Grok has its own feel too. It's not as bad as Claude, but one of the things that bugs me is that it is far too terse.
It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
I find I often have to ask it to re-explain what it means.
First, after a while it's just as grating as Claudeish. Second, my hunch is that it constricts the actual thinking of the LLM, like the same way that Newspeak does in 1984. It shrinks the range of thought that can be expressed if used as an input.
I think the real way to do it is to have another Claude entirely deal with the user as a liaison, but to keep the thinking in whatever format it came in.
Latent space reasoning, if you think about it, is exactly this to a crazy degree: why even formulate a thought as words if you can just keep it as matmuls until the user needs it? And then, if the user needs it, have it always specifically formulated for the user by another LLM rather than constrict its range of thought? Anyway, that's my take.
Given that the decrease in their margin and the fact they delayed the release of Grok 4.7 almost two weeks past the original date, XAI must not have been happy with the results for 4.7. And XAI also waited the day before Opus 5.5 is rumored to launch. I imagine Opus 5.5 will blow Grok 4.7 out of the water benchmark wise.
However, I have become skeptical of benchmarks. Grok 4.5 solved some issues setting up a buildroot system that Fable 5 couldn't do. I find the post cursor groks are phenomenal at frontend web development, though Claude is much better at backend ruby.
My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish. Or even GPT, which doesn't have Claude's ticks but definitely likes to handwave explaining technical concepts. Still, nothing beats Claude 3.5 and 4 with explaining since it seems all models have regressed. I wonder if Grok 4.7 will also regress with English because of all the RL.
* Ads in YouTube feeds for other google products and services.
* Ads underneath videos for products from the channel owner.
* Sponsorships within videos from the channel owner.
* Advertising overlays (supported IN THE APP BY GOOGLE) for products and services from the channel owner.
* Email advertisements for Google products and services.
* Community post advertisements from channel owners which show up in the YouTube feed.
I contacted support to enquire and they state these are not considered advertising.
Yes I see that as an ad. Do you not? Does anyone not? And if I'm on the highest paying ad free plan, what are they promoting to me?
If you pay to avoid ads, you are merely letting them know that you have disposable income to spend on this sort of stuff. You're doing their job for them by segmenting yourself into the upper echelons of the market.
At some point, some shareholder value maximizing CEO is going to show up and notice how much money he's leaving on the table by not advertising to all of those people full of disposable income.
Why would anyone give them the benefit of the doubt?
[0] https://lawcouncil.au/international-law/ils-insights/tangled...
I’m all for consumer awareness but I’m begging everyone to stop freaking out over prosaic non-issues like this.
Of course there is. For example, SeL4’s security and reliability proofs still hold in the world of LLMs. The problem is that most software isn’t written on that firm foundation. Instead, most software is made by people with the philosophy of “if it looks like it works, ship it”. You don’t get secure software by working like that, because security vulnerabilities aren’t visible.
We - humans - know how to write secure software. Just like we know how to make safe aeroplanes. The problem isn’t that we lack the capability to make secure computers. The problem is we don’t have a culture of security. Secure software is - somehow - niche. And as such, it’s much more expensive. And nobody wants to pay.
For example, do not hook your goddamn water or traffic or electricity infrastructure up to the goddamn Internet, and then, do fire the guy who suggested it.
The correct analogy for computer security is not locks and keys and doors and gates. It is a house in a floodplain. Your house will not survive the flood of it hits you. Do not store anything critical or irreplaceable in that house.
In the pilot, the Galactica was literally being turned into a museum, and that's why they lived.
China hacked 22.1 million records of US government employees:
https://en.wikipedia.org/wiki/2015_Office_of_Personnel_Manag...
Perhaps firing expertise and hiring incompetents wasn't a good idea.
- For general chat and web search, occasional image editing, small coding work, document review etc. ChatGPT Plus is basically limitless and “just works” since 5.6. I’ve yet to give it some task it cannot do.
- When given sensible instructions, it hardly annoys with weird phrasing, glazing, or annoying constructs.
- The apps are very good (ignoring the initially terrible Codex app)
It’s easily my best spent $23 a month.
Cached Read: ~6,500M
Input: ~150M
Output: ~20M
Approx $40 worth of usage across DeepSeek V4 Flash + MuseSpark Contributor 1.3. And a bit of both the GLM models. This is covered in a $10 subscription.
If I were to use Luna's API pricing:
$0.02 x 6,500 = $130
$0.20 x 150 = $30
$1.20 x 20 = $24
So $184. And this is assuming smaller coding sessions (<272K) beyond which Luna pricing doubles.
--
Cost wise, these models are nice for small stuff. Translations etc. Any model that does not provide multiple Mtoks of cached reads per cent is not very useful to me for coding workflows.
I dont know how they make money here
Snark aside, Luna 5.6 was (is) an incredible game-changer.
Here's GPT-6 Luna pelicans: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
And GPT-6 Sol: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Scroll to the bottom for the GPT-6 Sol max one: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
For comparison, here are the pelicans I got for GPT-6 Astra: https://tools.simonwillison.net/markdown-svg-renderer?url=ht... - I still like the Astra Max one best.
Here's a comparison grid showing all of the GPT-6 and GPT-5.6 pelicans at all effort levels: https://static.simonwillison.net/static/2026/gpt-6-and-5.6.h...
The grid is actually really interesting, because it shows that the 5.6 family default to brighter colors than the 6 family.
1/ Usage limits: downstream of input/output cost, but resets and obscure windows and odd 20x plan / 5x plan != 4x usage math throw a wrench into it. Winner right now is Codex by a mile, especially when you factor in the fact that ChatGPT usage (even 6 Astra Pro) is essentially unmetered on the 20x plan. Always a bummer when asking if I should see a doctor about a rash means I can't code as much. It's also is a godsend if you use an MCP like oracle to automate the process of calling the Pro model on particularly tough problems, giving better planning results or deeper code analysis without burning usage.
2/ Context window in the harness. Claude Code wins on this. There used to be a toml file workaround for Codex to extend the GPT context window to 1m, but this stopped working on the plans and only on per-token billing (ETA: noname120 pointed out this is no longer the case and it can be enabled again [1]). 252k is just not enough. Codex's compaction is very good, fwiw, but it happens so frequently that even a model as powerful as Astra sometimes loses the plot on long-running tasks.
3/ Ability to use the plan outside of the official harness. Codex wins. Anthropic does shit like bills requests as extra usage if it sees a hermes.md in a commit.
I've subscription hopped a bunch, and at times I've had both, but I keep coming back to Codex because it wins on 2/3.
ETA: apparently I haven't been Keeping Up With the Altmans and new 20x signups have been disabled for a few weeks. I am grandfathered in, which makes the comparison above pretty much moot.
Prices per 1M tokens Claude Opus 5.5 Claude Opus 5
Cache reads $0.20 $0.50
Input tokens $4 $5
Output tokens $20 $25
Cache writes $5 $6.25
Input
Output
Price reduction
GPT‑6 Sol vs. GPT‑5.6 Sol
$4 → $2
$20 → $10
50% cheaper
GPT‑6 Luna vs. GPT‑5.6 Luna
$0.20 → $0.10
$1.20 → $0.50
50% cheaper
But the Apple with ads is not the Apple that had some taste and discernment in the past. For a long time I’ve visited the App Store’s app update page directly (tap and hold on App Store icon to see the context menu option). Anytime I inadvertently go to the App Store home page or the few times I search, it’s an ad filled disaster!
From this article
> repeatedly attempting to prod customers towards even more of the company’s products might seem cheap, even distasteful.
From a recent post by John Gruber:
> Steve Jobs in 2011: 'We Build Products That We Want for Ourselves, Too, and We Just Don't Want Ads' [1]
Looks like Tim Cook, John Ternus and Eddy Cue really enjoy being swamped with ads in their products. Will there soon be a time when Apple executives start carrying some other brand’s devices with them to avoid having a rotten experience?
[1]: We don’t want ads https://daringfireball.net/linked/2026/07/28/jobs-we-dont-wa...
Recently I am starting to question some of the design decisions. Most native apps are bloated with functionalities, most of the time useless. Ads pop up everywhere, even before persistence; iCloud and backups are clearly designed to make you pay, the free space is useless.
I am not that sure my next phone will be an iPhone.
I have tried a recent Mac, and the system isn't intuitive at all. The first time I used GNOME, it was a breeze. Not the same with the MacBook system.
"However, the most astonishing thing about this break is that the GPT–6 Astra did it entirely on its own."
Stubborn for a long time because the message used a completely different key from the rest of that day's traffic. Everyone assumed it shared the daily key. The original transcription had errors. The left rotor turned over at letter 72, which is rare and breaks standard crib attacks.
What is cool, if true, is that it was a 2 day collab between the Leffer and Astra. To me this shows the importance of human in the loop, was still all also showing how immensely power of llm tools. But I think it’s getting a bit silly how much anrticles ignores the driving force (the person) in breakthroughs like this.
BTTE UM ANGABE DES MARSQWEGES X BEFINDE MIQ IN X ROSENOW ROSENOW X SOFORT FUNKANTWORT X WASCHBBSCH
Please specify the route of march. I am in Rosenow, Rosenow. Immediate reply by radio. Waschbusch.I’ve been dabbling in Linux since the early 2000s with Yellow Dog, SUSE, and Ubuntu, tried making it my daily driver in the early-mid 2010s, and for the past 5 or so years have been using it in a secondary/tertiary role.
Recently I’ve been trying to move my gaming box from Windows to Fedora (which I’ve found mostly makes the right tradeoffs to suit my preferences). I’ve gotten things to mostly work, including games that have been blockers in the past, but just yesterday my ethernet connection listing disappeared and broke network connectivity. No big deal I thought, just go into settings and add it back, but as it turns out the KDE settings UI for NetworkManager is horrifically flaky and most critically, its add interface wizard doesn’t set correct settings for a successful connection. No, you have to drop down and use nmcli afterwards to fix things because the UI also doesn’t surface the right checkbox. I wouldn’t have even figured that out if it hadn’t been for a chatbot’s assistance.
This is the exact sort of time-burning nonsense that isn’t a problem on either of the commercial platforms that I tend to encounter under Linux. Is this single instance going to push me to reboot that box back into Windows? No, but if issues like that keep cropping up eventually one will become the straw that breaks the camel’s back. Desire to avoid continued frustration will win over the logical portion of most peoples’ brains.
That's defined as "the device has become a brick, cannot be recovered". It was mostly popularized by broken firmware updates which effectively destroyed the device, as it could no longer be salvaged.
You calling a borked package install in userspace almost bricked is telling about your technical capability.
And I agree that people that are basically technologically illiterate should not use Linux.
They could if someone administers it for them, but if nobody can help them - it's just not the right choice.
It's placed right next to a similar report for the user to see which apps generally connect to the internet and to which domains.
Basically, the main point of the article, "I discovered that macOS 15.3 had enabled a feature that was phoning home every 15 minutes with personal data" is not true.
Some people understand the concept of trade-offs but don’t understand how other people don’t want the same trade-offs as they do
If you're not willing to stop using harmful software that's your problem.
And let's not forget that cheating in online games is only an issue because companies have co-opted what was a social activity between people you know (or at least can get to know) into a more profitable asocial e-peen measuring contest between randoms.
But don't fool yourself. You're subverted to Apple's rules the same as Microsoft users are to Windows.
I prefer to fight a little bit on an update on a unix system on something that is not working 100% to give up choices. When you use Windows, MacOS, Android you do not have choices. Choices are dictated from above, like it or not.
As for me every time I have to interact with something that is not BSD or Linux I suffer miserably.
Build quality (aluminum), charging (magsafe), screen resolution, battery life, noise (or lack thereof), trackpad…
It’s like other vendors aren’t even trying.
Also the OS mostly “just works”, just last week my Linux laptop disabled NVIDIA GPU (and almost bricked itself?? Not sure, had to fix apt) during automated updates
It's always "maybe later" and never "no seriously, I don't want this, go away"
Looking at Nvidia revenues in the past few years, there's maybe $300 billion worth of GPUs currently deployed in the U.S. The B200 costs ~$40k, so we have 7.5 million B200-equivalents, which draw 1000W. Running these at full capacity requires 66 TWh a year, or ~1.5% of total current U.S. electricity consumption. Maybe a bit more to account for inefficiencies, cooling, and other components, but not more than ~2.5% total I would guess.
So it's not that much in reality, but will definitely grow fast.
No matter how much cash you throw you can't just materialize a 100 nuclear reactors to power the data centers.
They invested billions into training the models but there is no competitive advantage, we see that within a couple of months everyone catches up. There is no way to profitability unless they get some policies to shields them against competitors that can't comply with the regulatory requirements.
That is also why there are things like Claude, Codex and Cursor. They are trying hard to build a customer relationship with a higher switching cost that hopefully sticks.
But the problem is that the AI buildout has become a large percentage of GDP. So obviously the government wants to keep it going because these companies are pumping enormous amounts of money into the economy.
whey they all singing the same tune. it make me question what is their real motives.
they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.
Here is one really neat bit:
A cutting edge training idea (for agents, it's been used elsewhere for ages) is on-policy RL, basically, it's not enough to say "here is an end to end agentic sequence (including tool calls etc.) that is perfect" you want to say "here is a sequence you might actually have generated that turns out to be correct".
Basically, it's more training efficient to improve models with small tweaks to do more of the right thing they are already doing sometimes than from some perfect oracular "this is the way" answer.
(if you've ever tried to teach humans new skills, you’ve probably noticed this too!)
When you do that, you care about how far the model you are updating (improving) has deviated from the one being used to generate rollouts (agentic rollouts for hard problems can take hours with lots of tool calls, so you can't keep redeploying every slight improvement).
Lo and behold, the dashboard literally has:
partial/avg_staleness (likely the measure of how many micro iterations the "generate answers" model is behind the "improving based on the occasional right answer" model)
train_infer_diff/new_infer/kl (a more direct KL divergence based way of measuring how differently the two models generate tokens)
How cool is that?!
And don't get me started on the clever ideas hiding behind dynsam/avg@n ...
Pelicans for Pro: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Pro [2]:, 1.02T total / 42B activated parameters
The realtime dashboard they shared during training (https://mimo.xiaomi.com/rl/) was an incredible learning and teaching tool for me, and they’ve been unusually comprehensive in sharing details about their methodology (check out that tech report - it's got lots of clever behind the scene tricks like Google or Deepseek writeups) and benchmark scores (even the stuff they didn’t do well on).
If you’re releasing an open model going forward, please consider offering the community more of this transparency!
- Cancelling -if briefly- Solaris on x86 in 2002. This killed Solaris in the minds of many who didn't want to be locked into Sun for SPARC.
- Failing to make a deal with Google in 2002. Apparently Sun insisted on knowing how many servers Google had, something that Google considered a high-value secret, so Sun failed to make a deal with Google, so Google ended up using Linux and contributing to Linux. This was a tremendous mind-share disaster -- it's hard to overestimate the damage done by this.
- Closing Sun PS (professional services). Bad bad move, possibly the worst of them.
- Not giving up on J2ME earlier -- it's not the sort of thing that could last forever, and Steve Jobs killed it with the iPhone. This was a case of vendor lock-in clouding Sun's decision making.
- Failure to recognize that Sun needed to become a systems company, not a CPU company.
- Failure to respond to Active Directory. This was yet another case of vendor lock-in clouding Sun's decision making: the Sun DS product team was milking their existing customers more than they wanted to go after more business with a sustainable strategy.
- UltraSPARC was more than a decade too late to make up for SPARC falling way behind x86_64. Sun needed to give up on SPARC, but again, vendor lock-in sounds sweet but turns out to be poison.
- Failure to make a deal with Apple for it to use ZFS in OS X.
- The MySQL purchase. WTF, this was horrible and stupid. The only interesting effect of this was to make Sun a target of acquisition for Oracle. But of course, it turns out that Oracle -a company built on building mind-share- had become too blinded by vendor lock-in just like Sun, so...
There were numerous other mistakes along the way. These are the most salient, for me anyway.
What's shocking is how long it took Sun to fail under those circumstances!
Also shocking is how much amazing stuff came out of Solaris engineering and the systems division!
At this point I have the feeling dell just charges you what they think they can get out of you. You push back and the price drops, I don't want to negotiate.
However you sourced your target is irrelevant to the activity at hand. When I first heard of this news I knew immediately someone skipped target verification or that it was simply no longer a policy of the DOD.
> The Pentagon chief last year slashed offices that didn’t contribute to his goal of “lethality,” including the group that assists in limiting risk to civilians, known as the Civilian Protection Center of Excellence. Around 200 employees who worked on the issue, including at that office, have been reduced by about 90 percent, according to two current and former officials and a person familiar with the effort. The team that handles civilian casualties at Central Command, which oversees the Middle East, has dropped from 10 to one.
Negligent homicide in pursuit of their DEI goals, basically.
Reading the details, "AI" doesn't really seem like the culprit -it's a scapegoat.
The intelligence that it was no longer a military target never entered the target database, the team that was responsible for vetting the target list was gutted, and said team was never even consulted.
The White House wanted 1000 targets and pulled from their database without any due diligence. Whether it was an AI call or an SQL query - this was from pure human maliciousness and incompetence.
I cannot believe this has come to pass.
At the end of the article is this excerpt
>> Results from this research were generated using an AI computer vision model developed by the research team on Bristol’s Isambard-AI, the UK’s most powerful AI supercomputer. Isambard-AI powered the analysis of 172.6 hours of live match footage from all 104 games played between 11 June and 19 July 2026.
So, basically now merchandizers, auditors, compliance monitors, etc. have a tool using which they can quantify whether broadcaster complied with contractual requirements of showing their ads/merch/logos as per contract. And if they missed, then they can quantify the delta - to sue the broadcaster for the balance as well.
Also, it can enable many countries, where certain content is not allowed, then this tool can simply give them data points without having to employ someone, or have someone review the output of the AI tooling.
Media: "Scientists claim their discoveries are useless"
My instinct says that these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher, and the potential depth of the problem may be much much larger. It may mostly manifest as increasing cost over time - the agents grind for longer and longer, iterating over and over to fix all the failing tests, and the breaking point will be where it never converges and you come back to millions of dollars in budget spent and still tests are failing and effective gridlock on system changes.
But this may be all my human-biased fantasy that justifies still taking a role in software development.
Giving someone the text output of a LLM is very similar to publishing a summary without links to the referenced material. When you were querying your LLM, you could have asked specific questions or asked for a custom focus or point of view. Your intended audience might have questions or different concerns, but they're unable to interact with your LLM. What you have delivered is static and unresponsive. It has all the disadvantages of being machine output without the advantage of being interactive, the way your LLM was for you.
It may have to wait until compute is cheap enough that tokens are essentially free, but we need a system to pass "hyperlinks" to LLM's primed with context, ready to be interactively queried on a chosen context. It's being overly generous to assume that people are putting even 300 bits into a LLM for every 1000 bits of regurgitated writing they try to pass off as their own. When people post LLM output as if it were their own, I have no choice but to assume they had zero knowledge of the subject, but this query taught them what they wanted to learn, and now they're sharing that. That's fine, but please pass an interactive LLM link rather than static text.
Once we have "hyperlinks" for LLM sessions, perhaps we can share LLM output a little more usefully and honestly.
Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
I'm good with DeepSeek v4.1 set to high. It is a relentlessly "hardworking" dirt cheap model.
Told it to convert a products page (that had two different fonts based on language) from two columns layout to 5 columns on desktop and 2 columns on mobile ensuring typography is readable.
My man went into spawning sub agent which failed to drive chrome so it wrote its own chrome driver protocol server in Typescript then generated a prototype website then downloaded the images and rendered each variation in a directory taking 100+ screenshots analyzing the typography depth and then delivering detailed report and then writing the whole thing with new page layout testing it again with several dozen screenshots using its driver and then saying all good and all really was good and whole thing took 25 minutes or so (including double visual validation) because it generates token at an incredible speed.
Total cost of the above? $0.07 cents.
PS: It generates token at such a blazing fast speed that you can't recognize the words as they are being added and can't read it without scrolling and pausing even if you're Jimmy Carter.
"Ah, yes. This is a classic dog-breed-to-appliance-failure mapping problem."
ChatGPT 6 Pro answered it without issue.
Isn't this basically the model admitting it was trained on this? Otherwise why would it think a pelican svg is a usual request?
All four levels have a correctly shaped bicycle frame. The differences between the pelicans aren't huge, but the xhigh one has a better beak.
I haven't managed to get one for level "max" yet, it hit the limit of 128,000 cap for output tokens while it was still reasoning about the question!
Max started its thinking trace like this:
> This is a classic test request, so I want to plan out a well-composed pelican with its distinctive beak and pouch riding a bicycle with proper wheels, frame, and pedals, set against a simple sky and ground backdrop.
So that failed attempt on max cost me $2.56.
I ran this using my llm-anthropic plugin:
uv tool install llm
llm install llm-anthropic --upgrade
llm keys set anthropic
# paste key here
llm -m claude-opus-5.5 -o thinking_effort low "Generate an SVG of a pelican riding a bicycle"
# Then to save the markdown logs
llm logs -cu > logs-with-usage.mdThe user is right. The outage is a real concern, and the issue is worse than we realized. Requests to Claude Mythos 5.1, Claude Fable 5.1, and Claude Opus 5 encountered elevated error rates. Worth stating plainly: these are not just models — they are load bearing rungs on the software development tooling ladder, and a blocker on this level makes the outage really bite.
One decision that is yours to make, not mine: should an email be drafted to Anthropic support? This issue has teeth, and a canonical handoff can land us where the main gate is no longer breaking silently.
I think this is what I'm most interested in. I mostly moved to Astra because I just can't work all day with the Claude Opus 5/Fable writing style. I don't think Astra is a better model, but it's the first OpenAI one that seemed good enough to me. Definitely keen to try Opus 5.5 and see if this claim is real.
I'm on the fence about calling out AI-isms but I think it's definitely worthwhile to call out ones that actually don't make sense.
Edit: In response to the initial replies. To me it clearly means "releasing frontier models at any pace less than as fast as possible". It implies relative restraint compared to the previous state and without stating the degree of restraint.
Nice. I was starting to think that Haiku got abandoned.
Interesting how the very first line is used to remind the reader of their call to pace the frontier just last week, and everything else after that line is to demonstrate with very specific numbers how they absolutely are not pacing.
I could see them accomplishing it and seeing gains like this in roughly the correct timeframe, and when I heard about that development I assumed the frontiers would probably jump on it.
How it works: https://miraflow.ai/blog/deepseek-v4-1-flash-causal-encoder-...
Ah, they're spreading their limits to all their models it seems. Definitely not a good thing long term in my opinion.
Prices per 1M tokens Claude Opus 5.5 Claude Opus 5
Cache reads $0.20 $0.50
Input tokens $4 $5
Output tokens $20 $25
Cache writes $5 $6.25
If you are forced to reduce price despite raising capabilities, that certainly tells something about the market, and potentially about Anthropic future profitability too, since this model is their biggest topline contributor
God I hope so
> Input and output tokens are $4 and $20 per million, 20% less than Opus 5. Cache reads (which make up the majority of agentic and coding work costs) are $0.20 per million tokens, 60% less than Opus 5. Opus 5.5 also generates output more than 30% faster than Opus 5.
Better than Fable, cheaper than even the last Opus. I use Opus as my main driver so this is very exciting!
> We want to make dealing with agentic infrastructure easier so you can focus on your work. AX is designed with an uncompromising focus on ergonomics, rapid iteration, and joyful workflows for both application developers and AI researchers.
On the the other hand, the readme quickstart section says
> You need a Kubernetes cluster, ko (brew install ko), a container registry your cluster can pull from, and a reachable Agent Substrate Control API (in-cluster default: api.ate-system.svc.cluster.local:443).
Call me old-fashioned but I don't find this "easier". Maybe it's easier in the same way that Kubernetes itself is easier than managing VMs and container deployments at massive scale without such a tool. But there's a vast chasm between what this tool is being sold as and what it actually is.
There are several projects that repeat benchmarks on published models. None has ever found significant fluctations
Here's one example https://marginlab.ai/trackers/claude-code/
Fluctuations of a few percentage points are to be expected and should not surprise anyone who knows how LLMs work.
This Twitter analysis of Fable 5 is not that at all. They analyzed their coding sessions and blamed all of the fluctuations on Fable changing. They then compared to ARC-AGI-2 questions as the benchmark for thinking tokens and tried to stir up anger that coding turns don't produce as many thinking tokens as the ARC-AGI-2 problems.
Exactly what I am saying for months now. And it's exactly the reason why I am shifting to open weight models now. Just bought myself a 2x DGX Spark Cluster. Will run Qwen3.8 Flash Next on it, maybe Qwen4 when it comes out.
Not only do I have full control over quantization and inference, but also will I experience a constant level of quality. It won't be frontier. But it will be stable, and that's enough reason for me to switch. Also I will likely save some money on subscriptions.
For an industry that’s stagnant in progress yet relies on new frequent releases to survive (non-progress being an existential risk), this could make sense.
I have no idea if that’s what’s happened, I completely pulled it out of my butt. And I have no idea is the actual frontier is stagnating.
AI companies should be subject to the OWM like any other company that sells a product that varies in weight. Perhaps when a sane administration is re-elected; one that can read history books and comprehend why our regulations exist in the first place. Or have even a semblance of respect for its citizenry.
I wonder what their official explanation for this behavior is.
Upon Googling "ClickFix":
> "A ClickFix attack is a social engineering technique... It typically compromises devices by manipulating victims into copying and pasting malicious commands directly into system-level tools"
> On that note, one way we can prevent it is to assert that all our content is byte-for-byte identical with the last known trusted stage of what we have produced
That doesn't help with things like the typical use of SynthID where the spymarking is done by the same process generating the content, so there is never a clean comparator. (It also wouldn't be useful anytime it is inplemented as part of a transformation—compression, etc. —step, for the same reason.)
First the low-end laptops and phones (and probably later, most of them) will incorporate some low-level driver that is constantly scanning for these and passing them to a helper app to phone home. I assume this is something Apple will, to their credit, refuse to do[1] but I don't think other OEMs will have any qualms based on what they already do with their TVs.
[1] (though they don't do this kind of thing out of altruism, but because their cash cow is app store rents and fat hardware margins, not third-party advertising.)
Everyone’s trying to figure out how to convert this speed to product features at scale, but enterprises are like container ships. Lots of might but slow to turn. The littler companies can actually take advantage of this and produce higher quality products at much faster speed. I think you’re expecting too much in the short term and too little in the long term. AI-native companies are gonna eat everyone’s lunch, once they figure out how to actually do it reliably.
Do you have actual productive examples? As in, products with a real userbase that couldn't exist or be scaled pre-AI? Genuinely asking, I might have missed some large hits. The closest I can remember was bun rewrite kerfuffle, which seemed more a marketing action than anything.
For the question where are the alternative OSes? Here is one that I've seen. There's probably more - https://www.reddit.com/r/ClaudeAI/comments/1wfpydl/i_asked_c...
For that other stuff you mentioned like the right click menu. Those huge corporate projects suffer more from layers of institutional dysfunction and will be very very slow to show any improvement. Their dysfunction can't be solved with just faster coding.
Using AI to build more features is easier than using AI to improve existing projects. People will gradually figure out how to do latter too, it'll just take longer.
Our QA, formerly a fairly frequent blocker of all our releases, are doing more in-depth reviews and catching issues earlier in our release process. They have become unblocked to the point they are actively chasing down work that starts to slip.
We have cleaned up and tuned both our security alerts and operations logs and improved our tenant isolation in our service in a way that makes customer and formal audits SIGNIFICANTLY easier.
We're setting ourselves up for faster human development of the hard-things. Our development environment and infrastructure are faster, cleaner, more auditable processes, and cheaper overall to operate.
These fixes mostly don't show up in our product change logs, and definitely don't fall into "new features". It would largely be invisible to the outside world, but our costs are going down (though to be fair, not offsetting the spend on AI to date), internal productivity has improved, operational incidents are down, and customer satisfaction is up.
Everyone's going so fast that they keep hitting walls. Review, CI, product asking for things, whatever.
Why have we not seen an improvements in products?
While every post and thread feels like a 90's wall street office, the new android and iphone ship with fewer features than usual. No indie guys come up with a linux-sized alternative OS. Switch 2 remains unhacked. Windows takes 3 seconds to show the right click menu.
Is everyone just running full speed in circles or something?
It's a bold strategy, Cotton. Let's see if it pays off for 'em.