David Ayoola

I help LLM infrastructure founders turn complex technical ideas into clear, compelling narratives that build credibility and authority in the AI community.

[David Ayoola @Dave412025]

5 reasons to articulate the exact pain your LLM solves:

Buyers don’t decode vagueness.

VCs back clarity, not buzzwords.

Pilots depend on precision.

Sales decks become obvious.

You sound like the only one solving that pain.

Clarity closes. Vagueness delays.

[David Ayoola @Dave412025]

The opposite of a good idea can also be a good idea.

[David Ayoola @Dave412025]

Got hit hard by malaria over the last three weeks, but finally on the mend. Next two weeks, the AI Infra Ledger returns with a deep dive into Migration Infrastructure bottlenecks, followed by a teardown on why multi-node scaling fails when NCCL tail latency spikes your MFU.

[David Ayoola @Dave412025]

It's very important to avoid glorifying learning from feedbacks in production so much that your first in-production system is plagued with problems you could have fixed.

[David Ayoola @Dave412025]

The thing is the question is not HOTL vs HITL in general. Rather it is this: For this present task, which one is most effective? The singular most important parameter to judge with is the nature of the decision, whether high-risk irreversible or not.

[David Ayoola @Dave412025]

Sometimes, writing is just thinking on paper. You won't delegate that one to a model or will you?

[David Ayoola @Dave412025]

Here's what you should do on Sundays?

Nothing. Rest. Just rest.

[David Ayoola @Dave412025]

What do you think are wrong with the current AI Engineering practices?

[David Ayoola @Dave412025]

Though many people would want us to believe the opposite, data is still very important in AI engineering. Data quality & relevance most importantly, especially when you're no frontier lab.

[David Ayoola @Dave412025]

Hybrid approaches are effective in AI system engineering, especially when the tasks are huge and diverse. Make no mistake, orchestrating a hybrid system is challenging.

[David Ayoola @Dave412025]

This is cliche & rightly so.

[David Ayoola @Dave412025]

New tech create new problems.

[David Ayoola @Dave412025]

Unsupervised training sounds good until you start engineering one. A simple clustering algorithm, for example, can take a gallon of sweat.

[David Ayoola @Dave412025]

Though many people would want us to believe the opposite, data is still very important in AI engineering. Data quality & relevance most importantly, especially when you're no frontier lab.

[David Ayoola @Dave412025]

Your LLM Infra must be solving difficult problems. It's half of the whole thought leading thing. This is the combo you want to have: great service + great takes.

[David Ayoola @Dave412025]

Expect my new Substack next week's Tuesday/Wednesday.

[David Ayoola @Dave412025]

New tech create new problems.

[David Ayoola @Dave412025]

SATD gets expensive every single time the lab upgrades. Then you need a serious solution. What's the way out? Build your own system from the ground, hey? Perhaps if you have the money. The other choice: have a working archi that helps with transitioning.

[David Ayoola @Dave412025]

How are companies who built their enterprise on a particular model coping in the age of updates upon updates?

[David Ayoola @Dave412025]

@VidhiWaghela @ProductHunt Thanks.

[David Ayoola @Dave412025]

This week's newsletter of LLM Infra Ledger: https://t.co/BFeGd9zw2n

[David Ayoola @Dave412025]

@VidhiWaghela @ProductHunt I saw it and was gobsmacked. If Emberis is all that you said, then this is a purple cow. Would love to hear more. Any paper or something already?