
Inkling is Thinking Machines Lab's first open-weights foundation model.
Why it mattersInkling is a 975B-param (41B active) multimodal MoE released under Apache 2.0 with 1M-token context and same-day fine-tuning on Tinker, giving builders a permissively licensed U.S. open base model to customize rather than a locked-down API.

A recap article covering five major trends from the AI Engineer World's Fair 2026, exploring how AI engineering has shifted from building individual agents toward engineering the reliable systems, loops, and skills that surround them.
Why it mattersA concise, well-sourced map of where AI engineering is heading — the shift from building individual agents to engineering the reliable loops, skills, and systems around them.

An experimental Adobe concept where websites assemble themselves in real time around each visitor's intent.
Why it mattersIf you're building web products in the agentic era, this frames a concrete shift.
AINews is a newsletter and searchable archive that recaps daily AI developments across Twitter, Reddit, and Discord, summarizing model releases, tooling launches, and research.
Why it mattersAINews condenses the firehose of daily AI activity across Twitter, Reddit, and Discord into a single searchable recap of model releases, tooling launches, and research.

Impressions from visiting the offices of OpenAI, Anthropic and Cursor in San Francisco, and what the engineers there say about how they actually work day to day.
Why it mattersFirsthand reporting from inside OpenAI, Anthropic, and Cursor reveals where coding-agent workflows are heading — cloud agents, agent-environment engineering, and aggressive per-token cost optimization.

A keynote argument that slowing down is how teams speed up, revisited after six months in which almost everything about the tooling changed.
Why it mattersA grounded field report on how top labs and companies are restructuring engineering around the newer generation of coding agents.

The promotional website for Ethan Mollick's book 'Co-Existence,' notable for including a dedicated version of the page addressed to AI agents that read on behalf of human users.
Why it mattersIf you build websites or content that AI agents will read on a user's behalf, this shows a concrete pattern for designing dual human/AI-facing pages and A/B testing presentation against LLMs.

An essay by Ethan Mollick exploring how to use AI thoughtfully for writing and learning without surrendering the cognitive work that builds real skill.
Why it mattersIt gives a research-backed framework for using AI on writing and learning tasks in ways that build skill instead of eroding it, with specific prompting patterns like activating tutor modes rather than accepting the frictionless default.



An article examining how AI interfaces shape usability, highlighting Anthropic's Claude Cowork (a Claude Code variant for knowledge workers) and its Dispatch feature.
Why it mattersA sharp argument that chatbot interfaces impose cognitive load and that specialized or on-demand interfaces (like controlling a desktop agent from your phone via QR code) unlock more of AI's real capability.




An essay by Ethan Mollick analyzing the current state of AI, arguing that we've entered an era of managing AI agents rather than working alongside them.
Why it mattersIt gives practicing engineers a concrete mental model for the transition from working alongside AI to orchestrating fleets of agents, grounded in a real 'software factory' case study where humans no longer write or review code.



An explanatory guide that breaks down how to choose AI tools in 2025 by understanding three layers.
Why it mattersIt gives you a reusable three-layer framework — models, apps, and harnesses — for reasoning about which AI tool fits a given task, cutting through the confusion of overlapping ChatGPT/Claude/Gemini coding agents and desktop products.







An essay by Ethan Mollick analyzing why AI capabilities improve unevenly (the 'jagged frontier') and how single bottlenecks hold back entire systems until they suddenly break through.
Why it mattersGives builders a practical lens — watch which single capability is bottlenecking a workflow rather than chasing benchmark deltas.




An essay by Ethan Mollick arguing that organizations should evaluate AI models like job candidates rather than relying on public benchmarks.
Why it mattersIf you're picking an AI model for real work, this shows why leaderboard scores mislead and gives you concrete alternatives—vibes-testing, GDPval-style real-task benchmarks, and ambiguous-judgment probes.



An analytical essay by Ethan Mollick (One Useful Thing) examining how AI agents have crossed a threshold into performing economically valuable tasks.
Why it mattersA grounded read on where AI agents actually produce economic value versus noise, anchored by a concrete demo of Claude Sonnet 4.5 reproducing an academic paper's findings.



An essay by Ethan Mollick exploring how the nature of working with AI is shifting from collaborative 'co-intelligence' to a 'wizard' model, where AI produces sophisticated outputs through opaque processes that users can only verify, not shape.
Why it mattersIt gives practitioners a sharp mental model for working with frontier models like GPT-5 Pro and Claude Opus.

An analytical essay by Ethan Mollick (One Useful Thing) exploring how powerful AI is becoming as accessible as a Google search, covering the economics, ease of use.
Why it mattersA clear-eyed framing of what happens when advanced AI becomes as cheap and ubiquitous as web search, with concrete cost/energy figures and capability demos that help builders reason about pricing, adoption.

GPT-5 is OpenAI's flagship AI model that automatically selects the right reasoning level for each task and proactively takes action on vague prompts.
Why it mattersGPT-5's default toward autonomous, multi-step action on vague prompts changes how you scope tasks for it.

An essay exploring whether AI agents can navigate messy, undocumented organizational processes by training on outcomes rather than encoding human expertise.
Why it mattersIf you're deploying agents inside real organizations, this reframes the problem: instead of mapping chaotic, undocumented workflows, you may only need to define good outputs and let agents learn the path.

An essay by Ethan Mollick (Wharton Generative AI Lab) exploring how AI can help or hurt human thinking across learning, creativity, and teamwork.
Why it mattersA research-grounded rebuttal to the 'AI rots your brain' narrative, with concrete frameworks ('think first, write first, meet first') and shareable prompts for tutoring, ideation, and meeting facilitation.






An index of the vibe-coding frontier. Corrections welcome.