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crazy that X open-sourced their algo. i spent last night studying it and here are the main takeaways for viral posts:

1. Early, strong in-network engagement. Your followers are your first gatekeepers. Rapid likes, retweets, and replies in the first minutes signal quality. This helps you break out of your followers (also means you should definitely reply to comments)

2. High-quality engagement signals. The algorithm prioritizes high dwell time, sustained video watch time, and deep thread reads. Avoid anything that leads to quick bounces. Your content needs to be genuinely satisfying.

3. Good social proximity. Who engages with you matters. Tweets from authors close to you in RealGraph, or liked by your close contacts, get a significant boost. Strong local social proof is a powerful amplifier. Interactions from highly connected followers definitely help as well

4. Topic and cluster relevance at scale. For out-of-network reach, your content needs to align with large, active SimClusters (broad topics/communities). Niche tags won't get you broad distribution. This is where researching trending topics becomes critical. Original content also does MUCH better than retweets or replies

5. Freshness and sustained momentum. Recent tweets with ongoing engagement are heavily favored. The algorithm tracks "last engagement since creation" - it wants content that keeps generating interest over time, not just a one-off spike. Consistency is key.

My AI transformation business is growing faster than any business I've ever started.

Funny enough, it started as a very high-end engineering agency.

Why?

1) We believe the best way to drive AI transformation is to use AI internally first, where we have wayyy more control.
2) We believe the best way to build a massive company is to hire the best technical talent, which our model allows us to do.

This is just a small taste of the very different approach we're taking to building McKinsey for AI.

Typical AI consultancy strategy:

• Charge by the hour
• Focus exclusively on net new Gen AI projects
• Go straight for Fortune 50 companies

Our strategy at @tenex_labs:

1) Start with engineering-as-a-service

Focus on the core technologies of our clients vs. net new Gen AI projects. Most Gen AI projects die on the vine, so the risk profile is way higher.

By having our Applied AI engineers speed up the pace of shipping on client’s core product, we're anchoring our business to a way more foundational need in a client’s business.

Then, once we've built trust, we can always explore net AI features, products, etc.

2) Change the business model for service businesses

We charge our clients based on software shipped & we pay our engineers based on software shipped.

This does two things:
1) Clients are pumped to pay for output/value.
2) Engineers are paid like salespeople, which means they can make a ton & are incentivized to create as much leverage with AI as possible.

We believe every service firm will ultimately go to a value-based pricing model because of the way AI changes labor dynamics.

3) Focus on the forgotten mid-market

Mid-market clients ($25-500m in revenue) are the forgotten stepchild of business.

Too big & slow growth to be interesting to startup agencies and too small to be interesting to the massive management consultancies (MBB).

We're happy to show this customer segment as much love as they need. Less red tape, less competition, but plenty of appetite to pay for AI engineering & transformation.

If you're interested in supercharging your engineering org or just want to chat about how we see the future of services, shoot me a DM!

The key is choosing a path that`s realistic to get into, pays well, and sets you up for long-term success. Cybersecurity is a great option because you can break in with affordable training, land entry-level roles that already pay strong, and quickly build toward six figures as your skills grow.

Tech sales is another solid reset because you don`t need a degree, the barrier to entry is low, and commission-based roles can scale your income fast.

Skilled trades are also powerful because they let you earn while you learn, and they`re always in demand + many people eventually start their own businesses.

If you`re drawn to data and AI, there are analyst and support roles you can prepare for online that lead to serious career growth in just a few years.

Healthcare support careers, like nursing or radiology tech, offer steady demand and stability.

At the end of the day, if I had to start over, I’d pick cybersecurity or tech sales, because both give you a fast entry point, strong pay, and the flexibility to pivot into even bigger opportunities down the road.