Twishmay

Founder @PsyTech_AI | MBA @Wharton | Engineer @iitbombay | Music, magic, & physics

[Twishmay @twishmay]

Nice! Try this out folks.

[Nishkarsh @contextkingceo]

Introducing HydraDB.

The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do.

We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now.

@hydra_db combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise.

We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.

[Brian Halligan @bhalligan]

I don't remember where I found this, but its spot on.

[Twishmay @twishmay]

How do you even catchup with the daily pace of progress in ai?

Only seems to be accelerating.

[Twishmay @twishmay]

hmm

[Twishmay @twishmay]

@mayankltgm @gauravtoshniwal Hopefully Bob was / is first of many to come ...

[Aakash Gupta @aakashgupta]

Microsoft sold every spare CPU it had to Anthropic and OpenAI. Amazon tripled its CPU buys year over year and still can't keep up. Two of AWS's biggest customers asked Andy Jassy if they could buy the entire 2026 production run of Graviton chips. He said no.

The ratio inside an AI datacenter used to be 100 megawatts of GPUs to 1 megawatt of CPUs. CPUs handled storage, checkpointing, pre-processing. Light work. GPUs did the actual training and inference.

Then OpenAI shipped o1-preview in September 2024. RL post-training went from "check the model output with a regex" to "run classifiers" to "compile the code and run the unit tests" to "spin up a sandbox, call three databases, run a physics simulation, verify the answer."

Every rollout now needs a CPU-backed environment to verify against.

Codex 5.4 runs agentically for 6-7 hours at a time. Each database call, each cron job, each scraped URL is CPU work. Coding agent revenue went from a couple billion to north of $10B in six months. That compute is sitting on CPUs.

The CPU to GPU ratio is now approaching 1:1. The entire global cloud was built for 1:8.

That's why GitHub has been unstable for weeks. Nvidia and Arm both announced they're entering the server CPU market in March. TSMC will only meet 80% of server CPU wafer demand this year. High-end server CPU prices are already up 50%.

When the GPU king and the IP licensor both pivot to CPUs in the same month, the boring chip isn't boring anymore.

[Isaak @isaakfreeman]

I’m leaving MIT and not continuing into my PhD. AI is coming too fast for humans to keep up.

But there might be a way: I realized digital humans are more possible than most think. With capable AI researchers helping, maybe for $10B, maybe in less than 10 years, on 50k H100s.

[Paul Graham @paulg]

There's never been an investment like the investment in railroads. (This graph has a log scale!)

[François Chollet @fchollet]

There's a broadly held misconception in AI that methods that scale well are simple methods -- even, that simple methods usually scale. This is completely wrong.

Pretty much none of the truly simple methods in ML scale well. SVM, kNN, random forests are some of the simplest methods out there, and they don't scale at all. Meanwhile "train a transformer via backprop and gradient descent" is a very high-entropy method, easily 10x more complex than random forest fitting. But it scales very well.

Further, given a simple method that doesn't scale, it is usually the case that you alter it to make it scale by adding a lot of complication. For instance, take a simple a simple combinatorial search-based method (not scalable at all) -- you can make it scale by adding deep learning guidance (which blows up complexity). Scalability usually belongs to high-entropy, complex systems.

[Twishmay @twishmay]

@pratikpoddar Super useful read !

[Pratik Poddar @pratikpoddar]

A key insight looking at India-first startup playbook 🇮🇳:

US: optimize for high AOV + margins

India: win with Low AOV, Low margins, High frequency, High retention, Massive throughput

Thread 👇

[Chris Bakke @ChrisJBakke]

Running a company:

2020: can you survive a pandemic?

2021: still here? we’re going to give all of your competitors $100m series A rounds.

2022: wow, you made it? okay, all engineers cost $600,000/year now.

2023: nice job! okay, SVB failed and we’re going to take away your bank account.

2024: a survivor I see. but can you pivot from ai to crypto to defense tech back to ai-enabled defense tech in a 12 month period to stay relevant?

2025: unfortunately all of your competitors have raised $2b series B rounds. oh and only 500 engineers are relevant and they cost $100m/yr each.

2026: well, well, well. you’re still in business? let’s deploy the thunderclap of godlike LLMs from the heavens so all of your customers can rebuild your app in 2 hours. can you survive?

[François Chollet @fchollet]

The reason symmetry is so important in physics is because symmetry is a highly effective compression operator. If a system is invariant under some symmetry, you only need to explain one axis of it. Scientific models represent the systematic exploitation of the universe's internal redundancies through symbolic logic.

[Tim Urban @waitbutwhy]

There are 303 visuals in What's Our Problem? Here are 15 of them.

First, the path of a maturing thinker. I still often find myself on this roller-coaster when I learn about a new topic. It's human nature. But as we grow as thinkers, we can get better at skipping steps 1-4.

[Twishmay @twishmay]

https://t.co/hg4CUH1dKw

[Twishmay @twishmay]

The appearance of stone tools in the archaeological record is often treated as the moment “we” arrived.

They offer a clear boundary between “clever humans” and the animals around us. But what if that assumption is wrong?

A recent archaeological find at Lomekwi in Kenya suggests that the first stone tools weren’t made by humans at all, but by small-brained, long-extinct hominins who lived more than three million years ago.

Who exactly were these mysterious toolmakers, and how does the discovery of their creations change the story of our evolution?

[Twishmay @twishmay]

In a world moving faster than our brains and institutions can track, the people with better sense-making systems keep more agency.

[Twishmay @twishmay]

Thoughts? If this is interesting can we start a WA group to collab on this and improve this?

Please comment below and will DM you the full pdf.

[Twishmay @twishmay]

Whats new this week?

[Elon Musk @elonmusk]

Banger 😂