Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building AI Agents that scale | 10 years in AI & ML | Building AI Flow, an AI & ML focused consultancy that designs, builds, and scales end-to-end AI products for startups and mid-market companies.


> What we do at AI Flow

- AI consulting - understand how to make most of your data and benefit from the Agentic AI wave.
- AI Strategy & Roadmapping - cut through hype, map the fastest path to ROI
- Custom ML & Gen-AI systems - from data pipelines to fine-tuned LLMs and Agents
- Data platforms - ETL, ingestion pipelines, document understanding end to end solutions
- Embedded AI Teams - senior engineers who plug in and deliver from day 1

> Proof over promises

- 45% pipeline cost & time drop for hyper-realistic video at Metaphysic
- 65% faster ML iterations for Google internal ML teams
- Millions saved automating invoice processing for a top-5 Oil & Gas firm
- Built tools used by 200K daily users, with more than 200 request/second at Bloomberg.
- 85% RAG & LLM pipeline cost drop through data cleaning pipelines for law companies.

> Why clients trust us

“I trusted you to deliver a good result, and that’s exactly what happened.”
- Josua Vieten, CEO @ ExoMatter

“Deep technical expertise across the stack. Mihai understands business context and proposes scalable solutions.”
- John Hack, CPO @ Interflexion

“Mihai makes it easy to get projects done. He is pragmatic, ambitious, and takes initiative.”
Paul Craciunoiu, Founder @ Uplift


> Let’s talk about building the AI future

Open to B2B, remote projects. If you need a sharp, senior team to ship real AI, book a 30-min call: antonmih.ai/meet

Mihai Anton

You wouldn't hire a mechanical engineer who only used AI-generated blueprints.

You wouldn't trust a doctor who only used ChatGPT for diagnosis.

Yet many companies hire "AI engineers" who've never debugged a production system.

Tools change. Fundamentals don't.

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Comment

Watch it here: https://www.youtube.com/watch?v=CLuv-X2Fm_s

Machine Learning by Stanford University on Coursera

Cambridge English Advanced English

Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building AI systems that scale | 10 years in AI & ML

Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building AI systems that scale | 10 years in AI & ML | Building AI Flow, an AI & ML focused consultancy that designs, builds, and scales end-to-end AI products for startups and mid-market companies.


> What we do at AI Flow

- AI consulting - understand how to make most of your data and benefit from the AI wave.
- AI Strategy & Roadmapping - cut through hype, map the fastest path to ROI
- Custom ML & Gen-AI systems - from data pipelines to fine-tuned LLMs
- Data platforms - ETL, ingestion pipelines, document understanding end to end solutions
- Embedded AI Teams - senior engineers who plug in and deliver from day 1

> Proof over promises

- 45% pipeline cost & time drop for hyper-realistic video at Metaphysic
- 65% faster ML iterations for Google internal ML teams
- Millions saved automating invoice processing for a top-5 Oil & Gas firm
- Built tools used by 200K daily users, with more than 200 request/second at Bloomberg.
- 85% RAG & LLM pipeline cost drop through data cleaning pipelines for law companies.

> Why clients trust us

“I trusted you to deliver a good result, and that’s exactly what happened.”
- Josua Vieten, CEO @ ExoMatter

“Deep technical expertise across the stack. Mihai understands business context and proposes scalable solutions.”
- John Hack, CPO @ Interflexion

“Mihai makes it easy to get projects done. He is pragmatic, ambitious, and takes initiative.”
Paul Craciunoiu, Founder @ Uplift


> Let’s talk about building the AI future

Open to B2B, remote projects. If you need a sharp, senior team to ship real AI, book a 30-min call: antonmih.ai/meet

AI Flow | We build AI powered products | AI, Machine Learning, and Data managed services

AI Flow

Senior Software Engineer | AI consultant | Working in AI before it was cool

Resume

Created a social network for frequent travelers, where they can post experiences, see what others explore, and follow their steps. Take a look to see how it works!

IdeaList

MihaiAnton has 14 repositories available. Follow their code on GitHub.

MihaiAnton - GitHub

Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building Agentic AI & ML systems that scale | 10 years in AI & ML

AI Flow #2 is up.

This week: how we helped a client cut spending from $10K/month to $2K/month, without touching the model.

After analyzing the situation, we found the problem was the input.

They were feeding entire HTML pages to the LLM (cookie banners, navigation menus, scripts, SEO content). The model was drowning in noise.

The fix was simpler than they expected. I break down what we did and the results in this week's edition.

Every time a new model drops, I get the same wave of messages from clients: should we rebuild? Should we switch? Are we falling behind?

This week it's GPT-5.4. Next month it'll be something else.

The honest answer is always the same: it depends on what you're optimizing for, and most teams haven't written that down anywhere.

- If cost is the constraint and the new model delivers similar quality for less, run the eval and test it.
- If accuracy drives revenue or directly reduces support volume, measure whether the delta is real enough to justify the migration work.
- If you're upgrading because it's newer and that feels like the right move, that's the one reason I'd push back on.

The things that determine whether your system works didn't change this week. Your data quality didn't change. Your evaluation criteria didn't change. The problem your users need solved didn't change.

A better model sitting on top of a broken pipeline is still a broken pipeline. Test it against the metrics you already track. If they move, ship it. If they don't, look at other areas that actually move them, like the data sources feeding the system.

AI Flow #3 is up.

This week: a client came in ready to invest in AI. Budget approved, timeline set.
We told them: not yet.

After analyzing their setup, we found data in 6 different systems. No tagging, no structure, half of it contradicting the other half.

Building on that foundation would have guaranteed a broken system. So, we made a different plan instead.

"How fast can we see ROI from AI?"

Depends on what you're measuring.


- Time savings? Weeks. We automated proposal generation for a construction company. From 4 hours per proposal to 20 minutes, they felt the ROI immediately.

- Revenue increase? Months. You need time to test, iterate, and prove the feature changes behavior.

- Cost reduction? Somewhere in between.


Most companies expect all three at once, immediately.

My advice: focus on time savings first, as it’s fastest to prove.

"Should we rebuild for the new model?"

The problem isn't the question itself. It's what comes next, as most teams usually ask, "Is it better?" instead of "Is it better for what we need?"

The answer depends on what you're optimizing for. Here are 3 examples.

Should we use the latest model?

2026 isn't about chasing what's new. 

It's about building systems that work regardless of which model is trending.

AI Flow #4 is up. This week: how fast can you actually see ROI from AI?

I’m sharing one case we had with AI Flow: We helped a construction company cut proposal time from 4 hours to 20 minutes; they felt the ROI in week one.

But that's time savings. Revenue increase? Cost reduction? Each has a different timeline.

The problem is that most companies expect all three immediately, and that's why projects stall before showing value.

“Can we automate this?” is one of the most common questions I get.

Sure, we can. But that’s not the first thing you should ask.

Before you jump into workflow scripts or n8n hacks, zoom out:

- Does this process actually need to exist?
- How much time or cost will automation really save?
- Do you have clarity on the pain points you’re trying to remove?

Most teams skip that step. That’s how you end up automating messy processes, which only automate more mess.

"We need AI to analyze our customer feedback."

This request came from a head of product at a 200-person company.

What they really had:

- Feedback in 7 different places
- Support tickets in Zendesk
- Feature requests in Productboard
- Sales calls in Gong
- User interviews in Dovetail
- Social mentions everywhere
- NPS surveys in their own tool
- Churn interviews in random Google Docs

Now, they wanted AI to magically connect all of it.

And here's what we told them: AI can't fix a structural problem.

First step: pick one source, the one with the highest signal (for them, it was support tickets because every issue was already tagged and categorized).

We built an AI analysis for that one source, and it worked. From that, they got insights they could act on.

Now they're adding the others. One at a time.

If you have data everywhere, don't start by connecting everything. Start by getting value from one source first.

Client last month: "We just need you to make our chatbot smarter."

Turns out their chatbot was giving wrong answers 40% of the time.

We dug deeper, and we found out that the problem wasn't the AI, but their knowledge base:

- Outdated pricing in 12 different PDFs
- Product specs scattered across Slack threads
- Support answers that contradicted each other

No model can fix messy data. It just makes confident mistakes faster.

What we did next was to spend a couple of weeks organizing their data before touching the AI. Now the chatbot works.

If your data lives in 10 different places with no single source of truth, start there.

A founder told me they spent 6 months building the perfect AI architecture, while never talking to a single user.

They launched last month, and it turns out users needed something quite different. 

Perfect systems built for the wrong problem are just expensive mistakes. I recommend prioritizing talking to users as much as possible to ensure you build what they really need.

A16z recently led a $30M round into Lio, a startup automating enterprise procurement with AI agents.

Sourcing, contract negotiation, spend management. All of it.

I'm not surprised by the funding. Procurement is one of the most automation-ready functions in a mid-size company, and one of the most overlooked.

When I talk to operations leaders about where AI could save them time, they usually mention customer-facing workflows first. Support, sales, marketing. Procurement comes up later, almost as an afterthought.

But when we actually map where decisions are slow and errors are expensive, procurement keeps appearing: requests sitting in inboxes waiting for approval, vendor comparisons done manually in spreadsheets, contracts reviewed line by line every single time.

The pattern underneath all of it is the same: high volume, repetitive structure, clear rules for what good looks like. That's exactly where automation compounds.

The question worth asking isn't whether procurement can be automated. It's which part of your procurement workflow is costing you the most time right now, and whether the process is clean enough to hand to an agent.

If the answer to the second part is no, that's where to start.

Most enterprise clients are not ready to build AI. This would be the wrong first move, anyway.

Very often, they are excited about building AI. Budget approved, timeline set, excited to start building.

Then I look at their setup. Data scattered across 6 systems with no structure. records contradicting each other, no single source of truth.

In such cases, AI won’t be a positive addition. It will only automate the chaos.

So I get back to them with a different plan. Spend two months organizing the data first, establish structure, then build AI that actually works. 

This approach might feel slow when you're excited to start. But it’s faster than building something that breaks and spending the next 6 months trying to fix it.

If everyone you talk to says yes immediately, you're probably not getting honest advice.

AI Flow #5 is up.

This week, I'm exploring the gap between demo and production, using a client situation as an example.

They had a recommendation engine already working in production. Users were getting suggestions, and the feature was live. But it performed at maybe 70% of where it could be.

After digging into the case, I noticed some problems in how it was built. It was rigid, with hard-coded paths, no observability, and every new requirement broke previous flows.

"We built a demo that works great. Now what?"

Now you find what breaks in production. 
What you should prioritize:

- Add monitoring
- Add fallback logic
- Test with real users, not perfect test cases.

Keep in mind that the gap between demo and production is where most projects fail.

AI Flow #6 is up.

This week, I'm looking at procurement, one of the most automation-ready functions in most businesses, and the one that rarely makes it onto the AI priority list.

When we map where decisions are slow and errors are expensive across the companies I work with, procurement keeps appearing. Vendor comparisons rebuilt from scratch every time. Purchase requests waiting three days for a two-minute approval. Contracts reviewed line by line for suppliers the company has worked with for years.

Nobody designed it this way. It just accumulated.

This edition covers why this happens, what to look for in your own workflows, and where to start if the process isn't clean enough to automate yet.

Cosmin Rusu

Transforming Visions into AI MVPs & Products | Founder @Duty Ventures | Ex {Google, Apple and Amazon}

Hi, I’m Cosmin, the Founder and Lead Engineer of Duty Ventures. 

After some experience as a software developer in companies such as Google, Apple, and Amazon, I decided to launch my project.

With several former colleagues and college friends, we launched our own startup specializing in software development for entrepreneurs and customers needing software development services. 

Duty Ventures is all about developing software products from start to finish, thanks to the experiences we have all acquired in the past years.

One of our most successful services is aimed at entrepreneurs and early-stage founders, for whom we provide an MVP-builder service getting their product from 0 to 1 within weeks!

However, our services are not limited to that. The teams we create do much more than just create MVPs and are able to adapt to the different technologies requested, all of this in an agile way.

Our most recurrent services are the following:
- MVP creation
- Custom Software Development
- Blockchain Development
- Mobile Development
- Checkout pages
- E-Commerce

And our most used technologies are:
- Ruby on Rails
- React
- React Native
- Java
- NodeJS


If you want to learn more about how we can help you, head over to: https://dutyventures.com


Phone: (+1) 669 200 9568 / (+40) 754 973 314
Email: cosmin@dutyventures.com

Founder & Lead software engineer

I'm leading a software development agency focused on fast iterations. Most of our clients are shipping more features, responding faster to support tickets, and have accelerated growth because of us.

We focused on HealthCare a lot lately due to the accelerated digitalization in this industry. I am a strong believer that we can make healthcare more technically advanced via pure software. 

In terms of technologies, our previous projects involved Ruby on Rails, React, React Native, Scala, Java, Kubernetes, GCP, AWS (ECS, RDS, AKS), Stripe Connect, HIPAA compliance.

Some of our previous clients: 
- Spotify
- Colgate
- Go4Ellis (incl. HIPAA compliance)
- KindBody (+1 HIPAA)
- NXT Sports
- Noom (Healthcare)
- Mochila Fulfillment
- 3Step
- Mylk Guys (YCombinator)
- NLV Productions
- SH Technologies
- One Step Software (Techstars)
- Ergodic Labs
- Med Prep
- Testing Psych
- Abundance Digital
- Tookitaki

Everyone is cutting juniors.

AI writes code. AI reviews code. AI tests code.
So why would anyone hire a junior developer in 2026?

I'll tell you why. Because I do it. Every quarter.

Here's what the "AI replaces juniors" crowd doesn't understand:

AI doesn't build culture.
AI doesn't grow into your next team lead.
AI doesn't challenge your senior engineers with fresh perspectives.
AI doesn't stay loyal for 5 years because you gave them their first real shot.

The data says junior positions dropped 20-30% in some regions.
The industry says "juniors are obsolete."

I say that's short-sighted and dangerous.

If we stop training juniors today, who leads your engineering team in 2030?

We'll face a competence gap worse than any talent shortage.
A generation of engineers who never learned to think... because a machine did it for them.

At Duty Ventures, we pair every junior with a senior + AI tools.
They learn faster. Ship sooner. And they bring something no LLM ever will:

Hunger.

The companies cutting all junior roles to save money?
They're eating their seed corn.

Cosmin Ionut

AI-forward engineers. Embedded fast. Delivering from day one. | Founder @Duty Ventures | Ex {Google, Apple and Amazon}

Anthropic just played the smartest marketing move in AI.

While OpenAI announced ads are coming to ChatGPT, Anthropic did the opposite - they ran a Super Bowl ad saying Claude will stay ad-free.

But that's not even the best part.

They started sponsoring Google search results for developer error messages and stack traces that return zero results.

Think about it: you're debugging at 2 AM, you paste an error into Google, nothing comes up - and right there is Claude offering to help.

That's not advertising. That's solving a real problem at the exact moment of frustration.

The marketing lesson here is massive:

Don't interrupt people. Show up where they already need you.

Most companies spend millions trying to grab attention from people who don't care. Anthropic spent it reaching developers at their most desperate moment - when no other solution exists.

This is the difference between push marketing and pull marketing done right.

What's the smartest developer-targeted ad you've seen?

Stop paying for ngrok. There's a better alternative.

Meet zrok - an open-source tunneling tool built on zero-trust networking principles.

Why I switched:

1. Free subdomains - no paying for stable URLs
2. Open source - self-host it or use their free SaaS tier
3. Built on OpenZiti - actual zero-trust security, not just marketing buzzwords
4. Supports public AND private shares
5. Auto HTTPS/TLS out of the box
6. Custom domains supported

If you're a developer who regularly needs to expose local services (demos, webhooks, testing) - ngrok's free tier feels increasingly restrictive.

zrok gives you what you actually need without hitting a paywall every 5 minutes.

The setup is straightforward. The docs are solid. And because it's open source, you can inspect every line of code handling your traffic.

In a world where we're tunneling more and more traffic for dev and testing, having a free and secure option matters.

Check it out:

zrok.io

Home - zrok by NetFoundry

Unpopular opinion: most people using AI to build apps have no business deploying anything to production.

A 20-year sysadmin just quit his job this week. Not burnout. Not pay. His company offered bonuses for the best "vibe coded" app and got a tsunami of shadow IT - unsecured apps, unvetted AI agents on corporate machines, and a team of THREE sysadmins drowning under "innovation" built by people who can't explain what their own code does. When he raised security concerns? Debated. Overruled. Approved anyway. Because a VP saw a demo and got excited.

Copying ChatGPT output into production doesn't make someone an engineer. It makes them a liability with a deployment pipeline. A CTO in the thread admitted his marketing team had secretly built 11 apps - one storing customer data in a public S3 bucket. Eleven apps. Zero reviews. This is where we are now.

The predictable response: "That's just gatekeeping. Engineers are afraid AI will replace them." No. Good engineers use AI daily. The difference is they can read it, debug it, secure it, and explain it to an auditor. AI didn't lower the barrier to building software. It lowered the barrier to building dangerous software. Most organizations can't tell the difference until something breaks, leaks, or lands on a regulator's desk.

That sysadmin isn't the problem. He's the canary. Companies will keep ignoring people like him until they're explaining to a regulator why customer data ended up on the open internet because Dave from accounting "had a great idea for an app."

Stop handing out innovation awards. Start asking who's going to maintain it, secure it, and answer for it when it fails.

Open article: From the sysadmin community on Reddit by reddit.com

A friend of mine had his first meeting with a potential client in Germany.

He didn't prepare a pitch deck. Didn't try to "understand the project." Didn't walk through his company's services.

Instead, he asked one question:

"If I fly from Romania to Germany on Friday, can we meet face to face?"

They said yes instantly.

That was 5 years ago. They're still working together.

Here's what most agencies get wrong: they lead with capabilities. "We do React, we do mobile, we do cloud."

Nobody cares.

What founders actually want to know is: will this person show up when it matters?

The best business relationships I've seen didn't start with a scope of work. They started with someone proving they gave a damn before the contract was signed.

If you're choosing between a partner with a perfect proposal and one who'll get on a plane for you — pick the plane.

For years, I thought the best way to create more value for clients was to charge less.

Lower rate = more value for their money. Simple math, right?

Wrong.

Here's what actually happened when I charged less:

→ Clients treated the work as disposable
→ My team felt undervalued, so they gave B-level effort
→ I attracted price-shoppers who churned the fastest
→ We couldn't invest in better processes, tooling, or talent

The moment I started charging what the work was actually worth, everything changed.

Clients were more engaged. More committed. More respectful of timelines.

My team felt ownership because the stakes matched the effort.

And the clients who stayed? They became long-term partners, not one-off projects.

If you're a founder evaluating dev partners and your main filter is "who's cheapest" — you're optimizing for the wrong thing.

The most expensive mistake isn't paying too much. It's paying too little for a team that doesn't care enough to get it right.

Hot take on vibe coding:

If frontend is mostly about calling APIs, and those APIs should be secure by default - does vibe coding make the most sense on the frontend?

Think about it. The frontend is essentially a curl wrapper. A UI layer. Less security risk.

Here's where I think things are heading:

Senior engineers will migrate towards building secure backends. Modular, composable backend "components" that can easily interact with each other.

Open-source microservices you can plug and play.

Need auth? Spin up your own Auth0-like instance. Need user-generated content? Spin up a UGC microservice - posts, comments, likes, replies, threads. The same patterns we use on LinkedIn, Facebook, X.

You reuse what exists. Then you generate your own microservice for what's specific to your product.

And once you become big enough? You open source it.

The frontend becomes the place where AI can truly shine - generating UIs, connecting to secure APIs, iterating on design fast.

The backend stays the domain of engineers who understand security, data, and system design.

Vibe coding isn't replacing developers. It's splitting the role in two.

Stop hiring 10 developers when 2 + Claudes can do the same job.

I know. Controversial. Especially coming from someone who runs a dev agency.

But here's what nobody in outsourcing wants to say out loud:

The old model is dying.

1. Junior positions in outsourcing dropped 20-30% last year
2. Clients are asking: "Why pay for 8 devs when 1 senior + AI does 80% of the work?"
3. McKinsey predicts a 20-30% reduction in the global outsourcing market by 2030

And yet... most agencies are still selling headcount.
Still billing by the hour.
Still pretending nothing changed.

Here's the uncomfortable truth from someone who runs an agency:

If your only value proposition is "cheap developers" - you're already dead.
You just don't know it yet.

At Duty Ventures, we made a decision 2 years ago:
Stop being a code factory. Start being a product partner.

Don't sell hours. Sell shipped features.
Don't sell headcount. Sell outcomes.

The agencies that survive 2026 won't be the cheapest.
They'll be the ones who embraced AI, specialized in complex domains, and aligned incentives with their clients.

The rest? They'll be replaced by prompts...

Heroku just entered "maintenance mode."

Except this time, it's not your app - it's the entire platform.

Salesforce announced Heroku is transitioning to a "sustaining engineering model." Translation: no new features, no new enterprise contracts, just keeping the lights on.

If you've been a developer long enough, you know what this means. Standing still in tech is the same as moving backward.

Here's the thing though - Heroku's ideas won.

Every git push deploy. Every platform that "just works." Vercel, Render, Railway, Fly - they all carry Heroku's DNA.

The lesson for anyone building software products:

Your product can pioneer a category and still lose if the parent company stops investing. Innovation without sustained commitment is just a head start for your competitors.

If you're still running on Heroku - your apps are fine for now. But start planning your next move.

The platform that taught us what great developer experience looks like deserved better stewardship.

"We spend $XX,XXX/month on LLMs" is the new "we hired 200 people in 6 months."

Both sound impressive until the board asks: what did we actually get for it?

The smartest teams I see aren't spending more on AI. They're spending less, and ACTUALLY shipping more.

So if AI will write code, and we treat that as a commodity, and as a pure blackbox inputs / outputs type of thing - what's next? Maybe we fully automate QA? Maybe we fully automate a simulation on how that code would interact with actual users? So we "simulate" a real world usage of an app / a website over time to find bugs / issues? Then, what next?

2018 Software Engineering problems: waiting for the build to finish

2026 Software Engineering problems: "You've hit your limit · resets 12pm (UTC)"

Better problems?

Marc Andreessen on how to get people to join your 3-person startup:

You have exactly 2 tools - stock options and vision.

Stock options they'll tell you aren't worth anything. So it really comes down to vision.

The best founders can explain how the world is going to look in a way that's so compelling, people leave Google and Microsoft to build it with them.

It's essentially sales. Selling to employees.

But here's the part most founders miss:

Hiring is also a self-selection process. The best companies are polarizing on purpose. They give a stark picture of what they are and what they aren't.

"We work 18-hour days - if that's not you, don't come."
"We go home at 5 every day - if that frustrates you, don't come."
"We do yoga as a company - if that's weird to you, don't come."

The founders who try to be everything to everyone end up hiring people who leave in 3 months.

The ones who are radically honest about their culture? They attract true believers.

Running a staff augmentation agency, I see this daily. The clients who know exactly what culture they're building find the right talent 10x faster.

graphical user interface, text, application, email

There are two types of early-stage technical decisions:

Type A: Decisions that get your product in front of users.
Type B: Decisions that prepare for users you don't have yet.

Most founding teams spend 80% of their time on Type B.

Kubernetes, microservices, auto-scaling, blue-green deployments... all Type B.

A $10 VPS with your app deployed on it? Type A.

And here's the uncomfortable math:

3,000 users paying $30/mo = $1M ARR.

A single server handles that without breaking a sweat.

Every week you spend on Type B before you have paying users is a week you're not learning whether anyone even wants what you're building.

Ship boring infrastructure.
Build interesting products.

Someone replicated Monday.com's core features in 60 minutes using AI.

Cost: $15.

Monday's stock took a hit. People panicked.

But here's the thing nobody's talking about:

Code has been getting cheaper for 20 years. AI just made the trend impossible to ignore.

If your entire business defensibility is "we built an app," you were already vulnerable. AI just made it obvious.

The real moat was never the code. It's:

→ Deep understanding of a specific customer problem
→ Product decisions that reflect real usage, not assumptions
→ An architecture that actually scales when users show up
→ A team that iterates based on data, not vibes

I see founders still spending months planning features that an AI tool could scaffold in an afternoon. And I see Silicon Valley founders shipping AI-generated MVPs that fall apart the moment real users touch them.

Both are missing the point.

The winners will be the ones who combine fast tooling with sharp product thinking.

Code is a commodity. Product judgment is not.

monday.com

monday.com | Outpace everyone with the best AI work platform

I think we're gonna start seeing a lot of these in the following month, if not even weeks!! Not sure where this is going but we re getting there 200mph 🚀🧨

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