Michael.

[VALIX @RetroValix]

This guy built an HFT algorithm on Polymarket with an average trade size of $10

Result: +$262,402

His bot trades 15-minute BTC/ETH Up/Down markets using a hybrid of two-sided market making, volatility harvesting, and asynchronous complete-set accumulation:

1. It builds positions where Up + Down < $1

It constantly keeps small BUY orders on both sides of the market and accumulates them during different phases of the underlying asset’s movement

For example:

> Buys Up at 42¢, then later buys Down at 53¢
> Complete set = 95¢ -> 5¢ gross edge

2. It leaves a directional residual on top of the paired inventory

For example:

> Buys 100 Up + 150 Down
> 100 Up + 100 Down form complete sets, while the remaining 50 Down maintain directional exposure

Around 86% of all purchased shares are part of the paired position, while the remaining inventory acts as the directional layer of the strategy

His Polymarket nickname: aswfadq1555

You can build your own trading bot here:
https://t.co/JVofcJa07i

(Trial period available after registration)

This guy’s stats:

> Trades / active hour: 324
> Average trade: $10.51
> Win Rate: 50%

The main edge of his HFT algorithm comes from using volatility within 15-minute markets to asynchronously accumulate Up and Down for less than $1. The unmatched remainder is kept as directional exposure

Using this strategy and his algorithm, this guy steadily grows his capital

[Latest in Cosmos @latestincosmos]

🚨: This is Mars!

140 million miles away from us!😮

[engineer's world @bitejoi]

A night light every guy would love.
This dragon breath lamp brings epic fantasy vibes and makes the perfect unique gift.

[Sam ꕤ @SamInkedArt]

Cosmic Wonders

[UFO mania @maniaUFO]

Night Sky on Mars as seen from NASA Curiosity Rover.

[Michael. @MichaelBayeh]

@Oscarrjavier95 Mariguaneras

[The Astronomy Guy @astrooalert]

A night on Mars.

This is 140 million miles away from us!

[Amazing Physics @amazing_physics]

Night Sky on Mars as seen from NASA Curiosity Rover.

[Latest in Cosmos @latestincosmos]

NASA releases insane new image of Helix Nebula.

700 light-years away from us.

It's the closest nebula to Earth.

Constantly watching us.

[Moffett Empire 🌐 ⚜️ @MoffettEmpiree]

His wife heard "nice butt" from 10 meters away but he missed the target from 2 meters

😂

[Michael. @MichaelBayeh]

@billieeilishtrs @eilishfragrance Jajaja no entiendo nafa pero es un encanto🥴😂❤️❤️

[Sprytix @Sprytixl]

ANTHROPIC LEAKED A FILE THEY SPENT 2 YEARS BUILDING WHERE 4 DEPARTMENTS CLOSE 90% OF YOUR WORK FOR $4 A DAY

you do only 10% - the deciding, and the other 90% runs on different versions of Claude at a fraction of the price.

research → marketing → sales → finance → back into the file

research pulls in hundreds of sources and a handful survive - the cheap model filters, the expensive one reads only what got through.

paying the top rate for a page you'll discard anyway is the commonest overspend there is.

marketing is high volume with a low stake per item - so you generate 3 variants at once instead of one.

one variant is a guess, three variants are a choice, and they cost the same.

in sales the cheap model scores 400 leads and the expensive one writes only to the few worth it.

a personal letter to a lead that was never going to convert is a paid guess.

6 of every 8 tasks in this file never need the priciest model - it answers at 5x the rate of the cheap one.

and there's exactly one department where saving on the model makes no sense - unit economics, funnel, forecast.

one wrong number here costs more than a full year of token savings.

finance is the only department that writes back into the file - next month's marketing runs on rules analytics wrote, not you.

overpaying is annoying, underpaying is expensive - route by the cost of being wrong, not the price per token.

save this and paste it into Claude Code - 4 departments execute, you choose ↓

[wast3 @0xWast3]

A million-token window solved the wrong problem, and Kimi K3 shipping one made that harder to see.

2.8T parameters, weights on your disk, an entire codebase in one prompt. Feels like memory. Isn't.

Memory is what survives the session ending. A window is what survives the next token.

Self-hosting sharpens it. Now you pay in GPU seconds, not API credits.

Every re-read of the same history burns the same compute. Owning the weights didn't make the past free.

Kimi Delta Attention cuts what long context costs. Cheaper repetition is still repetition.

And a bigger window buys the one thing you don't want: room to keep everything.

Nothing in a transcript is dated, ranked, or retired. Old and current load at identical weight.

The fix runs beside the model, not inside it. Capture rejects, consolidation merges, decay forgets on schedule.

Then a million tokens becomes headroom instead of a bill. You fill 5,000 and leave the rest empty.

Look at what your longest thread reloads every run. Then ask how much of it earned the slot.

[Hanako @hanakoxbt]

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES

you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested.

repo → map → 4 auditors → rank → fix → verify → report → back into the map

the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken.

4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings.

rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens.

the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit.

verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch.

the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended.

one human step in all of it: which fixes ship - 20 minutes instead of 3 days.

save this and read the full graph engineering course below ↓

[VALIX @RetroValix]

This trader made $132,356 on Polymarket using trading bot, buying markets for $11

His bot trades 5-minute crypto Up/Down markets and combines high-frequency directional trading with dynamic hedging

Its strategy is simple:

> Finds mispricing with its probability model and buys the outcome where it sees an edge

> Increases the position if the signal is confirmed, and partially reduces exposure when the signal weakens

> When the underlying asset reverses, it buys the opposite side as a hedge

His Polymarket nickname: trinity42

You can build your own trading bot here:
https://t.co/JVofcJa07i

(Free trial available after registration)

Using its strategy and high-quality execution, this guy turned $2,000 into $132,356 in 4 months of trading

[Daniro @Dan1ro0]

This trader used Claude to build a Quant Bot and made +$280,122 on Polymarket

33,043 predictions in 144 days with a 51% win rate

How is this account bringing in about $1,945 per day? The logic is simple:

1. It concentrates on short crypto “Up / Down” markets, averaging about 10 trades per hour
2. Rather than treating every window as a single prediction, the system appears to keep reevaluating the probabilities while the market is still open
3. It can build a directional position first, then use the opposite outcome later to reduce risk or improve the structure when conditions change

This trader’s Polymarket account:
https://t.co/Evd5uNWCDf

Most profitable trades:
$10,219 → $19,041 (+$8,822 +86.3%)
$12,355 → $20,800 (+$8,445 +68.4%)
$7,605 → $15,571 (+$7,966 +104.8%)

The 51% win rate is only part of the picture. Most of the performance comes from how the system sizes, hedges, and restructures exposure across more than 33,000 entries

[Michael. @MichaelBayeh]

@ZorritoMood ReclutaPaez.

[Gente Que Se Extingue Sola 🤦🏻‍♂️ @extinguiendonos]

Cuando ella te dice que es su primera vez

[Comunidad Biológica @Bio_comunidad]

La inteligencia artificial acaba de alcanzar algo inquietante: diseñar genomas completos de virus capaces de multiplicarse en el laboratorio.

Investigadores de Stanford usaron Evo 1 y Evo 2 para generar bacteriófagos dirigidos contra la bacteria Escherichia coli.

Estos virus no infectan personas: atacan bacterias específicas, introducen su material genético y utilizan sus células para multiplicarse.

El equipo sintetizó 302 diseños prometedores y descubrió que 16 producían bacteriófagos viables, capaces de replicarse.

Algunos superaron al fago natural ΦX174 al competir, reproducirse y destruir bacterias durante las pruebas de laboratorio.

Además, una mezcla de estos virus venció rápidamente la resistencia desarrollada por tres cepas diferentes de E. coli.

Esto podría impulsar terapias contra infecciones resistentes a los antibióticos, uno de los mayores desafíos médicos actuales.

Pero diseñar genomas virales con IA también exige controles estrictos para impedir que esta capacidad sea utilizada peligrosamente.

DOI: 10.1126/science.aec2657