dltHub

dltHub is the creator of data load tool (dlt)

[dltHub @dltHub]

Building pipelines with AI usually means losing context between tools.

dltHub AI Workbench runs the full 12-step workflow as a continuous session, schemas, incrementals, traces, transformations, and notebooks share context across the stack.

https://t.co/o8k2ht3KrU

[dltHub @dltHub]

@inngest @lightdash_devs @Streamkap At dltHub, @elviskahoro will demo our new Transformations public preview.

Expect demos from practitioners and startups building real-world AI and data systems, and a look at what agentic analytics looks like in practice.

📅 June 3 · SF

https://t.co/uc6ozcHiLZ

[dltHub @dltHub]

Agentic Analytics Demo Night lands in San Francisco this Wednesday 🚀

We're joining @inngest, @lightdash_devs, and @streamkap for an evening of demos from teams building at the intersection of AI, analytics, and data infrastructure.

🧵

[dltHub @dltHub]

@sameer_alsakran @metabase Sameer started Metabase as a side project, waited years before charging, ignored conventional SaaS advice, and built a product now used by 90k+ companies with 8-figure ARR.

We'll discuss OSS, AI, customer feedback, and building products that last.

https://t.co/BnEJoBLmi4

[dltHub @dltHub]

What does it take to build a product people actually keep using?

On Wednesday, we're hosting @sameer_alsakran, Founder & CEO of @metabase, at our office in Berlin for a live conversation with Francesco Mucio from Data Berlin.

🧵

[dltHub @dltHub]

@modal We’ll cover:

→ turning agent traces into dashboards for AI adoption, cost, and real business impact

→ how teams use Modal Sandboxes to run agents safely, plus a live background agent demo

📅 May 27 · 6–8 PM GMT+2

📍 Berlin

https://t.co/FSDDKF11gn

[dltHub @dltHub]

Berlin’s Applied AI Week is looking pretty special.

Tomorrow, dltHub and @modal are hosting an evening in Berlin for founders, engineers, and builders shipping agents in production.

Featuring Kenny Ning, Jefferson Girao & Alena Astrakhantseva.

[DataHub @DataHubCloud]

The open source AI stack walked into a bar in Silicon Valley. Ingestion, retrieval, and metadata all showed up.

🚀 The community came through at our event with @dltHub & @LanceDB for a night of technical talks built for engineers actually shipping AI in production. No fluff. Just engineers talking shop.

Stay tuned for more community meetups!

[DataHub @DataHubCloud]

Three production stories from @GrabID, @dltHub, and @iFood. Three very different starting points. One shared foundation. ����

Our May Town Hall brings together the teams answering the same question from very different angles: how do you make context actionable for AI at scale? 📈

Register: https://t.co/y5DdLtTEIJ

[Matthäus Krzykowski @matthausk]

https://t.co/ZNDeyvpegK

[dltHub @dltHub]

@ChanChan_Mao @lancedb @DataHubCloud Excited for this lineup 🔥

[dltHub @dltHub]

@DataHubCloud @lancedb See you in Menlo Park 🔥

[DataHub @DataHubCloud]

Some of the best open source AI conversations happen in person. We're joining @dltHub and @LanceDB in Menlo Park on May 21 for technical talks and demos from the engineers building the ML stack. Join us!

👉 https://t.co/5ceLRQpe18

[chanchan mao @ChanChan_Mao]

Next Thur May 21: @lancedb x @dltHub x @DataHubCloud demystifying the missing data layer for ML:
- Lance, the default storage layer for multimodal AI
- dltHub, the connective tissue of your AI data stack
- Supercharging Cortex with DataHub

https://t.co/1yBTXcKMWt

[dltHub @dltHub]

The AI stack is evolving fast, but reliable data movement is still the foundation.

Join dltHub, @LanceDB and @DataHubCloud on May 21 in Menlo Park for talks on multimodal AI storage, AI data pipelines, and trusted lineage systems.

🔗 https://t.co/u50aE57vaF

[dltHub @dltHub]

@elviskahoro @temporalio @nyghtowl @cecilphillip In under 10 minutes, we’ll cover:
- AI-assisted pipeline setup
- GitHub → dlt workflows
- Where AI helps vs where engineers still need to steer
- How to inspect & validate pipelines

📅 May 20 · 10 AM PST
📍 Live on YouTube

Watch live: https://t.co/9cTzRCInbE

[dltHub @dltHub]

How fast can you go from zero to a production-ready data pipeline when AI is your copilot?

Next Wednesday, @elviskahoro joins @temporalio alongside @nyghtowl and @cecilphillip for a live Vibe Check building a GitHub-powered pipeline with AI + dlt.

[dltHub @dltHub]

Explainer on ontology engineering and what we're building around it: why just clean schemas and prompts aren’t enough, and how adding a canonical model + taxonomy + ontology changes what agents can correctly compute (ARPU being the clearest example).

https://t.co/n1v6XylAy6

[dltHub @dltHub]

@probabl_ai We’ll show how agents can power data pipelines as code, turning traces into fresh, reliable datasets.

Stack: dlt, LanceDB, Pydantic, Ibis, HuggingFace + more → behind our agent evaluation platform.

📅 May 5 | 🕒 12:20–12:40
📍 Station F, Paris

https://t.co/9h6n2FrqhH

[dltHub @dltHub]

We’re excited to share that Violetta Mishechkina will be speaking at GOSIM Paris 🇫🇷

Invited by @probabl_ai to join the “Own Your Data Science and AI” workshop.

🎤 From Agent Traces to Analytics

Agents generate code, text, telemetry, yet most teams still rely on stale datasets.