Pg

PgBeam – A globally distributed PostgreSQL proxy

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PgBeam – A globally distributed PostgreSQL proxy

PostgreSQL connections from distant regions are expensive. A new connection from Tokyo to a database in us-east-1 costs 400-800ms before the first query runs: TCP handshake, TLS (2 RTTs), PG startup and auth. - PgBouncer pools connections but doesn't cache and runs in a single region. - Hyperdrive does both but only works from Cloudflare Workers. - Prisma Accelerate requires the Prisma ORM. PgBeam is a PostgreSQL proxy that speaks the wire protocol natively. You only change one environment variable: Before: postgresql://user:pass@prod.c7k2dfh4jk3l.us-east-1.rds.amazonaws.com:5432/postgres After: postgresql://user:pass@02ljaccjaffjy8xvsw1xq6fdra.gw.pgbeam.app:5432/postgres Three things happen: 1. Routing: GeoDNS points to the nearest proxy (6 regions today) 2. Connection pooling: Warm upstream connections, no TLS/auth cost per query 3. Query caching: SELECTs cached at the edge with stale-while-revalidate. Writes, transactions, and volatile functions like NOW() or RANDOM() are never cached. Live benchmark at https://pgbeam.com/benchmark with real TLS PostgreSQL connections from 20 global regions, comparing direct vs. PgBeam (cached and uncached). No synthetic data. This is a technical preview meant for design partners and early customers via a private beta before scaling the infrastructure. Feedback is welcomed!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, single · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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