HuaJiao DJ

A life coach, AI Startup Founder ( let's Speak like King & Queen, with Power and Grace!
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[HuaJiao DJ @UncleFlowerDJ]

🚀 8 Things I Wish I Knew When I Was 20: It's Never Too Late

Hello Flowers,

UncleFlower花叔DJ here.

I am an AI startup founder, a life coach, and a lifelong learner.

I'm here to share tips about psychology, life, health, AI, technology, startups, and business—all the things I've learned along my life journey.

My goal is to help everyone foster a happy, more fulfilled, and healthy lifestyle.

🧵Here are 8 things below ⬇️

[HuaJiao DJ @UncleFlowerDJ]

I Recreated Beijing’s Forbidden City with AI https://t.co/hXfC2MaNye via @YouTube

[HuaJiao DJ @UncleFlowerDJ]

@NadzuAI Send

[HuaJiao DJ @UncleFlowerDJ]

@mikefutia CLAUDE

[John @johnAGI168]

Seedance 已经走得太快太远了，感觉其他模型已经无法追赶其脚步了！

Seedance 2.5 能够直出 30 秒视频，参考素材可以达到恐怖 50 个，这次真的是遥遥领先遥遥领先🥇

Seedance 2.0 原生直出 4k 测试，细节直接拉满，好就是贵贵就是好😄

prompt 👇

【风格】国家地理自然纪录片（National Geographic Wildlife Documentary），IMAX电影画质，4K超清，真实摄影（Photorealistic），高帧率升格慢动作
【时长】10秒
【场景】海拔4500米的雪山岩壁，灰白碎石坡上薄雪覆盖，背景是青灰色的连绵山脊，天空阴沉，雪粒在风中横飞
[00:00-00:04] 镜头1：潜伏（Telephoto Close-Up, 600mm）
超长焦特写：一只雪豹趴在岩石后方，只露出半张脸和前爪。风吹动它脸颊两侧蓬松的灰白长毛，每一根毛尖带冰晶。琥珀色虹膜中瞳孔猛地收缩——它锁定了猎物。前爪无声地收紧，爪垫上粗糙的肉垫纹理清晰可见。
[00:04-00:07] 镜头2：爆发（High-Speed Tracking, 240fps Super Slow-mo）
侧面高速跟拍：雪豹从岩石后弹射而出，四肢完全伸展成一条直线，脊椎弯弓蓄力。后腿蹬踏岩面的瞬间，碎石和雪末被踢成扇形飞散。升格慢动作捕捉：尾巴像一条粗绳在空中画弧维持平衡，腹部的白色长毛在气流中翻卷。
[00:07-00:10] 镜头3：一击（Extreme Close-Up, Macro Lens）
极端特写：雪豹前爪拍击地面的瞬间，五指张开，弯曲的黑色利爪刺入薄雪层，溅起细碎的冰晶粒子在镜头前飞过（浅景深虚化）。定格在爪尖没入雪面的一帧，能看到爪钩弧度上反射的冷光。

[Justine Moore @venturetwins]

Found this Korean guy using AI video to make ads for products that should exist.

No idea what he’s saying, but I’m a fan 😅

(this is 404product on IG)

[HuaJiao DJ @UncleFlowerDJ]

@TommiPedruzzi System

[HuaJiao DJ @UncleFlowerDJ]

@expertwith_AI cash

[Jami @expertwith_AI]

I'm quitting my job to go full-in on Claude.

Just Asked it to:

Analyze mispriced Polymarket markets for arbitrage opportunities and find wallets using it to copy.

Turned $2K into $12K overnight.
Monitored 1,000+ wallets.

I realized something fast.

There are arbitrage bots I can't beat without code knowledge.

But I can find them. And copy them.

Claude built a monitoring terminal and connected it to a Telegram copytrading bot.

It's not a script. Not even a bot.

It's an AI agent that improves with every wallet it finds.

Fetches wallet behavior. How it trades. Arbitrage patterns. Position sizing. Timing.

70% win rate.
7 wallets copytrading right now from 500+ monitored.

Bot never pauses. Never gambles. Just math and profit.

You only need: Claude + a device + 1 hour per day.

Giving this free for 24 hours.

To get it:

1. Comment "cash"
2. Like and retweet this
3. Follow me @expertwith_AI so I can DM you

[Esan @esandurrani]

Introducing Honen

The world's fastest way to train skills needed to succeed in the Al era

Simply drop in your org's docs

Get interactive courses that self-improve over time
To prove out our vision, we partnered with NVIDIA to bring AI literacy to 250,000 learners

By 2030, 78M jobs will need reskilling due to Al, and we're ensuring no person or company gets left behind

Learn more https://t.co/4fOh4My1Bf

[Alfie Carter @AlfieJCarter]

R.I.P. paying full Opus prices for every single AI task.

A properly routed open-source Claude stack can replace $200+ a month in frontier model spend.

It is not as easy as just swapping the model name and hoping for the same output.

But if you start today, you can have GLM 5.2 wired into Claude Code, a local model running on your machine with zero token cost, and your first autonomous loop built, verified, and running unsupervised by end of this week.

I usually charge $99 for access to this playbook but today, it's free.

Like this post + comment 'STACK' and I'll DM you the full guide for free.

The guide covers three things.

How to set up local models on your hardware in 15 minutes using Ollama, which model runs best at your RAM level, and the decision engine that tells you which of your tasks belong on local, which go to a cheap API like GLM 5.2 at $1.40 per million tokens, and which 20% actually justify Opus.

How to wire GLM 5.2 into Claude Code in under 5 minutes by editing one JSON config file so the same harness, skills, and workflows you already have run on a 5x cheaper engine for 80% of your tasks.

How to stop prompting and start building loops. The 4-condition test that tells you which tasks are ready to loop, the four blocks every loop needs, and the copy-paste prompt that builds your first loop orchestration skill with training mode, memory, and a verification step included.

(Must be following, or I can't message.)

Taking this down in 48 hours.

[Dawn Song @dawnsongtweets]

Everyone says the latest AI agents will be "job-ready" soon, especially after the release of Fable 5 this week. But is that really the case?

Over the past many months, my group and collaborators have been building Agents' Last Exam (ALE), a benchmark designed to test exactly that claim on real digital labor-market work.

My group and collaborators previously have created many of the benchmarks the field runs on, including MMLU, MATH, CyberGym, and ExploitGym. Today, I'm excited to share Agents' Last Exam (ALE): a rolling benchmark that measures whether AI agents can actually perform economically valuable work across a broad range of real-world domains.

With ALE, we evaluated Fable 5, GPT-5.5, Composer 2.5, and other frontier agent systems across more than 1,500 expert-sourced tasks spanning 55 occupations.
The result is both impressive and sobering.

Today's agents can solve a meaningful fraction of professional tasks. But when we look at the hardest tasks, the ones requiring sustained reasoning, deep domain expertise, and reliable execution over long horizons, they are still far from human-level performance.

On ALE's hardest tier, every frontier agent we tested, including Fable 5, achieved a 0% success rate.
The age of useful agents is here.

The age of truly job-ready agents is not.

We hope Agents' Last Exam (ALE) will serve as a new guidepost and north star for developing agents capable of reliably performing economically valuable work across a broad range of domains.
🧵

[NO1ennn @N01ennn]

A 21-YEAR-OLD FROM CHINA RUNS 300 AI AGENTS AT ONCE. THE PART THAT MATTERS ISN'T THE SPEED, IT'S THAT NONE OF THEM CAN LIE TO HIM

he opens the dashboard and shows the swarm live, 300 Kimi K2.6 agents firing in parallel, then Opus 4.8 checking every single output against its source. this is not just a faster swarm. it is a loop that refuses to stop while anything is still wrong

he pointed it at 100 EV-market companies. first pass: 12 failed. wrong revenue, dead citations, empty fields. second pass: 3 failed. third pass: zero

this is not another agent demo. it is a system that catches its own mistakes before he reads a single row

[el.cine @EHuanglu]

AI is getting ridiculous.. it can turn disney storyboard into an actual film

[HuaJiao DJ @UncleFlowerDJ]

@JulianGoldieSEO Yes

[Marry Evan @marryevan999]

A 29-year-old sales consultant from China quit his job thanks to AI: now he earns in just two weeks what his boss makes in an entire year.

$306,000 in profit in the last month.

He replaced an entire team of quantitative analysts with Claude and a swarm of AI agents, and built his own ETH price simulation engine.

Each of the 6 agents validates its own trading decisions and, thanks to its precision, is generating at least $15,000 a day.

The coverage and analysis speed of his agent system far surpasses that of any top-tier trading team.

They collect data 24/7 and run simulations with that data in the MiroFish engine, completely autonomously.

They memorize every pattern, every market reaction, and every trading signal. This marks a new milestone in algorithmic trading.

Every trade is a perfect cycle. Every dollar earned is pure exploitation of market inefficiencies.

It doesn't predict the future: the math already knows it. It simply interprets the numbers correctly and takes the money.

Save this post if you really want to learn how to build something like it.

You only need Claude + Device + 1 hour/day.

Giving This Free for 24 hours. To get it:

1. Comment What Ever you think about it. ( Mandatory )
2. Like and Retweet this post
3. Follow me @marryevan999 ( I will DM You)

[HuaJiao DJ @UncleFlowerDJ]

@marryevan999 Cool, please share it

[HuaJiao DJ @UncleFlowerDJ]

@expertwith_AI Stack

[Muskan Jain @Muskanjain0401]

i built a job dashboard for yc companies powered by @supermemory to make finding founders & landing calls 10x faster.

→ 1,371 ranked YC roles from the last 2 years
→ T1 / T2 / T3 by reply probability
→ AI search ranks by meaning
→ 2,496 founders · 2,436 with LinkedIn · 1,235 with X · 853 with comp listed
→ outreach notes saved per company, close the tab, the dashboard remembers what you sent

without supermemory this is a sortable spreadsheet. with it, the search ranks by meaning and the dashboard remembers every founder i've touched.

stack: @claudeai code + @supermemory + @perplexity_ai API + @firecrawl + @MiniMax_AI 2.7 + @UnipileAPI for messaging

comment for access

[Kōda @aimikoda]

No matter how much we try, multiple-opponent scenes always seem to be challenging for AI video models. Let's see if the upcoming Seedance 2.1 / 2.5 next week can finally improve this.

What do you think about the petal effect? I'm considering using it in future videos as well.

Midjourney + Gpt Image 2 + Seedance 2.0

You can find the prompts in the first reply.

[AYi @AYi_AInotes]

有点炸裂，Cascadeur把AI动画的token计费直接干没了🤯

AI inbetweening、智能摆姿、物理工具，全部本地跑，没token、没credits、没云端排队，就四个字——无限生成

视频里最狠的演示是拿一个低多边形blockout rig直接出复杂奔跑动画，多场景秒切，没有任何生成等待画面
核心就三层:
1️⃣ 导入你自己的blockout角色rig
2️⃣ 用AI inbetweening补帧，智能摆姿调关键动态
3️⃣ 物理工具自动算重量感和接触——全程不联网，本地算力直接跑

这事真正的意义不仅仅是又一个AI动画工具，关键是它把动画迭代从"每次实验都要花钱/排队"变成在你本地就可以随意试了，
我印象里以前用云端工具改一个动作，脑子里得先算token，
现在不用了，Photoshop滤镜怎么试，这就怎么试🤣