Follow me for system design & book-writing tips.

Alex Xu is a software engineer and author. His book System Design Interview - An Insider’s Guide is one of Amazon's best-selling books in the “Distributed Systems & Computing” category.  As of December 2020, his book is being translated into 7 different languages (Traditional Chinese, Russian, Japanese, Spanish, Polish, Simplified Chinese, and Korean).

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Newsletter: https://blog.bytebytego.com
Twitter: https://twitter.com/alexxubyte
YouTube: https://www.youtube.com/c/ByteByteGo

Alex Xu

MCP vs RAG vs AI Agents

Everyone is talking about MCP, RAG, and AI Agents. Most people are still mixing them up. They’re not competing ideas. They solve very different problems at different layers of the stack.

MCP (Model Context Protocol) is about how LLMs use tools. Think of it as a standard interface between an LLM and external systems. Databases, file systems, GitHub, Slack, internal APIs. 

Instead of every app inventing its own glue code, MCP defines a consistent way for models to discover tools, invoke them, and get structured results back. MCP doesn’t decide what to do. It standardizes how tools are exposed.

RAG (Retrieval-Augmented Generation) is about what the model knows at runtime. The model stays frozen. No retraining. When a user asks a question, a retriever fetches relevant documents (PDFs, code, vector DBs), and those are injected into the prompt.

RAG is great for:
- Internal knowledge bases
- Fresh or private data
- Reducing hallucinations

But RAG doesn’t take actions. It only improves answers.

AI Agents are about doing things. An agent observes, reasons, decides, acts, and repeats. It can call tools, write code, browse the internet, store memory, delegate tasks, and operate with different levels of autonomy.

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Author of 4 Bestselling Books | Co-Founder of ByteByteGo

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RabbitMQ vs Kafka vs Pulsar

RabbitMQ, Kafka, and Pulsar all move messages, but they solve very different problems under the hood.
This diagram looks simple, but it hides three very different mental models for building distributed systems.

RabbitMQ is a classic message broker. Producers publish to exchanges, exchanges route messages to queues, and consumers compete to process them. 

Messages are pushed, acknowledged, and then gone. It’s great for task distribution, request handling, and workflows where “do this once” really matters.

Kafka flips the model. It’s not a queue, it’s a distributed log. Producers append events to partitions. Data stays there based on retention, not consumption. Consumers pull data using offsets and can replay everything. 

This is why Kafka works so well for event streaming, analytics, and pipelines where multiple teams need the same data at different times.

Pulsar tries to combine both worlds. Brokers handle serving traffic, while BookKeeper stores data in a durable ledger. Consumers track position with cursors instead of offsets. 

This separation lets Pulsar scale storage and compute independently and support both streaming and queue-like patterns.

Choosing between them isn’t about “which is faster” or “which is popular.” It’s about how you want data to flow, how long it should live, and how many times it needs to be read.

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Al Topics to Learn before Taking Al/ML Interviews (Part 1)

AI interviews often test fundamentals, not tools. This visual splits them into two buckets that show up repeatedly: Traditional AI and Modern AI.

Traditional AI focuses on fundamental ML topics, mostly from before neural networks became dominant.

Modern AI focuses on neural network foundations and newer concepts like transformers, RAG, and post-training.

Interviewers generally expect you to know both. They expect you to explain how they work, when they break, and the trade-offs.

Use this as a checklist, and make sure you can explain each topic clearly before your next AI interview.

Part 2 in the comments.

Over to you: which topic here do you find hardest to explain under interview pressure? What else is missing?

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12 Architectural Concepts Developers Should Know

1 - Load Balancing: Distributed incoming traffic across multiple servers to ensure no single node is overwhelmed.
2 - Caching: Stores frequently accessed data in memory to reduce latency.
3 - Content Delivery Network (CDN): Stores static assets across geographically distributed edge servers so users download content from the nearest location.
4 - Message Queue: Decouples components by letting producers enqueue messages that consumers process asynchronously.
5 - Publish-Subscribe: Enables multiple consumers to receive messages from a topic.
6 - API Gateway: Acts as a single entry point for client requests, handling routing, authentication, rate limiting, and protocol translation.
7 - Circuit Breaker: Monitors downstream service calls and stops attempts when failures exceed a threshold.
8 - Service Discovery: Automatically tracks available service instances so components can locate and communicate with each other dynamically.
9 - Sharding: Splits large datasets across multiple nodes based on a specific shard key.
10 - Rate Limiting: Controls the number of requests a client can make in a given time window to protect services from overload.
11 - Consistent Hashing: Distributes data across nodes in a way that minimizes reorganization when nodes join or leave.
12 - Auto Scaling: Automatically adds or removes compute resources based on defined metrics.

Over to you: Which architectural concept will you add to the list?

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bytebytego

9 Key AI Concepts Explained in 7 minutes (YouTube video link at the end).

- Tokenization
- Text Decoding
- Prompt Engineering
- Multi Step AI Agents
- RAGs
- RLHF
- VAE
- Diffusion Models
- LoRA

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10 Types of API Testing

1 - Smoke Testing: Quickly checks if the core functionalities of the API are working as expected.
2 - Functional Testing: Validates that the API behaves as specified in the functional specifications.
3 - Integration Testing: Ensures different modules or services interact correctly through the APIs.
4 - Regression Testing: Verifies that new changes don’t break existing API functionalities.
5 - Load Testing: Measures how the API performs under expected load conditions.
6 - Stress Testing: Tests the API behavior under extreme or peak load conditions.
7 - Security Testing: Assesses the API for vulnerabilities and potential external threats.
8 - UI Testing: Checks the interaction between the user interface and the API.
9 - Fuzz Testing: Sends random or invalid data to the API to detect unexpected crashes or errors.
10 - Reliability Testing - Evaluates the API’s consistency and stability over an extended period.

Over to you: Will you add any other API testing type to the list?

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11 Ways To Use AI To Increase Your Productivity

AI is changing how we work. People who use AI get more done in less time. You do not need to code. You need to know which tool to use and when.

For example, instead of reading long technical blogs, you can upload them to Google’s NotebookLM and ask it to summarize the key points.

Or you can use Otter.ai to turn meeting transcripts into action items, decisions, and highlights.

Here is a list of 19 tools that can speed up your daily workflow across different areas. Save this for the next time you feel stuck getting started.

Over to you: What’s the underrated AI tool that others might not know about?

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Agent Skills, Clearly Explained.

Why do we need Agent Skills? Long prompts hurt agent performance. Instead of one massive prompt, agents keep a small catalog of skills, reusable playbooks with clear instructions, loaded only when needed.

Here is what the Agent Skills workflow looks like:

1. User Query: A user submits a request like “Analyze data & draft report”.
2. Build Prompt + Skills Index: The agent runtime combines the query with Skills metadata, a lightweight list of available skills and their short descriptions.
3. Reason & Select Skill: The LLM processes the prompt, thinks, and decides: "I want Skill X."
4. Load Skill into Context: The agent runtime receives the specific skill request from the LLM. Then, it loads SKILL. md and adds it into the LLM's active context.
5. Final Output: The LLM follows SKILL. md, runs scripts, and generates the final report.

By dynamically loading skills only when needed, Agent Skills keep context small and the LLM’s behavior consistent.

Over to you: What skills would you find most useful in agents?

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GPT-5 is not one model. 

It is a unified system with multiple models, safeguards, and a real-time router.
This post and diagram are based on our understanding of the GPT 5 system card.
When you send a query, the mode determines which model to use and how much work the system does.

Instant mode sends the query directly to a fast, non-reasoning model named GPT-5-main. It optimizes for latency and is used for simple or low-risk tasks like short explanations or rewrites.

Thinking mode uses a reasoning model named GPT-5-thinking that runs multiple internal steps before producing the final answer. This improves correctness on complex tasks like math or planning.

Auto mode adds a real-time router. A lightweight classifier looks at the query and decides whether to use GPT-5-main or GPT-5-thinking when deeper reasoning is needed.

Pro mode does not use a different model. It uses GPT-5-thinking but samples multiple reasoning attempts and selects the best one using a reward model.

Across all modes, safeguards run in parallel at various stages. A fast topic classifier determines whether the topic is high-risk, followed by a reasoning monitor that applies stricter checks to ensure unsafe responses are blocked.

Over to you: What's your favorite AI chat bot?

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Luiz Mosciaro

How does gRPC work?

RPC (Remote Procedure Call) is called “remote” because it enables communications between remote services when services are deployed to different servers under microservice architecture. From the user’s point of view, it acts like a local function call.

The diagram below illustrates the overall data flow for gRPC.

Step 1: A REST call is made from the client. The request body is usually in JSON format.

Steps 2 - 4: The order service (gRPC client) receives the REST call, transforms it, and makes an RPC call to the payment service. gPRC encodes the client stub into a binary format and sends it to the low-level transport layer.

Step 5: gRPC sends the packets over the network via HTTP2. Because of binary encoding and network optimizations, gRPC is said to be 5X faster than JSON.

Steps 6 - 8: The payment service (gRPC server) receives the packets from the network, decodes them, and invokes the server application.

Steps 9 - 11: The result is returned from the server application, and gets encoded and sent to the transport layer.

Steps 12 - 14: The order service receives the packets, decodes them, and sends the result to the client application.

Over to you: Have you used gPRC in your project? What are some of its limitations?

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We started a new YouTube channel dedicated to AI fundamentals, infrastructure and models.

So far, we’ve published four videos.

We plan to publish 1 video every week.

Since this channel is new, we’re still shaping its direction and would genuinely love your input. 

If there are topics you’d like to see covered, please share them with us. Thank you!

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#systemdesign #coding #ai
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A nice cheat sheet of different monitoring infrastructure in cloud services.

This cheat sheet offers a concise yet comprehensive comparison of key monitoring elements across the three major cloud providers and open-source / 3rd party tools.

Let's delve into the essential monitoring aspects covered:
- Data Collection: Gather information from diverse sources to enhance decision-making.
- Data Storage: Safely store and manage data for future analysis and reference.
- Data Analysis: Extract valuable insights from data to drive informed actions.
- Alerting: Receive real-time notifications about critical events or anomalies.
- Visualization: Present data in a visually comprehensible format for better understanding.
- Reporting and Compliance: Generate reports and ensure adherence to regulatory standards.
- Automation: Streamline processes and tasks through automated workflows.
- Integration: Seamlessly connect and exchange data between different systems or tools.
- Feedback Loops: Continuously refine strategies based on feedback and performance analysis.

Over to you: How do you prioritize and leverage these essential monitoring aspects in your domain to achieve better outcomes and efficiency?

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🚀 Last Day to Enroll: Become an AI Engineer | By building, not just watching | Cohort 4!

After the amazing response to our first two cohorts, with nearly 1,000 people joining, we are excited to share that we’re opening the next round of Become an AI Engineer.

This is not your typical AI course focused only on tools and frameworks. The mission is simple: help engineers build strong foundations and practical end to end skills to grow confidently into AI engineering roles.

What makes this cohort stand out:
- Learn by building: You will create real world AI applications instead of just watching videos
- Clear and structured path: A thoughtfully designed curriculum that takes you from core concepts to advanced topics, step by step
- Live feedback and mentorship: Get hands on guidance from instructors and learn alongside peers
- Strong community: Learning is faster and more motivating when you are not doing it alone

We care deeply about skill building, not passive learning or surface level theory. The goal is for every participant to leave with the confidence and ability to build real AI systems.

Today is the last day to enroll before it starts.

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#AI #AIEngineer #MachineLearning 
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Deploying or upgrading services is risky. In this post, we explore risk mitigation strategies.

The diagram below illustrates the common ones.

Multi-Service Deployment
In this model, we deploy new changes to multiple services simultaneously. This approach is easy to implement. But since all the services are upgraded at the same time, it is hard to manage and test dependencies. It’s also hard to rollback safely.

Blue-Green Deployment
With blue-green deployment, we have two identical environments: one is staging (blue) and the other is production (green). The staging environment is one version ahead of production. Once testing is done in the staging environment, user traffic is switched to the staging environment, and the staging becomes the production. This deployment strategy is simple to perform rollback, but having two identical production quality environments could be expensive.

Canary Deployment
A canary deployment upgrades services gradually, each time to a subset of users. It is cheaper than blue-green deployment and easy to perform rollback. However, since there is no staging environment, we have to test on production. This process is more complicated because we need to monitor the canary while gradually migrating more and more users away from the old version.

A/B Test
In the A/B test, different versions of services run in production simultaneously. Each version runs an “experiment” for a subset of users. A/B test is a cheap method to test new features in production. We need to control the deployment process in case some features are pushed to users by accident.

Over to you - Which deployment strategy have you used? Did you witness any deployment-related outages in production and why did they happen?

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Joshua Debayo -

Nobody cares, just win | Stack up your wins & be so good they can’t ignore you.

Company Owner

Joshua Debayo

Anthropic just hired an SEO Lead at $320k.

In the last 6 months alone:

→ Meta brought on an SEO Lead ($211k)
→ OpenAI hired a Content Strategist ($393k)

This signals a clear shift.

SEO and content are no longer “nice to have.”
They’re core growth functions tied directly to revenue.
Top companies are investing heavily in talent that understands distribution, demand capture, and long-term visibility.

For SEO professionals who are adapting to the new landscape, this is a strong market.

For businesses, SEO has moved from a cost center to a growth lever.

For recruitment agencies that can source elite SEO talent, opportunities like this will be endless this year.

Signal Based Marketing For Recruitment Agencies & Wealth Management Firms

📌 Case study: AMS HealthCare Staffing placed 4 permanent EMTs for a training centre. 📌

Today, I recorded a quick video with one of our partners who runs a staffing agency based in Kentucky.

In the last few weeks, we helped him place 4 permanent EMTs for a training centre.

One client.
4 permanent placements.

All from outbound.

No posting content.
No fancy funnels.
No ads.

Just clean outreach.

This was done with a simple campaign we’re running on Instantly.ai –

 → Targeting the right decision-makers
 → At the right type of organisations
 → With the right positioning and messaging

We didn’t change his whole business.
We just got his offer in front of the right people… consistently.

In the video, he talks about:

→ What his outreach looked like before
→ How the campaign went from first message to placements
→ What it’s like working with JDebz Media behind the scenes

Apply here to partner with us → https://lnkd.in/eS6E_-wS

Keep working,
You’ve got this,
I’m rooting for you.

Signal Based Marketing For Recruitment Agencies | Click ‘Visit My Website’ To Learn More ↓

📌 Case study 📌 Upgrade Resources is averaging 3-5 warm intro’s every month with Hiring managers actively building teams 👇🏾

I’m pretty fortunate to attract some really good clients.

And honestly, I learn a lot from them too:

→ The way they communicate
→ The way they manage their time
→ The way they stay ruthless about growth

Karen Beltethon is definitely up there with one of the best clients I’ve ever had.

For context, Karen runns a recruiting firms finding skilled talent in specialised industries, including Mechanical, Sales, Industrial, and Medical.

She’s made me a good amount of money 🤣
Anytime I need a reference, she’s there backing what I do.

Where I’m from, we don’t take stuff like that for granted.

If you’re running a business and you’ve got genuinely good clients – but more importantly, good people – don’t take that for granted.

Do whatever it takes to get them exceptional results.

In this video, Karen shares her experience working with JDebz Media over the past 3 months.

Keep working,
You’ve got this,
I’m rooting for you.

PS – Go to the comment sections to see if you qualify to partner with us.

R.I.P. to AI agents that businesses don’t actually care about… 

I’m running a simple email script on Instantly.ai .ai that just works...

Stop selling AI agents.
Start selling tangible results. 

No one cares about your agents.

We launched a campaign for a recruitment client
→ Within 24 hours, we had positive replies.

Had an onboarding call with a client yesterday.
A few hours later, a reply already came in.

He even dropped this in Slack:

“That was quick.”

I CC’d him in.
Job done.

Onto the next lead.

It was a law firm in desperate need of legal assistants.
Simple post we found on LinkedIn jobs on this occasion.
A company still in need of talent 3 weeks after posting the role.

Here’s the reality:

❌ You don’t need fancy AI agents
❌ You don’t need insane personalisation
❌ You don’t need to overcomplicate outreach

What you do need:

→ One simple question
→ Backed by a solid case study
→ Followed up properly

That’s it.

Outreach doesn’t fail because of tools.
It fails because people try to be too clever.

Want the exact framework for FREE?

• Like this post
• Follow
• Comment “script”

Let’s run this up before it gets saturated.

Keep working.
You’ve got this.
I’m rooting for you.

PS - Apply here to work with us → https://lnkd.in/eddJ8CKc

We only partner wit recruitment agencies.

That is our only focus.

I don’t split my attention across SaaS, coaches, ecom, or random B2B offers.

I work exclusively with recruitment agencies.

That means 100% of my time is spent on:

→ Getting recruiters in front of hiring managers
→ Identifying hiring signals before jobs hit the boards
→ Crafting offers and outreach that actually get replies
→ Helping agencies build visibility and authority on LinkedIn

Recruitment is unique.
Your revenue lives and dies by pipeline.

Feast-and-famine cycles are real.
And generic B2B outreach doesn’t solve that.

Hiring signals work especially well for recruitment.
That’s why our systems are built specifically for this industry.

No learning curve.
No guesswork.
No diluted focus.

When you go niche, you don’t just get better results, you build better systems.
That’s why I only partner with recruitment agencies.

Keep working.
You’ve got this.
I’m rooting for you 

PS - Apply here to work with us → https://lnkd.in/eddJ8CKc

OpenAI  is hiring a Head of Sales Industries at $302k–$560k in SF.

Posted 11 hours ago on LinkedIn.  

That’s not a sales role.
That’s a revenue leadership mandate.

This isn’t about closing deals one by one.
It’s about building industry-level go-to-market motion inside one of the most influential companies in the world.

Titles like this signal a clear shift:

→ Fewer generalists
→ More vertical ownership
→ Sales leaders who understand ecosystems, not just pipelines

AI companies aren’t experimenting anymore.
They’re operationalising growth.

For senior sales leaders with deep industry experience, this is a top-tier market.

For companies watching closely, the signal is clear:

👉🏾 Enterprise sales is becoming more specialised, more strategic, and far more valuable.

For recruitment agencies that can place revenue leaders at this level, demand is accelerating and deal sizes are growing.

If they block you.
If they decline your call.
If they don’t want to work with you. 
If they say no leave me alone. 
If they say you just ain’t good enough.
If they say you sound like everyone else. 

Dial back in.
Go again. 
Focus.

Make them regret it all.

And later down the road when you’re crushing it & they come back and beg for your help, triple charge them.

If I help you for free you will NEVER take action.

Last year, I launched a free LinkedIn community on Skool.

We grew it to 298 members in 3 months.

I’m not an expert.
I’m not a guru.

I’ve just built a solid acquisition channel to find high-paying clients.
That’s it.

So it only makes sense to give back and help others who are finding this challenging.

I’m relaunching the community 28th Feb.

It’ll be affordable for anyone running a business who wants more clients using LinkedIn.

Stay tuned.
Until then, stay locked in.

Keep working.
You’ve got this.
I’m rooting for you

Apply here to partner with us → https://lnkd.in/eddJ8CKc

We’ve generated 426 positive replies from cold outreach on LinkedIn since November last year.

I’m giving away the exact blueprint for FREE.

Getting strangers to reply to your messages isn’t easy.

Most people either:

❌ Sound robotic
❌ Write paragraphs nobody reads
❌ Or pretend to be someone’s best friend before asking for a meeting

Steal this process for FREE.

None of that works.

We’ve spent years testing what actually gets responses from decision-makers.
And we’ve finally simplified it into a very direct LinkedIn DM framework.

No bs.
No fake rapport.

Just clear, honest outreach that gets replies.
If you like getting straight to the point, this will help you.

If you prefer sending three small-talk messages before mentioning business…
This probably isn’t for you.

Want the framework?

1️⃣ Like this post
2️⃣ Comment “JDEBZ”
3️⃣ Follow & connect with me

I’ll be sending it to 53 business owners who are currently struggling to book meetings consistently on LinkedIn.

If you want it, grab it before the list fills.

We managed to get 9 job orders for one of our recruiting clients.

No ads.
No inbound.
No job boards.

Here’s exactly what moved the needle:

☑️ Follow-ups ×8
☑️ Dialed-in target audience
☑️ Weekend outreach

Most recruiters stop after one message.

Some send a second.

Almost nobody sends eight.

That’s the difference.

The first message introduces you.
The second reminds them.
The third builds familiarity.
By the sixth, you’re no longer cold.

Consistency beats cleverness.

Second: target audience.

Not “healthcare.”
Not “construction.”
Not “tech.”

Specific titles.
Specific company size.
Specific pain.

When the message feels precise, replies go up.

Third: weekends.

This one surprises people.

Most agencies pause outbound Saturday and Sunday.

We lean in.

Decision-makers are quieter.
Less noise in their inbox.
More headspace.

Some of our best reply rates come between Saturday morning and Sunday evening.

The bar is lower than people think.

Most recruiters quit too early.
Target too broadly.
And send when everyone else sends.

Do the opposite.

Cold outreach isn’t dead yet.

You’re just messaging the wrong companies at the wrong time.

Let’s break down what hiring signals actually are.

Most recruitment agencies fail at outreach for one reason:

They spray and pray.
They message:

→ Companies that aren’t hiring
→ People with no authority
→ Prospects after they’ve already signed with another agency

Here’s the truth:

If a company has an urgent hiring need, you’re a hero.
If they don’t, you’re just noise.

Timing is everything.

Just the other day, we got a reply from an 8-figure construction company:

“Yes, please introduce me.
Thanks for thinking of me.
It’s all about timing for us.”

That’s not luck.
That’s a hiring signal.

So what are hiring signals?

Hiring signals are indicators a company is about to hire before the job hits job boards.

When you reach out at this stage, you’re not interrupting them.
You’re arriving exactly when they need help.

Here are the main ones we track:

→ Funding raised – cash in = hiring soon
→ Leadership hires – new VP = team growth
→ New office/location – new headcount needed
→ Key departures – gaps that must be filled
→ New contracts won – workload increases
→ Competitor expansion – forces them to scale too

This is who you target.

Not HR coordinators.
Not inbox gatekeepers.

You target:

→ Hiring managers
→ Department heads feeling the pain
→ Founders and CEOs during expansion

The difference is simple:

Old way:

100 generic messages
Random companies
Template offers
Cold, ignored outreach

Hiring signal way:

Fewer, targeted messages
Right company
Right person
Right time

Cold outreach isn’t dead.

Doing it without hiring signals is.

If you want systems built to:

• identify hiring signals
• craft outreach that lands
• get in front of hiring managers before roles hit job boards

That’s exactly what we do.

Keep working.
You’ve got this.
I’m rooting for you

PS – Apply here to work with us → https://lnkd.in/eddJ8CKc


Apply to partner with us → https://lnkd.in/eddJ8CKc

Most recruiters wait MONTHS for outbound campaigns to work on Instantly.ai. 

This one got 3 interested leads in 48 hours. Here’s why it worked:

To give you some context:

This client places paralegals, case managers, and intake specialists for personal injury law firms.

A tight niche.

Which makes results easier to produce.
Here’s what all our winning campaigns have in common:

→ The client knows exactly who they place
→ And exactly where they place them

One niche.
One target.

We run two simple campaigns:

→ Campaign #1 targets hiring signals (firms actively looking for talent)
→ Campaign #2 asks a simple question to uncover firms quietly looking for A-players

Most companies are always open to better talent.
You just need to ask the right way.

Last piece: volume.

This is a numbers game.

The more shots you take, the more goalzooo's you'll score.
The more qualified people you reach out to, the more replies you get.

Good offer + volume = results.

PS – Apply here to work with us → https://lnkd.in/eddJ8CKc

How to make intro's the right way

Most recruiters spend weeks trying to get an HR mangers to reply. Some spend a month.

It took us 24 hours. Here’s how: 

We launched a new campaign for a client.
Within a day, their recruiter had already sent a qualified candidate to a warm lead.

The hiring manager replied:

“We would need more information about the profile… we’d appreciate the option to interview.”

That’s not a fluke.
That’s positioning.

Most agencies:

→ Wait for roles to be posted
→ Blast InMails
→ Chase job ads
→ Hope for a response

We:

→ Target companies before roles go public
→ Lead with a relevant candidate
→ Create context before the ask

24 hours.
First interested lead.
Candidate submitted.

Speed isn’t about luck.
It’s about targeting the right people at the right time with the right message.

If you run a recruitment agency and you’re tired of waiting weeks for replies, book a walkthrough & I will personally show you 5 early hiring signals 99% of your competitors aren’t seeing 👇🏾

https://lnkd.in/eTJKvwDd

graphical user interface, text, application, chat or text message

Why posting on LinkedIn brings inbound leads to recruitment agencies.

Posting on LinkedIn isn’t about likes...

It’s about trust, visibility, and leverage
I started doing this for one of my clients.
Building her online presence alongside outbound.

What started happening:

→ More replies
→ Warmer conversations
→ Easier closes

Here’s why it works:

When hiring managers check you out (and they do), 
they don’t just look at your website.
They look at your LinkedIn

If they see:

→ Consistent posts
→ Clear opinions
→ Proof you’re active
→ A real human behind the brand

You instantly separate yourself from agencies spamming their inbox.
This is exactly what we’re doing with one of my clients right now.

→ Posting 3x per week
→ No ads
→ No fancy production

In the last 7 days alone, her impressions are up 55%
That’s thousands of decision-makers seeing her name for free.

The content is simple:

→ Industry insights
→ Short stories
→ Lessons from the field
→ Personality

You don’t need to post daily
 You don’t need to go viral

→ Monday
→ Wednesday
→ Friday
That’s enough

Recruitment is getting more competitive every year
New agencies launch daily
Brand is no longer optional

It’s your edge
Outbound + content is how you win

Keep working.
You’ve got this.
I’m rooting for you

PS – Apply here to partner with us → https://lnkd.in/eddJ8CKc

Sales Navigator is the only tool you need to sign high-paying clients.

Every client that’s paid me thousands per month
 → Came from Sales Navigator

No extra tools
No guru stacks
No distractions

Just Sales Nav.

If you’re serious about LinkedIn:

→ Building a clean lead list
→ Saying the right thing
→ Following up properly

You’ll make money.
That’s it.

When my Sales Nav runs out, we panic.

Why?

Because that’s where the deals come from.
$100/month

Best ROI you’ll find.
I’ve broken everything down already:

→ Lead list building
→ Messaging
→ Follow-ups

One video shows how I made $12k in 3 days
 On a $250 budget
 Using Sales Navigator only.

The tool isn’t the secret.
The system is.

Keep working.
You’ve got this.
I’m rooting for you

PS – Apply here to work with us → https://lnkd.in/eddJ8CKc

10 red flags we look for before partnering with recruitment agencies

At this stage of the business, we don’t work with everyone.

Not because we’re arrogant,
but because alignment matters.

If we partner with the wrong agency,
results suffer and reputations take a hit.

So here are some red flags we look for 👇

→ No follow-up culture 
→ No CRM or clear pipeline tracking 
→ No documented wins or case studies 
→ Unrealistic expectations on timelines 
→ Chasing every sector with no clear niche 
→ Haggle-heavy mindset around client acquisition 
→ Blaming the market instead of fixing the process 
→ Refusing to share basic numbers (fees, fill rates, revenue) 
→ Burned by multiple agencies and still looking for a silver bullet 
→ Founder doing everything with no bandwidth to handle opportunities 
→ Believing outreach is "beneath" them and relying only on referrals

None of this makes you a bad agency.
But it does mean we’re probably not a fit.

We work best with established recruitment agencies that:

→ Want a predictable pipeline
→ Can convert conversations
→ Understand systems take time
→ Are serious about growth

In the video, I break each of these down in detail.

Keep working.
You’ve got this.
I’m rooting for you 

PS - Apply here to work with us → https://lnkd.in/eddJ8CKc

taplio

The Battle

Alex Xu

Joshua Debayo

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Performance ScoreAlex Xu's LinkedIn Performance

Performance ScoreJoshua Debayo's LinkedIn Performance

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taplio

The Battle

Alex Xu

Joshua Debayo

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Alex Xu's LinkedIn Performance

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Performance ScoreAlex Xu's LinkedIn Performance