Owner @TheWriteCure | Tech Marketing Copywriter & Editor | I help tech startups and enterprises create more value through the right words.
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Joined November 2008
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Anya Sage retweeted
My best post ideas come from working on other content like books, blog posts, newsletters.
There is so much potential in larger content pieces, we just need to open our eyes and see it.
Anya Sage retweeted
In a benchmark against Ignition’s existing SQL Historian on Postgres, the new Enterprise Historian Module for TimescaleDB ingested 10x more data, queried it 10x faster, and used 10x less storage. Full SQL access throughout.
Travis Cox (@InductiveAuto) and Doug Pagnutti (@TigerDatabase) walk through how Ignition and TimescaleDB fit together, plus TimescaleDB for Ignition: unlimited tags, one price, optional HA cluster at no extra charge.
Thursday Oct 22, 9am PDT / 4pm GMT. Register and you'll get the recording either way.
tsdb.co/ignition-webinar-x
Anya Sage retweeted
Storing vectors is easy. Retrieving the right evidence is harder.
Introducing searchgres.js: BM25, semantic + hybrid search in Postgres, with hierarchy, metadata, regex + time filters.
Filtering belongs inside retrieval.
tsdb.co/searchgres-x
Anya Sage retweeted
The HNSW build took 42 hours.
We indexed 100M Cohere embeddings with pgvector. On a 128 GiB machine, the build couldn’t finish. On a 512 GiB machine (4x the cost), it took 42 hours and served 95% recall in 8 ms.
Today we’re releasing pg_vectorsearch, an open-source Postgres extension with a new index called PRISM. On the 128 GiB machine, the one HNSW couldn’t build on, PRISM built in 1h 40m and serves 95% recall in 6 ms. The index is 25 GB to HNSW’s 180 GB.
It holds at smaller scale too. At 1M vectors, PRISM serves 99% recall in 15 ms on 2 GiB of RAM. HNSW on twice the memory takes 392 ms.
How it works: PRISM groups similar vectors into partitions and stores them as compressed codes of about 1 bit per dimension. A query finds the nearest partitions and scans them sequentially, which keeps the CPU prefetcher ahead and lets SIMD score many vectors at once. Graph hops are unpredictable, so the CPU spends most of its time waiting on memory.
It builds directly on existing pgvector columns. No rewrite, no copy, and your pgvector queries run unchanged.
It’s an alpha. Heavy churn needs a manual rebalance, deletes don’t reuse space yet, and filtered streaming search is on the GA roadmap. Requires Postgres 18+.
Run it on your data and tell us where it falls short: tsdb.co/ugw9oocq
Anya Sage retweeted
A remote inverter drops off for 3 days, then flushes every buffered reading with its original timestamp.
Those rows land in aggregate buckets materialized days ago. A 12-hour lookback never sees them.
5 refresh policy shapes for solar and wind SCADA: tsdb.co/cagg-refresh-x
Anya Sage retweeted
I turned my AI-draft review process into a kit: a 10-minute checklist, a scorecard, and 20 prompts with "check before you publish" lists.
$15.
writecure.com/ai-content-qa-…
Anya Sage retweeted
Reviewing an AI draft?
Facts first. Claims second. Voice last.
Polish makes errors harder to see, so don't start there.
Anya Sage retweeted
Words that should make you ask "what's the proof?"
best, only, always, never, guaranteed, 100%
Keep one only if you can back it up.
Anya Sage retweeted
A line worth adding to every content prompt:
"Use only the facts below. If something is missing, write [NEEDS SOURCE] instead of guessing."
Then paste your real facts. 30 free prompts built this way:
writecure.com/business-writi…
Anya Sage retweeted
Don't ask the AI to verify its own draft.
Check numbers, names, and quotes against a real source: your product docs, your pricing page, your release notes.
Anya Sage retweeted
What does a 40-year-old database have to do with the AI boom?
Explore why PostgreSQL is adapting to AI workloads while giving developers a trusted foundation for applications, data, and more: hackernoon.com/why-a-40-year…
Anya Sage retweeted
What happens when 1.7 TB of sensor data needs to become easier and faster to query?
See how Companion Energy cut its data footprint to 39 GB, reduced query latency by 25×, and built a database architecture designed to scale with future data growth: hackernoon.com/how-companion…
Anya Sage retweeted
Big news from #ICC2026: Tiger Data is now a strategic partner of @InductiveAuto. 🐯🤝
Today, CEO Colby Clegg and Tiger Data CTO Mike Freedman took the stage to introduce Ignition Enterprise Historian, powered by TimescaleDB.
We’re excited to build what’s next for industrial data together.
Anya Sage retweeted
We're excited to announce that Tiger Data now connects to Amazon Quick via MCP for energy & utilities customers.
Learn more at aws.amazon.com/energy-utilit….
Anya Sage retweeted
Learn how high-cardinality time-series data affects PostgreSQL index size, query planning, and ingest performance, and how schema normalization can help.
#postgresqlhighcardinality #postgresqlindexwidth...Show more
Anya Sage retweeted
Some of your most expensive database queries may not be the ones you expect.
Learn from @TimescaleDB how PostgreSQL's pg_stat_statements can reveal which queries are consuming the most resources—and where to look next: hackernoon.com/what-pg_stat_…
Anya Sage retweeted
I've written before about how much innovation we're seeing across the energy ecosystem.
Companion Energy is a great example.
They manage 2+ TWh of energy annually and 200+ MW of distributed energy assets across solar, wind, batteries, EV charging, and more. And increasingly, this isn't just about monitoring energy infrastructure. Their software is actively making operational decisions about batteries, generation, and loads.
As they scaled, the team looked at MongoDB, InfluxDB, and ClickHouse. MongoDB lacked the relational joins they needed, while ClickHouse brought excessive tuning and operational complexity.
Ultimately, they decided they didn't need another database. They stayed with Postgres, moving from Azure PostgreSQL to @TimescaleDB on Tiger Cloud, combining their operational database with real-time analytics across billions of time-series records.
The results are pretty remarkable:
• 4,950 ms → 202 ms: 25x faster
• 1.7 TB → 39 GB: 43.5x compression
• 1.5 seconds for a 90-day range query against a 4.64-billion-row sensor table
Great story written by the Companion team:
tigerdata.com/blog/companion…
Anya Sage retweeted
Learn how to use PostgreSQL EXPLAIN ANALYZE to distinguish planning time from execution time and fix partition, memory, statistics, and caching bottlenecks.
#postgresqlplancaching #postgresqlwork_mem...Show more
Anya Sage retweeted
pg_textsearch 1.4: Faceted search gets a lot faster.
Highly selective WHERE filters used to force repeated BM25 passes over ever-larger candidate sets. Now we use Postgres' own selectivity estimates to size the initial top-K search more intelligently.
Result: ~5x lower latency across selectivities on MS-MARCO v2 (138M docs).
⚡️
Anya Sage retweeted
Why can yesterday's dashboard numbers change when nobody changed the data?
@TimescaleDB explains how late-arriving records, invalidation, and refresh windows affect continuous aggregates—and how to keep historical results consistent: hackernoon.com/continuous-ag…
