Women in AI & Analytics

Oct 24

The AI graveyard is a reproducibility audit, not a failure ledger

#ai #machinelearning #analytics #wiaia

4 min read

Women in AI & Analytics

Oct 19

Latent-space agents erase audit trails women in AI can't afford to lose

#ai #machinelearning #analytics #wiaia

4 min read

Women in AI & Analytics

Oct 10

Where AI capital flows next, and what open-source practitioners must build without it

#ai #machinelearning #analytics #wiaia

5 min read

Women in AI & Analytics

Oct 7

The open-source AI infrastructure layer is being rebuilt in public, with real trade-offs

#ai #machinelearning #analytics #wiaia

4 min read

Women in AI & Analytics

Oct 6

When AI breakthroughs outpace the governance that should contain them

#ai #machinelearning #analytics #wiaia

5 min read

Women in AI & Analytics

Oct 5

When deployment outpaces governance, measurement, and documentation

#ai #machinelearning #analytics #wiaia

5 min read

Women in AI & Analytics

Oct 5

What 125B parameters at 100 tok/s on one GPU costs to reproduce

#ai #machinelearning #analytics #wiaia

5 min read

Women in AI & Analytics

Oct 4

When hardware, culture, and governance become the new AI bottleneck

#ai #machinelearning #analytics #wiaia

4 min read

Women in AI & Analytics

Oct 4

When hype stops, practitioners build for reproducibility and governance

#ai #machinelearning #analytics #wiaia

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Women in AI & Analytics

Oct 4

Sovereign models, hard budget caps, and what they mean for practitioners without enterprise budgets

#ai #llm #opensource #devops

5 min read

Women in AI & Analytics

Oct 4

Running LLMs locally on Linux: what actually works on a Raspberry Pi

#ai #linux #llm #raspberrypi

5 min read

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Writing Debut

The AI graveyard is a reproducibility audit, not a failure ledger

Latent-space agents erase audit trails women in AI can't afford to lose

Where AI capital flows next, and what open-source practitioners must build without it

The open-source AI infrastructure layer is being rebuilt in public, with real trade-offs

When AI breakthroughs outpace the governance that should contain them

When deployment outpaces governance, measurement, and documentation

What 125B parameters at 100 tok/s on one GPU costs to reproduce

When hardware, culture, and governance become the new AI bottleneck

When hype stops, practitioners build for reproducibility and governance

Sovereign models, hard budget caps, and what they mean for practitioners without enterprise budgets

Running LLMs locally on Linux: what actually works on a Raspberry Pi