It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for May!

Data science & AI

• Agentic AI Flywheels (15 minute read)
  explains how production feedback, observability and evals create a flywheel that helps AI agents improve with every release.

  

• Choosing the Right Graph (36 minute read)
  Detailed breakdown by comparing labeled property graphs and RDF knowledge graphs, offering a practical framework for choosing between them.

• How AI agent memory works (14 minute read)
  walks through how AI agents use context, embeddings, retrieval and memory stores to remember useful information across conversations.

  

• Building Your First Claude Skill (19 minute read)
  shows how to build a Claude Skill that checks whether an analysis is correct, answers the right question and can be trusted before it reaches stakeholders.

• What political censorship looks like inside an LLM’s weights (45+ minute read)
  This article finds that Qwen 3.5’s political censorship is a small, identifiable circuit layered over facts the model already knows.

• Agent Evaluation: A Detailed Guide (60+ minute read)
  explains how to evaluate AI agents using realistic tasks, outcome-based metrics and detailed analysis of their tool use and decision paths.

  

• Is logistic regression regression? (6 minute read)
  Richard Vale explains why logistic regression is genuinely regression because it predicts a probability, only becoming classification once a cutoff is applied.

• What Matters in Production RAG (13 minute read)
  explains how production RAG depends on thoughtful chunking, reliable index updates and retrieval tracing that shows why the system returned a particular answer.

• How Lyft Uses AI to Scale Translation Across 150+ Products (18 minute read)
  This article walks through how Lyft used an LLM drafting and evaluation loop to cut translation times from days to minutes across 150+ services.

Data engineering

• Quick insights on materialized views (11 minute read)
  explains how materialized views speed up queries, how incremental refresh keeps them current and when they make sense for real-time processing.

  

• Five Worlds of Data Engineering (12 minute read)
  Chris hillman explains why data engineering advice often clashes because teams operate across five very different worlds, each with its own tools, constraints and priorities.

• Why I Set Up Databricks and Then Walked Away From It (17 minute read)
  walks through why he dropped Databricks for a simpler open-source stack that better matched his company’s data scale, budget and team maturity.

• Does ELT vs. ETL Even Still Matter? (9 minute read)
  argues that ETL versus ELT matters far less than building reliable, affordable pipelines that help the business make better decisions.

• Complete End-To-End Build of ETL Pipeline in AWS (12 minute read)
  walks through building ETL with DuckDB and ECS Fargate on AWS for under 50 cents.

  

• The Cognitive Overload of AI Development (7 minute read)
  Interesting article by on how AI coding has turned senior engineers into exhausted code reviewers, forever one giant AI-generated pull request away from chicken farming.

• Event-Driven vs. Polling Architectures for Agent Triggers (17 minute read)
  explains why production agent triggers need a mix of events, reconciliation polling, idempotency and durable runtimes rather than relying on webhooks alone.

• Progressive Disclosure: The Core Pattern for Analytics Agents (10 minute read)
  explains why analytics agents should load only the schema and business context they need, when they need it, to reduce confusion and hallucinated queries.

• Strong views on PostgreSQL VIEWs (24 minute read)
  Radim Marek explains why PostgreSQL views remain useful abstractions, but hidden dependencies, rigid schemas and complex migrations make them easy to misuse.

• How Grab Reclaimed Hundreds of Data Engineering Hours With Multi-Agent AI (17 minute read)
  This article explains how Grab used specialist AI agents to cut repetitive data support from hours to minutes and return hundreds of engineering hours to roadmap work.

  

Data analysis and visualisation

• Of Hammers and Nails: What AI Can and Cannot Do for a Data Analyst (5 minute read)
  argues that AI makes analysts faster at coding and drafting, but still struggles with the judgement, context and consistency needed for reliable analysis.

• Dead on Arrival: The AI Dashboard Problem (17 minute read)
  argues that AI can build polished dashboards in minutes, but good analysis, domain knowledge and human judgement still decide whether anyone should trust them.

• How to Visualize Plan vs Actual (6 minute read)
  explains how to make plan-versus-actual gaps obvious through distinct visual styles, selective color and the right choice of bars or lines.

  

Other interesting reads

• Cloudflare And Meta Just Told You Which Jobs Get Cut Next (12 minute read)
  Vin Vashishta argues that AI-driven layoffs will move from reporting roles to coordination roles and finally anyone whose work cannot be tied to measurable business value.

• Notes from inside China’s AI labs (18 minute read)
  shares what he learned inside China’s leading AI labs, from their fast-moving research culture and open-model mindset to their hunger for more compute.

• The economics of superstar AI researchers (10 minute read)
  Interesting article looking at how small differences in research ability can lead to enormous pay gaps when one researcher’s work reaches billions of users and cannot be replicated by simply hiring more people.

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for April!

Data science & AI

• Components of a Coding Agent (19 minute read)
  provides a great break down of the six core components of coding agents, showing why tools, repo context, memory, caching and context management often matter as much as the underlying LLM.

  

• The Revenge of the Data Scientist (8 minute read)
  Hamel Husain argues data science is making a comeback in the LLM era because evals, traces, metrics, labels and experimental design are exactly the skills needed to make AI systems work reliably.

• Machine Learning Visualized (4 minute read)
  Gavin Hung’s Machine Learning Visualized is a free, notebook-based guide that derives ML algorithms from first principles and uses interactive visuals to show how models train, learn and converge.

  

• Your AI Might be Lying to Your Boss (17 minute read)
  William O’Connell argues while AI IDEs can help developers save time, their metrics around AI-generated code are inaccurate and can't be trusted because of their self-serving interests.

• Why Most Time Series Models Fail Before They Start (18 minute read)
  This article explains why many time series models fail before they start, showing how stationarity checks, visual diagnostics and transformations like differencing can stop models from chasing trends instead of learning real signal.

• Multi-agent coordination patterns: Five approaches and when to use them (5 minute read)
  Anthropic breaks down five multi-agent coordination patterns, showing when to use generator-verifier, orchestrator-subagent, agent teams, message bus or shared state instead of overcomplicating agent systems too early.

  

• AIs can now often do massive easy-to-verify SWE tasks and I’ve updated towards shorter timelines (20 minute read)
  Ryan Greenblatt argues that AIs are now much better at massive, easy-to-verify software engineering tasks than expected, which has pushed him toward shorter AI timelines even though judgment-heavy work remains a major bottleneck.

• Being a Staff+ Data Scientist in 2026 (14 minute read)
  argues staff+ data science is less about fancy models and more about navigating messy stakeholder incentives, uncertainty, self-serve myths and the human work needed to turn data into decisions.

• How Pinterest Used Multimodal AI to Help Millions of Shoppers (14 minute read)
  Pinterest turned billions of products into 4.2 million shopping landing pages and improved search performance by 35%. This piece breaks down how Pinterest used vision-language models, contrastive learning and distributed inference to make products easier to discover.

Data engineering

• Architectural Foundations & Infrastructure (12 minute read)
  kicks off a good data platform architecture series, arguing that good infrastructure choices start with business requirements, data shape and boring fundamentals like scalability, resilience, modularity, observability and cost.

  

• The 5 Silent Failures in Data Pipelines (11 minute read)
  breaks down five silent pipeline failures, from schema drift to stale data and brittle logic, showing how bad data can reach dashboards without throwing a single error.

  

• Data Observability Fundamentals for Data Engineers (18 minute read)
  Good article by explaining data observability as a tool-agnostic way to catch silent pipeline failures, using freshness, volume, schema, quality and lineage signals to keep data and AI systems trustworthy.

• The Power of Data Sketches: A Comprehensive Guide (19 minute read)
  explains how data sketches trade perfect accuracy for tiny memory, fast queries and bounded error, making them useful when exact counts and aggregations become too expensive at scale.

  

• Stop Letting Tools Lead Your Platform Decisions (5 minute read)
  argues data platform decisions should start with use cases, constraints and users, not fashionable tools, because the simplest architecture is often the one that actually fits.

• Databases Were Not Designed For This (16 minute read)
  argues agentic AI makes databases riskier, so teams need defensive patterns like least privilege, timeouts, idempotency, soft deletes and query tagging before giving agents access to production data.

• The Last Mile to Apache Iceberg - Building a Basement Data Platform (13 minute read)
  shows how he solved the ‘last mile’ into Apache Iceberg by building a tiny low-cost event ingestion path for side-project analytics without Kafka, SaaS pipelines or a scary cloud bill.

• Lyft Data Tech Stack (5 minute read)
  breaks down Lyft’s data stack, showing how Kafka, Flink, Trino, Airflow, Flyte and 100+ PB on S3 support real-time analytics, ML and millions of daily rides.

  

• 30 BI Engineering Interview Questions That Actually Matter in the AI Era (32 minute read)
  Anusha Kovi reframes BI engineering interviews for the AI era around the skills that still matter: SQL judgment, semantic modeling, data governance, trustworthy metrics and turning messy business questions into reliable analytics.

• How Airtable Saved Millions by Cutting Archive Storage Costs by 100x (16 minute read)
  Airtable moved petabytes of archive data out of MySQL, made storage 100x cheaper and kept interactive query latency intact. This piece breaks down how Airtable handled the migration, why it chose DataFusion and the optimizations that made cold storage feel fast.

Data analysis and visualisation

• The data role is being reborn (8 minute read)
  thinks the data role is being reborn, with AI shrinking old output-heavy analyst work while increasing the need for judgment, business context and decision-shaping.

• Stop Coloring Retention Tables the Classic Way (3 minute read)
  shows that retention tables become much more useful when you color cohorts by deviation from the period average, rather than using classic gradients that mostly show the obvious decline over time.

• Dashboard rot as org attention grave markers (10 minute read)
  Randy Au argues dashboard rot is not really a dashboard problem, but a sign of shifting organisational attention, stale priorities and abandoned decisions fossilised as BI clutter.

Other interesting reads

• We’re in 1905: Why Electricity (Not Dot-Com) Is the Right AI Analogy (12 minute read)
  Interesting article by arguing AI is less like the dot-com boom and more like early electricity: the technology works but the real productivity gains will only come when companies redesign their workflows, org structures and data “factories” around it.

• Keeping up with the GPTs (24 minute read)
  thinks compute-poor AI labs can narrow the gap through efficiency tricks like distillation, but compute still dominates, so catching up with frontier labs is much easier than overtaking them

  

• Seeing like a spreadsheet (23 minute read)
  Interesting article by discussing how spreadsheets reshaped American business into a numbers-first optimisation machine and that AI may reorganise work and decision-making in a similar way.

Quick favor - need your take

Was there any standout article or topic from April I missed? Feel free to drop a comment or hit reply, even a quick line helps.

If you are already subscribed and enjoyed the article, please give it a like and/or share it others, really appreciate it 🙏

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for March!

Data science & AI

• State of Context Engineering in 2026 (12 minute read)
  argues that context engineering is evolving from prompt tinkering into a structured discipline where managing memory, retrieval and state becomes the core challenge of building reliable AI systems.

  

• Bayesian statistics for confused data scientists (15 minute read)
  Nicolas Chagnet explains Bayesian statistics in plain terms by showing that its real strength is not mathematical elegance but giving data scientists a cleaner way to reason about uncertainty and sparse real-world data.

• Agentic AI Engineering Guide (10 minute read)
  argues that most agentic AI systems fail not because the model is weak, but because teams make avoidable engineering mistakes around context, architecture, planning and evaluation.

  

• The Context Problem (29 minute read)
  argues that the AI industry has turned context into a token-priced billing unit even though context should really mean the relational structure that makes information coherent and useful.

• A Visual Guide to Attention Variants in Modern LLMs (26 minute read)
  shows how modern LLM attention has evolved from standard multi-head attention into a growing set of variants each designed to balance quality, memory use and inference efficiency.

  

• The Boon of Dimensionality (6 minute read)
  Amandeep Singh shows that high-dimensional space creates the geometric conditions that make embeddings, random projections and feature separation work in modern machine learning.

• GPT 5.4 is a big step for Codex (9 minute read)
  writes that GPT 5.4 feels like a real step forward for Codex, with gains in usability, speed, context handling and agent reliability that matter more in practice than benchmark scores alone.

• How Shopify Scales Taxonomy Evolution Across 10,000+ Categories With Multi-Agent AI (14 minute read)
  This piece breaks down how Shopify moved from reactive manual updates to a multi-agent system that scans taxonomy branches in parallel, proposes new categories/attributes from merchant data, detects duplicates and runs automated QA through domain-specific judges.

Data engineering

• Layer by Layer, We Built Data Systems No One Understands (9 minute read)
  writes that modern data stacks keep piling on layers in the name of simplicity but the result is often more sprawl, more cost and systems that are harder to understand or tie back to business outcomes.

  

• ETL is Dead (12 minute read)
  argues that ETL is not disappearing in volume but it is fading as the core identity of data engineering as AI shifts the real work toward context and semantics.

• The Data Engineer’s GitHub Portfolio (2026 Edition) (10 minute read)
  writes that a strong data engineering GitHub portfolio should prove technical taste, system thinking and real-world problem solving, not just show a pile of tutorial projects.

  

• The Data Engineering Mindset Every AI Builder Needs (14 minute read)
  writes that most AI products do not break because of the model but because builders ignore the data foundations early on, especially data flow design, data quality and monitoring.

• The absolute beginners guide to databasemaxxing (18 minute read)
  This article walks through database internals from a beginner’s perspective, showing how concepts like parsing, binding, scans and index seeks fit together under the hood.

• Your Data Model Isn’t Broken, Part I: Why Refactoring Beats Rebuilding (12 minute read)
  Chris Hillman makes the case that most broken data models are really bundles of hard-won business knowledge and that careful refactoring is usually smarter than blowing everything up and starting again.

• ClickHouse -> Real-time insight in 15 minutes (19 minute read)
  shows that ClickHouse is built for real-time analytics but getting that performance in production usually means handling enough operational complexity that makes platforms like Tinybird appealing.

  

• How Notion Scaled AI Q&A to Millions of Workspaces (14 minute read)
  This article walks through how Notion scaled AI Q&A to millions of workspaces while increasing onboarding throughput 600x and cutting costs by up to 90%.

Other interesting reads

• Relevance of Business Models for Data (10 minute read)
  makes the case that data teams should start with the business model first, because strategy, architecture, governance and analytics all work better when they are tied to how the company actually creates and captures value.

  

• It’s about the strategy, stupid (14 minute read)
  Timo Dechau makes the case that data work becomes far more useful when it starts with business strategy, not with dashboards, tracking audits or whatever tactic happens to be fashionable.

• What do frontier AI companies’ job postings reveal about their plans? (9 minute read)
  Interesting article suggesting that frontier labs’ job postings reveal where the market is heading, with hiring patterns pointing to heavier go-to-market pushes, new product bets and different strategies for securing compute and data.

Quick favor - need your take

Was there any standout article or topic from March I missed? Feel free to drop a comment or hit reply, even a quick line helps.

If you are already subscribed and enjoyed the article, please give it a like and/or share it others, really appreciate it 🙏

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for February!

Data science & AI

• Inside OpenAI’s in-house data agent (14 minute read)
  OpenAI explains how its in-house data agent combines rich internal context, live querying and self-learning memory to help employees go from vague business questions to trustworthy analysis in minutes.

• A Guide to Which AI to Use in the Agentic Era (17 minute read)
  breaks down the current AI tool landscape into a simple question: which model is best for this specific task, not which one wins the internet on any given day.

• Judgment isn’t uniquely human (19 minute read)
  argues judgment and taste are not uniquely human, and that treating them as off-limits to AI is another case of people underestimating how quickly models can learn high-level cognitive tasks.

  

• I spent $10,000 to automate my research at OpenAI with Codex (6 minute read)
  A researcher from OpenAI argues that people still underestimate what Codex can do in real workflows, sharing a high-usage setup and the practical lessons from using it at serious scale.

• Semantic Linking: the Aboutness of Data (12 minute read)
  expalins that semantic linking is the missing connection between data and meaning, where the real job is not adding labels to tables but explicitly mapping data objects to shared business concepts.

  

• AI Is Finally Eating Software’s Total Market: Here’s What’s Next (11 minute read)
  argues that AI won’t just disrupt software products, it will collapse whole layers of software value unless companies control the workflow, the customer relationship or the data moat.

• World Models and the Data Problem in Robotics (13 minute read)
  Nvidia researcher makes the case that robotics hits a data wall long before an algorithm wall and that world models learned from human first-person video are the most plausible route to scalable robot intelligence.

• Lessons learned from scaling data scientists with AI (10 minute read)
  Whatnot’s lesson from deploying AI for data science is that LLMs don’t remove the need for data scientists, they force teams to get serious about semantic layers and production-grade context management.

  

• How Shopify Scales Taxonomy Evolution Across 10,000+ Categories With Multi-Agent AI (14 minute read)
  This piece breaks down how Shopify moved from reactive manual updates to a multi-agent system that scans taxonomy branches in parallel, proposes new categories/attributes from merchant data, detects duplicates and runs automated QA through domain-specific judges.

Data engineering

• A Portable Analytics Stack (13 minute read)
  shows how a portable analytics stack built on DuckDB, DuckLake, dlt and SQLMesh can replace warehouse-heavy setups with lightweight, version-controlled pipelines that run locally or on cheap scheduled compute.

  

• Healing Tables: When Day-by-Day Backfills Become a Slow-Motion Disaster (24 minute read)
  Chris Hillman shows why incremental historical backfills corrupt dimensions and proposes a healing table pattern that separates change detection from period building so history can be rebuilt cleanly.

• 2028 - THE GREAT DATA RECKONING (16 minute read)
  A speculative but funny take by where he imagines a 2028 data industry shakeout where AI wipes out much of the tooling and data theater while the people who survive are the ones with real business context and architecture skills.

• Data Engineers Are Becoming MetadataOps Engineers (10 minute read)
  An interesting take by that the next layer of data engineering is MetadataOps: building AI-ready metadata, semantic structure and agent-facing governance so LLMs stop guessing and start using data reliably.

  

• How Data Engineers Can Leverage AI Tools Without Losing Fundamentals (13 minute read)
  makes the case that data engineers should use AI to accelerate the boilerplate, not outsource the fundamentals because the real leverage still comes from owning modeling, architecture and performance

• Backfills - The Necessary Evil of Data Engineering (12 minute read)
  A practical look by at why backfills happen, why engineers hate them, and how better parameterization, rerunnability, and storage-aware design can make them less painful.

  

• Why Your 5-Second BigQuery Query Isn’t Cheap (13 minute read)
  A practical breakdown of BigQuery pricing by that shows why short query runtimes are a misleading proxy for cost, and why slots are the compute metric that actually matters.

• How LinkedIn Built a Pipeline That Scales to 230M Records/sec Without Breaking SLAs (12 minute read)
  LinkedIn pushed Venice to handle 175M+ lookups per second while ingesting 230M writes per second. This piece breaks down how they balanced compaction, CPU bottlenecks and adaptive throttling to scale ingestion under eventual consistency.

Data analysis and visualisation

• The State of Machine Learning Competitions (34 minute read)
  This report maps the 2025 competition landscape and finds that winning solutions are getting more compute-hungry, transformer-heavy and increasingly shaped by Qwen in NLP while classic tabular methods still hold their ground.

  

Other interesting reads

• AI Doesn’t Reduce Work - It Intensifies It (9 minute read)
  Interesting findings in HBR that AI doesn’t really remove work so much as intensify it, speeding up expectations and raising the risk of burnout instead of delivering the productivity gains companies hoped for.

• The Anthropic Hive Mind (21 minute read)
  Steve Yegge’s take is that Anthropic’s real edge is not just Claude but a hive-mind way of working where humans and AI operate in a shared, high-speed loop that most companies aren’t built for yet.

• The least understood driver of AI progress (36 minute read)
  Anson Ho highlights that software progress, not just bigger chips or more spending, is a major and underappreciated reason AI keeps getting better faster than many people expect.

Quick favor - need your take

Was there any standout article or topic from February I missed? Feel free to drop a comment or hit reply, even a quick line helps.

If you are already subscribed and enjoyed the article, please give it a like and/or share it others, really appreciate it 🙏

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for January!

Read more

It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for December!

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It’s time for another round-up on all things data!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for November.

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It’s time for another round-up on all things data!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for October.

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It’s time for another round-up on all things data!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for September.

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It’s time for another round-up on all things data!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for August.

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As I mentioned last week, I’m making a few changes to the newsletter based on your feedback. Here’s what’s new:

1- Data roundup: I’m trialing a new format that focuses more on deep dives, practical how-tos, and longer-form content to help you sharpen your craft. Less hype about the latest model, more substance. Each month, I’ll share a curated roundup of the most useful pieces I’ve come across in AI and data across data science, data engineering and data analysis.
So: less news, more depth.

2- Referral prizes: I’m changing the referral prizes to:

1 share - 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by skill, level or free/paid

2 shares - 100+ cheatsheets for all things data (science, engineering, analysis)

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It’s time for another weekly round-up on all things Data and AI. But before that, I wanted to mention If keeping up with the latest news is not your jam, you can click on settings and turn it off (Data Bytes) and keep receiving emails for the other topics you are interested 👇

I’d rather you receive fewer emails that you are interested in than getting stuff you are not crazy about :)

Now, with that out of the way, let’s get to this week’s round-up!

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It’s time for another weekly round-up on all things Data and AI. But before that, I wanted to mention If keeping up with the latest news is not your jam, you can click on settings and turn it off (Data Bytes) and keep receiving emails for the other topics you are interested 👇

I’d rather you receive fewer emails that you are interested in than getting stuff you are not crazy about :)

Now, with that out of the way, let’s get to this week’s round-up!

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It’s time for another weekly round-up on all things AI and data. But before that, I want to mention again, if you are a data person in the trenches and interested in sharing your experience and learnings with other people, reply to the email or message me and we can work it out. We get almost 10k views every month and your experience would be valuable to others

Now, with that out of the way, let’s get to this week’s round-up!

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Today I’m announcing a brand new addition to my Substack publication: Data Tinkerer subscriber chat. This is a conversation space for subscribers. kind of like a group chat or live hangout. I’ll post questions and updates that come my way and you can jump into the discussions and or make suggestions.

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1. Get the Substack app by clicking this link or the button below. New chat threads won’t be sent sent via email, so turn on push notifications so you don’t miss conversation as it happens. You can also access chat on the web.

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2. Open the app and tap the Chat icon. It looks like two bubbles in the bottom bar, and you’ll see a row for my chat inside.

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Now let’s get to this week’s round-up!

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It’s time for this week’s round-up on all things data and AI. Before that, just sharing that you can have access to 100+ cheat sheets like below, if you share Data Tinkerer with just 2 other people

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Now, with that out of the way, let’s get to this week’s news round up on all things data and AI

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It’s time for this week’s round-up on all things data and AI. Before that, just sharing that you can have access to 100+ cheat sheets like below, if you share Data Tinkerer with just 2 other people

So if you enjoy reading Data Tinkerer, share with friends and earn referral rewards!

Refer a friend

Now, with that out of the way, let’s get to this week’s news round up on all things data and AI

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Before we get to this week’s round up of all things data and AI, just wanted to remind you that you can unlock the following prizes by referring Data Tinkerer to others:

1 share - a PDF of top 15 articles on Data Tinkerer

2 shares - 100+ cheat sheets for all things data. From Python, SQL, Power BI to Tableau, Spark, Git and many more

4 shares - 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by skill or whether it’s free or paid

Read more

Before we get to this week’s round up of all things data, just wanted to mention the referral program with the new subscribers. Based on the number of people you share Data Tinkerer with, you can unlock the following rewards:

1 share - a PDF of top 15 articles on Data Tinkerer

2 shares - 100+ cheat sheets for all things data. From Python, SQL, Power BI to Tableau, Spark, Git and many more

4 shares - 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by skill or whether it’s free or paid

Read more

Before we get to this week’s round up of all things data, I’m excited to announce that I’m making the referral gifts even easier to grab. So based on the number of people you share Data Tinkerer with, you can unlock the following rewards:

1 share - a PDF of top 15 articles on Data Tinkerer

2 shares - 100+ cheat sheets for all things data (science, engineering, analysis)

4 shares - 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by skill or whether it’s free or paid

Read more