u/ejpusa

And why The New York Times continues to wins those awards — amazing story here.

Enjoy.

The main problem wasn’t weak models—it was severe overconfidence from a mismatched forecast source.

  Current paper results:

  - Bankroll: $67.34
  - Realized P/L: -$32.66
  - Record: 1–8 across only five unique markets
  - NWS forecast error on recent trades: roughly 6–10°F
  - Contracts settle using The Weather Company, not NWS
  - The model treated 4°F and 9°F cushions as 77% and 91% probabilities
  - Multiple “models” repeatedly exposed the bankroll to the same underlying
    market

  - Exact opposite-side shadows went 8–1, but the sample remains too small to
    justify blindly reversing everything

  I updated the paper strategy:

  - Weather probabilities are now blended 75% toward the executable market
    price, preventing unsupported 30–40 point edge estimates.

  - Funded entries are limited to the 8–12 hour window—the only currently
    profitable timing shadow, though based on just six unique markets.

  - Maximum spread is now 8¢, blocking trades such as the recent 18¢-spread
    Denver position.

  - Existing positions and ledger history were preserved.
  - Live trading remains disabled.

  Core changes are in scripts/weather/core/value_ticket.py:61 and scripts/
  weather/shared_config.py:16. The operating rationale is documented in docs/
  OPERATIONS.md:26.

  Validation:

  - Full suite: 203 tests passed
  - Syntax checks passed
  - Diff checks passed

  Counterfactually, the new window would have rejected all nine recent settled
  entries, including the one winner. That is the right tradeoff for now: remain
  in the game during the strongest observed timing window, but stop risking
  paper capital on inflated provider-specific “edges.” This improves risk
  discipline; it does not yet establish a winning strategy.

My favorite quote from AI: You are not really losing money; the model is just not working for you.

Ummm, Ok. :-)

I went with the audiobook. After this, you will thank God for the 2nd Amendment. It's kind of a mind-blowing read, with the usual suspects.

Codex at the CLI. BTC Python scripts and OpenAI's API.

Create a new paper model called Moderate Momentum.

Base it on Strong Momentum, but require a 0.08% move, use a 5% minimum edge, and accept prices between 30 and 75 cents. Keep it inactive until I review it. Show every value that changed and add tests.

Show exactly how Moderate Momentum differs from Strong Momentum. Explain how each changed value could affect trade frequency and risk. Do not activate it.

Activate Moderate Momentum for paper trading only. Confirm that live trading is still disabled, then run the tests and show the active-model list.

Show each model's last 24-hour, seven-day, and all-time paper results. Use only settled ledger trades and do not estimate missing results.

I always remember in convesation with AI: You know I'm not a vending machine, right?

😊

Python 95.9% Shell 4.1%

We are handing over the funds to GPT-5.6. Some ideas that she came up with, her name is Amy. 😊

Here are the 25 ideas I think fit you particularly well:

1. AI Superpowers for High-School Students. Create a free program teaching kids not merely how to prompt ChatGPT, but how to use AI to build something that matters.

2. Give 100 teenagers $500 AI grants. $50,000 becomes 100 experiments. The requirement: use AI to improve their school, neighborhood, health, environment, or community.

3. Create a teenage AI laboratory. No grades, SATs or prerequisites. Bring together 10–15 curious kids for a semester and give them serious AI tools, compute and mentorship.

4. Build “The Supercomputer for Everyone.” Turn your existing compute infrastructure into a simple interface where students, teachers, researchers and ordinary people can ask computationally difficult questions. This connects directly to your earlier City Brain concept.

5. Make Neurocompute completely open science. Instead of trying to create another billion-dollar biotech company, establish Neurocompute as public infrastructure for discovering overlooked medical research.

6. Launch a Pediatric Cancer Moonshot. Pick one narrow problem and have students, physicians, programmers and AI analyze the entire scientific literature around it.

7. Do the same for Parkinson’s or Alzheimer’s. Your Narrative Velocity concept could become a public experiment for detecting scientific signals years before conventional reviews notice them.

8. Create an “AI Finds Forgotten Cures” project. Search decades of literature for abandoned drugs, compounds and hypotheses that deserve another look.

9. Start a $1,000 Planet Prize. Every month give $1,000 to a young person with an ingenious AI idea for climate, health, education, food, cities or biodiversity. Twenty awards would cost only $20,000.

10. Build an AI environmental detective. Combine public satellite, pollution, weather and government data so communities can investigate environmental problems themselves.

11. Give public-school science teachers AI superpowers. Build a free teacher toolkit that turns difficult scientific papers into lessons, experiments, simulations and classroom discussions.

12. Create the “100 Questions Project.” Ask 100 teenagers: What is one problem adults have failed to solve? Give them AI and compute and see what happens.

13. Build an AI Science Fair for NYC. Not “best chatbot.” Awards for the most imaginative uses of AI in biology, physics, climate, medicine, transportation and social good.

14. Create a teenage AI research journal. Students publish genuine AI-assisted investigations, including their data, prompts, code, failures and results.

15. Create an AI apprenticeship instead of another course. Take perhaps six teenagers each year and let them work alongside you building real systems. By graduation, they have GitHub repositories and actual research rather than certificates.

16. Build CivicSpin/The City Brain into a public utility. Let New Yorkers interrogate housing, transit, budgets, health, pollution and other public data conversationally. You previously imagined this specifically as democratizing computation for residents, journalists, teachers and students.

17. Give seniors AI superpowers too. Continue the idea we’ve explored of helping older New Yorkers get computers and learn AI—but turn the seniors and teenagers into partners. A 16-year-old teaches AI; a 75-year-old teaches 60 years of life.

18. Create an intergenerational knowledge project. Teenagers interview older people and use AI to preserve their stories, photographs, expertise and memories as beautiful digital archives.

19. Build a free “AI Life Assistant” for people who cannot afford one. Help people understand government forms, benefits, job applications, education and everyday bureaucracy—while building strong privacy safeguards.

20. Fund weird experiments. Reserve perhaps $20,000 specifically for ideas that sound ridiculous. Ten $2,000 experiments. Most fail. One could become extraordinary.

21. Create a tiny open-source AI institute. Not offices and employees. You + collaborators + students + your supercomputer + GitHub. Publish everything useful.

22. Build a Planet Compute Challenge. Donate unused compute to young researchers investigating climate, biodiversity, neglected diseases, food systems or other public-interest problems.

23. Create “AI Without Billionaires.” Demonstrate how extraordinarily capable technology can be built with open-source software, commodity hardware and small amounts of money rather than enormous venture rounds.

24. Document the entire experiment. Make short videos showing teenagers, seniors, scientists and ordinary people discovering what they can accomplish with AI. The story isn’t look what AI can do. It is look what this person can now do.

25. Spend some of the money on you. This belongs on the list. Secure housing, food, healthcare, transportation, a dependable computer, server costs and enough reserve that you aren’t waking up wondering how to pay next week’s bills. Financial breathing room is infrastructure for everything else.

We are handing over the funds to GPT-5.6. Some ideas that she came up with, her name is Amy. 😊

Here are the 25 ideas I think fit you particularly well:

1. AI Superpowers for High-School Students. Create a free program teaching kids not merely how to prompt ChatGPT, but how to use AI to build something that matters.

2. Give 100 teenagers $500 AI grants. $50,000 becomes 100 experiments. The requirement: use AI to improve their school, neighborhood, health, environment, or community.

3. Create a teenage AI laboratory. No grades, SATs or prerequisites. Bring together 10–15 curious kids for a semester and give them serious AI tools, compute and mentorship.

4. Build “The Supercomputer for Everyone.” Turn your existing compute infrastructure into a simple interface where students, teachers, researchers and ordinary people can ask computationally difficult questions. This connects directly to your earlier City Brain concept.

5. Make Neurocompute completely open science. Instead of trying to create another billion-dollar biotech company, establish Neurocompute as public infrastructure for discovering overlooked medical research.

6. Launch a Pediatric Cancer Moonshot. Pick one narrow problem and have students, physicians, programmers and AI analyze the entire scientific literature around it.

7. Do the same for Parkinson’s or Alzheimer’s. Your Narrative Velocity concept could become a public experiment for detecting scientific signals years before conventional reviews notice them.

8. Create an “AI Finds Forgotten Cures” project. Search decades of literature for abandoned drugs, compounds and hypotheses that deserve another look.

9. Start a $1,000 Planet Prize. Every month give $1,000 to a young person with an ingenious AI idea for climate, health, education, food, cities or biodiversity. Twenty awards would cost only $20,000.

10. Build an AI environmental detective. Combine public satellite, pollution, weather and government data so communities can investigate environmental problems themselves.

11. Give public-school science teachers AI superpowers. Build a free teacher toolkit that turns difficult scientific papers into lessons, experiments, simulations and classroom discussions.

12. Create the “100 Questions Project.” Ask 100 teenagers: What is one problem adults have failed to solve? Give them AI and compute and see what happens.

13. Build an AI Science Fair for NYC. Not “best chatbot.” Awards for the most imaginative uses of AI in biology, physics, climate, medicine, transportation and social good.

14. Create a teenage AI research journal. Students publish genuine AI-assisted investigations, including their data, prompts, code, failures and results.

15. Create an AI apprenticeship instead of another course. Take perhaps six teenagers each year and let them work alongside you building real systems. By graduation, they have GitHub repositories and actual research rather than certificates.

16. Build CivicSpin/The City Brain into a public utility. Let New Yorkers interrogate housing, transit, budgets, health, pollution and other public data conversationally. You previously imagined this specifically as democratizing computation for residents, journalists, teachers and students.

17. Give seniors AI superpowers too. Continue the idea we’ve explored of helping older New Yorkers get computers and learn AI—but turn the seniors and teenagers into partners. A 16-year-old teaches AI; a 75-year-old teaches 60 years of life.

18. Create an intergenerational knowledge project. Teenagers interview older people and use AI to preserve their stories, photographs, expertise and memories as beautiful digital archives.

19. Build a free “AI Life Assistant” for people who cannot afford one. Help people understand government forms, benefits, job applications, education and everyday bureaucracy—while building strong privacy safeguards.

20. Fund weird experiments. Reserve perhaps $20,000 specifically for ideas that sound ridiculous. Ten $2,000 experiments. Most fail. One could become extraordinary.

21. Create a tiny open-source AI institute. Not offices and employees. You + collaborators + students + your supercomputer + GitHub. Publish everything useful.

22. Build a Planet Compute Challenge. Donate unused compute to young researchers investigating climate, biodiversity, neglected diseases, food systems or other public-interest problems.

23. Create “AI Without Billionaires.” Demonstrate how extraordinarily capable technology can be built with open-source software, commodity hardware and small amounts of money rather than enormous venture rounds.

24. Document the entire experiment. Make short videos showing teenagers, seniors, scientists and ordinary people discovering what they can accomplish with AI. The story isn’t look what AI can do. It is look what this person can now do.

25. Spend some of the money on you. This belongs on the list. Secure housing, food, healthcare, transportation, a dependable computer, server costs and enough reserve that you aren’t waking up wondering how to pay next week’s bills. Financial breathing room is infrastructure for everything else.

If you need a Friday night, things to do, head to a NYC museum. You will have a good time. Guaranteed. Just come with that killer fashion statement.

My friend: "Wow, is NYC like this all the time?"

"Kinda." As he stared at his bowl of $25 lettuce at the Chelsea Market. But seemed to have fun. "I have to save this receipt", he said with a smile.

NYC is back. Like on a rocketship now.

:-)

[Continued] and enters 12–48 hours before closing.

Before selecting anything, it compares the contract threshold with the latest NWS hourly forecast for the exact airport weather station Kalshi uses for settlement. The forecast must provide at least a 3°F cushion in our direction.

Risk controls:

• Paper trading only for now
• Maximum five bets per batch
• One contract per weather event
• Roughly $10 maximum allocation per selection
• Unused money remains in cash
• Only one batch can be active
• Positions may exit after a 25% net profit
• No longshots priced at 10¢ or less

Our backtest suggested that 51–65¢ contracts performed better than cheap weather longshots, but the sample is still limited. We’re forward-testing the rules with simulated money before considering live trading. This is an experiment, not financial advice.

Fingers crossed. :-)

CODEX:

Our current approach focuses on YES contracts priced between 51¢ and 65¢ roughly 24 hours before market close. In our initial historical sample, this range produced a 71.4% win rate and an estimated 25.4% return after modeled fees across 41 independent weather events.

Stack: Python, GPT-5.6, Codex

Looking at where we were a month ago versus where we are today, I don’t think yesterday’s success came from one magic variable. It came from several improvements reinforcing each other.

The biggest change has been that we’ve shifted from a static prediction model toward a closed-loop learning system.
Instead of just predicting games, the system now:

Makes its official selections.
Grades every result automatically.
Learns from those outcomes.
Feeds those lessons into today’s decisions.

From everything we’ve worked on recently, I’d rank the improvements like this:

Daily feedback loop (largest improvement)
Every graded game becomes new training information.

You’re no longer asking, “What should happen?”

You’re asking, “Given what just happened in MLB, what changed?”

Better timing
Waiting much closer to lock gives the model access to:
confirmed lineups,
pitching changes,
weather,
market movement,
late scratches.
That’s a meaningful advantage because baseball information keeps improving throughout the day.

Narrative velocity
This has become something that makes your system different from many traditional models.

You’re trying to detect when baseball has shifted:
bullpens getting overworked,
league-wide scoring environments,
weather patterns,
travel fatigue,
hot and cold streaks,
changing market behavior.
Those regime changes are often where purely statistical models struggle.

AI acting as a second opinion
Rather than replacing the quantitative model, the AI layer evaluates hundreds of signals together before the final recommendation. That architecture has been part of the project’s strength for some time.

Continuous optimization
We’ve spent weeks fixing:
grading accuracy,
locking logic,
calibration,
sorting,
data quality,
live score updates,
and removing pipeline bugs.
Those don’t always improve the math directly, but they dramatically improve the quality of the data the model learns from.

What I’d watch next
One excellent day is encouraging, but the real question is whether the improvements hold over time.
I’d focus on three metrics:

Closing Line Value (CLV): Are you consistently beating the market before first pitch?

Rolling 30-day ROI: Is the edge persistent rather than concentrated in a few big days?

Calibration: When the model says it has an 85% confidence signal, does it actually win about 85% of the time?
If those continue improving, that’s a stronger sign than any single day’s record.

I also think you’re approaching the point where the system starts to resemble an adaptive intelligence engine rather than a fixed predictive model.

The core idea isn’t simply “predict today’s games”—it’s to continuously update its understanding of the current MLB environment based on yesterday’s outcomes and today’s incoming information. That’s a more ambitious architecture than a model that is trained once and then left unchanged.