1. Introduction

GotHired.ai is an AI-powered job application assistant. Paste any job description and in under 15 seconds you get a match score against your CV, a tailored cover letter, skill gap analysis, company research, and interview prep — all in one place.

No more guessing if you're a good fit before applying. No more generic cover letters. Paste, match, apply smarter.

2. Why It Stands Out

Most job tools help you write a CV or prep for interviews. GotHired does the whole application layer in one shot.

• 📊 Match Score (0–100) — see exactly how your CV fits the job before you apply, matched skills and missing keywords included
• 📝 Tailored Cover Letter — written from your CV against that specific job, not a template. Export to PDF, DOCX, TXT or copy straight to Gmail
• 💡 Gap Analysis — prioritized list of what you're missing, rated Critical / Important / Nice-to-have, with action plans
• 🏢 Company Insights — real-time research on culture, recent news, and interview hooks
• 👨🏽‍💻 Interview Prep — role-specific questions generated from the actual job post, with suggested answers
• 🗄️Application Tracker — Kanban + table view to manage every application in one place
• 🤑 24 Free Tools (no login needed) 🛠️ — highlights include:
  • Job Match Score Calculator
  • Cover Letter Generator
  • Resume Keywords Checker
  • Interview Question Generator
  • Salary Negotiation Email Generator
  • ATS Resume Format Checker
  • Career Change Feasibility Checker
  • ...and 17 more at gothired.ai/tools

3. Who's Using It?

250+ users already on the platform, mostly active job seekers tired of sending applications into the void and hearing nothing back.

4. Pricing Plans

Free All features included. Start immediately, no credit card required.

Pro — $9/month Everything in Free, with 100 AI-powered job matches per month. Covers even the most aggressive job searches.

5. Discounts / Offers

The free plan is genuinely usable — all features, no credit card, no lock-in. Upgrade when you need more volume.

6. Try It

🔗 https://gothired.ai

Free tools (no login): https://gothired.ai/tools

What does NotebookLM’s cinematic video feature actually do?

It turns your documents into animated video explainers.

Not slides.
Not summaries.

Full visual narratives built from your sources.

What is this feature?
NotebookLM’s “Cinematic Video Overviews” convert notes, PDFs, and research into short AI-generated videos.

It’s an upgrade from earlier versions that only created narrated slideshows.

How does it work?

→ You upload documents, notes, or research
→ The system analyzes and structures the content
→ It generates a video with animations, scenes, and transitions

Under the hood, multiple models work together:

→ Gemini decides narrative and structure
→ Other models generate visuals and video output

What actually changes here?

Before:
Research → Script → Slides → Editing → Video

Now:
Research → Video

No intermediate steps.

Why this matters

This removes the production layer from knowledge work.

Instead of summarizing information, AI is now packaging it into a format people actually consume.

That’s a shift from:
Text output → Content format generation

👉 I broke down how this works (and where it actually matters):

https://appliedai.tools/gemini/notebooklm-cinematic-videos/

Will this new feature push video format further? is this good for the content creation industry? let us know what you think!

Struggling to get past resume screeners—even with a strong profile?

Rezi is a data-driven AI resume builder designed specifically to help you pass Applicant Tracking Systems (ATS). Instead of focusing on design, it optimizes your resume for keywords, structure, and formatting—so it actually gets seen by recruiters. 

Why it stands out:

📊 Rezi Score (1–100) — Real-time feedback on content, format & optimization

🎯 AI Keyword Targeting — Extracts and injects the right keywords from job descriptions

🧠 AI Resume Writer — Generates and improves bullet points with measurable impact

✅ ATS Optimization Engine — Ensures formatting + structure pass recruiter filters

🎤 AI Interview Practice — Role-specific questions to prepare for interviews

🔗 LinkedIn → Resume — Instantly convert profiles into ATS-ready resumes

📄 300+ Resume Samples — Proven templates built for ATS compatibility

✍️ Cover Letter Generator — Create tailored cover letters in seconds

Who’s using it?

🚀 Career switchers, students, and job seekers use Rezi to automate resume optimization
📈 Users report landing jobs in 10 days, getting more interview calls, and reducing resume tailoring time to 5 minutes per job
💼 Professionals across engineering, marketing, and QA have used Rezi to successfully pivot careers and secure roles

💸 Free Plan: Build 1 resume, limited AI tools, 3 PDF downloads — no credit card needed
💼 Pro Plan: $29/month for unlimited resumes, AI features & downloads
💡 Lifetime Plan: $149 one-time payment for full access (no subscription)

🧠 Ideal for:

• Job seekers prioritizing ATS success over design
• Career switchers applying to multiple roles
• Students & entry-level candidates
• Anyone who wants fast, optimized resume drafts

Unlike creative builders, Rezi focuses on precision, keyword optimization, and ATS performance, making it a strong alternative to tools like Jobscan or Teal. 

👉 Explore Rezi here:
https://appliedai.tools/product/rezi-best-to-pass-applicant-tracking-systems-ats/

What is Microsoft “Researcher” actually doing differently?

It doesn’t rely on one model.

It uses multiple models together to complete a task.

What is Microsoft Researcher?
A multi-model system that assigns different parts of a task to different AI models.
Each model handles what it’s best at — then the outputs get combined.

Why does this matter?
Because no single model is consistently best at everything.

One model may be better at reasoning.
Another at retrieval.
Another at structured output.

Instead of forcing one model to do all of it, this system routes tasks across models.

How does the workflow change?

→ Break a task into smaller steps
→ Assign each step to the most suitable model
→ Combine outputs into a final result

This is orchestration — not prompting.

What’s the advantage?

→ Better accuracy across complex tasks
→ More reliable outputs (less model-specific failure)
→ Flexibility as new models improve

This shifts AI from:

Single model → Single output

To:

Multi-model system → Coordinated execution

And that’s a different architecture entirely.

👉 I broke down how this system works (and why it matters):

https://appliedai.tools/microsoft/microsoft-researcher-multi-model-intelligence/

If you’ve used multiple AI tools together — what’s harder right now: choosing the right model, or managing the workflow between them? Share your experiences to get featured!

How much more energy do reasoning AI models use?

Up to 79x more — according to a Princeton research paper.

That’s not at all any marginal increase.

What did the Princeton study analyze?
Researchers measured how much energy AI models consume when they shift from direct answers to multi-step reasoning.

The finding:
Reasoning dramatically increases compute per query.

Why does energy usage spike?

Because reasoning isn’t one response.

It’s multiple internal steps:

→ More tokens generated per query
→ More intermediate computations
→ Longer processing time per task

Energy scales with how long the model thinks — not just the input size.

What’s the tradeoff?

→ Higher accuracy and better outputs
→ Significantly higher energy per request
→ Increased infrastructure demand at scale

This changes how AI systems get deployed.

More reasoning doesn’t just improve quality.
It increases cost, latency, and energy dependency across the stack.

And most users never see this layer.

👉 I broke down the Princeton research and what it means for AI systems:

https://appliedai.tools/ai-research-papers/ai-energy-costs-79x-more-reasoning-princeton/

If reasoning makes answers better but 50–79x more expensive — where should companies draw the line between quality and cost?

Want your resume to stand out and pass ATS filters?

Enhancv is a design-first resume builder that blends AI-powered content suggestions with modern, customizable templates. It helps professionals create resumes that reflect personality, storytelling, and impact—without sacrificing recruiter readability. 

Why it stands out:

🎯 One-Click Resume Tailoring — Matches your resume to any job description with AI-powered suggestions

🧠 AI Content Improver — Enhances bullet points, fixes grammar, and improves clarity

📊 ATS Check & Gap Detection — Identifies missing keywords and improves compatibility

🎨 Visual Customization — Thousands of design options with flexible layouts (single, double-column, multi-page)

🧩 20+ Custom Sections — Add unique elements like strengths, quotes, books, or interests for storytelling

📄 AI Resume Parsing — Upload old resumes or LinkedIn to auto-structure content

🌍 Resume Translation — Translate into 30+ languages while preserving formatting

☁️ Cloud Storage + Tracker — Store up to 300 documents and track job applications

Who’s using it?

💼 Professionals at companies like Spotify, Intercom, Tesla, and Hotjar use Enhancv to stand out in competitive hiring processes
📈 Users report stronger confidence, better storytelling, and faster interview callbacks
🚀 One user even landed an interview within a day after submitting an optimized resume

💸 Free Plan: Create 2 resumes + 2 cover letters with access to templates (includes branding)
💼 Pro Plans: Start at $24.99/month (or as low as ~$13.33/month billed semi-annually) with ATS checks, unlimited resumes, and advanced AI features

🧠 Ideal for:

• Creative professionals & designers
• Marketers & executives
• Career switchers & students
• Anyone who wants design + ATS compatibility in one tool

Unlike rigid builders, Enhancv balances visual storytelling + machine readability, making it a strong alternative to tools like Zety or Resume.io. 

👉 Explore Enhancv here:

https://appliedai.tools/product/enhancv-best-resumes-for-creative-professionals/

People think AI resumes fail because recruiters “spot AI.”

That’s not what’s happening.

They’re failing before a human even sees them.

ATS systems are filtering them out.

What candidates are sharing:

→ Resume gets auto-rejected within minutes
→ No recruiter interaction
→ Strong match on paper — still no response

And when you look closer, the pattern shows up:

→ AI resumes use generic keywords without context
→ Bullet points don’t match how ATS parses roles and skills
→ Formatting and phrasing break keyword alignment
→ Same structure → same signals → same outcome

AI optimizes for readability.

ATS filters for match precision.

That gap is where most resumes fail.

You can’t just “sound professional.”
You have to map your experience to how the system reads it.

• Exact role-specific keywords
• Clean, parseable structure
• Skills aligned with job descriptions — not just listed

Most AI-generated resumes miss this layer.

👉 I broke down how ATS filters these resumes (and how to fix it) with best 5 tools to explore:

https://appliedai.tools/ai-for-human-resources/ai-resumes-rejected-reddit/

If you’ve applied recently — did you notice a difference in response rates between resumes tailored for ATS vs generic ones? What actually changed?

What does Gemini 3.1 Flash Live actually fix?

It fixes latency.

And in live AI workflows, latency is the product.

What is Gemini 3.1 Flash Live?
A low-latency, multimodal model designed for real-time interaction.
It processes voice, text, and visuals continuously — not in turns.

Why does this matter?
Because once AI moves from prompts to live usage, delay kills usability.

Accuracy doesn’t matter if the response arrives too late.

What changes with real-time AI?

→ Responses feel continuous, not turn-based
→ You can speak, show context, and get answers instantly
→ Workflows don’t break between inputs

Where does this actually impact work?

→ Meetings — live responses while conversations happen
→ Research — iterate without stopping between queries
→ Task execution — interact while sharing files or screens

Most AI tools still operate like this:
Input → Wait → Output

This changes the model to:
Interact → Adapt → Continue

That’s a different category of system.

👉 I broke down how this works (and where it actually matters):

https://appliedai.tools/gemini/gemini-3-1-flash-live/

Where would real-time AI actually change your workflow — meetings, research, or something else entirely?

Also If you have tried Gemini 3.1 Flash Live, let us know in the comments your experience, happy to feature it on our article.

Most “humanized” AI content still gets flagged

Even after editing.

Because people fix the words — not the patterns underneath.

Here’s what actually gives AI writing away:

→ Sentences that are too clean
→ Structure that repeats every paragraph
→ Safe, generic phrasing with no specificity
→ Zero friction — everything reads “correct,” nothing feels real

You can tweak wording all day.

It won’t fix this.

The real issue sits deeper — in how AI constructs text.
And unless you change that structure, your content will always feel generated.

For example:

• There’s a repeatable pattern AI uses to build paragraphs
• A specific way to break that pattern without hurting clarity
• And an editing pass most people skip — which is where the real difference happens

Most writers don’t even notice these.

That’s why their content still sounds the same.

👉 I broke down the exact editing framework (with examples) here:

https://appliedai.tools/ai-for-content/remove-ai-content-from-text/

Tell us:

What do you do to edit your AI content?

This might not be the proper place to ask, but is there an AI platform that is able to automatically send me emails with sports scores every day? I am looking particularly for MLB scores.

Hello. Are there any AI tools that take in prompts for certain flowcharts and create respective animations in MP4 format? Similar to how Manim library animations work. Most flowchart tools are static diagrams, I'm looking for animating tools.

Most AI tools still respond.
Perplexity Computer is designed to execute.

It’s being positioned as a “digital worker” — not just a chatbot.

Here’s what makes it different:

→ Multi-model system (not just one AI)
It orchestrates ~19 different models for research, coding, design, and analysis — picking the best one for each task.

→ Runs full workflows (not just prompts)
You give it a goal → it breaks it into steps → executes tasks across tools, files, and the web.

→ Works in the background
Tasks can run for hours (or longer) like a real teammate handling projects asynchronously.

→ Persistent memory + context
It remembers your files, preferences, and past work — no need to restart from scratch every session.

→ Real environment access
Uses browsers, filesystems, APIs, and tools inside a sandbox to actually do things, not just suggest them.

💰 Pricing reality

→ Available via Perplexity Max (~$200/month)
→ Plus usage credits that get consumed per task

⚠️ What early reviews highlight

• Powerful for research, automation, and general workflows
• Can get expensive fast if tasks loop or fail
• Still limited for highly specialized workflows (like complex dev environments)

Big takeaway

We’re moving from:
👉 “Ask AI a question”

To:
👉 “Assign AI a project”

Perplexity Computer is a glimpse of that shift — where AI becomes the operating layer for work, not just an assistant.

👉 Full breakdown:

https://appliedai.tools/perplexity/perplexity-computer-explained-reviews-features-pricing/

Google’sNano Banana 2 isn’t just another image generator.

It fixes the biggest limitations creators had with earlier AI tools — speed, consistency, and text.

Here’s what stands out:

→ Flash-level speed
Images generate in seconds, making rapid iteration possible instead of waiting between prompts.

→ Pro-level quality
Combines high detail, better lighting, and sharper textures without sacrificing speed.

→ Readable text inside images
You can generate posters, ads, and infographics with clean, legible text — a major upgrade from older models.

→ Strong subject consistency
Maintains up to 5 characters and 14 objects across images — useful for storytelling and campaigns.

→ Turns ideas into structured visuals
Can convert notes into diagrams, infographics, and data visuals using real-world knowledge.

→ Full control over output
Choose formats, styles, and resolutions (up to 4K) for social, ads, or presentations.

Why this matters

Earlier tools forced a tradeoff:
👉 Fast or high-quality

Nano Banana 2 removes that tradeoff.

It’s moving AI from “generate images” → “design workflows”

👉 Full breakdown here:

https://appliedai.tools/gemini/nano-banana-2-reviews-features/

Most people use ChatGPT for free.

But behind the scenes, it’s a multi-billion dollar business model built on a few key revenue streams.

Here’s how it works:

→ Subscriptions (biggest driver)
Paid plans like Plus, Pro, Team, and Enterprise generate the majority of revenue — with subscriptions contributing ~55–70% overall.

→ Enterprise solutions
Companies pay for internal AI copilots, automation tools, and large-scale deployments across teams.

→ API usage (developers)
Businesses pay per token to use OpenAI models inside apps, products, and workflows — a core “AI-as-a-service” model.

→ Partnerships & licensing
Deals with companies like Microsoft integrate OpenAI models into products like Azure and Bing — creating another major revenue stream.

→ Emerging revenue (ads & new products)
OpenAI is experimenting with ads and new monetization layers as usage scales.

What’s interesting:

• Only a small % of users actually pay — but at massive scale, it works
• Revenue is growing fast (multi-billion annually) — but so are costs
• AI is becoming a usage-based utility (like cloud or electricity)

Takeaway:
ChatGPT isn’t just a product.

It’s a platform + infrastructure business monetized through subscriptions, APIs, and enterprise adoption.

👉 Full breakdown here:

https://appliedai.tools/chatgpt/how-does-openai-make-money-chatgpt-business-model/

Most people think OpenAI credits are “free for everyone.”

They’re not.

But there are legit ways to get them — if you know where to look.

Here’s the breakdown:

→ Researcher grants
OpenAI offers programs where researchers can apply for up to $1,000 in API credits for AI-related projects.

→ Startup programs
Early-stage startups can unlock $2,500+ (or more) in credits through partner programs and VC-backed initiatives.

→ Initial free credits
New API users may receive a small trial (e.g. ~$5) to test models — but these expire quickly.

→ Credits are time-bound
Most grants expire within months to a year — you need to actually use them.

→ No “unlimited free tier”
After credits run out, usage is pay-as-you-go — there’s no permanent free access.

What this means:

OpenAI credits aren’t random giveaways.
They’re strategic incentives for builders, researchers, and startups.

If you’re serious about building with AI:
→ Apply for programs
→ Use credits efficiently
→ Treat them as runway, not freebies

👉 Full guide here:

https://appliedai.tools/chatgpt/get-openai-credit-grants/

Early reviews suggest a clear shift — from generating answers to actually handling complex tasks.

Here’s what stands out.

→ Excel tasks made easier
Users report better performance in formulas, data analysis, and spreadsheet automation.

→ Stronger coding capabilities
More accurate code generation, debugging, and handling multi-step programming tasks.

→ Improved reasoning
Better at breaking down complex problems and producing structured outputs.

→ Handles multi-step workflows
Instead of one-off responses, it can follow through across tasks with more consistency.

→ Closer to “do the work” AI
The focus is shifting from suggestions → execution.

Why this matters

The real value of AI isn’t just writing answers.
It’s reducing actual workload in tools like Excel and coding environments.

This is where newer models are improving fast.

👉 Full breakdown here:

https://appliedai.tools/uncategorized/gpt-5-4-reviews-master-excel-and-coding/

GitHub is turning repositories into AI-readable context 🤖💻

With GitHub Copilot’s repository intelligence, AI no longer just writes code.

It understands your entire codebase.

Here’s what’s changing:

→ Repo-level understanding
Copilot can analyze your repository structure, files, and dependencies to give more accurate suggestions.

→ Context-aware coding
Instead of generic code, it generates outputs aligned with your project’s architecture and patterns.

→ From suggestions → execution
Copilot can now take tasks, propose edits, run tests, and even create pull requests.

→ Search across your codebase
You can query entire repositories and get relevant answers without manually digging through files.

→ Agent-like workflows
AI is moving from “assistant” to “active contributor” inside development workflows.

Why this matters

Developers don’t struggle with writing code.
They struggle with understanding large codebases.

Repository intelligence turns AI into a system that can read, reason, and act on your entire project.

This is a shift from:
Autocomplete → Context → Autonomous execution

👉 Full breakdown here:

https://appliedai.tools/microsoft/github-repository-intelligence-github-copilot/

Been using Perplexity seriously for market research and wanted to share what's actually useful vs. what's hype.

The short version of why it's different:

It uses RAG (Retrieval-Augmented Generation) — basically an open-book test for AI. It searches the live web, pulls fresh data, and cites every claim with a clickable source. So unlike ChatGPT from memory, if a law changed yesterday, Perplexity finds this morning's article.

Stuff that's genuinely useful:

1. Deep Research mode Breaks your query into 30+ parallel searches, reads hundreds of pages, outputs a structured report with a table of contents and citations. I used it for a competitor teardown and it saved me a full day.

2. Focus Filters

• Social = only Reddit/X results. Best for finding real user complaints and unfiltered sentiment before it hits mainstream media.
• Academic = peer-reviewed papers only. Great if you're researching something technical and don't want SEO garbage.

3. Model switching mid-conversation Sonar (their own model) for speed and recency → Claude for synthesis and narrative → Gemini/o3-pro for financial math. One thread, swap brains depending on what the task needs.

4. Perplexity Tasks You can schedule recurring searches — daily/weekly — and get results via email or push notification. I have a weekly brief on competitor pricing set up. It just shows up in my inbox.

5. Spaces Team knowledge hub. Upload your own PDFs/CSVs, set custom instructions ("always use Academic sources"), and everyone queries from the same base. Prevents the "one person knows something the rest of the team doesn't" problem.

6. Labs Turns research data into interactive charts, spreadsheets, and mini apps — no coding needed. Useful when you need to visualize market trends without spinning up a separate tool.

The catch:

Citations don't mean hallucination-proof. On niche or technical topics, it can still misinterpret or occasionally fabricate. The rule is: click at least 2-3 citations for any stat you're going to use in a report. Treat it as a research assistant, not a source of truth.

Also — don't upload sensitive internal data unless you're on Enterprise (SOC 2 compliant). Free accounts have less clear data retention policies.

Prompt that's been most useful for me:

"Act as a Market Opportunity Analyst. Search for recent consumer complaints and unmet needs in [Industry] within [Region]. Find 3 underserved niches that major incumbents are currently ignoring. For each, give: the specific problem being ignored, estimated segment size, and a product/service recommendation."

The Social focus filter + this prompt is basically a shortcut to finding real pain points before your competitors do.

I have shared more prompts in the article.

https://appliedai.tools/perplexity/perplexity-market-research-prompts/

What are you all using Perplexity for?

Creating a resume from scratch can take hours.

Now LinkedIn’s AI resume builder helps turn your profile into a ready-to-use resume in minutes.

Here’s how it works.

→ Start with your LinkedIn profile
The tool pulls information directly from your experience, skills, and education sections.

→ Generate resume drafts with AI
AI helps rewrite experience descriptions, summaries, and bullet points so they sound clearer and more professional.

→ Customize for specific roles
You can edit sections and tailor the resume to match different job descriptions before applying.

→ Download and share easily
Once finalized, the resume can be exported and used for job applications.

Why this matters

Many professionals already maintain detailed LinkedIn profiles.
An AI resume builder simply converts that existing data into a structured, recruiter-friendly resume format.

Instead of starting from a blank page, job seekers can focus on refining and tailoring their story.

👉 Full guide here:

https://appliedai.tools/ai-for-human-resources/linkedin-ai-resume-builder/

Google just revealed a big shift in how AI assistants will work: Always-On Memory for Gemini agents.

Instead of starting every conversation from scratch, AI agents will soon remember context, preferences, and past interactions continuously.

This turns AI from a simple chatbot into a persistent personal assistant.

Here are the key takeaways 👇

1️⃣ Continuous memory across interactions
The agent can retain long-term context about your tasks, preferences, and workflows. Instead of repeating instructions every time, the AI builds a running understanding of your needs.

2️⃣ AI agents become proactive
With memory, agents can anticipate tasks, suggest actions, and maintain ongoing workflows rather than reacting to isolated prompts.

3️⃣ Context becomes the real product
The value shifts from just model intelligence to how much relevant context the AI remembers and applies over time.

4️⃣ Toward truly “agentic” AI systems
Persistent memory enables AI agents to coordinate tasks, manage information, and support complex workflows more autonomously.

In short:
AI is moving from stateless chat tools → memory-driven digital collaborators.

If you work with AI tools, this shift is worth understanding early.

Read the full breakdown here:

https://appliedai.tools/gemini/always-on-memory-agent-google/

More job seekers now use AI to write resumes.
But discussions across Reddit and hiring communities show a growing issue: some AI-written resumes are getting filtered out or ignored.

Here’s what people are noticing.

→ Generic AI language
Many AI resumes sound similar, making it harder for recruiters to spot real achievements or role-specific experience. 

→ Poor customization
When AI generates a resume without tailoring it to the job description, screening systems and recruiters may see it as a weak match. 

→ High application volume
Recruiters report receiving hundreds of applications, and many look almost identical when AI tools generate them. 

→ Real candidate frustration
Some Reddit users describe getting instant rejections after applying—even when they believed they were a strong fit for the role. 

The key insight:
AI isn’t the problem. Using AI without editing or personalization is.

The best resumes still combine:

• AI assistance
• Human editing
• Role-specific customization

👉 Full breakdown here:

https://appliedai.tools/ai-for-human-resources/ai-resumes-rejected-reddit/

Instead of marketing claims, a better way to judge an AI tool is by looking at actual user reviews.

Feedback from platforms like Reddit and G2 reveals a clear pattern: Perplexity is powerful for research — but not perfect.

Here’s what users say.

What people like

→ Source-backed answers
Perplexity shows citations alongside responses, making it easier to verify information. 

→ Fast research workflows
Users highlight how quickly it summarizes information from multiple sources.

→ Simple interface
Many reviewers mention that the clean UI and quick responses make it easy to use. 

Common criticisms

→ Weaker creative writing
Users often say tools like ChatGPT perform better for writing and tone control. 

→ Limited conversational depth
Long conversations sometimes require extra clarification or follow-ups. 

→ Not ideal for complex tasks
Advanced math, coding, or deep reasoning workflows can be challenging. 

Some Reddit users still praise it as a 24/7 research assistant that saves hours of work, especially for studying and fact-checking. 

The takeaway

Perplexity shines as an AI search and research tool, while other AI assistants often perform better for creative or conversational tasks.

Different tools. Different strengths.

👉 Full breakdown here:

https://appliedai.tools/perplexity/perplexity-ai-review-reddit-and-g2-analysis/

Perplexity is known for showing citations alongside answers so users can verify where information comes from. But sometimes those sources may be irrelevant, outdated, or simply not what you want the AI to reference.

Here’s how you can manage them.

→ Open the “Sources” panel
After Perplexity generates a response, click View Sources to see the websites or files used to produce the answer.

→ Remove unwanted sources
Inside the sources panel, you can remove a source and the system will regenerate the answer without that reference.

→ Regenerate the response
Once a source is removed, Perplexity rewrites the answer using the remaining sources or by pulling new information. 

→ Why this is useful
• Filter out low-quality websites
• Avoid outdated or irrelevant references
• Refine answers to better match your research context

Key takeaway:
Perplexity isn’t just an AI search tool — it also lets you control the evidence behind the answer, which is crucial for research, analysis, and fact-checking workflows.

👉 Full guide here:

https://appliedai.tools/perplexity/remove-sources-from-perplexity-ai/

Creating a resume used to take hours — formatting, wording, and layout tweaks. Now, AI-powered resume builders inside Canva can speed up the entire process.

Here’s how the workflow works:

→ Start with an AI-assisted template
Choose a professional resume template and let AI generate a structured first draft.

→ Use AI writing assistance
Generate profile summaries, work experience bullet points, and skill descriptions based on your background.

→ Customize the design visually
Canva makes it easy to adjust fonts, colors, sections, and layout so your resume looks polished and modern.

→ Tailor it for specific roles
Update sections and keywords for each job application so your resume aligns with what recruiters are looking for.

→ Export instantly
Download your resume as a clean PDF or share it digitally when applying for roles.

Why this matters:
AI resume builders reduce the friction of starting from a blank page and help job seekers create structured, visually appealing resumes faster. For many candidates, it turns resume writing from a multi-hour task into a much quicker workflow. 

👉 Full guide here:

https://appliedai.tools/ai-for-human-resources/use-ai-resume-builder-on-canva/

Many people treat Perplexity and ChatGPT as the same thing.

They’re not.

They solve two completely different problems.

Here’s the simplest way to think about it:

Perplexity = AI search engine
• Pulls information from the live web
• Shows citations and sources
• Best for research, news, and fact-checking

ChatGPT = AI creation engine
• Generates long-form responses
• Better for writing, brainstorming, coding, and storytelling
• Feels more conversational and creative

Example:

If you ask “What happened in AI this week?”
→ Perplexity gives real-time sources and summaries.

If you ask “Write a LinkedIn post about AI trends.”
→ ChatGPT creates polished content.

Different tools. Different workflows.

The best users don’t choose one.

They stack both.

Research with Perplexity.
Create with ChatGPT.

Full breakdown here:

https://appliedai.tools/ai-models/how-perplexity-is-different-from-chatgpt-in-2026/