Mr Tung

Building @stickyqr and @hmpmaster

[Mr Tung @mrtungdev]

@ashoKumar89 MySQL (planetscale)

[Mr Tung @mrtungdev]

I use GPT, Claude, and Gemini almost every day — not because I love paying for 3 AI subscriptions, but because each one is genuinely better at different things.

That’s why AI pricing feels awkward right now. $20 is often not enough, but paying for multiple $100+ plans is too much. I wish more AI companies had a Cursor-like $50–$60 tier for people who actually use AI seriously every day.

[Mr Tung @mrtungdev]

I’m on ChatGPT Business with 5 paid seats.
In User Settings, one user has the Analytics Viewer role, which OpenAI describes as not having invite permissions.
However, this user was still able to invite another member, and Billing now shows 6/5 seats in use.
Can @OpenAI confirm whether this is expected behavior or a permission enforcement issue?

[Mr Tung @mrtungdev]

@yulikay Food

Food is a big part of Vietnamese culture, and each city has its own special dishes.

Hanoi is famous for delicious Pho, while Da Nang is known for Mi Quang, Banh Trang Cuon, Bun Cha, fresh seafood, and many local dishes you should try.

[Mr Tung @mrtungdev]

@DurovPD I’d buy two months of focus to build an open-source Mass times platform for my city. I’ve wanted to do it for three years, but never had the time.

[Miles Deutscher @milesdeutscher]

https://t.co/042ZheWx7F

[Mr Tung @mrtungdev]

My DJI Osmo Pocket 4 took a small fall (around 50cm) and the screen edge got chipped 😅. Still works fine, but yeah… it’s more fragile than it looks. Definitely get a protective case if you don’t want this to happen.

[Mr Tung @mrtungdev]

@suniljoshi19 @ShadcnSpace I just switched from NextJS to Tanstack Start. Is there a support plan for Tanstack in the Templates?

[Mr Tung @mrtungdev]

Opus 4.7 has been pretty good compared to the past three weeks, but even on Max, the usage limit runs out so fast 😓

[Karri Saarinen @karrisaarinen]

https://t.co/srw9NQMkH2

[Mr Tung @mrtungdev]

Super Hero by @hero_ui + Uniwind + @expo is truly an elite combo.
Will be migrating the Flutter app to this stack.

[𝕏 Travels, Hotels and Resorts @em3dia]

Vietnam is definitely a place that should be on your list of places to explore. It’s definitely on my Bucket list!
Here are some awesome, fun and unique activities in Vietnam that you could try
🎥 vietnam_expressdiary | IG

[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.

[TypeScript @typescript]

TypeScript 6.0 is now available!

This release brings better type-checking for methods, new standard library features, new module features for Node.js, and more!

But most important, this release brings us one step closer to the upcoming native-speed 7.0!

https://t.co/hon0RU1L5B

[Akhilesh Mishra @livingdevops]

Kubernetes is beautiful.
Every Concept Has a Story, you just don't know it yet.

In k8s, you run your app as a pod. It runs your container. Then it crashes, and nobody restarts it. It is just gone.

So you use a Deployment. One pod dies and another comes back. You want 3 running, it keeps 3 running.

Every pod gets a new IP when it restarts. Another service needs to talk to your app but the IPs keep changing. You cannot hardcode them at scale.

So you use a Service. One stable IP that always finds your pods using labels, not IPs. Pods die and come back. The Service does not care.

But now you have 10 services and 10 load balancers. Your cloud bill does not care that 6 of them handle almost no traffic.

So you use Ingress. One load balancer, all services behind it, smart routing. But Ingress is just rules and nobody executes them.

So you add an Ingress Controller. Nginx, Traefik, AWS Load Balancer Controller. Now the rules actually work.

Your app needs config so you hardcode it inside the container. Wrong database in staging. Wrong API key in production. You rebuild the image every time config changes.

So you use a ConfigMap. Config lives outside the container and gets injected at runtime. Same image runs in dev, staging and production with different configs.

But your database password is now sitting in a ConfigMap unencrypted. Anyone with basic kubectl access can read it. That is not a mistake. That is a security incident.

So you use a Secret. Sensitive data stored separately with its own access controls. Your image never sees it.

Some days 100 users, some days 10,000. You manually scale to 8 pods during the spike and watch them sit idle all night. You cannot babysit your cluster forever.

So you use HPA. CPU crosses 70 percent and pods are added automatically. Traffic drops and they scale back down. You are not woken up at 2am anymore.

But now your nodes are full and new pods sit in Pending state. HPA did its job. Your cluster had nowhere to put the pods.

So you use Karpenter. Pods stuck in Pending and a new node appears automatically. Load drops and the node is removed. You only pay for what you actually use.

One pod starts consuming 4GB of memory and nobody told Kubernetes it was not supposed to. It starves every other pod on that node and a cascade begins. One rogue pod with no limits takes down everything around it.

So you use Resource Requests and Limits. Requests tell Kubernetes the minimum your pod needs to be scheduled. Limits make sure no pod can steal from everything around it. Your cluster runs predictably.

[Kings of JDM @kingsofjdm]

Best Alarm Clock Ever 🤯🔥

[HeroUI @hero_ui]

Introducing HeroUI v3 🔥

A complete rewrite for React. A brand-new library for React Native.

🌐 Web → 75+ components, 21 new
📱 Native → 37+ components, built from scratch
🎨 Styling → Tailwind CSS v4, CSS variables, OKLCH, BEM
⚡ Performance → CSS animations, no JS runtime
♿ Accessibility → React Aria Components
🧩 Architecture → Compound components, headless-ready
🤖 AI → MCP Server, Agent Skills, LLMs.txt
📎 Figma Kit v3 with 1:1 component parity

One design system. Beautiful everywhere.

[Mr Tung @mrtungdev]

@fiynraj I’ve used them all before, but switched to PlanetScale over 2 years ago—it’s been the right choice for our company.

[🧬Maxpein🧬 @maximumpain333]

Why some people never posted in social media?

✨🙌🏽💫

[Mr Tung @mrtungdev]

@fmdz387 🙂