Very curious whether anyone has tried this and can comment on the experience.
A text-first terminal thin client strikes me as a compelling use-case for a pair of smart glasses, depending on the execution. Only thing I'd really want is to be able to connect a keyboard.
While it's easy to look at it that way on the surface, from reading the blog post, it sounds like a big part of it may just be the nature of Bun as a project.
If Mastodon.social disappeared tomorrow, aren't everyone's accounts on that instance effectively just gone?
> If I am fully self-hosting the entire bluesky app, I need to spend ten thousands of dollars a month, but it'll keep working.
That strikes me as a point in favor of AtProto for decentralization (unless it's possible for others to cheaply/simply run their own backups of the entire mastodon.social instance & I'm just unaware of that fact).
> With bluesky it's different.
The key AtProto differentiator I find most compelling (which it seems you omitted from your list) is that Bluesky can disappear tomorrow and, if I'm running my own PDS, I can simply log in to another different platform using my same username & account and immediately see my entire message history w/o skipping a beat.
From the standpoint of an individual user who just wants to own their own data w/o needing to worry about hosting entire apps or manage scaling, the fact that AtProto allows me to host a PDS and do just that while still using any of big-name apps w/o needing to create new logins and fracture my online presence feels like a far more pragmatic trade-off, IMO.
I guess it comes down whether you care more about data decentralization, or platform decentralization. Personally, I care far more about the former, and to that end the AtProto model feels much easier to set up & far more seamless in practice.
How is that trivially answered with Mastodon? And what does "the rest of the world disappearing" actually mean in your example?
Everything I'm seeing about hosting costs in the current day and age is that the full AtProto stack (PDS, Relay, AppView) is roughly in par with hosting an ActivityPub instance of equivalent size (if not a little cheaper).
And with AtProto, folks get to pick and choose what slice of the stack they wanna host, and opt-in to more of it gradually as they see fit. With ActivityPub, you are either opting in to hosting an entire instance, or fully reliant on someone else.
I'm open to the idea I'm misunderstanding some aspect of ActivityPub, given I've not really explored the hosting side of all that deeply.
This is an interesting take because AtProto feels both more accessible AND more decentralized to me (at least with my current mental model).
With ActivityPub, because running an instance requires hosting the data, the application, and dealing with all the subsequent scaling challenges, you kinda have to choose between being taking on active ops responsibilities or tying yourself to someone else's instance (which will probably be one of the bigger, more centralized ones).
If you decide you don't like an instance you picked and decide to move (unless things have changed) you're kinda stuck needing to start fresh.
With AtProto, it's trivial to jump ship to a different application platform and continue using your same identity. Exporting your data from a platform and self-hosting is a bit of a UX challenge, but at least it's possible.
As an example, I recently started using Tangled for the first time and was able to login using my existing bsky-backed domain (h14h.com). No need to create a new account or pick a new username -- it was as if I were already there. Then getting set up w/ self-hosting my git repos on a VPS was an afternoon of work at most, and it's just some backend service chugging away that I almost never have to think about.
The worst that will ever happen is I see a banner message in tangled.org saying something like "your repo is out of date and may be compatible with the latest version of Tangled", which I can solve by simply rebuilding & redeploying a docker image w/ the latest versions.
Granted, AtProto is definitely harder to wrap your head around architecturally. But actually interfacing it with a user is much simpler, IMO.
I've been doing some testing with GLM 5.2 on Fireworks and it looks like the "High" reasoning level uses fewer tokens than even K2.7 Code by a considerable margin (roughly half).
Don't have any evals indicating how it compares on upper-bound quality, but for a well-defined task it seems like GLM 5.2 on "High" is remarkably token efficient. Looking forward to seeing where it lands on the AA index.
Grok 4.3 was made on Colossus 1? It's not frontier quality, but it's definitely not nothing. Also, Cursor brings more expertise to help them improve their utilization further.
Additionally, I struggle to see how renting out excess capacity is anything but good. It brings in a ton of cash at a price premium, and ensures they have headroom to gradually phase out rental capacity as their internal demand increases.
> You think they have more compute than Google?
Fair point. No way they have more raw compute, but I do think it'd be fair to say they have more "excess compute capacity" than Google, but even that is pretty speculative.
> You think they have the most cashflow?
Again fair point. I was overestimating how much cash comes from a $2T IPO before actually looking at the numbers. My revised take is that SpaceX/xAI are now in-line with the other labs on cash liquidity, rather than leading (where pre-IPO they were way behind)
> You think they have higher quality training data than Anthropic, Google, and OpenAI?
Yes -- I'm standing by this one. Cursor has a multi-year head start on agentic coding data collection, and a GUI UX that likely provides richer user sentiment & quality signals than something like Claude Code.
Obviously OpenAI & Anthropic have far larger proprietary datasets for chat histories, and Google is an undisputed leader in data hoarding. But when it comes to agentic coding specifically (AI's most compelling use-case, IMO), I think Cursor's data is a HUGE deal. This is backed up by how good Composer 2.5 was given it was essentially Kimi K2.5 + Cursor data.
Additionally, I also suspect its possible to mine Twitter for user engagement & sentiment analysis to create surprisingly useful datasets.
> Training on what? They rented both of their data centers away!!!
I thought they only rented Colossus 1, but you're right they're also renting out a portion of Colossus 2. That said, they still have 2/3 of their Colossus 2 facility available for internal, which is likely enough to build something competitive. They also have 90-day termination agreements in place once they forecast a need for more internal capacity.
All that said, while I appreciate your pushback & I was overly hyperbolic on a few key points in my last comment, I stand by my core theses: The Cursor acquisition (assuming it goes through) combined with the SpaceX IPO puts xAI/Grok right in-line with the other big labs, at least in terms of positioning.
Whether they're able to execute remains to be seen, but I would not at all surprised to see a frontier-quality Grok 5 or Composer 3 release before the end of the year.
The key point I think you're missing is Cursor's "moat" isn't around their product or brand, it's around the gigantic corpus of usage data they've almost certainly collected.
It is simply not feasible train an LLM to be as good as these frontier models are without a TON of high-quality examples of what "good" looks like. Every time a Cursor user (who didn't opt out of analytics) does/doesn't hit a "retry" button, or rejects/accepts an LLMs output, it allows Cursor to log record of a specific LLMs output and a binary signal of that output's quality.
Given they've been at this since 2022, and for most of that time sat comfortably at #1 in market share among comparable AI coding tools (only recently getting topped by Claude code), Cursor likely has the largest, highest-quality, SWE-specific dataset in the industry by a sizable margin.
Grok being so late to the party could only train on twitter data in combination with whatever they could source publicly or purchase privately, and likely hasn't had anywhere near the usage they'd need to build up their own competitive dataset from scratch anytime soon.
If you believe (as SpaceX seems to) that the AI's total addressable market is over $26T, and acquiring proprietary, high-quality training data is the difference between capturing ~1-2% of that market and ~10-20%, then $60B starts to look like a bargain.
SpaceX isn't a space company anymore, it's an AI company. In their IPO filing, of their projected $28T total addressable market, only $370B (~1-2%) of that is from space [1]. The rest is primarily AI, with a sprinkling of Telecom revenue from Starlink.
Given xAI's Grok is way behind ChatGPT & Claude on coding capabilities, whereas Cursor was able to get in spitting distance of them w/ Composer 2.5 by simply running post-training on Kimi K2.5, I'm not sure Elon could dream up a more perfect strategic fit.
Cursor likely has the largest, highest quality dataset of any private firm for training new coding models, which would compete SpaceX's trifecta of becoming a viable competitor in the AI race:
1. Access to compute (they have so much that they're renting capacity to Anthropic & Google)
2. Liquidity for R&D+M&A (largest IPO in history)
3. High quality training data (this Cursor acquisition)
> Isn't it kinda bizarre that Elon is pivoting SpaceX to something else?
In a vacuum, absolutely yes. But in the bizarre context of the AI economics, chaotically scrambling to bring everything you need to compete in-house makes perfect sense.
Arguably, when compared with either OpenAI, Anthropic, or Google, SpaceX/xAI now own the most compute, are the most financially liquid, and (assuming the Cursor acquisition goes through) have the largest corpus of high quality training data.
We may very well be a couple of months away from a Grok release that goes toe-to-toe w/ other American frontier models, IMHO.
So when you look at this as a $60B play to capture an additional 10-20% of an estimated $26T total addressable market, it makes a lot more sense. Now, whether that projected TAM is even remotely close to reality (or even just enough to make Cursor worth $60B) is another question entirely.
Apple Shortcuts have felt like a blatantly obvious AI play to me for a while now.
The interface for creating them manually has been so bad for so long, it feels clear to me that LLM-driven shortcut orchestration was always the endgame. Apple built up their ecosystem of composable "tools", and then trained an LLM on how to call them.
The result, IMO, is the first OpenClaw/Hermes competitor that's feasible for use by the general public.
Everyone with a paid Claude or ChatGPT that they're struggling to use to the fullest is going to have very little reason not to swap over to an upgraded iCloud+ plan (if they don't already have one). I suspect we're going to see mass cancellation of $20/mo plans very soon.
OpenAI's timing for removing their temporary increased usage limits is looking pretty unfortunate...
The gated "ultra-speed" phenomenon seen here and with the Cerebras Kimi K2.6 release, while understandable, is somewhat troubling IMO.
Getting ~1000 TPS on near-frontier intelligence is a step change, and enables whole new use-cases for applications. Seeing limited compute resources beget selective access makes me worry for the future of competition.
I don't think anyone serious in the Elixir community ever said "you don't get bugs". Maybe you do get fewer bugs as a result of immutability and pattern matching features, but "no bugs" is definitely not a promise I've ever heard.
The thing you DO hear a lot, though, is that you don't need to worry about bugs nearly as much as you do in other languages. But that's not because Elixir is "magic", rather, it comes from Elixir's runtime (Erlang/BEAM) providing best-in-class fault tolerance primitives like lightweight process isolation and supervision trees.
In practice that means the blast radius of bugs is generally tiny and any resulting crashed processes are often recoverable. The phrase you often hear is "let it crash", since the effort that goes into exhaustive defensive programming is usually more costly than the bugs you'd be trying to prevent.
The performance hangup is definitely a barrier, but I think LM Studio and other similar apps are still too far on the "techy" end of the spectrum and have UX barriers that will need to be addressed. IMO for most people, exposing things even as "basic" as the official model name is a leaky abstraction that could be overwhelming.
If the first thing (for example) my mom sees upon installing the app is a dropdown model picker that contains things like "Qwen3.6-35b-a3b-mlx" she will 100% be bouncing off of it.
IMO the best version of this is a custom app/harness with a couple of pre-selected (and ideally fine-tuned) open models that immediately start downloading after checking the system's hardware specs. This would likely be a turn-off to most devs, but is absolutely essential if building an app for general consumers.
But honestly, obsoleting a huge number of otherwise great Apple Silicon machines is something Apple would moment consider a major "pro" of building a compelling local AI stack.
With how much speculation around the difficult time Apple has had getting people to upgrade from M1, I'm sure they'd jump at such an opportunity.
Fair points all around. Ultimately it all comes down to execution.
In theory, Apple SHOULD have an advantage given they have everything they need in house and can all pull in a unified direction. In practice, it's not always the case that all the teams in a large corporation are all that much better at pulling in the same direction than multiple different corporations in a partnership. And all this will be moot if Local LLMs never catch up to cloud LLMs in terms of quality.
Regardless, it'll be very interesting to see how Nvidia's partnerships with Microsoft & hardware OEMs play out. If the AI inference compute share shifts appreciably to local consumer hardware, I'll want to see strong competition.
I think a major incentive could be to sell hardware. If Apple is able to get their hands on a local LLM capable of covering a significant % of what people use ChatGPT for, the pitch they can offer is:
"Free, private, offline ChatGPT so long as your laptop has X GB of RAM"
Beyond that, I wouldn't underestimate the incentive of "because I can". The "secret sauce" you refer to is effectively just a DB & a while loop that feeds text to a bunch of tensors. If an indie dev decides they want to release something that dismantles the OpenAI & Anthropic moats, there really isn't all that big of a technical barrier stopping them.
That said, Apple's vertical integration is a massive competitive advantage here, IMO. Nvidia's reliance on Microsoft & Windows for software support likely makes competing w/ Apple an uphill battle.
If/when Local AI gets good enough to compete with Cloud AI on most inference workloads, Apple starts to look like Nvidia's biggest competitor.
While this is admittedly a dream scenario, the biggest downside would be Apple effectively having a monopoly in "Agent-ready" consumer electronics. Hopefully local AI both becomes the norm, and there is sufficient competition among the consumer platforms.
Side-note: I would love to see an "RTX Spark" Framework 13 mainboard at some point.
A text-first terminal thin client strikes me as a compelling use-case for a pair of smart glasses, depending on the execution. Only thing I'd really want is to be able to connect a keyboard.