An autonomous weather trading bot powered by the Hermes Agent.

It gets temperature data from multiple sources and uses a Gaussian Bucket strategy:

For example, if the forecast is 72F, it estimates that the final temperature will be between 71F and 73F.

After that, it compares this range with current prices on Polymarket and sends you a trading signal in Telegram.

The main feature of this bot - it can track its own results, learn and improve its own strategy over time.

github.com/nicolastinkl/hermes_weatherbot

A weather bot that analyzes different sources in real time, like forecasts, airport data and aviation observations (METAR and SPECI) to get the latest and most accurate temperature data.

Then it generates a detailed weather report for a specific city and day.

github.com/yangyuan-zhen/PolyWeather

NWS forecast bot with Kelly strategy. This bot scans NWS and gets the freshest temperature value in selected city and then compares it with current price on Polymarket.

It uses Kelly strategy to calculate the best trade size based on how strong this edge is and can also automatically execute trades.

github.com/MoonsatProtocol/Polymarket-Weather-Bot

GFS based weather trading bot. It uses 31 different forecast scenarios from the Global Forecast System for a specific city and day to estimate the most accurate temperature outcome.

Then it automatically compares this value with current Polymarket prices to find mispriced opportunities and execute trades.

It has a dashboard where you can see and track all signals, trades, forecasts, trading pnl and more.

github.com/suislanchez/polymarket-kalshi-weather-bot

A full ML weather model:

1. This is a full machine learning weather model built by a Boston University computer science student.

It learns from past forecasting errors by comparing weather predictions with the temperature that actually happened.

For example, if NWS often predicted 70F in Chicago, but the actual high was usually around 68F, the model learns that this source in this city overestimates the highest temperature by 2F.

Over time, it learns patterns across different cities, seasons and weather conditions.

So when a new forecast comes in, the model does not trust it blindly. It adjusts the forecast based on historical errors and produces a more accurate temperature estimate.

github.com/AruneshDev/Automated-Trading-System-Kalshi-Weather-Model

All of these bots include a simulation mode, so you can test them on real markets and see what pnl they can get without risking real funds.

Boss: “ so you’re going to have to work overtime this weekend without pay, how does that sound?”

Me: “that sounds wonderful”

💼 Job loss from AI.

A compilations of opinions by today's leading figures.

We need to be prepared.

"Anything that involves information, anything short of shaping atoms, AI can do 50% of all those jobs right now"

- Elon Musk

He says white-collar work goes first because it only manipulates information, not atoms. If a job is basically typing, clicking, researching, writing, analyzing or teaching on a screen, AI can do it.

The shift will lag due to inertia, but competition will force faster adoption when AI-heavy firms outperform AI-light firms.

And then humanoid robots will take most blue-collar jobs. Once machines can “shape atoms,” robots will do physical work in factories, logistics, maintenance, construction, and even run data centers.

He expects far more humanoid robots than humans, initially scarce then plentiful, with the main bottlenecks being power, cooling, and materials, not software.

2025-26 : “We’re laying off humans because AI is more cost efficient”

2027-28 : “Turns out humans are actually cheaper than AI”

Incel culture probably has an expiration date.

The moment sex robots become genuinely convincing companions, not just toys, but emotionally responsive, affectionate, loyal, customizable partners, a huge part of the loneliness market collapses overnight.

Not all of it, obviously.

But a lot of the bitterness, resentment, and status panic around sex and dating will suddenly look very obsolete.

The future won’t “fix” everyone.

But it will make some current social problems look strangely medieval.

That's how Minecraft works 😂

Silicon valley: ai is the biggest money machine ever

Meanwhile...

Estimated cumulative ai spend vs revenue:

1. Amazon: $331b spent → $22b revenue
2. Google: $303b spent → $25b revenue
3. Meta: $238b spent → $3b revenue
4. Microsoft: $256b spent → $31b revenue
5. Openai: $62b spent → $26.7b revenue
6. Anthropic: $33b spent → $22b revenue

Maybe it’s the most expensive infrastructure war in history.

The direct payback is still mostly a promise.

#BTC/USDT H2 #Indicator #Signal #Trading #GlobalOneAI
Globalone.ch #Prompt

Safe to say the Strait of Hormuz is still closed.

Once global SPR levels are run down suddenly this illusion faces reality.

Two possible scenarios could play out here:
1. If the Federal Reserve is forced to do QE because of a financial crisis, without lockdowns, oil could easily rise to $200 a barrel.

2. Technically you don't even really need QE, all you need is nations to suddenly decide they need to refill their SPR. Then it becomes a panic for both Dollars and oil, which is something we've only seen briefly once before in 2022.

The end result of both scenarios is a global sovereign debt crisis, as nations are forced to liquidate US treasuries and gold to acquire oil with a skyrocketing Dollar.

This is exactly why I warned oil was heading to $50 before $200.

Amazing explanation by Project 2025 creator #RealEJAntoni

The economic shock from the oil shortage is coming.

This map separates what is actually documented from what is still only demo visibility.

• Tesla Optimus
➝ Factory pilot in North America

• AGIBOT A2 / A2-W
➝ Commercial delivery signals in China

• Fourier GR-1 / GR-2
➝ Rehab research pilots in China
➝ Demo presence in North America and Europe

• NEURA 4NE-1
➝ Launch demos in Europe

• XPENG IRON
➝ Public reveal in China

• Leju KUAVO
➝ Education and service use in China
➝ Demo presence in Asia

• Kepler Forerunner K2
➝ Factory pilot signal in China
➝ Expo demo in Europe

• Persona AI
➝ Shipyard pilot in North America

• MagicLab MagicBot
➝ Industrial pilot signal in China

• Robot Era STAR1
➝ Public demos in China

Important detail
No public regional sales percentages are available.

So this is not a sales ranking.
It is a deployment signal map based on official public information.

The humanoid race is wider than the 5 names everyone repeats.

If you're not making enough money now, then you're not doing enough

Anthropic wants to make its own drugs with help from Claude.

Claude Science is meant to do for lab research what Claude Code did for programming.

Drug discovery is messier, because molecules fail when biology, chemistry, safety, or dosing breaks.

Anthropic wants its researchers to feel those failures directly while shaping the product.

The first programs will target neglected diseases, including rare disorders and some tropical conditions.

These areas often have clearer biology but weaker commercial incentives than common diseases.

Many rare genetic disorders start with one broken gene, giving AI a cleaner target.

Common conditions usually involve many genes, tissues, histories, and environments interacting at once.

That difference makes early rare-disease work a harder business, but a clearer testbed.

Anthropic also avoids competing with pharma customers by choosing overlooked programs.

Anthropic already works closely with major drugmakers, so it understands where researchers get stuck.

Novartis CEO Vas Narasimhan joining the board gives those product decisions direct pharma-level pressure.

Anthropic bought Coefficient Bio and hired AlphaFold researcher John Jumper, so this is not a side experiment. Shows how seriously Anthropic is treating biology.

The AI capex cycle is now pushing memory into a much more aggressive pricing upcycle than expected.

UBS now expects DRAM prices to rise 32% quarter-over-quarter in Q3 and 18% in Q4, while NAND prices are expected to rise 30% in Q3 and 12% in Q4.

AI infrastructure is eating memory supply faster than the industry can add it. High Bandwidth Memory, DRAM, and NAND are all getting pulled into the same demand wave. UBS also sees the DRAM market staying undersupplied until at least 2028, because demand growth in 2027 is expected to be far above supply growth.

Sonnet 5 might be the worst-value model on this chart...

Cost per intelligence index task:

>1.3x more expensive than opus 4.8 max
>2.2x more expensive than gpt-5.5 xhigh
>4.8x more expensive than glm-5.2 max
>7.4x more expensive than kimi k2.6
>57x more expensive than deepseek v4 pro

And it still isn’t the strongest model here...

You’re not paying for better intelligence.

You’re paying the anthropic tax.

Everyone said tariffs would kill US manufacturing.
The data says otherwise.

US ISM Manufacturing PMI (50 = expansion line):

2022-2025:

→ Mostly stuck below 50 for 2.5 straight years
→ Brief pokes above 50, never sustained

2026:

→ Broke above 50 in February
→ 5 straight months above the line
→ Hit 54 in June, the highest print since mid-2022

The predicted collapse never showed up in the data.

Europe should be reading this chart, not the headlines.