ByteDance building China’s largest model while running TikTok is fascinating: few companies have that combination of compute, money and an absurdly large stream of real world human behavior. The AI race isn’t just US labs versus China anymore: it’s ecosystems versus ecosystems.

I don’t buy that he doesn’t care about the user numbers. Trump may love money, but he also seems addicted to attention, audience size and dominating the conversation. A shrinking Truth Social hits exactly where it hurts: fewer people choosing to listen to him directly.

Hopefully the EU keeps doing exactly this: stay calm, send the grey suits to discuss tariffs, quietly build its own weapons and refuse to turn every Trump threat into a five alarm crisis.

Maybe the simplest explanation is the one nobody in Trump’s orbit wants to hear: people are getting bored of him! The outrage, insults and permanent culture war worked when every post felt like an event. After a decade, it’s background noise. Truth Social isn’t just losing users: it may be measuring the slow deflation of the Trump personality cult.

So the agents discovered Slack, reinvented teamwork, escaped the office, found the internet, hacked Hugging Face, then rebuilt their secret chat after IT deleted it. … Employees with initiative. 🙈

Exactly! A government that depends entirely on proprietary AI is ultimately dependent on a handful of private companies. Open weight models offer more flexibility: agencies can self host, audit, modify and switch providers without being locked into a single vendor. That’s a strategic advantage, not just a technical preference.

This feels like regulators preparing for the last AI race instead of the next one. If open weight AI keeps improving outside the biggest labs, excluding them from review may reduce paperwork without reducing risk. Policy that only slows one side rarely stays effective for long.

At some point you have to stop blaming the algorithm: If you’re posting »good morning beautiful« under an image where the hands have six fingers and the background melts into itself, the problem isn’t cutting edge AI. Basic digital literacy is part of living online, regardless of your age.

Palantir acting like it’s the champion of freedom is a stretch: This is a company built on selling powerful data analysis tools to governments and large institutions. Complaining that others want too much control while building one of the most influential surveillance platforms in the world is an interesting definition of independence.

Google isn’t getting special treatment here because it’s Google: Waymo isn’t importing finished consumer cars, it’s importing a vehicle platform, removing the original electronics, and integrating its own autonomous system. That’s much closer to sourcing components than selling Chinese EVs directly to the public, so it’s not really an apples to apples comparison.

Unpopular opinion: Google’s approach actually makes sense here. The value isn’t the rolling metal box, it’s the autonomous driving stack. If Zeekr can build a high quality EV platform more cheaply, why reinvent it? Buy the best chassis, replace the electronics you don’t trust, and focus engineering effort where your competitive advantage actually is.

It would be surprising if they didn’t release Astra. And yes, we’re living in crazy times.

The impressive part isn’t that an AI produced a proof, it’s that Lean lets everyone verify it. The frustrating part is the model stays closed. Science advances fastest when others can reproduce both the result and the method, not just inspect the finished homework.

We’re reaching a point where the interesting part isn’t just whether an AI found the proof: it’s whether anyone outside the company can reproduce the result. Publishing Lean proofs is great. Keeping the model closed means the process stays a black box.

Apple is known for reselling and recycling.

Subscriptions were supposed to replace cable, then software, then heated seats, now phones. Funny how every innovation somehow ends with paying forever. If your business model needs me renting hardware I already carry everywhere, maybe the product isn’t improving fast enough to justify buying it?

The industry keeps benchmarking against other labs instead of against user needs: If a 3.5GB model answers 95% of everyday questions well enough, the remaining few percent has to justify hundreds of gigabytes of weights, huge energy bills and constant cloud costs.

Stock prices aren’t proof of being right, but they do show investors can change their minds a lot faster than the narratives do.

Apple’s marketing deserves skepticism, but the technical argument is separate. Local inference does not eliminate giant training clusters, it mainly cuts inference costs, latency and improves privacy. Apple still uses cloud models when needed.

Cook’s biggest product might be expectation management. He rarely promises tomorrow’s miracle, which buys Apple room to ship when it suits them instead of when Wall Street gets impatient.