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Almost Timely News: 🗞️ How I Keep Up With Everything in AI (2026-03-08) :: View in Browser
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What’s On My Mind: How I Keep Up With Everything in AI
This week’s newsletter is about something I get asked constantly - usually in the hallway after a talk or in the Q&A at the end of a workshop: how do you stay on top of all of this? How do you know what’s real and what’s hype? How do you actually keep up when the field moves this fast?
The answer is that I treat it like a data problem - because that is what it is. If you have ever worked in data engineering, you know the concept of ETL: Extract, Transform, Load. You pull data from sources (extract), you clean and shape it into something meaningful (transform), and then you put it to work somewhere useful (load). That three-part process maps onto how I manage the constant flood of AI news, research, tool releases, and practitioner discussion that flows through my desk every single day.
Part 1 of this newsletter covers extraction - where I actually get my information and how I have structured those sources to give me signal without burying me in noise. Part 2 is transformation - the mental framework I use to make sense of what I find, so I can quickly judge what deserves my attention and what I can safely ignore. Part 3 is loading - what I actually do with the information, from testing new models to building production tools for clients. And because this newsletter covers a lot of technical ground, I put a glossary up front so you have definitions in hand before you need them.
Part 0: Glossary of Terms
This issue covers a lot of ground across AI research, tooling, and workflows - which means it drops a fair number of technical terms along the way. Not everyone lives and breathes this stuff daily, and even experienced practitioners occasionally hit a term they have seen a dozen times without a clean definition. Here is your cheat sheet, up front.
ETL (Extract, Transform, Load): The classic data engineering workflow: pull data from sources (extract), clean and shape it into something useful (transform), then store or deliver it somewhere you can act on it (load). This newsletter borrows that same three-step structure as a framework for staying current on AI.
Foundation model: A large AI model trained on broad data - text, code, images, or all three - that serves as the base for many different tasks. Think of it as a well-educated generalist. GPT-5.4, Gemini 3.1, Qwen3.5, GLM-5, and Claude Opus 4.6 are all foundation models.
Open-weights model: An AI model whose internal parameters have been publicly released so anyone can download and run it locally. Qwen 3.5 is an example. Contrast with proprietary models, where the weights stay locked inside the company that built them.
Local LLM: A large language model you run on your own hardware rather than through a cloud service. Your data stays on your machine and there are no per-use costs. The trade-off: local hardware limits how large a model you can run.
Context window: The maximum amount of text an AI model can hold in working memory at one time. Everything it reasons about in a single session must fit inside this window. Bigger windows mean the model can handle longer documents or conversations without losing the thread.
Harness: The software framework, interface, and tooling built around an AI model to make it useful for real work. If the model is the engine, the harness is the rest of the car - transmission, steering, safety systems. Most real-world AI value comes from well-built harnesses, not just from better models.
Agentic / AI agents: AI systems that take autonomous actions - browsing the web, writing and running code, calling APIs, reading files - and chain those steps together to complete multi-part tasks with minimal hand-holding. A super-agent coordinates multiple sub-agents or tools to tackle complex workflows.
RAG (Retrieval-Augmented Generation): A technique that gives an AI model access to an external knowledge base at response time. The model retrieves relevant documents first, then uses that content to ground its answer - accurate responses about your specific data without retraining from scratch.
Scaffolding: The supporting structure built around a task before the detailed work is filled in - file structure, function signatures, placeholder logic. Good scaffolding lets smaller, cheaper models do solid work because much of the thinking has already been done.
Proof of concept (POC): A quick prototype built to test feasibility - not polished, not production-ready, just functional enough to answer “can we actually do this?” POCs validate an approach before you commit real resources to it.
Hallucination: When an AI model confidently generates something factually wrong or simply made up - an inherent property of how these models work, not a random glitch. Tasks requiring net-new content carry higher hallucination risk than tasks where you have already provided the source material.
Preprint: An academic paper posted publicly - typically on arXiv - before peer review. Preprints let researchers share findings fast, which matters enormously in a field moving as quickly as AI. Treat them as promising leads, not settled science.
arXiv (arxiv.org): The dominant preprint server for AI and machine learning research. Most major labs post papers here first. It is where you see what is happening in research now, not six months from now.
RSS (Really Simple Syndication): A standardized feed format that lets you subscribe to many sites at once and get new content automatically - a practical way to monitor dozens of blogs, news sites, or developer announcements without visiting each one.
Talkwalker / Brand24: Social listening and media monitoring platforms that track mentions, discussions, and news across the web and social channels in near real-time. They can ingest enormous volumes of content and make it available for automated processing.
Discord announcement channels: A Discord feature that lets servers designate specific channels as announcement channels. Other servers can follow them, so official posts appear automatically in your own server - high-signal release announcements without the noise.
Subreddit / Reddit community: A topic-specific forum on Reddit. Communities like r/LocalLLaMA are often the fastest signal for how practitioners actually receive a new model or tool - reactions, use cases, and bugs included.
ICLR, ICML, NeurIPS: The three most prominent academic conferences in machine learning - International Conference on Learning Representations, International Conference on Machine Learning, and Neural Information Processing Systems respectively. Major research from Google, Anthropic, Meta, and Alibaba is timed to these events, so paper volume spikes dramatically around them.
Qwen / Qwen 3.5: A family of open-weights AI models from Alibaba, available in multiple sizes. Smaller versions handle summarization well on modest hardware; larger versions are competitive with frontier proprietary models - a useful illustration of how capable open-weights models have become.
Claude Code: Anthropic’s AI-powered coding assistant and agentic development environment. It writes, edits, and runs code; manages files; and executes multi-step tasks with real autonomy. A prime example of a purpose-built harness.
Deerflow: An agentic super-agent tool from ByteDance - an orchestration system that chains multiple AI actions together to complete complex, multi-step tasks autonomously.
API (Application Programming Interface): A defined interface that lets one piece of software talk