HN user
I built a very similar experiment about 10 days ago and shared it here (the post is in Chinese):https://x.com/nake13/status/2000401664923324439
My focus was on finding a good text→URL-slug compression strategy. I used ChatGPT-5.2-Pro mainly to explore and compare different compression approaches and trade-offs.
"Over six times the horsepower of Steam Deck" ≈ RTX 3060 Laptop?
The post is gaining traction as more than a dozen users, including me, report the same issue.
Wikipedia’s coverage looks broad, but it still can’t keep up with how fast knowledge grows. And the gaps are even more severe in non-English versions of Wikipedia.
If you’ve tried OpenAI’s Deep Research or similar tools, you’ll know they pull far more info than Wikipedia. But if you’re an expert, you’ll quickly spot errors since the breadth is huge but the depth and accuracy are only so-so.
For non-experts just exploring new topics, it’s still perfectly useful. Grokipedia probably uses a similar search, verify, summarize workflow, so it naturally inherits mistakes from the internet, which isn’t really an LLM problem.
Grok is just the first to make it public, and other AI companies could easily build their own synthetic data Wikipedias, and some probably already have.
I quite like the ASCII art animation from DevDay.
That just shows they were in a rush to release the product rather than taking time to refine it. You can tell it’s quite rough and simple, with almost no distinctive visual style.
They should really back projects like Servo or Ladybird to cut down on their reliance on Chromium/Webkit. It’s the only way to truly support an open-source ecosystem and push forward the next generation of browser engine, even if it takes time.
ChatGPT Atlas just packages several existing ChatGPT features into Chrome, for example, the basic Chat UI and an Agent mode. By turning the browser into the product, it gives them access to more user data, enabling more personalized recommendations. It also allows them to execute specific tasks once users are logged into certain services.
It seems this generation focuses more on GPU and AI acceleration rather than CPU. The M5 chip allows Apple Vision Pro to render 10% more pixels and operate at up to 120 Hz. It delivers up to four times the peak GPU compute performance compared with M4, provides 30% higher graphics performance, and offers 15% faster multithreaded CPU performance.
Bitcoin Core v30.0 now relays/mines txs with multiple OP_RETURN outputs and raises the default -datacarriersize to 100,000 bytes. This is a mempool policy change, not consensus. The move has already gotten negative feedback from devs worried it invites more arbitrary “junk” data on-chain. Bitcoin Core’s stance is harm-reduction: OP_RETURN is prunable and cleaner than UTXO-stuffing workarounds that people have been using anyway. Economically, more fee-paying bytes means miners are unlikely to reject it.
The Magistral Small can fit within a single RTX 4090 or a 32GB RAM MacBook once quantized.
I’ve noticed something no one has mentioned yet: Liquid Glass is natively HDR.
It does not require a centralized server or service. It uses IPFS to achieve peer-to-peer content distribution. Furthermore, you can link your content to an Ethereum Name (.eth) so that others can follow you via Planet by the .eth name. Since both IPFS and ENS are decentralized, you can build your websites or follow others, all in a decentralized manner.