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Processing 100M samples/s on ingestion takes serious database engineering. At P99CONF, VictoriaMetrics Co-Founder Roman Khavronenko breaks down the Go optimizations needed to build a blazing-fast TSDB from scratch, covering string interning, goroutine tuning, and sync.Pool.

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Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe and a forecasting layer that predicts weather 3 days ahead. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts and forecast layer from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build Find the sign-up link in the replies.

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PBD Podcast on Elon Musk: “Whoever creates the most value for everybody else should end up with the most resources. Name somebody who’s done it better than him. Elon Musk for 30 years has averaged the creation of a multi-billion dollar company every 5 years. There’s nobody in the world who’s ever done that before. It is incredible, and it’s not like, ‘Oh, let me go do a roll-up of everybody else’s businesses’—it’s not financial engineering, it is from scratch: ‘we’re going to build a product to solve something at scale’.”
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PBD Podcast on Elon Musk: “Whoever creates the most value for everybody else should end up with the most resources. Name somebody who’s done it better than him. Elon Musk for 30 years has averaged the creation of a multi-billion dollar company every 5 years. There’s nobody in the world who’s ever done that before. It is incredible, and it’s not like, ‘Oh, let me go do a roll-up of everybody else’s businesses’—it’s not financial engineering, it is from scratch: ‘we’re going to build a product to solve something at scale’.”

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Qwen3.8 Flash Next is starting to look ridiculous on Apple Silicon. I’m running the 4-bit MTP build locally on an M3 Ultra Studio, and the latest OMP run hit: 97.1 tok/s decode 23K context ~1,131 tok/s uncached prompt processing That first number is the one that caught my attention. Nearly 100 tokens per second from a local Qwen3.8 Flash Next setup is already fast enough that the usual “local models are slow” argument starts feeling pretty outdated. And the prompt processing speed is even crazier. Over 1,100 tok/s on an uncached prompt means the model can chew through a large amount of context before generation even starts. The setup matters here. This isn’t just Qwen3.8 Flash Next running untouched. It’s a 4-bit quantized build with MTP, and the inference stack is clearly doing a lot of work behind the scenes to make the hardware perform like this. There’s already a PR open for the implementation on oMLX, so this isn’t just a one-off local experiment either. If these optimizations make their way into the broader MLX ecosystem, running large models locally on Apple Silicon gets even more interesting. The other thing I like about numbers like this is that they put the focus back on the entire inference stack. Model size is one variable. Quantization is another. Then you have MTP, KV cache configuration, runtime optimizations and the hardware itself. Change the recipe and the same model can feel completely different. Qwen3.8 Flash Next at ~97 tok/s on an M3 Ultra is a pretty good demonstration of that.
0:36

Qwen3.8 Flash Next is starting to look ridiculous on Apple Silicon. I’m running the 4-bit MTP build locally on an M3 Ultra Studio, and the latest OMP run hit: 97.1 tok/s decode 23K context ~1,131 tok/s uncached prompt processing That first number is the one that caught my attention. Nearly 100 tokens per second from a local Qwen3.8 Flash Next setup is already fast enough that the usual “local models are slow” argument starts feeling pretty outdated. And the prompt processing speed is even crazier. Over 1,100 tok/s on an uncached prompt means the model can chew through a large amount of context before generation even starts. The setup matters here. This isn’t just Qwen3.8 Flash Next running untouched. It’s a 4-bit quantized build with MTP, and the inference stack is clearly doing a lot of work behind the scenes to make the hardware perform like this. There’s already a PR open for the implementation on oMLX, so this isn’t just a one-off local experiment either. If these optimizations make their way into the broader MLX ecosystem, running large models locally on Apple Silicon gets even more interesting. The other thing I like about numbers like this is that they put the focus back on the entire inference stack. Model size is one variable. Quantization is another. Then you have MTP, KV cache configuration, runtime optimizations and the hardware itself. Change the recipe and the same model can feel completely different. Qwen3.8 Flash Next at ~97 tok/s on an M3 Ultra is a pretty good demonstration of that.

FHILY👑

15,378 görüntüleme • 1 ay önce

EXCLUSIVE: The Sales Machine Behind $17.3B Deel Co-Founder & CRO Shuo Wang (Shuo Wang) $1M → $100M → $1B → $1.5B+ ARR I sat down with Shuo in the South of France to break down the *sales machine* behind one of tech’s fastest-growing companies. deel stats: › $1.5B+ in revenue › $17.3B valuation (Ribbit, Coatue, a16z) › 40,000+ customers › 15 acquisitions › Launched Akai, its AI agent platform (Rumors a crazy new ai product launching soon 👀) Deel added >$140M ARR in 90 days without increasing headcount by automating~600 Full Time Employees' equivalent in work with Akai Shuo co-founded Deel alongside Alex Bouaziz (Alex Bouaziz), Co-Founder & CEO, in 2019. They built Deel as a global company from day 1. Today, the company helps businesses hire, manage & pay teams around the world. We also get into: › Why Shuo thinks founders simply aren't selling enough › Building & incentivizing a remote sales organization across 120 countries › How an MIT-trained robotics engineer became CRO › Shuo and Alex's founder dynamic, going global from day one › What she's learned from James Dyson & Jeff Bezos 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Shuo Wang, Co-Founder & CRO at Deel (00:41) How Deel went from $100M to $1B ARR in 3 years (02:43) Why Deel went global from day one (03:36) Who uses Deel? From ElevenLabs to Chanel (04:16) Hot take (05:15) Why sales is really about systems (06:18) How to build a winning remote sales team (07:48) Going global with Snoop Dogg (08:34) How Shou & Alex divide roles (09:23) The founder who inspired Shuo (10:24) The Bezos mindset (11:36) NEW Hot take
12:48

EXCLUSIVE: The Sales Machine Behind $17.3B Deel Co-Founder & CRO Shuo Wang (Shuo Wang) $1M → $100M → $1B → $1.5B+ ARR I sat down with Shuo in the South of France to break down the *sales machine* behind one of tech’s fastest-growing companies. deel stats: › $1.5B+ in revenue › $17.3B valuation (Ribbit, Coatue, a16z) › 40,000+ customers › 15 acquisitions › Launched Akai, its AI agent platform (Rumors a crazy new ai product launching soon 👀) Deel added >$140M ARR in 90 days without increasing headcount by automating~600 Full Time Employees' equivalent in work with Akai Shuo co-founded Deel alongside Alex Bouaziz (Alex Bouaziz), Co-Founder & CEO, in 2019. They built Deel as a global company from day 1. Today, the company helps businesses hire, manage & pay teams around the world. We also get into: › Why Shuo thinks founders simply aren't selling enough › Building & incentivizing a remote sales organization across 120 countries › How an MIT-trained robotics engineer became CRO › Shuo and Alex's founder dynamic, going global from day one › What she's learned from James Dyson & Jeff Bezos 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Shuo Wang, Co-Founder & CRO at Deel (00:41) How Deel went from $100M to $1B ARR in 3 years (02:43) Why Deel went global from day one (03:36) Who uses Deel? From ElevenLabs to Chanel (04:16) Hot take (05:15) Why sales is really about systems (06:18) How to build a winning remote sales team (07:48) Going global with Snoop Dogg (08:34) How Shou & Alex divide roles (09:23) The founder who inspired Shuo (10:24) The Bezos mindset (11:36) NEW Hot take

Molly O’Shea

105,731 görüntüleme • 20 gün önce

Where will sustainable Bitcoin-native yield come from - without compromising decentralization? 🟠 At House of ZK’s Bitcoin Economy Conference, this panel explored the tradeoffs behind BTC-native lending, institutional inflows, and yield mechanisms - and what it takes to build sustainable yield models that preserve decentralization and align with Bitcoin’s core values. Featuring: 🔸 norbert, Global Growth at A Lee 🔸 Ben Sigman, CEO of Libre | Bitcoin L2 🔸 Hobart of StarkWare 🥷 🔸 Professor Redouane Elkamhi, Chief Economist at GOAT Network 🔸 Toby Lewis, Co-founder of OrdinalsBot ⚡ Also available on YouTube ▶️
41:03

Where will sustainable Bitcoin-native yield come from - without compromising decentralization? 🟠 At House of ZK’s Bitcoin Economy Conference, this panel explored the tradeoffs behind BTC-native lending, institutional inflows, and yield mechanisms - and what it takes to build sustainable yield models that preserve decentralization and align with Bitcoin’s core values. Featuring: 🔸 norbert, Global Growth at A Lee 🔸 Ben Sigman, CEO of Libre | Bitcoin L2 🔸 Hobart of StarkWare 🥷 🔸 Professor Redouane Elkamhi, Chief Economist at GOAT Network 🔸 Toby Lewis, Co-founder of OrdinalsBot ⚡ Also available on YouTube ▶️

House of ZK

94,302 görüntüleme • 1 yıl önce