Deedy Profile picture
Jul 19 2 tweets 1 min read Read on X
🚨 BREAKING: Detailed list of all 44 people in Meta's Superintelligence team.

— 50% from China
— 75% have PhDs, 70% Researchers
— 40% from OpenAI, 20% DeepMind, 15% Scale
— 20% L8+ level
— 75% 1st gen immigrants

Each of these people are likely getting paid $10-$100M/yr. Image
Source: anonymous Meta employee

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More from @deedydas

Aug 10
I'm using GPT5 Pro to find me the best stocks and startup investments.

Asked it to use modern portfolio theory and size investments.
—Top Privates [+9.7%]: Databricks, Stripe, Anthropic, SpaceX
—Top Publics [+14.2%]: Nvidia, TSMC, Microsoft, Meta

Just put $1000 into the stocks! Image
Prompt: "Check all public / private stock market companies and tell me what I should invest in from first principles reasoning. You have $1000.

Please do deep research and present rationale for each investment. Each one should have a target price and expected value. Use advanced math for trading. Draw research from authoritative sources like research and unbiased pundits. Size my bets properly and use everything you know about portfolio theory. Corroborate each decision with a list of predictions about those companies.

Your goal is to maximize expected value. Make minimum 5 investments. Write it in a table."
This follows my previous experiment on Polymarket, which seemingly had ~2-4x the expected returns!
Read 4 tweets
Aug 8
Ridiculous that OpenAI claimed 74.9% on SWE-Bench just to prove they were above Opus 4.1’s 74.5%…

By running it on 477 problems instead of the full 500.

Their system card only says 74% too. Image
And yes, I know they’ve always reported on the 477 denominator, but that’s NOT “SWE-Bench verified”, that’s an entirely different metric, it’s “OpenAI’s subset of SWE Bench Verified” and that number can’t be compared
Read 4 tweets
Jul 31
🚨Anthropic is at $4.5B annualized revenue and is the fastest growing software company in history!

They just overtook OpenAI to become the market leader in LLM API cost.

We just dropped this and more in our mid year Enteprise AI report:

1/7 Image
Enterprise LLM API spend has exploded from $3.5B to $8.4B by mid year, and that number is already stale!

2/7 Image
Enterprises and startups are choosing closed source models.

Only 11% of enterprises show high open source model usage.

3/7 Image
Read 7 tweets
Jul 31
Microsoft just leaked their official compensation bands for engineers.

We often forget that you can be a stable high-performing engineer with
great work-life balance, be a BigTech lifer and comfortably retire with a net worth of ~$15M! Image
On the top chart:
Blue is base, purple is stock, green is bonus
Read 4 tweets
Jul 22
The best open-source AI model just dropped a detailed report on how it was trained, a rare resource for students given no frontier lab is publishing!

Kimi K2's estimated total cost of training is ~$20-30M, roughly in line with pricing: $0.6/M in $2.5/M out tokens.

10 highlights:Image
1. Generating tokens by rewriting high-quality tokens with LLMs in pre-training
2.  Mining 3000+ MCPs and using LLM-generated personas to improve agentic tool calling
3.  10,000 parallel Kubernetes sandboxes to solve Github issues
4.  New scaling laws for sparsity in MoE models
5. RL with verifiable rewards (RLVR) for math, coding, safety with self-critique model with long-reasoning penalty, causing direct, desisive answers
6. Training recipe of 4k sequences, then 32k then 128k with YaRN
7. High temp during initial RL training to promote exploration
Read 5 tweets
Jul 18
The hardest high school math exam in the world, the 6 problem 9 hour IMO 2025, was this week.

AI models performed poorly.

Gemini 2.5 Pro scored the highest, just 13/42, costing $431.97, in a best of 32 eval. Bronze cutoff was 19.

Long way to go for AI to solve hard Math. Image
Here's a more beautiful visualization of model performance on MathArena Image
Read 5 tweets

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