Technical creator. Involved in everything AI, data, and tech
Joined March 2023
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The edge computing market hit $168B in 2025. IoT devices are headed to 39 billion by 2030.
And yet: most vector database are mot built for air-gapped or latency-critical environments.
Healthcare. Manufacturing. Military. The cloud can't serve them, and nobody's building for them.
Your favorite tech creator retweeted
For under 10M vectors, pgvector is usually enough if you already run Postgres. Move when you hit heavy filtering, high QPS with tight latency, or memory costs start climbing. We built a free 30-sec check for exactly this: actian-devs.github.io/vector…
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Pls don't act like you are not seeing this post you can also contribute by reshare or repost for more people in helping 2 locating her 🙏
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Would you sacrifice some data freshness to keep analytical queries away from production traffic?
I'd prioritize workload isolation unless the app needs near-real-time insights.
Databricks has a good breakdown of this trade-off:
databricks.com/blog/what-hta…
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An HTAP database can look fast until you run analytical queries alongside production traffic.
Benchmark both workloads together. Watch OLTP p99, replication lag, and data freshness.
Databricks explains what to test before choosing an HTAP architecture:
databricks.com/blog/what-hta…
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Google Custom Search JSON API is gone on Jan 1, 2027.
If it is in your stack, do not just swap endpoints. Check your parser, pagination, domain restrictions, query params, quotas, and failure handling too.
This migration guide by Octen breaks it down well
octen.ai/versus/google-custo…
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You're building an offline AI agent on a Raspberry Pi 5.
It needs to remember conversations after a restart and find old information when you ask in different words
What would you use for its memory?
A) JSON files
B) Bigger context window
C) Vector DB + embeddings
D) Fine-tuning
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100K vectors on a Raspberry Pi 5.
Actian tested VectorAI DB on an 8GB Pi and got 4.98 ms p99 for top-5 search.
That's synthetic-vector retrieval.
Now I want to see how far this little board can go.
Full benchmark:
actian.com/blog/developer/bu…
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One result surprised me while benchmarking web scraping tools.
Browserbase Fetch averaged 0.61s on TripAdvisor.
But all 100 requests returned 403.
Zenrows took longer but retrieved the page.
Speed means little if you can't access the data.
My benchmark:
zenrows.com/blog/best-browse…
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I tested Zenrows Fetch vs Browserbase Fetch on 4 protected sites.
100 requests per target. 2 req/s.
Results:
• Zenrows: 100%
• Browserbase: 62.25%
These were default Fetch configurations for both tools, not full browser tests.
I documented the methodology:
zenrows.com/blog/best-browse…
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Choosing a web search API for your AI agent?
Check:
1. Freshness
2. p95/p99 latency
3. Result quality
4. Coverage
5. Output format
6. Integration
7. Cost per answer
8. Reliability
This guide by Octen includes a scoring matrix. Save this post.
octen.ai/guides/how-to-choos…
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I’ll give you first access to insane tech and pay you to share your experience whether good or bad with full disclosure
We work with frontier model and tech companies that want to scale early access and growth programs
Comment ‘access’ and I’ll DM you an invite. Adding just 50 more to the next cohort
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Octen compared 8 embedding models for RAG.
On RTEB's 16 public datasets:
• Octen-Embedding-8B: 0.795
• Nemotron 3 Embed: 0.785
• Voyage-4-large: 0.762
But benchmark scores aren't a substitute for testing your data.
Full comparison:
octen.ai/versus/best-embeddi…
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Before choosing an embedding model for RAG:
1. Collect real queries
2. Label relevant documents
3. Test 2–3 models
4. Compare NDCG@10
5. Check storage + re-embedding costs
Octen explains this approach while comparing 8 models:
octen.ai/versus/best-embeddi…
#rag #embeddings
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You can search thousands of images using a text description.
The approach:
1. Encode images with CLIP
2. Store vectors + metadata
3. Encode the text query
4. Retrieve similar images
Actian built an offline defect-search demo with Python and VectorAI DB:
actian.com/blog/developer/bu…
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What if your AI coding agent fixed a bug today, but then forgot the solution in the next session?
Came across a GPT-6 Astra experiment where they used Actian VectorAI DB as external memory to store and retrieve previously tested fixes across sessions.
It worked by:
-Search a vector DB for similar previous fixes
-Check if the old fix applies to the current code
-Save new fixes only after tests pass
They also tested it across 5 sessions and the agent successfully retrieved earlier fixes. I liked the idea of remembering tested fixes instead of the whole conversation.
I'm curious what others think about giving AI coding agents this kind of memory. Would it be useful in your workflow?
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Zekeri throws a challenge to all the Abuja residents to disappoint Wike in 2027.
This is just the beginning, when the game finally starts, even Wike will plead for mercy.
How can one man say he will decide who wins elections in Federal Capital Territory?
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Half the AI timeline right now: "You don't need a vector database."
The other half: "you'll regret not having one."
Both are right, depending on your workload.
So we built a tool that tells you which camp you're in.
Answer a few questions about your data, latency, filtering and deployment.
In 30 seconds, you'll know whether a flat file, keyword search or pgvector is enough, or whether you really need a vector database.
Sometimes the answer is no. We'll tell you that too.
bki.sh/RyqhiNN
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Building an AI agent with memory?
Know these 4 types:
1. Working: current task
2. Episodic: past interactions
3. Semantic: learned facts
4. Procedural: workflows
Actian breaks down how they fit into production agent architectures:
dev.to/actiandev/what-you-ne…
