Curious Google Results Via URL Manipulation

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The Google URLs have changed for image search; it now has the URL including &udm=2. I have decided to play with the parameter’s value.

Playing with the &udm parameter shows interesting search results categories: images perspectives (!) learn (!) video (redirects to news (redirects to web, no AI (!) forums

I do not think this is documented.

By Googling, I also discovered this interesting reading about the last “no AI” option – Wrenching Around Google URLs, Get Your Old Skool Search Back (for now).

It is fun playing with URLs. 🙂



Emails to LinkedIn URLs In Bulk: Five Major Options

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Last week, a colleague messaged me:

“Hello Irina! how are you? I just love your GitHub Search tools! it’s awsome! such a gift for the community.

I wonder if you can help me with the easiest way to enrich github search results with LinkedIn profiles… LinkedIn URL’s. in bulk.”

If you only have a few addresses to check, the old Outlook online trick will do the job. But my friend had several hundred records.

So here are some approaches to mass-cross-referencing lists of emails against LinkedIn and getting lists of the matching profile URLs and other profile data.

  1. In LinkedIn Recruiter, you can upload 200 emails at a time and get the URLs back. (paid subscription; reliable matching)
  2. In SeekOut, you can upload a long list of emails and export the info, including the URLs. (paid subscription)
  3. Cleabit allows you to upload a list of emails for free and will show you the percentage identified and some demographics, but you will need to pay per record for individual contacts.
  4. In ContactOut, you can upload a list of emails and download the profiles. Each enrichment costs points. I do not have enough experience with this reverse look-up to say anything about its quality or completeness.
  5. Or, alternatively,

USE A HM-HACK; see below:

If you do not have access to LinkedIn Recruiter, here is a little-known way to still get yourself the ability to upload lists of emails and get the members’ basic info and URLs back, just like subscribers do. Find a friendly colleague, your first connection, who does subscribe to LinkedIn Recruiter, and ask them to make you a “Hiring Manager.” This will give you the full function of email list uploading without either side paying extra.

As a reminder, our fullest sourcing course for Recruiters is coming up. – JUNE 4-7 & 11-13 @ 8 AM TO 9 AM PACIFIC EACH DAY:

Seven-Day Sourcing Bootcamp, June 2024





AI Inventions in LinkedIn Collaborative Articles and Risky Strings

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I received an invitation to collaborate on “What do you do if you want to break into the job market as an AI career changer? ” on LinkedIn. I did not understand the question but finally looked at the full list of collaborative articles.

The AI generating these categories, article titles-questions, and answers outlines has been “creative” over the top!

Naturally, I went to explore the Boolean search category. Since Boolean search syntax differs on different sites, I thought that questions in the category should mostly be related to specific platforms, like Google or LinkedIn.  It’s not the case.

What LinkedIn AI has generated (italicized, with my comments):

  1. How do you design XOR and NAND puzzles that are challenging but not frustrating? What? I’d say it’s a cool question to ask ChatGPT in your spare time, but it is unrelated to Boolean search. It’s about complex logic – not implemented in our practical search systems.

  2. What are the advantages and disadvantages of using Boolean strings in job boards and resume databases? When we are talking about disadvantages, I expect to hear about alternatives. Do not use Boolean search to find resumes on databases or LinkedIn, and do what – avoid search operators? But here is what it says:

Using Boolean strings in your recruitment process can be challenging and risky.[…]

A lack of familiarity with the syntax and logic of Boolean strings can result in a steep learning curve and a high margin of error, leading to frustration and reduced search productivity.

I never knew Boolean searches could be “risky” and reduce productivity, but here you go!

3. How do you evaluate the relevance and ranking of web pages based on their titles, URLs, and snippets? That one went to the SEO land, which uses some Boolean searching but differs from “Boolean search.”

4. How do you test and debug your Quine-McCluskey method and prime implicants solutions? I Googled the method name – it is about math functions with only 1 and 0 values, referred to as Boolean. It’s not about search.

5. How do you communicate and collaborate with others using Boolean derivatives? (Do you know the answer? lol)

(There are more interesting questions.)

It looks like LinkedIn’s “collaborative article” AI generates these barely relevant questions with semi-hilarious suggestions based on a large pool of knowledge—too large and without context and proper training. It picked the word “Boolean” (which can mean several things) from the request and went for wide interpretations. In this case, it also gathered irrelevant knowledge from scientific systems or libraries. Some questions I saw seemed OK and relevant, but most were off.

You will observe the same story—unrelated or odd questions and suggestions—with another familiar collaborative topic: Sourcing. For example, it has this question (and I don’t know why): How do you design and implement a fair and transparent competency-based pay structure?

Members who come in to respond, I’d say, act “politely,” try to make sense of the questions, and if a question is about something obscure (like you saw above), even Google and paste answers! LinkedIn promises you will stand out as a collaborator.

I previously wrote AI and LinkedIn Are Like Oil And Water; this post is related. So is this – LinkedIn Wants Me to Pack Groceries and Learn Fluoroscopy.

We have just announced a new hands-on practical

ChatGPT for Recruitment Workshop

on April 23, 2024 Tuesday. Participation is limited to 10 people.

LinkedIn Assumes Skills And It Is a Problem

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From LinkedIn Engineering:

“Don’t confuse skills with “that list of skills you entered on your LinkedIn profile”.  We intuit skills from MANY places.  For example, we will give you “credit” for a skill if you:

-Have it in your profile some place.  We convert all the text in your whole profile in to one big field behind the scenes and use it to keyword search 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.”

(Bold is mine – IS)

Let me show you what happens.

Developers in El Cerrito, CA (where I live) with:

  • keyword = javascript — 430 members
  • (LinkedIn) skill = javascript — 416 members
  • Self-entered skill = javascript — 347 members

I.e., searching by “LinkedIn Skills” in that wider sense outlined by the Engineering have found 97% of members with the keyword. The stats are similar if you use other keywords for skills.

How is this useful? I am glad we have the hidden skills: operator to search for self-entered skills.

It is not too early to sign up for our most extensive

Seven-Day Talent Sourcing Bootcamp for Recruiters,

starting on June 4, 2024.

Exclusively for Technical Recruiters

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Sourcing Software Developers? There is no better place than Github. And our tool, GitHub User Search, is the best tool for finding lists of Developers. I want to tell you how to integrate it into your sourcing process by combining it with LinkedIn.

First, search with our tool. The search syntax is complex. But typically, you will only need to search for:

  1. Programming language, e.g. language:python
  2. Location in every way you can think of spelling it out, e.g., location:SF location”San Francisco”

Example: language:java location:NYC location:”New york” location:NY.

You can also search for keywords, but note that Github does not have a field for the job title, and few have filled out the company field.

As a result, you obtain an Excel file with up to 1,000 records with these fields:

  • Username
  • URL
  • Name
  • Company
  • Location
  • Bio
  • Social (up to four social profiles and a blog URL)
  • Number of followers
  • Emails  (which we discover and provide).

You can also search several times in different ways to merge the exports for further processing.

Some of the records would already have LinkedIn profile URLs. You can collect the URLs, run them as a batch with SalesQL, filter, and review the output. This makes part of your ready-to-email list of potential candidates.

Then, if you have LinkedIn Recruiter, upload a CSV file into a designated project with a list of all the emails from the export and any first and last names (the names do not matter). This will cross-reference the batch against LinekdIn and let you search within it, creating an additional distribution list.

Email is a unique identifier, but you can also work with other information within the records. For example, you can search on LinkedIn for the location and the language name plus an “OR” of first and last names from the export in keywords.

This makes for a productive, fast process with rich results. The combined information collected from LinkedIn and GitHub facilitates personalizing the outreach.

We will be running various scenarios of the tool usage along with tons of other material in the upcoming in-depth

Three-Day Tech Sourcing Bootcamp

 (March 26-28, 2024, Tuesday -Thursday)

Please see the full agenda and register on the site – and hurry! Only a few seats are left.










Unlocking a New World of Sourcing : A Bigdata Approach

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Guest post from Ashfaq Ahmed.

Continuing from my previous post – Death of LinkedIn X-Ray, What next?

Before talking about Bigdata concepts, let me demystify a common sourcing myth that is widely prevalent in the industry:

“There is no one approach to sourcing. Everyone can have their own strategies/approaches.”

If you’d be interested in why such a thought process is prevalent in the industry, read my linkedin post (to keep this blog short, I’ll jump onto Bigdata concepts).

Bigdata concept is nothing but a structured approach to organizing your data sets and addressing them as small & unique data slices, one at a time.

Your candidate data is scattered in so many forms & shapes, because of which we think & believe there isn’t one sourcing approach. However, the reality is that there is some structure/shape to how candidates’ data is presented.

For example, assume you are getting a Software Engineer role with C#,, SQL, Rest as skills.

Below is a visual representation of how candidates would have presented themselves on a platform like Linkedin. It is a probabilistic view of their representation; some would have written all 4 skills, some 3, etc.

Note : Red means they don’t have that skill on their profile but have the rest.

When some anchor skills aren’t present, like “,” the data becomes noisy, meaning it can bring irrelevant profiles. The data noise increases further when more than one anchor could be missing in a profile, like the below image

So, this is how data slices can be built based on how candidates write. For data-abundant roles on a platform like LinkedIn, one can build 100+ unique searches to spot candidates in different forms & shapes.

Sourcing does have a structure, form and shape. It’s about understanding your Persona of the role, how your data is presented & chalking out data-slicing strategies.

For the last 8+ years, I’ve been training these to tech recruiters & have been actively writing about this on my LinkedIn. Feel free to reach out to me on LinkedIn if you have any questions or if you’d like to know more.


Death of LinkedIn X-Ray, What Next?

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Guest post from Ashfaq Ahmed.

I got on a call with Irina & David & we discussed how sourcing will be impacted in the Post- X-Ray era.

First things first, why were we able to access LinkedIn via Xray before? Because when you’ve logged out from LinkedIn and visited a profile via Google, the profile data was more or less similar to what you saw when logged in.

Now, LinkedIn didn’t want scrapers or even Recruiters to access their data from search engines, and thereby, they changed the UI of Public Profiles by hiding almost all crucial data.

Sourcing Tools :

This change will not affect Large enterprise sourcing tools because they never relied on Google/Xray for scraping data at scale. Most Sourcing tools buy data from large data providers like Brightdata and others.

Recruiters :

In the post-x-ray world, if recruiters have to master something at all, then it is their foundational sourcing. Knowing to write a good boolean is just one step, and the others are – strong personal understanding + Big Data Concepts in Sourcing.

The Big Data concept is nothing but the ability to write mutually exclusive searches for a given JD. For instance, you write Devops & you spot 10,000 candidates. Can you slice this data into 10 search buckets, each consisting of 1000?

Such data slicing helps you organize your sourcing approach & classify your data based on the efficacy of results.

In the next post, I will touch on the details of how to implement big data concepts with an efficacy framework. Meanwhile, want to test your tech sourcing skills? Take up a couple of quizzes right here:


Google’s filetype: Decline and Workaround

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The Google advanced search dialog still shows file types:

But search using the operator filetype: and keywords no longer works. For example, filetype:xlsx attendees returns all sorts of pages.

For now, you can use a workaround: filetype: will still work if your search contains either site: or inurl: operator. We can use them with the minus, essentially excluding nothing, like this:

We all wonder how long Google will support its operators (I will need to update the operator list).

[edited] Danny Sullivan says it is probably a bug.

I Want My Profile To Be Public, But I Have Lost Control – So Have You

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LinkedIn X-Ray is gone (or soon to be gone, depending on your location).

So, what did LinkedIn do?

The screenshot above shows my LinkedIn public profile settings.

For the majority of members I know, the settings are the same. We want to be found on Google and Bing, which the UX promises.

However, this is my experience and education on the public profile. Profiles all went to “extreme privacy” starting about a month ago. Most data went to the deep web, which search engines cannot access.

1. Members were never informed of the changes – or asked whether we wanted them.

2. LinkedIn Help still says we are in control. Not true.

Here is a quote in response to my inquiry from a LinkedIn Engineering Director:

“Our trust team is rolling out (As they always have, none of this is new, it is an ongoing thing) changes to what is visible in public profile.  The idea behind public profile is to identify a person, to decide if that person is someone you wish to connect with, or reach out to.  If you want to connect, then of course that person can choose to accept or decline.  Same with messages and outreach.  But all of this is being done for member trust and member data.  Members expect us to help them control exactly where their data shows up, is used, and how it appears.”

This – Members expect us to help them control exactly where their data shows up, is used, and how it appears. – is exactly what has stopped happening. And it is new, and unfortunate.

How does it support our trust in LinkedIn? is a question for LinkedIn’s Trust Team.

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.

Sales Navigator Revelations and Function Codes

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The death of X-Ray prompts everyone to study LinkedIn’s search closer.

LinkedIn’s Sales Navigator has different algorithms from both Premium and LinkedIn Recruiter. There is no good reason for that. Different Developer teams fell out of sync.

Sales Navigator “thinks” most of LinkedIn. (I am being sarcastic.) It has a huge population of

1,665M+ profiles.

What are the 665M profiles unaccounted for in other accounts and in press? It is hard to say. In 2022, I posted LinkedIn Software Crisis (a Summary). At that point, LinkedIn Recruiter showed 400M “extra” profiles. It was a bug, that was fixed then due to the post and subsequent connection with an Engineering Director for LIR. Maybe it is the same bug in Sales Navigator, counting uploaded resumes as profiles.

Despite that, I think Sales Navigator is a fine choice for sourcing. It has a nice set of search filters. If it understood the hidden operators, it would be awesome. But it does not.

What I like about Sales Navigator is its search URLs. They are “readable,” can be shared, and expose various internal codes.

For example, I selected every function in the search, and from the URL, got this list of Function codes, which are not officially documented:

Accounting = 1
Administrative = 2
Arts and Design = 3
Business Development = 4
Community and Social Services = 5
Consulting = 6
Education = 7
Engineering = 8
Entrepreneurship = 9
Finance = 10
Healthcare Services = 11
Human Resources = 12
Information Technology = 13
Legal = 14
Marketing = 15
Media and Communication = 16
Military and Protective Services = 17
Operations = 18
Product Management = 19
Program and Project Management = 20
Purchasing = 21
Quality Assurance = 22
Real Estate = 23
Research = 24
Sales = 25
Customer Success and Support = 26

The secret operators do not work in SN, but you can use these codes in Recruiter or Lite with the operator functions:.

As always with LinkedIn-computed data, there is a warning. LinkedIn is not good at interpreting its data. Lots of members do not have functions assigned. Sales Navigator shows 1BN results without functions. It is 2/3 of the profiles. Since it has some ghost profiles, the high number may be the consequence of that. But always make sure that some of your searches do not include calculated values such as function (or seniority, etc.)

LinkedIn, unfortunately, falls behind in these AI times. No matter what account you use, it is best to use Boolean search where possible and understand how selections work.

Our latest webinar LinkedIn 2024 Solved is up-to-date.