Hi! I'm Tatenda.

Who to follow

• One Useful Thing (Ethan Mollick)newsletterEvidence on how AI changes work and organisations, written for leaders
• The Every PodcastpodcastWeekly conversations with operators putting AI to work inside companies, including Every's enterprise adoption practice
• Elena VernanewsletterWrites plainly about how AI is changing jobs, teams and growth work
• Lenny's NewsletternewsletterThe largest product community; its AI surveys and practitioner interviews show what teams actually adopt
• Engineering Enablement (DX)newsletterEvidence on AI productivity in engineering teams, measured rather than claimed
• MIT Sloan Management RevieworgResearch-backed articles on operating models, leadership and getting value from AI
• Behind the CraftnewsletterHands-on tutorials showing non-engineers how to prototype and work with AI tools
• Wharton Knowledge: AIorgPublishes the annual enterprise AI adoption report and research on AI at work

Start with these

• Navigating the Jagged Technological FrontierDell'Acqua, Mollick et al. (Harvard Business School working paper): Field experiment with BCG consultants showing where AI lifts performance and where it hurts
• AI tools are overdelivering: results from a large-scale AI productivity surveyLenny's Newsletter: What practitioners say AI actually changed in their work
• 2025 AI adoption reportWharton: How large US firms are using and measuring generative AI
• AI adoption is a UX problemNan Yu: Why rollouts stall on interface and habit, not capability
• Guidance for AI AdoptionNational AI Centre / Department of Industry, Science and Resources: Current government guidance on the practices organisations should put in place when adopting AI
• Unlocking productivity with generative AI: evidence from experimental studiesOECD: A sober summary of measured productivity gains

Who to follow

• Anthropic EngineeringorgFirst-party write-ups on agent design, tool writing, context engineering and multi-agent systems from people who ship them
• Model Context Protocol BlogorgSpec changes, roadmap and security guidance for the protocol most agent tool integrations now use
• LangChain BlogorgRegular posts on agent architectures, agent evaluation and production case studies
• Microsoft Copilot Studio BlogorgWhat is new for building agents in Copilot Studio, with governance and licensing changes
• Designing with AI (Victor Dibia)newsletterFocused on multi-agent design patterns and how to evaluate them
• Drew BreunigpersonClear framing of how context, prompts and tools fit together in agent systems
• Philipp SchmidpersonShort, code-heavy guides on agent frameworks, function calling and managed agents
• Foundation CapitalorgCoined 'service as software' and the system-of-agents thesis for enterprise AI

Start with these

• Building effective agentsAnthropic: The standard reference for when to use workflows versus agents, with the core patterns named
• Effective context engineering for AI agentsAnthropic: Explains how to manage what goes into an agent's context window over long tasks
• Agentic AI in organisations: early insights from practitioner interviewsOECD: How organisations are deploying agents and what governance they need
• How we built our multi-agent research systemAnthropic: A production multi-agent architecture with honest notes on cost, prompting and evaluation
• Don't Build Multi-AgentsCognition (Walden Yan): The strongest counter-argument: why shared context beats splitting work across agents
• Owning the workflow in B2B AI appsa16z: Why value accrues to whoever owns the end-to-end workflow
• A system of agents brings service-as-software to lifeFoundation Capital: The thesis behind agents replacing outsourced services

Who to follow

• APRAorgPrudential regulator for banks, insurers and super; its operational risk and tech expectations bind AI use
• Australian Signals Directorate / ACSCorgIssues security guidance on AI systems and advisories that feed into board cyber risk
• Gradient InstituteorgLocal, technical work on AI risk and assurance, often with government and industry
• AI Safety NewsletternewsletterFortnightly roundup of AI safety research and policy, for risk teams tracking frontier issues
• Joanne C KleinpersonDetailed posts on labelling, retention and data controls that decide whether Copilot is safe to roll out

Start with these

• Voluntary AI Safety StandardDepartment of Industry, Science and Resources: Australia's ten guardrails for deploying AI, the local reference point for governance
• AI Risk Management FrameworkNIST: The most widely used structure for identifying and managing AI risk
• ISO/IEC 42001 overviewMicrosoft Learn (compliance documentation): Plain summary of the AI management system standard and how a major vendor certifies to it
• Operational risk management (CPS 230)APRA: The prudential standard that governs AI vendors and critical operations for regulated entities
• Advancing accountability in AIOECD: How to connect risk-management frameworks to accountability across the AI lifecycle
• Can the finance sector oversee AI innovation while maintaining its rapid progress?OECD: Supervisory thinking on AI in financial services
• Towards a common reporting framework for AI incidentsOECD: A template for logging and reporting AI incidents consistently
• Disrupting the first reported AI-orchestrated cyber espionage campaignAnthropic: A real incident showing why agent security belongs on the risk register

Who to follow

• Hamel HusainpersonThe best practical source on error analysis, LLM-as-judge and building evals that matter
• Eugene YanpersonDeeply researched patterns for evals, retrieval and recommendation systems; posts less often but each one lasts
• Jason Liu (jxnl)personConcrete playbooks for improving RAG with measurement rather than guesswork
• Arize AI BlogorgFrequent material on tracing, evaluation and monitoring LLM apps in production

Start with these

• Your AI Product Needs EvalsHamel Husain: Shows why evals drive iteration speed and how to start with simple assertions and traces
• Evals FAQHamel Husain: Practical answers to the questions every team asks when starting evals
• Demystifying evals for AI agentsAnthropic: How to evaluate multi-step agents, not just single answers
• What We've Learned From A Year of Building with LLMsYan, Bischof, Frye, Husain, Liu, Shankar: Tactical, operational and strategic lessons from six practitioners in one place
• A five-step roadmap to closing the AI evaluation gapOECD: The policy view of why evaluation lags deployment, and what to do

Who to follow

• Joe ReispersonPractical, opinionated commentary on data teams, modelling and the industry
• benn.substack (Benn Stancil)newsletterSharp, often contrarian essays on what data teams are for and where the market is going
• Data Products (Chad Sanderson)newsletterThe main source on data contracts and shifting data quality upstream; posts less often
• Juan SequedapersonConnects data semantics and metadata to making AI work on enterprise data
• dbt Labs BlogorgProduct direction and practice for analytics engineering and the dbt semantic layer
• The Analytics Engineering RoundupnewsletterWeekly curation and commentary on analytics engineering and data plus AI
• Databricks BlogorgPrimary source for lakehouse architecture, governance and data plus AI platform changes
• Snowflake BlogorgPrimary source for Snowflake platform, Iceberg and data sharing changes
• Data Engineering WeeklynewsletterRounds up the best engineering posts on pipelines, platforms and data quality each week
• Simon Späti (ssp.sh)personDetailed comparisons of data tools, semantic layers and open table formats
• Kai WaehnerpersonReal enterprise case studies of Kafka, Flink and streaming architectures by industry
• Jack VanlightlypersonRigorous deep dives into how Kafka, Iceberg, Delta and Hudi really behave

Start with these

• The Rise of Data ContractsChad Sanderson: Makes the case for agreed schemas between producers and consumers to stop pipeline breakage
• What is analytics engineering?dbt Labs: Defines the analytics engineer role and the software practices it brings to analytics
• Data Mesh Principles and Logical ArchitectureZhamak Dehghani: The four principles and the data product idea in their defined form
• Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced AnalyticsArmbrust, Ghodsi, Xin, Zaharia (CIDR 2021): The paper that defined the lakehouse architecture
• AI, data governance and privacyOECD: Where data governance and privacy law meet AI
• Context graphs: capturing the why in the age of AIDharmesh Shah: Why agents need the reasons behind data, not just the data

Who to follow

• Office 365 for IT ProspersonNear-daily, candid analysis of Microsoft 365 Copilot features, licensing and admin gaps
• Microsoft 365 Copilot Blog (Tech Community)orgFeature releases, admin controls and deployment tips straight from the product team
• Microsoft Foundry BlogorgHands-on posts on models, agents and evaluation tools in Azure's AI platform
• Microsoft 365 RoadmaporgItem-level feed of upcoming Microsoft 365 and Copilot changes, useful for change management
• AWS Machine Learning BlogorgWorked examples on Amazon Bedrock and SageMaker, often from named enterprise customers
• Google Cloud Blog: AI & Machine LearningorgUpdates and case studies on Vertex AI and Gemini Enterprise
• Google Workspace UpdatesorgEach Gemini-in-Workspace change and its admin settings, as it ships
• Salesforce NewsorgAgentforce and Einstein announcements, pricing changes and customer stories
• ServiceNow BlogorgNow Assist and AI agent updates for IT and service workflows

Start with these

• Microsoft 365 Copilot adoption hubMicrosoft: Official rollout playbooks, scenario libraries and success measures for Copilot
• Data, privacy and security for Microsoft 365 CopilotMicrosoft Learn: What Copilot can see, where data goes and which controls apply; the first doc risk teams ask for
• How many products does Microsoft have named Copilot?Tey Bannerman: A map of every Copilot, useful before any licensing decision
• AI adoption in the Cloud Adoption FrameworkMicrosoft Learn: Step-by-step guidance on strategy, governance and platform setup for AI on Azure
• Generative AI Lens, AWS Well-Architected FrameworkAmazon Web Services: Checklist for building reliable, secure and cost-aware generative AI workloads
• Building A Generative AI PlatformChip Huyen: A step-by-step reference architecture from simple calls to routing, guardrails and caching

Who to follow

• Benedict EvanspersonLong-view essays and an annual deck on where AI fits in the wider tech and business cycle
• StratecherynewsletterExplains the strategic moves of the big AI vendors you buy from; free posts appear in the public feed
• Exponential ViewnewsletterWeekly read on AI adoption, costs and economic impact, with data a board can follow
• Platforms, AI and the economics of BigTechnewsletterSharp analysis of how AI reshapes business models, workflows and competitive position
• Tomasz TunguzblogShort, data-led posts on AI economics, pricing and the software market
• Clouded JudgementnewsletterWeekly view of software and AI company metrics for anyone building a business case
• The LeveragenewsletterStrategy essays on profit and power in AI, from a former Every lead writer
• a16zorgRegular data on what enterprises are buying and building with AI, from an investor's seat
• McKinsey InsightsorgSource of the widely cited State of AI survey and operating-model research; feed covers all topics
• Understanding AInewsletterClear, sober explainers of what AI can and cannot do yet, useful for setting realistic expectations
• Big TechnologynewsletterInterviews and reporting on how large companies are deploying AI and what it costs them
• Epoch AIorgData-backed forecasts on scaling and cost that are useful for planning

Start with these

• AI Index ReportStanford HAI: Annual, data-heavy baseline on AI capability, cost, adoption and investment
• Presentations (including the annual AI deck)Benedict Evans: Board-ready framing of how AI shifts the tech industry and where value may land
• AI horseless carriagesPete Koomen: Why bolting AI onto old software designs disappoints
• Slow-burn AI: when augmentation, not automation, is the real threatSangeet Paul Choudary: How augmentation quietly reshapes roles and margins
• Venture capital investments in AI through 2025OECD: Where the money in AI is going, by country and sector

Who to follow

• Simon Willison's WeblogpersonThe most consistent hands-on log of what coding agents, tool use and new models actually do when you try them
• The Pragmatic EngineernewsletterThe most-read engineering newsletter; tracks how AI tools change real software teams
• Andrej KarpathyblogClear explanations of how LLMs work and where software is heading
• Latent SpacenewsletterLong interviews with the people building frontier models and AI products
• AI News by Latent SpacenewsletterThe fastest way to catch each day's model releases and discussion in one email
• InterconnectsnewsletterInsider-level analysis of RL, post-training and open models
• Import AInewsletterReads important papers each week and explains why they matter for policy and capability
• Ahead of AI (Sebastian Raschka)newsletterClear monthly reviews of new LLM architectures and papers, often with code
• Martin FowlerpersonSober, engineering-first essays and fragments on using coding agents in real teams
• Elevate (Addy Osmani)newsletterPractical guidance on working with AI coding agents and keeping code quality up
• Armin RonacherpersonDetailed first-person accounts of building software with coding agents, including what fails
• Geoffrey HuntleypersonPushes coding agents to their limits and documents the techniques, such as running agents in loops
• Hugging Face BlogorgRelease notes and tutorials on open models, fine-tuning, quantisation and inference
• vLLM BlogorgWhere to learn how serving performance, batching and new model support actually work
• Modal BlogorgEngineering posts with real numbers on GPU inference, latency and cost
• Together AI BlogorgResearch and benchmarks on fast inference, speculative decoding and fine-tuning
• Doug TurnbullpersonGrounded view of retrieval and search ranking that RAG teams often skip
• Deep (Learning) Focus (Cameron R. Wolfe)newsletterThorough explanations of post-training, RL and fine-tuning methods with paper references
• Vicki BoykispersonCareful, unhyped writing on embeddings, ML infrastructure and building with models
• Lil'Log (Lilian Weng)personRare but exhaustive literature reviews that become standard references
• Anthropic ResearchorgPrimary source for interpretability, alignment and Claude capability work
• OpenAI NewsorgPrimary source for OpenAI model, research and product releases
• Google DeepMind BlogorgPrimary source for Gemini and DeepMind science results
• Google Research BlogorgSteady stream of applied research across ML systems, health and science

Start with these

• Patterns for Building LLM-based Systems & ProductsEugene Yan: Seven patterns (evals, RAG, caching, guardrails and more) that still map most production systems
• Introducing Contextual RetrievalAnthropic: A measured technique for cutting RAG retrieval failures, with numbers
• LLM Powered Autonomous AgentsLilian Weng: The canonical breakdown of planning, memory and tool use that most later work builds on
• How to write a good spec for AI agentsAddy Osmani: Specs as the main lever on agent output quality
• The learning loop and LLMsMartin Fowler: Why developers still need to learn while AI writes code
• How generative and agentic AI shift concern from technical debt to cognitive debtMargaret-Anne Storey: The hidden cost when teams stop understanding their own systems
• The Bitter LessonRich Sutton: The short essay explaining why general methods that scale with compute keep winning
• Scaling MonosemanticityAnthropic (Transformer Circuits): Landmark interpretability result showing interpretable features inside a production model
• Can AI Scaling Continue Through 2030?Epoch AI: Quantifies the power, chip, data and latency limits on training scale

Who to follow

• iTnewsorgDaily reporting on how Australian banks, agencies and large firms run tech and AI projects
• InnovationAus: Artificial IntelligenceorgClose coverage of federal AI policy, procurement and regulation debates
• The MandarinorgHow government agencies are adopting and governing AI and data
• CSIRO's Data61orgAustralia's largest AI research group, including responsible AI engineering work
• AI Forum New ZealandorgNZ adoption research, policy submissions and member case studies
• Tech Council of AustraliaorgIndustry positions on AI regulation, skills and investment that shape national policy
• MemianewsletterWeekly ANZ-aware digest of AI and tech developments with local policy commentary
• The Next Billion SecondspersonAustralian podcast and essays on where AI is heading and what it means for society
• SmartCompany: Artificial Intelligence (Neural Notes)orgHow Australian businesses are using AI, with local vendors and data
• IT Brief AustraliaorgHigh-volume local vendor, partner and customer AI announcements

Start with these

• National AI PlanDepartment of Industry, Science and Resources: The Australian Government's overall plan for AI capability, adoption and safety
• Australia: national AI policy initiativesOECD.AI: Every Australian AI policy initiative in one place, compared with other countries
• Engaging with artificial intelligenceAustralian Signals Directorate and international partners: Security questions to ask before adopting an AI system
• Public Service AI FrameworkNew Zealand Government (digital.govt.nz): New Zealand's framework for responsible AI use across the public service

Who to follow

• Office of the Australian Information Commissioner (OAIC)orgSets privacy expectations for AI use under the Privacy Act and publishes enforcement decisions
• Department of Industry, Science and Resources: Artificial intelligenceorgHome of the Voluntary AI Safety Standard, AI adoption guidance and regulatory proposals
• Digital Transformation AgencyorgOwns the policy for responsible use of AI in Australian Government agencies
• National AI CentreorgPublishes practical adoption guidance, tools and events for Australian organisations
• NIST Information Technology newsorgAnnounces AI risk, agentic AI security and standards work that global frameworks borrow from
• The EU AI Act NewsletternewsletterFortnightly summary of EU AI Act implementation, useful for firms with EU exposure or copying its model
• Luiza's NewsletternewsletterFrequent, readable analysis of AI regulation, lawsuits and governance practice
• IAPPorgDaily coverage of AI governance and privacy law worldwide, written for practitioners
• Human Technology Institute (UTS)orgAustralian research on AI governance for boards and AI law reform

Start with these

• OECD AI PrinciplesOECD: The intergovernmental baseline most national AI policies trace back to
• Policy for the responsible use of AI in governmentDigital Transformation Agency: Mandatory rules for Commonwealth agencies, and a useful template for regulated firms
• Guidance on privacy and the use of commercially available AI productsOAIC: How the Privacy Act applies when staff use tools like Copilot or ChatGPT with personal data
• Common guideposts to promote interoperability in AI risk managementOECD: Compares major AI risk frameworks side by side
• Bridging frameworks: how HAIP 2.0 supports interoperability with the EU AI ActOECD: How one reporting effort can serve several regimes
• Intellectual property issues in AI trained on scraped dataOECD: The copyright and data-scraping questions in plain terms