Morris Simons

M. Sc. Eng. AI & machine learning student at Blekinge Institute of Technology.

[Gergely Orosz @GergelyOrosz]

Why did Uber build thousands of microservices? No better person to answer than Uber's first CTO, Thuan Pham. Timestamps:

00:00 Intro
05:32 Getting into tech
16:09 The dot-com bust
20:42 VMware
26:29 Getting hired by Travis at Uber
33:22 Early days at Uber and scaling challenges
40:57 Uber’s China launch
47:12 The platform and program split
50:26 From monolith to microservices
53:38 Internal tools at Uber
57:05 Helix: Uber’s mobile app rewrite
59:55 Thuan’s email about naming
1:02:03 Org structure changes under
1:06:34 Thuan’s work philosophy
1:12:23 The “three tours of duty” at Uber
1:15:37 Why Thuan left Uber
1:17:34 Coupang and Nubank
1:21:59 Faire
1:25:31 How Faire uses AI
1:28:24 AI’s impact on software engineering
1:31:09 The role of the CTO
1:35:13 Career advice

Brought to you by:

• @statsig – ⁠ The unified platform for flags, analytics, experiments, and more. https://t.co/ZCSOIcWv31

• @WorkOS – Everything you need to make your app enterprise ready. https://t.co/xUBBN8VuwQ

• @SonarSource – The makers of SonarQube, the industry standard for automated code review. Check out SonarQube Advanced Security: https://t.co/WbRTb55FL6

Three interesting parts from this conversation:

1. The program/platform split came before microservices.

The concept of cross-functional “program” teams and dedicated “platform” teams became necessary because an org split across backend, frontend and mobile engineers slowed down in execution speed when Uber grew to around 100 engineers. Every feature required negotiating bandwidth across the mobile, backend, and dispatch teams. Thuan, Travis Kalanick, and Jeff Holden literally used color-coded sticky notes with people’s names to reorganize into self-sufficient teams. We cover more about this split in this The Pragmatic Engineer deepdive, The Platform and Program split at Uber: https://t.co/E4W3FJ07Gj

2. Expect multiple rewrites during hypergrowth.

The right architecture depends on how fast a product and company are growing. At Uber, repeated rewrites were common because each one “bought” another window of survival for the company. Thuan’s recommendation is to understand that a rewrite simply means a company is outrunning its existing architecture: this is not necessarily a bad thing!

3. Uber is the only major company that had a “Senior 1” and “Senior 2” level – and Thuan is unapologetic.

Thuan introduced the Senior 1 (L5A) and Senior 2 (L5B) levels because the jump from senior (L5) to Staff (L6) became very big, and larger than between previous levels. One problem this split level created was that Uber’s L5B was akin to Google’s and Facebook’s L6/E6. Thuan resisted the title inflation of just renaming L5B to ‘Staff’.

[Morris Simons @MorrisSimons1]

@TAbrodi You can add a bunch of data in images metadata. Its a common CTF challenge to extract flags from images in many different ways.

[boris @boristane]

I'm trying something new with my blog, making it interactive

first article is about something I care deeply about: logs

logging sucks so much

https://t.co/mdFhxEvpjB

[Morris Simons @MorrisSimons1]

Startup Experts Discuss Doing Things That Don't Scale https://t.co/RcJRyiE0Y6 via @YouTube

[eric zakariasson @ericzakariasson]

this is the most used slash command internally at cursor to remove ai slop

install it to your project with this link:
https://t.co/ufnOZMPzIk.

[Prudhvi Reddy ⚡️ @prudhvir3ddy]

Indian developer reverse engineered the protocol that AirPods use to communicate with Apple devices and implemented those features on Android (and Linux) |

[Mindset Machine @mindsetmachine]

Steve Jobs definition of focus is insane:

[Gajesh @gajesh]

one more karpathy tweet going on the wall

[Daniel San @dani_avila7]

Exploring @karpathy Nanochat through a Knowledge Graph

Love how this project combines simplicity with power.
Want to navigate and understand the entire repo?

Check it out here: https://t.co/X0gr0WAq79

Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository.

Powered by @deepgraphMCP @AIatMeta and @GroqInc

[Mayank Agarwal 💡 @TheAIWorld22]

𝟕𝟎 𝐓𝐨𝐮𝐠𝐡𝐞𝐬𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

Mandatory Bookmark

► Thread [1/20]

[Chris @ChrisGPT]

GPT-1 when asked

“What would you say if you could talk to a future OpenAI model?”

There’s something beautiful about this

[Rohan Paul @rohanpaul_ai]

Github 👨‍🔧: Awesome-GraphRAG

A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.

github .com/DEEP-PolyU/Awesome-GraphRAG

[Elliot @elliotnorrevik]

Normal companies have "Bring your kids to work Day", Lovable has "Bring your parents to work Day"

[Vik Paruchuri @VikParuchuri]

Parsing PDFs has slowly driven me insane over the last year. Here are 8 weird edge cases to show you why PDF parsing isn't an easy problem. 🧵

[Morris Simons @MorrisSimons1]

Cool!!

[Anton Osika @antonosika]

Lovable just crossed $100M ARR in 8 months.

Faster than OpenAI, Cursor, Wiz, and every other software company in history.

Today we're launching a game-changing update, it reduces the error rates by 91%.

Introducing Lovable Agent:

[Nirant @NirantK]

Apple just dropped a killer open-source visualization tool for embeddings — Embedding Atlas — and it’s surprisingly powerful for anyone working with large text+metadata datasets.

This reminds me of Nomic's Atlas, but I never got around to using it 😅

We’re talking real-time search, multi-million point rendering, and automatic clustering with labels.

One of their showcase examples visualizes ~200K wine reviews using embeddings + metadata like price, country, and tasting notes. And it is lightning fast even on my browser! No separate code needed!

It nails what most LLM devs need but often hack together:

✅ UMAP projections
✅ Faceted search across metadata (e.g. “country vs. price”)
✅ Hover + tooltip on raw points
✅ Interactive filters, histograms, and cluster overlays
✅ Cross-linked scatterplot + table views

Under the hood:
• Fast rendering using WebGPU (with WebGL fallback)
• Embedding-based semantic similarity search
• Kernel density contours for spotting clusters or outliers

You just upload your .jsonl or .csv with text + vector + metadata. It handles the rest: clustering, labeling, UI layout, everything.

This feels like the LLM-native version of Tableau — but optimized for text, chat and modern data needs

If you’re building RAG evals, search tuning, clustering explainability, or even dataset audits — this could be your new favorite tool.

[ℏεsam @Hesamation]

Vibe-coded AI startup 2025

[Felix Haas @felixhhaas]

We just hit $100M ARR in 8 months 🔥

Faster than OpenAI, Cursor, Wiz, and every other tech company in history.

And today, we’re launching our most powerful update yet:

Introducing Lovable Agent:
→ 91% fewer build errors
→ Smarter reasoning
→ Real dev-like behavior

Instead of following your prompts, Lovable now thinks, takes actions, checks its work, and keeps going until it’s done.

It reads files, debugs logs, searches the web, and builds products like never before.

With this new update, it now feels less like a tool and more like a teammate.

The old Lovable helped us go from $1M to $100M ARR.
Today's update will (hopefully) take us to $1B.

LFG

[alex @shitshowdotinfo]

Printing this out and pasting it above every thermostat at work