R[AI]DAR

Category intelligence

Social Media Briefing

All 293 current items, analyzed and ranked.

Executive synthesis

Social Media Summary

Coding agents dominated leadership commentary, with OpenAI's Greg Brockman framing Codex as an AI teammate and arguing a large capability overhang exists—users underutilize it due to habit, not model limits.

AI economics and bubble skepticism, largely driven by Gary Marcus, formed a heavy counter-current: critiques of half-trillion-dollar industry losses, SpaceX IPO hype, and claims that open-sourcing Llama catalyzed China's AI rise. On the tooling side, coverage of Microsoft Research's SkillOpt—self-evolving agent skills—rounded out the most technically substantive discussions.

Key Themes

AI Safety and Model Uncertainty · 1Coding Agents and Codex · 3AI Industry Leadership and Talent · 3AI Economics and Bubble Concerns · 12Open Source and AI Geopolitics · 4AI Agents and Governance · 2Robotics and Embodied AI · 16AI Productivity and Innovation · 2AI Devices and App Ecosystem Disruption · 3AI-Native Applications and Tooling Strategy · 4

Primary evidence

All Ranked Signals

Showing 10 of 293 signals 2 remaining
Social Twitter Jun 7

Whenever I don’t use codex for a task, I ask myself why and usually realize that there’s some missin...

By @gdb

75 score
AI Analysis

Greg Brockman observes that when he avoids using Codex it is usually due to missing context or habit rather than model limits, suggesting a large capability overhang.

Source details
Whenever I don’t use codex for a task, I ask myself why and usually realize that there’s some missing context, I needed to write a skill, or I just didn’t think to use it. Rarely is it because the task is outside of the capabilities of the model. Overhang right now feels large.
coding agentsCodexOpenAIAI capabilities
Social Twitter Jun 7

Something to show people that don't get AI safety at least a little bit. We have so much we don't kn...

By @natolambert

72 score
AI Analysis

natolambert shares something he frames as a demonstration of AI safety concerns, emphasizing how much remains unknown and uncontrolled in models.

Source details
Something to show people that don't get AI safety at least a little bit. We have so much we don't know and don't currently control in the models. (extreme content warning, but you're on X)
AI safetymodel interpretabilityalignment
Social Twitter Jun 7

Codex use-cases: “From software engineering and design to data analysis and operations, Codex is bec...

By @gdb

70 score
AI Analysis

Greg Brockman highlights Codex use cases, framing it as becoming an AI teammate rather than just an assistant across engineering, design, and operations.

Source details
Codex use-cases: “From software engineering and design to data analysis and operations, Codex is becoming an AI teammate instead of just an AI assistant.”
coding agentsCodexOpenAIAI productivity
Social Twitter Jun 7

In her first wide-ranging interview since leaving OpenAI, @miramurati shared more than ever before ...

By @emilychangtv

70 score
AI Analysis

Emily Chang highlights Mira Murati's first wide-ranging interview since leaving OpenAI, where the former CTO describes Thinking Machines' vision of humans and AI collaborating like a tandem bike and keeping people in the loop.

Source details
In her first wide-ranging interview since leaving OpenAI, @miramurati shared more than ever before about what she’s building at her AGI startup, @thinkymachines lab. The former OpenAI CTO laid out her vision for a future where humans and AI work together more closely -- “like a tandem bike” -- and where people aren’t pushed out of the loop as machines become more capable. via @BloombergLive
AI industry leadershipAGI visionThinking Machines
Social Twitter Jun 7

Zuckerberg and LeCun’s unilateral decision to open source Llama likely (partly) catalyzed China’s AI...

By @GaryMarcus

65 score
AI Analysis

Marcus argues that Zuckerberg and LeCun's decision to open-source Llama catalyzed China's AI industry and may have harmed US business interests.

Source details
Zuckerberg and LeCun’s unilateral decision to open source Llama likely (partly) catalyzed China’s AI industry — and may have done truly massive harm to American business interests. We are now starting to see the consequences.
open sourceChina AIAI geopoliticsMeta
Social Twitter Jun 7

one popular theory is that research paper alpha* and lab publishing ~died when researchers realized ...

By @swyx

65 score
AI Analysis

Swyx argues research-paper alpha and lab publishing died as researchers realized they could leave for over $100M for their tacit knowledge, claiming California non-compete rules spread knowledge more than GitHub, arxiv, and Hugging Face combined; pitches his AI Engineer conference as a product-centric complement.

Source details
one popular theory is that research paper alpha* and lab publishing ~died when researchers realized that instead of fighting with marketing depts they could simply walk out the door and get >$100m for their legally protected tacit knowledge gained california non-noncompetes have a bigger impact on knowledge spreading than github, arxiv, and huggingface combined *btw this is a motivator for me to set up @aidotengineer as a product-centric industry conference to complement the paper-centric rese
AI talent and mobilityresearch culturenon-competesAI industry
Social Twitter Jun 7

It is a really good time to store up a few of your hardest, most valuable, and most unusual ideas - ...

By @emollick

62 score
AI Analysis

Ethan Mollick advises stockpiling your hardest and most unusual ideas because AI makes good ideas cheap to implement but no easier to find.

Source details
It is a really good time to store up a few of your hardest, most valuable, and most unusual ideas - whether for work, hobbies, or a new venture. Thanks to AI, really good & unique ideas are getting extremely cheap to implement, but not necessarily easier to find. Big opportunity
AI productivityinnovationfuture of work
Social Twitter Jun 7

Only in an America can an industry that has collectively lost over half a trillion dollars —at a pac...

By @GaryMarcus

60 score
AI Analysis

Marcus criticizes the AI industry for losing over half a trillion dollars while seeking government subsidies.

Source details
Only in an America can an industry that has collectively lost over half a trillion dollars —at a pace of roughly a million dollars a minute — ask for (and likely get) government subsidies. t.co/DAbr5vnU6J
AI economicsAI bubblesubsidiesAI criticism
Social Twitter Jun 7

If you aren’t one of the banks running the SpaceX IPO, you’re the mark: https://t.co/EbQjGV6e40

By @GaryMarcus

58 score
AI Analysis

Marcus warns that anyone not among the banks running the SpaceX IPO is the mark.

Source details
If you aren’t one of the banks running the SpaceX IPO, you’re the mark: t.co/EbQjGV6e40
IPOSpaceXAI hypeinvesting
Social Twitter Jun 7

Handwritten skills are dead. Microsoft just taught them to self-evolve. Most agent skill docs are ...

By @AlphaSignalAI

55 score
AI Analysis

AlphaSignalAI summarizes a Microsoft Research paper called SkillOpt that treats an agent skill file as trainable state, using a second model to propose add/delete/replace edits validated against a held-out set, borrowing gradient-descent discipline; claims wins on 52 setups across six benchmarks.

Source details
Handwritten skills are dead. Microsoft just taught them to self-evolve. Most agent skill docs are handwritten and brittle. They rarely improve once shipped. Microsoft Research just released a paper fixing this. It treats the skill file as a frozen agent's trainable state. A second model reads execution traces. It then proposes small add, delete, or replace edits. A candidate ships only if it beats the prior version. A held-out set decides every round. The system borrows discipline
AI agentsagent skills and self-improvementresearch