LinkedIn X-Ray No More. Now What?

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Last week, Google has adjusted its index. It no longer finds me and many others by the Headline, About, Experience, and Education.

Bing is in the process. It already does not find some profiles:

Any search engine is affected. (A search engine does not have a LinkedIn login, right? Even if Microsoft owns them both.) X-Ray has lost its power. If it “works” for you, it is only a matter of days, maybe a few weeks before it stops. And it will no longer find updated info on profiles, i.e., your results get outdated daily.

X-Ray going down is a new challenge to respond to!

Nobody has data comparable to LinkedIn. Many would love this not to be the case, but such is life. Because a Premium account does only partial indexing, in order to source within, you need to have LinkedIn Recruiter, Lite, or Sales Navigator and learn how they search. It is not straightforward and is poorly documented. Learn how to search on LinkedIn in our comprehensive class.

You can also X-Ray the shallow data and go from there.

A productive way to source is to come to LinkedIn with some data collected to cross-reference. It can be emails, names, and other things you find outside of LinkedIn. While names are not unique, running a long OR of names combined with what you know about your target profiles, such as industry keywords or companies, can narrow it down to your prospects.

If you are after Software Developers, try our GitHub User Search Tool – no login required. (But please make sure you read the Help!) Once you have the data, go to LinkedIn to cross-reference.

How do you assess continued access to the full profile data if you are using a search engine or an outside system? Here is what I recommend. To (collectively) test how well systems deprived of LinkedIn profile public data will perform:
1) Change YOUR LinkedIn headline
2) Set up alerts on search engines and systems to check whether and when they will catch up, i.e., search for yourself with the new headline in keywords.
Let us know what happens 😉

Join us at the brand-new class

LinkedIn Sourcing in the Post-X-Ray Era

on February 28-29 and figure out how to source in the new circumstances.

The Adventures of X-Ray Are Mysterious

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I have “Saved As” my public LinkedIn profile, and it looks like this:

This is my “Experience” and “Education.” Nothing much is visible without logging in. A large percentage of profiles look the same.

But things are in flux on the web.

Google LinkedIn X-Ray is back!

Mike Santoro has noticed. We think it is temporary.

In his post, Marcel has a screenshot of this search, pulling words from my now-hidden public LinkedIn profile: social list elsevier sourcing training verge brain gain. Here is Marcel’s image;  Google could not find me two weeks ago.

Here is today’s screenshot. Google has reverted to the previous Index state of the page! I believe it is currently true for all public profiles.  You will find them all.

Custom Search Engines have not been affected all along. Yet.

Bing finds me, but without a name (and finds my two partners):

It shows me again on page 3, with a location of “45K followers:”

Locations are pretty messed up in Bing’s snippets for most profiles.

It’s unclear what has happened with Google’s Index. Common sense dictates that Google and Bing will adjust to redacted profiles in the future.

It’s an adventure! To search on LinkedIn correctly, consider getting a recording of the recent class,

LinkedIn 2024 Solved.




The Best Github Tool In Industry

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Guest post from Julia Tverskaya.

Are you a Technical Recruiter? Are you looking for Software Developers?

On January 16th, Irina announced a new GitHub search and export tool in her LinkedIn article.  We are happy to report that the tool has immediately attracted hundreds of users and we hope many more will discover its value.

The GitHub User Search tool empowers sourcers to find, export, and contact GitHub users. Our email search is optimized to be as precise as possible. You can find and export up to 1,000 results in each run. At this time, we do not require a login. There is nothing comparable available in the industry.

However, using the tool efficiently requires a solid grasp of GitHub’s search, including its peculiarities and constraints. Although the tool’s help page provides guidance, errors still happen.

Let’s look at the most common mistakes to help you improve your searches.

Qualifiers (GitHub search operators) are your friends!

GitHub search distinguishes between searching users’ general information, such as their names and bios, and searching within special fields. Keywords are used to search in general information; reserved words called “qualifiers” are used to search in fields. These fields include a user’s primary language and their location, parameters Sourcers are interested in.

Not using qualifiers, or using them incorrectly, leads to getting poor results – or in some cases, not getting any results. Here’s how to use the qualifiers correctly.

Searching for location

Use qualifier location:

This qualifier directs the search to exclusively examine the location field in users’ profiles.  Without this qualifier, any geographical term used in your search query is treated as a general keyword. Consequently, the GitHub search engine would only scan through keyword-searchable fields like usernames and bios.

Let’s say we are looking for users with experience in AI who live in Prague.
This search: AI location:Prague specifically targets users who have indicated Prague in their location field. It returns 89 results.
Compare it with AI Prague which returns only 20 (the ones where “Prague” is used in the users’ bios). 

Searching for two locations or different spellings of the same location?

Use qualifier location: twice, e.g.:  AI location:Prague location:Praha searches for users with AI who spell their location as either “Prague” or “Praha”. 

Searching for a programming language

Use qualifier language:

This qualifier allows you to refine your search for GitHub users by their primary programming language. For example, to find users proficient in C++, use: language:C++

Compare these two searches looking for users who live in Latvia and whose primary language is JavaScript:
location:latvia language:javascript returns 619 results. 
location:latvia javascript returns 49 results – all people who mention JavaScript in their bios. Not only did we get fewer results, we cannot be sure that JavaScript is indeed their primary language.

Searching for different languages?

Use the language: qualifier twice. E.g, to find people in Latvia whose primary language is either C++ or Python: location:latvia language:c++ language:python

Using Boolean operators in GitHub searches

While the Boolean operators AND, OR, and NOT are fundamental in many search contexts, their application in GitHub searches comes with specific considerations.

  • Keep it simple

GitHub’s search engine is designed to interpret only simple boolean expressions. Parentheses do not work (the search will not return any results). Combining operators AND and OR in a single query often leads to unexpected results, and we do not recommend it. 

  • Boolean with qualifiers is restricted   

The words “AND”, “OR”, and “NOT” can only be used with keywords and in: qualifiers (in:email, in:name, in:login). You cannot use them with other qualifiers. AND and OR are always implied, and NOT is expressed as a minus sign. 

For example, “java developer” AND location:barcelona does not return results because the qualifier location: cannot be used with the operator AND. Omit AND to get the desired result: “java developer” location:barcelona (remember, AND is implied).

Do you remember how we looked for people whose primary language is either C++ or Python? language:c++ language:python (OR is implied). language:c++ OR language:python does not work.

The operator NOT is always expressed as a minus sign when used with qualifiers, e.g. location:”estonia” -location:”Tallinn” returns people who live in Estonia but not in Tallinn. 

Because we cannot use AND and OR with qualifiers, some searches are simply not possible.  For example, we cannot search for users whose primary language is Java and who also mention Java in their bios. language:java AND java returns an error (and omitting AND is interpreted as OR by the Github internal algorithms). 

Our help page contains additional details and examples of Boolean search. 

Final notes:

  • Boolean operators are always capitalized: AND, OR, NOT
  • There is no white space between the minus sign and a search term, e.g. -language:javascript
  • Qualifiers are always lowercase: location:, user:, fullname:, etc.
  • There is no white space between most qualifiers and the search term, e.g.language:java, location:london, etc. 
  • Do not forget about the colon after qualifiers, e.g. repos:, followers:, etc.

Do you want to learn more? 

Sign up for our three-day tech sourcing bootcamp for an in-depth discussion


LinkedIn X-Ray Workaround

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The loss of LinkedIn X-Ray saddens our community. Indeed, we can no longer find the About, Experience, and more on Google. Bing will naturally follow.

However – here is a productive way to use the “remaining” X-Ray.

Step 1. Start with X-Rays for the location, job titles, and, optionally, companies, like “san francisco bay area” python developer at google.

Step 2. Collect the URLs from the search with Julia‘s brilliant Google Search Results Scraper.

Step 3. Use SalesQL’s bulk profile URL upload. Alternatively, use SeekOut or Phantombuster. (Beyond a limit, these tools are paid.)

Step 4. Download the enriched list. It has all the names, current companies, past companies, titles, and more, plus contact information.

Step 5. Filter the Excel file for promising profiles and review them on LinkedIn.

What are the benefits?

  1. As a result, you have an outreach email list that is highly personalizable. (Put the company name in the subject for a high opening rate.)
  2. Search results will not depend on your LinkedIn network or personal data. They can complement your LinkedIn searches.
  3. You can run the process regardless of your LinkedIn account type. You will not be blocked by LinkedIn, for sure.

That said, correctly searching on LinkedIn is crucial. Unfortunately, LinkedIn’s help documentation is incomplete and misleading,

Join us at the brand-new class

LinkedIn Sourcing in the Post-X-Ray Era

on February 28-29 and figure out how to source in the new circumstances.

LinkedIn Profiles Redacted, X-Ray Dying, Aggregators Will Suffer

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My friend Marcel van der Meer’s message shocked me this morning. He was the first to notice a huge reduction in LinkedIn public profiles. The Headline, About section, Experience, and Education are gone. There are still some public profiles with the full data, but it’s a matter of time before all will be reduced. LinkedIn has notified Google, so there is no old indexed data for us to X-Ray.

Why did LinkedIn do that? Not because some of us use LinkedIn X-Ray for sourcing. The obvious reason is harming LinkedIn’s competitors, people aggregators like SeekOut that provide a superior search over LinkedIn’s data. If a tool depends on using public data, its LinkedIn-copied information is now becoming obsolete by the minute.

The good news is that tools like PhantomBuster and my favorite SalesQL will continue to work for now because they interact with the data from your login.

LinkedIn X-Ray is not completely dead; you can still do things with the inanchor: operator. But with so much data hidden, it is not a tool of choice for most tasks.

Some things will now never be found. LinkedIn does not index grades at school, and public profiles have lost them. I would have a huge problem running a (past) sourcing request for CS grads with good grades in Europe.

With that in mind, our focus is now on utilizing LinkedIn’s not-so-great search. To find the fullest data on profiles, you must have the Recruiter, Recruiter Lite, or Sales Navigator subscription.

Join us at the brand-new class

LinkedIn Sourcing in the Post-X-Ray Era

on February 28-29 and figure out how to source in the new circumstances.




AI and LinkedIn Are Like Oil And Water

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My AI-related experience as a LinkedIn member has been interesting. I have been getting hilarious (or sad?) notifications for my potential job-hunting from LinkedIn – see LinkedIn Wants Me to Pack Groceries and Learn Fluoroscopy.

This morning, I took a look at my skill suggestions. Unfortunately, they were not any better – I have as much skill in Manufacturing Process Improvement as in Finance (none):

Next, go suggested skills. I have nothing to do with 6 out of 8 and little with the other two (while I do have a few other, not listed here, skills):

And my desired roles are those I would dread of. Seriously.

I hope not too many members rely on LinkedIn’s suggestions!

I believe that LinkedIn is not doing a great job incorporating AI into any of its products. Here is what this means for Recruiters and Sourcers:

  1. Keep your BOOLEAN up! It is and will be the best way to search in LinkedIn Recruiter going forward.
  2. (If you have the budget), reconsider people aggregators. While their deficiency is out-of-date data, they may run well-functioning AI over the data they have.
  3. Become a ChatGPT Prompter. While LinkedIn’s implementation of AI is not quite there, you can improve work significantly with AI.

If you want to catch up on all things AI in recruiting – our January ChatGPT class is sold out; you can register for the February 2024 class at

ChatGPT and AI for Sourcing and Recruitment.


Julia’s Google Search Results Extractor

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I have been missing a convenient tool for Google search results scraping ever since Google redesigned the output screen. The tool’s need arose from Google switching the output format to scrolling with “more results” from the previous convenient page-by-page view. The change broke the beloved Instant Data Scraper.

Julia Tverskaya is our long-term Partner, Sourcer, Recruiter, and a good friend. She also writes code! And now Julia has created a Chrome Extension that does even better than automatically pressing the “more results” button.

(It is her second published extension. Julia’s Email Extractor, the first extension she coded, now has 9,000 users. The Extractor uniquely follows the user on all open pages, collects every email from the pages’ HTML, and stops at a user’s signal.  Its parsing formula is solid. It also cleans up the list: deduplicates, alphabetizes, and removes non-personal and junk emails. I use it regularly for sourcing. Our sourcing tool Social List is Julia’s creation as well.)

The brand new Julia’s second extension is

Google Search Results Scraper.

The tool relies on the trick described in the post Get Back More Google Results By Not Using More.

What the new Google Search Results Scraper does:

  • Collects Google search results for a query, as many as possible
  • Increases the max number of results returned to 300-400 from the current 100-130
  • Exports results in Excel in this format:
    • URL
    • Title
    • Snippet
    • Emails (if found)

Get it here – and we would be glad to hear from you!

Please also share with colleagues who may benefit from the tool.





Have Skills, LinkedIn?

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LinkedIn tells us we are moving toward skills-based hiring and stresses the updated skills search that is smart about the content of members’ profiles and can guess skills. Is that so? Please review LinkedIn Wants Me to Pack Groceries and Learn Fluoroscopy if you haven’t. It’s a (sad) joke.

LinkedIn Engineering observes that Recruiters find and contact only a small percentage of members. The idea to expand Skills beyond what members enter comes from their wish that we find others. (But we would find others if not for the issues described in LinkedIn’s Members and Companies Disconnect, You Are Missing Seniority, LinkedIn, and other similar cases where LinkedIn misinterprets profile data!)

So here is how the “new,” wide skills are assigned, per LinkedIn Engineering:

“Please don’t confuse skills with “that list of skills you entered on your LinkedIn profile”.  Yes, of course, we use those, but we intuit skills from MANY places.  For example, we will give you “credit” for a skill if you (these are examples and not exhaustive):

  • Have it explicitly listed as a skill on your LinkedIn profile (what you were talking about)
  • Have it in your profile some place.  We actually convert all the text in your whole profile into one big field behind the scenes and use it to keyword search for skills.  The goal is to give people “credit” for skills they likely have, even if they didn’t think to explicitly list it.  We don’t want everyone having to do some crazy optimization of entering every keyword they can think of.  That sucks.  We want to give people credit for skills.
  • Have it in a resume we have access to (this is permission dependent).  We scan resumes for skills. 
  • We also “give credit” for skills based on connections at times.  So, let’s say you have a bunch of connections, and you are all “similar” profiles, and all those other folks have X skill and you don’t.  We will “infer” that you have that skill.”

What happens in practice is that the Skill search more or less equals the keyword search. As an example, it would find an experienced Developer for “Java” which she used in college years ago. Or she didn’t, but her connections did!

Additionally, Skill search in Recruiter has a unique syntax (why?). Looking for clinical therapy is not the same as looking for clinical AND therapy.

However, there still is a way to search by self-entered skills, via search  operators.

Join us for the class

LinkedIn Recruiter Mastery

coming up on November 29, 20, 2023, and learn all about the tool that the LinkedIn documentation does not have.

AI Can Search But Not Source: More On AI-Search-Engine Integration

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At the beginning of this AI wave, services popped up that offered search engines and AI “integration,” like this:

There is no integration here other than combining results on the same screen. However, the latest developments in search-the-web-plus-AI tools are promising. It was welcome news that Bing is back with ChatGPT and the GPT-4 data has been updated to 2023. (I hope this stays in spite of the weekend news!) Check out our AI Chatbot Comparison Chart.

Algorithms behind major search-engine-AI integrations are complex and, to a large extent, unknown. Practice, discussions, and trusted newsletters will help us navigate them. But, say, ask Bing to source profiles of people with certain skills and experience, and it will, in most cases, either refuse or hallucinate. (Most AIs do not feel enthusiastic, particularly, about reading LinkedIn profiles.)

However, there are ways to combine search engines’ results and AI “on your own,” having clarity about the process. It is similar to scraping and processing search results but can be much smarter and faster with the right prompts. The approach can be customized in many concrete circumstances.

Utilizing AI and Search Engines in Candidate Sourcing

Search Engines: They provide an extensive range of basic candidate information, pulling from professional networks, publications, and online profiles. Their utility lies in initial data aggregation.

AI Tools like GPT: These tools offer a nuanced analysis. AI interprets and evaluates a candidate’s professional nuances, assessing skills and cultural alignment more deeply than surface-level data.

Integrated Approach: Begin with search engines for broad data collection. Then, apply AI for detailed analysis. For instance, in technical roles, search engines identify candidates with relevant backgrounds, while AI evaluates project intricacies and technological adaptability.

Summary: Search engines offer volume; AI provides insight. Combined, they form an effective strategy for informed candidate sourcing.

Our ChatGPT webinars have been a continued success. Please do not miss the next one –

ChatGPT and AI for Sourcing and Recruitment

December 20-21, 2023. Materials are provided; seating is limited.

Creating Batch Match GPT

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GPTs are fascinating and promising. I want to share my experience creating one.

I regularly use SalesQL batch import of LinkedIn profiles and get a rich output where each record has detailed info about the members. The other two systems I use for the rich output are SeekOut and ContactOut. These outputs have much more detail than export from LinkedIn Recruiter.

Creating the “Batch Match” GPT, I told it that I would give it a job description and a list of potential candidates (which I would export from SalesQL), and wanted to see a table with assessments and scores.

I picked a job description from LinkedIn for a Unicorn Hunter (Corporate Recruiter) in Automation Machinery Manufacturing. I also gave BatchMatchGPT a SalesQL export with a list of profiles of Recruiters. The GPT replied: “The assessment will focus on their alignment with the key requirements of the job, which include experience in in-house recruiting, expertise in the industrial automation sector (if any), proficiency in various recruitment strategies and tools, and strong interpersonal and communication skills.” It sounded reasonable.

Here is what it gave me based on a job description and profile list – by all means, useful and time-saving:

GPT’s “understanding” of the job requirement was adequate. You can see in the table how it tried to derive info, even the soft skills, from the input. I could further refine the output by setting the column configuration.

To become an everyday tool, the GPT needs more corrections, rule setting, and debugging. This would probably take a few hours. I will take this route and hope to make BatchMatchGPT public. (You can also try to create and test your own, following the description.)

ChatGPT is always a bit stubborn. The GPT did not want to read the job description from the link, though it previously read my blog with no problem. It wouldn’t read profiles from URLs either. This could be a specific LinkedIn limitation imposed by Microsoft. The GPT also told me it couldn’t access external files when I imported an Excel file. But when I converted it to PDF, it digested the info.

Please join us at

“ChatGPT and AI for Sourcing and Recruitment”

November 15-16 (Wed-Thu), 2023, and learn to up your game in the new world of AI.