zyad

CEO & Founder @thelensai

[zyad @itszyadd]

I bought a Youtube channel last year for $10k, when I was $70k in debt. This month alone it made $10k

The channel is now worth over $180,000, a %1800 return.

@leopoldasch recruit me.

[zyad @itszyadd]

Considering FIFA world cup is around the corner, this asset is a gold mine. I predict the revenue to reach $13,000 per month, easily.

The asset is now worth $180,000, which is a 18x return, or %1800.

[zyad @itszyadd]

Last year, i was $70,000 in debt, and bought a YouTube channel with my remaining $10,000 when it was making essentially $0 because the previous owner had stopped uploading and the channel had gone inactive.

My thesis was that if I reactivated the channel, the evergreen library would start getting pushed by the algorithm again (my thesis was right)…

[zyad @itszyadd]

For reference:

• March: $7,300
• April: $7,200+
• Past 28 days: $8,400, highest revenue period in channel history
• Upload frequency: roughly once every 1–3 months
• Majority of revenue (98% of revenue) still comes from videos uploaded back in 2022

[zyad @itszyadd]

@aviralbhat VC slop

[zyad @itszyadd]

If you’re building RAG pipelines, test this idea.
Treat vectors as a fallback, not the foundation.
You’ll cut latency, reduce cost, and make your system actually think.

[zyad @itszyadd]

I built a hybrid retrieval layer for RAG/AI agents that makes vector search the last resort.

Most RAG stacks jump straight to embeddings while ignoring the two things that actually make retrieval efficient, semantic/non-semantic caching and BM25 keyword search.

ValeSearch routes queries through cache → semantic cache → BM25 → rerank → vectors only when confidence is low (cosine setup with my testing but it can differ)

In early tests on a 9-figure real estate company’s internal RAG setup, on paper it proved to cut 70% of queries before they ever touched the LLM, in the long run.

Repo’s still unfinished, but everything is in the readme for a better understanding.
https://t.co/J2yr2w0k2W

[zyad @itszyadd]

The more your system is used, the more queries start to repeat.
Different wording, same content.
Caching that intelligently saves massive LLM cost over time.

[zyad @itszyadd]

@Aurimas_Gr Yet not many deploy semantic caching for their agentic pipelines

[zyad @itszyadd]

@rryssf Everyone is saying the context is the new route for AI, but if you feed an AI so much context that everything is explained, its predictive analysis would effectively just be the context you gave it

[zyad @itszyadd]

Participating in meta’s $2.5 million “hackathon” having never built a game on their platform.

I bought the quest 3 just for this so lets see how it goes.

[zyad @itszyadd]

@Yuchenj_UW now repeat this 20 times lets see if qwen can consistently choose the best line of reasoning or if its just luck

[zyad @itszyadd]

@adithya_s_k Have you tested stanford’s new paper’s findings on the efficacy of context augmentation vs fine tuning?

[zyad @itszyadd]

How does one land a research position at a t20 uni without being a student there?

[zyad @itszyadd]

@0xsero

[zyad @itszyadd]

@hendrycks Auditory intelligence > speef AND visual intelligence??? Are we using the same gpt-5?

[zyad @itszyadd]

“AI agents” have become such a buzzword its actually insane.

But no one’s talking about the infrastructure that makes those agents actually work inside a business.

I’ve recently been building AI systems for companies that are way slower than I expected. They’re all trying to integrate AI, but the moment you ask how their data flows, it’s a mess, which makes me so bullish on data-related startups.

It’s weird… I feel like I’ve been selling shovels during a gold rush. Everyone wants AI, but very few have the people who can actually build it properly within their own ecosystem.

Unless you’re a massive consulting firm charging half a million just for a “strategy session,” these companies can’t find anyone reliable to get it done.

There’s such a massive gap between what AI could do for enterprises and what’s actually being done right now.