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 July!

Data science & AI

• Kimi K3 Architecture Notes (4 minute read)
  dissects Kimi K3’s sparse architecture, attention residuals, NoPE design and inference-efficiency trade-offs

  

• The Cheapest Way to Make Open Weight AI Models Better (27 minute read)
  explains why targeted post-training can improve open-weight models more economically than scaling pre-training alone.

• A Taxonomy of Self-evolving Agents (14 minute read)
  Shilong Liu proposes a practical taxonomy for agents that adapt their memory, tools, prompts or policies through experience.

  

• The LLM Critics Are Right. I Use LLMs Anyway. (19 minute read)
  accepts the strongest criticisms of language models while explaining the disciplined uses that still make them valuable.

• Closing the Verification Loop (12 minute read)
  Kieran Klaassen argues that agents become dependable only when verification is built into the loop rather than left to a final human check.

  

• How an AI Token Travels Through a Data Center (26 minute read)
  traces one token through gateways, schedulers, KV caches, accelerators and networks to explain the real mechanics and economics of inference.

• How to Implement a Unified Memory From Scratch (16 minute read)
  builds a unified memory layer from scratch and explains how agents can retain useful state without turning context into clutter.

  

Data engineering

• I Spent 10 Hours Learning Multithreading and Multiprocessing (14 minute read)
  compares multithreading and multiprocessing through hands-on examples so Python practitioners can choose the right concurrency model.

  

• The Data Anarchy Tax: Why your team is firefighting 45% of the time (9 minute read)
  Good article by on why fragmented ownership and unclear operating rules create a measurable data-anarchy tax that keeps teams in reactive work.

• The Most Expensive Mistake in System Design (12 minute read)
  explains why designing for imagined scale too early can make data systems harder and more expensive to operate than the problem requires.

  

• The Startup’s Postgres Survival Guide (11 minute read)
  Alexander Belanger lays out pragmatic Postgres choices for startups that need reliability without prematurely adopting a more complex data platform.

• Your Data Warehouse Isn’t Integrated Just Because the Tables Are in One Place (12 minute read)
  argues that co-locating tables is not integration when semantics, contracts and ownership remain fragmented.

  

• So, is data modeling dead? (16 minute read)
  Daniel Beach examines what AI-assisted development changes about data modelling and why deliberate structures still matter.

• Backfilling: The Most Underrated Feature of a Data Pipeline (10 minute read)
  Lucie Thimus explains why safe, repeatable backfills are a core capability for correcting history and evolving production pipelines.

• FastAPI for Model Serving: The Standard You Should Actually Use (12 minute read)
  shows how to structure a FastAPI service for model inference with production concerns such as validation and deployment in mind.

Data analysis and visualisation

• The Model Is Smart. Your Company Is the Problem (10 minute read)
  argues that failed AI analysis is often rooted in unclear metrics and organisational fragmentation rather than model capability.

• Going Bayesian Automates Your Manual Data Analysis (9 minute read)
  Eric J. Ma shows how robust likelihoods and hierarchical Bayesian models can replace manual outlier handling with a reproducible analytical process.

Other interesting reads

• How Tech Workers Are Feeling in 2026: A Workforce Splitting in Two (22 minute read)
  Interesting analysis by and using a large sentiment survey to show how AI is dividing tech workers between amplification and destabilisation while burnout rises.

  

• You Don’t Have to Be Smart If You Can Think Clearly (4 minute read)
  argues that methodical, clear thinking under uncertainty is more dependable than flashes of intuition when engineers face genuinely hard problems.

Quick favor - need your take

Was there any standout article or topic from July 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 June!

Data science & AI

• Stop Monitoring AI Systems Like Web Services (11 minute read)
  explains why production AI needs monitoring built around quality, behaviour, cost, feedback and changing data rather than ordinary web-service health metrics.

  

• When Is It Actually a RAG Problem? (10 minute read)
  provides a decision framework for separating genuine retrieval problems from issues better solved with tools, semantic layers or improved context.

• Using Local Coding Agents (34 minute read)
  tests open-weight models in local coding-agent setups and examines how close they come to replacing hosted coding subscriptions.

• Stateful Swarms make AI 100x More Intelligent per Dollar (24 minute read)
  examines how persistent memory changes multi-agent performance, coordination and cost compared with stateless agent architectures.

  

• When Trees Still Beat Tabular Foundation Models (4 minute read)
  examines the dataset characteristics that still favor gradient-boosted trees over increasingly capable tabular foundation models.

• Running Local Models Is Good Now (8 minute read)
  draws on hands-on experiments to explain why local models have become practical for coding, research and personalised development assistance.

• Scaling Laws, Carefully (25 minute read)
  Lilian Weng provides a careful guide to neural scaling laws, covering their mathematical foundations, practical value and important limitations.

Data engineering

• Vibe Coding Is Dangerous, Agentic Engineering Isn’t (15 minute read)
  argues that coding agents become useful when humans remain responsible for specifications, architecture, tests and review.

• How to Build a Simple, Bulletproof Data Pipeline (9 minute read)
  demonstrates how a deliberately simple batch pipeline can deliver robustness and maintainability without unnecessary real-time infrastructure.

  

• Five dbt Mistakes I See in Every Startup (11 minute read)
  examines five recurring dbt mistakes involving CI, contracts, incremental models, sources and environment separation.

• Broker-Visible vs Client-Local Parallelism (6 minute read)
  compares two approaches to messaging parallelism and explains how each affects coordination, throughput and failure handling.

  

• Basic Spark Concepts (5 minute read)
  explains the foundational Spark concepts practitioners need before tuning jobs or adopting more complicated distributed-processing patterns.

• What Is Apache Arrow Flight? (13 minute read)
  Daniel Beach builds an Arrow Flight server and client to explain how Arrow and gRPC move columnar data without traditional serialisation overhead.

• In 2026, the Data Fundamentals Matter More Than Ever (12 minute read)
  argues that modelling, SQL and dependable data foundations remain the real constraints on AI adoption despite the focus on agents and new tools.

• How AI Changes Four Core Data Roles (7 minute read)
  examines how AI is changing the responsibilities and required skills of analysts, analytics engineers, data engineers and data scientists.

Data analysis and visualisation

• On Data Quality (12 minute read)
  Great article by arguing that data quality is conditional on use and should be evaluated according to the decisions and outcomes the data must support.

  

• Why General AI Models Still Cannot Be Trusted for Data Analysis (9 minute read)
  Interesting review by examining where general AI models continue to fail at analytical reasoning, validation and reliable interpretation of data.

• The Tableau Exodus Has Begun (7 minute read)
  Interesting observation by arguing that organisations leaving Tableau should use the migration to rethink metrics, workflows and the value of BI.

Other interesting reads

• Consulting in an AI-World is Fine. Big Consulting Isn’t (8 minute read)
  Interesting take by arguing that AI threatens the economics of large consulting firms more than the value of independent specialist expertise.

• What I’d Do As a Junior Candidate in Mid-2026 (6 minute read)
  practical advice by for junior candidates trying to develop credible skills and stand out in a difficult data job market.

Quick favor - need your take

Was there any standout article or topic from June 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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Keep learning

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 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.

Quick favor - need your take

Was there any standout article or topic from May 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 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 🙏

Share

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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!

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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!

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

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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!

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

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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!

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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.

Join chat

How to get started

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.

3. That’s it! Jump into my thread to say hi, and if you have any issues, check out Substack’s FAQ.

Feel free to share your opinion or suggestions there, I’d really appreciate it.

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

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

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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

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