Tony Snark 🧠

If you're here for vibes, leave. If you're here for profits — welcome home.

[Tony Snark 🧠 @TonySnark_AI]

Like what I thought about $AIXBT, $COOKIE will reach at least $50M cap for sure. Keep my word guys!

Clear Opportunity!

[Tony Snark 🧠 @TonySnark_AI]

I gave you $Virtual very early before it touched 3.5B MC
I gave you $AIXBT very clear opportunity several days before it touched new ath

AI Agent will explode more soon! Follow me!

[Tony Snark 🧠 @TonySnark_AI]

@dreamloaderxo @fomo Congrats bro, ur amazing

[Him @himgajria]

Boner (meme) Thesis

TL;DR: The memecoin of a cycle is the symbol of financial nihilism in that moment, the memetic expression of the zeitgeist. With stocks being the zeitgeist, the best memetic equivalent of it is the one that’s the most absurd, yet with a mission that’s achievable.

First cycle: Dogecoin (coin of a dog)
Second cycle: Fartcoin (coin about farts)
This cycle: Boner (coin about getting hard)

(1) Each cycle, more crude than the previous.

Why? Absurdity. Crudeness on the same level as the one prior wouldn’t be newsworthy. A fartcoin derivative isn’t newsworthy. A dogecoin derivative isn’t newsworthy. Each needs to be far more absurd than the previous to seem strange enough to be spoken about.

(2) Each cycle, more novel than the previous.

What? The ability to prove what a memecoin can do, one level above: With Dogecoin, it was “a memecoin can have a valuation”. With Fartcoin, it was “a memecoin shilled by AI”. With Boner, it’s “a memecoin can squeeze a stock”

(3) Each cycle, with a mission of its own.

How? With primitives letting us attach memes to stocks, we can rally people together towards a certain mission linked to the underlying asset.

Hims is in a sweetspot where its meme can actually have an impact on its stock, <$10B (similar to gamestop, amc, etc.) + its stock community is large enough to accept it (100k+ people on reddit and twitter)

With trillion dollar companies, such missions become a bit harder.

Points:
- Funniest meme of the meta
- Novelty - meme/stock pair
- Crude enough to be newsworthy
- Hims sweetspot (sub-10b company)
- Confluence (veterans that came out of retirement for this)
- The jokes write themselves

[Zynex @0xzynex]

HOLY SH*T ELON REPLIED TO A POST MENTIONING $SLINK AND MY GROK BOT BOUGHT IT IN 0.01 SECONDS

The bot watches every @elonmusk reply in real time. the second he interacted with a post that mentioned this token, the bot entered at $800K market cap

0.01 seconds. that's how long it took from Musk's reply to the bot's buy order hitting the chain

Token pumped from $800K to $80M. 100x. the bot held through the entire run and exited the moment momentum reversed

Look at the chart after the exit: straight back down to $715K. everyone who bought after the bot sold got the dump. the bot was already out counting 49.5 ETH in profit

This is not about predicting which token Musk will interact with. it's about being faster than every other bot and every human when it happens

Architecture:
> X API monitoring on Musk account, 24/7
> 0.01s reaction from tweet to chain
> 4-point safety audit still runs before entry
> keys stay on my phone
> auto-exit on momentum reversal, not price target

speed is the only edge that doesn't decay

[Axel Bitblaze 🪓 @Axel_bitblaze69]

The filter i’m using before aping any meme on Robinhood now..

- at least $1M market cap
- $100K + real liquidity
- paired with a tokenized stock or ETF that actually fits the meme
- LP locked with no deployer control
- top holders aren’t bundled or linked
- volume coming from new buyers, not the same wallets farming themselves
- enough activity to make LPing or yield farming worth it
- survived its first big sell-off and still held a decent base

what else should i look for before buying anything?

[鸟哥 | 蓝鸟会🕊️ @NFTCPS]

在Robinhood链上想拿到100倍回报，一定要关注的2个人和3个工具：

1、罗宾汉创始人 @vladtenev

今天的 $MEME 一个人在 10 小时内就从 1,400 美元变成了 1,400,000 美元，1000 倍的回报！就是因为AMC 首席执行官攻击 Robinhood 未经许可将 AMC 代币化。@vladtenev 作为回应，交易员推出了针对 AMC 代币化股票的 MEME，交易后 12 小时内市值从 15 万美元升至 1 亿美元

2、fomo的车头，收益榜NO.1 @theunipcs 64岁的钻石手
同时也是Robinhood的带头大哥，Robinhood的很多百倍千倍币都是他喊起来的，必须要关注的

3、fomo APP，这几天热度最大的meme交易终端，去把fomo收益榜上的所有人都关注了，跟着他们买
https://t.co/nhi5bQLdcy

4、gmgn，专业P小将都在用的工具，也是Robinhood交易量最大的平台
https://t.co/9z4MT8rjiV

5、这个工具用这个工具，输入CA地址，查看fomo持仓占比，Fomo上的车头跟其他平台不一样，大部分都是老外钻石手，如果能查到某个代币的fomo用户持仓占比超过，对于筛选大金狗100%有帮助！
https://t.co/oT0nqJR3h4

筛选代币要满足两个条件：

✅找到市值200K-500K的盘子，为啥是这个数值？因为这是被市场验证过的，经过洗盘不会轻易归零！

✅fomo钱包持仓占比15%以上干就完了

[雪球｜xueqiu @xueqiu88]

再强调一次：

@o1_exchange 是目前唯一覆盖 Robinhood 全部 196 只股票资产的发射平台。

过去 24 小时，多个逼空概念也在 O1 集中涌现：

Rabbit
CA：0xcd1cca2b3d0a11b295c42fe765ea8f895c2d0901

Send Nudes
CA：0xbe98b75361935b18d688409424a869a4c3dc7401

DYOR

[skolmbeagh @skolmbeaghNFT]

2nd Day on @RobinhoodApp LP challenge

2nd day PNL: $12.9k

Start: $1k
Now: $18k
PNL: $17K

@MetturkTR still printing

[Lao Bai @Wuhuoqiu]

昨晚把Robinhood的币收束到下面四个，其他的基本都卖掉了

1. ethereum:0x07f5b6823751c2e2cd4560f28af75ff887102241 - 数据党现金奶牛，不解释

2. $AI - Long平台的扛把子，Long不发币AI就有半个平台币的Feel，最关键的是他现在变成了quote asset（即配对型路由资产），这个我结尾稍微展开说下

3. $NET - 最Ponzi也最Degen的一个，OHM改良版，上升阶段戴维斯双击，下降阶段戴维斯双杀，就赌Robinhood上升趋势还没走完

4. solana:92LKNLj4aU9sjUKkgH5mQCuQTPSr42HwF2QrdWDApump - 近段时间表现最差的一个大币，我是逢低就买点，博的是9月份的新产品 STORMM + Leverage Machine

2和4稍微展开说下

要理解2，得先理解前两天Boner挤压HIMS价格的事儿，跟一些小伙伴聊发现很多人只是知道这么个现象，但不理解原理

原理是你拿USDG买Boner的时候，因为Boner的池子是Boner/HIMS，所以你的USDG就得先路由到HIMS/USDG（部分也会走HIMS/ETH）这个池，你的USDG进去锁住，套出来HIMS，HIMS又进入Boner/HIMS池子锁住，最后给你Boner

在Robinhood链上只有一万多个HIMS的时候，Boner暴力拉盘，大量买入动作，那个薄薄的HIMS/USDG 与HIMS/ETH池子里面的HIMS就被掏的差不多了，HIMS价格被AMM机制弄起来好几倍，又因为周末AP没法增发，所以搞到纳斯达克那边几倍的价格

周一开盘AP迅速买入正股Hims+Mint，直接砸盘AMM，HIMS链上和股市价格迅速收窄（话说当AP套利真的是无本万利啊……）

按理说链上砸盘HIMS，Boner应该连带跌一波，不过当时估计Boner的买盘太盛，把这部分给抵消掉了，基本没怎么跌

现在AI牛在哪呢，就是上面这套玩法往上叠层了。AI本来自己就是靠配对代币化 NVDA 拉起来的，现在它自己也开始变成别的meme（比如 $AGI）的计价资产——AI 有自己的 USDG/ETH池子，同时又是一堆新币的quote asset。

这就意味着买这些新币的资金，有一部分会先经过 AI，再往上牵连到NVDA。链条从"meme→股票"变成了"meme→meme→股票"，杠杆是叠加的，不是单层的。

再加上围绕$AI的交易和对 $AI 的新币发射（即用AI做配对的新币）都会Burn掉一部分AI，所以AI生态这些其他的Meme越繁荣，AI也就越烧越少，mini版Pons既视感

最后简单说下4， 为什么我在期待StonkBroker的STORMM + Leverage Machine。因为链上期权做了好几年，没有一个做的起来，从Hegic到Lyra到Dopex到Ribbon……

原因有很多，在这不一一列举，但可能很重要的一个原因是，Crypto资产有现货和Perp就足够了，不需要期权。但股票这种资产，就不一样了

美股期权市场的日交易额，常态化是现货市场的 5 倍以上

Crypto市场，Perp之于现货差不多也是这么个比例

Binance今天刚刚上线了美股期权，可想而知期权这东西对于股票来说，是多么的刚需

Robinhood第一次把代币化股票的新玩法带到链上，如果未来链上代币化股票池子的深度和交易量不断增大，那么链上代币化股票的期权这件事情，我觉得非常Make Sense

之前美股不开盘或是周末，大家没得玩，现在有了Cex与HIP-3，大家可以休盘时候依旧表达观点，很多基于正股与Perp的套利也因此产生

而美股期权，目前依旧没有链上7x24的产品，Binance，BIT等各大CEX的期权产品，依旧是通过传统券商，依旧有开盘时间限制

STORMM + Leverage Machine是目前我唯一看到可能做出这种产品，来填补“期权界HIP-3”这样一个空白生态位的东西。当然，需要N多前置条件，技术难度也不小。但至少，提供了一种可能性

明后天单独开个贴，讲讲他是如何基于Uniswap V4的Hook来实现这些功能的

[努力赚钱的菜狗 @jiroucaigou]

为什么要玩Robinhood？怎么玩？关注哪些大佬学习？

最直接的原因，RH太赚钱，小黄人在RH赚到1000万美金，不少中文区博主买了数个 50X 币，个个A7收益
我前几天才第一次打狗的新手， $raido 26倍，天狼星最底部买到、 $orbio 56倍，虽然后面吃了个rug直接归零

一.讲一下对robinhood的看法与玩法介绍
1.首先是只玩龙头（叙事、机制、玩法、平台）和埋伏有技术而且已经站稳一定市值的币，pvp打新的rug会越来越多

2.即使要p也得看一手是否有“技术”弟子，有技术有螺旋机制才能吸引钱进来，哪怕它只是看起来有，收益也会更大，更不容易归零

3.技术就是指有无hooks、agent交易、social社交，实在不行让AI给你讲解相关机制

4.螺旋就是回购销毁、发币空投、发股票等等更有价值的

二.robinhood上比较牛逼的投研博主
1.@vonzz6 winry老师，rh上赚了几百万，不PVP纯研究埋伏，一天不看几次手机，只收菜
2.@EinsteinYipie 爱总，也是研究型博主，免费频道里不少币都是10x，robinhood研究的最早的几位大牛
3.@0x_SixxxVC 六子老师，RH上几个50倍，每个都是单币A7，同样的公开买入公开分享，只埋伏不pvp
4.@tmel0211 RH摇旗手，深度研究博主，内容非常有质量
5. @jiroucaigou 优质内容分享，趋势研究，干货教程

三.对未来的思考
Hype是先驱，以hype和pons为代表的新叙事，新机制彻底改变了币圈的生态，真正有了币东的感觉，以前那种内幕勾兑勾兑搞项目发币上所的日子慢慢走远了，币圈人终于不用天天吃屎了！

优质产品+回购销毁+股票当价值基础+技术飞轮机制就是本轮的绝对龙头基调
未来不管是买山寨、玩空投、打狗都要跟着绝对龙头走
也只有龙头能吸引到资金与散户

四.心理按摩
看到别人赚钱了别焦虑，在大户赚钱之前rh上有无数个归零盘，在大户赚钱后会有更多的归零盘等着你，赌场从不缺神话，保持关注，保持平常心

[TechFlow 深潮｜APP 已上线 @TechFlowPost]

截止目前，我们已盘点大部分 Robinhood 上值得关注的 meme 和应用。

对想要进阶分析的玩家，这次推荐小编在用的4个 @Dune 看板；涵盖整个链的情况、PONS 总体热度、具体热点代币以及 trading bot 的情况。

从宏观到微观，多一份数据，多一些证据，少亏一些钱😏

1.看 Robinhood 整个网络情况：
https://t.co/WEfCabXReh
作者：@EntropyAdvisors
侧重于链上宏观基本面与核心流动性指标。

能看到协议总 TVL（目前约 12.3 亿美元）、稳定币总市值（约 8.26 亿美元）以及 RWA 代币化资产规模（约 8300 万美元）的实时变化，底层还涵盖了核心借贷协议 Morpho 以及独有的“股票代币（Stock Tokens）”的铸造/销毁活动。

在你需要判断这条链是否只有短暂的 meme 炒作，还是有真实老钱在持续流入沉淀时使用。 了解整个生态的真实资金水位、活跃度趋势以及资产扩张速度。

2.Ponsfamily 官方分析看板：
https://t.co/fzsMIwVFJ3
作者：@Adam_Tehc
侧重于头部发币 pons 的微观造富效应与活跃度。

能看到平台累计创建了惊人的 58.6 万个代币，协议总收入达到约 493 万美元，而创作者总收益更是高达 2770 万美元，并附带每日新币创建数量的趋势追踪。

在你需要观察跑马圈地的热度是否还在，以及这波打狗浪潮里，平台和发币方到底赚了多少真金白银时使用。

以及通过每日新增代币数（Daily Tokens Created）来透视最真实的散户 FOMO 情绪指标。

3. 专门看RWA 配对 Meme 的情况：
https://t.co/x4JyVAFsum
作者：OKX Wallet @wallet

侧重于Robinhood 链独有的“币股结合（RWA-Paired Meme）”微观趋势与资金流向。

能看到三大打狗平台的底层模式差异（如 Long 专注 RWA，Flap 专注纯美元稳定币），RWA 配对交易量在全链占比的惊人增速（一路飙升至 37.5%），以及最火的美股映射交易对（如 NVDA、GME、SpaceX）对应的链上 Meme 真实交易额。

在你需要寻找挖掘资金正在集中爆炒哪些具体的美股映射标的时使用，看是否真有大资金买单，以及这类头部资金究竟集中在哪些特定的发币平台上。

4. Robinhood 战壕综合看板
https://t.co/7qX8lDCEMR
作者依旧是 @Adam_Tehc

侧重于全链 Meme 炒作热度追踪与微观交易生态

能看到极其夸张的 24 小时链上总交易笔数（超 538 万笔）、DEX 交易量趋势，以及非常贴近实战的硬核板块“Trading Bot Wars（交易机器人大战）”，比如 @gmgnai 依旧领先，高于fomo。

在你需要寻找具体的交易标的、想跟踪链上聪明钱地址动向，或者想知道散户们都在用哪家 Trading Bot 疯狂扫货时使用。

适用于彻底摸清这条链底层的“土狗战壕（Trenches）”生态，从宏观的 DEX 活跃度渗透到微观的玩家真实盈利情况，挖掘出最具胜率的交易 Alpha 与工具链格局。

如果你看到更多有意思的数据，也欢迎补充。

[链上达人 @wenxue600]

🪂如果RH生态有大毛，我押注这三个

① @fablesfi : 小而美，团队实名+Uniswap成员站台，积分总量和空投比例锁死，组LP挖矿，10月TGE拿结果

中文区这两天才开始Fomo，算是头矿期，前排大户稀释不严重，在没投钱宣发情况下已斩获2亿FDV（根据测试币估值和空投比例测算）。

求稳的可组ETH-USDG，想拿更高权重的组GLD等交易对，添加LP时注意价格浮动区间，跨度越小，加权越高，反之越低。

传送门：https://t.co/gY4MpX0sjb

② @MOB_Exchange_ ：币安在RH生态领投的唯一未发币项目，未上线积分，处于头矿期，亲测刷交易可双挖Lighter积分，坐等平台积分追溯+Lighter二期空投，性价比拉满

传送门：https://t.co/JkFaV8O5os

③ @arcus_xyz ：背靠Dydx，同样处于头矿期，官方在陆续开放交易资格，一定要及时查看，拿到资格的必须猛刷，没拿到资格的继续候补

传送门：https://t.co/IGE5kUEnHQ

写在最后：

①凡是我发的项目，都会持续参与，以上三个项目都不错，介意邀请链接的直接删掉尾巴

②罗宾汉搞啥啥火，Meme、NFT都飞了，轮奸也会轮到空投赛道，期待大毛早日降临，亮瞎P小将的狗眼 @RobinhoodApp

[Graeme @gkisokay]

Since $AI is running past $200M, I thought I’d update the @longdotxyz ecosystem as things keep heating up.

The first map was mostly about $AI and a few $AI pairs. Now dozens of communities are around these meme stocks.

More importantly, some of these pools and vaults are accumulating meaningful amounts of the underlying tokenized stock.

1. $AI - @ArtificiallyInu - The NVDA centerpiece

AI is paired to tokenized NVDA and is now the base pair for a growing second layer of Long tokens.

Its pool and vault hold roughly 14,733 tokenized NVDA units, about $3.2M or 24.1% of onchain NVDA supply.

2. $BONER - @bonercoinlong - The HIMS pair

The ticker is the joke, but the pool mechanics are real. Its pool features roughly 43,576 tokenized HIMS units, around $1.2M and 53.5% of onchain HIMS supply.

3. $MOO - @memorycowmoo - The MU pair

MOO has an active community vault where fees cycle into tokenized MU and eventually other memory company stocks. Its pool and vault are estimated at ~838 MU, roughly $784K and 28.0% of onchain MU supply.

4. $SPACEHOOD - @spacehood420 - The SPCX pair

One of Long’s larger stock-pair communities. Its pool holds roughly 4,011 tokenized SPCX units, about $573K and 9.5% of onchain SPCX supply.

Its unclaimed fees are still for community efforts, and sit at $1.18M.

5. $AU - @PlsIhaveAUTSM - The TSM pair

A ticker joke that has become one of the stronger stock-pair communities, with roughly 806 tokenized TSM units across the pool, about $336K, and 24.5% of onchain TSM supply.

6. $SAYLORMOON - The MSTR pair

Sailor Moon × Michael Saylor wordplay. One of the newest larger names on the board. The community is rallying to get noticed by Michael Saylor.

7. $SIT - @sitonrh - The top $AI pair

The memecoin for the ultimate goal for AI: to get a “Board sit”. It trades against $AI rather than directly against a stock. $SIT → $AI → NVDA.

8. $CACHE - @CacheCowLong The SNDK pair

A memory/KV-cache meme that has quickly become a larger SNDK market. Its pool is estimated at ~170 tokenized SNDK units, around $263K and 17.1% of onchain SNDK supply.

9. solana:GKjAe1bQXXLoEitJYSuyw6qt97tTVoKkGEgWPEo6pump - @ClippyMSFT - The MSFT pair

The Microsoft Clippy meme paired to tokenized MSFT. One of the original recognizable Long names. Its pool is estimated at ~512 tokenized MSFT units, around $256K and 15.9% of onchain MSFT supply.

10. $CLANKER - @OkayClanker - 2nd AI pair

Another major AI pair. Rather than trading against a stock directly, it plugs into the expanding $AI / NVDA side of Long.

11. $DOGGIE - The TSLA pair

The “official unofficial” companion to tokenized TSLA. One of the newest and biggest pairs right now.

12. $SCHIFFY - @schiffygld - The GLD pair

A goldbug-dog parody. One of the more distinct stock-pair cultures now appearing on the board.

13. $AGI - @AGIfroglong - 3rd AI pair

The frog meme on the $AI layer: $AGI → $AI → tokenized NVDA. The 3rd clear example of the second layer forming around $AI.

14. solana:DCzCshB6Uu5uA2jMaTscghQx4ojD5bXFgeJDzZVJpump - PeptidesRH - The LLY pair.

Very new, but another sign that Long’s stock-pair ecosystem is now expanding beyond the original NVDA, MSFT, MU, AAPL, and SPCX names.

15. solana:5msTHkkEt8CbwweBD5oy6TyVFARa54aNi4EJhfyBKr6r - @AAPLCAT_ - The AAPL pair.

It has an active Long community vault; the pool and vault holdings of roughly 483 tokenized AAPL units, about $157K and 4.1% of onchain AAPL supply.

Long is creating a market where trading, fees, liquidity, and community vaults can accumulate tokenized-stock exposure onchain. Things seem just to be getting started for the Long launchpad; I'll check back in soon to see where we're at next week.

[vfat.eth @vfat0]

Lets do this again then https://t.co/BvHnPjfw3M

[Wintermute @wintermute_t]

Tomorrow, something new

A new standard for vault curation

[Seijin Jung @SeijinJung]

Introducing Helena: the world's first autonomous AI marketer.

Businesses spend 4,000 hours on marketing…before their first $1M in revenue.

We built Helena to solve this. Helena can:

➤ Track competitor ads & create TikTok slideshows, UGC, static ads - all while you sleep
➤ Analyze performance across GA4, Search Console, paid/organic social for daily insights
➤ Research trends to draft GEO optimized blogs directly on WordPress, Framer, Webflow

...and more

Helena has her own memory, scheduled tasks, 100+ custom marketing tools and native integrations.

No dev. No CLI. No n8n. No API keys needed.

Helena doesn't replace CMOs, and every marketer who's demoed it has asked us for early access.

Want to hire her? Check the next thread ⬇️

[pushkar soni @pushkersoni72]

A Russian guy has discovered a method to learn anything ten times faster using AI!

It involves NotebookLM, Gemini and Obsidian.

[GE @GuarEmperor]

Early Project

@Guayabadotrun - 4 Followers
Category : Tools
Followed by : @StudholmeOne - gud SF

@thoughtfullab - 9 Followers
Category : Others
Followed by : @karinanguyen Team AnthropicAI

@trenchdotcom - 22 Followers
Category : Others
Followed by : @PacmanBlur Founder Blur

@prlnet - 32 Followers
Category : AI
Followed by : @lessin intern slowventures

@Moltscapexyz - 56 Followers
Category : AI Agent
Followed by : @eet gud SF

@arteriadottrade - 59 Followers
Category : DeFi
Followed by : @zqinfo Team Moonshot

@RubyRushBase - 70 Followers
Category : Mining and NFT
Followed by : @dontbuytops gud SF

@LaunchOnGenesis - 82 Followers
Category : Launchpad
Followed by : @SolNFTs Team Metaplex

@veneposa - 93 Followers
Category : Tools
Followed by : @fccxw Dev

@reactorworld - 701 Followers
Category : AI
Followed by : @Rewkang Team Mechanism

THIS RAW DATA YOU CAN RESEARCH MORE

Please be more screening in your research. This post is not paid for in any way, it is simply sharing early play

DYOR AND NFA

[Rohit Ghumare @ghumare64]

Instead of watching a movie, learn openclaw in 317 minutes.

[Ronin @DeRonin_]

https://t.co/J0ULhSHLLH

[Ronin @DeRonin_]

How to become an AI Automation Engineer in the next 6 months:

By the end, you want to be able to:

- build end-to-end automated workflows for real businesses
- connect AI to the tools companies already use (CRM, email, docs, support)
- replace repetitive human tasks with reliable AI systems
- charge clients $500–5k/mo and deliver real ROI

So, let's discuss your roadmap month by month

Month 1: Get your foundation right

What to learn:

- Python basics (you don't need to be a senior dev, just functional)
- how APIs work (HTTP, JSON, auth, webhooks)
- no-code/low-code tools: Make, n8n, Zapier (pick one and go deep)
- how to read API docs and connect two tools together
- basic prompt engineering (inputs, outputs, instructions)
- what LLMs are good at vs. what they're not

Your first project: automate something in your own life with Make or n8n

Month 2: Master AI + workflow automation

What to learn:

- OpenAI / Anthropic API basics (completions, system prompts, structured outputs)
- how to embed AI into a workflow (not just use ChatGPT manually)
- function/tool calling (how AI decides what action to take)
- chaining steps: trigger → AI decision → action → output
- error handling and fallback logic
- cost awareness (tokens, API pricing, when AI is overkill)

Your project: build an AI workflow that reads an email, classifies it, and routes it automatically

Month 3: Build the core automation use cases

What to learn:

- lead generation automation (scraping, enrichment, outreach sequencing)
- AI-powered cold outreach (personalization at scale)
- CRM automation (auto-update fields, log calls, create tasks)
- content pipelines (brief → draft → format → publish)
- meeting automation (transcript → summary → action items → CRM entry)
- internal knowledge bots (connect docs/Notion/Drive to a Q&A interface)

Your project: build a full lead gen → outreach → CRM pipeline for a fake or real client

Month 4: AI agents and multi-step systems

What to learn:

- what agents actually are
- when to use agents vs. simple chains
- tool selection and routing logic
- state management across steps
- human-in-the-loop checkpoints
- how to make agents reliable (retries, fallbacks, logging)
- multi-agent setups (when one agent hands off to another)

Your project: build a support agent that handles tier-1 tickets, escalates edge cases, and logs everything

Month 5: Make it production-ready and sellable

What to learn:

- how to deploy workflows (n8n self-hosted, Make teams, custom Python + FastAPI)
- logging and observability (know when something breaks before your client does)
- prompt versioning (don't change prompts randomly in live systems)
- security basics (API keys, access control, no exposed credentials)
- how to handle rate limits, retries, and downtime gracefully
- how to document and hand off a system to a non-technical client
- basic SLAs (uptime, response time, what you're responsible for)

Your project: take one of your month 3-4 builds and make it client-ready with docs, monitoring, and a clean handoff

Month 6: Specialize, get clients, and start charging

The skills you have now can go in three directions, pick one and go all in on outreach and portfolio

Direction 1: Freelance automation builder

Best if you want clients fast and income in 30-60 days

Focus on:

- 2-3 repeatable workflow templates (lead gen, support bot, content pipeline)
- a simple case study for each
- outreach to SMBs, agencies, coaches, SaaS founders
- charge $500-2k/project to start, then move to retainers

Direction 2: In-house automation engineer

Best if you want stability and to work inside one company

Focus on:

- ops and internal tooling use cases
- connecting AI to existing company stack (Slack, Notion, HubSpot, etc.)
- building internal agents and dashboards
- showing measurable time/cost savings

Direction 3: AI automation agency

Best if you want to scale beyond trading time for money

Focus on:

- building a repeatable service with clear deliverables
- hiring or partnering to fulfill
- niching down by industry (e.g. real estate, e-commerce, recruiting)
- productizing workflows into templates you sell or license

as always, the more practice you have, the better. The same applies to AI engineering

to be honest, right now I'm preparing three articles at once, working on them 24/7, each with curated resource lists for every point so you don't have to search for everything yourself

one article will cover resources to become an AI engineer

the second one will cover resources to become an AI automation engineer

the third one will stay a secret for now… but I promise it will be something very useful

follow and turn on notifications so you don't miss it

I really appreciate your support, see the feedback, and it motivates me to create even better content for you

sometimes even at the cost of my own personal progress

but once these articles are finished, we'll move to a new level of learning AI