (PSI) and Lighthouse is that PSI involves both real-world and lab data, while Lighthouse performs a page loading simulation by modifying the connection and user-agent of the device.
Another point of difference is that PSI doesn’t supply any information related to web accessibility, SEO, or progressive web apps (PWAs), while Lighthouse provides all of the above.
Thus, when we use PageSpeed Insights API for the bulk URL loading performance test, we won’t have any data for accessibility.
However, PSI provides more information related to the page speed performance, such as “DOM Size,” “Deepest DOM Child Element,” “Total Task Count,” and “DOM Content Loaded” timing.
One more advantage of the PageSpeed Insights API is that it gives the “observed metrics” and “actual metrics” different names.
In this guide, you will learn:
- How to create a production-level Python Script.
- How to use APIs with Python.
- How to construct data frames from API responses.
- How to analyze the API responses.
- How to parse URLs and process URL requests’ responses.
- How to store the API responses with proper structure.
An example output of the Page Speed Insights API call with Python is below.
Libraries For Using PageSpeed Insights API With Python
The necessary libraries to use PSI API with Python are below.
- Advertools retrieves testing URLs from the sitemap of a website.
- Pandas is to construct the data frame and flatten the JSON output of the API.
- Requests are to make a request to the specific API endpoint.
- JSON is to take the API response and put it into the specifically related dictionary point.
- Datetime is to modify the specific output file’s name with the date of the moment.
- URLlib is to parse the test subject website URL.
How To Use PSI API With Python?
To use the PSI API with Python, follow the steps below.
- Get a PageSpeed Insights API key.
- Import the necessary libraries.
- Parse the URL for the test subject website.
- Take the Date of Moment for file name.
- Take URLs into a list from a sitemap.
- Choose the metrics that you want from PSI API.
- Create a For Loop for taking the API Response for all URLs.
- Construct the data frame with chosen PSI API metrics.
- Output the results in the form of XLSX.
1. Get PageSpeed Insights API Key
Use the PageSpeed Insights API Documentation to get the API Key.
Click the “Get a Key” button below.

Choose a project that you have created in Google Developer Console.

Enable the PageSpeed Insights API on that specific project.

You will need to use the specific API Key in your API Requests.
2. Import The Necessary Libraries
Use the lines below to import the fundamental libraries.
import advertools as adv import pandas as pd import requests import json from datetime import datetime from urllib.parse import urlparse
3. Parse The URL For The Test Subject Website
To parse the URL of the subject website, use the code structure below.
domain = urlparse(sitemap_url) domain = domain.netloc.split(".")[1]
The “domain” variable is the parsed version of the sitemap URL.
The “netloc” represents the specific URL’s domain section. When we split it with the “.” it takes the “middle section” which represents the domain name.
Here, “0” is for “www,” “1” for “domain name,” and “2” is for “domain extension,” if we split it with “.”
4. Take The Date Of Moment For File Name
To take the date of the specific function call moment, use the “datetime.now” method.
Datetime.now provides the specific time of the specific moment. Use the “strftime” with the “%Y”, “”%m”, and “%d” values. “%Y” is for the year. The “%m” and “%d” are numeric values for the specific month and the day.
date = datetime.now().strftime("%Y_%m_%d")
5. Take URLs Into A List From A Sitemap
To take the URLs into a list form from a sitemap file, use the code block below.
sitemap = adv.sitemap_to_df(sitemap_url) sitemap_urls = sitemap["loc"].to_list()
If you read the Python Sitemap Health Audit, you can learn further information about the sitemaps.
6. Choose The Metrics That You Want From PSI API
To choose the PSI API response JSON properties, you should see the JSON file itself.
It is highly relevant to the reading, parsing, and flattening of JSON objects.
It is even related to Semantic SEO, thanks to the concept of “directed graph,” and “JSON-LD” structured data.
In this article, we won’t focus on examining the specific PSI API Response’s JSON hierarchies.
You can see the metrics that I have chosen to gather from PSI API. It is richer than the basic default output of PSI API, which only gives the Core Web Vitals Metrics, or Speed Index-Interaction to Next Paint, Time to First Byte, and First Contentful Paint.
Of course, it also gives “suggestions” by saying “Avoid Chaining Critical Requests,” but there is no need to put a sentence into a data frame.
In the future, these suggestions, or even every individual chain event, their KB and MS values can be taken into a single column with the name “psi_suggestions.”
For a start, you can check the metrics that I have chosen, and an important amount of them will be first for you.
PSI API Metrics, the first section is below.
fid = [] lcp = [] cls_ = [] url = [] fcp = [] performance_score = [] total_tasks = [] total_tasks_time = [] long_tasks = [] dom_size = [] maximum_dom_depth = [] maximum_child_element = [] observed_fcp = [] observed_fid = [] observed_lcp = [] observed_cls = [] observed_fp = [] observed_fmp = [] observed_dom_content_loaded = [] observed_speed_index = [] observed_total_blocking_time = [] observed_first_visual_change = [] observed_last_visual_change = [] observed_tti = [] observed_max_potential_fid = []
This section includes all the observed and simulated fundamental page speed metrics, along with some non-fundamental ones, like “DOM Content Loaded,” or “First Meaningful Paint.”
The second section of PSI Metrics focuses on possible byte and time savings from the unused code amount.
render_blocking_resources_ms_save = [] unused_javascript_ms_save = [] unused_javascript_byte_save = [] unused_css_rules_ms_save = [] unused_css_rules_bytes_save = []
A third section of the PSI metrics focuses on server response time, responsive image usage benefits, or not, using harms.
possible_server_response_time_saving = [] possible_responsive_image_ms_save = []
Note: Overall Performance Score comes from “performance_score.”
7. Create A For Loop For Taking The API Response For All URLs
The for loop is to take all of the URLs from the sitemap file and use the PSI API for all of them one by one. The for loop for PSI API automation has several sections.
The first section of the PSI API for loop starts with duplicate URL prevention.
In the sitemaps, you can see a URL that appears multiple times. This section prevents it.
for i in sitemap_urls[:9]: # Prevent the duplicate "/" trailing slash URL requests to override the information. if i.endswith("/"): r = requests.get(f"https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url={i}&strategy=mobile&locale=en&key={api_key}") else: r = requests.get(f"https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url={i}/&strategy=mobile&locale=en&key={api_key}")
Remember to check the “api_key” at the end of the endpoint for PageSpeed Insights API.
Check the status code. In the sitemaps, there might be non-200 status code URLs; these should be cleaned.
if r.status_code == 200: #print(r.json()) data_ = json.loads(r.text) url.append(i)
The next section appends the specific metrics to the specific dictionary that we have created before “_data.”
fcp.append(data_["loadingExperience"]["metrics"]["FIRST_CONTENTFUL_PAINT_MS"]["percentile"]) fid.append(data_["loadingExperience"]["metrics"]["FIRST_INPUT_DELAY_MS"]["percentile"]) lcp.append(data_["loadingExperience"]["metrics"]["LARGEST_CONTENTFUL_PAINT_MS"]["percentile"]) cls_.append(data_["loadingExperience"]["metrics"]["CUMULATIVE_LAYOUT_SHIFT_SCORE"]["percentile"]) performance_score.append(data_["lighthouseResult"]["categories"]["performance"]["score"] * 100)
Next section focuses on “total task” count, and DOM Size.
total_tasks.append(data_["lighthouseResult"]["audits"]["diagnostics"]["details"]["items"][0]["numTasks"]) total_tasks_time.append(data_["lighthouseResult"]["audits"]["diagnostics"]["details"]["items"][0]["totalTaskTime"]) long_tasks.append(data_["lighthouseResult"]["audits"]["diagnostics"]["details"]["items"][0]["numTasksOver50ms"]) dom_size.append(data_["lighthouseResult"]["audits"]["dom-size"]["details"]["items"][0]["value"])
The next section takes the “DOM Depth” and “Deepest DOM Element.”
maximum_dom_depth.append(data_["lighthouseResult"]["audits"]["dom-size"]["details"]["items"][1]["value"]) maximum_child_element.append(data_["lighthouseResult"]["audits"]["dom-size"]["details"]["items"][2]["value"])
The next section takes the specific observed test results during our Page Speed Insights API.
observed_dom_content_loaded.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedDomContentLoaded"]) observed_fid.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedDomContentLoaded"]) observed_lcp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["largestContentfulPaint"]) observed_fcp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["firstContentfulPaint"]) observed_cls.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["totalCumulativeLayoutShift"]) observed_speed_index.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedSpeedIndex"]) observed_total_blocking_time.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["totalBlockingTime"]) observed_fp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedFirstPaint"]) observed_fmp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["firstMeaningfulPaint"]) observed_first_visual_change.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedFirstVisualChange"]) observed_last_visual_change.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedLastVisualChange"]) observed_tti.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["interactive"]) observed_max_potential_fid.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["maxPotentialFID"])
The next section takes the Unused Code amount and the wasted bytes, in milliseconds along with the render-blocking resources.
render_blocking_resources_ms_save.append(data_["lighthouseResult"]["audits"]["render-blocking-resources"]["details"]["overallSavingsMs"]) unused_javascript_ms_save.append(data_["lighthouseResult"]["audits"]["unused-javascript"]["details"]["overallSavingsMs"]) unused_javascript_byte_save.append(data_["lighthouseResult"]["audits"]["unused-javascript"]["details"]["overallSavingsBytes"]) unused_css_rules_ms_save.append(data_["lighthouseResult"]["audits"]["unused-css-rules"]["details"]["overallSavingsMs"]) unused_css_rules_bytes_save.append(data_["lighthouseResult"]["audits"]["unused-css-rules"]["details"]["overallSavingsBytes"])
The next section is to provide responsive image benefits and server response timing.
possible_server_response_time_saving.append(data_["lighthouseResult"]["audits"]["server-response-time"]["details"]["overallSavingsMs"]) possible_responsive_image_ms_save.append(data_["lighthouseResult"]["audits"]["uses-responsive-images"]["details"]["overallSavingsMs"])
The next section is to make the function continue to work in case there is an error.
else: continue
Example Usage Of Page Speed Insights API With Python For Bulk Testing
To use the specific code blocks, put them into a Python function.
Run the script, and you will get 29 page speed-related metrics in the columns below.

Conclusion
PageSpeed Insights API provides different types of page loading performance metrics.
It demonstrates how Google engineers perceive the concept of page loading performance, and possibly use these metrics as a ranking, UX, and quality-understanding point of view.
Using Python for bulk page speed tests gives you a snapshot of the entire website to help analyze the possible user experience, crawl efficiency, conversion rate, and ranking improvements.
More resources:
Featured Image: Dundanim/Shutterstock
SEO
4 Ways To Try The New Model From Mistral AI

In a significant leap in large language model (LLM) development, Mistral AI announced the release of its newest model, Mixtral-8x7B.
magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%https://t.co/uV4WVdtpwZ%3A6969%2Fannounce&tr=http%3A%2F%https://t.co/g0m9cEUz0T%3A80%2Fannounce
RELEASE a6bbd9affe0c2725c1b7410d66833e24
— Mistral AI (@MistralAI) December 8, 2023
What Is Mixtral-8x7B?
Mixtral-8x7B from Mistral AI is a Mixture of Experts (MoE) model designed to enhance how machines understand and generate text.
Imagine it as a team of specialized experts, each skilled in a different area, working together to handle various types of information and tasks.
A report published in June reportedly shed light on the intricacies of OpenAI’s GPT-4, highlighting that it employs a similar approach to MoE, utilizing 16 experts, each with around 111 billion parameters, and routes two experts per forward pass to optimize costs.
This approach allows the model to manage diverse and complex data efficiently, making it helpful in creating content, engaging in conversations, or translating languages.
Mixtral-8x7B Performance Metrics
Mistral AI’s new model, Mixtral-8x7B, represents a significant step forward from its previous model, Mistral-7B-v0.1.
It’s designed to understand better and create text, a key feature for anyone looking to use AI for writing or communication tasks.
New open weights LLM from @MistralAI
params.json:
– hidden_dim / dim = 14336/4096 => 3.5X MLP expand
– n_heads / n_kv_heads = 32/8 => 4X multiquery
– “moe” => mixture of experts 8X top 2 👀Likely related code: https://t.co/yrqRtYhxKR
Oddly absent: an over-rehearsed… https://t.co/8PvqdHz1bR pic.twitter.com/xMDRj3WAVh
— Andrej Karpathy (@karpathy) December 8, 2023
This latest addition to the Mistral family promises to revolutionize the AI landscape with its enhanced performance metrics, as shared by OpenCompass.
What makes Mixtral-8x7B stand out is not just its improvement over Mistral AI’s previous version, but the way it measures up to models like Llama2-70B and Qwen-72B.
It’s like having an assistant who can understand complex ideas and express them clearly.
One of the key strengths of the Mixtral-8x7B is its ability to handle specialized tasks.
For example, it performed exceptionally well in specific tests designed to evaluate AI models, indicating that it’s good at general text understanding and generation and excels in more niche areas.
This makes it a valuable tool for marketing professionals and SEO experts who need AI that can adapt to different content and technical requirements.
The Mixtral-8x7B’s ability to deal with complex math and coding problems also suggests it can be a helpful ally for those working in more technical aspects of SEO, where understanding and solving algorithmic challenges are crucial.
This new model could become a versatile and intelligent partner for a wide range of digital content and strategy needs.
How To Try Mixtral-8x7B: 4 Demos
You can experiment with Mistral AI’s new model, Mixtral-8x7B, to see how it responds to queries and how it performs compared to other open-source models and OpenAI’s GPT-4.
Please note that, like all generative AI content, platforms running this new model may produce inaccurate information or otherwise unintended results.
User feedback for new models like this one will help companies like Mistral AI improve future versions and models.
1. Perplexity Labs Playground
In Perplexity Labs, you can try Mixtral-8x7B along with Meta AI’s Llama 2, Mistral-7b, and Perplexity’s new online LLMs.
In this example, I ask about the model itself and notice that new instructions are added after the initial response to extend the generated content about my query.


While the answer looks correct, it begins to repeat itself.


The model did provide an over 600-word answer to the question, “What is SEO?”
Again, additional instructions appear as “headers” to seemingly ensure a comprehensive answer.


2. Poe
Poe hosts bots for popular LLMs, including OpenAI’s GPT-4 and DALL·E 3, Meta AI’s Llama 2 and Code Llama, Google’s PaLM 2, Anthropic’s Claude-instant and Claude 2, and StableDiffusionXL.
These bots cover a wide spectrum of capabilities, including text, image, and code generation.
The Mixtral-8x7B-Chat bot is operated by Fireworks AI.


It’s worth noting that the Fireworks page specifies it is an “unofficial implementation” that was fine-tuned for chat.
When asked what the best backlinks for SEO are, it provided a valid answer.


Compare this to the response offered by Google Bard.


3. Vercel
Vercel offers a demo of Mixtral-8x7B that allows users to compare responses from popular Anthropic, Cohere, Meta AI, and OpenAI models.


It offers an interesting perspective on how each model interprets and responds to user questions.


Like many LLMs, it does occasionally hallucinate.


4. Replicate
The mixtral-8x7b-32 demo on Replicate is based on this source code. It is also noted in the README that “Inference is quite inefficient.”


In the example above, Mixtral-8x7B describes itself as a game.
Conclusion
Mistral AI’s latest release sets a new benchmark in the AI field, offering enhanced performance and versatility. But like many LLMs, it can provide inaccurate and unexpected answers.
As AI continues to evolve, models like the Mixtral-8x7B could become integral in shaping advanced AI tools for marketing and business.
Featured image: T. Schneider/Shutterstock
SEO
OpenAI Investigates ‘Lazy’ GPT-4 Complaints On Google Reviews, X

OpenAI, the company that launched ChatGPT a little over a year ago, has recently taken to social media to address concerns regarding the “lazy” performance of GPT-4 on social media and Google Reviews.

This move comes after growing user feedback online, which even includes a one-star review on the company’s Google Reviews.
OpenAI Gives Insight Into Training Chat Models, Performance Evaluations, And A/B Testing
OpenAI, through its @ChatGPTapp Twitter account, detailed the complexities involved in training chat models.


The organization highlighted that the process is not a “clean industrial process” and that variations in training runs can lead to noticeable differences in the AI’s personality, creative style, and political bias.
Thorough AI model testing includes offline evaluation metrics and online A/B tests. The final decision to release a new model is based on a data-driven approach to improve the “real” user experience.
OpenAI’s Google Review Score Affected By GPT-4 Performance, Billing Issues
This explanation comes after weeks of user feedback about GPT-4 becoming worse on social media networks like X.
Idk if anyone else has noticed this, but GPT-4 Turbo performance is significantly worse than GPT-4 standard.
I know it’s in preview right now but it’s significantly worse.
— Max Weinbach (@MaxWinebach) November 8, 2023
There has been discussion if GPT-4 has become “lazy” recently. My anecdotal testing suggests it may be true.
I repeated a sequence of old analyses I did with Code Interpreter. GPT-4 still knows what to do, but keeps telling me to do the work. One step is now many & some are odd. pic.twitter.com/OhGAMtd3Zq
— Ethan Mollick (@emollick) November 28, 2023
Complaints also appeared in OpenAI’s community forums.


The experience led one user to leave a one-star rating for OpenAI via Google Reviews. Other complaints regarded accounts, billing, and the artificial nature of AI.


A recent user on Product Hunt gave OpenAI a rating that also appears to be related to GPT-4 worsening.


GPT-4 isn’t the only issue that local reviewers complain about. On Yelp, OpenAI has a one-star rating for ChatGPT 3.5 performance.
OpenAI is now only 3.8 stars on Google Maps and a dismal 1 star on Yelp!
GPT-4’s degradation has really hurt their rating. Hope the business survives.https://t.co/RF8uJH1WQ5 pic.twitter.com/OghAZLCiVu
— Nate Chan (@nathanwchan) December 9, 2023
The complaint:


In related OpenAI news, the review with the most likes aligns with recent rumors about a volatile workplace, alleging that OpenAI is a “Cutthroat environment. Not friendly. Toxic workers.”


The reviews voted the most helpful on Glassdoor about OpenAI suggested that employee frustration and product development issues stem from the company’s shift in focus on profits.


This incident provides a unique outlook on how customer and employee experiences can impact any business through local reviews and business ratings platforms.


Google SGE Highlights Positive Google Reviews
In addition to occasional complaints, Google reviewers acknowledged the revolutionary impact of OpenAI’s technology on various fields.
The most positive review mentions about the company appear in Google SGE (Search Generative Experience).


Conclusion
OpenAI’s recent insights into training chat models and response to public feedback about GPT-4 performance illustrate AI technology’s dynamic and evolving nature and its impact on those who depend on the AI platform.
Especially the people who just received an invitation to join ChatGPT Plus after being waitlisted while OpenAI paused new subscriptions and upgrades. Or those developing GPTs for the upcoming GPT Store launch.
As AI advances, professionals in these fields must remain agile, informed, and responsive to technological developments and the public’s reception of these advancements.
Featured image: Tada Images/Shutterstock
SEO
ChatGPT Plus Upgrades Paused; Waitlisted Users Receive Invites

ChatGPT Plus subscriptions and upgrades remain paused after a surge in demand for new features created outages.
Some users who signed up for the waitlist have received invites to join ChatGPT Plus.

This has resulted in a few shares of the link that is accessible for everyone. For now.
Found a hack to skip chatGPT plus wait list.
Follow the steps
– login to ChatGPT
– now if you click on upgrade
– Signup for waitlist(may not be necessary)
– now change the URL to https://t.co/4izOdNzarG
– Wallah you are in for payment #ChatGPT4 #hack #GPT4 #GPTPlus pic.twitter.com/J1GizlrOAx— Ashish Mohite is building Notionpack Capture (@_ashishmohite) December 8, 2023
RELATED: GPT Store Set To Launch In 2024 After ‘Unexpected’ Delays
In addition to the invites, signs that more people are getting access to GPTs include an introductory screen popping up on free ChatGPT accounts.


Unfortunately, they still aren’t accessible without a Plus subscription.


You can sign up for the waitlist by clicking on the option to upgrade in the left sidebar of ChatGPT on a desktop browser.


OpenAI also suggests ChatGPT Enterprise for those who need more capabilities, as outlined in the pricing plans below.


Why Are ChatGPT Plus Subscriptions Paused?
According to a post on X by OpenAI’s CEO Sam Altman, the recent surge in usage following the DevDay developers conference has led to capacity challenges, resulting in the decision to pause ChatGPT Plus signups.
we are pausing new ChatGPT Plus sign-ups for a bit 🙁
the surge in usage post devday has exceeded our capacity and we want to make sure everyone has a great experience.
you can still sign-up to be notified within the app when subs reopen.
— Sam Altman (@sama) November 15, 2023
The decision to pause new ChatGPT signups follows a week where OpenAI services – including ChatGPT and the API – experienced a series of outages related to high-demand and DDoS attacks.
Demand for ChatGPT Plus resulted in eBay listings supposedly offering one or more months of the premium subscription.
chatgpt plus accounts selling ebay for a premium 🫡🇺🇸 https://t.co/VdN8tuexKM pic.twitter.com/W522NGHsRV
— surya (@sdand) November 15, 2023
When Will ChatGPT Plus Subscriptions Resume?
So far, we don’t have any official word on when ChatGPT Plus subscriptions will resume. We know the GPT Store is set to open early next year after recent boardroom drama led to “unexpected delays.”
Therefore, we hope that OpenAI will onboard waitlisted users in time to try out all of the GPTs created by OpenAI and community builders.
What Are GPTs?
GPTs allow users to create one or more personalized ChatGPT experiences based on a specific set of instructions, knowledge files, and actions.
Search marketers with ChatGPT Plus can try GPTs for helpful content assessment and learning SEO.
Two SEO GPTs I’ve created for assessment + learning 👀👇
1. Content Helpfulness and Quality SEO Analyzer: Assess a page content helpfulness, relevance, and quality for your targeted query based on Google’s guidelines vs your competitors and get tips: https://t.co/LsoP2UhF4N pic.twitter.com/O77MHiqwOq
— Aleyda Solis 🕊️ (@aleyda) November 12, 2023
2. The https://t.co/IFmKxxVDpW SEO Teacher: A friendly SEO expert teacher who will help you to learn SEO using reliable https://t.co/sCZ03C7fzq resources: https://t.co/UrMPUYwblH
I hope they’re helpful 🙌🤩
PS: Love how GPT opens up to SO much opportunity 🤯 pic.twitter.com/yqKozcZTDc
— Aleyda Solis 🕊️ (@aleyda) November 12, 2023
There are also GPTs for analyzing Google Search Console data.
oh wow. I think this GPT works.
Export data from GSC comparing keyword rankings before and after an update and upload it to ChatGPT and it will spit out this scatter plot for you.
It’s an easy way to see if most of your keyword declined or improved.
This site was impacted by… pic.twitter.com/wFGSnonqoZ
— Marie Haynes (@Marie_Haynes) November 9, 2023
And GPTs that will let you chat with analytics data from 20 platforms, including Google Ads, GA4, and Facebook.
Google search has indexed hundreds of public GPTs. According to an alleged list of GPT statistics in a GitHub repository, DALL-E, the top GPT from OpenAI, has received 5,620,981 visits since its launch last month. Included in the top 20 GPTs is Canva, with 291,349 views.
Weighing The Benefits Of The Pause
Ideally, this means that developers working on building GPTs and using the API should encounter fewer issues (like being unable to save GPT drafts).
But it could also mean a temporary decrease in new users of GPTs since they are only available to Plus subscribers – including the ones I tested for learning about ranking factors and gaining insights on E-E-A-T from Google’s Search Quality Rater Guidelines.


Featured image: Robert Way/Shutterstock
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