Staying Current — Newsletters, People, Communities

Who and where to follow for staying current on AI-assisted dev tooling. Nothing goes in this file without meeting the evidence bar: named practitioners with a real, checkable identity (org affiliation, public repo, publication history), current docs, or repo-health signals. For verified tools/repos/products themselves, see _ai-tooling-recommendations.md. Process notes live in decisions/.

Newsletters

TLDR AI — daily digest, ~1.1M subscribers. Breadth over depth; good for not missing a major release, not for understanding one.
Latent Space — Substack, swyx + Alessio Fanelli. The defining "AI Engineer" publication; same brand runs the podcast and Discord below. Closest thing to a beat-reporting operation for this specific space.
The Batch — DeepLearning.AI / Andrew Ng. General AI-industry framing, not dev-tooling-practice specific — peripheral, not core.
Ben's Bites — established AI product/tool newsletter, product-launch and funding-news focus rather than technical practice (exact current URL not confirmed, search before subscribing).
Interconnects by Nathan Lambert — 79k+ subscribers. Real ML-research background (Berkeley AI, Meta, DeepMind, HuggingFace) — writes about training/alignment research with the technical depth of someone who's done the work, not summarized it.
Ahead of AI by Sebastian Raschka — same author as the YouTube channel below; paper-to-practice explainers from someone with a real research and textbook-authoring background, not a news aggregator.

Newsletters on a breadth to depth line.

People to follow

X/social handles are inherently a lower evidence bar than repos/docs — real accounts posting real content, not vetted authorities. Split below by whether the content is actually about building something — a tool, a skill, an agent, an automation, code or no-code — versus commentary/coaching that isn't. Each entry states specifically what it's good for.

How much weight each kind of source earns.

Breadth, depth, practitioners, and community. Weight each by evidence.

Core — building tools, skills, and agents (code or no-code)

Andrej Karpathy — YouTube, @karpathy. Former Tesla AI director, OpenAI founding member. The "Zero to Hero" series builds a neural net and then an LLM from scratch in code, no slideware — the most technically rigorous "how transformers actually work" education available from any single source. Highest-confidence entry on this whole list.
Sebastian Raschka — YouTube, Ahead of AI Substack above. ML researcher and staff member at Lightning AI, author of multiple deep-learning textbooks including a from-scratch LLM-building book. Reliable bridge between new papers and practical implementation — technically literate, not hype-summarizing.
@simonw (Simon Willison) — creator of Datasette, long-running independent writer on local models, CLI tooling, and LLM security. Dates everything, links primary sources, states uncertainty explicitly rather than hedging vaguely. The highest signal-to-hype ratio individual voice on this entire list.
@swyx (Shawn Wang) — Latent Space co-host, ex-AWS/Temporal/Airbyte before going independent. Coined/popularized "AI Engineer" as a distinct role. Tracks dev-environment and agent-harness shifts in real time — less a single-topic expert, more the person watching the whole tooling ecosystem move.
Greg Kamradt — YouTube (Data Independent), gregkamradt.com. Creator of the "Needle in a Haystack" test — now a standard benchmark for LLM long-context retrieval, used and cited across the industry including by model vendors themselves. Data Independent covers hands-on RAG/agent-framework build-alongs. Worth following specifically for context-window and retrieval-evaluation methodology.
Sam Witteveen — YouTube (exact current handle not confirmed, search "Sam Witteveen AI"), @Sam_Witteveen. Co-founder of Red Dragon AI, a working AI company in Singapore. One of the earlier LangChain-era educators; channel now covers agentic-application patterns and local/open-source model walkthroughs. A practicing company co-founder narrating real build decisions, not tutorial content alone.
Mervin Praison — YouTube, mer.vin, github.com/MervinPraison. Creator of PraisonAI, a multi-agent orchestration framework supporting 100+ LLMs with built-in memory/RAG. Real, checkable identity and active repo. Relevant specifically if evaluating multi-agent frameworks — a first-party source on that framework's own design tradeoffs, narrower relevance outside that.
@skirano (Pietro Schirano) — profiled by Replit's own blog as a notable independent builder; known for fast, design-literate AI-assisted app-building demonstrations on X. Real identity, but the content is technique-and-demo, not documented methodology — useful for seeing what's currently possible in AI-assisted UI building; treat as inspiration, not a source to cite claims from.
@realrileybrown (Riley Brown) — 329K followers, real person, associated with the VibeCode app/newsletter, one of the more visible faces popularizing "vibe coding" — building real software with AI assistance, no traditional dev background required. Useful for tracking that workflow and vocabulary as it evolves; the "$9M app" figure circulating about him comes from marketing-adjacent blogs, not an audited source, so weight the outcome claims separately from the technique itself.
@nick_saraev — 550K followers, real person, founder of Maker School — an AI-automation-agency community teaching n8n/Zapier-style agent and workflow building. No-code, but it's genuinely about constructing agents/automations, which is this list's actual subject. Caveat is on the packaging, not the content category: this is a paid course/community business, and several independent "is this legit" review sites exist specifically because of that — read the technique, weight the outcome claims skeptically.
@nathanhodgson_ (TikTok) — 141.8K followers, real and active, Make/Zapier-centric AI-automation-building content — same category as nick_saraev, same caveat on outcome claims vs. technique. Note: original source had this as @nathanhodgson.ai, corrected to @nathanhodgson_.
The Cognitive Revolution (Nathan Labenz) — podcast. Long-form interviews where guests are often the actual researchers or builders behind a given model or paper, not press-circuit talking heads — gets more technical candor than most coverage of the same announcement.

Audience size is not evidence. Outcome claims are unaudited.

Technical voices and what each is for.

Adjacent — real and checkable, but not about building

Kept for completeness, not endorsed at the same level as the section above. The distinguishing feature here isn't "no-code" or "not a developer" — it's that the content itself isn't instructional about constructing a tool, skill, or agent; it's commentary, career content, or using an AI product as an end user.

Real people, kept for completeness, not endorsed like the core list.

@thevarunmayya (Varun Mayya) — 1M+ followers, real entrepreneur (founder, Avalon Labs; previously connected to Furlenco). Broad startup/career/tech commentary — general tech-culture awareness, not building instruction.
@sabrina_ramonov — real, founder of Blotato (an AI content-distribution tool — she has built something), but the audience-facing content is mostly "how to use AI for content/marketing output" rather than "how to build an agent or skill." Worth a second look specifically for how Blotato itself is built, less so for the broader content stream.
@mavgpt (Maverick Maltin) — real, prompt-engineering/AI-productivity creator. Actionable technique underneath (persona framing, multi-persona "Board of Directors" prompting, chain-of-thought breakdowns) but packaged as viral-hook marketing funneling to paid guides — treat as a prompting-technique source, not a build-instruction source.
@gayatri.tech (Gayatri Agrawal) — real, AI/ML background, founder of AI-services startup ALTRD. Content is workflow/adoption-focused (where AI fits in business ops, tool-to-task fit, AI literacy) rather than hands-on building; short-form format trades off technical depth. Funnels to consulting work, not courses.

Communities (Discord/Reddit/etc.)

Latent Space Discord — tied to the Latent Space brand above.
Anthropic Developers Discord — official Anthropic community.
OpenAI Developers Discord — official OpenAI community.
Cursor Community Discord — official Cursor community.
r/LocalLLaMA — open-weights/local-hardware focus, fast honest benchmarking of new claims.
r/mcp — smaller/newer, MCP-specific.
r/PromptEngineering
ODS.ai (Open Data Science) — long-running practitioner ML community, Telegram-based.

Where to ask questions and watch claims get tested.