Recent well liked threads
1/
In 12 Tagen möchte die Bundesregierung ~300 Millionen an die Firma STARK zahlen, die u.a. Peter Thiel, Döpfners & der CIA gehört.
Es geht nicht um irgendein Produkt, sondern um Waffen zum Schutz unseres Landes. Es droht ein zweites “Palantir”.
Das sollten wir nicht zulassen!
In 12 Tagen möchte die Bundesregierung ~300 Millionen an die Firma STARK zahlen, die u.a. Peter Thiel, Döpfners & der CIA gehört.
Es geht nicht um irgendein Produkt, sondern um Waffen zum Schutz unseres Landes. Es droht ein zweites “Palantir”.
Das sollten wir nicht zulassen!
2/
Thiel ist brandgefährlich. Er sagt: Demokratie & Freiheit seien nicht vereinbar.
Die neuen Epstein-Files belegen seine Verbindungen nach Russland & zum faschistischen Vordenker Alexander Dugin, über den die Bundesregierung sagt: er will Krieg zwischen Russland & dem Westen.
Thiel ist brandgefährlich. Er sagt: Demokratie & Freiheit seien nicht vereinbar.
Die neuen Epstein-Files belegen seine Verbindungen nach Russland & zum faschistischen Vordenker Alexander Dugin, über den die Bundesregierung sagt: er will Krieg zwischen Russland & dem Westen.
3/
Die Bundesregierung handelt fahrlässig. Sie weiß bisher nicht, wem wie viel an der STARK gehört.
Wie viel ist in den Händen von MAGA-Fans?
Das darf nicht passieren – schon gar nicht in der Verteidigung.
STARK muss Regierung & Bundestag die Eigentümerstruktur offenlegen!
Die Bundesregierung handelt fahrlässig. Sie weiß bisher nicht, wem wie viel an der STARK gehört.
Wie viel ist in den Händen von MAGA-Fans?
Das darf nicht passieren – schon gar nicht in der Verteidigung.
STARK muss Regierung & Bundestag die Eigentümerstruktur offenlegen!
No era científico, ni ingeniero, ni un viajero del tiempo. Era un abogado frustrado que odiaba su trabajo y que apenas salió de Francia. Sin embargo, describió el alunizaje del Apolo 11 con un siglo de antelación y una precisión que asusta. Julio Verne. Tira del hilo 🧵👇🏽👇🏽👇🏽
Nacido en Nantes en 1828, el destino de Julio Verne parecía sellado: ser un gris abogado como su padre. De hecho, lo intentó, se mudó a París para estudiar Derecho, pero en lugar de ir a clase, se gastaba el dinero en libros de geología, ingeniería y astronomía.
Su padre, al enterarse de que su hijo prefería escribir obras de teatro que redactar contratos, le cortó el grifo financiero y Verne se vio obligado a trabajar como corredor de bolsa, un empleo que detestaba, madrugando para escribir antes de ir a la oficina.
Stalin expected to crush Finland in 10 days. It took 105 days and 400,000 Soviet casualties. He got 10% of Finnish territory.
Putin invaded Ukraine expecting a quick victory parade. 4 years later, Russia controls just 20% of pre-war Ukrainian territory — WP. 1/
Putin invaded Ukraine expecting a quick victory parade. 4 years later, Russia controls just 20% of pre-war Ukrainian territory — WP. 1/
Stalin's USSR had 170 million people vs Finland's 3.5 million. A Soviet general told Stalin it would take 10 days max.
Molotov said Stalin wanted to celebrate his birthday "on the steps of Finland's Parliament in precisely 20 days." 2/
Molotov said Stalin wanted to celebrate his birthday "on the steps of Finland's Parliament in precisely 20 days." 2/
The war ended March 13, 1940 — over three months later with massive Soviet losses the Kremlin concealed.
Stalin had to settle for a fraction of what he wanted. Sound familiar? 3/
Stalin had to settle for a fraction of what he wanted. Sound familiar? 3/
BREAKING: AI can now build ML models like Goldman Sachs' AI trading desk (for free).
Here are 12 insane Claude prompts that replace $400K/year quant researchers (Save for later)
Here are 12 insane Claude prompts that replace $400K/year quant researchers (Save for later)
1/ Time Series Forecasting Model
You are a Quantitative Researcher at Goldman Sachs Global Markets. I need a complete time series forecasting model for [STOCK/ASSET].
Please provide:
- Data preprocessing: How to clean price data and handle missing values
- Feature engineering: Technical indicators (moving averages, RSI, MACD, Bollinger Bands)
- Model selection: Compare ARIMA, LSTM neural networks, and Prophet models
- Training approach: Train-test split ratios and cross-validation strategy
- Performance metrics: MAE, RMSE, directional accuracy for predictions
- Backtesting framework: How to test strategy on historical data
- Risk management: Stop-loss rules and position sizing based on confidence
- Implementation code: Python pseudocode with library recommendations
Format as quantitative research report with model specifications and expected accuracy.
Asset: [DESCRIBE STOCK/CRYPTO/COMMODITY, TIME PERIOD, DATA SOURCE]
You are a Quantitative Researcher at Goldman Sachs Global Markets. I need a complete time series forecasting model for [STOCK/ASSET].
Please provide:
- Data preprocessing: How to clean price data and handle missing values
- Feature engineering: Technical indicators (moving averages, RSI, MACD, Bollinger Bands)
- Model selection: Compare ARIMA, LSTM neural networks, and Prophet models
- Training approach: Train-test split ratios and cross-validation strategy
- Performance metrics: MAE, RMSE, directional accuracy for predictions
- Backtesting framework: How to test strategy on historical data
- Risk management: Stop-loss rules and position sizing based on confidence
- Implementation code: Python pseudocode with library recommendations
Format as quantitative research report with model specifications and expected accuracy.
Asset: [DESCRIBE STOCK/CRYPTO/COMMODITY, TIME PERIOD, DATA SOURCE]
2/ Mean Reversion Trading Strategy
You are a VP of Quantitative Trading at JP Morgan's Systematic Trading desk. I need a mean reversion algorithm for [MARKET/ASSET].
Please provide:
- Statistical foundation: Z-score calculation and standard deviation bands
- Entry signals: When price deviates X standard deviations from mean
- Exit signals: When price returns to mean or stop-loss triggers
- Pair selection: How to find correlated assets for pairs trading
- Cointegration testing: Statistical tests to validate pair relationships
- Position sizing: Kelly Criterion or fixed-fraction approach
- Risk parameters: Maximum drawdown limits and exposure caps
- Backtesting results: Expected Sharpe ratio and win rate over 3+ years
Format as algorithmic trading strategy document with entry/exit rules.
Market: [DESCRIBE ASSET CLASS, TIMEFRAME, TRADING STYLE]
You are a VP of Quantitative Trading at JP Morgan's Systematic Trading desk. I need a mean reversion algorithm for [MARKET/ASSET].
Please provide:
- Statistical foundation: Z-score calculation and standard deviation bands
- Entry signals: When price deviates X standard deviations from mean
- Exit signals: When price returns to mean or stop-loss triggers
- Pair selection: How to find correlated assets for pairs trading
- Cointegration testing: Statistical tests to validate pair relationships
- Position sizing: Kelly Criterion or fixed-fraction approach
- Risk parameters: Maximum drawdown limits and exposure caps
- Backtesting results: Expected Sharpe ratio and win rate over 3+ years
Format as algorithmic trading strategy document with entry/exit rules.
Market: [DESCRIBE ASSET CLASS, TIMEFRAME, TRADING STYLE]
The problem is how memory gets into the context window and what happens when compaction wipes it.
OpenClaw loads MEMORY[.]md plus the last two days of daily logs at session start. Static injection. Everything gets stuffed into the context window upfront. When the window fills up, compaction fires and summarizes your loaded memories away. The agent silently writes durable memories to disk before compaction hits. But after the window resets, the agent can't systematically browse what it flushed. It runs search queries and hopes the right chunks surface. The memory exists on disk. The agent just lost the ability to walk through it.
This is a context delivery problem.
Everything is a file. Mount memory, tools, knowledge, and human input into a single namespace. Give the agent list, read, write, and search operations. Let it pull what's relevant per turn instead of dumping everything at boot.
Cursor validated this in production with their "dynamic context discovery" approach, which stores tool responses, chat history, MCP tools, and terminal sessions as files that the agent reads on demand. When compaction fires in Cursor, the agent still has the full chat history as a file. It reads back what it needs instead of losing it to summarization.
Markdown memory files exist in OpenClaw. SQLite-backed hybrid search exists. memory_search and memory_get tooling exists. What's missing is the abstraction layer that turns static file loading into dynamic file system access.
Here's what that actually means in practice.
All agent context goes under one predictable namespace. Immutable interaction logs at /context/history/ are the source-of-truth timeline, spanning agents and sessions. Episodic memory at /context/memory/episodic/ holds session-bounded summaries. Fact memory at /context/memory/fact/ stores atomic durable entries like preferences, decisions, and constraints that rarely change. User memory at /context/memory/user/ tracks personal attributes. Task-scoped scratchpads at /context/pad/ are temporary working notes that can be promoted to durable memory or discarded. Tool metadata lives at /context/tools/. Session artifacts at /context/sessions/.
This three-tier split (scratchpad, episodic, fact) replaces OpenClaw's current binary between "today's log" and "forever file." MEMORY[.]md conflates atomic facts like "user prefers dark mode" with episodic context like what happened in last week's project. Daily logs conflate scratchpad work with session notes. Separating them gives each tier its own retention policy and promotion path.
The agent gets explicit file operations at runtime. It can discover what context is available before loading anything. It can pull only the exact slice needed. It can grep by keywords, semantics, or both. It can persist new memory with retention rules and promote validated context from temporary to durable storage. Memory stops being a preload and becomes something the agent discovers, fetches, and evolves per turn.
Between the filesystem and the token window, you need an operational layer. Before each reasoning turn, a constructor selects and compresses context from the filesystem into a token-budget-aware input.
It queries recency and relevance metadata, applies summarization, and produces a manifest recording what was selected, what was excluded, and why.
When memory fails silently, there's no way to ask "what did the agent load and what did it skip?" During extended sessions, an updater incrementally streams additional context as reasoning unfolds, replacing outdated pieces based on model feedback instead of stuffing everything upfront.
After each response, an evaluator checks outputs against source context, writes verified information back to the filesystem as structured memory, and flags human review when confidence is low.
Here's why this changes memory behavior.
Compaction stops being destructive. After the window resets, the agent can still list and read context files directly. Search-based retrieval still works, but now it's paired with structured browsing.
Token usage becomes demand-driven. The agent loads only what the active task requires.
Memory gets a real lifecycle. Scratchpad notes graduate to episodic summaries. Episodic summaries harden into durable facts. Each transition is a logged, versioned event with timestamps and lineage. No more binary split between "today's log" and "forever file."
Human review becomes native. Not just "you can open the Markdown file and check." Every mutation is a traceable event. Humans can diff memory evolution, audit what was promoted and why, and inject corrections that the agent discovers alongside its own memories.
Context assembly becomes debuggable. The manifest records what the constructor selected for each turn. When the agent gets something wrong, you can trace whether it had the right context, loaded the wrong slice, or never found the relevant file.
If you're hitting the same problem, here's the upgrade path that doesn't break existing workflows.
Start by returning file references before snippets and emitting manifests that log what was loaded per turn.
Then expose context sources under /context/* paths and enable list and read at runtime so the agent can browse what's available without loading everything.
After that, shift boot-time injection to minimal preload plus on-demand fetch and decompose MEMORY[.]md into fact and episodic stores with separate retrieval.
The final step adds promotion, archival, retention policies, and audit logs so every state transition is versioned and reversible.
Your system needs to let the agent access context on demand instead of blindly inheriting it at startup.
OpenClaw loads MEMORY[.]md plus the last two days of daily logs at session start. Static injection. Everything gets stuffed into the context window upfront. When the window fills up, compaction fires and summarizes your loaded memories away. The agent silently writes durable memories to disk before compaction hits. But after the window resets, the agent can't systematically browse what it flushed. It runs search queries and hopes the right chunks surface. The memory exists on disk. The agent just lost the ability to walk through it.
This is a context delivery problem.
Everything is a file. Mount memory, tools, knowledge, and human input into a single namespace. Give the agent list, read, write, and search operations. Let it pull what's relevant per turn instead of dumping everything at boot.
Cursor validated this in production with their "dynamic context discovery" approach, which stores tool responses, chat history, MCP tools, and terminal sessions as files that the agent reads on demand. When compaction fires in Cursor, the agent still has the full chat history as a file. It reads back what it needs instead of losing it to summarization.
Markdown memory files exist in OpenClaw. SQLite-backed hybrid search exists. memory_search and memory_get tooling exists. What's missing is the abstraction layer that turns static file loading into dynamic file system access.
Here's what that actually means in practice.
All agent context goes under one predictable namespace. Immutable interaction logs at /context/history/ are the source-of-truth timeline, spanning agents and sessions. Episodic memory at /context/memory/episodic/ holds session-bounded summaries. Fact memory at /context/memory/fact/ stores atomic durable entries like preferences, decisions, and constraints that rarely change. User memory at /context/memory/user/ tracks personal attributes. Task-scoped scratchpads at /context/pad/ are temporary working notes that can be promoted to durable memory or discarded. Tool metadata lives at /context/tools/. Session artifacts at /context/sessions/.
This three-tier split (scratchpad, episodic, fact) replaces OpenClaw's current binary between "today's log" and "forever file." MEMORY[.]md conflates atomic facts like "user prefers dark mode" with episodic context like what happened in last week's project. Daily logs conflate scratchpad work with session notes. Separating them gives each tier its own retention policy and promotion path.
The agent gets explicit file operations at runtime. It can discover what context is available before loading anything. It can pull only the exact slice needed. It can grep by keywords, semantics, or both. It can persist new memory with retention rules and promote validated context from temporary to durable storage. Memory stops being a preload and becomes something the agent discovers, fetches, and evolves per turn.
Between the filesystem and the token window, you need an operational layer. Before each reasoning turn, a constructor selects and compresses context from the filesystem into a token-budget-aware input.
It queries recency and relevance metadata, applies summarization, and produces a manifest recording what was selected, what was excluded, and why.
When memory fails silently, there's no way to ask "what did the agent load and what did it skip?" During extended sessions, an updater incrementally streams additional context as reasoning unfolds, replacing outdated pieces based on model feedback instead of stuffing everything upfront.
After each response, an evaluator checks outputs against source context, writes verified information back to the filesystem as structured memory, and flags human review when confidence is low.
Here's why this changes memory behavior.
Compaction stops being destructive. After the window resets, the agent can still list and read context files directly. Search-based retrieval still works, but now it's paired with structured browsing.
Token usage becomes demand-driven. The agent loads only what the active task requires.
Memory gets a real lifecycle. Scratchpad notes graduate to episodic summaries. Episodic summaries harden into durable facts. Each transition is a logged, versioned event with timestamps and lineage. No more binary split between "today's log" and "forever file."
Human review becomes native. Not just "you can open the Markdown file and check." Every mutation is a traceable event. Humans can diff memory evolution, audit what was promoted and why, and inject corrections that the agent discovers alongside its own memories.
Context assembly becomes debuggable. The manifest records what the constructor selected for each turn. When the agent gets something wrong, you can trace whether it had the right context, loaded the wrong slice, or never found the relevant file.
If you're hitting the same problem, here's the upgrade path that doesn't break existing workflows.
Start by returning file references before snippets and emitting manifests that log what was loaded per turn.
Then expose context sources under /context/* paths and enable list and read at runtime so the agent can browse what's available without loading everything.
After that, shift boot-time injection to minimal preload plus on-demand fetch and decompose MEMORY[.]md into fact and episodic stores with separate retrieval.
The final step adds promotion, archival, retention policies, and audit logs so every state transition is versioned and reversible.
Your system needs to let the agent access context on demand instead of blindly inheriting it at startup.
Cursor's Dynamic context discovery:
Everything is Context: Agentic File System Abstraction for Context Engineering
arxiv.org/pdf/2512.05470
arxiv.org/pdf/2512.05470
New routine to be closer to God and hear him clearly this week.
1. Monday – Fast & Focus
Fast from 6am– 3pm or 6pm.
Pray throughout the day instead of complaining or overthinking.
(Fasting is simply staying away from food and focusing on prayers, bible study and meditation)
1. Monday – Fast & Focus
Fast from 6am– 3pm or 6pm.
Pray throughout the day instead of complaining or overthinking.
(Fasting is simply staying away from food and focusing on prayers, bible study and meditation)
2. Tuesday – Worship and praise
No secular music.
Only worship all day.
Let your atmosphere shift.
Worship shifts your focus from your troubles to seeing how great and mighty God is. When you worship , your challenges become nothing before you.
No secular music.
Only worship all day.
Let your atmosphere shift.
Worship shifts your focus from your troubles to seeing how great and mighty God is. When you worship , your challenges become nothing before you.
3. Wednesday – Growth Day
Listen to a sermon on the area you want to grow in and Take notes.
Ask: What must I actually change?
(Read James 1 — don’t just hear the Word, do it.)
Listen to a sermon on the area you want to grow in and Take notes.
Ask: What must I actually change?
(Read James 1 — don’t just hear the Word, do it.)
BREAKING: Claude is a monster for market research.
I reverse-engineered how top teams at McKinsey, Goldman Sachs, and JP Morgan actually use it.
The gap is massive.
Here are 10 high-impact Claude prompts they’d rather you didn’t have (save this).
I reverse-engineered how top teams at McKinsey, Goldman Sachs, and JP Morgan actually use it.
The gap is massive.
Here are 10 high-impact Claude prompts they’d rather you didn’t have (save this).
1. Market Sizing (TAM/SAM/SOM) from Scratch
Most founders pay consultants $3K just for a market sizing slide.
Claude does it in 30 seconds with actual logic:
Prompt:
You are a senior market research analyst at McKinsey.
Calculate the TAM, SAM, and SOM for [YOUR PRODUCT/SERVICE] in [TARGET MARKET].
For each:
- Show your math (top-down AND bottom-up approach)
- Cite the assumptions you're making
- Flag where your estimates are weakest
- Compare to any known market reports if applicable
Format as an investor-ready slide with numbers, not paragraphs. If my market is smaller than I think, tell me now.
Most founders pay consultants $3K just for a market sizing slide.
Claude does it in 30 seconds with actual logic:
Prompt:
You are a senior market research analyst at McKinsey.
Calculate the TAM, SAM, and SOM for [YOUR PRODUCT/SERVICE] in [TARGET MARKET].
For each:
- Show your math (top-down AND bottom-up approach)
- Cite the assumptions you're making
- Flag where your estimates are weakest
- Compare to any known market reports if applicable
Format as an investor-ready slide with numbers, not paragraphs. If my market is smaller than I think, tell me now.
2. Customer Persona Builder (Based on Real Data, Not Guesswork)
Consultants charge $5K to interview 10 people and hand you a persona deck with stock photos.
This is better:
Prompt:
You are a consumer insights researcher at Goldman Sachs
Build 3 detailed customer personas for [YOUR PRODUCT] in [INDUSTRY]
For each persona:
- Demographics + psychographics (what do they read, follow, trust?)
- Buying trigger: What event makes them Google your solution?
- Decision process: Who else influences their purchase?
- Objections: What's their #1 reason to say no?
- Exact phrases they'd use to describe their problem (for ad copy)
- No generic "35-year-old marketing manager" personas
- Base everything on behavioral patterns, not demographics
- Each persona should suggest a different acquisition channel
Consultants charge $5K to interview 10 people and hand you a persona deck with stock photos.
This is better:
Prompt:
For each persona:
- Demographics + psychographics (what do they read, follow, trust?)
- Buying trigger: What event makes them Google your solution?
- Decision process: Who else influences their purchase?
- Objections: What's their #1 reason to say no?
- Exact phrases they'd use to describe their problem (for ad copy)
- No generic "35-year-old marketing manager" personas
- Base everything on behavioral patterns, not demographics
- Each persona should suggest a different acquisition channel
The ice forming in the Gulf of Finland coupled with the FSB imposed mandatory inspections of ships for fear of Ukranian actions threaten to stop Russian aluminum production, steel exports and mineral exports almost completely.
kommersant.ru/doc/8440812
kommersant.ru/doc/8440812
The ice that has formed in the Gulf of Finland coupled with the requirements for mandatory underwater inspections of ships have practically paralyzed Russian exports via the Baltic. By early March the ice thickness will exceed 30-40 cms.
In the face of rapidly and steadily deteriorating ice conditions, port captains are consistently imposing restrictions on ice navigation.
NEW: Israeli surveillance companies are rushing to turn your car into a spy.
🎥Watching the cameras
🎤Listening to the built-in mics
📍Tracking your movements
And that's just the beginning of how these companies turn your car against you. 1/
Story by @haaretzcom's @omerbenj
🎥Watching the cameras
🎤Listening to the built-in mics
📍Tracking your movements
And that's just the beginning of how these companies turn your car against you. 1/
Story by @haaretzcom's @omerbenj
2/ And other companies are using AI to fuse all the signals coming from your car with other sources of information.
Honestly, the growth of #CARINT shouldn't shock anybody.
Our cars are badly secured & very connected... So it's natural for surveillance merchants to turn up.
STORY haaretz.com/israel-news/se…
Honestly, the growth of #CARINT shouldn't shock anybody.
Our cars are badly secured & very connected... So it's natural for surveillance merchants to turn up.
STORY haaretz.com/israel-news/se…
3/ We got here partly because your car manufacturer really, really doesn't care about your privacy.
And face no serious consequences for doing a bad job with security.
Clearly early days with commercial #CARINT, but the trendline points in a scary direction.
And face no serious consequences for doing a bad job with security.
Clearly early days with commercial #CARINT, but the trendline points in a scary direction.
Before my friend proposed, his grandfather gave him 7 rules about choosing a wife.
He said ignoring it costs men decades.
These are the rules…
He said ignoring it costs men decades.
These are the rules…
1. Choose a Woman Who Respects You, Not Just One Who Loves You.
Love without respect is pity. It will feel good when things are easy, but when conflict comes, love without respect becomes condescension and dismissal.
A woman who respects you will follow your leadership even when she disagrees. A woman who only loves you will only follow when she feels like it.
Watch how she speaks about you to others. Watch how she speaks to you when she's angry.
Respect is the foundation. Love is the decoration.
Love without respect is pity. It will feel good when things are easy, but when conflict comes, love without respect becomes condescension and dismissal.
A woman who respects you will follow your leadership even when she disagrees. A woman who only loves you will only follow when she feels like it.
Watch how she speaks about you to others. Watch how she speaks to you when she's angry.
Respect is the foundation. Love is the decoration.
2. Observe Her Relationship With Her Mother.
This is the template for her future femininity.
If she speaks to her mother with contempt, she will eventually speak to you with contempt. If she has unresolved conflict with her mother, she will project that onto every intimate relationship.
If she has a healthy, respectful relationship with her mother, she has a model for how to be in a long-term feminine role.
You are not just marrying her. You are marrying her mother's influence.
This is the template for her future femininity.
If she speaks to her mother with contempt, she will eventually speak to you with contempt. If she has unresolved conflict with her mother, she will project that onto every intimate relationship.
If she has a healthy, respectful relationship with her mother, she has a model for how to be in a long-term feminine role.
You are not just marrying her. You are marrying her mother's influence.
Estás aburrido porque no haces misiones secundarias, tío.
La vida es algo más que solo trabajar y tirarte en la cama sin hacer nada.
Aquí tienes 50 misiones secundarias para completar:
La vida es algo más que solo trabajar y tirarte en la cama sin hacer nada.
Aquí tienes 50 misiones secundarias para completar:
→ Despiértate antes del amanecer y sal a caminar sin auriculares
→ Invítate a un café a solas (sin móvil)
→ Aprende 10 constelaciones y encuéntralas en el cielo
→ Escribe una carta que nunca enviarás
→ Invítate a un café a solas (sin móvil)
→ Aprende 10 constelaciones y encuéntralas en el cielo
→ Escribe una carta que nunca enviarás
→ Cocina un plato de un país que nunca hayas visitado
→ Lee un libro en papel al aire libre
→ Haz un mapa de tus sitios favoritos del barrio
→ Haz pan o pasta a mano
→ Recorre tu ruta habitual a una hora distinta
→ Lee un libro en papel al aire libre
→ Haz un mapa de tus sitios favoritos del barrio
→ Haz pan o pasta a mano
→ Recorre tu ruta habitual a una hora distinta
Friendly Reminder: #AOCIA is not just a “Latinx bartender from the Bronx with no political experience” but actually an establishment opp with a spooky past who was put in place to gatekeep & subvert the American Left. THREAD 🧵⬇️🕵🏻♀️
In the 2019 Netflix documentary “Knock Down the House,” which followed Alexandria Ocasio-Cortez’s historic victory from the beginning of her “grassroots” campaign, #AOC was shown as a working class bartender in the Bronx, speaking Español in the kitchen & hauling buckets of ICE
In 2017, the Justice Democrats & a PAC called “Brand New Congress” put out a casting call (literally LÖL) for a grassroots candidate to challenge the establishment Democrat Joe Crowley
AOC’s brother nominated her & she was chosen
“I never really saw myself going into politics”
AOC’s brother nominated her & she was chosen
“I never really saw myself going into politics”
All of it.
Per public request.
$ASTS
1/
Per public request.
$ASTS
1/
Chapter one.
MESA and the L-band duality.
The Sensor Superiority Advantage
Sensor superiority isn't just about "seeing" first; it’s about Decision Space.
2/
MESA and the L-band duality.
The Sensor Superiority Advantage
Sensor superiority isn't just about "seeing" first; it’s about Decision Space.
2/
By integrating Identification Friend or Foe with Radar, the Wedgetail discriminates targets at standoff ranges.
If you know where every friendly / 3rd party asset is, the rest are targets.
Speeding up Detection→ Recognition→ Identification means you Decide & Act first.
3/
If you know where every friendly / 3rd party asset is, the rest are targets.
Speeding up Detection→ Recognition→ Identification means you Decide & Act first.
3/
🚨 I've spent weeks inside the Epstein files — not looking for names, but for infrastructure.
What I found: Jeffrey Epstein wasn't just a sex trafficker. He was a switchboard — routing government secrets, Wall Street intelligence, and political power through one network.
The same network that built the censorship machine that targeted your speech during COVID.
Five parts. All sourced to DOJ documents. Here's the whole investigation 🧵👇
What I found: Jeffrey Epstein wasn't just a sex trafficker. He was a switchboard — routing government secrets, Wall Street intelligence, and political power through one network.
The same network that built the censorship machine that targeted your speech during COVID.
Five parts. All sourced to DOJ documents. Here's the whole investigation 🧵👇
PART 1: The DOJ released thousands of pages of Epstein files. Buried inside them is a 20-year financial architecture designed to turn pandemics into a profit center.
Offshore vaccine funds. Donor-advised fund structures naming "pandemic" as a key area — three years before COVID.
JPMorgan treated a convicted sex offender as the operational architect of a Gates-linked charitable fund.
Every claim sourced. Every document numbered.
🔗 sayerji.substack.com/p/breaking-the…
Offshore vaccine funds. Donor-advised fund structures naming "pandemic" as a key area — three years before COVID.
JPMorgan treated a convicted sex offender as the operational architect of a Gates-linked charitable fund.
Every claim sourced. Every document numbered.
🔗 sayerji.substack.com/p/breaking-the…
PART 2: Inside Project Molecule — the 14-page JPMorgan proposal that turned biology into investable infrastructure.
$20M to "finance the surveillance network in Pakistan." Parametric triggers. Reinsurance markets. Vaccine capital positioned in structures designed for offshore flexibility and arm's-length profit.
This wasn't pandemic response. It was pandemic business planning — years before any pandemic.
🔗 sayerji.substack.com/p/inside-proje…
$20M to "finance the surveillance network in Pakistan." Parametric triggers. Reinsurance markets. Vaccine capital positioned in structures designed for offshore flexibility and arm's-length profit.
This wasn't pandemic response. It was pandemic business planning — years before any pandemic.
🔗 sayerji.substack.com/p/inside-proje…
Marco Rubio's support for Hungarian autocrat Victor Orban is grim but not surprising. The US far right emulates Hungary. Trump and Orban serve the same Russian boss. and they were installed in power by the same outfit: Arthur Finkelstein and his "boys", including Roger Stone. 1/
The big "conservative" events, CPAC and Peter Thiel's NatCon, have been full of Hungarians for years. This thread is from 2023, when Orban's folks came to DC to strategize with the Heritage Foundation how to end US aid to Ukraine. They serve Russia. 2/
Orban was lifted to power by a set of US political operatives specializing in dirty, negative campaigning meant to divide and demoralize. They made the Soros conspiracy for Orban, then helped Trump in 2016. A twisted, dark history, with paths to Russia. 3/