for them.

Following up on yesterday’s GitHub livestream, I’II use other repos to build these

deliverables before my interview:

• A coach who prepares me by asking tailored and personal questions

• A one-pager that summarises my strategy for the first 90 days in the role

See you in 4 hours (10pm, GMT+8):

Join the live

You spend so much time trying to get the hang of all the new terms, but you still get frustrated with the output because AI continues to produce slop for you.

This was not what the experts promised when they said that AI would make you more productive.

No matter how hard you try and how many more tutorials you watch, you struggle to get good outputs with AI.

Not forgetting the limited time you have left in your day to experiment with these tools, so it feels like you’re constantly failing.

But the key to using AI effectively is simpler than you think.

Your ultimate goal with AI

Forget about all the engagement bait you see online about AI running an entire company for you, while you just tell it something vague and it does everything for you.

For most people like you and me, this level of output is unattainable (unless you’re willing to burn tons of tokens and hit your rate limits).

To paint a more realistic picture of using AI in my workflows, I set this goal for myself:

AI executes a task and produces an output so good that it’s as if you did the work yourself.

From the outside, your employer would think that you did an amazing job, when all you did was delegate that task to AI.

This takes practice, and it’s not possible to achieve this with just a simple prompt of ‘do all of my daily tasks in my work for me’.

AI is your world-class football player, but it still needs direction from the manager so it performs well for you.

The CSS framework to become better at AI

To stop producing slop with AI, you need to give better instructions to AI.

If you are not specific in what you want it to do, it’ll continue producing slop.

If it doesn’t understand the outcome, workflow, and the bottleneck, it’ll give something generic.

If it can’t connect to the tools that you use in your work to produce that output, you’ll only be left with text instructions that you still need to find a way to implement it.

For AI to do the work that you are already doing, you need these 3 components (CSS):

• Context: Give AI every minute detail about the task you want it to execute so it knows the Outcome, Workflow, and Bottleneck (OWB) you face

• Skills: A repeatable SOP that you give AI to execute so that it will give the same output day in, day out (just like teaching a new intern at your job)

• System: You build the necessary infrastructure to connect AI to your files and tools, so it can do all the work for you

Once you master building all 3 components, you go from basic AI user to advanced power user.

This is possible for anyone, even though you have zero coding experience (like me).

All you need is to describe everything in plain English, and AI will handle the rest for you.

Let me build an AI-native workflow for you

If you’re still skeptical on how easy it is for AI to do your work for you, let me show it to you.

Tell me one painful workflow that you absolutely hate doing in your work or daily life.

Is it the same, boring task that you have to do every day that drains you?

Fill out this form below, and I’ll build out the entire workflow live on YouTube:

Build my workflow

Probably.

But as a football fan since 2006, I can’t help but think how managing a football team and AI models are exactly the same.

And when I saw some of the favourites like Portugal and Spain fumble games that they should easily win, it made me realise the reason why so many are struggling with AI.

The best players (models) won’t give you the results

Both Portugal and Spain had some of the best players in the world. And yet, they struggled to score against opponents that ranked way lower than them.

If you just looked at the teams on paper, it should have been an easy win for both (hopefully, you didn’t bet too much on that result).

But what happens when the tactics don’t seem to work, or bring the best out of these players?

Talent means nothing without direction.

If the manager can’t find the optimal way to use the players he has at his disposal, they will fail to win.

We have seen teams that had all the talent, but still came up short. I remember when England tried to make playing both Lampard and Gerrard work, but it didn’t produce the results because they had the same play styles.

The same happens when you’re using AI in your workflows:

You could have the best models in the world. GPT-5.5, Opus 4.8, Fable 5. But if you don’t know how to direct them to do work for you, it produces slop.

You spend more time trying to troubleshoot and correct the output than generating anything meaningful.

You need to be a better manager (director of AI)

While the manager doesn’t kick the ball during the match, they play the most important role.

They come up with the tactics and clear instructions for the players to perform on the pitch.

They tell the players exactly what they need to do in every scenario they can think of when playing against the opponent.

And in a similar way, AI needs direction from you (its manager) on how to beat the opponent (the bottleneck you’re facing at work).

Every model is like a world-class player that has all the talent in the world, but it can’t perform without your vision.

AI is excellent at execution. When you don’t give it your tactics and instructions, it starts underperforming by giving average outputs.

It’s impossible to just tell a football team with the best players in the world to just ‘score more goals than the opponent’.

And likewise, you can’t just tell AI to ‘make me 6-figures a month’ and hope that they can perform magic for you.

So if you want to master AI, you have to become a better manager:

• Tell AI what outcome you want to achieve

• Tell AI the steps you need to achieve the outcome

• Tell AI the bottlenecks you face when executing your workflow

Start by building a system

Lionel Scaloni won the last World Cup because he built his team around Messi.

And if you want to succeed with AI, you have to build a system that lets you direct your players (the models) in the most efficient way.

Yesterday, I shared the system that I use to work with AI effectively:

• I’m not locked in with one model provider and I can switch models anytime

• I have full access to all my data instead of having to export my data

• I create more meaningful outputs with my system (instead of just words through a chatbox)

I showed how I built this system in a livestream, and here’s the recording if you missed it:

Watch the recording

AI usage has become a KPI that defines your performance, where you have to prove that you’re building AI-native workflows or you won’t get a pay raise (or even keep your job).

So it feels like a second job that you didn’t ask for, yet you’re forced to learn everything by yourself, while you’re struggling to keep up with just your daily tasks.

And no matter what you try with whatever free time you have, it doesn’t seem to work.

You struggle with AI not because you don’t have the knowledge, but because you see AI in the wrong light.

Because while it can be an amazing tool that helps you do the tasks that you hate doing, you need to learn how to use it effectively:

AI is not a magic pill

If AI is accessible to anyone today, why aren’t we all rich yet?

Anyone can type into the chatbox ‘make me 6 figures a month, make no mistakes’, yet most people are not achieving that outcome yet.

And that’s because AI is not something that can magically drop a sum of money in your lap.

It needs to know what steps are required to make these 6 figures, before it goes out and does the work for you.

And if you don’t give it enough direction, it will come up with a generic plan that won’t give you any results.

So here’s how I prefer to see AI while working with it:

You are the conductor, AI is a highly-skilled musician

In an orchestra, the conductor actually plays the most important role, even though they don’t play any instruments (and likely aren’t skilled at any of them).

It may seem like they’re just waving their hands or baton around and it’s all just a show, but it’s far beyond that.

They decide the speed (tempo) of the music, the volume, and the style (tone) of the piece. The musicians take cues from the conductor on what the piece should sound like.

And in a similar way, you’re the conductor of an AI orchestra.

The music piece is the output that you want to achieve (for example, write a high-converting blog post for your company).

And as the musicians, AI is highly skilled at their craft, but they still need direction on how the piece should be played.

That’s when you come in as the conductor. You don’t need to be as highly skilled as the musician (AI) in playing the instrument, but you have the vision on what the piece should sound like (or what output should be created).

You just need to care about the ‘what’, while the AI creates the output (song) for you.

Become a better conductor

To continue providing value to your employer, it’s no longer about how many tasks you can complete.

That was the mistake that cost me my job too, where I was just a mindless task executor that AI could easily replace.

Instead, the key to surviving in this new age of work is to become a conductor.

How can you direct and manage one (or multiple) agents to execute the tasks for you?

And even though this seems extremely daunting, it’s not as bad as it seems.

All you need is to describe your problems and outcomes in plain English, without having to code anything at all.

And that’s what I’ll be sharing more inside The AI-Native Sprint, a 90-minute crash course that gives you a clear path to becoming AI-Native for busy 9-to-5 professionals.

If you’d like to join us, sign up for the waitlist here:

Save my seat

All the noise about having to use a new tool, or you’ll get left behind. When in fact, none of it really matters.

After watching the latest @navalpodcast episode, I gained more clarity on how I should use AI in my work, and here’s what I learned:

The tools, agents, or models do not matter

While everyone online insisted that you need to use OpenClaw, Hermes, Ralph Loops (and now /goal), Naval ignored all of it.

And he was right:

The days I spent troubleshooting all of my agents were completely wasted because I no longer use them.

I’ve tried plenty of prompts from AI influencers, but they didn’t give the output that I wanted.

If you want to use AI in a way that works for you, it doesn’t matter what tools or models you use, because all of them are interchangeable.

You will waste so much time getting it to work, and the opportunity cost is producing more meaningful outputs.

Or creating more value for your employer or your own business.

These are huge time sinks, but everyone wants to play with them because it’s the hottest thing right now.

Yes, it’s cool that agents can do all of the tasks you hate for you.

But so many things can (and will) go wrong.

You will get mediocre outputs or even worse, do something harmful that can’t be reversed (and the agent can only apologise).

The tools, models, and agents are all major distractions.

Most models are good enough to execute the tasks for you, so long as you give them this:

Focus on shaping the problem

If you want to become AI-native, the very first step is having a complete understanding of the problem you want AI to solve for you.

I call this the OWB of the problem:

• Outcome: What do you want to achieve?

• Workflow: What are the steps required to achieve that outcome?

• Bottleneck: What blockers are present in the workflow that prevent you from achieving that outcome?

You have to be clear about the outcome, so the AI stays aligned with you.

You have to be clear about the workflow, so you can give AI clear instructions and it executes the task just like how you’d have done it.

You have to be clear about the bottlenecks, which are potential steps that AI can automate the task for you (especially if they’re soul-sucking and mindless).

This is the boring part about learning how to use AI:

You can’t automate what you don’t know. You have to become the subject matter expert of the workflow so you know how to direct AI to replicate it for you.

Only then can you automate the workflow in an effective way.

So if you want a clear path to becoming AI-native without all the noise, sign up for The AI-Native Sprint waitlist.

This is a 90-minute live session where I’ll help you install an AI system that works for your specific workflows.

Save my seat

Everyone’s telling you that you’re going to get left behind. Your job, your livelihood, they’ll all be gone if employers don’t see you using AI effectively.

But no matter how hard you try, nothing seems to work.

And the frustration gets worse when you see others making millions with AI, while you have nothing to show for it. The gap continues to widen each day, and it becomes an additional stressor in your life.

The online posts you consume make you feel that the only reason that you’re failing is because you are lazy.

“You’re not putting in the work, you don’t want this enough to make a change”.

But if you’re already hard-pressed for time both in your work and personal life, how else can you become AI-native?

Laziness has nothing to do with it

Everyone knows that becoming AI-native is so important to stay valuable to your employer.

Without it, you could be the next person they decide to replace in the ever-growing list of layoffs.

So it’s not that you’re lazy, but you’re completely overwhelmed with what to do to get started.

• Should I buy a completely new Mac Mini, or can I just use my own computer with AI?

• Should I be using Discord or Telegram for my Hemes agent?

• What tools should I be using to achieve this task?

But these are the wrong questions to be asking when learning how to become AI-native.

Because it was never about the tools, models, or agents.

Instead, becoming AI-native is more about these questions:

• How can I describe a problem clearly enough to AI so it understands fully, with all the required context?

• How can I build a repeatable workflow with clear instructions so AI can execute it on my behalf?

• How can I break down one task into its simplest units so I know what to delegate to AI, and what I should be doing myself?

AI-native is not about the number of tools you use, but how deeply you understand the task.

If you have a workflow that you do every single day, there are steps that can be automated and ones that you should still be doing manually (or with minor assistance from the AI).

So stop listening to the ‘advice’ you see online. It makes you feel inadequate about yourself, and you’ll lose all motivation to continue learning AI.

And they just want your eyeballs to continue monetising the attention they got through fear-mongering.

You’d want to:

• List out every single task you do in your work or daily life

• Decide which are the ones you want to continue doing, and ones you wished someone could do for you

• Define the clear objective you want to achieve with that task you hate doing (Outcome)

• Break it down into atomic steps (Workflow)

• Identify blockers that prevent you from doing this task faster (Bottleneck)

• Work together with AI (preferably with voice) to turn this workflow into a repeatable Skill

It’s not the flashiest or something that you can brag about on Twitter.

But by looking at AI with a first-principles thinking, you can achieve so much more with it.

And this is what I’ll be sharing more in The AI-Native Sprint, a 90-minute live workshop for busy 9-to-5 workers who want a clear path to become AI-native.

We’ll work together to turn one soul-sucking task you hate in your job and build a repeatable system that completes these tasks for you.

While having proof that you’re AI-native so you stay valuable to your employer.

If this sounds like you, join the waitlist here:

Save my seat

All that hard work that you put in, just to be labelled as ‘lower-value human capital’, because a machine can do your work faster, better, and cheaper than you.

So of course you’ll hate AI when it takes everything from you.

But this is the harsh reality we’re in right now, especially when we’ve been doing things that AI can now do better than humans:

Capitalism is all about value flow

Love it or hate it, the core of capitalism was always about value.

And in the business sense, how you are making or saving money for that business.

The more heavily involved your role is in this, the more perceived value you have to your employer.

Most of our jobs are not directly tied to revenue or expenditure, so it’s far easier for us to be laid off.

We’re the ‘context carriers’, the ones in the middle that coordinate between the product and marketing.

Or the only 2 core roles of a company that won’t be easily replaced by AI.

With businesses and companies becoming leaner than ever with AI, there are fewer seats left for these context carriers.

Instead of fighting this uphill battle, a shift in perspective is much needed:

Become valuable in areas that AI can’t replace yet

If you’ve been spending time doing boring, soul-sucking tasks in your job, it’s time for a change.

These tasks were meant to be executed by machines with a clear workflow, not humans with a brain.

So instead of being the one who executes these mindless tasks, become the one who directs AI on how to complete them.

You give it clear instructions because you know:

• The outcome of the workflow, and what defines a good output

• The exact steps to execute the workflow

• What bottlenecks are present that prevent you from achieving the output

You build a clear SOP that lets AI execute it, as if you’ve done it yourself.

And because you’ve done the same task day in and day out, you’re the subject matter expert at it.

You are high-value human capital

So who cares if your employer thinks you’re low-value human capital?

They see the shiny features that AI brings, and are quick to believe that it can solve all the bottlenecks for them.

But if they don’t know the workflow, they can’t get AI to give them the outputs they need.

They still need someone to guide and orchestrate the AI, and that is your edge.

Learn to build AI-native workflows even without coding experience, all through talking in plain English.

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that gives you a clear path to becoming AI-Native for busy 9-to-5 professionals.

If you’d like to join us, sign up for the waitlist here:

Join the waitlist

But when you try out the latest model or the latest tool, you get frustrated.

Everyone makes it seem so easy. Just give Claude these prompts and you’ll become a 10x employee.

But how do you actually do that?

In the end, you burn all your time trying to figure out a tool that you won’t use in 2 months.

And you’re back to square one.

But this was never the way to learn AI.

Everyone wants to sell you on their latest feature

You’re constantly faced with over-sensationalised news whenever it comes to AI, because they generate the most clicks.

“Claude is replacing a new industry today, ChatGPT is killing all designers”.

The same, boring news that makes you feel completely inadequate about yourself if you continue consuming it day in, day out.

They do this to force you into chasing the latest feature or tool because they proclaim how amazing it is.

But sooner or later, you burn out if you’re constantly stuck in this hamster wheel of trying everything new.

You think that the only way to get ahead is to be up-to-date, but no one cares whether you used Opus 4.7 or an old model to produce an output.

The output has to be meaningful enough to justify the amount of time and money you’re spending on it.

And if you’re building something flashy just to impress others, it’ll never work out in the long run.

AI is just an excellent execution machine

So instead, view AI as a tool to either help you (or your company) make more or save more money.

That’s all it is, really.

And it usually comes in the form of saving time. Saving precious time that you were spending doing the same old repetitive tasks that you hate doing.

Saving precious time that you could better use for higher-leverage tasks that AI can’t fully help you with.

AI is excellent at following orders (like I was in my previous company), and it needs someone to direct it.

If you give it bad or vague instructions, it won’t give you the output that you want or need.

But if you give it a clear workflow and the bottleneck it needs to solve, it becomes a world-class intern.

So it was never about the tools, models, or features.

All AI needs from you are these 3 things:

The OWB of your problem

To be successful at AI, you need to explain these clearly to AI:

• Outcome: What do you want to achieve?

• Workflow: What are the steps required to achieve that outcome?

• Bottleneck: What bottlenecks are you facing in the workflow?

Give these clearly to AI in as much detail as you can, and you’ll be amazed at how much better the outputs are.

AI just needs the direction, or the ‘what’ from you.

Not everything can be outsourced to AI, and I’d prioritise giving them mindless tasks that you hate doing.

Because if your workflows are not clear and specific enough, you will spend more time troubleshooting them instead of getting anything usable.

Get better outputs with AI

Being AI-native was never about the number of tools you know, or the tokens you burn.

It’s all about giving clear instructions to AI so it can execute a task just as you would do it yourself.

And the only 2 things you should be focusing on are:

• Gathering your context: Giving AI detailed information about who you are, what you do, and how you think

• Building Effective Skills: Repeatable workflows that you can give AI so it executes the task based on your instructions

And after spending the past 4 months learning how to get good at AI, I built out a system that only concentrates on these, so you don’t waste your time on any other distractions.

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that helps busy 9-to-5 professionals master AI.

If you’d like to join us, sign up for the waitlist here:

Join the Waitlist

So those who prove they can burn the most tokens, even if it doesn’t produce anything useful, get the pay raise.

This is exactly what’s happening at Amazon, and quite likely at other tech companies too.

Employers have bought into the AI hype train and insist that everyone has to use AI in every single workflow.

Scoreboards are used to track your AI usage, and employees will use them in any other way except for being productive, just to burn more tokens in this ridiculous PvP game.

This will not end well

Goodhart’s Law was something I learned when I was interested in crypto airdrops:

These were essentially free money that you get when you interact with a project before they launched their token.

And because this ‘free money glitch’ worked so well, others started using the airdrop criteria used to reward past airdrops and use it for future ones.

Some rewarded the number of transactions signed on a blockchain, so one person can spin up 1,000 wallets doing the exact same actions.

It was hoped that all of them would be seen as ‘real humans’, so they can get the tokens and dump them (they’re also known as Sybils).

This is the perfect example of Goodhart’s Law:

When a measure becomes a target, it ceases to be a good measure.

And this is happening to AI usage as well:

When companies reward token burn, employees will do whatever it takes to burn more tokens than their colleagues.

Even if they’re using it for non-productive work.

Everyone starts to optimise for token burn, which directly goes against the business goals:

Your employer needs to see a return on token spend

Any employee and any tool will only make sense for your employer if they do either of these:

• Make more money

• Save more money

That’s essentially how you prove your value to your company, when you tie it directly to revenue.

But now that tech companies are prioritising token spend, it’s encouraging employees to waste money instead.

This can’t go on forever.

AI subsidies will soon be removed once VCs want to profit from their investments.

Tokens will get more expensive, and employees who focus on token burn will become a cost liability.

So instead of playing this ridiculous game, here’s what I’d do to prove that I’m using AI effectively:

Build real substance with AI

Instead of building a flashy website, I’d focus on building practical workflows with AI:

What are some bottlenecks in the company that stop it from making or saving more money, and how can I use AI to remove them?

There’s no need to pretend that you’re using AI well, when you have real outcomes and proof that you are AI-native:

You automate the tasks that you hate doing so that you have more time to do the high-leverage tasks that AI can’t do for you.

And if you want to learn how to build these workflows, I’ll share what I learnt after spending 4 months with AI in The AI-Native Sprint.

This is a 90-minute crash course that gives you a clear path to becoming AI-Native for busy 9-to-5 professionals.

If you’d like to join us, sign up for the waitlist here:

Join the Waitlist

Mo Bitar’s hilarious video painted the current state of AI hype in both tech and AI Twitter/YouTube, where the only way to keep your job was to sell hype to your employer.

Make it known that you’re the expert in AI and find ways to replace others before they replace you, so that your trigger-happy employer doesn’t add you to the layoff list.

You have to sell that you’re all in on AI, even when you’re a skeptic of it, just to be in the good books of your employer.

You have to constantly stuff AI buzzwords down people’s throats that you don’t even know how they work, just to stay relevant.

But, what if there’s another way to use AI in your job that keeps you safe without becoming another obnoxious co-worker?

You don’t need to know everything about AI

Being AI-native was never about using every single feature that launches every day.

The engagement farmers will constantly tell you that you’ll be left behind if you don’t try them, but it’s just a major distraction.

Most of the models and new tools that are hyped online won’t be relevant in the next few months.

I’ve never tried a Ralph loop before, and I don’t intend to, until I’m sure that I can give clear instructions to the AI to achieve the outcome I want.

Falling into the AI Tool Trap puts you into another hamster wheel that you can’t escape:

• A new feature gets released

• Everyone hypes it up on Twitter and YouTube

• You fall for the hype and try using it

• You spend more time troubleshooting than doing anything meaningful

• You get frustrated and revert to your old workflows

Which is ultimately just a huge waste of time.

So ignore all the buzzwords that everyone is throwing around.

Harness, context engineering, model quantisation, none of that matters to you now.

Instead, just focus on this:

Build practical workflows with AI to automate the tasks you hate doing

While automation seems like a dream that everyone wants to achieve, we don’t want to give AI the wheel so it runs our lives entirely.

There are still things that we enjoy doing, and AI’s main aim is to automate the tasks that we absolutely hate.

The soul-sucking, draining, repetitive tasks that we do day in, day out, that are mindless, and we wish someone could do them for us.

Those are the tasks that we should be outsourcing to AI, while we have more cognitive load to focus on the tasks that we enjoy doing.

So instead of building a flashy tool with the sole purpose of impressing your employer (who couldn’t care less), build with AI to achieve these 2 outcomes:

You prove your value by making or saving more money

The more directly tied to revenue your job is, the more valuauble are to your employer.

If you can prove that whatever tool you build with AI makes or saves money for the business, you will be safe.

And it all starts with this:

Find one painful bottleneck that stops the company from making or saving more money in your workflow, and build a solution with AI to remove the bottleneck.

There’s no need for any hype when you have substance and real outcomes (instead of burning tokens and raising the expenditure of your company).

What if you could become AI-native in less time?

You can build practical workflows with AI, even if you have zero technical experience like me.

After spending the past 4 months with the sole purpose of getting good at AI, I have built a system that helps me automate all the tasks that I hate doing.

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that gives you a clear path for busy 9-to-5 professionals to become AI-Native.

If you’d like to join us, sign up for the waitlist here:

Join the Waitlist

But I want to help you avoid going through all the frustrations as I did.

2 months ago, I was excited to build my OpenClaw agent.

I spent a week researching the perfect setup and model to use with it. But when I bought my Tencent Cloud VPS, I had no idea of the challenges I faced.

It became a nightmare just trying to get everything to work, where I didn’t even reach the stage of creating meaningful outputs with it yet.

And that’s when I saw the hype over Hermes, and decided to switch over too. Everyone was saying how it was better and the setup was more beginner-friendly.

So I bought another VPS (Hetzner). The setup was definitely easier (likely because I was more experienced after the OpenClaw nightmare), but there were still parts that kept breaking.

Of course, I’m not saying that it’s a bad technology. I’m still amazed at how I could pop into Discord, chat with my agent to carry out a task, and it just gets it done.

But at this stage, for beginners like myself, it’s a major time sink and distraction. You’ll likely spend more time getting things to work than doing anything meaningful with it.

I recently shut down my Hermes agent for this very reason,

and here’s what I’d recommend doing instead of building an agent:

The learning curve is still too steep

While installing OpenClaw, there were so many things I had to learn along the way:

• Reading and understanding the documentation (it’s still not beginner-friendly yet)

• Navigating the Terminal setup

• Finding files within the explorer

Most YouTube videos talk about how easy it is to set up OpenClaw, but they glaze over all the important details that a beginner would need to know.

And of course, a bunch of them were just shilling Hostinger as the VPS provider because of the affiliate commissions, even though it was one of the costliest plans.

Even though Tencent Cloud provided a one-click install for OpenClaw, I had absolutely no idea what to do next after launching the VPS.

And their documentation was horrendous too.

I knew nothing about SSH and the ability to connect to my VPS via the Terminal.

I didn’t know that VS Code had an extension so I could access all of the files within my VPS.

I didn’t know that I should not have installed OpenClaw on the root account.

There was no clear guide that went through all of these specifics, and I had to figure it out all by myself.

Using OpenClaw or Hermes via a VPS is not the only way, and the other method was running it on a local computer?

But what about those who do not have a spare MacBook lying around?

Because you wouldn’t want it anywhere near your main computer:

They are still not secure yet

I don’t fully trust either agent with my API keys or main accounts.

Not after what happened when I tried to create an onchain trading bot. I specified that each trade should be $20 as I was still testing out the trades.

But my mistake was giving it $400, and it decided to make a trade with my full amount.

It’s possible that an agent could do something irreversible to your finances or accounts, and the only thing it’ll tell you is: “Let me be honest, I made a mistake”.

At that point, there’s nothing else you can do except regret giving your agent full access to your accounts.

Because there’s no one else you can blame at that point in time.

Memory is still a major issue for most agents. Some can continue to execute the tasks, until one day, it decides to forget everything.

Are you willing to risk ruining your entire life to automate some of these tasks? I’m not, especially when it comes to my finances.

So the ideal way to set up your agent is by giving it completely separate accounts that are not linked with yours.

Though now, its capabilities will be limited and you can’t automate tasks that require your credentials.

Most APIs still lack scoped access and guarded controls, so it will take a while before agents become more useful.

But at this point, it’s more like an all-or-nothing scenario:

Either you give it full access to your account (which is risky), or none at all (which limits its usefulness).

Most of the features are just shiny tools

Unless we’re business owners who have extremely complex workflows, most of what we see online doesn’t apply to us.

Yes, they are cool and flashy. They make you feel productive by setting it up.

But in the end, do we really have a need for them?

These were some comments I’ve received on my YouTube videos that talked about Hermes:

Some people know how to set it up, but they don’t know what to use it for.

Others were saying how they are just shiny toys that don’t do anything useful.

I know this too, because I spent days trying to get a multi-agent setup to work, without even learning how to run OpenClaw in the first place.

I was inspired by the hype posts that I saw online, and convinced myself that I should set it up now so that I don’t have to worry about the infrastructure.

But instead, I wasted time on creating new Discord bots, getting them to work in each channel, and having the gimmick of agents being able to talk to each other.

And at the end of the day, I had no idea what I should be using my 5 different agents for.

I have shiny object syndrome and feel that the only way to keep up with AI is by trying new tools. But you’ll just be stuck inside the AI Tool Hamster Wheel of starting from scratch and having nothing to show for it in the end.

I no longer touch my OpenClaw agent, and I completely shut down my Hermes agent once I finally understood this.

You can’t automate what you don’t know

Agents seem fantastic because you could tell it to make you 6-figures every month, and in theory, it will help you to do just that.

But because you (and me) have no idea how to create a concrete plan to make these 6 figures, the agent will spew out average advice that anyone can give you, so there’s no edge at all.

Without proper guidance and context, because you’re not even sure of the workflow:

You can’t expect AI to do anything useful, since it’s an incredibly intelligent intern but is completely lost at execution.

That’s why I completely switched my focus to building Effective Skills:

These are repeatable workflows in your .claude or .agents folder that are invokable by a slash command, and you can run an entire workflow with it.

Here are some examples of the Skills I built or adapted from other Skill libraries.

Before using Hermes and OpenClaw, I was building out an Obsidian + LLM tool (which I call my Portable AI System), where the core of it was my:

• Context (who I am, what I do, how I think)

• Skills (repeatable SOPs)

And now, I’ve completely switched back to this system.

Yes, I lose out on automation and cron jobs. Yes, I will lose out to anyone who builds insane workflows and runs everything on autopilot.

But I’d rather spend my time nailing the gold standard of my workflow now, instead of stressing over Hermes or OpenClaw messing up everything because my instructions weren’t clear.

So I’ve made a promise to myself that I will only touch agents again once I’m confident that my AI-native workflows will consistently produce the output that I want.

Build workflows first before agents

It will be tempting to jump straight into building agents, because of how easy everyone online makes it seem.

But once you’re in, you could be spending hours on fake productivity, troubleshooting all the connections without producing real outputs from it.

So instead, focus on building Effective Skills:

Turn the current tasks that you have into an AI-native workflow.

Determine what tasks you hate doing, and find a way to outsource them to AI.

Test out the workflow through multiple runs (because you won’t get the right workflow immediately), before moving to full autonomous automation.

After spending 4 months learning how to get good at AI, I’ve settled on a system to build these workflows that can be integrated into your job (and even your daily life).

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that gives you a clear path to becoming AI-Native for busy 9-to-5 professionals.

If you’d like to join us, sign up for the waitlist here:

Join the Waitlist

What vibe-coded site can I build with all the fancy features that will wow my employer?

What multi-tool and agentic workflow can I build, so I can show I know how to use all of these tools?

But all of these are just distractions that waste your time.

Especially if you’re new to coding (like I was), and suddenly the world is your oyster. Where you could literally build anything so long as you describe it to AI.

As someone who fell into that trap (and eventually got laid off because I had nothing to show for with my AI usage except for burning unnecessary tokens), I should have known this instead:

Being AI-native is about building something useful

Your employer doesn’t care about the number of tokens you burned or the number of tools you integrated into your workflow.

But this is the only thing they care about:

Does this site, tool, or workflow help to make the company more money, or save more of it?

If whatever you built doesn’t do either of these, it’s just a poor ROI in the eyes of your employer.

They are paying you a salary to figure things out with an AI subscription they paid for, and will be disappointed if you have nothing to show for it.

Especially if you took time off from your actual work to build it.

So rather than focusing on something flashy, this is what I’d do:

Start with one problem

AI is excellent as a mindless task executor, or a knowledgeable but clueless intern. If you give it a clear SOP (Skill) and context of what task it needs to complete, it can give you an output that is close to what you expect.

Of course, this takes many iterations (and time) to reach that stage, but I ask myself this question when integrating AI in my workflow:

What is the single most annoying, soul-sucking, repetitive task that I absolutely hate doing inside this workflow, and how can I outsource it to AI?

Usually, this involves a lot of mindless clicking.

One task I absolutely hated was the copying and pasting of every image I had inside my article draft (written in Notion or Obsidian) to the publishing platform (Twitter, Substack, Medium).

Each image involved 5-10 seconds of copying and pasting, and you could imagine the time I wasted doing this for 20 images and repurposing across all 3 platforms.

So I built a workflow with the help of AI to convert my Obsidian markdown file into an HTML file with all the embedded images.

Instead of copying and pasting every image, I could now do this in 5 seconds per platform.

And it all started out with the task that I didn’t want to do anymore, and it was perfect for AI to execute it for me.

I’m sure that in your own workflows, there are tasks that you hate doing too.

The ones that drain so much of your time and you wish someone else could do it for you.

Now, you don’t need to hire an intern, and just outsource it to AI instead.

Being AI-native starts with understanding your workflows

Instead of trying to build something fancy, find ways to make or save money for your employer with AI.

And all it takes is:

• Understanding the workflows you do in your 9-to-5

• Working together with AI to find an automated solution

After spending 4 months learning how to get good at AI, I’ve settled on a system to build these workflows that can be integrated into your job (and even your daily life).

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that gives you a clear path to becoming AI-Native for busy 9-to-5 professionals.

If you’d like to join us, sign up for the waitlist here:

Join the Waitlist

And who knows if your company will be next.

It’s frustrating that your job has never felt so unsafe. At any moment, your employer could pull the plug and decide to go all in on AI.

And that leaves you with the unhappy process of finding yet another job, in a market where most employers are questioning why they should hire someone else instead of just using AI.

But, I don’t think this will happen to most of you, unless you fall into this group:

The mindless task executors are unsafe

I got laid off in Feb. At first, I felt that it was unfair.

But then, I finally understood why I was no longer valuable to my company:

I was excellent at following orders, but had zero agency. I was too afraid of making mistakes, and I became an executor, rather than a strategist.

As a marketer, I followed whatever that my manager planned to do. Write this article, create this graphic. And whenever I was asked to give ideas, I had none.

Because I was too scared that my ideas sounded stupid.

Since AI could do my job better, faster, and cheaper, it made sense why my employer would swap out my salary for a $100/month Claude plan.

For those who fall in the same category as me (a mindless task executor):

There could be a higher chance that AI replaces you.

AI is excellent as an execution engine. So long as you give it the direction, the expected output, and a clear workflow:

It will do that task almost to perfection.

And those who survive the next wave of layoffs are those who possess this skill:

Being AI-native starts with mastering Skills

Everyone talks about the high-income skills that you need to survive the AI era.

To me, the only one that you should focus on is building good Skills:

These are repeatable SOPs that you give to Claude, ChatGPT, or any other LLM, and they execute them according to that workflow.

Skills are the most effective when they tell LLMs to execute tasks that you hated doing every single day:

• Searching old docs and chat messages to find something that your teammate said previously

• Filling in claim forms manually for your claimable expenses

• Formatting reports so they look presentable

When employers want you to be AI-native, it’s not about how many vibe-coded projects you can produce every week.

Instead, it’s finding ways to integrate AI into your workflows:

What steps can be automated by AI, while which ones should a human still do?

When you free up your time and cognitive load by outsourcing the right tasks to AI, you become more valuable to your employer.

So if there was only one skill that I would master now, it would be to build Effective Skills with the SIGNAL Framework:

• S: Spot the bottleneck

• I: Integrate the stack

• G: Guide the LLM

• N: Nail the standard

• A: Automate the logic

• L: Loop recursively

So long as you can describe the problem and bottleneck in plain English, you can build a Skill without any coding experience.

You decide on the ‘what’, while AI finds the ‘how’ to execute it.

Become AI-native in less time by learning this skill

Building good Skills will be something that your employers will value, especially when it saves them time on the soul-sucking, boring tasks that you and they hate doing.

And after spending the past 4 months learning how to get good at AI, I perfected the SIGNAL framework to build any Skill to automate my work, instead of relying on another automation platform like n8n.

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that gives you a clear path to becoming AI-Native for busy 9-to-5 professionals.

If you’d like to join us, sign up for the waitlist here:

Join the Wailist

• Claude adds a new feature

• A new platform releases its MCP

• ChatGPT launches a new model

Infographics like this go viral online, and you’re overwhelmed by the sheer number of new tools to try.

Someone proclaims on Twitter that this new agent changes everything.

A YouTuber warns that if you’re not using this feature, you’re already falling behind.

And if you’re constantly consuming content like this, it’s the easiest way to feel inadequate about yourself.

You know that you need to catch up with the latest tools to stay ahead, but you don’t have time to spare.

So you burn whatever free time you have trying to understand how it works.

You get frustrated when nothing seems to be working, and ultimately give up with nothing to show for.

But this tool-chasing habit that everyone tells you to adopt is the completely wrong approach to becoming AI-native.

Tools are just distractions meant to trap you in this hamster wheel

The platforms and models that you use today won’t be the same ones that you use in 3 months.

So it’s a huge waste of time comparing them, because most are already capable enough.

Time that you can’t afford at all.

I know this because I was stuck in this loop too.

When I first started learning how to use AI effectively, I was too focused on choosing the right model and tool for me.

I saw everyone shilling a new tool that supposedly automates my entire life, and I wasted precious time getting it to work.

Not forgetting the days that I burned just getting OpenClaw and Hermes to work, when I could have used that time to create real outputs.

I was constantly stuck on this hamster wheel:

• A new feature gets released

• Everyone hypes it up on Twitter and YouTube

• I fall for the hype and try using it

• I spend more time troubleshooting than doing anything meaningful

• I get frustrated and revert to my old workflows

Whatever you see online is meant to trap you inside this hamster wheel.

They use fear-mongering tactics to convince you that your existing setup is useless (when that’s completely false).

And then I finally realised that none of these matters if I wanted to get good with AI.

What if you could become AI-native in less time?

There’s no point in spending all your free time trying to master Claude Code, when it could be replaced tomorrow.

Instead of focusing on the tools, I’ve learned that building an AI system that automates all the work I hate is the fastest way to become AI-native.

Your employer won’t care if you’re using the latest tools, if you can’t give them the outputs that they need.

And after spending 4 months learning how to get good at AI, I’ve settled on a system that completely removes all the distractions and focuses on automating workflows instead.

This is what I’ll be sharing more of in The AI-Native Sprint, a 90-minute crash course that gives you a clear path to becoming AI-Native for busy professionals.

If you’d like to join us, sign up for the waitlist here:

Join Waitlist

Time is so tight, and it’s impossible for you to try everything that gets shilled on Twitter or YouTube.

As a non-coder, you think that you’re at a disadvantage because that’s the only way to become AI fluent.

But that’s completely wrong.

You don’t have to spend time learning code to become good at AI. Of course, it’s more beneficial for you to know it, but anyone can build anything with AI now.

If you know how to describe your problem in plain English, AI can create a solution for it and execute it for you.

While I’ve been unemployed over the past month, I’ve been using AI extensively to get better at it.

So I’ve gone through all the failures, getting frustrated when things don’t work, and spent all the time finding out what works for non-coder like myself to become AI fluent.

And here’s the framework I wish I knew before I started:

What AI is, and isn’t

Before we start, let’s get this point out of the way.

AI is not a magic pill that can drop 6 figures in your lap with just one simple prompt.

If that were the case, everyone would be millionaires already.

But after using AI extensively since January, this is what I realised about AI:

It is an amplifier of systems by automating the soul-sucking, repetitive tasks.

But if you don’t have a system or clear workflows, it will amplify that as well. And the result comes out as an amplification of a messy system that you’ll spend more time troubleshooting the system than getting real outputs you could use in your life or job.

Trust me, I’ve wasted weeks of my life just getting OpenClaw and Hermes to work, while accomplishing nothing.

There’s no point in building agents that automate every task for you, when you don’t even know your workflow well enough.

It’s impossible to hand off everything to AI completely. You still need to give inputs on what works and what doesn’t, based on your own taste and judgment, while AI is just the execution engine.

Quoting from Albert Romero, who introduced me to this concept:

AI collapsed the how, so the only thing that matters is determining on the what.

So to get good at AI, you need to get good at determining what to use AI for (because it’s great at some things, but not at others).

And this is how you can do it:

The FOCUS Method to get good at AI

With all the shiny new tools that are constantly being shilled on Twitter and YouTube, you’re likely overwhelmed.

And with every new feature that Claude or ChatGPT launches, you’re tempted to throw whatever that you’ve built away to start using this new feature.

But to become AI fluent, you just need focus.

Focus on the fundamentals, understand what AI can do for your workflows, and then try the tools afterwards once you have a better understanding.

With time as your most precious resource, we need to optimise it so you don’t waste it on actions that won’t help you become AI fluent.

This is the FOCUS Method that I use to learn AI:

• F: Filter the noise

• O: Optimise your context

• C: Concentrate on one win

• U: Use it daily

• S: Scale your Skills

And here’s each step in detail:

Filter the noise

AI Twitter and YouTube are the perfect places to make you feel completely inadequate.

There are so many proclamations of how everyone is:

• Running a fully autonomous company with AI

• Replacing a $120k/month department with just Claude Code

• Using 10 agents to get them to 100 million views

But 90% of what you see online is just noise. The ultimate skill to possess right now is skepticism, because nothing can be trusted.

It’s just a waste of time scrolling on the timeline, when you could be building something that solves a painful problem in your own life.

I found one way to solve this is by DM-ing the account that makes all of these insane claims. How did they actually do it?

If they can’t answer you clearly, then it’s likely just farming engagement.

It’s impossible for someone to become an expert at a new feature just 5 hours after it was launched.

So don’t fall into the trap of feeling bad about yourself by comparing with accounts that do nothing, yet are acting as they do.

I approach this by taking inspiration from what others post and see how I can add it to my current AI system.

Since the AI knows me enough after already executing multiple tasks for me, it provides a recommendation on whether this new feature is something worth integrating or not.

And it all starts with providing this to AI:

Optimise your context

AI works best when it knows who you are, what you do, and how you think.

And the easiest way to provide this is through your past work (blog posts etc.) or conversations.

Otherwise, it’ll just give you generic outputs when you don’t provide it with enough direction on what it should focus on.

So throughout your day, start compiling everything that you’re willing to share with the AI, so that it understands you better:

• Your tweets, articles, and meeting transcripts

• Your chat history with AI platforms

• Important documents about your personal life or work

The more you share, the more AI knows you better. But of course, there’s this fine line of privacy where anyone who gains access to your system knows everything about you.

So it really depends on how much you’re willing to share.

LLMs love markdown (.md) files as it’s LLM-friendly, so I spent my time converting all of my files into this format.

While it was still a painful process, I’m grateful that most of my work was done in Notion, so I could export my pages as markdown files.

For any files (.docx, pptx) that are not already in the markdown format, I like using this MarkItDown library to convert anything into a .md file that I can give to the LLM to understand me better.

This is a one-time task that can be time-consuming at the start, but it saves you the hassle in the future.

Once you have all of your context compiled, you can throw it inside any LLM to analyse and understand everything about you.

This also means that proper file organisation is crucial inside your AI system.

For mine, I organise it between Context and Tasks, so everything about me goes into the Context folder that is used by the AI to complete the Tasks that I want to complete.

Concentrate on one win

I have a bad habit of shiny object syndrome, where I have so many workflows that I want to automate with AI.

Even when one workflow is not fully fleshed out yet, I want to immediately move on to the next one.

And that could be a problem that you face too.

It’s better to fully flesh out one workflow that consistently gets you the output that you need, instead of building multiple AI-assisted workflows that require you to constantly troubleshoot them.

Something boring that gets the job done is way more valuable than a flashy workflow that you’re just building to get views on Twitter.

Everything else about AI doesn’t matter, like:

• What model you should use to achieve the task (because most are already capable enough)

• What tools you should be using (some of the ones that exist today won’t last in a few years’ time)

We don’t need to waste time worrying about the most cost-efficient model when we’re on a monthly plan and can swap models easily while finding the best one that meets our needs (some are even free too).

And this is only possible when you build a Portable AI System that lets you switch out components (like your models or tools), and it still gets the job done.

So instead of worrying about any of this, find one bottleneck that’s stopping you from doing a task better or faster, and work on using AI to remove that bottleneck.

This involves building Effective Skills, which are hyper-personalised SOPs that give you high-quality outputs, no matter what model you use.

And you don’t need to write them out at all, or even know the right script that automates the workflow.

Just by going through a dry run together with the AI, it has enough context to build the Skill for you.

I shared more about the framework I use to build these Skills here.

Use it daily

AI is not a tool that you can use once and get good at instantly.

It has to be something that you do every single day, just like practising the piano.

So commit to spending at least one hour a day playing and experimenting with AI.

This doesn’t mean you have to build out a vibe-coded platform every day.

Instead, work together with AI on finding bottlenecks in your workflows, and finding ways to integrate AI inside.

One of the first tasks that I automated was generating summaries for YouTube videos.

In the past, this was the workflow that I used:

• Copy the URL from YouTube

• Paste it into a YouTube-to-transcript site

• Copy the transcript

• Paste it into ChatGPT

• Paste my summariser prompt inside ChatGPT

• Generate the summary

This took about 2-3 minutes and a lot of mindless copy-pasting and getting frustrated when I couldn’t find the summariser prompt.

So instead, I built a Skill that helps me achieve this:

I type /learnings-youtube with the URL, and it captures the transcript and generates the summary as specified by my prompt without me doing anything else.

Everything is saved inside my Portable OS, so the LLM can retrieve it in future when I ask it to.

From 2-3 minutes of work being reduced to less than 30 seconds.

And all of this was done with the help of AI.

I gave it this problem and the workflow that I wanted to achieve (including where the files should be stored), and it came up with the solution itself.

I’m sure that many of your workflows can be automated by offloading all of the boring, soul-sucking tasks that you hate doing to AI.

And it all starts with:

• shaping your problems together with AI

• Solving them with automated scripts that AI generates for you

• Telling AI what went right and wrong

The more you work with AI, the more you understand its capabilities, and the more work you can outsource to it.

Scale your Skills

After building out a single Skill, it’s time to start scaling them.

You now have the knowledge to:

• Identify the bottleneck in your workflow

• Shaping the problem together with AI

• Directing AI to the ideal output

And this can now be used across every workflow that you hate doing.

But this final step takes practice. You have to keep playing with AI in this manner to get better at building Effective Skills.

Becoming AI fluent doesn’t mean getting another certificate

It is hard to learn AI when you’re already pressed for time every day.

Your time is precious, so don’t waste it on getting another certificate that means nothing.

Instead, what others are looking out for are real workflows that you have automated for yourself or your clients that save them time.

Being AI fluent doesn’t have to be anything fancy. So long as you can show a result for someone (especially saving time), your perceived value increases to your employer or future clients (just like what I’m trying to do here).

But of course, please don’t farm engagement like what most are doing on Twitter or YouTube.

In the end, the only two things that differentiate you from everyone else are your:

• Context: AI will continue giving generic outputs, unless you give it enough context about who you are, what you do, and how you think

• Skills: Repeatable SOPs that any model can follow to automate all the boring, soul-sucking tasks that we hate doing

Anything else can be swapped out if you build your AI system with portability in mind, especially when the AI cost war will make subscription plans worse in the future.

So to automate the tasks that you hate with a system you truly own and is not locked in with any providers:

Get the blueprint to build your Portable AI System here.

I’m trying to understand who currently reads Stack Signal and how I can make it more valuable for you.

If you have two minutes, take the reader survey here, and I’ll share what I find in a future issue.

I write about AI every week for non-coders who are overwhelmed by all the noise online. If you want to build workflows that automate the tasks that you hate doing, subscribe below:

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I get it, because that was exactly me back in Jan 2026 as a non-coder.

Everyone on Twitter is giving you the wrong advice:

• “Steal my prompt to make your first 6 figures with AI”

• “Use the latest models, or you’ll get left behind”

• “Learn prompt engineering so you don’t get replaced by AI”

I tried all the latest tools, watched all the YouTube videos out there, but nothing seemed to click.

I kept getting generic outputs that I’d never use.

I spent more time teaching AI how to perform the tasks I did with it a few days ago, rather than executing them.

After locking in for 2 months (while I was on hospitalisation leave), I realised I was approaching AI the wrong way.

To be truly successful with AI, you need to build an effective AI system.

And all of them have 2 things in common:

• They include strong context about the user

AI will continue giving generic outputs, unless you give it enough context about who you are, what you do, and how you think.

The more context you give it, the more it understands about you, and the less time you take to re-explain yourself with every session.

• They have Skills that automate their work

Models are just our employees, and we need a way for any model to understand how we work and execute tasks according to how we want.

Skills are these repeatable SOPs that any model can follow to automate all the boring, soul-sucking tasks that we hate doing.

It was never about the best model or prompt.

So long as you can articulate your workflow and problem clearly, AI will help you automate any task (even if you have no coding experience).

You don’t need agents or a complex setup

The more time I spend with AI, the more I realise how Hermes and OpenClaw are major distractions.

Yes, they are effective and can do amazing work. But only for those with clear systems and workflows that AI can understand and execute immediately.

While for the rest of us, it’s a waste of time building out these automations when we don’t even know what to automate yet.

There’s no need to use all these shiny tools. Instead, we just need a working system where we can use AI to free us from all the tasks we hate doing.

Over the past 3 months, I’ve been trying and failing with different setups, until I found the most adaptable way to build one.

I call it the Portable AI System, which is designed to:

• Understand your workflows and save you time by doing the tasks you hate

• Be flexible enough so you can switch model providers and still get a similar quality of outputs

In this course, I am focused on building out a system that saves you 2+ hours every week.

And all you need is a clear path to achieve that, without any fluff or over-sensationalised news.

Here’s what you receive that makes it very difficult to fail:

• The fastest way to set up an AI client, connect a free model, and run your first Skill in one session

• The fundamentals of the Portable AI System, so you know exactly how AI can automate the work you hate without wasting time

• A pre-built repo structure that gives you the starting point, which you can structure and re-organise as you wish

• Learn the SIGNAL framework to turn any annoying daily task into a repeatable Skill (no matter which model you end up using)

• How to turn your prompts that are found in 5 different folders into a system that works on any model you choose

I created this course for my past self.

This is the course I wish I had, so I didn’t waste my time watching YouTube tutorials or trying the latest shiny tool that added nothing to my workflows.

This is what I would do if I wanted to become good at AI in the shortest time possible, without worrying about the best model or prompt.

If you want to stop getting overwhelmed by AI and start building a system that is adaptable to any new change, now is the perfect opportunity to start.

Navigating the course

This course is a full newsletter with multiple sections. Depending on your device, here’s how you can get to your intended section:

• Desktop: A table of contents can be found on the left-hand side

• Mobile: No navigation, but your scrolling progress will be saved so you can go back to where you left off

The 8 components

The Portable AI System is made up of 8 components for it to fully function:

Read more

Models are more expensive now, rate limits are getting worse on coding plans, and us consumers are all trapped in subscriptions that no longer serve our purpose.

For those who don’t have any additional laptops or GPUs lying around (like me) and don’t want to buy new hardware just to run local models, the options of using AI are getting increasingly limited.

Within these few weeks, we’ve seen instances like:

• GitHub Copilot pausing signups for their Pro plan (just when I wanted to try it out)

• Claude silently removing Claude Code from the Pro plan and putting it back (likely after the backlash)

This is only the start, and other model providers and aggregators will follow the same route of nerfing their plans:

Because it no longer makes any sense for them to give out inference that costs more than what consumers are paying in their plans.

My first experience with AI was through Claude Code and I grew to love the Claude Code extension inside VS Code because of the great UI.

But when I wanted to switch from Claude and try out other models inside my system, it was complicated and I spent too much time troubleshooting it.

It’s likely easy for someone who understands code and the config.json files, but I didn’t want to continuously find different configs each time when I switched plans.

And that’s when I finally realised something about the state of models today:

Models are just commodities

Whenever a new model launches like Opus 4.7 or GPT-5.5, the whole timeline goes crazy.

There are many claims of ‘it’s so over’ all over the timeline because the new model is more capable and will replace jobs in many industries.

The posts go on to show the new benchmarks of the latest model updates and how it surpasses other existing models.

Everyone on Twitter tells you to upgrade to the latest models to get the latest benefits.

Most of it is usually engagement bait. But because of all the over-sensationalised news and fear-mongering, you’re convinced that the latest models are the only way to get work done with AI.

You drop everything and sign up for the latest Claude or ChatGPT plan, having to start again from scratch because the model knows nothing about you.

You waste time re-explaining everything to the model, because importing everything from your previous system is too time-consuming.

And all that time is wasted when you could be producing real outputs that go towards achieving your goals.

So now comes the most important question:

Do you really need the latest model to suddenly become good with AI?

If your work involves building and running applications that are used by millions, probably.

But for non-coders like myself, we don’t need the best model to execute tasks. The ones that can give us the output that we want and at a lower cost is more ideal.

As compared to running a more expensive model that gives us a slightly better output.

But maybe it’s because I’m using AI differently from others. Instead of consistently vibe coding (I did build a few projects but it’s not my priority), I’m using AI to automate all the tasks that I hate doing.

The soul-sucking, repetitive tasks that do not require a lot of thinking, but I still have to do them for my life to function.

And that’s where I see AI is most effective at:

I can outsource these tasks that I hate doing to AI, while I have more cognitive load to worry about the tasks that I enjoy (or those that require thinking).

Functionality beats aesthetic, and all that matters is getting a result.

One example I use AI now is to generate HTML content for my articles, so I can repurpose them in 1 copy-paste function.

In the past, I had a nightmare when I wanted to repost my article across Substack, Twitter, Medium, and any other long-form platform, mainly because of the images.

Copying directly from Notion or Obsidian didn’t work. My old workflow involved manually copying and pasting every single image into each section within the article.

And it was a mindless, tedious chore to do this same task over and over again across all of these platforms.

But now with the help of AI, I generate an HTML file that I can easily copy and paste directly into any long-form platform.

Though I still have to do it manually for Twitter, but it saved so much of my cognitive load.

This is just one example of how AI has become an executor of the tasks that I hate doing.

And back to the point, it doesn’t matter what model you’re using.

Most models are capable enough to execute daily tasks that we hate doing. Just tell AI the problem that you’re facing, the platforms you use to get the task done, and it will figure out how to do it.

At the end of the day, models are just commodities.

You can easily switch between any of them and still get similar results.

Of course, the frontier models will give you better results. But sometimes, a lower-quality output at a cheaper cost is good enough.

And I wanted to build an AI system that gives me that portability to switch between providers at any time:

Most of your AI system is easily interchangeable

When I first tried Obsidian many years back, I thought it was extremely bare bones with a steep learning curve (and decided to use Notion instead).

But when I tried Obsidian again to build out my AI system, I finally learnt how to appreciate its open system.

For my existing Notion system, I had to use an API or MCP for the LLM to understand and work with it, which was more tedious.

Meanwhile, Obsidian was just a collection of folders and markdown files that the LLM can easily read (.md files are LLM-friendly) and edit at any time.

While all my notes truly belong to me, instead of being held ‘hostage’ by another software company.

This goes along well with @kepano’s whole principle of self-guaranteeing promises:

One that is verifiable, non-reversible, and does not require you to trust anyone.

Anything within your current system can break, or better options will pop up.

Is your system adaptable enough to switch any of these components and still function?

Ever since I wanted to move away from Claude Code, I’ve been obsessed with building such a system, which I now call the Portable AI System.

Inside, I have 8 core components that are required for my system to function:

• *P*ersonalised Context: A file system that includes your context and tasks so any model knows who you are, what you do, and how you think

• *O*wning Your Keys: A BYOK provider that lets you easily switch between models

• *R*equests: Your input (voice or text) that gives commands so the AI runs tasks for you

• *T*hought Processor: The LLM acts as the brain that receives an input and provides an output

• *A*ctionable Workflows: Skills that are repeatable and can be invoked by any LLM

• *B*uilding Capabilities: Tools like MCPs or APIs that increase the capabilities of your system to external platforms

• *L*inking Across Devices: Syncing your portable system across multiple devices

• *E*diting & Managing: Using a markdown editor to write and manage anything in your system

Most of my system is interchangeable:

• Models will come and go, and I can switch to the most cost-effective one that gives me the outputs I want

• There are many BYOK providers that let me add my own API keys to use their platform (just that I like OpenCode for now)

• I can use any tool for voice requests (just that I bought a lifetime licence for Letterly)

• There are plenty of markdown editors (but Obsidian is still king)

• APIs and MCPs can be switched whenever I use a new platform

• GitHub is the best for syncing and version history, but a new platform may pop up at any time

At any time, I can switch any of these 6 components to something newer or better. And the system still continues to function while giving me the outputs that I want.

Though there are only 2 components that truly belong to me (and that’s how I won’t be replaced by AI):

Nothing else is more valuable than your context and skills

The best outputs from a model come when it has these 2 things from you:

• Your context (a file system that helps the LLM understand who you are, what you do, and how you think)

• Your Skills (repeatable workflows that you’ve built out for the AI to execute for you with clear instructions on your specifications)

If you ask AI to ‘write a viral tweet’, the output will be average because it knows nothing about the type of content you write, or the templates that you want it to follow.

But if you have a library of your past tweets that display your tone and your main ideas, and you have some tweet templates (or a prompt) that you want to follow:

It will give a tailored output that fits your needs, instead of something that is so generic.

And that’s something that becomes the most valuable asset of your entire system:

• Your thoughts, perspectives, and the type of content you consume

• The specific workflows that you use to get an output that you want

Everyone has their own way or workflow of doing the same thing, like writing an article or managing their to-do calendar. The steps we take to complete the same task will be different and cannot be copied.

And if you can articulate that workflow clearly to AI (via creating a Skill), it can execute it in exactly the way you want.

Which is why I don’t recommend downloading and using someone else’s skill, and I prefer adapting those skills to fit my own context.

The models you use are the commodity, while your context and Skills are the true assets that are irreplaceable and are the core for your system to continue functioning.

Any of the 6 components can be swapped out from the system, but context and Skills are eternal.

You need to have zero dependencies in your system

Most YouTube videos out there share all the amazing things with Claude Code.

But you can execute the exact same tasks in a system that doesn’t require you to just be locked in with Claude or any other ecosystem.

There are many models that will offer promotions or free trials, just like what OpenAI did for me.

Or you could get sign-up bonuses like Kiro with 500 free credits that you can use.

And because my system is flexible enough, I can switch between any IDE or model and still get the outputs I want (because everything is written inside of my Skills).

Especially when a model or coding plan gets progressively worse or more expensive.

The AI cost war will only get worse, and the only way to escape is by building a Portable AI System:

One that lets you automate the tasks you hate with a system you truly own.

So focus on building out your context and your Skills, while worrying less about what models you are using.

The answer to ‘what is the best model’ is ‘all of them’. Give them the right context and workflows, and they’ll execute tasks in the exact way you want them to be done.

Learn more about how to build out good Skills here.

If you want more guidance on building out your own Portable AI System, Signal Starter is a live session where I’ll be going through the entire process step by step.

In 90 minutes, you’ll:

1. Identify one painful workflow you actually want to automate (not what someone else tells you to)

2. Build the Portable AI System from scratch with the 8 components

3. Turn that task into a working Skill you can invoke anytime

No coding experience is required, and I’ll show you how to build your system your way and automate all the tasks that you hate doing.

The session will be live on 29 May and includes the full recording if you can’t make it:

I want in

The AI cost war is just getting started, and inference will no longer be as cheap as it currently is right now.

I’m facing this constant struggle of managing my usage very carefully to avoid being rate-limited, and it has been a pain (and somewhat stressful experience).

Those who constantly hit rate limits will be those who are in a constant loop of asking AI a question and it executes, which burns too many tokens.

Meanwhile, the ones who survive are those who know how to spend their tokens wisely. Instead of prompting simply every time, they build out repeatable workflows that have clear instructions for any model to execute.

With models now being a commodity (so many are good now), the only key differentiating factor becomes the workflow and instructions that you give AI to execute.

And today, I’ll be showing you that key component you need in your system to escape this cost war:

Models will only get more expensive from here

Vibe coding used to be fun. AI used to be accessible to anyone who subscribed to a Coding Plan.

We saw posts about how someone can vibe code a cool tool that helps them with their workflows, and I remembered no one complaining about the rate limits.

A few months back, I knew nothing about this term. I never worried about them because it seemed like I would never hit them with my paid subscriptions.

I just kept prompting simple requests and asking AI to make simple changes to my vibe-coded projects (which was the completely wrong way to do it).

But it’s not the same now. If I were to do the same actions, I’d likely hit my rate limits with just a few requests (when I could have made the edits directly via a code editor).

Models used to be cheap because model providers wanted to acquire more users. They cut costs and give usage limit promotions to lock you in with their annual plans. Most are not profitable, and that has to change.

Right now, most of our costs are still being subsidised by the VCs. But it’s only a matter of time before model providers are forced to make profits (they’d want a return on their investment).

And that’s when AI will no longer be for everyone. We start to face the real costs we incur when using AI, which are exponentially higher than the costs we have right now.

A simple chat with the AI will use 20% of your weekly limit. Soon, AI will only be accessible to those who can afford the higher-tier subscriptions.

While everyone else is left with the scraps of poor rate limits or low-tier models.

Rate limits get hit more easily, and providers decide to cancel third-party/agent integrations when it becomes too expensive to run them with a coding plan.

The $10/month Coding Plans will be pitiful, and we barely get any usage out of them. Or, they could just be like Z.AI and increase their subscription plan costs without informing anyone.

And those who use AI as a simple chatbot without building anything repeatable will be stuck in this cost war.

Most people are too focused on the models

Every now and then, a new model will launch with all the hype and engagement farming on the timeline, just like Opus 4.7.

Most talked about the benchmarks that they exceeded every other model.

But does that matter to you?

The ‘best’ model may not always perform the task in the way that you like or expect. In other cases, a ‘lower-tier’ model could give you the same output at a lower cost.

The ‘best’ model today won’t be the same tomorrow, since everything changes so quickly in AI.

Maybe it’s because I’m using AI differently from what others are used to.

I’ve stopped vibe-coding extensively, unless there’s something that I’d believe is truly useful to myself (like FreeTierModels).

Instead, I’m using AI to automate tasks that I hate doing myself. The soul-sucking, repetitive actions that I do every day can be offloaded to AI.

So instead of stressing over what model you should use, I’m focusing on building this instead:

You need to build a Portable AI System

I’ve been obsessed with building a system that lets me easily switch between any model or subscription, while still getting good outputs.

Everything needs to be modular and flexible enough so all of my data remains truly mine, while the model is just the executor that automates the workflow for you.

These are the 8 PORTABLE components of my system:

• Personalised context: A file system that includes your context and tasks so any model knows who you are, what you do, and how you think

• Owning your keys: A BYOK provider that lets you easily switch between models, even within the same chat window

• Requests: Your input (voice or text) that gives commands so the AI runs tasks for you

• Thought processor: The LLM acts as the brain that receives an input and provides an output

• Actionable workflows: Skills that are repeatable and can be invoked by any LLM

• Building capabilities: Tools like MCPs or APIs that increase the capabilities of your system to external platforms

• Link across devices: Syncing across multiple devices

• Edit and manage: Using a markdown editor to write anything in your system

Any of the external components (like the models) can be switched out and the system still works.

We are not locked in with one provider, while still having access to all of our data (instead of having to export it).

And the only way for the entire system to work with any model is through repeatable workflows:

Skills let you Build Once, Switch Anytime

Systems thinking is something that I didn’t care much about. But the more I looked at how any business runs, everything has an SOP.

Someone like McDonald’s, which I quote this idea that Ali Abdaal makes in his videos:

McDonald’s has built systems so easy and repeatable that they can replicate across any outlet.

Any employee can be hired, and they can immediately start contributing to the system because of the clear workflows and SOPs that have been built.

And it’s not just McDonald’s, any food outlet is the same:

• Someone takes orders

• Orders are passed to the kitchen

• The kitchen prepares the food and passes to the server

• The server gives the food to the customer

Everyone has well-defined roles and workflows with clear instructions on how to execute the tasks they are expected to.

We can build our AI system in the same way.

Our employees (models) can be switched at any time, while we get the same output because we gave them clear instructions on how to execute the task, regardless of the model we choose.

If you’d heard of Claude Skills before, these are repeatable SOPs written in .md files that tell Claude how to execute anything to get an output that you want.

In my current AI system, I’ve added 190 different Agent Skills (it’s not just limited to Claude) to automate all of the tasks in my daily life.

Some are ones that I built, others I’ve downloaded or adapted from skill repositories online, and these let me carry out certain actions like:

• Converting my long-form article into an HTML file that can be easily repurposed into Substack, Medium, and LinkedIn

• Fetching an onchain summary from DeBank from the wallets that I track

• Syncing between my lead magnets and email platform

• Generating colour palettes for different sites and my own brand

• Adding YouTube transcripts and summaries to my knowledge base

• Converting different files into markdown for easy reading by AI

• Full workflows that keep track of all of my articles and YouTube video scripts

These are just some of my examples, but there are many others that you can add to your own workflows.

But I’m not going to bore you with the technicalities of how a Skill works. What’s more important is using the right steps to build out a Skill that you can use with any model:

The framework to build effective Skills

You may be wondering, why even bother with learning how to build Skills?

There are so many repositories out there with Skills for almost any use case.

But what works for someone else may not necessarily work for you, because everyone’s workflows will be different.

And there’s a risk of prompt injection attacks when installing someone else’s public Skill, so I’d adapt my workflow from them but never copy it directly.

You have a unique way of carrying out a certain task to get to an output, so there’s no point in copying someone else’s workflow.

While it seems daunting to write out a SKILL.md file, it’s not that hard. You just need to run the entire workflow together with AI, and work on solving the problem together.

There’s no need to write out the entire SOP (which I hate doing). Once you get a ‘gold standard’ run that gets the output that you want, the LLM will write out the full spec while taking note of what they should avoid (through bad runs).

So there’s no coding involved either.

I’ve developed a SIGNAL framework that I use whenever building a new Skill, and here’s how you can use it too:

(I’ll be using examples from a recent Skill that I built with this framework):

• Spot the bottleneck

Instead of building out your entire system from day one, let’s start small and find one annoying problem to solve first.

Throughout your entire life, there are many tasks that are extremely repetitive and soul-sucking, and you just wish there were a way to complete them much faster.

This is exactly how AI can help you automate your workflow: you only need to give AI a command, and it executes the entire workflow for you.

First, spend some time trying to identify that annoying problem you have. What is something you wish you didn’t need to do every single day?

It doesn’t matter how small it is, it just needs to be something that you hate doing.

That gives you more motivation to find a way to solve that bottleneck.

It can be as simple as connecting a new provider to OpenCode. I didn’t want to constantly:

• Go back to their documentation

• Find the right page and give it to the AI to execute it

So I built out a Skill to lay out exactly how I want this action to be done (just like all of my other OpenCode skills).

If it’s something that you do repeatedly, it can be converted into a Skill.

And this saves so much time because you can just invoke it with a slash command.

Frame your problem by describing exactly what you want to achieve, and what bottleneck you face.

The more details you give the AI, the better it can understand your problem and suggest a solution.

I’d suggest using a voice transcription tool (I’m using Letterly) to describe your problem via speaking instead of typing.

Here’s a recent example of mine when building out a Skill:

• Integrate the stack

After telling AI what the problem is, and if you give enough details to it, the AI should come up with a comprehensive plan on how to automate that workflow for you.

Some skills don’t require complex tools. They can just exist as a simple SKILL.md file with written text on what to execute.

Others will require connecting to APIs or MCPs (if you’re using external platforms), or Python scripts to execute tasks (like getting all of the recent tweets I posted via Typefully).

Depending on what platform you use, the LLM will recommend a way to connect with them so you can carry out the task.



That’s why Skills are more versatile than prompts that just give you text-based outputs.

• Guide the LLM

I saw a recent Reddit post that complained about how confused an intern was when executing tasks that the OP instructed him to do.

A similar concept can be applied to AI:

They are just an execution tool, and if you don’t define your instructions clearly:

They’ll come up with extremely poor outputs.

Ones that you don’t expect because you didn’t guide them enough.

So we have to walk through the entire workflow with AI step by step. We can’t expect it to know what we want, especially if we don’t articulate it clearly enough.

That’s why after framing the problem, I’d always want to run the entire workflow once to see how the AI understands and executes the task.

It will never be perfect during the first pass, and the only way to build the Skill is through failing repeatedly:

• Nail the standard

I can guarantee that the AI will make mistakes when running through the workflow.

I’ve experienced it so many times, but it’s actually good that they make the errors now.

By telling it what went wrong and what it shouldn’t do, it has more context on the wrong executions and what to avoid.

That’s why I like failures at the start:

We get to iterate on the workflow together with AI and get it to clarify anything that they’re still unsure (which could be something we didn’t think of too).

Constant iteration is the only way to build a good Skill. It’s tedious, but once you get the ‘gold standard’ that gives you the output you want:

It’s finally time to turn it into a Skill.

• Automate the logic

This is probably the easiest step, after getting the output that you want.

Claude has a skill-creator Skill that outlines exactly how a Skill should be created and how to reduce the token burn through progressive disclosure.

There’s no need to understand how it all works.

Once you get a successful run, tell the AI to convert the entire session into a Skill, including all of the mistakes made and bad runs with the skill-creator Skill.

While this Skill is focused on creating Skills inside the .claude folder, it’s also applicable to any Agent Skill. All of my Skills are in the .agents folder to be invokable by OpenCode, and they were all created by the same skill-creator Skill.

It has rich context on what worked and what didn’t, so the Skill becomes more personalised and specific with a higher success rate.

I’d also be more conscious about naming my Skills. When I want to search for them inside my .agents folder, it’s easier to find them if I organise by their domains, including:

• core-*

• learnings-*

• article-*

This is entirely optional, and you can choose to name your skills however you like.

But that’s not the end of the entire Skill creation process:

• Loop recursively

Whenever we run a Skill, there’s likely a new problem or output expectation that we have.

Sometimes it could be as simple as changing the file directory.

Skills are not static, and they will constantly evolve.

So whenever there’s something wrong or new when you execute them, just ask the AI to update the Skill to fit the new workflow.

Consistently iterate on the Skill until it gets the outputs that you want.

Exit the AI cost war with Skills

Skills let you be truly portable with your AI system:

They are clear SOPs and instructions given to any model that can execute them and get similar results.

And you won’t get locked in with any provider because you built the Skill once that can be repeated at any time with any model.

Of course, that’s not always the case, especially when asking the LLM to write good content (that doesn’t sound like AI slop).

The more expensive models may produce better outputs, but for other workflows that have been converted into reusable scripts, AI is just there to execute them.

So it’s possible to use a cheap model like GLM 4.7-Flash, and you’d still get the same results.

To start building out your own Skills, I’ve included both my SIGNAL Skill framework and the skill-creator Skill inside this folder.

If you want more guidance on building out your own Portable AI System, Signal Starter is a live session where I’ll be going through the entire process step by step.

In 90 minutes, you’ll:

1. Identify one painful workflow you actually want to automate (not what someone else tells you to)

2. Build the Portable AI System from scratch with the 8 components

3. Turn that task into a working Skill you can invoke anytime

No coding experience is required, and I’ll show you how to build your system your way and automate all the tasks that you hate doing.

The session will be live on 29 May and includes the full recording if you can’t make it:

I want in

I’ve been a heavy user of this free tier ever since I found out about this ‘good deal’, and it’s starting to get worse with the lower rate limits and greater congestion.

So before you use it, here are some key considerations and caveats about OpenRouter’s free models from my experience:

The ‘completely free’ tier is extremely limited

If you want to use OpenRouter completely free, you’ll be severely disappointed with how quickly the requests get used up every day.

The ‘truly free’ tier is limited to only 50 requests per day (different from token-based usage).

Depending on how complex your request is, it is possible to use multiple requests within the same prompt.

So if it’s very complex, you could reach this limit quickly.

To get more free requests per day, I would recommend topping up ≥ $10.50 to your account.

This now gives you 1,000 free requests per day, which is much more generous.

Right now, I’m comfortably below the rate limits every day, where I’ll send at most 500 requests a day.

Though I don’t use OpenRouter’s free tier for all of my tasks with AI, so it doesn’t paint the full picture.

While 10 credits cost you 10,OpenRouterchargesa50.80 minimum fee if you top up via a credit card).

The fees are definitely much more expensive than other model providers, and I’m just focused on making this one-time top-up so I get access to the higher rate limits.

But I’ve already spent $1.50 with this mistake:

Your credits may get used unknowingly

I added my OpenRouter API key to my Hermes agent, with the main aim of using the free tiers for simple tasks.

But what I found was that my API key was being used for the paid version of Gemini 3 Flash, and I spent $1.50 without intending to.

Though the good news is that you don’t need to maintain 10 credits in your account. So long as you have topped up 10 credits, you’ll gain access to the 1,000 requests per day.

If you’re just intending to use the free tiers, set a usage limit of 0 and you won’t face the same problem as me.

I likely incurred paid model usage on OpenRouter because of this:

You get rate-limited when servers are overloaded

Hermes may have fallbacks when the free tiers are overloaded, and that’s why Gemini 3 Flash was used instead.

Since we’re on the free tier, our requests will be the lowest priority.

There will be certain timings when the demand for compute is extremely high, and our requests will take much longer to be completed.

You’ll likely face rate limit errors like these.

Sometimes, when the model performs multiple steps before giving an output, you could be rate-limited during the entire process.

So I’m only using this free tier to execute tasks that I don’t require the result to be instant.

The tasks that require more thinking will be outsourced to my paid subscriptions instead.

Some of the models won’t be rate-limited as much, but that comes at a price:

Your data can (and will) be used for training

At the time of writing, NVIDIA’s Nemotron 3 Super is the most popular free model on OpenRouter.

I’ve used it before and it’s rather stable, but it comes with a catch:

NVIDIA will use whatever data you send to the free version to train its models.

If you’re conscious about privacy, this is a key consideration before you select the Nemotron family of models.

Other free models do not have the same disclaimer, but it’s possible that they may use your data too (there’s no such thing as a free lunch).

I’m fine with this disclaimer since I’d outsource more of the monotonous tasks to Nemotron instead of ones that heavily involve my personal data.

Models will change frequently

I wouldn’t get too reliant or comfortable with any of the free models.

Without warning, they could remove any model from the free tier, just like what they did for Qwen 3.6 Plus.

When a new model launches, they’ll give it away for free on OpenRouter as a marketing stunt. For Qwen 3.6, it was only available for a week.

I was a heavy user of the Step 3.5 Flash free tier, because it was fast and could execute most tasks.

But a few days ago, it was removed from the free tier with no announcements at all.

There will be some, like Arcee, that disclose when they’ll be removed, but I wouldn’t count on it.

After the Step incident, I’m showing less loyalty to any models on the free tier. They will come and go, and I’ll just enjoy the free inference while it lasts.

It’s getting worse, but OpenRouter is still worth $10.50 (for now)

Since I started using the OpenRouter free tier a month ago, the quality has dropped significantly:

• Fewer free models to choose from

• Servers are getting overloaded constantly

I see the OpenRouter free tier as a backup plan, and not my main model provider.

The free inference is great, but it won’t last forever. Sooner or later, OpenRouter will nerf this tier further once the costs of giving away free tiers are no longer profitable.

So I’m only using it for executing tasks where I don’t need the outputs immediately and don’t require thinking capabilities.

Tasks like updating my calendar or fetching my latest Twitter posts from Typefully.

That way, I’m able to save those inference costs on my main subscription plans (I’m currently using ZAI and OpenCode Go), so I don’t hit the rate limits that quickly.

As a one-time payment of $10.50, I still think it’s worth it for now, so long as you build up good skills that give clear instructions to the LLM on:

• What the desired outcome is

• What are the steps required to reach that desired outcome

To learn how to set up OpenRouter’s free tier with your AI system (via an API key), the full steps can be found in my YouTube guide.

That’s why I’m focused on building a Portable AI System where I can switch models at any time, and still get the same quality of outputs.

If you want more guidance on building out your own Portable AI System, Signal Starter is a live session where I’ll be going through the entire process step by step.

In 90 minutes, you’ll:

1. Identify one painful workflow you actually want to automate (not what someone else tells you to)

2. Build the Portable AI System from scratch with the 8 components

3. Turn that task into a working Skill you can invoke anytime

No coding experience is required, and I’ll show you how to build your system your way and automate all the tasks that you hate doing.

The session will be live on 29 May and includes the full recording if you can’t make it:

I want in

I’m lucky that I didn’t get stuck in one. But with the recent Claude policy changes for OpenClaw, many on Reddit and Twitter are trapped.

Anything can happen in 1 year, and you’re left frustrated with lower rate limits, limited integrations, or a plan that’s completely unusable.

So to avoid getting locked into any provider, I made 2 key rules for my AI system:

• Only buy monthly plans

• Make my system completely portable with different providers

Here’s the exact setup I’m using to switch providers without losing output quality:

You’re paying more than what you save

Most model providers want to lock you into annual plans. They dangle the perception of saving 20% or 2 months from your plan, which makes it seem like a good deal.

While deciding which AI plan to go with, I was tempted to go with annual plans for these extra cost savings.

But a Reddit comment made me change my thinking completely:

Once you lock in with them, you’re essentially trapped. So go for the monthly plan instead.

Anything can happen during this 1 year, especially when things change so quickly in this space.

Will Claude or ChatGPT still be the best models in 2027? Maybe, but it’s hard to say.

Other providers are getting better each day. Every new model claims they are smaller than Opus, but with almost the same capabilities.

Open-source models are competing with frontier models, and they can significantly reduce your costs (if you already have the right hardware).

While you save money with the annual plan, you’re incurring costs in other ways.

Right now, all of our coding plans are cheaper than direct API calls.

OpenAI did amazing PR by resetting their rate limits twice over the past 2 weeks.

Why would they do that when it incurs more costs for them?

We’re at the stage of hyper-competition in the AI space, as every model provider rushes to acquire new users:

• Free tiers with generous rate limits

• Heavy discounts on their annual plans

• Attractive pricing for new business seats

All of these marketing tricks are aimed at building loyalty, so you stay inside their ecosystem.

But once the cost of acquiring a new customer no longer makes sense, things will change for the worse.

Most of our token usage is subsidised by VCs.

OpenAI just announced its latest funding round of $122 billion, which is just insane.

It’s all the same for tech companies, just like how Uber underpriced their rates to get more users.

But eventually, the funding will dry up.

The costs of large-scale inference will increase to meet the demand for more users. And VCs aren’t a charity, they expect these AI companies that they’ve so heavily invested in to start turning in profits.

Once that happens, you get trapped.

The rate limits decrease rapidly. Typing in one simple request can cause a spike in your usage.

Companies start removing integrations because their infrastructure can’t handle the sheer amount of requests.

We’re at the tail end of the golden era for AI.

I have many regrets about not starting earlier before Claude Code blew up, as that would have given me more freedom to play around with AI.

The free inference, generous rate limits, and any other perks will soon be a thing of the past.

And those who are locked in are the ones who are trapped the most:

You need a Portable AI System

The best model that works for you today, won’t be the best one that works for you next month.

And when you’re locked in with one model, it’s so hard to make the switch a few months later.

That’s why I’ve been so obsessed with building a Portable AI System for my workflows, which needs to meet this criteria:

• I have full control over my data and workflows (it’s not locked into one provider)

• I can easily switch between any model (my system can connect to almost any model provider)

• I still get a similar quality of outputs, but at a lower cost

The key characteristic of this system is adaptability. I’m only locked into a plan for 1 month, so I can try it and switch to another one (if it doesn’t meet my needs).

My data, my context, and my skills are all fully owned by me. And because I build highly specific workflows (Skills) through iteration, I can get almost any model to execute the same workflow with similar results.

Inside this system, there are 5 key components required for it to run successfully:

• A file system that includes my context and Skills (in the .agents or .claude folder), all in the .md format

• A monthly LLM coding plan or API (this will change frequently)

• A Bring Your Own Key (BYOK) interface that lets the LLM access my files

• An editor platform to do all of my writing

• A syncing platform so all of my work is accessible across any device

I’ve been trying multiple setups, and I’ve finally settled on a Claude Code-Obsidian hybrid system:

• A simple folder on my Mac desktop for my context and Skills

• OpenRouter for free tier models (1,000 requests/day after a $10.50 deposit) and other coding plans as a backup (I’m currently using @Zai_org’s GLM)

• @opencode to connect to these models (it’s available on both Desktop and Terminal)

• Obsidian for editing

• GitHub for syncing between my desktop and laptop

I’ve moved on from the OpenCode Desktop app (because it became too laggy with the number of chats that I have).

Right now, I’m using the Terminal because I can run multiple windows at the same time, each for one specific task I want to complete.

Though it’s messier than I would have liked, and I should have stuck to at most 2 windows for my setup.

You may be wondering, why is there no OpenClaw or Hermes at all?

There’s too much noise right now with everyone bragging about the complicated workflows on Twitter, making you feel so inferior.

Instead of focusing on what to run with these agents, build out high-quality workflows first.

Once you get the outputs that you want and like, then it’s time to go for ‘always-on’ automation.

Not everything you build with AI has to be impressive or make you 7 figures.

All you need is getting AI to solve one painful problem in your life, and that’s enough to change the way you see AI.

Don’t get trapped by convenience and take action instead

You don’t have to get locked into one ecosystem just because they offer you a seemingly good deal.

Yes, it’s so tempting to just stay with a Claude or ChatGPT plan. It feels so convenient and easy. But that only benefits them, and not you.

Anything can happen inside the full year that you lock in.

So instead, you need something portable enough so you can switch out models to whatever you like and still get the same outputs.

If you’re done being locked into one provider, start with this free setup video guide.

If you want more guidance on building out your own Portable AI System, Signal Starter is a live session where I’ll be going through the entire process step by step.

In 90 minutes, you’ll:

1. Identify one painful workflow you actually want to automate (not what someone else tells you to)

2. Build the Portable AI System from scratch with the 8 components

3. Turn that task into a working Skill you can invoke anytime

No coding experience is required, and I’ll show you how to build your system your way and automate all the tasks that you hate doing.

The session will be live on 29 May and includes the full recording if you can’t make it:

I want in