WFCN 👽🖖🏼

Network Engineer.
Não fui eu que lhe ordenei? Seja forte e corajoso! Não se apavore, nem se desanime, pois o Senhor, o seu Deus, estará contigo. Js1:9

[Rimsha Bhardwaj @heyrimsha]

10 free textbooks from MIT, Stanford, and Berkeley that you can download legally right now.

→
Introduction to Linear Algebra — Gilbert Strang (MIT)

This is textbook for one of the most-watched math courses ever. The course has about 20 million views on MIT OpenCourseWare. A lot of machine learning engineers learned these ideas from one calm, clear professor.

https://t.co/21ZEyYt0Q8

→ Mathematics for Computer Science (MIT 6.042)

This course covers proofs, discrete math, and probability. These topics are a big part of what computer science is built on, but a lot of undergrads don’t hear about them until later.

https://t.co/n6iYa2nTxZ

→ Convex Optimization - Stephen Boyd, Stanford

This is used in almost every serious machine learning and control systems class in the world. Cambridge University Press let Boyd keep it free on his own website.

web. stanford. edu/~boyd/cvxbook/bv_cvxbook.pdf

→ CS229 Machine Learning Notes - Andrew Ng, Stanford

Not the Coursera version. The actual Stanford graduate course notes. Dense, precise, and the closest thing to a grad school education you can download in one PDF.

https://t.co/lMbgoQpuzB

→ An Introduction to Statistical Learning - Stanford / USC
The book three statisticians from Stanford and USC made free because they wanted everyone to learn it. 290,000 people have taken the companion course on edX.

https://t.co/pzr1qxZwLR

→ Computational and Inferential Thinking - Berkeley Data 8

The textbook behind Berkeley's most popular course. Data science from scratch, built to be understood without a math degree first.

https://t.co/SJKJHKwOUj

→ Dive into Deep Learning - Berkeley / Amazon

Jensen Huang called it "excellent." 500 universities across 70 countries use it. Every concept runs as live code directly in the browser.

https://t.co/AxpcyRsVFw

→ Introduction to Probability - Blitzstein & Hwang, Harvard

The official textbook of Harvard's Stat 110, which has been called the best probability course ever put on YouTube. Free second edition online.

https://t.co/T8mDwKLPvp

→ The Elements of Statistical Learning - Hastie, Tibshirani, Friedman, Stanford

This is the grad-school version of ISLR. Springer offers it for free as a PDF. A lot of researchers download it and keep it saved on their computer.

https://t.co/8ApXy767id

→ MIT OCW Online Textbooks Index - 45+ books across every department

One page with every free MIT textbook, organized by subject. You can find books on algorithms, physics, economics, and engineering. All of them are open access.

https://t.co/qafq5b2tsa

Save this before someone makes them take it down.

(They won't. But save it anyway.)

[Daniel Romero @HyperTechInvest]

Industries AI won’t replace:

• Energy generation

• Grid infrastructure

• Raw materials

• Heavy manufacturing

• Construction

• Warehousing

• Ports and shipping

• Agriculture

• Water utilities

• Waste management

• Chip manufacturing

• Weapons manufacturing

• Chemicals

• Forestry

• Livestock farming

• Prisons

• Animal care

• Elderly care

• Healthcare

[Shay Boloor @StockSavvyShay]

10 NAMES TO WATCH IN THE SPACE ECONOMY

1. $RKLB becoming the second pillar of U.S. launch and space systems infrastructure with an $805M firm fixed-price Tranche 3 Tracking Layer award validating it as a trusted defense prime while Electron cadence, rising ASPs & vertically integrated manufacturing drive durable margins & a funded path to Neutron.

2. $ASTS building the D2D comms layer that turns space into a carrier-grade broadband network with BlueBird 6 proving the physics of tower-in-the-sky connectivity & SHIELD selection pulling AST into the Golden Dome stack as a long-duration, recurring wholesale revenue platform for global operators.

3. $PL selling recurring Earth-imagery and analytics to defense & intelligence customers with ~60% of revenue from sovereign contracts while scaling Satellite Services where governments fund dedicated constellations that also expand Planet’s proprietary data archive.

4. $BKSY selling high-cadence defense ISR subscriptions from its Gen-3 constellation that's differentiated by sub-30-day commissioning & low-latency imagery with ~25% long-term growth potential but burning ~$15M per quarter as it scales capacity.

5. $RDW supplying space-grade hardware & autonomous drones to U.S. + European defense programs with 2026 growth hinging on converting a $350M+ backlog while burning ~$20M per month in cash.

6. $FLY running a small-lift launch platform (Alpha) plus Blue Ghost lunar delivery with upside dependent on stabilizing launch cadence & converting NASA/DoD backlog into recurring missions.

7. $SPIR selling subscription space-intelligence data from its LEO constellation targeting >30% growth in 2026 off government backlog.

8. $SPCE is still pre-commercial suborbital spaceflight bet with commercialization pushed to late 2026 with an unproven revenue model while burning ~$100M per quarter.

9. $FJET developing an air-launched microsatellite platform using supersonic F-104 jets but still pre-revenue with no proven launch demand, competing against Electron-class incumbents.

10. $GSAT repositioning its L-band satellite network as a space-based private-network backbone for IoT, robotics & defense comms with XCOM RAN monetizing spectrum & orbit through industrial deployments as capex rolls off.

[The Market Mind @Market_Mind_]

What are Rich are Buying

[Fortune MMXM @FortuneMMXM]

Trading Without Gambling, a book you must read.
Here are 21 important Quotes from it 🧵.

[InvestmentGuru @InvestmentGuru_]

Nuclear Power: The Next Energy Catalyst for AI

Key Companies to Watch Across the Nuclear Value Chain:

Upstream (Materials & Fuel):
•Uranium mining: $CCJ, $UEC, $UUUU, $DNN, $NXE
•Nuclear fuel processing: $LEU

Midstream (Design, R&D, Construction):
$SMR – NuScale Power: SMR pressurized water reactors, commercialization-ready
$OKLO – Oklo Inc: Generation IV fast reactors, recent defense contracts
$NNE – NANO Nuclear Energy: Generation IV thermal reactors, “walk-away safe” design
$BWXT – BWX Technologies: Reactor components & nuclear tech for gov’t and commercial sectors

Downstream (Operations & Waste Management):
•Utilities: $CEG, $VST, $AEP, $SO, $EXC, $DUK, $ETR, $PEG
•Equipment & services: $GEV, $ETN, $HON, $EMR, $GHM

Nuclear energy isn’t just about power—it’s becoming a strategic backbone for AI and technology growth.

With COP28 calling to triple nuclear capacity by 2050, nuclear energy is entering a new growth cycle, poised to power AI, data centers, and the global push for low-carbon, stable energy.

[Market Rebellion @RebellioMarket]

Market Wizard Linda Reschke's 12 Technical Trading Rules: @SJosephBurns

1. Buy the first pullback after a new high. Sell the first rally after a new low.

2. Afternoon strength or weakness should have follow through the next day.

3. The best trading reversals occur in the morning, not the afternoon.

4. The larger the market gaps, the greater the odds of continuation and a trend.

5. The way the market trades around the previous day’s high or low is a good indicator of the market’s technical strength or weakness.

6. The previous day’s high and low are two very important “pivot” points, for this was the definitive point where buyers or sellers came in the day before. Look for the market to either test and reverse off these points, or push through and show signs of continuation.

7. The last hour often tells the truth about how strong a trend truly is. “Smart” money shows their hand in the last hour, continuing to mark positions in their favor. As long as a market is having consecutive strong closes, look for up-trend to continue. The up trend is most likely to end when there is a morning rally first, followed by a weak close.

8. High volume on the close implies continuity the next morning in the direction of the last half-hour. In a strongly trending market, look for resumption of the trend in the last hour.

9. The first hour’s range establishes the framework for the rest of the trading day.

10. A greater percentage of the day’s range occurs in the first hour then was the case in the past, and thus it has become increasingly important to trade aggressively if there are early signs of a strong trend for the day.

11. There are four basic principles of price behavior which have held up over time. Confidence that a type of price action is a true principle is what allows a trader to develop a systematic approach.

The following four principles can be modeled and quantified and hold true for all time frames, all markets. The majority of patterns or systems that have a demonstrable edge are based on one of these four enduring principles of price behavior.

Charles Dow was one of the first to touch on them in his writings. Principle One:

A Trend Has a Higher Probability of Continuation than Reversal Principle Two:

Momentum Precedes Price Principle Three:

Trends End in a Climax Principle Four:

The Market Alternates between Range Expansion and Range Contraction!

12. In the world of money, which is a world shaped by human behavior, nobody has the foggiest notion of what will happen in the future. Mark that word –

Nobody! Thus the successful trader does not base moves on what supposedly will happen but reacts instead to what does happen.

[Market Rebellion @RebellioMarket]

SWING EMOTIONAL TRADING:

1. Exiting Too Early (Fear of Losing Profits)

Symptom: You close a trade at a small profit, then the stock flies 20% higher.

Consequence: You miss the real move—swing trading is about catching big swings, not scalping.

2. Cutting Winners, Holding Losers (Fear & Hope)
Fear: You panic and sell winners fast.

Hope: You "pray" that your losing trade will turn around.

Consequence: Completely reverses risk/reward—small wins, big losses.

3. Revenge Trading
After a loss, you jump into another trade instantly to “get it back.”
Consequence: You’re trading anger, not a setup. Usually leads to bigger losses.

4. Overtrading Due to Boredom or FOMO

You trade random setups just because others are talking about them on Twitter/Discord.

Consequence: You burn capital, break your edge, and ruin confidence.
5. Lack of Patience to Let Setups Play Out

You exit trades too soon or enter too early due to anxiety.

Consequence: Miss the full swing or get shaken out during healthy pullbacks.

6. Changing Strategy Mid-Trade
You entered based on a breakout, but when it pulls back, you suddenly treat it like a long-term hold.

Consequence: No consistency. No accountability.

7. 📉 Ignoring Your Stop Loss (Ego)

You refuse to be wrong, so you move your stop lower or delete it.
Consequence: One bad trade destroys 10 good ones.

8. Burnout and Decision Fatigue

Emotional trading drains your energy. You obsess over charts, miss sleep, overanalyze.

Consequence: Poor decision-making and burnout.
9. Repeating the Same Mistakes

You keep promising, “Next time I’ll follow my plan,” but you don’t.

Consequence: Stagnation. No growth. You lose faith in yourself.

10. Loss of Confidence and Discipline
Emotional trading causes regret. Regret breaks confidence. No confidence = no discipline.

Consequence: You start doubting your system, your skills, and your future.

Final Truth:

Swing trading is a game of delayed gratification. Emotional trading is instant gratification. One builds wealth. One burns it.

[Daniel Romero @HyperTechInvest]

Best Stocks by Industry to Invest in the Future

1. Data Center Operators / Neocloud

$IREN | Iren
$CLSK | CleanSpark
$NBIS | Nebius Group
$WULF | TeraWulf
$AMKR | Amkor Technology
$HUT | Hut 8
$VRT | Vertiv Holdings
$CIFR | Cipher Mining
$BITF | Bitfarms
$APLD | Applied Digital

A Thread 🧵

[Futurum Equities @FuturumEquities]

FUTURUM AI FIFTEEN REBALANCE

We just completed the first quarterly rebalance of the Futurum AI Fifteen -- our curated list of non-Mag 7 names we believe are building the next layer of AI infrastructure.

Ranked by our proprietary AIRometer Score -- here are the top 15 👇

[Kirk Borne @KirkDBorne]

[700-page PDF e-Book] #Algorithms for Decision-Making — brilliant and comprehensive eBook from MIT: https://t.co/cZde89YnmQ

[Dhanian 🗯️ @e_opore]

The Complete LLM & Generative AI Tech Stack

Foundation Models & Core Technologies
├── Large Language Models
│ ├── OpenAI: GPT-4, GPT-4o
│ ├── Anthropic: Claude 3
│ ├── Meta: Llama 2, Llama 3
│ └── Google: Gemini, PaLM 2
├── Open Source Alternatives
│ ├── Mistral AI models
│ ├── Falcon, Vicuna
│ ├── CodeLlama, StarCoder
│ └── Custom fine-tuned models
└── Model Architectures
├── Transformer-based
├── Attention mechanisms
├── Encoder-decoder models
└── Auto-regressive models

Development Frameworks & SDKs
├── Python Libraries
│ ├── LangChain, LlamaIndex
│ ├── Transformers
│ ├── PyTorch, TensorFlow
│ └── JAX, Flax
├── API Integration
│ ├── OpenAI Python SDK
│ ├── Anthropic API
│ ├── Google AI Python SDK
│ └── REST API wrappers
└── Development Tools
├── Jupyter Notebooks
├── VS Code with AI extensions
├── Google Colab, Kaggle
└── Weights & Biases

Prompt Engineering & Optimization
├── Prompt Design Patterns
│ ├── Zero-shot, Few-shot learning
│ ├── Chain-of-Thought
│ ├── ReAct
│ └── Self-consistency, Tree of Thoughts
├── Prompt Management
│ ├── Prompt templates
│ ├── Version control for prompts
│ ├── A/B testing frameworks
│ └── Prompt optimization tools
└── Advanced Techniques
├── Function calling
├── Tool use & planning
├── Memory management
└── Multi-modal prompting

RAG & Knowledge Enhancement
├── Retrieval-Augmented Generation
│ ├── Vector databases
│ ├── Document loaders & parsers
│ ├── Embedding models
│ └── Hybrid search systems
├── Vector Databases
│ ├── Pinecone, Weaviate
│ ├── Chroma, Qdrant
│ ├── Milvus, Vespa
│ └── PostgreSQL with pgvector
└── Data Processing
├── Text chunking strategies
├── Embedding generation
├── Semantic search
└── Knowledge graph integration

Application Patterns & Architectures
├── Common Patterns
│ ├── AI Agents
│ ├── Conversational AI
│ ├── Content generation systems
│ └── Code generation
├── System Design
│ ├── Chat interfaces
│ ├── Streaming responses
│ ├── Caching strategies
│ └── Rate limiting
└── Integration Patterns
├── API gateways for AI services
├── Middleware for LLM call
└── Circuit breakers

Model Management & Deployment
├── Model Serving
│ ├── Hugging Face Inference Endpoints
│ ├── AWS SageMaker, Google Vertex AI
│ ├── Azure Machine Learning
│ └── Custom model servers
├── Fine-tuning
│ ├── LoRA,
│ ├── Parameter-efficient fine-tuning
│ ├── RLHF
│ └── Custom training pipelines
└── Optimization
├── Quantization
├── Pruning
├── Model compression
└── Hardware acceleration

Monitoring & Evaluation
├── Performance Metrics
│ ├── Latency
│ ├── Cost per request tracking
│ ├── Token usage analytics
│ └── Quality metrics
├── LLM-specific Monitoring
└── Evaluation Frameworks

Security & Responsible AI
├── Safety Measures
│ ├── Content moderation
│ └── Ethical guidelines
├── Privacy
│ ├── Data anonymization
│ ├── GDPR
│ └── Audit trails
└── Risk Management
├── Bias detection
├── Fairness metrics
├── Transparency reports
└── Response plans

Emerging Technologies & Future Trends
├── Multi-modal AI
│ ├── Vision-Language models
│ ├── Audio processing
│ ├── Video understanding
│ └── Cross-modal retrieval
├── Advanced Architectures
│ ├─MoE
│ ├── Retrieval-based models
│ ├── Neuro-symbolic AI
│ └── World models
└── Production Considerations

Get my Complete LLM and Generative AI Development eBook here: https://t.co/DBXPEOcHrI

[WFCN 👽🖖🏼 @WalteneFred]

#NewProfilePic

[Michael Timothy Bennett @MiTiBennett]

happy to announce i just got examiner feedback on my thesis. recommended for acceptance as is, no changes, no corrections :)

[Nicholas Fabiano, MD @NTFabiano]

This is the Atlas of Cognitive Disorders.

[Kirk Borne @KirkDBorne]

[700-page PDF e-Book] #Algorithms for Decision-Making — brilliant and comprehensive eBook from MIT: https://t.co/cZde89XPxi

[Math Cafe @Riazi_Cafe_en]

The Algorithm Design Manual
by Steven S. Skiena

Archive link: https://t.co/HQ94G4btws

[ₕₐₘₚₜₒₙ @hamptonism]

Quant Finance Roadmap:

[Yaashaa Golovanov @Golovanov_ammoc]

This requires mathematical maturity, but every effort invested to read this “classic” is worth your time and devotion. It’s a “Socratic dialogue between guru & disciples.” This book, Tarasov (and Arnold) shaped my pedagogical philosophy, leading to the creation of AMMOC.
See ToC.

[Orbion @cryptorbion]

🚨A 200-year-old farmer chart peaks in 2026

The 18-year real estate cycle shows the same pattern

2026 will be the BIGGEST super cycle in history...

Here's what's coming and list of alts I'm buying👇🧵