Generative AI ALLY

Obsessed with AI, Possessed By Generative AI

[codila @0xCodila]

Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%:

10% → 17:44 - build your first agent
30% → 39:30 - Loop engineering: iterate, check, break
60% → 1:12:38 - Graph engineering
75% → 1:34:26 - agents that throttle themselves
100% → 1:55:05 - full graph for multi-agentic systems

everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph

watch the course, build the graph - then read the full architecture below ↓

[Prajwal Tomar @PrajwalTomar_]

Your AI coding agent is quietly ignoring the rules you give it.

My AI tried to sneak Postgres into a project I told it to keep on SQLite. Three sessions later, like we never talked about it.

This time it got caught.

I built two local agents that share one memory:

→ One writes the code and logs every decision I make
→ One reviews new code and flags anything that breaks a past decision

It caught the Postgres import instantly and named the exact rule it broke.

Runs fully on my laptop. No cloud.

Here's how I built it.

[Base44 @Base44]

Your users are your customers.
Now your app knows how to keep them.

Base44 now connects to @Mailchimp, @klaviyo, @intercom, @gorgiasio, and more.

[Logan Kilpatrick @OfficialLoganK]

This model is much more efficient and spend a lot less tokens to deliver better performance!

[Google @Google]

Today we’re expanding the Gemini family with three new models built to be faster, more token efficient, and reliable at scale.

Meet the new Gemini models ↓

[Logan Kilpatrick @OfficialLoganK]

Say hello to Gemini 3.6 Flash, designed to be higher intelligence, more token efficient, and with a new lower price, based directly on developer feedback!

3.6 Flash continues our progress towards models that are deeply usable in real world scenarios!

[matt @mattknowsai]

CLONÓ UNA APP DE $10 MILLONES EN 45 MINUTOS CON 2 PROMPTS.

10 minutos pensando. 15 minutos para enseñarte los 4 pasos.

Guárdalo, lo agradecerás. ⬇️

[Superior @andreysuperior]

You already know AI can run a business. But do you actually understand what it's deciding, or do you just trust the output.

Seven Claude workflows handle scheduling, invoicing, reporting, support.

$200 a month in tools. Most people running one of these have no idea why it actually works.

Somewhere inside is Matt, the manager agent. Pull up his file and you see the whole map.

Every agent he talks to. Every file that changes when he makes a decision, and what breaks downstream if he's wrong.

Bunk makes the products. Dennis researches ideas. Luke turns them into videos. Matt watches all of it and writes the report himself, every Friday, unasked.

Now other founders pay to have one. $2,500 a month per client. Eight clients. $20,000 a month, recurring, from something he built once.

Most founders guess why something worked. He can just check.

[Woody @woody_research]

One guy spent $6,000-$8,000 building what looks like an expensive gaming PC.

He was actually building his own AI data center around two RTX 5090s.

Now he can keep a local language model running on one GPU while the second handles image or video generation. His prompts and files stay on his machine, and every new workload runs on compute he owns instead of compute he rents.

For him, the two GPUs are not a hardware flex. They are two production lines.

The PC used to access AI. Now it hosts it.

Hook-pattern: character + identity reveal / Proof: named hardware + human workflow / Ending: infrastructure reframe

[Dekos @PolyDekos]

This trader turned $250 into $24,540 in just 4 months, using ONLY Polymarket's weather markets.

Geography: Wellington, Seoul, Tokyo, Chicago, New York

- Total profit: +$24,540.28
- 1,387 trades, win rate on closed positions = 72%
- Largest profit: $3,407.07

This is simply a search for low-cost contracts, and scaling up involves the trader entering with large amounts ($2-8K) even in contracts with an average price (50-90¢), relying on the consistency of the forecast rather than just cheap “lottery-style” bets.

Top 3 deals:

1. $25.33 → $938.33 (+3,604%)
2. $298.83 → $3,705.90 (+1,140%)
3. $34.52 → $478.00 (+1,285%)

In fact, this is compounding in its purest form: a small amount of capital + a high win rate + reinvesting profits = exponential growth in just a few months

[VALIX @RetroValix]

This guy built a trading bot with Claude and made $671,000 on Polymarket

His bot trades Up/Down markets at high frequency and combines asynchronous two-sided arbitrage with short-term directional trading

His Polymarket account:
https://t.co/TUBSyic1At

His strategy is simple:

> Places limit orders on both sides of crypto markets

> Buys Up and Down one after another when each side temporarily becomes undervalued

> At the same time, keeps a directional skew toward the side his model sees as more likely

Using this strategy, he captures a consistent edge, repeats it thousands of times, and keeps growing his capital

[VALIX @RetroValix]

This trader built a trading bot with Claude and made $101,220 on Polymarket

His bot uses a hybrid system that combines temporal arbitrage between Up and Down with a controlled directional skew

His strategy has 4 steps:

1. The bot starts by buying one side of the market

2. After the underlying asset moves and the market quotes change, it accumulates the opposite side so that Up + Down is below $1

3. The equal part of the Up and Down position becomes a hedged set

4. The unequal part of the position stays directional and captures extra edge

This guy’s Polymarket username: almach

Using this strategy and fast, high-quality execution, he turned $33k into $101k in 2 months of trading

[kyrox @kyroxxxq]

THIS ENGINEER FROM AMSTERDAM NETWORKED 4 M2 MACS TOGETHER TO BUILD A MASSIVE LOCAL AI SERVER IN HIS KITCHEN.

Running enterprise-grade neural networks usually requires renting expensive cloud GPUs and paying thousands in hourly hosting fees. This hardware architecture completely bypasses the cloud. By physically linking four separate Apple desktops, he created a unified computing cluster capable of processing massive language models entirely offline.

The interface on the screen is a custom node-based routing system that distributes the heavy computational load across all four machines simultaneously. Instead of one single machine struggling to generate the output, the network splits the cognitive processing. This setup allows him to run heavy, uncensored intelligence locally without ever sending a single byte of private data to an external corporate server.

You no longer have to rely on paid monthly subscriptions or worry about data privacy to access elite-level computing power. With the right clustering software and a stack of standard consumer hardware, you can engineer a private, fully autonomous Jarvis right on your standing desk.

[Sweex @sweexx9]

🚨 Gobiernos pagan millones de dólares al año a Palantir por su sala de guerra secreta.

Un solo tipo llamado Elie la reconstruyó desde cero, la subió a GitHub y la regaló GRATIS para todo el mundo.

Se llama Monitor Mundial.

Ábrelo y verás esto:

🌍 Globo terráqueo 3D en vivo con +500 noticias llegando en tiempo real, resumidas por IA
(15 categorías: movimientos militares, shocks económicos, desastres, ciberataques, rutas de aviones y barcos)
🗺️ 56 capas de mapas que puedes apilar y combinar como quieras

📊 Índice de estrés que puntúa 31 países y se actualiza solo

📈 Un solo panel con 29 bolsas, materias primas y criptomonedas

🤖 Todo con IA local vía Ollama (sin ninguna clave API)

App nativa para Windows, macOS y Linux • 25 idiomas • Funciona al instante después de clonar

Mientras las élites pagan fortunas por el poder de ver el mundo en tiempo real…

Tú lo tienes gratis
Clónalo ahora.
El monopolio de la información se acaba de romper.
¿Lo vas a probar?

[NO1ennn @N01ennn]

FOUR MAC STUDIOS JUST RAN A TRILLION-PARAMETER LLM LOCALLY AT 23 TOKENS PER SECOND, REPLACING A $2,000 A MONTH CLOUD GPU BILL FOR $40 IN ELECTRICITY

00:04 the laptop shows four inference streams firing in parallel. the 4-bit model fits on one machine, but he split it across four on purpose

MLX distributed handles the tensor parallelism. instead of one Mac carrying the full model, all four process the same layers simultaneously. the workload splits, the speed goes up, and the cluster beats the single unit

the same model class costs $3 to $8 per hour to rent on an H100 pod, which is $2,000 a month if you run it daily. this cluster runs it for the cost of electricity, roughly $40 a month

four M3 Ultra Mac Studios land around $16,000 all in. against a $2,000 monthly cloud bill that is an 8-month payback, and after that every trillion-parameter token you generate is free, private, and never leaves the room

the article ranks local AI from $180 up to the $3,999 DGX Spark. this is the cluster tier, where four consumer machines pool into a frontier model that would otherwise live behind a rented rack and a monthly invoice

save this before every builder paying $2,000 a month for cloud inference does the math and pulls it onto four Macs ↓

[VALIX @RetroValix]

He used Claude to build a trading bot on Polymarket

Result: +$110,343 in 1.5 months

His bot trades short-term crypto Up/Down markets, combining a hybrid of 2 components:

1. Temporal arbitrage between Up and Down: the bot accumulates both sides at different moments, trying to get the combined cost below $1

2. Directional residual: when his algorithm gives a strong signal, the bot increases one side and creates a directional skew

His Polymarket username: bosona

Using this strategy, clean execution, and a strong mathematical model, he turned $20k into $110k

[Granite @Granite0x]

Met a guy making $1.1 million a year as an agents engineer at Google Cloud.

Asked him how he gets agents 20x better without changing the model.

He sent me the exact thing he uses himself. A repo he open-sourced 2 days ago.

You won't find anything better about harness engineering, in the open.

Cloned it and pointed my agent at it last night.

Ryan Lopopolo. Google Cloud engineer.

'harness-engineering' - anthology + field guide + agent context bundle. You reference his docs from your CLAUDE.md.

633 stars. 48 hours old. MIT.

-> https://t.co/AOykHplNNX

bookmark this before it gets lost.

[0XDegen @b3785486614474]

这哥们儿用Claude搭了个量化机器人，直接在Polymarket上干了11.2万多美元

71天，4518笔预测，胜率稳定64%！

每天平均稳定进账1586美元，机器人24小时不间断运行。

策略其实不复杂：
专注短期加密“涨/跌”盘口，专门等窗口快关闭的时候进场，那时候市场一边的价格还明显低于模型算出来的合理价。

不需要每把都赌大的，机器人就是反复用同一个快进快出的策略，靠高频次小单把利润硬生生滚起来！

最狠的几笔：
1454刀 → 4775刀（净赚3321，+228.4%）
50刀 → 2500刀（净赚2450，+4900%）
50刀 → 2500刀（净赚2450，+4900%）

钱就是这么���出来的——同一个短线策略���复执行，直到小钱变成大钱！

#Polymarket

[0xAkky @AKKY33157665]

一个中国大学生用 Bot 在 Polymarket 跑，10 天赚了 21.8 万美元。

策略极简：实时盯 Binance 和 Coinbase 的 BTC 价格，#Polymarket 赔率延迟半拍时就���先入场，几分钟平仓。

354 次操作，大多在 BTC 市场。

散户还在争论谁当总统，它已在闷声收割 5 分钟行情。

真正的优势有时不是信息，而是速度

[0Xmood @7mood10061]

这哥们儿把 Claude Fable 5 当外挂，直接把自己的量化机器人干成了印钞机

他的主页：https://t.co/eZTDjoTDCP

从4月30日开始，他用优化后的系统在比特币5分钟涨跌盘上狂刷，42天内完成了3万多笔交易，平均每天净赚6878美元。目前已经把初始的5.43万美元干到了+531.8%的收益率，总利润达到28.8万美元。

更夸张的是，这套系统还在持续进化。

他的打法核心其实就一句话：专门抓比特币短期市场里那些还没反应过来的定价偏差，在市场完全修复之前提前卡位，然后把同一个小优势用极高的频率反复执行。

��笔赚得不多，但靠海量重复，把微小的定价错误一点点堆成了大利润。代表性交易里，有几笔直接干出几倍的收益。

现在 Fable 5 回来了，而他又把系统再优化了一轮。按照目前这个趋势继续跑下去，单日过万、单周三四十万的利润，已经不是什么遥远的事情。

这已经不是人在和市场博弈了。这是在用目前最强的 AI 之一，把捕捉定价偏差这件事，做成了一个24小时不间断、高频重复的自动化流水线。

别人还在研究怎么看盘、怎么判断方向的时候，这套系统已经把“低胜率但极高频的微优势重复执行”这件事，跑成了稳定且高效的赚钱机器。

Fable 5 一回来，他就又开始加速了。