FEDERAL RATES

Personal rumination of federal reserve overnight rates. federal_rates on mastodon

[Dan Kornas @DanKornas]

Kaggle Competitions

What you will learn:
- Kaggle Competition - House Prices: Advanced Regression Techniques Part1
- Kaggle Competition - House Prices Regression Techniques(Hyperparameter Tuning)-Part 2
- Used Deep Learning Technique and did the Accuracy Increase? Part 3
- Kaggle Competition- Predict Stock Price Movement Based On News Headline using NLP
- Kaggle Competition- Implement A DNA Classifier using NLP
- Kaggle Competition- Predicting PIMA Diabetes Prediction using Machine Learning
- Kaggle Competition- Dengue or Malaria Prediction Using Transfer Learning VGG19

Link is in the first comment 👇

♻️ Share this with your network if you found it useful or insightful.

[slash1s @slash1sol]

IN 2005 MIT FILMED THE LECTURE THAT EXPLAINS WHY CODE THAT FLIES ON YOUR LAPTOP DIES THE SECOND REAL DATA HITS IT -- EXACTLY WHAT AI WRITES BY DEFAULT

80 minutes from Charles Leiserson, co-author of the book every CS student calls the algorithms bible.

-> The idea that lands: a program isn't fast or slow. It's a curve. The wrong one looks fine on six rows and dies on six million.

You were taught to make it work. Never to ask what happens when the input explodes.

AI hands you code that passes every test on small data -> then hides an O(n²) bomb that goes off in production.

Making it run was never the skill -> knowing how it scales is.

Most people ship whatever works today. The ones who watched this see the wall coming a mile out.

Bookmark and Watch this legend ↓

[Cliff Pickover @pickover]

FREE Math Book. 470 pages.

"Complex Analysis" by Howell & Mathews. Supports a one-semester undergraduate course. Suitable for math, engineering, and physics students.

1 Complex Numbers
2 Complex Functions
3 Analytic and Harmonic Functions
4 Sequences, Series, and Fractals
5 Elementary Functions
6 Complex Integration
7 Taylor and Laurent Series
8 Residue Theory
9 Conformal Mapping
10 Applications of Harmonic Functions
11 Fourier Series and the Laplace Transform

Link: https://t.co/xGdcl0JNNQ

[venus @RitOnchain]

AI Phd Professor just showed how to build Hidden Markov Models for time-series data.

61-minutes. free. By AI Researcher for Quants.

"for stock market prediction is the same idea you want to predict what prices are going to be in the future"

here's what they cover:
•why the independent data assumption fails
•calculating transition and emission distributions
•predicting future states step-by-step
•extracting hidden sequences with the Viterbi algorithm

Bookmark it & watch today. Then read the article below.

[Pallavi @pallavishekhar_]

- Math behind Attention- Q, K, and V
- Math behind √dₖ Scaling Factor in Attention
- Math Behind Backpropagation
- Math Behind Gradient Descent
- Math Behind Cross-Entropy Loss
- Math Behind RoPE (Rotary Position Embedding)
- RMSNorm (Root Mean Square Layer Normalization)

[Ruben Hassid @rubenhassid]

Instead of watching Netflix tonight.

Spend a day mastering Claude here: https://t.co/Vn60ElPZ2i

→ Level 1 - 24 min: The basics.

Claude For Dummies: https://t.co/jw2qdIcjnh
Claude Certified: https://t.co/9jKsXWOt66
Stupid simple Claude: https://t.co/SVGd967eMQ

→ Level 2 - 1 hour: Real workflows.

Claude Cowork: https://t.co/uWTpOI3Woc
Claude for teams: https://t.co/qxlcqhf8bM
Claude Design: https://t.co/ZY8Fg5D2ea
Cowork + Projects: https://t.co/Q7AN9CZAbO
Claude for slides: https://t.co/L0bPMgXci6
Claude Skills: https://t.co/6cHYYfjXEA

→ Level 3 - 3.5 hours: The pro moves.

Claude to sound like you: https://t.co/kDGBpSF7Wh
Stop hitting Claude limits: https://t.co/j5fEzSH5br
Stop Prompting: https://t.co/j1LATSJiat
Claude replaced me: https://t.co/pNs1hPNDy5
Stop sounding like AI: https://t.co/JWKUGNKgOS
Excel with Claude: https://t.co/7g3CFNcKrs

→ Level 4 - 8 hours: Expert mode.

Claude Code: https://t.co/UgE9xBXVbE
Claude Connectors: https://t.co/TSAQqOpDeV
Stop using Claude at work: https://t.co/c6X55Thy6t

Pro tip: Don't binge it. Do one level per sitting.

Actually apply each guide before moving to the next.

[Roan @RohOnChain]

Jim Simons (Quant King), literally tells you exactly how he built the quant strategies behind the most profitable hedge fund in history:

[Kirk Borne @KirkDBorne]

[Free PDF Download] Introduction to Probability for Data Science: https://t.co/L51j3udY5j by @stanley_h_chan
—————
#StatisticalLiteracy #Mathematics #ML #MachineLearning #DataScience #DataScientist #Statistics

[Antonio Lupetti @antoniolupetti]

"An Introduction to Flow Matching and Diffusion Models" is a set of MIT lecture notes for the course "Generative AI With Stochastic Differential Equations" (2026) that provides a clear introduction to the mathematics behind modern generative AI.

The notes discuss flow matching and denoising diffusion models as core techniques behind many advanced generative systems, with references to models such as Stable Diffusion 3, FLUX, VEO-3, and AlphaFold3.

They develop the mathematical foundations of generative modelling, covering topics such as sampling from probability distributions, ordinary and stochastic differential equations, Brownian motion, diffusion processes, flow matching, score matching, classifier-free guidance, architectures for image and video generation, latent spaces, autoencoders, and discrete diffusion models for language generation.

What I particularly appreciated is the teaching style. The notes first build geometric and probabilistic intuition and only then derive the complete mathematical formulations. The result is a treatment that is rigorous, visual, and remarkably approachable.

This is probably one of the best freely available resources for understanding what is actually happening under the hood of diffusion models from a mathematical perspective.

https://t.co/J96rHCBPrb

[Kirk Borne @KirkDBorne]

Graph Algorithms for Data Science: https://t.co/1x1iT3sPoP

I have said this for years: "All the world is a graph!" The natural data structure of the world is a graph, not rows and columns:

Connected things (IoT)
Context engineering
Graph analytics
Graph databases
Knowledge graphs
Linked data
Network science
PageRank search algorithm
Semantic metadata
Six degrees of separation
etc.

[Kirk Borne @KirkDBorne]

Download 244-page PDF “Introduction to Neural Networks” at https://t.co/0nNNi7ezRu
————
#DataScience #AI #MachineLearning #ML #Mathematics #Calculus #DataScientist

[Rohit Kumar Tiwari @_rohit_tiwari_]

AI Engineering from Scratch.

503 lessons. 20 phases. 320 hours.

https://t.co/UuX9N62nNm

Phase 00: Setup & Tooling (12 lessons)
Phase 01: Math Foundations (22 lessons)
Phase 02: ML Fundamentals (18 lessons)
Phase 03: Deep Learning Core (13 lessons)
Phase 04: Computer Vision (28 lessons)
Phase 05: NLP (29 lessons)
Phase 06: Speech & Audio (17 lessons)
Phase 07: Transformers Deep Dive (14 lessons)
Phase 08: Generative AI (14 lessons)
Phase 09: Reinforcement Learning (12 lessons)
Phase 10: LLMs from Scratch (22 lessons)
Phase 11: LLM Engineering (15 lessons)
Phase 12: Multimodal AI (25 lessons)
Phase 13: Tools & Protocols (23 lessons)
Phase 14: Agent Engineering (42 lessons)
Phase 15: Autonomous Systems (22 lessons)
Phase 16: Multi-Agent & Swarms (25 lessons)
Phase 17: Infrastructure & Production (28 lessons)
Phase 18: Ethics, Safety & Alignment (30 lessons)
Phase 19: Capstone Projects (85 lessons)

[Himanshu Kumar @codewithimanshu]

Jim Simons turned $100 into $130 billion using math.

He just gave the entire playbook in a free 1-hour MIT lecture.

You've been picking stocks based on Reddit and vibes.

He returned 66% per year for 30 years using equations.

This is the most valuable hour you'll spend this week.

Save the video. Watch it tonight. Build the bot this weekend. Follow
@codewithimanshu

for more high-signal content that turns lectures into income.

↓

Below what nobody is telling you about this lecture.

Everything Jim Simons taught Renaissance Technologies in the 1980s is now buildable in a weekend with Claude Code.

Pattern recognition across thousands of assets. Signal detection in noise. Automated execution.

Risk management at scale. In 1988, this required a team of 50 PhDs and millions in infrastructure.

In 2026, one person with Claude + a laptop can build a working version in 7 days.

The knowledge gap between you and a Renaissance trader is now smaller than it has ever been in history. Follow

Follow
@codewithimanshu
for daily breakdowns of the AI tools that turn this knowledge into income.

↓

What this lecture actually teaches you to build.

Simons walks through the core principles that printed $130 billion:

> Find statistical edges that are invisible to humans
> Trade only when the math says yes, never on emotion
> Run hundreds of small bets simultaneously, not one big bet
> Cut losses ruthlessly when signals weaken
> Compound relentlessly across decades These aren't trading tips.

These are the foundational principles of every AI trading bot worth running. Watch the lecture. Take notes. Then turn it into a system. Follow
@codewithimanshu

for the exact Claude prompts that turn quant theory into deployed bots.

↓

Your weekend playbook to turn this into profits.

Friday night: watch the Simons lecture. Take notes on every signal he mentions.

Saturday: open Claude Code. Build a backtesting framework using historical price data.

Test 3-5 of his core signal ideas.

Sunday: paper trade your best signals on Polymarket, Toobit, or Alpaca. Validate before risking real capital.

Monday: deploy a small position. $100. $500. Whatever you can lose without flinching.

Compound. Iterate. Scale.

That's how a Simons-grade trading system gets built in 2026. Not over 30 years. Over one weekend. Follow
@codewithimanshu

for the exact templates and prompts to build each piece.

↓

for more high-signal content that turns lectures into income.his is the best time in history to build wealth with AI trading.

Jim Simons needed:

> A team of 50 PhDs
> $25 million in compute
> 10 years of infrastructure
> Custom data feeds nobody else had

You need:
> Claude Code
> A laptop
> 7 days of focus
> $20/month in API costs

Same math. Same principles. Same edge.

Different barrier to entry.

People who watch this lecture and build with the knowledge will compound for the next decade.

People who save it for later, will still be picking stocks based on vibes in 2027.

Save the video. Watch it tonight. Build the bot this weekend. Follow @codewithimanshu

[slash1s @slash1sol]

IN 1986 MIT FILMED A LECTURE THAT OPENS BY TELLING YOU COMPUTER SCIENCE IS NOT A SCIENCE AND HAS ALMOST NOTHING TO DO WITH COMPUTERS

72 minutes from Hal Abelson and Gerald Sussman, the lecture an entire generation of engineers calls the one that rewired how they think.

-> The line that lands: computer science is about computers the way astronomy is about telescopes. The tool was never the point.

The real subject was always one thing -- controlling complexity. Everything else is detail.

Forty years later it reads like a prophecy. AI writes the syntax now. What's left is exactly what they taught: taming complexity nobody can hold in their head.

The language was never the skill -> the thinking was. This is where you learn it.

Most people chase the newest framework. The ones who watched this think on a level frameworks can't touch.

Bookmark & Watch today it, this one's a legend ↓

[Scott Redler @RedDogT3]

220K views on this video lesson.

Once $SPCX has an 8 and 21 day, you'll need this:

https://t.co/ASxwSr70HB

[Dan Kornas @DanKornas]

Get your copy of the book here: https://t.co/k8WRbUlcmp

[Dan Kornas @DanKornas]

Most AI/ML books are only useful if they change how you build.

Kubernetes in Action, Second Edition is useful because it maps the topic to engineering work you actually have to operate.

The book covers:

• Deploy and scale beyond a demo
• Tens of thousands of developers have learned how to develop and run a…
• From building your first cluster, you’ll steadily expand your initial…
• Understand the core system design choices
• Map the topic to real engineering workflows
• Avoid common production failure modes

The production angle is the part I would pay attention to.

The demo is the easy part. The useful engineering work is making the system reliable once it touches real workflows.

Good fit for engineers building real AI systems and wanting a stronger mental model than another clean demo.

Link in the first comment.

[Livsun @L1vsun]

math behind how market makers price every trade against you has been sitting in a free PDF since 2008

not charts. not patterns. a literal equation that tells you where to place the bid and ask at every single moment, updating in real time

it accounts for three things: how much inventory you're already holding on one side, how volatile the market is

and how likely whoever is trading against you knows something you don't

if you're loaded up on one side you widen the spread on that side and shift your reservation price inward

you slow down new exposure without closing positions. the formula tells you exactly how much to adjust

retail traders spend years studying which direction price will move

market makers solved a completely different problem: how to profit from the spread regardless of direction

Bookmark before it get buried

prop shops have been rebuilding this math for almost two decades

it shows up in quant interviews and internal research docs, not in trading courses or youtube tutorials

the document is 17 pages, free, in a university archive

the whole time you were learning to read the market, the other side was running an equation

[OS Dev @OSdev_]

An Introduction to Programming with Threads by Andrew D. Birrell (1986)

https://t.co/b7BCuMhOCC

[bodila @51bodila]

"Einstein of Wall Street" couldn't afford a seat on the exchange - now he trade $1 billion daily for over 40 years and survived 5 global crashes

he just revealed the 5 rules that 80% of traders break every day

"FOMO, hype and hope are not sustainable trading strategies"

twice tried to buy a seat on the exchange - twice someone outbid him - so he leased one and never left

40 years later he watched everyone on the NYSE floor buy Yahoo at $3 - it went to $600 - they all went broke - one man sold in pieces and made $20 million

that's when he learned the rule he still follows today -"nobody ever went broke taking a profit "

bookmark & watch today ↓