Raghav Dixit

technology nerd, music lover, ML/DL engineer
AI @tenex_labs

[Alex Lieberman @businessbarista]

After selling Morning Brew for $75m, I never thought I'd build a media company again.

I was wrong. I am.

The media company is Tenex Media. It's the digital destination for AI education & applied AI, and it sits on top of @tenex_labs, the applied AI consultancy & lab my cofounder & I launched 15 months ago.

We're actively hiring for key founding team roles at Tenex Media, which I'll share below.

And in sharing these roles, you'll also get a sense of our strategy—what feels like lessons learned from Morning Brew & what feels like new, unexplored territory.

1) We're hiring 2-3 Full-Stack AI Creators.

Personality-driven content is becoming a bigger and bigger part of consumers’ media diets. The democratization of creation & distribution means that people can attract as much attention as institutions & consumers have always longed for content that feels personal/relatable.

And we're seeing this take shape in the AI space with personalities like @karpathy, @bcherny, @signulll, @levie, @jainarvind and others.

You have maybe the best job in the world as a full-stack AI creator. You build with AI. You talk about what you build. You teach people how to use AI through content. And you train F2000 enterprises on AI.

2) We're hiring a Content Engineer, Editorial.

Brand-driven content isn’t dead. The expectations of it have just changed & it serves a more important role in media than ever before.

There is one foundation, two channels & three franchises that you will own in your role.

The foundation: the content machine. We've developed an internal AI system that allows us to create editorial at scale without sacrificing voice or quality. You will use & improve the content machine to become a one-person wrecking ball.

Two channels: website and newsletter. 30% of our inbound MQLS in Q2 came from AEO. That's crazy because we haven't done jack shit to optimize our website or long-form content. Next, is newsletter. I'm a little biased. But great editorial newsletters have never been more important to own & nurture your audience. You'll take our newsletter, Ultrathink, from 1x to 3x per week and make it the best applied AI newsletter in the world.

Franchises:
- Ultrathink: tenex's editorial newsletter
- Hey chat: site essays answering the most pressing questions enterprise executives have around AI transformation
- LabNotes: site essays sharing step-by-step playbooks to understand AI concepts & how to apply the technology in real business contexts

Content Engineering is to modern marketing what DevOps was to software. A new discipline that makes scale, governance, and speed possible without sacrificing craft.

For more info about the roles, check out the JDs below...

[Raghav Dixit @_raghavdixit_]

@businessbarista @tenex_labs This is a great opportunity!

[Raghav Dixit @_raghavdixit_]

@dan_zakon great insight! switching to pi now

[Arman Hezarkhani @ArmanHezarkhani]

Extremely proud of our work with Advantage Solutions and @AnthropicAI.

Advantage is a people-first business in a dynamic market. Dave Peacock (CEO) and Bethany Miles (CAIO) are market leaders in their AI roll out.

Their roadmap shows a balance of ambition and practicality that has driven measurable results. And it's backed by the power of Anthropic's platforms.

We're incredibly excited about the future of these partnerships.

https://t.co/y6QwK4EHGF

[Alex Lieberman @businessbarista]

I’m in a text group with 30 enterprise execs driving ai transformation at their companies.

It is the #1 community I’ve become a part of this year.

Battle tested AI tools, challenges, and wins are shared constantly and we are starting to meet as a group monthly for live discussion.

I want more of the energy and value this group provides, but I don’t want to dilute it, so I'm spinning up a few more of these this quarter.

Each group is capped at 30 people and the rules are simple…

No sales pitches. Just smart people sharing how they’re using AI personally and how they’re transforming their org with AI (successfully and unsuccessfully).

If you want to join one of these groups, reply with "ai,” and if it’s a good fit I’ll DM you an invite.

[Raghav Dixit @_raghavdixit_]

@businessbarista you do NOT want to miss this

[Raghav Dixit @_raghavdixit_]

Part A of the first topic in the series is out! A friendly introduction to the world of vectors: how words become numbers, how meaning becomes geometry, and how a machine learns. Explained symbol by symbol, with animations. Part B soon!

Hope it helps!

https://t.co/bDRN4XMAmB

[Raghav Dixit @_raghavdixit_]

https://t.co/RVnYCtg2wO

[Raghav Dixit @_raghavdixit_]

Billions of people use AI every day. Almost none of us actually know what's happening underneath.

And I think that quietly gets to people. You lean on something this powerful, it gets smarter every month, and there's this low hum in the back of your head..."I don't really understand how any of this works"

It's easy to feel behind. Sometimes a little scared of it.

I've spent a while buried in the math behind these systems, and the thing I keep coming back to is this: the ideas underneath are simpler than the people explaining them make them sound. The jargon is the hard part. Not the machine.

So I'm going to start writing the version I wish I'd had, taking one concept at a time and pulling it apart from the ground up, until the gears actually make sense. No hand-waving, no equations dropped on you out of nowhere.

Some of what we'll touch:
• How LLMs actually work, and the history that led there
• How machines see and generate images
• Reinforcement learning, in plain words
• The architectures behind the models you already use
• The stranger frontiers, like physics-based and quantum AI

If you've ever used AI and felt that gap, this is for you.
First one's coming soon.

[Raghav Dixit @_raghavdixit_]

https://t.co/RVnYCtg2wO

[Arman Hezarkhani @ArmanHezarkhani]

Generalists are so important for early-stage startups

Specialists require specialized roles, which adds rigidity to an organization.

If you're hiring a specialist, you better be confident that you'll need that role for the next few years. Otherwise, you'll have to constantly churn through people, or push them to roles they're ill-suited for.

Generalists solve this problem.

They fill the gaps of an imperfect or incomplete organization.

Adding a forgiving layer of flexibility to an organization as the org figures out what it is.

But here’s the thing.

Eventually, the organization will figure out what it is — at least parts of it will — and in that stage, specialists become the better option.

So what to do with the generalists that built the firm?

Traditionally, they’re fucked.

They’re pigeonholed in to a position. They’re forced to become a specialist, but it doesnt work. So they leave and go to build the next company. Sometimes their own company.

But at @tenex_labs , we’re constantly growing new arms and legs. We’re constantly expanding and trying new things.

We’re not a specialized organism, we’re an ecosystem. And within that ecosystem, generalists and specialists can live and thrive together.

[Raghav Dixit @_raghavdixit_]

@beanlawler_ @swill1ams 🙌❤️

[Raghav Dixit @_raghavdixit_]

@seejayhess Likewise brother, grateful to share this journey with you!

[Raghav Dixit @_raghavdixit_]

Working at startups is never easy. Doing it as an international hire, half a world from home, is a different game entirely.

I kept making that bet anyway at lean, venture-backed teams, one of which became a unicorn...and every time I noticed the same arc. The high of building "the next cool thing," and once it wore off a feeling I couldn't name. For years I assumed that's just what startups felt like.

Last September, around 2am, I was doom scrolling LinkedIn when I saw posts from @businessbarista , @ArmanHezarkhani , and @beanlawler_ about @tenex_labs . All three within five minutes.

The company was only a few months old. I had almost no information about the problems I'd get to work on. And for a foreign worker, an early-stage startup isn't one bet, it's three: on yourself, on the team, and on a visa clock that doesn't care about either.

What made me apply anyway was the vision: building a company that lasts in a post-AGI world, with a business model to back it up.

Then came the conversations with Arman, Alex and the rest of the team at the time(basically @seejayhess and @dan_zakon ). I wont ever forget this voice in my head saying "this is it." when I walked back home from the final round. The intellect, the caliber, the desire to push the vision through...It made my blood rush and fortunately, I got a chance to join as the third engineer and the first international hire.

Ten months in, after many learnings, complex challenges, and a lot of memories, I can finally name the feeling I could never shake at every startup before this one.
It was the lack of a home away from home.

That's what Tenex turned out to be. People I genuinely love, respect, and look up to. A team this nerdy, rigorous, fast-paced, and fun at the same time is rare. I didn't just join another startup. I found my tribe.

If you want to find yours while working at the frontier of AI, we're hiring!

https://t.co/LsarWgH41Y

[Raghav Dixit @_raghavdixit_]

@ArmanHezarkhani Thanks for giving me a chance 🫶🏻

[Dean @beanlawler_]

https://t.co/3G9LRiU7eB

[Raghav Dixit @_raghavdixit_]

@beanlawler_ Great stuff!

[Scott Williams @swill1ams]

People don’t talk about Gemma 4 enough.

For context: @cerebras recently made Gemma 4 31B by @GoogleDeepMind available in public preview, and I think that the published numbers are worth testing for agentic workflows.

For my use case, it is the perfect scout model for coding agents like Oh My Pi. Not the model that makes every hard call. The model that does the parallel discovery work needed to make decisions.

Things like:
- scanning a repo
- analyzing images
- mapping a project
- summarizing sources
- triaging risks
- doing first-pass review
- finding the files a stronger model should inspect

That matters because coding agents spend a lot of time gathering context before they do the “smart” part.

The published numbers make it worth a look:

1. Speed: ~1,800 output tokens/sec
2. Cost: $0.99/M input tokens and $1.49/M output tokens
3. Quality: Analysis Intelligence Index score of 29, near Claude Haiku’s 30 on that benchmark, while delivering much higher throughput on Cerebras.

The point is not “small models beat frontier models.”

The point is routing.

The outcome for me: faster discovery loops, better context capture from text and images that previously got missed, and more workflow speed without forcing every step onto the expensive model or degrading response and code quality.

PS: At Tenex, we spend a lot of time testing models in real workflows, not benchmark slides. If you like building with new models, arguing about what they are actually good at, and turning that into product, you should apply.

Tenex Careers Page: https://t.co/4UuZjneXHA

[Raghav Dixit @_raghavdixit_]

@ArmanHezarkhani Excited!!

[Alex Lieberman @businessbarista]

The demand for exceptional AI-native software engineering is skyrocketing right now.

It's why 50%+ of our business at @tenex_labs is elite engineering-as-a-service for midmarket & enterprise companies.

@levie talks about it a lot, but as the power of AI defuses from engineering to all other areas of knowledge work, the laundry list of needs for builders that have AI chops, strong systems thinking, and deep process knowledge is going parabolic.

Moreover, a list of patterns is emerging for where companies need the most engineering help in a post-AI world. I thought it'd be helpful to share.

Here's the internal list we've been keeping:
- Data engineering & organization: helping companies unify, clean, and get their data to be agent-ready
- Old-fashioned software engineering, supercharged by agentic workflows
- Build "the brain": single source of truth with RBAC upon which information can be queried or transformed with customized agents
- Cyber hardening: reduce AI-era cyber risk by identifying highest-risk exposure and rapidly implementing practical controls, automations, and monitoring
- Token audits: helping AI-native software orgs build & deploy strategies for token efficiency/costs
- Generative UI: building intelligence dashboards engines that enable every role in a company to act fast on data-driven insights
- Edge ML: building and scaling resource-constrained computer vision model for retail applications
- BYOA: build custom agents for any repetitive business process, from deep process mining to build, enablement, and enhancement
- SDLC colonoscopy: studying a company's software lifecycle, identifying bottlenecks, and driving efficiency so code isn't metered by organizational inefficiency
- Stack rebuilds: Rebuilding old systems for 1/10th of the cost at 10x the speed
- Stack speed-ups: Rebuilding systems to be most agentic engineering-friendly
- Autonomous SRE: setting up agents in CI/CD and connected to monitoring tools to automatically raise PRs for bug fixes

What's missing from this list?

P.S. if you need an elite squad of AI-native engineers to supercharge your business, shoot me a DM.

[Alex Lieberman @businessbarista]

I want to start an AI community for executives.

This will be a space for people to share killer use cases, agentic workflows/agents, post-AI org structure, AI governance, AI training/enablement, change management, and more.

Comment “AI-native” if you want to join.