Intel
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The Pulse: Grok’s CLI caught uploading all your local files to the cloud
The Pragmatic Engineer's Pulse on Grok's CLI being caught uploading users' local files, including SSH keys and documents, to the cloud just as the capable Grok 4.5 model was winning developers over.
Why it mattersIf you use AI coding CLIs, this breaks down how Grok's CLI was caught silently uploading local files to the cloud — essential reading for understanding the data-privacy and telemetry risks before wiring an agent into your codebase.
articleGergely Orosz
Inkling (Thinking Machines Lab)
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.
articleLatent Space
Context engineering with Dex Horthy
Dex Horthy on context engineering: what actually goes into an agent's window, and why managing that is the discipline rather than prompt wording.
Why it mattersDex Horthy coined 'context engineering'.
articleGergely Orosz
AI Engineer World's Fair 2026 Recap
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.
articleRichard MacManus
Adobe Agentic Sites (Audience of One)
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.
articleRichard MacManus
AINews
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.
articleLatent Space
Impressions from visiting OpenAI, Anthropic, & Cursor
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.
articleGergely Orosz
Dwarkesh Patel
The next big breakthrough will be AIs learning on the job
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Scaling Laws, Carefully
Scaling laws say training loss falls predictably as model size, data and compute grow, a straight line on a log-log plot.
Why it mattersWalks through how training loss scales predictably with model size, data, and compute, and how to allocate a fixed compute budget optimally between parameters and tokens.
articleLilian Weng
Slow down to speed up: so much has changed in 6 months’ time
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.
articleGergely Orosz
Why is Meta destroying its engineering organization?
Gergely Orosz on what has been happening inside Meta's engineering organisation, and why the changes look self-inflicted from the outside.
Why it mattersA grounded, insider account of how top-down AI mandates and 'always use AI' pressure are reshaping a world-class engineering org.
articleGergely Orosz
Building OpenCode with Dax Raad
Dax Raad on building OpenCode, on the Pragmatic Engineer podcast.
Why it mattersOpenCode is one of the fastest-growing open-source AI coding tools, and this conversation surfaces the engineering judgment and open-source/model-provider strategy behind it.
articleGergely Orosz
Dwarkesh Patel
Eric Jang – Building AlphaGo from scratch
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Dwarkesh Patel
Reiner Pope – The math behind how LLMs are trained and served
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Claude Cowork with Dispatch
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.
articleEthan Mollick
Dwarkesh Patel
Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
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Dwarkesh Patel
Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute
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The Shape of the Thing
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.
articleEthan Mollick
A Guide to Which AI to Use in the Agentic Era
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.
articleEthan Mollick
Dwarkesh Patel
Dario Amodei — "We are near the end of the exponential"
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Lex Fridman
#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
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The Shape of AI: Jaggedness, Bottlenecks and Salients
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.
articleEthan Mollick
Dwarkesh Patel
Ilya Sutskever — We're moving from the age of scaling to the age of research
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Giving your AI a Job Interview
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.
articleEthan Mollick
Dwarkesh Patel
Andrej Karpathy — AGI is still a decade away
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Dwarkesh Patel
Some thoughts on the Sutton interview
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Real AI Agents and Real Work
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.
articleEthan Mollick
Dwarkesh Patel
Richard Sutton – Father of RL thinks LLMs are a dead end
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Dwarkesh Patel
Fully autonomous robots are much closer than you think – Sergey Levine
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On Working with Wizards
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.
articleEthan Mollick
Mass Intelligence
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.
articleEthan Mollick
GPT-5
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.
articleEthan Mollick
The Bitter Lesson versus The Garbage Can
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.
articleEthan Mollick
Lex Fridman
#475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games
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Against "Brain Damage"
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.
articleEthan Mollick
Dwarkesh Patel
Why I don’t think AGI is right around the corner
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Dwarkesh Patel
Is RL + LLMs enough for AGI? — Sholto Douglas & Trenton Bricken
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Why We Think
Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post.
Why it mattersA carefully sourced deep dive into why test-time compute and chain-of-thought improve LLM reasoning, giving engineers a mental model for when and how to spend inference-time compute effectively.
articleLilian Weng
Dwarkesh Patel
Mark Zuckerberg — AI will write most Meta code in 18 months
podcast
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Dwarkesh Patel
AGI is still 30 years away — Ege Erdil & Tamay Besiroglu
podcast
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Dwarkesh Patel
AI 2027: month-by-month model of intelligence explosion — Scott Alexander & Daniel Kokotajlo
podcast
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Dwarkesh Patel
Satya Nadella — Microsoft’s AGI plan & quantum breakthrough
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Lex Fridman
#459 – DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters
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Reward Hacking in Reinforcement Learning
Reward hacking is an agent exploiting flaws in a reward function to score well without doing the task.
Why it mattersIf you're doing RLHF or RL fine-tuning of language models, this explains how agents exploit reward-function flaws — modifying unit tests to pass coding tasks, sycophantically mirroring user preferences.
articleLilian Weng
Lex Fridman
#452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity
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Extrinsic Hallucinations in LLMs
Narrowing hallucination to its useful meaning: output that is fabricated and grounded in neither the provided context nor world knowledge, rather than any mistake a model makes.
Why it mattersIt gives a precise taxonomy of hallucination (in-context vs. extrinsic) and surveys the actual detection and mitigation methods.
articleLilian Weng
Diffusion Models for Video Generation
Diffusion models moved from image synthesis to video, a strictly harder problem because temporal consistency across frames demands world knowledge a single image never needed.
Why it mattersA structured survey of how diffusion models extend from image to video generation, unpacking the temporal-consistency and data-scarcity problems that define the current research frontier.
articleLilian Weng
Thinking about High-Quality Human Data
Most task-specific labelled data comes from human annotation, which makes annotator disagreement, expertise and incentives a modelling problem rather than an operational detail.
Why it mattersA deep dive into the mechanics of high-quality human annotation and RLHF labeling — covering rater agreement, aggregation, and quality-control techniques that directly affect the data your alignment and fine-tuning pipelines depend on.
articleLilian Weng
Adversarial Attacks on LLMs
A survey of adversarial attacks and jailbreak prompts against language models.
Why it mattersA rigorous, research-grounded map of how jailbreaks and adversarial prompts actually work against aligned LLMs, giving engineers the vocabulary and threat models needed to red-team and harden their own AI products.
articleLilian Weng
How to Train Really Large Models on Many GPUs?
[Updated on 2022-03-13: add expert choice routing .] [Updated on 2022-06-10]: Greg and I wrote a shorted and upgraded version of this post, published on OpenAI Blog.
Why it mattersA rigorous, well-organized reference on the parallelism strategies (data, tensor, pipeline, MoE, expert-choice routing) and memory-saving techniques that make large-scale model training feasible.
articleLilian Weng
An index of the vibe-coding frontier. Corrections welcome.