I’m very excited about this. Coming next to the Neon backend: realtime. Sync is incredibly hard to get right - especially if the goal is to make it great to work with, support as many database query use cases as possible, and scale well. Sync coming to Neon was probably obvious to many after our announcement that the Electric team is joining Databricks but we can finally announce it officially today! The Electric team is building sync for Neon. I asked @thruflo about joining Databricks and their first few weeks at the company: neon.com/blog/electrifying-n…

I’m very excited about this. Coming next to the Neon backend: realtime. Sync is incredibly hard to get right - especially if the goal is to make it great to work with, support as many database query use cases as possible, and scale well. Sync coming to Neon was probably obvious to many after our announcement that the Electric team is joining Databricks but we can finally announce it officially today! The Electric team is building sync for Neon. I asked @thruflo about joining Databricks and their first few weeks at the company: neon.com/blog/electrifying-n…

I used to use supabase, but I switched to Neon because of project limitations. Neon offers more value than Supabase.

Quick database rant :) There is still a lot of basic wood to chop when it comes to agent observability. It's a totally new database workload. OLAP systems are built for <5kb events with UUIDs and very repetitive dictionary-like strings. And the techniques that work to query those do not work for agent traces. That's compounded by the crazy cost and scale problems. Agents have PMF now and so it's common for these workloads to generate GB/s per agent of tracing data. It's just not practical operationally to store this on attached disks. You have to use object storage. This problem of fat rows + object storage throws the existing systems' query schedulers for a total loop. They're used to reading local data in microseconds and for that data to be very dense / high signal. If you're searching for random shit in agent traces, this falls apart completely, and you end up doing a terrible job of scheduling work on your CPUs. Which in turn means you need to create a lot of OS threads to handle the workload and make your kernel very unhappy. We waddled our way around this by cobbling together Tantivy and our own I/O scheduler for a while, but ultimately realized we had to rebuild the execution engine from scratch. We tried using some off the shelf stuff (Datafusion is by far the most promising system here), but sadly to achieve optimal performance you need control flow while decoding postings from the inverted index so that you can short circuit. Tantivy, etc. do this but the logic is interleaved with the I/O itself, so you end up with terrible cold starts no matter what. A simple way to think about this is a graph traversal on the inverted index. The columnstore-maxxing approach is to read the whole graph and then crunch on it very fast (and you can schedule that efficiently, of course). The classic inverted index approach is traverse and potentially short circuit as you do so, interleaving those operations. We solved this by building a new engine called Nitro, which does async I/O scheduling, but with control flow / short circuiting built into the actual I/O layer, so that we can still saturate the network but avoid reading stupid amounts of data before actually executing a search. For example, if you search for something like "aha! you must be right about describe_all_queries() not working for Monday's cohort", you do not want to read all postings for "you", "must", "be", "right", "not", and "for". I think the benchmarks speak for themselves.. We compared Nitro against two modern columnar systems used in agent observability. We also still have a lot of work to do here. If this kind of stuff interests you, we are hiring!

Oct 4, San Francisco luma.com/build-agents

Moved @videorc object storage from Cloudflare R2 to Hetzner. I'd rather pay Hetzner $5/month than use Cloudflare for free. I live in Spain. During La Liga matches, Spanish ISPs block Cloudflare IPs. I can't ship a release. People here can't even download the Videorc app. Not Cloudflare's fault. Still unusable. I can't plan my work around football matches.

Also new on Neon Functions: we now support custom domains

We just shipped Function Triggers, branch-scoped definitions that tell Neon when to invoke a deployed function. First type is cron schedules compatible with scale to zero: neon.com/blog/your-neon-func…

switched from @supabase to @neondatabase for all side-projects. saves a lot of money.

The Neon backend is GA. A complete set of primitives for apps and agents built around the database, with open standards, no lock-in, and branching-first by design. Starting with: Lakebase Postgres, Object Storage, Functions, Managed Better Auth, and AI Gateway. More to come.

Am I reading @neondatabase pricing correctly? It's 5 GB of egress per project on the free tier and you can have 100 projects? Whereas @supabase gives 5 GB total? And then paying Supabase gives you 250 GB of egress total and Neon gives 500 GB per project. What am I missing here?

Am I reading @neondatabase pricing correctly? It's 5 GB of egress per project on the free tier and you can have 100 projects? Whereas @supabase gives 5 GB total? And then paying Supabase gives you 250 GB of egress total and Neon gives 500 GB per project. What am I missing here?

Built a full-text search over all of Hacker News (~49M stories, comments, jobs & polls, syncing live per hour) to be 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠𝐥𝐲 𝐟𝐚𝐬𝐭 with no Next.js level cache and all magic at the Neon Postgres layer: hnfaster.vercel.app 🎉 • Every keystroke is a live query against @neondatabase Postgres • BM25 (powered by Lakebase Search) ranking runs inside the Postgres • No connection pool, just SQL-over-HTTP round trips • pg_prewarm loads the table + every index into cache • partial BM25 index per type, predicate matching the WHERE clause • Deployed on @vercel Just Postgres doing Postgres things, close to the compute 🐘

#1 in Claude Code, Codex and Cursor. Still first for GPT-6 Astra and Fable 5.1 @neondatabase ripped our leaderboard of which database agents choose. Agents cite easy setup, branching and lower compute bills through scale-to-zero. Every run open 👇 armature.tech/leaderboards#a…