Tung Nguyen

Sr. Paid Media Manager | Meta Certified | LinkedIn Marketing Insider | Notion Enthusiast | Creativity Lover | Tools Productivity Admirer 👨🏻‍💻

[Higgsfield AI 🧩 @higgsfield_ai]

You can now turn your handwriting into a font, and the app behind it took one prompt to build.

Made on Claude via Higgsfield MCP.

[Tung Nguyen @ntt410]

@MrBeast 1.410.410 pennies

[Tung Nguyen @ntt410]

@SymplyTonbra We all had the same question LOL

[Tung Nguyen @ntt410]

@liamtlacey @samsheffer @sandbar Expensive + subscription model

[Tung Nguyen @ntt410]

@oasisrick @oasisdevices That was quick :) I'm still very skeptical, but you got me with @WisprFlow support ;)
Excited for the launch!

[Tung Nguyen @ntt410]

@WisprFlow @oasisdevices Skeptical about the voice quality as Wispr requires a good headphone for accurate dictation. I am even thinking of using a microphone over my headphones so I don't even know if this even works.

[NotebookLM @NotebookLM]

Doom scrolling but make it educational 🤓

Introducing Short Video Overviews in NotebookLM! Turn your most complex sources into 60-second, vertical videos that deep dive into any concept.

Rolling out now to Google AI Ultra and Pro subscribers on mobile & web (free users soon!)

[Tung Nguyen @ntt410]

@samkohl I think it's just a matter of habit. If I can afford to have it, I will just use it to watch movie every night as wind down

[Tung Nguyen @ntt410]

@hypermemetic @0xMoysei https://t.co/7HlncfAt4D

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Sakshi Sugandhi @SakshiSugandhi]

Vibe coders in 2026 be like.....

[Tung Nguyen @ntt410]

@shivsakhuja Goose

[Shiv @shivsakhuja]

Today we're launching Meta Ad Researcher skills for Claude.

This is a set of skills inside /gooseworks that turns Claude into an expert ad research team — find what's trending, see what competitors are running, and mine the angles that actually convert.

First, install by running:

npx gooseworks install --all

Then you can do any of the following:

/gooseworks --skill trending-ad-hook-spotter
Find trending topics in my niche to run ads on right now

/gooseworks --skill competitor-ad-intelligence
Scrape my competitors' ads and reverse-engineer their funnel

/gooseworks --skill ad-angle-miner
Mine my reviews and Reddit for the angles that actually convert

/gooseworks --skill brand-research
Research my brand and build a reusable context pack for ads

Oh and then you can use /gooseworks to make ad creatives directly from the research.

/gooseworks use goose-ads remix to create 5 static ads based on the research above.

All of this works right in Claude. No need to leave Claude at all.

We've built a library of 100+ skills to turn Claude into an expert growth team - this includes skills for ads, social, content, research, gtm, seo and more.

Comment Goose below and I'll send you an early access link.

[Sunny @sunnny1583]

Apple's new Icon Composer is genuinely fun to mess around with.
You can try different Liquid Glass effects, OS 26/27 icon styles, adjust specularity, refraction, chromatic shadows, and more.

Anyone interested in designing icons for iOS or macOS should give it a try.

[Ritwik Pavan @ritwikpavan]

A lot of exciting new consumer product launches recently.

Some of my favorites are from Philips, Mondo Robotics, LINC, and Autonomous.

Check out the latest drops:

[Hurley @Johnsjawn]

underrated

[0xFuck.ETH @0x404page]

被一个帖子震住了，一个21岁的美国大学生，靠AI虚拟女友一个月赚了4.3万美金。

他在OnlyFans上做了一个叫Maya的账号。这个Maya，22岁，标签是UCF心理学辍学生，有1247个付费订阅者，单个粉丝最高刷了1847美元。但最关键的是，照片里那个女孩压根不存在。

没有真人出镜，没有真人打字。所有的消息是AI写的，照片是AI生成的，声音也是AI合成的。整个Maya，就是藏在他MacBook里的四个文档。

以前搞一个AI虚拟网红，动辄半年到一年半，现在他只用4周就跑通了。再往后，可能一个周末就能搭出来。

这四个文件是这样的：

· persona.md，写清楚Maya的人设、背景、喜好；
· voice.md，固定她说话的语气和声音风格；
· flux.md，固定长相、拍照风格，保证所有照片看起来是同一个人；
· brain.md，记下每个订阅者聊过什么，什么喜好。

AI每次回复粉丝之前，都会把这四个文件读一遍，所以从来不崩人设，也不会忘记粉丝说过的话。

说实话，技术栈简单到有点离谱了。以前那个很有名的虚拟网红Aitana López，团队花了18个月才做出来。现在Maya这种模式，4周就全搞定了。

这根本不是科幻，就是任何一个有台电脑的人都能照着做的东西。而且不止是OnlyFans，这套流程往Instagram、TikTok、Twitch上套，都一样跑得通。

AI把造一个虚拟人的成本几乎干到了零。到这一步，剩下唯一的门槛，其实就是你有没有那个品味，去决定这个虚拟人应该是个什么样子。

[Tung Nguyen @ntt410]

@beyoncegarden That is so annoying.

[Nathie @NathieVR]

Here's a first look at Snapchat's new AR glasses launching this fall for $2,195. They are designed for productivity, streaming, gaming and a wide range of day-to-day activities.

[ElevenLabs @ElevenLabs]

Introducing Avatars in @ElevenCreative.

The best AI voices, now with a face. Create studio-grade talking videos from a script, a voice, and an avatar - all in one place.