AcidiQ

El Qapitan • Rap Fela • GBEZ out now!

[AcidiQ @acidiqSZN]

It's AcidiQ szn!⏳🚀

[B/R Football @brfootball]

The boy from Rosario who changed the game forever.

Gracias, Leo 🐐🇦🇷

[AcidiQ @acidiqSZN]

@blizzyblade @Odumodublvck_ https://t.co/JUeELY6Cm4

[Obinna Iheanachor @DataSenseiObi]

Dashboards tell us what happened.

Decision intelligence explains why, whether it matters, and what to do next.

That is where AI belongs.

Great speaking yesterday at the @PBIMCR Power BI & Microsoft Fabric Meetup. 🎤

[Obinna Iheanachor @DataSenseiObi]

Yesterday at PyData Leeds I shared a simple idea:

The cheapest AI inference is the one you never make.

Instead of sending every document to an LLM, process locally first and escalate only uncertain cases.

The result:
✅ Lower cost
✅ Lower latency
✅ Better reliability

Thanks to everyone who attended the session on Local-First AI. Great questions and discussions afterwards. 🚀

#PyData #Python #AIEngineering #LLM #MLOps

[Obinna Iheanachor @DataSenseiObi]

Spoke at @JumpingRivers’ AI in Production conference today

Production AI. Trust by design.

Great room. Insightful questions. Fantastic experience overall!

[Obinna Iheanachor @DataSenseiObi]

Spoke at Leeds Data Science meetup last night - From £8,000 to £40: What I Learned Shipping AI in a UK Engineering Company.

4,700 engineering drawings. 4 weeks of manual work → 45 minutes. The model was the least interesting part.

Sharp room, great questions. Thanks @jumping_uk for the invite

[Towards Data Science @TDataScience]

Interested in the intersection of data science, electoral politics, and scenario analysis? Don't miss @DataSenseiObi's latest exploration, written just ahead of the May 7 UK elections. https://t.co/g5dPDrmTvF

[Obinna Iheanachor @DataSenseiObi]

Hey #datafam

UK local elections today. Just published in @TDataScience

Across 64 English authorities and six modelled scenarios, the strongest shock is only 13% of the calibrated uncertainty band.

Scenarios sit inside the noise.

https://t.co/LibEFMTnmV

[Obinna Iheanachor @DataSenseiObi]

Hey #datafam

Ahead of the UK local elections tomorrow, I just published a Tableau scenario dashboard for the 2026 cycle.

Finding: the strongest modelled shock is only 13% of the median uncertainty band. Scenarios sit inside the noise.

https://t.co/TWg77P2gFA

[Towards Data Science @TDataScience]

For his latest deep dive, @DataSenseiObi presents a data-quality case study on English local elections, covering categorical normalisation, metric validation, and why raw labels should never define analytical groups. https://t.co/xN0YcbBJpb

[Towards Data Science @TDataScience]

Whether you're interested in British politics or in smart data-analysis techniques, @DataSenseiObi's data quality case study is a must-read. https://t.co/xN0YcbBJpb

[Obinna Iheanachor @DataSenseiObi]

I got it wrong.

A bug flipped my entire result: fragmentation didn’t rise in 66/67 councils. It rose in just 18.

What actually changed? Voter churn doubled. The party system didn’t fragment.

@TDataScience https://t.co/JDCS8eVj54

@tableau https://t.co/0qplL03M9P

[Obinna Iheanachor @DataSenseiObi]

Quick survey for UK data professionals:

How is AI actually impacting your work? (not the hype)

2 mins. Anonymous.

👉 https://t.co/NNAx0W9bV3

[Obinna Iheanachor @DataSenseiObi]

I replaced £8,000 of manual work with ~$15 in API calls.

4,700 PDFs. 45 minutes. 96% accuracy.

Published on @TDataScience 👇

https://t.co/ujAFYYgDvc

Here’s the architecture (and why GPT-5 wasn’t the answer) 🧵

[Obinna Iheanachor @DataSenseiObi]

I spent 20 mins pitching an AI project

My manager asked: "How much time does this save us?"

I didn’t have an answer. Proposal died

Second time: one slide. 4 lines. Approved!

Same project. Same tech. Different framing

Here’s the exact framework ↓

https://t.co/1IhtFah3XF

[Obinna Iheanachor @DataSenseiObi]

First time I pitched an AI project, I talked about embeddings for 20 minutes.

My manager: "How much time does this save us?"

I didn’t have an answer.
Proposal died.

Here’s what I learned 🧵

[Obinna Iheanachor @DataSenseiObi]

SQL failed.

Not because the data was missing.

Because it didn’t understand the question.

RAG did instantly.

This changes how we query data.

2-min demo 👇
https://t.co/30IK28XbMT

[Obinna Iheanachor @DataSenseiObi]

I asked the SAME question on the SAME data.

SQL: 0 rows
RAG: exact answer + citations

Nothing was missing.

SQL just needed the *exact wording*.
RAG understood the intent.

This is the shift most data people are underestimating:

7 data skills that map directly to AI 🧵

[AcidiQ @acidiqSZN]

@JJEnglert Guide

[AcidiQ @acidiqSZN]

@FynCas V2