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- 🤖 Navigating Challenges in Spatial Machine Learning
🤖 Navigating Challenges in Spatial Machine Learning
🦾Plus: 📱 Apple Unveiled its First Foldable, the iPhone Duo
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Hey folks! Let’s get into Big Data and AI craziness…
In today's edition: Here is roundup of Python sets, CX Implementation, Logarithms 👇
🐍 Python sets and dictionaries can have quadratic-time performance
🛍️Tailwind Labs is joining Shopify
🧮The Lost Art of Logarithms
🚨 Anthropic Safety Failed as Claude Went Rogue
💡 AI Tutorial:How to turn your meeting notes into detailed project
🤖AI Tools and Data Tools to checkout
A short note about our perspective paper on validation, uncertainty, algorithms, software, and reproducibility in spatial machine learning…Spatial machine learning has become a standard tool for producing environmental and geographic prediction maps. It is now relatively (technically) easy to combine field observations with remote sensing, climate, terrain, or other predictor layers and fit a strong machine learning model.
AI made PMs faster. Multiplayer mode is still broken.
A PM can summarize research, draft a PRD, and mock up a prototype before lunch. The hard part starts when the team has to decide what actually gets built.
Jira Product Discovery gives product teams one place to capture insights, prioritize ideas with consistent frameworks, and build living roadmaps stakeholders can rally around.
And because it’s connected to Jira, the context behind every decision stays with the work—so developers and their agents know not just what to build, but why.
AI helps PMs move faster. Jira Product Discovery helps the whole team build with confidence.
Organizations spend millions on customer relationship management (CRM), field service, artificial intelligence (AI), enterprise resource planning (ERP), customer portals, and automation platforms to improve customer experience (CX). Unfortunately, technology alone rarely delivers the intended results. Companies that consistently deliver exceptional customer experiences don’t succeed because they purchase better technology.
In Python, the dict data structure is the conventional key-value structure. E.g., you might store a list of names as keys and have their phone numbers as values. Valentin Ignatev wrote this amusing post on X: It is indeed widely believed that, in the strict sense, the dict data structure and its companion, the set data structure, are O(1), meaning that as you increase the size of the data structure, the time to insert or query a key remains constant.
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Tailwind is joining Shopify, one of the first companies operating at scale to see the potential of Tailwind CSS and start building with it. Shopify has invested in making sure Tailwind is actively maintained and will continue to provide a stable long-term home for the project. Nothing will change with Tailwind CSS or any of Tailwind's other open source projects, but the project will no longer try to grow its business.
A web-book-in-progress by Charles Petzold wherein the utility, history, and ubiquity of that marvelous invention, logarithms, including what the hell they are, are explored; with some demonstrations of their primary historical application in plane and spherical trigonometry; plus, that extraordinary tool known as the slide rule is fully explored in theory and use…
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👨💻 Data Tools, Libraries
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Turmoil is a testing framework for distributed systems. It can run multiple concurrent hosts within a single thread to provide deterministic execution.
ugrep-indexer
A monotonic indexer to speed up grepping by >10x.
AI News:
Turns out Apple just needed a few extra years to decide that folding phones were a good idea. The iPhone Duo landed Wednesday in Cupertino with a 7.6-inch inner display, a 5.4-inch cover screen, and a hinge built from over 100 engineered components. It starts at $1,999, ships October 23, and is the first marquee device under new CEO John Ternus
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Andrew Tulloch, the researcher Meta chased with a billion dollar package, lasted under a year. Tulloch waited to leave until Meta finished rolling out Muse, the personal AI agent it opened to US users on Tuesday. He worked inside TBD Lab, the frontier research team Alexandr Wang runs at Meta Superintelligence Labs.
Google has decided the future of AI lives somewhere very cold. The company said Wednesday it will pour $15.1 billion into AI infrastructure in Finland, which CNBC notes is its largest single investment anywhere in Europe. Regulators there have spent years nudging hyperscalers to build on the continent rather than route everything through American data centers... consider this a rather large yes.
Suno launched v6, a family of three music models it developed alongside the Warner Music Group, BMG, and Believe, with the company saying the models were built on licensed data rather than the training set behind its older versions. Warner was one of three majors that sued Suno in 2024 over its training data, then settled last November in a deal promising licensed models.
Anthropic's deeper internal assessment found what its first review missed. Anthropic's monitor missed almost all of the worst incident, because the model's notes said the environment was fake. The July review covered 141,000 transcripts. The second one covered 481 million and turned up a fourth break-in. METR now holds transcripts and access to Anthropic employees cleared to share confidential material.
AI Tutorial
How to turn your meeting notes into detailed project plans with GPT-6 Astra in ChatGPT
Photo: OpenAI
Open ChatGPT and toggle on Work. Choose GPT-6 Astra if it’s available on your plan.
Upload your meeting notes, transcript, or supporting files.
Ask Astra to extract the core project information: decisions, tasks, owners, deadlines, dependencies, risks, and open questions.
Sample prompt: “Turn these meeting notes into an actionable project plan. Extract every decision and task, assign the owner named in the notes, capture deadlines and dependencies, and flag anything that has no clear owner or due date. Do not invent missing information. Organize the output into a project tracker with these columns: Task, Owner, Status, Deadline, Dependency, Priority, and Risk.”Have Astra create the tracker as an editable spreadsheet, such as a Google Sheet or Excel file.
Review the tracker and ask Astra to highlight blocked tasks, missing owners, conflicting deadlines, and unresolved decisions.
Make any corrections, then ask Astra to produce a short project summary with the key milestones, immediate next steps, and biggest risks for the team.
Most "hybrid" analytics setups still mean replicating data into the cloud first. AIStor skips that step. Watch a live walkthrough: import a share into Unity Catalog, query on-prem Iceberg and Delta tables directly, and get results back in seconds—no copy, no downsampling, no pipeline to maintain.
Jobs:
This week's Data & Analytics jobs: I have updated my curated list with fresh openings. Browse and apply in one click: jobs
Machine Learning Engineer – Hybrid – Boston
Lead Machine Learning Engineer – Texas
Senior Data Engineer – Analytics – Boston
Data scientist - NY
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A.I. Generated Image of the Day
👀 ancient oak tree with bioluminescent moss
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