Alexander CS Hendorf
where else to find them
what they talk about
all 12 in the filters
where they keep going back
- PyCon Italia2 editions2023–2024
- PyData London2 editions2018–2022
- PyData Berlin2 editions2017–2018
all 11 in the filters
on a stage with
- Alexander Beckonce
- Ines Montanionce
- Sylvain Corlayonce
all 5 in the filters
who talks about the same things
- Ines Montani17 talks
- Mario Munoz12 talks
- Tennessee J Leeuwenburg15 talks
- Tereza Iofciu15 talks
- Tania Allard15 talks
- Kojo Idrissa18 talks
| title | conference | date |
|---|---|---|
| Open-source Business | PyData Paris 2025 | 2025-09-30 |
Claims about funding and governance hit specifics: a €80M Series A example, government grants seeding projects, and companies of ~30 employees balancing paid… | ||
| AI in Reality Fireside Chat: Enterprise AI & Open‑Source Innovation | PyCon DE & PyData 2025 | 2025-04-23 |
Covers concrete enterprise moves: Merck runs a four‑year AI program for 63,000 employees, prototypes LLMs, then abandons an internal clone for a partnership to… | ||
| Beyond Agents: What AI Strategy Really Needs in 2025 | PyCon DE & PyData 2025 | 2025-04-23 |
GTC observations condense into concrete shifts: NVIDIA markets a compact developer box with 128 GB of VRAM and about one teraflop for local model work… | ||
| Ten Years of Community Organizer | PyCon Italia 2024 | 2024-05-22 |
Ten years of running community events culminate in concrete lessons: volunteer teams scale well until about 1,500 on‑site attendees, finance and legal need an… | ||
| Ten Years of Community Organizer | PyData NYC 2023 | 2023-11-01 |
Ten years of organizing work claim concrete wins: program chair for 5 years running selection and recordings, migration from ad hoc Google Drives to documented… | ||
| 5 Things about fastAPI I wish we had known beforehand | PyCon Italia 2023 | 2023-05-25 |
FastAPI runs on Pydantic, Starlette and Uvicorn, so Pydantic does parsing not strict enforcement: validators must return value and the default behavior lets… | ||
| Lessons Learned About Data & AI at Enterprises and SMEs | PyData London 2022 | 2022-06-17 |
Claim: enterprises and SMEs behave differently enough that cannot buy AI must build it becomes operational advice — commodity APIs ok, domain models require… | ||
| 5 Things You Want to Know About AI Adoption in the Enterprise | PyCon DE & PyData Berlin 2022 | 2022-04-11 |
Claims five concrete executive questions that decide scale not hype and shows why prototypes usually stall: a Jupyter prototype that fails in production… | ||
| Better Code for Data Science | PyData Global 2020 | 2020-11-11 |
Argues that pragmatic engineering habits — project layout, tests, packaging, dependency pinning, code review and reproducible environments — turn notebooks… | ||
| Speech Synthesis with Tacotron2 and PyTorch | PyData Amsterdam 2019 | 2019-05-10 |
Implements Tacotron2 with PyTorch and trains on mel spectrograms to map text to audio samples, using an English public dataset of ~24 hours and a selfmade… | ||
| How I Made My Computer Write it's First Short Story | PyData Berlin 2018 | 2018-07-06 |
Uses PyTorch and a local eGPU / GTX 1080 to train models that attempt full episodes: character-aware text generation from fan transcripts, TTS trained on the… | ||
| Databases for Data Science | PyData London 2018 | 2018-04-26 |
Shows concrete claims and runnable tools not buzzwords: compares Postgres, MongoDB, Cassandra and Neo4j with query examples, mentions MongoDB aggregation… | ||
| Effective Data Analysis with Pandas Indexes | PyCon.DE 2017 | 2017-10-25 |
Demonstration shows how Pandas indexing (especially TimeSeriesIndex) changes workflows: timestamps become index objects allowing dot-year/month access, slicing… | ||
| Introduction to Data-Analysis with Pandas | PyData Berlin 2017 | 2017-06-23 |
Covers a hands‑on Pandas workflow using Jupyter notebooks and examples that read CSV/Excel/JSON/SQL/HTML via read_csv / to_excel on a ~1,000‑row, 7‑column… | ||
| Data Analysis and Map-Reduce with mongoDB and pymongo | EuroPython 2015 | 2015-07-20 |
Live demos show mongoDB aggregation pipelines doing match project group unwind and date operators on a 37 GB dataset compressed to about 9 GB with 1.3 million… | ||
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