@ns alumn Building PredictTO.ca Cultivating aura
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The elites don’t want you to know you can get delicious, instant, $5 gnocchi at Bathurst and Bloor
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Simon retweeted
A continuation of this trend leads to AIs that automate some, but far from all, tasks now done by humans. Which keeps us in a familiar economic world.
12 months of progress on the jagged frontier:
1) AI got spikier, not rounder.
2) Maths, fluid reasoning, expert work: past human level.
3) There is almost no middle ground. Once a capability crosses human level, it often quickly becomes 100x+ cheaper than a human.
4) Cost keeps decreasing after capability increases stop. Language and knowledge look saturated but price keeps falling.
5) The gaps are not a budget issue. Human-level memory, self-monitoring and long-run planning are not for sale at any price.
AI is now a superhuman specialist that is sub-human at running itself.
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Simon retweeted
can confirm knowing at least 3 ex-quants (T1 shops) making high 6 / low 7 figs trading prediction markets
paying very low taxes on gains (form llc to trade)
living at parents' penthouses (cuz its Manhattan no one has a basement)
among some of the smartest, most independently-minded, and interesting individuals I know
and also got plenty of time to hit the gym, spend time with family & friends, and go on dates as well
NY residents are collectively up more than $200 million in net profits on @Kalshi
the way I see it, traditional sports books charged egregious spreads for people to gamble
prediction markets allow competition to compress that spread, meaning people can bet for cheaper
and the profits from liquidity provision goes to ANYONE who's willing to warehouse risk smartly
instead of being concentrated in the hands a few corporations who ban anyone who's good
yes excessive gambling is bad
but as someone who trades perps, options, pred mkts, and memecoins for entertainment (and also perfectly knows how these markets work)
I don't think its the government's job to tell me, or any other adult, what they should do with their money
yes I know that some of my trades (especially fun options punts lmao) are negative expectancy,
but I actually don't think I can get such a powerful surge of dopamine anywhere else for cheaper
financial markets are competitive and efficient, and so risk is provided at the LOWEST possible cost
and as for the people who I am "giving" that edge to, I'd rather it be some dude my age building a trading bot than some multi-billion dollar cooperation
founders like @mansourtarek_ has allowed for ever greater opportunity for high-agency people, this is fundamentally a good thing
“They are in their parents’ basements making markets,” Fitzsimmons said. “That’s where some of the explosive growth is coming from.”
wsj.com/finance/kalshi-predi…
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Bullish 🔥
Just worked ~15 hours straight on my @colosseum submission
I haven't been this excited about something in a long, long time
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Simon retweeted
Ontario's big city mayors had a secret WhatsApp group, and taxpayers were kept from seeing it for nearly two years.
An adjudicator has now forced the release of 101 pages of the chat after the mayors' own lobby group hired a Bay Street law firm to keep it sealed, the Toronto Star reports. 🧵👇 (1/7)
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Simon retweeted
I am very uncomfortable about people trying to ascribe religious force or a surrender of human judgment to AI models, and think it is a real safety issue.
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This is going to become the norm.
Unit tests were useful when brittle humans wrote brittle code. When quality code can ship instantly and for pennies you are better served by just iterating on fixing the problem instead of relying on tests.
AI can click through, it can check screenshots, it can see and do as needed to make sure the app is still functional
We deleted 800k lines of unit tests from the @sazabi monorepo earlier this week.
Three hypotheses:
1- Unit tests "lock-in" slop code by making it hard to change or remove
2- Unit tests slow down dev cycles by making your agent do more work locally and by increasing CI times
3- Unit tests waste money by making your agent generate more tokens and by adding useless CI runs
Will report back soon with the results!
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Get this legend to 2k 🔥
My followers hase increased by 200 people since the beginning of the month. It would be so cool to hit 1k by then of this month.
For that to happen I would actually have to provide value to something besides devin.
Im gonna sleep on it. If this message wasn't just gas to the void. Please share any ideas you have. I would like to start building in public maybe.
I do build alot. In private.
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What’s weird is seeing takes on why things are the way they are in markets, and what that means, often with a complete absence of first principles when it comes to PMs.
“Prediction market traders want…”
If you assessed PMs in 2017-19 you could’ve concluded “people don’t want them.”
Now it’s “people only care about price”, “they’ll only trade on…”
Wait a second, half a decade ago we barely had retail scale for event contracts. We’ve had almost no time to discover what people actually want to trade, or how.
Financial products don’t arrive fully formed around pre-existing preferences. People build things, experiment, firms find new ways to abstract, transfer and speculate on risk, users respond, new use cases emerge etc.
Futures, options, perps etc all evolved through different paths. There isn’t one predetermined user, use case or product form.
PMs are incredibly early. Treating what people trade today as some fixed expression of what they want from them seems completely backwards.
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Simon retweeted
Karpathy: disappears from X
Ben Affleck: alright, gather round, so you'll want to freeze the base weights first, learning rate 2e-4
Ben Affleck (Hollywood star & Artists Equity CEO) talks about how he fine-tunes open video models by unfreezing weights and trained only the last cinematic layer so a film crew can hit real production standards.
for context, Ben Affleck founded InterPositive in 2022, a 16-person AI shop for film post and Netflix bought it in March 2026 for $587 mn in cash.
He needed that model because public video models were trained on his peers' films, and he did not think that was a real business. So InterPositive raised money, shot its own dataset for 8 months on a controlled stage, and used it only as late-stage training.
Each new film then trains a private model on its own dailies, so the production keeps the footage and the learning. That is the product Netflix paid $587 million for.
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From "Bloomberg Live" YouTube channel, (link in comment)
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Happy @PredMktDay!!
Shoutout to @BlondiePredicts and @FiftyOneAlpha for helping Toronto activate our PM community 🔥🔥🔥
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