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AI agents for data engineering. Impact analysis, root cause debugging, model refactoring. All powered by full data stack context.

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@openrouter GitHub repo: https://t.co/0b0S4EWpCl

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@openrouter 1. Throughput, latency and cost management across providers with simple configuration.

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@openrouter documentation: https://t.co/HQGchabcEM

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@openrouter fenic was designed from the beginning to be model agnostic. You could run the same semantic operators on any model and even mix them together. With the new OpenRouter integration it's now also provider agnostic and you get the following important capabilities:

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The new integration with @openrouter makes fenic even more model and provider agnostic.

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Thanks to @lhoestq and @vanstriendaniel for the collaboration and feedback that made this possible and to @Vorkosigan05 who built and maintains the integration!

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fenic ❤️ Hugging Face Datasets!

You can now turn any fenic snapshot into a shareable, versioned dataset on @huggingface . Perfect for reproducible agent contexts and data sandboxes.
🧵

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A few things you can do with this new integration.

1. Rehydrate the same agent context anywhere (local → prod)
2. Versioned, auditable datasets for experiments & benchmarks
Docs: https://t.co/BADieNkC3h

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Too many AI projects stall out. Costs balloon, pipelines break, and adoption slows.
Yoni Michael’s latest piece in AI Journal shows how treating inference as a first-class data operation can change that story. Full article here: https://t.co/3QdYDjSflz

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"AI confidence is high — but production results still lag."
Our cofounder, Yoni Michael, shares why in CIO.

Read it here 👉 https://t.co/3niayOYbhZ

#CIO #AIinEnterprise #Typedef

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for more check here: https://t.co/Qmk63KaRu5

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fenic 0.5.0 is live!

With, smarter PDF parsing + metadata, timestamp & date data types, optimized planning, and OpenRouter support with reliability and docs to match.

Highlights: 🧵

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DX & docs: “Fenic in 120 Seconds” quickstarts; clearer errors, cleaner markdown output from parse_pdf.

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Common patterns: multi-step enrichment, RAG prep, nightly jobs with partial recomputes.

for more check the Github repo: https://t.co/0b0S4EWpCl

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fenic's Local Data Caching & Persistence keeps expensive AI steps from rerunning and your pipelines resilient.

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With fenic, it’s explicit and simple: call .cache() where it matters.

Protect pricey semantic ops (classify/extract) from re-execution

Reuse cached results across multiple downstream analyses

Recover from mid-pipeline failures without starting over