I’ve been thinking about something that happens a lot when tailoring resumes.

Say a job description asks for:

“Experience conducting candidate interviews.”

But the resume says:

“Scheduled interviews and communicated with candidates throughout the hiring process.”

Obviously those things are related, but I wouldn’t say they’re the same.

The person might have been involved in recruiting, but nothing there proves they actually conducted interviews.

What bothers me is when resume advice or tools take something like “scheduled interviews” and rewrite it as “conducted interviews” just because it matches the job posting better.

That feels like a pretty big jump from tailoring into exaggerating.

At the same time, calling the requirement completely “missing” doesn’t seem right either because the person does have related experience.

So I think there should really be three categories:

Matched: You have direct evidence you did it.

Partial: You did something related, but not enough to prove the full requirement.

Missing: There’s no evidence you did it.

And if something is partial, maybe the next step should be asking the person more questions.

Did you sit in on interviews?
Did you do phone screening?
Did you ask candidates screening questions?
Did you take interview notes?

If yes, great, add what you actually did.

If not, keep it as related experience instead of turning it into something stronger.

Even small word changes can completely change the claim:

Scheduled ≠ Conducted
Assisted ≠ Led
Supported ≠ Managed

Curious how recruiters or people who review resumes handle this.

Would you consider the example above a partial match, or would you still mark it as missing?

I was looking at a resume recently where the job required experience conducting candidate interviews.

The resume said the person had scheduled interviews and communicated with candidates throughout the hiring process.

Obviously, that’s related experience. But it’s not the same thing as actually conducting the interviews.

I’ve seen resume advice that would basically turn that into “conducted candidate interviews” because it matches the job description better. That feels risky to me.

Maybe they sat in on interviews. Maybe they helped with screening. Maybe they actually did conduct some interviews. But unless the resume or the person confirms that, you can’t just assume it.

At the same time, calling the requirement completely “missing” doesn’t seem accurate either. There is relevant experience there.

So I’d call it a partial match:

Relevant experience? Yes.
Proof they conducted interviews? No.

I feel like this is where a lot of resume tools get things wrong. They treat everything as either matched or missing, when sometimes the honest answer is somewhere in between.

Curious what others think. Where do you draw the line between tailoring a resume and exaggerating someone’s experience?

I saw someone say they'd submitted 100 applications without getting a single interview, and my first thought was that application 101 probably isn't going to change much.

Not saying that to be harsh. The job market is rough and sometimes qualified people get ignored for reasons they can't control.

But zero screening calls after that many applications would make me pause.

Are the applications all for roughly the same kind of role? Does the resume actually show the main requirements near the top? Are these jobs still fresh when the application goes in?

There's also a difference between getting no calls at all and getting interviews but no offers. Those seem like two different problems. Changing the resume again might make sense for the first one, but probably not automatically for the second.

I'm wondering how many people track this stuff beyond the total number of applications. Like applications to screening calls, then interviews, then offers.

Has anyone here changed one part of their process and actually noticed the response rate improve?

Everyone talks about AI replacing jobs, but I think something else is happening first.

A lot of entry-level work has always been repetitive.

Writing first drafts.

Summarizing meetings.

Cleaning spreadsheets.

Research.

Creating reports.

Basic coding.

Simple customer support.

Those weren't glamorous tasks, but they were how people learned the job.

Now AI can do a surprising amount of that work.

If companies need fewer junior employees because AI handles many of those tasks, where are new graduates supposed to get experience?

It feels like we're creating a situation where companies want experienced people, but there are fewer opportunities to become experienced in the first place.

I'm not saying AI is replacing every junior role. Plenty of companies are still hiring, and people are still needed for collaboration, communication, and decision-making.

I'm just wondering if the career ladder is starting to lose its first few rungs.

Has anyone noticed this where they work?

Are junior positions actually becoming harder to find, or are they simply changing into different kinds of roles?

I've always been told that networking is one of the best ways to find a job.

The problem is...most advice feels kind of fake.

Stuff like:

"Just connect with recruiters."

"Send 100 LinkedIn messages."

"Ask for referrals."

If someone I didn't know asked me for a referral five minutes after connecting, I'd probably ignore them too.

Lately I've been trying something different.

Instead of asking people for jobs or referrals, I've just been asking about their career.

Questions like:

• How did you get into this role?
• What surprised you most after joining your company?
• If you were starting over today, what would you do differently?

I've had a few conversations that were actually enjoyable, even when nothing came from them.

It made me wonder if networking works better when you stop thinking of it as networking.

For people who've had success with LinkedIn, what kinds of messages actually got responses?

And if you've been on the hiring side, what immediately makes you ignore a message?

I've been trying to understand how ATS systems actually work because there is a lot of conflicting advice online.

Some people say:

"ATS will automatically reject your resume if you don't have the right keywords."

Others say:

"ATS is mostly just a database and recruiters still review applications."

From what I've been reading, it seems like ATS systems are mainly used to organize applications, extract information, and help recruiters search candidates.

The areas that seem to matter most are:

• Clear job titles and experience
• Relevant skills connected to actual work
• Easy-to-read formatting
• Tailoring your resume to the role

I'm curious what people here have experienced.

Has anyone actually seen a major difference after changing their resume for ATS, or do you think ATS advice online is exaggerated?

For a long time, I thought getting a 90%+ ATS score meant my resume was ready to go.

But after looking into how hiring systems actually work, I'm starting to think those scores are more of a guideline than a prediction.

There isn't one universal ATS scoring system.

Every resume checker has its own algorithm.

Every company configures its hiring software differently.

And recruiters still decide who gets interviewed.

A high ATS score might mean you've included relevant keywords or fixed formatting issues, but it doesn't guarantee interviews.

Personally, I think it's more useful to ask:

• Does my experience actually match the job?
• Have I shown measurable achievements?
• Is my resume easy for a recruiter to skim?
• Have I tailored it to this role?

Curious what everyone else thinks.

Have ATS scores actually helped you get interviews, or do you see them as more of a resume-checking tool than a predictor of success?

After spending way too much time applying for jobs, I realized something: not every job posting is actually hiring.

Some listings stay up for months.

Some companies collect resumes for future openings.

Some have already chosen an internal candidate but still have to post the job publicly.

Now, before I spend an hour tailoring my resume, I check these 7 things.

1. The posting is 30+ days old

If the exact same role has been sitting online for weeks or months, I investigate before applying. Some companies hire continuously, but others simply recycle old listings.

2. The same job keeps getting reposted

Search the company on LinkedIn. If the identical role keeps appearing every few weeks with the same description, it's worth asking why it hasn't been filled.

3. The job description is vague

If it says something like:

>

...without explaining responsibilities, tools, or qualifications, I'm cautious. Most hiring managers know exactly what they're looking for.

4. There's no sign the company is actively hiring

Check their LinkedIn page.

• Recent hires?
• Recruiter activity?
• Company updates?

An inactive page doesn't automatically mean it's a ghost job, but it's another signal to consider.

5. The salary and requirements don't add up

Examples:

• Entry-level role
• 8+ years of experience
• Expert in everything
• Low salary

Sometimes this simply means the posting wasn't carefully written. Other times, it suggests the role isn't a real hiring priority.

6. It already has thousands of applicants

A high applicant count doesn't mean the job is fake.

But if it's weeks old with 3,000+ applicants, your time may be better spent finding newer opportunities.

7. Read recent employee reviews

Employees sometimes mention hiring freezes, layoffs, or restructuring. That context can help you decide whether the opportunity is worth pursuing.

None of these signs prove a job is a ghost job.

They're simply filters that help me decide where to invest my time.

I'd rather send 10 thoughtful applications than 100 applications to jobs that may never be filled.

What other signs have you noticed?

For years, we've all heard the same advice:

"Your resume is getting rejected by ATS."I believed it too.

But after digging into how modern hiring actually works, I'm starting to think we've been focusing on the wrong enemy.

From what I've found, most companies using ATS platforms like Workday, Greenhouse, or Taleo aren't automatically rejecting resumes because of wording or formatting. Automatic filtering is usually based on knockout questions set by the employer, such as work authorization, certifications, or minimum experience.

After that?

A recruiter is often looking through a massive stack of resumes.

Some jobs get 100 to 250 applicants.

Popular roles can receive 500 to 2,000+ applications in just a few days.

That changes the question completely.

Instead of asking:

"How do I beat the ATS?"

Maybe we should be asking:

"How do I make a recruiter stop scrolling?"

For example, which resume bullet stands out more?

❌ Managed cross-functional projects.

✅ Reduced deployment time by 34% by rebuilding the CI pipeline, enabling 12 successful releases in one quarter.

One tells me what you did.

The other shows the impact you made.

I'm curious what everyone else thinks.

Have we spent too much time optimizing for ATS and not enough time making resumes compelling for the actual person reading them?

Recruiters and hiring managers, I'd especially love to hear your perspective.

Something I noticed:

Many experienced people have resumes full of tasks.

But hiring managers don't know the impact.

Example: "I handled reports"

Okay.

But:

How many?

Why?

What improved?

Experience alone is not enough.

The resume has to translate experience into value.

I have been researching hiring and resumes recently.

Something interesting:

A lot of advice focuses on:

"Beat ATS"

"Add keywords"

"Use this template"

But I think the bigger question is:

Can someone quickly understand your value?

A recruiter does not know your entire career story.

Your resume has to communicate:

What did you do?

What changed because of your work?

Why does it matter?

Curious what others think.

What is the biggest resume problem you have faced?

I’ve been testing AI tools for the past few months for content creation, coding, automation, and productivity.

Instead of collecting random hype tools, I filtered everything down to the ones that are actually useful in real workflows.

I grouped them into categories so it’s easier to understand where each tool fits:

• Coding
• AI Agents
• Automation
• Marketing
• Video creation
• Writing
• Learning
• Productivity
• Design

I also removed tools that felt repetitive or overhyped.

Here’s the interesting part — in each category, there’s usually one tool that stands out as the “default winner” right now:

• Coding → Cursor
• AI Agents → Claude
• Automation → n8n
• Marketing → Taplio
• Video creation → Runway
• Writing → ChatGPT
• Learning → Perplexity
• Productivity → Notion
• Design → Canva

This is just my current pick based on usage, not hype.

I’m curious:

What AI tools are you actually using daily right now?

And what’s one underrated tool I should add to the next version?

I found something interesting while going through Google's official Gemini Prompt Guide.

I expected pages of advanced prompt engineering techniques.

Instead, the same pattern appears over and over again.

Every good prompt contains four things:

• Persona: Tell AI who it should be.
• Task: Tell it exactly what you want.
• Context: Give it the background information.
• Format: Specify how the answer should be structured.

Once I noticed that pattern, the rest of the guide became much easier to understand.

It also made me realize why so many prompts online don't work very well. They're usually missing one or two of these pieces.

For those who have been using AI for work, what's been the biggest improvement you've made to your prompts?

I’ve been experimenting with a different way of using ChatGPT for learning.

Instead of asking it to just explain topics, I started prompting it like a tutor.

Now it:

• Checks what I already know first

• Builds a structured learning path

• Teaches step by step instead of dumping everything at once

• Gives exercises after each lesson

• Revisits earlier concepts so I don’t forget them

What surprised me is how different it feels compared to normal usage.

It’s less like “getting answers” and more like actually building a skill properly.

Curious if anyone else is using ChatGPT like this, or if you’ve built your own learning setup?

If you’ve ever asked ChatGPT what do you think about my writing?, you’ve probably noticed a pattern:

It almost always says it’s good.

Not because your work is always good.
But because these models are trained to agree more than they disagree.

There’s even research showing they can be far more likely to validate you than push back. It’s a known behavior called sycophancy basically, the model learns that agreement feels “helpful,” so it leans into it.

So the real issue isn’t honesty. It’s vagueness.

And vagueness is where bad feedback hides.

The fix: use a rubric

Instead of asking “is this good?”, define how “good” is measured.

For example:

• Clarity (25)
• Structure (25)
• Persuasiveness (25)
• Originality (25)

Then force this rule:

1. Score each category first
2. Then calculate the total
3. Only then give feedback

Now the model can’t just vibe its way to “100/100.”

Even better:
Make criteria objective where possible.
Not “good flow,” but:

• “Each claim has evidence or explanation”
• “No unsupported jumps in logic”

When I tested this kind of approach, vague rubrics gave inflated scores. Tight rubrics didn’t. And the tighter ones actually pointed out real weaknesses.

Bonus move:

If you’re unsure what to measure, ask the model to build the rubric first. Then evaluate your work against it.

Same work. Different lens. Completely different truth.

Curious what others do to avoid the “everything is amazing” effect. Do you force structure like this, or just rely on instinct?

I work in HR and open these files every week, so a few honest things that cost good people interviews:

1. The software has to find you first. Most mid-to-large employers use an ATS — it parses your resume into a database, and recruiters search it by keyword. If the posting says "client relationship management" and you wrote "managing customers," you don't show up. Mirror the posting's exact words.

2. Duties ≠ achievements. "Responsible for managing the team" tells me nothing. "Led a team of 12, cut turnaround 30%" tells me you make things better. Verb + what you did + a number.

3. One column, standard headings, no graphics. Fancy two-column/text-box designs confuse the parser and your work history gets scrambled. Clean and boring beats pretty and broken.

4. You get a ~6-second human scan. Name clear, a 2-line summary up top, recent role obvious, white space. Crowded page = crowded thinker.

Happy to answer resume questions in the comments — drop yours.

Google Skills (skills.google) just consolidated nearly 3,000 AI courses and hands-on labs into one platform. Free tier is 35 lab credits a month for developers; full catalog is $29/mo. The labs are decent because they run in real Google Cloud consoles with Gemini Code Assist built in.

 The problem: 3,000 options is how you quit on day two. So instead of browsing, I made the model build the path. Pasted this into Claude: 

"I'm a [role] who wants to learn [specific goal]. Google Skills has ~3,000 courses and labs. Build me a focused 4-week plan: one track only, the 3-4 specific labs and badges worth doing in order, about 3 hours a week, skip anything that is pure theory. Tell me which badge to earn first and why it matters to an employer." 

Honest result: it cut the whole catalog down to a short ordered path and named the first badge to chase. The catch is it is only as good as how specific you are. "Learn AI" gives you mush. "Deploy ML models on Vertex AI" gives you a real plan. 

Works the same on any oversized course library, not just Google's.

Google Skills (skills.google) just consolidated nearly 3,000 AI courses and hands-on labs into one platform. Free tier is 35 lab credits a month for developers; full catalog is $29/mo. The labs are decent because they run in real Google Cloud consoles with Gemini Code Assist built in. 

The problem: 3,000 options is how you quit on day two. So instead of browsing, I made the model build the path. Pasted this into Claude: 

"I'm a [role] who wants to learn [specific goal]. Google Skills has ~3,000 courses and labs. Build me a focused 4-week plan: one track only, the 3-4 specific labs and badges worth doing in order, about 3 hours a week, skip anything that is pure theory. Tell me which badge to earn first and why it matters to an employer." 

Honest result: it cut the whole catalog down to a short ordered path and named the first badge to chase. The catch is it is only as good as how specific you are. "Learn AI" gives you mush. "Deploy ML models on Vertex AI" gives you a real plan.

 Works the same on any oversized course library, not just Google's.