A tale of two communities
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The document narrates the challenges faced by small organizations in adopting open source repository software, highlighting a case of two groups with different resources and capabilities. It discusses the need for better documentation and training to bridge the gap between resource-rich and resource-poor communities. Additionally, it suggests that software developers should test their products in diverse environments to ensure broader accessibility and functionality.
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Explore the ease of managing Firecracker microVM with the firecracker-python. In this session, I will introduce the basics of Firecracker microVM and demonstrate how this custom SDK facilitates microVM operations easily. We will delve into the design and development process behind the SDK, providing a behind-the-scenes look at its creation and features. While traditional Firecracker SDKs were primarily available in Go, this module brings a simplicity of Python to the table.A Constitutional Quagmire - Ethical Minefields of AI, Cyber, and Privacy.pdf
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The SQLExecutor is one of FMEs most powerful and flexible transformers. Pivvot maintains a robust internal metadata hierarchy used to support ingestion and curation of thousands of external data sources that must be managed for quality before entering our platform. By using the SQLExecutor, Pivvot can efficiently detect problems and perform analysis before data is extracted from our staging environment, removing the need for rollbacks or cycles waisted on a failed job. This presentation will walk through three distinct examples of how Pivvot uses the SQLExecutor to engage its metadata hierarchy and integrate with its Data Quality Management workflows efficiently and within the source postgres database. Spatial Validation Validating spatial prerequisites before entering a production environment. Reference Data Validation - Dynamically validate domain-ed columns across any table and multiple columns per table. Practical De-duplication - Removing identical or near-identical well point locations from two distinct source datasets in the same table.The Future of Product Management in AI ERA.pdf
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Hi, Im Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around.
Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as Ive transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs Well talk about it shortly.
But before that, I want to take you back to 1997. One of my favorite movies is Fifth Element. It wowed me with futuristic predictions when I was a kid and Im impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves:
Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink).
Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully).
On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldnt be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future.
So, we as humans, when we dont know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didnt steal jobsthey transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.
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A tale of two communities
- 5. The group油gets油the油repository油up油and油running,油but油only油precariously.油The油group油feels油like油 they油are油only油taking,油and油not油giving油back油to油the油community油because油of油their油frequent油pleas油 for油help油and油lack油of油resources. This油is油where油the油story油ends油for油now. The油story油I油just油told油was油a油conglomeration油of油the油experiences油that油many油small油organizations油 have油faced油when油trying油to油enter油and油participate油in油the油open油source油repository油community.油 Grinnell油College油implemented油Islandora油6油in油2012,油and油it油was油a油very油bumpy油ride.油But油 because油we油went油through油the油process油with油an油OS油repository,油other油small油organizations油 contacted油us油to油share油their油repo油experiences油and油ask油questions.油油I油learned油in油this油process油 that油we油 the油small油and油limited油resourced油 all油shared油similar油experiences.油 https://secure.flickr.com/photos/jollino/5588370858 https://secure.flickr.com/photos/nuytsia_pix/386214544 5
- 11. Here油is油the油second.油What油do油I油mean油by油echo油chamber?油油If油the油majority of油places油using油your油 software油with油the油least油amount油of油issues油look油like油your油group油in油terms油of油skill油sets油and油 resources,油congrats油 you油have油echo油chambered油your油software. If油a油group油cant油get油your油software油to油work,油they油more油than油likely油cant油participate油in油shaping油 the油cultural油memory油and油scholarly油record.油Think油about油that油for油a油moment.油The油amount油of油 resources油spent油on油yak油shaving油so油they油can油get油a油basic油installation油off油the油ground油and油stable油 enough油to油maintain油means油that油there油is油no油chance油to油go油beyond油treading油water油in油the油 community,油if油they油can油tread油the油water油in油the油first油place. Getting油out油of油the油echo油chamber油requires油at油least油these steps,油in油addition油to油taking油on油a油co design油development油approach: 1.油Look at油your油stack.油What油is油the油adoption油rate油for油parts油of油that油stack?油What油is油the油learning油 curve油to油implement油it?油Im油not油saying油that油you油cant油use油newer油languages油or油frameworks;油 Im油saying油that油if油you油know油youre油using油a油stack油that油is油not油widely油adopted油by油the油greater油 community,油you油need油to油lower油the油bar油considerably油in油order油for油the油greater油community油to油 use油your油software. 2.油Test油your油software油in油places油that油dont油look油like油yours.油If油they油cant油implement油your油stack,油 fix油it油on油your油end.油You油have油a油responsibility油to油the油community油to油make油your油software油 functional油as油the油users油in油the油community油have油a油responsibility油to油send油bug油reports油and油to油 help油troubleshoot. 11
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