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ServiceNow Published 64 Pages on People Intelligence. One Line Buried Inside Changes Everything.
ServiceNow just published a 64-page playbook on people intelligence. One buried statistic – only 24% of organisations have formal data cleaning processes – says more than the rest of the…
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Why your agent works on Tuesday: notes from a team building LLM features into a multi-tenant SaaS
We are a small team shipping LLM features into a multi-tenant SaaS. The model is rarely the part that breaks. Capacity, schema drift, prompt caching, tenant isolation – the work…
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One model won’t fit all: what we learned about churn prediction in People Analytics
We built a universal churn model for all our clients. It had a reasonable AUC. Then we ran a backtest and it caught zero actual departures. Here’s what happened next…
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The tracking isn’t the problem. The secrecy is.
LinkedIn allegedly scanned over 6,000 browser extensions and fingerprinted devices without disclosing it anywhere in its privacy policy. Meta does something arguably larger in scale and says so openly. That…
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Skills data is not a taxonomy problem. It is a signal problem.
The dominant bet in 2025 is that better AI inference will solve the skills data problem. It won’t. Not because the inference is bad – because the question is probably…
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Your engagement model is not broken. It’s just not yours.
Europe has the lowest employee engagement scores in the world – 13% according to Gallup. But European employees also report lower stress, higher wellbeing, and comparable productivity. Either Europe runs…
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What happens when you train people AI on behaviour, not records
Every people analytics model trains on data. The question most implementations skip is which layer. Most HR AI runs on records – structured, auditable, quarterly. But there is a second…
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Ten years of multi-tenant SaaS: the decisions we made, and the ones we’d unmake
GFoundry. What held for ten years, what didn’t, and what a small team learns when decisions made in year two still cost you in year eight.
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Stream mining in real time: what we built with Fraunhofer and what we learned
A €378,000 R&D project with Fraunhofer Portugal. MongoDB, ActiveMQ, FP-Growth, K-Means. What we built, what the data showed, and how it became GFoundry.
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How we built a music recommendation engine in 2007 – before Spotify existed in Portugal
Palco Principal. 70,000 tracks, three engineers, no library worth using. The algorithm, the A/B test numbers, and how the 2008 financial crisis ended it.
