Dr. Simon Harrer
Gerolzhofen, Bayern, Deutschland
6240 Follower:innen
500+ Kontakte
Info
Curious Software Engineer, Java Book Author, Data Mesh Book Translator, Data Contract…
Artikel von Dr. Simon Harrer
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Data Marketplace: Make or Buy
Data Marketplace: Make or Buy
I just returned from the POA Summit (Data Product-Oriented Architectures Summit), and one pattern kept standing out…
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Aktivitäten
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I'll be in Zürich from October 28th to 29th. Feel free to reach out if you want to meet up. Or simply come to TDWI Roundtable Zürich on 28.10.2025 at…
I'll be in Zürich from October 28th to 29th. Feel free to reach out if you want to meet up. Or simply come to TDWI Roundtable Zürich on 28.10.2025 at…
Beliebt bei Dr. Simon Harrer
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I'll be in Zürich from October 28th to 29th. Feel free to reach out if you want to meet up. Or simply come to TDWI Roundtable Zürich on 28.10.2025 at…
I'll be in Zürich from October 28th to 29th. Feel free to reach out if you want to meet up. Or simply come to TDWI Roundtable Zürich on 28.10.2025 at…
Veröffentlicht von Dr. Simon Harrer
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❓ How can AI automate Data Governance? Proud to share our latest research presented at #AIES2025 on how Generative AI can automate data governance —…
❓ How can AI automate Data Governance? Proud to share our latest research presented at #AIES2025 on how Generative AI can automate data governance —…
Geteilt von Dr. Simon Harrer
Berufserfahrung
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Ausbildung
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Otto-Friedrich-Universität Bamberg
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Dissertation: Effective and Efficient Process Engine Evaluation
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Activities and Societies: Scholarships of the Max-Weber Program and e-fellows.net
Bescheinigungen und Zertifikate
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Certificate for Teaching in Higher Education of the Bavarian Universities
Training Centre for University Teaching
Ausgestellt:
Ehrenamt
Veröffentlichungen
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Data Mesh (German translation)
O'Reilly Germany
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Java by Comparison - Become a Java Craftsman in 70 Examples
The Pragmatic Bookshelf
Improve your coding skills by comparing your code to that of expert programmers, so you can write code that’s clean, concise, and to the point: code that others will read with pleasure and reuse. Get hands-on advice to level up your coding style through small and understandable examples that compare flawed code to an improved solution. Discover handy tips and tricks, as well as common bugs an experienced Java programmer needs to know. Make your way from a Java novice to a master craftsman.
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BPMN 2.0: The state of support and implementation
Future Generation Computer Systems
Auszeichnungen/Preise
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Ranked 88 out of approx. 3000 in Google Hash Code
Google
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Good Teaching Award
Faculty of Information Systems and Applied Computer Science of the University of Bamberg
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Best Conference Paper Award
IEEE International Conference on Service-Oriented Computing and Applications
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2. Place at IEEE Services Cup
IEEE SERVICES
Sprachen
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Deutsch
Muttersprache oder zweisprachig
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Englisch
Fließend
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Jetzt anmelden und ansehenWeitere Aktivitäten von Dr. Simon Harrer
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#AIES2025: Automating Data Governance with Generative AI Managing data across decentralized systems is complex, and ensuring compliance with GDPR…
#AIES2025: Automating Data Governance with Generative AI Managing data across decentralized systems is complex, and ensuring compliance with GDPR…
Beliebt bei Dr. Simon Harrer
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Lucas Baião and I had the privilege of presenting our Data Mesh journey at Kuehne + Nagel during the Snowflake World Tour 2025 ❄️. Our session…
Lucas Baião and I had the privilege of presenting our Data Mesh journey at Kuehne + Nagel during the Snowflake World Tour 2025 ❄️. Our session…
Beliebt bei Dr. Simon Harrer
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CTO hired 20 engineers at $200K each. Features now ship 50% slower than before. $3M consulting firm disguised as a product company. Her exit…
CTO hired 20 engineers at $200K each. Features now ship 50% slower than before. $3M consulting firm disguised as a product company. Her exit…
Beliebt bei Dr. Simon Harrer
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It has been a while since the fifth anniversary of "Getting APIs to Work" YouTube channel, so it's time to use the timeline that I produced. Time to…
It has been a while since the fifth anniversary of "Getting APIs to Work" YouTube channel, so it's time to use the timeline that I produced. Time to…
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Weitere Beiträge entdecken
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Tomasz Tunguz
Walk into a bookstore’s self-help section , rows of productivity systems : Getting Things Done, Bullet Journal, Zettelkasten. Each promises transformation—if you commit weeks. The market thrives , hundreds of new titles annually. But something new is emerging. We’re discovering we can redesign workflows instantly with AI prompts instead of studying methodologies for weeks. Three recent experiments hint at what’s coming. First, Paper2Agent transforms research papers into working code agents in 30 minutes to 3 hours. Ask it to implement AlphaGenome’s genomic analysis from a published paper & it processes the code, builds the environment, runs the tutorials. Previously: read the paper (2 days), understand the methodology (1 day), code the implementation (3 days). Now: point AI at the repository, get a working agent by lunch. Second, when I discovered Google’s Agent Development Kit research on tool design patterns, I asked AI to redesign my automation tools following those principles. The result : a 41% reduction in AI operation costs, implemented while I was answering emails in another browser tab. Third, over the weekend I read Jesse Vincent’s architect/implementer workflow via Simon Willison’s write-up. The approach splits AI coding into two sessions: one architect to design, one implementer to execute. No weeks of trial & error to understand the nuances. Read, implement, test, done. The pattern emerging : instant implementation. We can now test productivity systems like trying coffee drinks. Monday : architect/implementer (cappuccino—structured , classic). Tuesday : traditional single-session (flat white—simpler , maybe better?). Wednesday : hybrid workflow (chai latte—wait , is this even coffee?). By Thursday , the data shows which saves 3 hours per week. The self-help aisle promised transformation through commitment. AI promises transformation through experimentation.
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Max Mergenthaler Canseco
🚨 New “foundation model” for reasoning on text and time series by Stanford Biodesign Digital Health, Google and ETH Zürich. Spoiler alert: OpenTSLM lacks evidence to support its claims, does not show evidence of working beyond a narrow problem set, and is not more accurate than classical models. 👉 OpenTSLM is marketed as a breakthrough multimodal foundation model for temporal reasoning with order-of-magnitude gains in medical reasoning and beyond. ‼️But... no evidence to support those claims is to be found. The paper overstates results, omits key literature, and cherry-picks baselines, falling short of minimal standards for methodological rigor and transparency. Here are 🔟 points that I find problematic and share in the spirit of academic exchange. ⚠️ Core concerns: 1️⃣ Misleading framing: Tasks are mostly templated classification, not real temporal reasoning. No forecasting, imputation, or anomaly detection. 2️⃣ False claims: “order-of-magnitude gains in temporal reasoning” is simply false and misleading. 3️⃣ Missing literature: Complete omission of related work. E.g. Chronos by Amazon , TimesFM by Google, Moirai by Salesforce, Toto by Datadog, TabPFN by Prior Labs, TiRex by NXAI, Moment by Carnegie Mellon University... 4️⃣ Weak baselines (cherry-picking): self constructed datasets and omission of standard baselines. (E.g., Sleep-EDF results are far below models from the last decade) 5️⃣ Template dependence — Models were trained to solve one very particular task and don't show any signs of generalization. 6️⃣ Compute inefficiency — Enormous complexity and compute for subpar results. 7️⃣ No forecasting, classification or anomaly detection: Using ChatGPT-style captioning is not forecasting. The authors do include code to handle the Monash Dataset and The UCR Archive but completely omit any reference in the paper. 8️⃣ Missing results, logs, checkpoints, and relevant ablations — No breakdown of curriculum, rationale, or encoder impact. 9️⃣ Code irregularities — >122 files deleted before publishing; including evaluation notebooks and dataset generation script. Also the code history shows that some standard benchmarks (e.g. PAMAP2) were introduced, presumably evaluated and then replaced. 🔟 Suboptimal quality — The paper is signed, endorsed, and shared by researchers from from top institutions. Such an amazing set of authors can and has to produce better work. 🔍 Transparency, quality, and rigor matter — especially in fields like healthcare. 🎖️ If we want trustworthy foundation models we need to raise the bar, not lower. Patrick Langer, Thomas Kaar, Max Rosenblattl, Paul Schmiedmayer, Filipe Barata Welcome to the time series field, and #happyforecasting. 💬 Thoughts, evidence, or corrections welcome. If I’ve missed something, please point it out — I’ll happily adjust or retract. #AI #MachineLearning #TimeSeries #FoundationModels #Transparency #MLResearch #AcademicIntegrity
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