OpenAI's Phone Ambitions, Gemini's Video Leap, and the AI Outsourcing Revolution

• LLM-generated fuzzers from SOTA models don't do a good job of thinking about how inputs should be varied to elicit bugs
• GPT-5.5 xhigh has 7.5% standard deviation between runs, which is less than 1 SD across different GPT models
• Running a simple agentic loop where the agent produces a result you know will be wrong, then fixing the parts that need fixing, took about five minutes of human time versus a traditional two-day analysis
• Coding agents are highly non-uniform in their effectiveness relative to humans, creating phase changes in how you work

zarzavat (5 replies)

Fable changes the game yet again, because it's API-only.

You're not likely to want to run Fable in a loop any more than you want to take a bunch of dollar bills and light them on fire. Every invocation of Fable has to be intentional, its context carefully managed. I feel like a babysitter.

vidarh (1 replies)

I just had Fable run overnight in a loop, and it fixed ~150 compiler crashing bugs that Opus had kept deferring.

I wouldn't start with Fable - when I use burndown loops I tend to include instructions to document progress and set aside anything that turns out to be harder than expected, and solve the easy stuff first. When a model runs out of easy stuff and start struggling to make progress on what is left, I can let it keep churning on that - they get there eventually - or I can bump it up to a smarter model if one is available.

Opus had churned a week driving down spec failures, and did a great job. The 150 Fable took overnight were the ones Opus had kept putting aside.

duckmysick (4 replies)

I'd like to highlight a different part of the article:

In general, when I talk to software folks about testing, I'm coming from such a different place that they immediately look at me like I'm an alien, so let's talk about how we tested at this hardware company I worked for, Centaur, which informs my biases about how I like to work. Some of the things that we did that were or are unorthodox in the software world are:

Hired dedicated QA / test engineers, with testing being a first-class career path on par with being a developer - No code review by default - Virtually no hand-written tests - Constant testing via what programmers sometimes called property based testing, randomized testing, fuzzing, etc., although we just called those tests (hand-written tests were called "hand tests"). - Large regeression test suite (3 months wall clock to execute on compute farm) - No unit tests

Anybody here tried that (or a similar) approach? Especially going all-in on property based testing and fuzzing with no unit tests.

I tried that approach somewhere before and the initial results were promising, but ran into political issues so the idea was canned.

• The AI tutor achieved 90% voluntary student engagement compared to 10-15% baseline textbook reading
• Effect sizes ranged from 0.71 to 1.30 standard deviations for students with full engagement
• The study acknowledges self-selection as a central threat and lacks randomized controls
• The headline result was based on a statistical model using lessons/reviews engaged with and mid-term scores

baq (4 replies)

I'm on record saying that a system like this with some extra hardware (i.e. a way for the LLM to have live understanding of the student's paper notebook or handout which are being written in with a plain old pencil) combines the best of both worlds - individual tutoring with approximately zero screen time which scales linearly with the number of students. The role of the teacher or professor then becomes a manager of the student - agentic tutor pairs, a referee when the student and model disagree, etc. and most importantly still being the human teacher you can just talk to in the human education process.

I'm convinced this is the future of education - models are there, we need the classroom tech to catch up. The alternative is obvious and quantified in the paper - students just use models to do their work for them and learn nothing.

radioactivist (4 replies)

I am somewhat skeptical of this.

First, the headline result of 0.7*sigma improvement is the output of a statistical based on lessons/reviews they engaged with and their mid-term score, with that shift being for "full engagement". Based on their tables something like ~16 students (11% of the group) actually reached that level of engagement

Second, trying to incorporate past grades into their modelling is not a substitute for a randomized trial.

Third, the headline engagement number of 90% is for "engaging with the platform, via Module Review or Lesson Quizzes, at least once". I don't know why much of that couldn't just be attributed to novelty. Or even partly a professor with all sorts of enthusiasm for the platform.

Fourth, the "full dosage" effectiveness is measured based the final exam scores. Were these exam questions produced independently from the "Phosphor" materials? (e.g. by blinding?) Were they checked for direct overlap with those materials? The 0.7 sigma shift is 3 points on a 24 point exam; if even a few of the questions on that exam were very similar to those materials it could account for almost all of it. This is not clear to me from the manuscript.

If this was the case, then it's a question less of "is AI effective" vs. "did the students look at the materials". You could still argue that the AI platform got them to read, but that is a somewhat different statement than the AI helped them learn.

or_am_i (2 replies)

The article explicitly calls out selection bias (this is entirely based on 90% that opted into using the tutor, there was no control group), I wish the headline did as well. "Engaged students score 0.71 - 1.30 SD better in tests" sounds like a much simpler explanation.

• Meta laid off approximately 8,000 employees and reassigned 7,000 to AI groups earlier this year
• Zuckerberg told staff the perceived upside of the AI restructuring hadn't come to fruition yet
• Meta is expected to spend as much as $145 billion on AI infrastructure this year
• Zuckerberg said he believed the company would begin seeing improvements from AI investments within three to six months

• The repository claims to contain a reverse-engineered system prompt for Claude Design with 20 chapters and 14 procedural skills
• The prompt covers content discipline, aesthetic principles, accessibility, visual hierarchy, and interaction states
• Commenters noted that Claude Design's actual skill list doesn't match the structure in the repository
• One commenter noted that Claude Design's prompt is trivially extractable from the frontend bundle sent on every network request

simonw (3 replies)

I can't even tell if this repository is based on prompts extracted from Claude Design or if the author had an LLM create all of these prompts in it from scratch.

The fact that they encourage and accept PRs indicates that this isn't intended as a direct prompt extraction exposure project - plus the license, which should indicate they have the authorship necessary to license that content.

Assuming this IS a complete ground-up implementation it really needs to link to demonstrations that it works. Without any evidence it's hard to justify spending time exploring it.

simonw (1 replies)

If you ask Claude Design itself to list the names of the skills available to it you get:

Animated video Interactive prototype Make a deck Make a doc Make tweakable Claude API in prototypes Frontend design Wireframe Export as PPTX (editable) Export as PPTX (screenshots) Create design system Save as PDF Save as standalone HTML Send to Canva Handoff to Claude Code

Which does not match the structure of this project at all.

smokel (2 replies)

> Open source, MIT licensed.

I don't think that is how copyright licensing works.

• Canada's Department of National Defence signed a secret Palantir contract that grew from $14.4 million to $44.4 million
• The Ontario Provincial Police have run Palantir's Gotham platform since 2015
• Only about 12% of Canadian businesses use AI, and barely 8% of small firms do
• The strategy allocates $500 million for equity stakes and $700 million for compute but no direct procurement mandates

jmyeet (0 replies)

I don't think there's a government in the world, including the US, that should allow Palantir anywhere near their data or systems. I consider Palantir a national security threat. I also feel this way about McKinsey (and Bain, BCG, etc).

I also think any form of platform AI usage to be a national security threat in the absence of stringent controls over that data and the platform. At some point I think governments and companies will wake up to this and demand local LLMs or, in the very least, a cloud platform within their jurisdiction, ownership and control.

toomuchtodo (0 replies)

They're arguably a US based technoauthoritarian think tank masquerading as an enterprise software and data analytics firm.

France, Germany, Spain, and Britain have or are in the process of disassociating from them.

altmanaltman (1 replies)

I mean no public strategy should include secret bills, Palantir or no Palantir.

If you're idealogically opposed to Palantir, how will a home-grown Palantir help? It would likely do the same things Palantir does but with a Canadian Alex Karp

• GPT-5.6 Sol Ultra will be available in Codex with a new ultra mode using subagents
• The ultra mode involves subagents trained to cooperatively pursue tasks and communicate with each other along the way
• This differs from Pro mode where agents worked independently and only merged results after all finished
• The Information reported OpenAI found ways to cut inference costs by half

layla5alive (4 replies)

https://archive.ph/NEwVz

"However, these inference optimizations, which rival Anthropic refers to as "compute multipliers," are a big focus for all the labs. Anthropic CEO Dario Amodei has been publicly talking about the concept since at least mid-2023, when he said on a podcast that the company limits "the number of people who are aware of a given compute multiplier" because it could give other AI labs a leg up if they were to be able to replicate them. (Compute multipliers can also refer to efficiency optimizations in the model-training phase.)"

Yes, on a world with finite resources where your industry is singlehandedly siphoning ALL THE RESOURCES - hoard general efficiency optimizations and treat them as trade secrets - winning is all that matters, normal people and other species and the planet be damned.

Everything I hear about Dario these days makes me like him less and less. He sure did seem to speed run the 'tech leader with scruples' to 'tech villain' path! I guess all the cycles are compressing as we approach the singularity..

changoplatanero (2 replies)

For pro mode the agents worked independently and only when they all finished did a new agent take a look at everything to merge the work into a single response. The new thing involves subagents that have been trained to cooperatively pursue a task and are allowed to communicate with each other along the way.

andai (2 replies)

For context:

Additionally, we're introducing a new ultra mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work.

https://openai.com/index/previewing-gpt-5-6-sol/

Can someone explain how this compares with Pro? I thought Pro was already something similar.

• Fable identified 5 release blockers including a critical bug where delete_where() never committed and poisoned the database connection
• GPT-5.5 found two additional P1 issues with db.query() transaction handling that Fable missed
• The work involved 37 prompts, 34 commits, and changes across 30 files
• The estimated API cost was $149.25, broken down as $141.02 for the main session plus smaller amounts for review agents

• The study interviewed 18 UK-based domestic workers about their experiences with AI-driven smart home devices
• AI-enabled features and opaque, agency-mediated employment arrangements intensified surveillance in employer-controlled homes
• The paper identifies domestic worker agencies as institutional adversaries in the threat model
• The paper was accepted for presentation at USEC 2026 (Symposium on Usable Security and Privacy)

• Across 660 trials with Claude Code, code cleanliness did not change the agent's pass rate
• Agents working on cleaner code used 7-8% fewer tokens
• Agents working on cleaner code reduced file revisitations by 34%
• The study used minimal pairs of repositories matching on architecture, dependencies, and external behavior

• Microsoft 365 Business Basic goes from $6 to $7 per user per month (16% increase)
• Frontline F1 plan jumps from $2.25 to $3 (33% increase), and the no-Teams version rises 43%
• Windows Enterprise per-device licensing goes up 31%, from $5.85 to $7.63
• Microsoft 365 E5 gets Security Copilot included at 400 SCUs per 1,000 licenses

ninko (4 replies)

Microsoft is committed to delivering continuous innovation and value through Microsoft 365. Over the past several years, we've invested deeply in security, compliance, productivity, AI, and IT management— helping organizations stay productive, secure, and competitive in a rapidly evolving landscape. This change reflects the significant innovation delivered over the past several years and the added value customers will gain with new additions to the suites, including major advancements in AI (ex. Copilot Chat, Copilot Chat Analytics), security (ex. Microsoft Defender for Office P1), and IT management (ex. Intune Suite).

chairmansteve (4 replies)

If AI is so great, these price rises will pay for themselves many times over.....

Have any HNer's experienced or observed any productivity increases, or even any utility increases from co pilot?

jinxmeta (2 replies)

so a long way of saying: we need to fund our AI.

jamesfinlayson (1 replies)

Had a clueless business analyst get Copilot to write a bunch of stories for an epic, then when she had a meeting with me (and three other people!) to go through the stories I had to tell them that 90% of what they had done was wrong.

If they'd come to me from the outset I could have told them the one story title and handful of acceptance criteria that was required but instead we wasted 5 person hours and whatever Copilot costs.

bombcar (0 replies)

Copilot actually can search outlook emails on Mac. This is the greatest achievement of any AI.

• 102 mentions of food poisoning at Riu Palace Santa Maria were missing from the AI summary
• Tripadvisor's AI described sexual harassment allegations as 'lapses in service'
• Seven deaths reported since 2023 at the Riu Palace, with a group legal action representing 412 holidaymakers
• Tripadvisor's Ollie chatbot told users food poisoning was 'quite unlikely' at the Riu Palace

solenoid0937 (1 replies)

Someone should get the bot to leak system instructions. I'd bet good money TripAdvisor has configured it to be positive.

The TripAdvisor spokesperson said people can just look at the reviews, "eliminating any need to blindly trust AI-generated content." How about these clowns just fix their harness instead?

userbinator (1 replies)

Is it actually a "summary" of the reviews, or did they just ask the AI to generate a flattering compliment? Because AI often seems very eager to do the latter, possibly due to an abundance of "toxic positivity".

aurareturn (0 replies)

The answer is obviously deliberate prompting to make negative reviews sound less bad.

1. Booking websites want to convert more users

2. Hotels will get angry if a booking website's AI summary is negative

raychis (0 replies)

This is exactly why AI summaries shouldn't be trusted blindly, especially when safety concerns can be buried under hundreds of positive reviews.

It is a big problem that the uncertainty in the text produced by LLMs isn't surfaced to users. It is also a big problem that companies think that shoehorning AI summaries everywhere is a good idea.

Fire-Dragon-DoL (0 replies)

Won't they have the same problem Google is having, where since the content is AI generated and the AI is controlled by TripAdvisor, they are liable for what it says?

u/Latter-Safety1055 (349 points · permalink)

I would kind of hope my Product & Design Lead knew what the heck we were competing against. It would give some credence to "You know how I said Fable was dank? Check this out!"

You don't have to denigrate your competitor.

u/TFenrir (169 points · permalink)

Well, "competition". Google owns like 18% of Anthropic and they have a very good relationship. Would be nice if Google for could make their own Fable class model though, they are being relegated to an even lower tier

u/XCherryCokeO (138 points · permalink)

I got like 100 downvotes because I said this would happen on the command and conquer subreddit 6 months ago. They said it would not be able to do that for years. Lmfao.

u/FriendlyTask4587 (226 points · permalink)

Didn't he say gpt-4o could do PhD level math? I'd take this with a grain of salt

u/CymonSet (181 points · permalink)

He is probably referring to the Erdos problems it recently solved.

u/demianin (93 points · permalink)

I don't think this guy could be any cornier if he tried

• Gemini Omni Flash is priced at approximately $0.10 per second via API
• The release sparked discussion about AI's potential to replace VFX, writing, and acting in film production

u/often_says_nice (133 points · permalink)

Hollywood is cooked

u/one_tall_lamp (82 points · permalink)

My grandma is going to see shit like this on her feed and think the second coming of Jesus has started lmao

Old people are going to get scammed so bad with this

u/Hotarusglaive87 (222 points · permalink)

I'm gonna need more context and to probably stop laughing cause the ending was chef's kiss. Like what did it do? Malfunction and go into martial arts demo mode?

u/AxiosXiphos (56 points · permalink)

• A Nvidia principal engineer noted that 5.6 Sol 'doesn't give up' on tasks the way Claude models tend to
• The engineer showed 5.6 Sol gave better results than Opus while using more tokens
• Commenters noted that Claude 4.7 and 4.8 were previously noted for discouragement and lack of bravery

u/biblecrumble (95 points · permalink)

"Doesn't give up" is kind of a big deal. I don't know how many times Opus 4.6/4.8 told me something was a dead end, only for me to push it over and over again until it somehow ended up being able to do it, but it's definitely an issue; I have strong suspicions that it is causing more anti-patterns and hacky fixes to be introduced into the codebase than necessary. Curious to see this in action.

u/GamablobYT (86 points · permalink)

In a following tweet he actually shows that 5.6 sol gave better results than opus while using more tokens than it

u/Ormusn2o (74 points · permalink)

Interesting, because discouragement and lack of bravery was noted for 4.7 and 4.8 few months ago when they released. I wonder if it's endemic problem with those models, or if it's OpenAI models that have some special sauce that makes them become discouraged less, as the same thing I have seen reported from 5.5-Pro.

• LongCat 2.0 has 1.6 trillion total parameters with ~48 billion active parameters
• The model was trained on 100% domestic Chinese chips
• BF16 weights are 3.55 TB, FP8 weights are 2.05 TB
• Released under the MIT license by Meituan

u/duhd1993 (66 points · permalink)

In case people don't know, Meituan is China's Groupon+Uber Eats. This model is trained on 100% domestic chips. When will wallstreet react to this?

u/Intrepid_Quantity661 (46 points · permalink)

1.6 total, 48B active and MIT lincesed? Meituan cooking. Downloading now to test against Qwen and Deepseek.

u/woahdudee2a (351 points · permalink)

if trends hold, high end consumer hardware will cost same as enterprise hardware

u/Real_Ebb_7417 (56 points · permalink)

There will be no consumer hardware in two years :(

u/geneusutwerk (56 points · permalink)

I'm not sure what you want us to do with this. Like good on you for trying but usually a post like this is either comes with a blog about what you learned or metrics for the model.

I don't want to dissuade anyone from trying things and learning but it would help to make it clearer what your expectations are for this.

u/cakes_and_candles (94 points · permalink)

Lmao

https://preview.redd.it/7rjap01b0hbh1.jpeg?width=1044&format=pjpg&auto=webp&s=2ed4b55f707e767515401700b221ff4e16cdbf34

u/shockwaverc13 (152 points · permalink)

old man voice back in my day we were content just using mixtral 8x7b and mistral 7b frankenmerges and we were celebrating the fact open models were lagging by only 9 months from GPT-4

u/erratic_parser (97 points · permalink)

Unless there is a breakthrough in inferencing bleeding edge models will always be out of reach of consumer GPUs. Only so much quantization and fine-tuning one can do.

u/ParaboloidalCrest (28 points · permalink)

• There will always be a decent model that runs on consumer hardware.
• That model will never compete with a model 10-100x its size + megawatts of infra + billions of dollars worth of top engineer hours building the surrounding tools.

Can we drop the hopium already? I'm not sure what that kind of silly recurring posts achieve, other than collecting upvotes for the OP.

u/ArtVoyager77 (107 points · permalink)

wait, "He searches for people in the group doing some good research, and asks that they put his name on the paper," and others are like, sure, I will put your name without any contribution whatsoever?

Putting one's name on paper is very common in (semi) senior positions, but not at the PhD level.

I am aware of "I am going to put your name in my paper, and you are going to put my name in your paper", but I have never heard of what you said.

u/Waste-Falcon2185 (74 points · permalink)

He is headed straight for the C-suite

u/appdnails (10 points · permalink)

If people accept putting his name without any contribution, you should be more worried about the people in your group. No researcher with integrity would do that. They are literally stating "this person helped conduct the research" without it being true.

Sadly, in many groups there is an "exchange" where people put each other names in their papers (author A put author B name and vice versa). But you are saying that the individual does nothing, so it seems the people in your group are being unethical without even having something to gain, which is even scarier. Like, no realization that they are losing their integrity.

u/didimoney (136 points · permalink)

Just work on actual research instead of engeneering LLMs

u/Ok-Addition1264 (67 points · permalink)

I don't think it's pointless. Divergent ideas always popup in specific research topics that build on each other. It's actually how research in general works no matter the field. They may even abandon theirs midway through completion or help yours out in the end.

Research is always complementary or you're doing it wrong.

I've been where you're at but for me in computational physics and cybersecurity over the past ~35 professional years.

u/mr_stargazer (28 points · permalink)

Actually, if I happen to know DeepMind or Anthropic are doing the exact topic I am, I would feel comfortable in two aspects.

1. I can leverage in what they're doing and use them as justification for my own work.
2. Be absolutely relaxed, because many of these companies have a specific bias on the way they approach some topics. E.g, "Transformers no matter what", "Jax, Diffusion and Transformers no matter what. ", " 32 GPUs no matter what".

Understand that you as an individual scientist is freer to pursue the science alone than these companies. Not only they need to publish to legitimize themselves, but at the same time they have to signal they're working on a specific topic, they need to justify lab structure, they need to signal they're helping the company bottom line, etc, etc.

I don't want to to be picky on lab a,b, c, but as a whole, I have yet to remember a specific paper coming from big labs that are 100% clean and reproducible. They've hijacked big conferences as their own marketing platform. So, if you are doing the exact same work as they are, but come up with a clean reproducible repository with statistics and hypothesis testing, this is way more valuable in the long run than a paper produced by a big lab, but it would take me 3 months and 10k worth of GPU to replicate.

Courage and keep on..

• Ford rehired over 300 veteran quality inspectors after AI quality checks fell short
• VP Charles Poon said the company mistakenly thought introducing AI and ingesting design requirements would produce high-quality results
• The AI tools lacked the training and expertise of veteran technicians who had left before their knowledge could improve the systems
• Ford reached number one in the JD Power Initial Quality Study for the first time since 2010

• Chinese models grew from 5 to 20 in the daily top 50 between January 2025 and May 2026
• Analysis based on OpenRouter's daily top 50 most-used models by token count
• Canada and France each have single representatives (Cohere's Command R and Mistral's NeMo)

u/phatrice (48 points · permalink)

This is based on openrouter data which is just a subset (or even a small fraction) of global AI usage

u/GraceToSentience (20 points · permalink)

Mistral really took a hit didn't they.

u/CrunchyMage (13 points · permalink)

Man, Europe really needs to get on the Draghi plan.

There’s no reason a EU with unified capital markets and 28th regime business registration can’t be competing and innovating at the cutting edge.

• Alibaba went proprietary/API-only for Qwen 3.7 Max and Plus launches
• A Qwen team member hinted open-source plans for Qwen 3.7 are 'under discussion'
• That comment was later hidden/deleted on Hugging Face

u/rerri (45 points · permalink)

Afaik, the only information with regards to possible Qwen 3.7 open weight release was a single comment on a HF paper page from one Qwen team member. It was an encouraging one sentence answer to a question by a random user, but nothing that I would view as definitive.

Alibaba hasn't really posted roadmaps for Qwen so if they release new models, it'll likely just happen out of the blue or maybe we see some vLLM/transformers pull requests slightly before.

late edit: I took a look at the guys twitter account and he posted a day after the HF comment:

While I am personally a staunch advocate of open source—a commitment I have consistently upheld and the very reason I joined Qwen—I must clarify that the open-source plan for Qwen3.7 is still under discussion, and I cannot make definitive promises on behalf of the team...

https://xcancel.com/xuanmingzhangai/status/2069880601398886802

u/dinerburgeryum (35 points · permalink)

Controversial take, but I think there’s a very strong possibility that we’ve seen the last open weights Qwen model. I had assumed the team shakeup after the 3.5 release was related to the original team wanting to make more general purpose models, and the higher ups wanting better agent models. As time has gone on I’ve started to think Alibaba wanted to pivot Qwen away from open weights and into pay-to-play inference. So far that has seemed correct, and the lack of messaging around more open weights models seem to only confirm that.

u/WiseVanilla2743 (17 points · permalink)

There is but it is little bigger than 9b Gemma4 12b qat should be the thing you might be looking for

• NVFP4 is blazing fast but has looping issues in copilot
• FP8 seems to be the right choice for coding purposes
• User switched from llama.cpp to vLLM for better performance and stability
• Test system: Asus Proart Z890, Intel 270K plus, 96GB DDR5, RTX 6000 Pro Blackwell 96GB

u/TripleSecretSquirrel (16 points · permalink)

You may know this already, but vLLM really shines for multiple concurrent requests, and you can get way more total tokens generated that way.

I recently made the switch to vLLM at least for bigger code sprints where fully autonomous agents can execute well and have been blown away at the results. I get ~5x the tokens per second by batching than I do from an optimized single stream on llama.cpp.

I’m on a much smaller, less powerful GPU (R9700), but I design my code sprints to be lots of very small atomized tasks, so a 50k context window is generally enough to work with. I can run 12 concurrent agents, each with 50k context. For task that need more context, I can always reduce the kv cache size, but again, the 50k ceiling is accounted for in sprint planning.

• 10MB LoRA adapter for Qwen3.5-4B that gates tool use on internal confidence
• Catches its own errors better than base model's tool calling (d' improvement of 0.46)
• Cuts rate of private questions sent to public search from 22% to 10%
• Every answer is traceable with citations and confidence bands

u/iKy1e (3 points · permalink)

That’s a really cool project! Thanks for sharing this. Going to dive into how this works later.

• OpenAI is building its own AI agent phone hardware, not just software
• Target launch date is 2027
• The goal is to challenge the iPhone directly

u/Cool_Samoyed (173 points · permalink)

Finally I can let ai scroll the reels for me so I can get some work done.

u/magic6435 (165 points · permalink)

Lol they can't possibly be that stupid

u/Noskaros (60 points · permalink)

Sweet Jesus, Altman is handling his company like an un manned ship at this point. Weren't they re orienting and focusing on profits or smth ? In what world is OpenAI phone gonna rival the iPhone ?

u/Dizzy_Ad2768 (58 points · permalink)

Apple: We will keep you as a side option in Siri AI.

OpenAI: This is threat. We shall take revenge.

• ChatGPT answered a Stack Exchange question that should have been closed as off-topic
• Commenters note LLMs don't require minimum point requirements to answer
• Some argue Stack Exchange's elitist design drove users to LLMs

u/girlgamerpoi (232 points · permalink)

Reminds me of Raj's twitter reply to sam altman's lol

u/ohnoplus (175 points · permalink)

To be fair: It is an opinion question and therefore off topic for stack overflow. Should go in the meta. But also LLMs don't dowvote and close your questions, they answer them, which is why I visit stack exchange way less than I used to.

u/BestLemonCheesecake (111 points · permalink)

I love this so much. It's a place where the most "elite" engineers and programmers are supposed to gather, yet they were, and still are, completely oblivious to actual tech. They are driven purely by pride, which ironically destroyed everything they had to brag about.

• ChatGPT's answer about humanity's biggest lie was described as 'simultaneously inspiring and depressing'
• One user's ChatGPT response described humans as 'emotional, tribal, self-protective animals who use reason as much to justify our beliefs as to find truth'

u/hoomanchonk (93 points · permalink)

simultaneously inspiring and depressing

u/SuperBowlXLIX (37 points · permalink)

Here's how mine answered:

Humanity's biggest lie is that we're mostly rational people seeing reality clearly, when in practice we're emotional, tribal, self-protective animals who use reason as much to justify our beliefs as to find truth; almost everyone thinks their worldview is built from facts and principles, but a lot of it is inherited from family, class, culture, media, trauma, incentives, and the need to belong, which is why people can defend cruelty as "common sense," inequality as "merit," propaganda as "truth," and fear as "realism" while feeling completely honest.

• Anthropic's Fable model went API-only just 2 days before ChatGPT 5.6 subscription announcement
• Anthropic is mostly sold to companies, and team/company deals aren't month-by-month
• Some speculate Opus 5.0 will be announced a week later

u/Basic-Magazine-9832 (20 points · permalink)

i did a chargeback for my max 20x subscription, get bent antropic, now you totally gonna go bankrupt

/s

u/waraholic (10 points · permalink)

Then a week later Opus 5.0 will be announced.

Anthropic is mostly sold to companies and the whiplash going from one to the other between model releases isn't worth it. Plus, those team and company deals are not month-by-month.

• AI performance per dollar continues to improve, raising questions about the units of measurement
• Commenters discuss the Jevons paradox in the context of computing efficiency
• One commenter noted that computing itself is the greatest example of Jevons' paradox — the market always consumes all available compute

u/CallMePyro (85 points · permalink)

Title gore, lol. How can performance per dollar get cheaper? What're those units? Performance per dollar per dollar? And if it gets faster do you measure that in performance per dollar per meter per second?

u/Bishopkilljoy (45 points · permalink)

The Jevons paradox is an economic principle stating that when technological progress increases the efficiency with which a resource is used, the rate of consumption of that resource often increases rather than decreases.

u/Specialist_Dark_3668 (14 points · permalink)

The greatest example of jevon's paradox is computing itself. There has never been a point in history where the market said "Oh, we have enough compute".

Whatever the compute producers can output is always consumed. Zero leftover. The only demand from the market is more, faster, more efficient. With no end in sight.

Science fiction even envisages Dyson Spheres; computers that envelop stars.

• AI models are 7-70x cheaper than engineers, while offshore teams are only 3x cheaper
• The real cost of offshore teams is writing tickets and reviewing work, not coding
• AI enables smaller, tightly integrated teams with AI supplying bulk labor

u/SoylentRox (31 points · permalink)

It's an extreme irony. Outsourcing - where in foreign countries (most commonly India but many other places are in use) you have "bulk labor" at about 1/3 the costs of US engineers (the actual ratio is about 3:1 with India, 1.5:1 with Europe or Canada) is too expensive.

These outsource campuses are never as well connected information wise. You have to constantly do late night or early morning calls to sync with them, and the US team usually has the core architecture and the core skills that created the original design for the hardware and software. The "bulk labor" people are given outlying tasks.

So it creates a situation where these people have less information that an AI model has access to - since an AI model understands US Bay Area English better than people in India, since that's what it's creators speak in - and are too expensive.

Even at today's high API costs, AI models are around 7-70x cheaper than engineers , and as mentioned, India is only 3x cheaper. Not to mention those sync delays - every time your core US engineers have to spend an hour in meetings syncing with the India team each day is an hour that they could have spent interacting with Fable just getting the work done directly.

There's also a quality problem - obviously outlying teams write lower quality code, it depends on the specific team and engineers . I've seen that Eastern European engineers particularly enjoy taking shortcuts. So yes AI slop is a thing, but we can control that by ordering models to do extra cleanup passes to a point - human slop is somewhat endemic.

It's not the only model - US engineers are so expensive that a different model is we just have entire products handled start to finish by the offshore team, and have the team augmented by AI models, compensating for differences in skills and education. This might be the cheapest of all.

Point is with AI you want smaller teams and tightly integrated single campuses, with all the bulk labor of hundreds of human-equivalent workers supplied by AI.

u/ikkiho (7 points · permalink)

When I ran an offshore team the coding was the cheap part. Where my week actually went was writing a ticket tight enough that I got back what I meant, then reviewing whatever came back for the subtle wrong stuff. Swapping the offshore team for a model doesn't touch either of those. You still spec, you still review, just faster and with a worker that fails more confidently. Pulling this in-house mostly moves that same overhead onto whoever writes the prompts now.

• Kitboga has developed techniques to disrupt AI-powered scam phone calls
• The community celebrates Kitboga's work with humor and inside jokes

u/Anen-o-me (20 points · permalink)

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u/YakzitNood (18 points · permalink)

I absolutely love kit boga. He is so funny to watch live

• The author argues cognitive dynamics are better modeled as invariant-preserving flow than as descent
• Invariants that matter tend to be structural rather than learned
• One commenter noted that biological learning is dissipative optimization and can't truly be a Hamiltonian system

u/TheHandsomePo-ta-to (17 points · permalink)

What part of intelligence behaves like Hamiltonian flow through a structured state space rather than optimization toward a loss minimum?

u/FlameOfIgnis (13 points · permalink)

If you want my honest take, I don't have a solid answer for you. The intuition I follow is that anything that has to keep moving without settling; reasoning, exploration, goal directed searching-- they don't really monotonically reduce an error towards a fixed point, instead they follow a trajectory guided by certain properties they hold invariant.

At the end of the day with HNN's, we are still optimizing towards a loss minimum but just one layer deeper, so we are reducing our problem space by utilizing a more structured state space that makes anything that would violate the invariances that our system has inexpressible.

To me, the most visually vivid comparison to this could be in 2D, trying to fit an arbitrary decision boundary vs knowing the decision boundary is a single concave down line. We could try to fit an arbitrary line, or we could define a parametric function that describes the correct shape and fit the parameters-- making anything that would violate our structure requirement inexpressible while also decimating the search space.

But if I may elaborate more, I don't think intelligence is purely a hamiltonian flow. Biological learning is a dissipative optimization at the end of the day so it can't truly be a hamiltonian system. But my actual research interest is in hamiltonian/lagrangian unified models and the core claim I believe to be defensible is narrower, and imo more interesting: cognitive dynamics are better modeled as invariant-preserving flow than as descent, and invariances that matter tend to be structural rather than learned.

u/ikonkustom5 (7 points · permalink)

I like this. I always thought ML/AI has been trying to map the river instead of mapping the canyon and the properties of water and letting the river fall out naturally.

• llama-server's KV cache restore feature was functionally broken across process restarts
• The bug: slot.prompt.checkpoints metadata existed only in process memory and wasn't serialized
• The fix restores the checkpoint metadata list so the reuse path can properly roll back via covering checkpoints

u/ikkiho (1 points · permalink)

yeah the nasty part is the restore succeeds mechanically so your dashboards all look green, and the miss only shows up as a latency spike on the first request after a restart. hit this exact shape before, some blob round-trips perfectly but a tiny bit of sidecar state never gets persisted so the consumer just quietly rejects the whole thing. what saved me was timing first-token after the restart instead of trusting that the restore returned ok.

u/lost-context-65536 (1 points · permalink)

This looks good, I'll merge it into CachyLLama as well.

• Community members shared model performance on new hardware setups
• Ornith 1.0 was noted for understanding agent harnesses and self-extension better than other models in its size class
• Models mentioned include Qwen 3.6-35B, Gemma 4-31B, GLM 4.7 Flash, and Mistral Medium 3.5

u/devoidfury (22 points · permalink)

I have a similar box, strix halo on 128gb UMA, different brand. Here's some models I've had good results on, using mostly llama.cpp + llama-swap:

• qwen3.6-35b
• qwen3.6-27b
• gemma-4-31b
• gemma-4-12b
• glm4.7-flash
• mistral-medium-3.5
• minimax-m2.7
• north-mini-code-1.0-30b
• ornith-1.0-35b
• ornith-1.0-9b
• llama-3.1-70b
• deepseek v4 (using antires/dsv4)

u/devoidfury (5 points · permalink)

It's neat! It seems to understand the idea of an agent harness and self-extension better than other models in the same size; however it's a little too dogged in how thoroughly it'll inventory things and makes a plan before it acts, so it is more prone to blowing out the context or going into reasoning loops at times where other models will act more incrementally with a less complete picture.

• DeepSeek charges 2 orders of magnitude less than OpenAI/Anthropic and is still profitable
• GitHub Copilot moved to per-token billing, with companies managing budgets by using smaller models where necessary
• Commenters noted the markup on tokens is similar to other industries with information asymmetry

u/Putrumpador (7 points · permalink)

Why are *some* tokens are so expensive?

u/WonderFactory (2 points · permalink)

It's a good explainer but his conclusions at the end are completely wrong, that regular companies wont be able to afford agentic coding. Co pilot moved to per-token billing this month, my company give all the devs a monthly token allowance and it's up to me to try to manage that budget by using smaller models where necessary, refreshing the context so I'm not sending unnecessary tokens etc.

Agentic coding has become indispensable, it's not going anywhere and it'll only get cheaper as inference costs drop