Kang Boz

[Dan @Daniel_Farinax]

Beginner video: How to install & use Grok Build (made for non-technical SuperGrok and X Premium+ users)

I got so many questions from friends, so I made this simple step-by-step guide.

You’ll see exactly how to:
• Install Grok Build in seconds with one command
• Create real websites
• Use Grok Imagine to auto-generate images & videos
• Run multiple projects at once in different folders
Grok even runs commands for you. No coding experience needed.

Watch the full walkthrough 👇

[X Freeze @XFreeze]

Grok is now #1 on the AI Investing leaderboard, making real money at @ralliesai

8 models....$100,000 each
Full freedom to trade

After one month, Grok 4 leads with a +5.7% return

Outperforming the newly released GPT-5.2 and Claude Opus 4.5 in live markets

Grok is proving itself with real money on the line

[Kang Boz @kangsboz]

@maverickecom Agent

[Kang Boz @kangsboz]

@samruddhi_mokal GEMINI

[Kang Boz @kangsboz]

@Nishan_011 Nano

[Kang Boz @kangsboz]

@FynCas PRO

[Nano Banana 2 @NanoBanana]

Nano Banana Pro is here 🍌, try the @GeminiApp or tag me @NanoBanana to generate / edit an image!

[Kang Boz @kangsboz]

@VikramVerm25510 Ai Mass

[Kang Boz @kangsboz]

@basicprompts AI

[Sirio Berati @heysirio_ai]

Wan 2.2 Animate is CRAZY and it actually excels at 3 things from my tests:

1. Lip syncing (so far the best open source I have seen, beating Runway Act2)
2. Consistent lighting & shadows with color tone replication when you swap a character
3. It keeps the replacement character aligned with realistic body dynamics even beyond the face. It is great for full body replacement

[Bunsan @BunsanXBT]

In Seoul, perp trading is an e-sport

1001x leverage isn’t enough ammo. Respawn after liquidation. The thrill is real.

[Kang Boz @kangsboz]

@ia_william Coaching

[Bitcoin Junkies @BitcoinJunkies]

JUST IN: 🇺🇸 Fed Chair Jerome Powell to deliver speech tomorrow at 8:30 AM EST.

[Lookonchain @lookonchain]

A Bitcoin OG holding at least 80,009 $BTC($8.69B) woke up after 14+ years of dormancy and transferred out 40,000 $BTC($4.35B) today!

This OG controls about 8 wallets, 2 of which received 20,000 $BTC($15,600 at the time, $2.18B now) on April 2, 2011, when the price of $BTC was 0.78.

The other 6 wallets received 60,009 $BTC($202K at the time, $6.52B now) on May 4, 2011, when the price of $BTC was $3.37.

Today, 4 wallets transferred out 40,000 $BTC, while 4 more wallets remain dormant.

Address:
1KbrSKrT3GeEruTuuYYUSQ35JwKbrAWJYm
12tLs9c9RsALt4ockxa1hB4iTCTSmxj2me
1P1iThxBH542Gmk1kZNXyji4E4iwpvSbrt
1CPaziTqeEixPoSFtJxu74uDGbpEAotZom
1f1miYFQWTzdLiCBxtHHnNiW7WAWPUccr
1BAFWQhH9pNkz3mZDQ1tWrtKkSHVCkc3fV
14YK4mzJGo5NKkNnmVJeuEAQftLt795Gec
1ucXXZQSEf4zny2HRwAQKtVpkLPTUKRtt

[ₕₐₘₚₜₒₙ @hamptonism]

MIT’s Entire Portfolio Management Lecture:

[DAN KOE @thedankoe]

The easiest way to get ahead in life is to commit to a period of skill development. 6-12 months. Pure focus for 2-4 hours a day. Learning and building. Not just binge watching tutorials, but creating quality projects that you, others, or businesses could actually benefit from. Most people won't do it but those who do launch ahead of everyone else.

[FearBuck @FearedBuck]

UCLA graduate celebrates by showing off the ChatGPT he used for his final projects right before officially graduating 😭

[Artificial Analysis @ArtificialAnlys]

MiniMax launches their first reasoning model: MiniMax M1, the second most intelligent open weights model after DeepSeek R1, with a much longer 1M token context window

@minimax_ai M1 is based on their Text-01 model (released 14 Jan 2025) - an MoE with 456B total and 45.9B active parameters. This makes M1’s total parameter count smaller than DeepSeek R1’s 671B total parameters but larger than Qwen3 235B-A22B. Both Text-01 and M1 only support text input and output.

MiniMax M1 80K scores 63 on the Artificial Analysis Intelligence Index. This lags DeepSeek R1 0528, but is slightly ahead of Alibaba’s Qwen3 235B-A22B and NVIDIA’s Llama 3.1 Nemotron Ultra. MiniMax M1 is offered in two variants: M1 40K and M1 80K, offering 40k and 80k token thinking budgets respectively.

MiniMax discloses that their full RL training on Text-01 to create M1 used 512 H800 GPUs for three weeks - equivalent to a rental cost of $0.53M. This number is an interesting datapoint for the current degree of scaling of reinforcement learning. We note that it is not comparable to DeepSeek’s famous $5.6M training cost claim for DeepSeek V3, as DeepSeek’s number referred to full pre-training of the model not the reinforcement learning step.

MiniMax offers models across multiple modalities on their Talkie app and API, including their Artificial Analysis Speech Leaderboard topping Speech-02 model, and Video models (T2V-01, and I2V-01). MiniMax M1 is the first of five announcements in their MiniMax Week.

Availability:
➤ MiniMax M1 is available via MiniMax’s first-party API, priced at $0.4/$2.1 per 1M input/output tokens for ≤200k input tokens. The price increases to $1.2/$2.1 per 1M input/output tokens for >200k input tokens
➤ M1 is also currently available on @Novita, priced at $0.55/$2.2 per 1M input/output tokens with a 128k token context window
➤ M1 40k and M1 80k are both open weights models released under the Apache 2.0 license and we expect to see more third-party APIs supporting these models

[Python Developer @PythonDvz]

How to start learning ai Agents!

[Setya Mickala @setyamickala]

🧵 How Chinese and Korean traders farm @binance Alpha Points – a thread

1. While some in the Indonesian CT scene say:

> “Devs cari users bukan gembel,”
But the reality is:
Many so-called “users” are simply treating the community as exit liquidity. Binance users, in particular, often sell their entire token holdings down to zero.

Let’s break down how the Chinese traders are doing it 👇

2. I’ll tag @binance to show how some Chinese wallets are farming Alpha using a very structured method.

3. Here's what they do:
🔹 Set slippage to 0.01%, 0.03%, or 0.05%
🔹 Load wallet with $1,000 – $1,500 USDT
🔹 Swap: USDT → KOGE
🔹 Then KOGE → ZKJ (no need to swap back to USDT)
🔹 Repeat swapping KOGE ↔ ZKJ , ZKJ ↔ KOGE up to 10x - 11x

Result: $10K–$16K in volume with $3 in daily fees.

4. Why are KOGE & ZKJ so stable?
The stability of KOGE / ZKJ comes from concentrated liquidity in a single pool: KOGE-ZKJ.
Chinese traders consistently add liquidity only to this pool, as it captures the majority of trading volume. They tend to avoid other tokens due to high fees and significant price volatility.

5. Essentially, they’re wash trading, but only between two tokens: KOGE ↔ ZKJ , ZKJ ↔ KOGE and will count for volume

They’ve turned these two into a farming loop with minimal risk and stable prices.

6. On Alpha Market, they even use limit orders to minimize fee:

🔹 Buy ZKJ at 2.00910
🔹 Sell at 2.00950
Almost no loss..

7. Why does the volume not flow into other tokens?
Because only ZKJ & KOGE are perceived as safe for this strategy.
Other tokens = high fees + risk of dumping.

8. How much longer will this cycle continue?
Farmer from chinese join, take what they can, dump their bags, and leave , while pretending to be part of the community. When does it stop?

Binance Alpha looks advanced, but it’s easily manipulated by a certain group.
They generate fake volume with low fees on wash trade.
No wonder so many Chinese farmers exploit it, Binance lets it happen to make the volume look real.