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`bunx pg-here` global one-liner to run a standalone postgres instance with all data within the current folder throw-away postgres anytime anywhere. cli args for version/user/pass/db configs. docs: stop using docker just to launch a postgres!!

murat 🍥

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50,537 views • 5 months ago •via X (Twitter)

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Just Use Postgres feat. Denis Magda Denis literally just published a book called “Just Use Postgres”. In this conversation, that turned out to be super fun, we talked about: • what MySQL got wrong w.r.t community • the recent explosion of Postgres development in the world (Supabase, Neon, Crunchy, ClickHouse) • when Postgres instead of Kafka • the significance of the meme “just use postgres” • Postgres for data analytics Chapters (00:00) Intro (04:26) The Just Use Postgres Movement (08:36) The Small Data Trend and Its Implications (15:49) The Shift from Distributed Systems to Simplicity (17:15) Updating your priors re: PG capabilities (20:28) MySQL's OSS community split & our experiences with such things (39:48) Postgres for Analytics (55:49) PG in the lake house (59:27) AI Marketing Slop (01:02:19) How you'd build a brand new app on Postgres (01:05:24) Speedrun through PG extensions (01:11:56) PG as a Message Queue

Stanislav Kozlovski

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Code along with me as I show you how to build a full-stack Uber Clone with #ReactNative! 🔥 We tried Expo's latest API route features to integrate a Postgres db (Neon Postgres) with payments using Stripe, and it just works. It's very easy to use and get started without any issues so far. Evan Bacon 🥓 See the tutorial here! ➡️ 🚀

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Adrian | JavaScript Mastery

14,514 views • 1 year ago

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

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dale

15,019 views • 1 month ago

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 views • 8 months ago

⏰ we brought time travel to replit! 1. see a global history of all your checkpoints 2. view a fully interactive version of your app at any point in that history 3. rollback and rollforward to any point in history (with your database data and schema, h/t Neon Postgres)

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Jacky

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Spinning up LangChainGo? This walkthrough shows how to launch a working agent with Docker Compose using the compose-for-agents repo. Local dev or cloud offload, one command to run. Watch here: Repo:

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Docker

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JSON columns in Postgres can be flexible, but too much flexibility can be risky as you might accidentally end up with malformed data. You can prevent this using the pg_jsonschema extension to easily define a schema for your JSON and JSONB columns!

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Tyler Shukert

17,480 views • 1 year ago

It's June 16. My birthday. So I built a Mini version of Kubernetes. You give it a container image → it schedules it, pulls it, runs it, and tracks its lifecycle. What it does: - Submit jobs via a REST API - Move jobs through SUBMITTED → RUNNABLE → RUNNING → SUCCEEDED / FAILED - Pull container images and run real Docker containers - Persist state in Postgres - Use BullMQ + Valkey for scheduling and execution Building this from scratch gave me a much deeper appreciation for how orchestration systems work behind the scenes. There's still plenty to improve, but seeing the control loop model in action was incredibly satisfying. Stack: Node.js · TypeScript · Express · BullMQ · Valkey · Postgres · Docker · Drizzle Not a bad way to spend a birthday. 🎂

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Pankaj Kumar

12,492 views • 1 month ago

Mike Stonebraker is a Turing award winner famous for his fundamental contributions to databases (e.g. Postgres, C-Store and much more). I interviewed him recently about: • The story behind Postgres & the hardest technical challenge in building it • Where he disagreed with Google's technical decisions • Future problems in databases • Literature recommendations to learn databases • Why LLMs score 0% on his text-SQL benchmark • What if you replaced all state in an OS with a DB Timestamps: 0:00 - Intro 1:03 - How he got into databases 6:43 - Competing with Oracle 9:07 - What made Postgres special 15:55 - One size fits none 21:37 - Why he disagreed with Google 29:14 - Why he chose academia over big tech 30:58 - Replacing state in an OS with a DB 42:02 - Future problems in databases 51:36 - Technical book recommendations to learn databases 52:20 - Advice for younger self 55:52 - Outro Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

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Ryan Peterman

109,827 views • 3 months ago

Your Postgres is 100x slower than traditional OLAP engines. A deceptively simple OSS extension fixes this. Here's an interview where we dive into the deep engineering around how this is achieved. Joining me (and leading the conversation) is Marco Slot: an engineer with an EXTENSIVE and impressive career history around PostgreSQL: 👉 Created pg_cron in 2017 (3.7k stars) - a tool to run cron-jobs in Postgres 👉 Built pg_incremental - fast, reliable, incremental batch processing inside PostgreSQL itself 👉 co-created pg_lake (after working on Crunchy Data's Warehouse, and getting acquired into Snowflake) 👉 Helped get pg_documentdb (MongoDB-on-Postgres) off the ground Marco Slot is a world-class expert in Postgres extensions. He seriously impressed me with his knowledge over the course of a private LinkedIn conversation, and now that I type out his resume - I understand where it came from. He should be on everyone's radar. So I brought him on the pod. In our full 2-hour deep-dive, we went over: • 🔥 how pg_lake makes analytics 100x faster (literally) • 🔥 perf internals like vectorized execution & CPU branching • 🤔 practical differences between OLTP and OLAP database development (and the age-old mission in uniting both) • 🤔 how (and why) pg_lake intercepts query plans and delegates parts of the query tree to DuckDB • 💡 why Postgres is architecturally terrible at analytical queries (and how vectorized execution fixes this) • 💡 Marco's hard-won experience through a decade+ career in Postgres • 🏆 Iceberg's role as the TCP/IP for tables • 🏆 what the real moat of PostgreSQL is Developments like pg_lake are a real reason why "Just Use Postgres" is much more than a meme, and it'll continue to dominate discourse. I promise you will learn a lot from this episode. Timestamps: (0:02) What is pg_lake? (2:23) Postgres' 100x slower problem and columnar storage experiments they had to make Postgres fast for analytics (6:00) practical examples and internals (16:20) perf internals - vectorized execution & CPU optimization (23:00) pg_lake architecture (why DuckDB isn't embedded) and the connection-per-process issue (29:16) how pg_lake intercepts the query plan tree and delegates parts to DuckDB (41:09) Iceberg catalogs (48:24) postgres to iceberg ingestion patterns (and pg_incremental) (53:40) Marco's (long) career: early AWS, Citus, Microsoft, Crunchy Data & Snowflake (1:04:20) Marco's observations around the merging between OLTP and OLAP (and the subtle dev differences there) (1:15:30) reverse ETL (1:33:08) Iceberg as the TCP/IP for tables (1:35:00) Marco's thoughts on the "Just Use Postgres" fever

Your Postgres is 100x slower than traditional OLAP engines. A deceptively simple OSS extension fixes this. Here's an interview where we dive into the deep engineering around how this is achieved. Joining me (and leading the conversation) is Marco Slot: an engineer with an EXTENSIVE and impressive career history around PostgreSQL: 👉 Created pg_cron in 2017 (3.7k stars) - a tool to run cron-jobs in Postgres 👉 Built pg_incremental - fast, reliable, incremental batch processing inside PostgreSQL itself 👉 co-created pg_lake (after working on Crunchy Data's Warehouse, and getting acquired into Snowflake) 👉 Helped get pg_documentdb (MongoDB-on-Postgres) off the ground Marco Slot is a world-class expert in Postgres extensions. He seriously impressed me with his knowledge over the course of a private LinkedIn conversation, and now that I type out his resume - I understand where it came from. He should be on everyone's radar. So I brought him on the pod. In our full 2-hour deep-dive, we went over: • 🔥 how pg_lake makes analytics 100x faster (literally) • 🔥 perf internals like vectorized execution & CPU branching • 🤔 practical differences between OLTP and OLAP database development (and the age-old mission in uniting both) • 🤔 how (and why) pg_lake intercepts query plans and delegates parts of the query tree to DuckDB • 💡 why Postgres is architecturally terrible at analytical queries (and how vectorized execution fixes this) • 💡 Marco's hard-won experience through a decade+ career in Postgres • 🏆 Iceberg's role as the TCP/IP for tables • 🏆 what the real moat of PostgreSQL is Developments like pg_lake are a real reason why "Just Use Postgres" is much more than a meme, and it'll continue to dominate discourse. I promise you will learn a lot from this episode. Timestamps: (0:02) What is pg_lake? (2:23) Postgres' 100x slower problem and columnar storage experiments they had to make Postgres fast for analytics (6:00) practical examples and internals (16:20) perf internals - vectorized execution & CPU optimization (23:00) pg_lake architecture (why DuckDB isn't embedded) and the connection-per-process issue (29:16) how pg_lake intercepts the query plan tree and delegates parts to DuckDB (41:09) Iceberg catalogs (48:24) postgres to iceberg ingestion patterns (and pg_incremental) (53:40) Marco's (long) career: early AWS, Citus, Microsoft, Crunchy Data & Snowflake (1:04:20) Marco's observations around the merging between OLTP and OLAP (and the subtle dev differences there) (1:15:30) reverse ETL (1:33:08) Iceberg as the TCP/IP for tables (1:35:00) Marco's thoughts on the "Just Use Postgres" fever

Stanislav Kozlovski

16,767 views • 3 months ago

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Avi Chawla

186,931 views • 6 months ago

Introducing db-mover A GUI tool to copy or download database content directly from your browser. Try it here: Check code: I work with a lot of databases: Mongo, Postgres, MySQL, Redis. Every time I need to move data, I am digging through old bash scripts and praying I do not mess up. Recently I got stuck. I was moving a staging database to a new server and I was not at my PC. I only had my phone. I could not run my scripts and had to wait until I got home to fix it. That's why I built db-mover. It is simple, paste your source URL, paste your destination URL, and hit start. You get real-time progress bars and live logs. 100% open source. Give it a try if you want to move data without the terminal. You can also suggest new feature , send in improvements you want added. #OpenSource #DevTools #Database #mongodb #postgres #mysql

Introducing db-mover A GUI tool to copy or download database content directly from your browser. Try it here: Check code: I work with a lot of databases: Mongo, Postgres, MySQL, Redis. Every time I need to move data, I am digging through old bash scripts and praying I do not mess up. Recently I got stuck. I was moving a staging database to a new server and I was not at my PC. I only had my phone. I could not run my scripts and had to wait until I got home to fix it. That's why I built db-mover. It is simple, paste your source URL, paste your destination URL, and hit start. You get real-time progress bars and live logs. 100% open source. Give it a try if you want to move data without the terminal. You can also suggest new feature , send in improvements you want added. #OpenSource #DevTools #Database #mongodb #postgres #mysql

JC Coder

13,607 views • 6 months ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 8 months ago

Just shipped a new Next.js template! • Uses Postgres and Drizzle for the database • UI components with Tailwind and shadcn/ui • Email/password authentication with JWTs stored to cookies • Dashboard pages with CRUD operations on users/teams • Basic RBAC with Owner and Member roles • Pricing page (/) which connects to Stripe Checkout • Subscription management with Stripe Customer Portal • Global middleware to protect logged-in routes • Local middleware to protect Server Actions / validate schemas • Activity logging system for any user events It's not finished, and there's probably bugs, but it's time to ship 🫡 • Demo: • Code:

Just shipped a new Next.js template! • Uses Postgres and Drizzle for the database • UI components with Tailwind and shadcn/ui • Email/password authentication with JWTs stored to cookies • Dashboard pages with CRUD operations on users/teams • Basic RBAC with Owner and Member roles • Pricing page (/) which connects to Stripe Checkout • Subscription management with Stripe Customer Portal • Global middleware to protect logged-in routes • Local middleware to protect Server Actions / validate schemas • Activity logging system for any user events It's not finished, and there's probably bugs, but it's time to ship 🫡 • Demo: • Code:

Lee Robinson

362,516 views • 1 year ago

🚨 LAUNCH ALERT 🚨 Tanplate is live 👉 🔁 RT for a launch discount code 👀 The ultimate TANSTACK AI boilerplate to build & ship AI/SaaS products fast. Everything you actually need. Nothing you don’t. AI-ready • Multi-model (OpenAI / Claude / Gemini / local-ready) • Streaming UI • Audio transcription (Whisper) • Multimodal context (text / images / files) Core stack • TanStack Start / Query / Store / Form • Vite • Docker • Drizzle ORM + Postgres Auth & Payments • Better Auth • Stripe • Polar Frontend • tailwindcss • shadcn External Services • Resend • S3 • i18n No boilerplate hell. No wiring for weeks. Just clone, tweak, ship.

🚨 LAUNCH ALERT 🚨 Tanplate is live 👉 🔁 RT for a launch discount code 👀 The ultimate TANSTACK AI boilerplate to build & ship AI/SaaS products fast. Everything you actually need. Nothing you don’t. AI-ready • Multi-model (OpenAI / Claude / Gemini / local-ready) • Streaming UI • Audio transcription (Whisper) • Multimodal context (text / images / files) Core stack • TanStack Start / Query / Store / Form • Vite • Docker • Drizzle ORM + Postgres Auth & Payments • Better Auth • Stripe • Polar Frontend • tailwindcss • shadcn External Services • Resend • S3 • i18n No boilerplate hell. No wiring for weeks. Just clone, tweak, ship.

rs

27,121 views • 6 months ago

Chrome DevTools MCP is going to be a Swiss-Army knife MCP. Watch me use it here with Gemini CLI to open Google Scholar, search for a term, and save the top 5 PDFs to a local folder. I cannot stop thinking of all sorts of personal automation possibilities with this. Amazing release from the Chrome for Developers team!

Chrome DevTools MCP is going to be a Swiss-Army knife MCP. Watch me use it here with Gemini CLI to open Google Scholar, search for a term, and save the top 5 PDFs to a local folder. I cannot stop thinking of all sorts of personal automation possibilities with this. Amazing release from the Chrome for Developers team!

Delip Rao e/σ

238,890 views • 10 months ago

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.

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.

Avi Chawla

14,579 views • 1 month ago

Tried out stripe projects this week! built a customer support bot with hermes from Nous Research + supermemory. bot remembers every customer across sessions. the piece worth highlighting, Stripe projects ships a hermes skill that lets the agent auto-provision supermemory itself. ask hermes to build the bot, it runs stripe projects add supermemory/memory. stripe creates the supermemory account, syncs the api key to .env, and bills it on your stripe invoice. and supermemory is one of 30+ providers. databases ( Neon Postgres, Supabase), hosting ( Vercel , Cloudflare), auth ( Clerk), search ( Exa), analytics ( PostHog). one CLI for the whole stack.

Tried out stripe projects this week! built a customer support bot with hermes from Nous Research + supermemory. bot remembers every customer across sessions. the piece worth highlighting, Stripe projects ships a hermes skill that lets the agent auto-provision supermemory itself. ask hermes to build the bot, it runs stripe projects add supermemory/memory. stripe creates the supermemory account, syncs the api key to .env, and bills it on your stripe invoice. and supermemory is one of 30+ providers. databases ( Neon Postgres, Supabase), hosting ( Vercel , Cloudflare), auth ( Clerk), search ( Exa), analytics ( PostHog). one CLI for the whole stack.

Muskan Jain

20,335 views • 1 month ago

This has been one of my favorites for a while, but now it's time to let it go. Here's my preferred way of getting the KeePass db that we often hunt for: downgrade the executable to version 2.53, use CVE-2023-24055 and wait for the busy admin to trigger the dump of the database. The target can remain clean and you can simply check for the dump creation. KeePass version 2.53 can still open kdbx created with the version 2.57 and if using a proper xml the user will likely notice nothing. Update alerts can also be disabled within the xml.

This has been one of my favorites for a while, but now it's time to let it go. Here's my preferred way of getting the KeePass db that we often hunt for: downgrade the executable to version 2.53, use CVE-2023-24055 and wait for the busy admin to trigger the dump of the database. The target can remain clean and you can simply check for the dump creation. KeePass version 2.53 can still open kdbx created with the version 2.57 and if using a proper xml the user will likely notice nothing. Update alerts can also be disabled within the xml.

Diego Capriotti

18,567 views • 1 year ago