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
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…
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Your AI agent has a web search tool but still answers current questions from memory?
Check the tool description.
"Search the web" isn't enough. Tell it WHEN to search: new releases, prices, versions, news, etc.
Octen explains this with a working LangChain example:
octen.ai/guides/how-to-add-l…
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Before connecting a scraping API to your AI agent, check:
- Anti-bot reliability
- JS rendering
- JSON/Markdown output
- Request latency
- Cost per usable result
This article compared 4 popular APIs with an 800-request benchmark.
Good reference: zenrows.com/blog/best-web-sc…
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A lot of “web-aware agents” are still:
fetch(url) → dump page into model → hope
Production gets messy fast.
JS, bot protection, noisy HTML, wrong page content, extraction formats.
This Mastra + Zenrows setup treats web access like infrastructure.
zenrows.com/blog/web-aware-t…
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5 code review habits you should steal today for AI-generated PRs:
1. Read the ticket first
2. Review schemas/types before dependent code
3. Separate logic from formatting noise
4. Hunt assumptions, not syntax
5. Give agents a diff-size budget
CodeRabbit breaks it down here: coderabbit.ai/guides/code-re…
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AI-assisted PRs are 2.6× larger on average in a recent benchmark data.
Once the diff gets huge, review quality drops fast.
A simple rule is to give coding agents a diff budget.
If the task crosses it, split the PR.
See the benchmark here
coderabbit.ai/guides/code-re…
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And when you're not "conventionally beautiful", you'll see shege. Any bad thing that happens when you're around, it go be like say na your bad aura cause am.
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Everyone's building AI lead gen agents, but how many are pulling from the same prospect databases?
This tutorial uses OpenAI Agents SDK + Zenrows to discover 78 companies from a live directory, then enrich and score prospects against an ICP.
Code included:
zenrows.com/blog/ai-lead-gen…
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Persistent coding-agent memory has a nasty edge case:
same error message, different root cause.
Retrieval can find the “right” old memory and still suggest the wrong fix.
I’d love to see this benchmarked adversarially.
Actian’s current Astra implementation: actian.com/blog/developer/bu…
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Your lead gen agent can get HTTP 200 and still extract zero leads.
Some protected directories return a challenge page instead of company listings.
This Zenrows + OpenAI Agents SDK tutorial shows how to fetch, extract, enrich and score real prospects:
zenrows.com/blog/ai-lead-gen…
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“If I see any of you at Onitsha Head Bridge celebrating that you maintained a 1950s bridge for the South-East, Holy Ghost will kpaiii you there. This country has no conscience. If you see the way the government is showing this bridge everywhere on the internet, you will think it is a newly constructed bridge, without knowing that it is a bridge built years ago. Imagine someone celebrating his car that he parked for over 50 years after repainting it.”
— Pastor Odumeje bl@sts government over celebration of Onitsha Head Bridge