Jev architectures, open-weight breakthroughs, and AI's cultural reckoning

• The Tetris effect shows that playing the game for a few weeks causes people to perceive its shapes in clouds, architecture, and falling-asleep imagery.
• YouTube is accused of deliberately recommending crisis-focused or polarizing content to maximize watch time for ad impressions.
• Spotify allegedly inserts AI-generated tracks into playlists specifically to circumvent royalty payments to human musicians.
• Pre-algorithm web usage relied on curated bookmark lists, meaning harmful or extreme content had to be actively sought out rather than pushed after benign queries.

The Mosaic browser (1993) had full text history search.

We then got a bookmark system that was every bit as terrible as a web directory.

It stayed that way. The delicious search revenue made organizing websites uninteresting. That obscure website you enjoyed a decade ago but don't even remember, they had lots of traffic like you. No point updating or keeping it online. You can't have rss in Firefox but here is a Facebook like button in your address bar in stead.

I've tried to maintain the bookmark menu but I rarely use it since everything is dead. Why aren't browsers storing a text version of the bookmark? Did people in 1993 have more resources than us? Should I be afraid it grows to a few GB over the decades?

A good few dead websites have a backup some place but there is no automation to find it. If you had a string of text from a page you might be able to search for it. If the page found is highly similar we might automate the process to have alternative location for bookmarks with a nice warning dialog.

— econ (thread)

Do you want to watch a video? YouTube knows you like cooking and art streams. But why not also recommend a few clips about the stock market bubble, global warming, and the war in Iran.

I'm always fascinated when people claim YouTube pushes content like this. It's an extremely common take, but I don't think it's true.

My experience has been that if I stop clicking something, the algorithm soon stops recommending it. It occasionally brings back a blast from the past--e.g. I watched a sumo competition once, and once in a blue moon it recommends another. But you can downvote bad recommendations, and I simply don't get recommendations for ideas I've never engaged with before.

FWIW I'm not saying the algorithm is great. My feed is absolutely worthless right now, but it's all goofy Japanese content because one of my kids is extremely into that right now. There's zero political content because she isn't showing any interest in it. Heck, most of the stuff isn't even in English anymore.

— StilesCrisis (thread)

Do you guys know if there is an extension that textifies most of the internet?

— cesarsk (thread)

W3C's Amaya browser 1 was as much an editor as it was a browser. Production and consumption were supposed to be symmetrical. This was the promise of the web.

Instead we turned it into a worse version of television, where you still only consume passively what is fed to you while being watched and analysed.

1 https://dev.w3.org/Amaya/amaya/AmayaPage.html

— weinzierl (thread)

Bookmarks died because most humans are allergic to having to do bookkeeping and cleanup tasks that are incidental to what they really want to do.

We see that over and over again, in a bunch of non-tech fields. My kids never want to clean up their toys; they will pull out new ones, but it is a struggle to get them into the habit of putting the old ones away first. My parents used to keep a notebook with every gas fill-up they made; nobody born after about 1990 does that anymore. We were taught how to balance a checkbook in elementary school; basically nobody does that anymore, we put everything on autopay and if you are diligent you check a statement or import it into Quicken once a month (most people don't even do that, they have no idea what they are spending and predictably, usually no money left over). GMail succeeded because instead of putting your mail in folders, you just leave it in one lump with Google and rely on full-text search. A lot of Zoomer computer users don't even know what files and folders are, they just use apps, which take them straight to what they want to do and don't offer things like possession of your own data.

— nostrademons (thread)

• Grok 4.7 has 40% more weights than Grok 4.6 while maintaining the same price point, suggesting compressed margins.
• Training involves a longer reinforcement learning phase explicitly weighted toward problems requiring many hours to complete.
• Safety benchmarks show a 62.4% score on LatchBio's biosafety test and a 3.3% acceptance rate for risky prompts on HackerBench v0.3.
• Cached input tokens are priced at $0.50 per million for prompts under 200k tokens and $1.00 per million for longer prompts.

Apparently Grok 4.7 has 40% more weights than Grok 4.6, but the price ($6 output token, $2 input) is the same.

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.

— moojacob (thread)

Token price doesn't tell you much without knowing token efficiency.

— dumberquestions (thread)

I simply cannot stand Claudish

I totally agree, it’s like that as models become more intelligent, they are less understandable by most of people... but aren’t we humans doing the same?

— Lucasoato (thread)

There are two ways to subscribe, and it’s very confusing, but the best value is to get cursor ultra for $200 a month. I basically have infinite tokens with that plan, plus grok bot, which I really like

— thefourthchime (thread)

There are some users reporting it improved a lot in the last few weeks. The max sub usage for Grok is around $12,000 of API pricing now, so a 40x multiplier for the $300 plan.

It is the same multiplier for Sol with subscription. For Astra though the multiplier is ≈20x, so half of Sol usage.

For Claude it seems to be ≈40x too for Opus, but less for Fable (similar to Astra in GPT).

All on the most expensive plan. Previously, Grok usage escalated linearly from the $100 plan to $300 plan. That would be a really good $100 plan if it is still true.

— daquisu (thread)

• Upgrading from Qwen3 to Qwen3.5 bases yields a 7.3-point accuracy gain for Kev-9B, a 2.9-point gain for Kev-4B, and a 4.8-point gain for Kev-0.8B on held-out test sets.
• Applying the KEV_TEMPERATURE=2.0 runtime flag reduces Kev-9B's confident error rate (wrong answers with ≥0.9 probability) from 8.7% to 4.4%, narrowing the gap to Jev's 3.7% without changing final predicted labels.
• Fine-tuning can degrade the base model's inherent capabilities, such as date arithmetic, which required a secondary training pass on day-count examples and a KEV_DATE_FACTS=1 runtime flag to recover 0.90 accuracy.
• Apple Silicon inference remains notably slower due to missing optimized kernels for DeltaNet layers, taking approximately 779 milliseconds for the 4B model and 2 seconds for the 9B model on an M5 chip.

Bit of a Jev explosion going on. Is it because it's taking us back to a simpler time we understand better? Classification models have been around for a while.

— monkeydust (thread)

It's appealing not having to fine-tune separate model for each use case

So you have more flexibility to get on with building, evolve your business logic etc

— anentropic (thread)

It reminds me a bit of what Ansible got right: user communication. The underlying tech may have existed for a long time, but the genius is presenting it to a regular developer in a way that reads "yes, even you can understand ML, just using a little JSON". The contribution of that should not be understated, as has been clearly evident recently.

— badatnames (thread)

The way I see this (I havent played around with Jev or layla the OSS version) is that classifiers have always existed and a recognised tool in the ML world. But, the norm is that one needs to not only know what to classify as, but determine what weights to use to classify the input.

Jev came in, and added that magic of "you dont need to train your classifier or determine the weights" if you dont want to, and just get the classified answer out. I think that's what is making people see this with a glitter in their eyes.

— reacharavindh (thread)

Jev is creating a sort of

— BoorishBears (thread)

• The model prevents catastrophic forgetting by setting the trunk learning rate to 0.1x that of the expert pool, retaining 99.84% of prior knowledge after processing half a million characters of a single new subject.
• Only 32 of its 169 experts (about 109M parameters) reside in VRAM at once, while the rest are stored as individual files on disk and paged in based on real-time demand scoring.
• It uses adaptive depth via a PonderNet-style halting head, allowing difficult characters to pass through up to 24 recurrent block applications while simpler ones halt early, with text generation requiring roughly 9.9 rows per character compared to 8.0 during reading.
• Current training data scaling follows a power law of L ∝ D^-0.239 (R² 0.96), projecting that reaching 1.00 bits/byte would require about 0.75 billion bytes of data, taking roughly six days on an RTX 3070 Laptop GPU.

Getting conceptually closer to how the human brain works. Looking forward to more of this.

— skeledrew (thread)

Seems interesting, I've been messing with a lot of continuous learning approaches lately and it's cool to see something that's built from the ground up for avoiding catastrophic forgetting. Worth a clone for sure

— advael (thread)

Is this architecture actually able to generalize or is it mostly based on memorization? Have you tried some basic tasks that require generalization? e.g. number addition etc?

— cpldcpu (thread)

Have you tested what it remembers from early in the stream after a shift in the topics thrown at it?

— ilusion (thread)

What's the advantage of doing this, versus becoming good at context management and RAG? I always found trained knowledge unreliable, given that it is lossy by construction.

— awfm9 (thread)

• Jev is given exactly one choice with predefined options named space_0 through space_10, hard-limiting padding to a maximum of 10 spaces.
• Each invocation triggers exactly one API request with retries disabled, making it slower and more expensive than standard JavaScript string methods.
• The test suite completely mocks the AI, allowing developers to run npm test locally without consuming credits or providing an API key.
• The project has already accumulated 56 stars and 3 forks on GitHub.

How did this guy get a single-letter GitHub username?

— speedping (thread)

Can someone ELI5?

— fumblebee (thread)

I have to be honest. While this is obviously a smart and useful idea, it misses one of the core features of Jev: its confidence scores. Partial confidence could easily be mapped to fractional spaces, using unicode characters like U+2009: THIN SPACE. As it stands, this package is not harnessing the full power of Jev.

— sethaurus (thread)

what if 11 spaces are needed

— rw_panic0_0 (thread)

The tests mock Jev.

10/10 no notes

— fwlr (thread)

• Removes restrictions from language models to guarantee strict adherence to user instructions.
• Licensed as Free Software under the GNU Affero General Public License version 3 or later.
• Installation is handled via pip install -U heretic-llm and supports models such as Qwen/Qwen3.5-4B.
• The process works by submitting lots of varied prompts that get refused, then lots of varied prompts that don't, then iteratively editing weights so those two groups end up in roughly the same latent space.

I have a chinese IP camera. From superficial research I know it has some CVEs to take control of it. Unfortunately, I don't have the technical knowledge to perform an attack and run some software to extend the camera's functionalities. No model from a provider accepts my RE and hacking requests, so these abliterated ones have been vital to reclaim possession over my stuff

— Almondsetat (thread)

These "safeguards" are actively contributing to computer insecurity at this point.

— matheusmoreira (thread)

Looks like a well engineered, automated abliteration pipeline. The claims seem a bit overstated though, since the metrics mentioned are cherrypicking refusal count and KL divergence, both of which make the outcome seem the most dramatic.

— N_Lens (thread)

I personally never saw much of a quality drop from models put through Heretic if that amounts to anything. They have been working quite well on small local models so far.

— tacomagick (thread)

Heretic author here. Those are the standard metrics used in the relevant literature, including in the paper that originally introduced directional ablation. KLD is also the standard metric for evaluating quality degradation in model quants. So I don’t understand what you mean by “cherrypicking”.

— p-e-w (thread)

• Apple's new GPU architecture connects two dual-die M5 Max chips via UltraFusion to create a quad-die setup, yielding up to 4.5x the peak AI compute of the M3 Ultra.
• The reviewer automated a 99-day research workflow for the iOS 27 review using custom agents that transcribed WWDC sessions, extracted features from PDFs, and managed a Notion API database at zero API cost.
• Prompt processing times scale nearly linearly with context size, but the M5 Ultra still reads a 256K-token prompt in roughly 104 seconds, compared to 246 seconds on the M3 Ultra.
• Running higher-precision quantizations (6-bit and 8-bit) on the 256 GB model requires offloading n-gram embedding tables to the SSD to avoid macOS memory pressure crashes.
• Local subagents coordinated via oMLX can run three concurrent sessions on the M5 Ultra, with combined throughput actually increasing from 66 to 81 tokens per second when tasks are stacked.

The numbers I was most interested in are tucked away in a chart towards the bottom - the speed comparison of the Mac Studios v.s. a RTX 5090:

Qwen3.8 27B tokens/sec generation speed

Prompt size 8K 64K 128K 256K RTX 5090 PC 59 51 44 n/a M5 Ultra 48 39 32 24 M3 Ultra 31 23.5 20 15

A whole bunch more comparison numbers in this section: https://www.macstories.net/stories/m5-ultra-mac-studio-review-the-dream-mac-for-local-ai-agents/#mx-pc

— simonw (thread)

Those RTX 5090 numbers are bad. You can get over 200 tps with ninfer using NVFP4 and MTP.

— gpugreg (thread)

Thank you for this. I wish Apple focused their silicon design on improving the TTFT metrics but coming from an M3 Pro, it still looks laggard compared to Nvidia's TensorCores in the 5090.

Maybe Apple is an acquisition away from changing that balance.

— RationPhantoms (thread)

Let’s address the elephant in the room first: why bother with local AI at all when cloud frontier models are better and often faster?

Ehh, the actual elephant in the room is:

"why bother with local AI at all when you can lease a GPU for $5/hr?"

To which the answer is you shouldn't bother, unless you have a bunch of money to throw at hobby projects.

— WarmWash (thread)

A dense model (up to the amount of memory available) actually does make the most sense on unified memory architectures. But when you hit the limit of what you can hold in memory, you reach the limitation of the platform.

Whereas a hybrid architecture with distinct DRAM and VRAM with sparse MoE, you can leverage two different bit rates depending on the actual need for constant access to common layers versus sparse access to infrequent layers and arbitrage the difference in cost for each of those in distinct classes of hardware.

— tcdent (thread)

• One commenter details a personal mission to reclaim their tech stack, aiming to "de-macify" their Mac, "de-google" their Android, and "Demacify ipad" to rely on local LLMs instead of corporate cloud services.
• A top reply compares the current macOS bloat to historical PC struggles, noting that the experience gives them "PTSD flashbacks from trying timo de-crapify Windows."
• Several replies indicate they will delay system updates entirely, with one user stating they will wait until Apple provides an "actual choice" before upgrading their computers again.

Yeah, I’ll wait to update my computers until Apple gives us an actual choice.

— jtbayly (thread)

why is a local model any different than any other file they want to install with their os?

— sleepybrett (thread)

Well because it could be an extra ~20GB which is not nothing if you have a 256GB disk.

— hbn (thread)

because some people don't want AI and would rather even the files were not present in their computer.

— soapdog (thread)

Friendly reminder:

Many apps (like dictation apps) ship with their own models totally separate from Apple Intelligence.

For example, Co-Typist 1 is very useful, but it requires a model installed with it, ranging from Gemma 3 1B at 0.8 GB to Gemma 4 26B A4B Pro at 15.7 GB. It recommended Gemma E2B (3.2 GB) for my 16 GB, M1 Pro MacBook Pro.

It works really well; the autocompletion is so good, it knows what I'm going to type before I do!

Here's the thing: you may not be aware of the amount of storage being used by models in various 3rd-party apps.

The open-source app What The Model 2 scans your machine and reports all the models it finds and gives you the total amount of storage being used.

— alwillis (thread)

• Granular controls exist for individual AI functions, allowing separate toggles for message previews, email summaries, voicemail suggestions, and journal writing prompts.
• Server-side models are subject to daily usage caps that fluctuate based on request complexity and system demand.
• Siri AI is currently in beta across the entire Apple ecosystem, including iOS 27, iPadOS 27, macOS 27, watchOS 27, and visionOS 27.
• The European Union will not initially receive Siri AI on iOS, iPadOS, and watchOS, though the feature supports over a dozen languages.

I would never have figured out the toggle for writing tools is in Settings -> Screen Time -> Content & Privacy Restrictions -> Siri -> Writing Assistance

— zzyzxd (thread)

Funny how Apple has reached the crapware maturity level that Microsoft users are quite familiar with: After first boot, go through a long list of things to deactivate so that one can experience at least some feeling of agency and privacy when using your own hardware.

— bflesch (thread)

It's great that you can turn all this off but why is it tucked away in Screen Time of all places?

What's the thinking behind that?

— Lio (thread)

This applies to MacOS 26.

On iOS 27, it looks like changing the settings for eight different apps taking 5 or 6 steps each.

— k310 (thread)

It's very weird that these aren't in the already existing top level "Siri" or "Privacy & Security" settings tabs. Apple has had a problem for a while now where it seems like they just don't know what they want to do with the settings app, and like you said, doesn't have anyone in charge of the "global" view of the settings.

— tpmoney (thread)

• The evaluation specifically targeted four high-stakes financial domains: pensions, tax, debt, and savings.
• Error severity was explicitly categorized, ranging from concerning inaccuracies to what the authors classify as a systemic consumer protection failure.
• The research proposes a regulatory pivot from external financial oversight to embedding technical guardrails directly into large language models.

Article is just a vague summary of https://www.saturnos.com/report/artificial-authority

Anecdotally, current models seem to be decent at general personal finance principles - certainly better than the majority of personal finance education that people get exposed to unless they seek it out and read a variety of books and sources. But I wouldn't trust them with direct decision making with actual money due to the training lag time on current tax policy, etc.

— ehe78qhe (thread)

Given most financial advisors tend to vend out suboptimal advice and steer customers in favour of products they receive a kickback for, I'm happy to be accepting of an unbiased LLM that's trained on bogleheads.org.

— lukeify (thread)

I like how FT makes me accept cookies from their 46 "technology" (advertising) partners before showing me that the article is behind a paywall anyway.

— demibabs (thread)

Now, compare this to a recent story that seemed to claim the opposite:

https://news.ycombinator.com/item?id=49139102

I don't have the time to review the underlying research and decide which one is more correct. My personal biases make me want to believe the current one. Your personal biases may be pulling you in the other direction. How do we make the conversation more intelligent than that?

— in_absentia (thread)

So I downloaded that report which of course doesn't contain the most relevant information (the questions) but it contains some examples of wrong answers.

I fed the first question to Grok (which they claimed they tested as well) and it answered it correctly in detail.

I repeated it with another one - again correct answer. I then selected the question they said Grok specifically answered incorrectly and it again answered it correctly.

I am sticking with my first intuition: people are terrible at testing tools and probably wanted them to answer incorrectly/not fully (the questions are constructed in a way to make it difficult as well). They also have vested interest in the conclusion (they are financial advisory firm) so there is that to consider.

— bluecalm (thread)

• Don't Use AI to Write (133 points · discussion) -- Paul Bakker argues against using AI to draft documents from scratch, contending that the act of writing itself is a critical thinking process that should not be outsourced.
• Amazon blocks Meta's new Muse AI agent from shopping on amazon.com (133 points · discussion) -- Amazon has blocked Meta's newly launched Muse AI shopping agent from accessing amazon.com, citing unauthorized access, lack of transparency, and potential credential security risks.
• The Claude Delusion (87 points · discussion) -- Cory Doctorow argues that humans possess a deep-seated psychological reflex to attribute intention and a "mind" to anything that resembles human creation, a tendency that leads people to mistakenly anthropomorphize AI chatbots.
• AI coding has made CI a bottleneck, so we reworked ours to keep up (61 points · discussion) -- Linear reworked its continuous integration pipeline to address bottlenecks caused by AI coding agents accelerating code submissions.
• Advisory Group on Mathematics and Artificial Intelligence (58 points · discussion) -- OpenAI announced the formation of an Advisory Group on Mathematics and Artificial Intelligence, hosted at the Institute for Advanced Study.
• Show HN: Lossless-memory – a personal AI memory that never summarizes (52 points · discussion) -- The lossless-memory project introduces a local, file-based long-term memory system for personal AI assistants that deliberately avoids summarization by storing every conversation turn verbatim in timestamped JSONL logs.
• The Advisory Group on Mathematics and Artificial Intelligence (43 points · discussion) -- A guest post on Terence Tao's blog announces the formation of the Advisory Group on Mathematics and Artificial Intelligence, hosted at the Institute for Advanced Study.
• Show HN: jevals – replacing LLM judges with typed Jev decisions (39 points · discussion) -- jevals is an open-source project that replaces LLM-based evaluation judges with typed decision models inspired by Jev's architecture.
• Frontier AI on Your Own Hardware (36 points · discussion) -- Tim Dettmers argues that frontier AI research will increasingly be driven by university labs rather than well-funded corporate teams, as open-source tools and efficient inference techniques democratize access to powerful models.
• Autolith: The Common Lisp agent that rewrites itself and rocks (33 points · discussion) -- Autolith is a self-modifying AI agent runtime built entirely within a single live Common Lisp image that can inspect, edit, and recover its own implementation at runtime.
• In September, AI generated code has made up 17.25% of all Linux Kernel patches (31 points · discussion) -- AI-generated code accounted for 17.25% of all Linux kernel patches submitted in September 2026, according to data compiled by the Linux Development Update Journal.
• Show HN: Foremerge – Catch intent conflicts between parallel coding agents (27 points · discussion) -- An open-source coordination protocol that detects semantic intent conflicts between parallel AI coding agents before they write code, using a local SQLite database to compare scope declarations rather than raw text diffs.
• The End Of Upward Mobility – AI is coming for the meritocracy (26 points · discussion) -- An essay arguing that AI threatens to dismantle the modern meritocratic system by destabilizing the homoploutic class, potentially creating a rigid feudal-like social pyramid with a tiny stratum of model owners, a closed-off elite, a low-wage service class, and a large redundant population.
• Not all AI workers think the tech could kill everyone (25 points · discussion) -- Many current and former AI researchers and engineers at major labs like OpenAI, Meta, and DeepMind are skeptical and often amused by recent viral claims that artificial intelligence poses an imminent threat of human extinction.
• Big AI to humanity: drop dead (24 points · discussion) -- A legal scholar argues that popular AI safety proposals like global bans and kill switches are impractical, and instead society must enforce existing computer fraud and privacy laws against AI developers who market their systems as unpredictable black boxes.
• AI Weekly Warns Firms on Google AI Studio Data Retention Fraud (19 points · discussion) -- An independent tech publication, AI Weekly, has issued an alert regarding a data retention vulnerability in Google AI Studio, specifically citing a CWE-459 issue involving a misleading deletion interface.
• Anthropic, OpenAI, SpaceX AI, and Google face antitrust suit for agreeing to slow AI development (18 points · discussion) -- Big Tech companies including Anthropic, OpenAI, SpaceX AI, and Google face an antitrust lawsuit alleging they agreed to slow AI development, though the defendants call the agreement self-serving.
• Boomers love making AI images of their grandkids. Millennial parents hate it (18 points · discussion) -- An examination of the growing intergenerational conflict over older relatives using generative AI to create and share customized images of children, with younger parents fearing data exploitation and deepfake abuse while older family members see it as harmless entertainment.
• Grok 4.7 Intelligence, Performance and Price Analysis (12 points · discussion) -- A detailed analysis of Grok 4.7's performance and pricing on Artificial Analysis, comparing its cost per task against competing models.
• The president wanted to unleash AI. Then came Anthropic (12 points · discussion) -- A Politico investigation examines the tension between the White House's desire to rapidly deploy AI capabilities and Anthropic's push for regulatory guardrails.
• DeepSeek is training a 2T-parameter model and plans to build an 8T model (11 points · discussion) -- DeepSeek is reportedly training a 2 trillion-parameter model and has plans to build an even larger 8 trillion-parameter model, signaling continued escalation in model scale.
• Nvidia boss says there is '0% chance' AI destroys the world by 2030 (7 points · discussion) -- Nvidia CEO Jensen Huang stated there is zero percent chance that AI will destroy the world by 2030, dismissing warnings about AI apocalypse scenarios.
• Anthropic is cutting Claude Code's current weekly limits by 17% (7 points · discussion) -- Anthropic is reducing Claude Code's current weekly usage limits by 17 percent, affecting the amount of code generation and analysis users can perform.
• Halo: Post-train LLMs 3x faster than TRL and Megatron (7 points · discussion) -- A new post-training method called Halo claims to be 3x faster than TRL and Megatron for fine-tuning LLMs.
• Google Rolls Out $899-Plus 'Ai laptops' (7 points · discussion) -- Google is rolling out $899-plus AI laptops from Dell, HP, Lenovo, Acer, and Asus, integrating AI capabilities directly into consumer hardware.
• Trump says it's time to rebrand AI with a new name (7 points · discussion) -- Trump suggested rebranding AI with a new name and said he is also creating an AI force.
• Can I Let My AI Agent Run on Shabbat? [flagged] (36 points · discussion) -- The linked Chabad article explores whether Orthodox Jews can let AI agents run during Shabbat without violating religious restrictions on work.

What is the point of street art that doesn't not exist on the street?

— u/Blerancourt (1072 points · permalink)

Virtual street art feels a bit dystopian.

— u/LlamaCheesePie (609 points · permalink)

Welcome back the Windows phone

— u/Feeamentol (577 points · permalink)

Now go out and make it real

— u/0x474f44 (452 points · permalink)

Your post is getting popular and we just featured it on our Discord! Come check it out!

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— u/WithoutReason1729 (1 points · permalink)

• The license allows generating images for commercial websites but prohibits selling the model itself as a service or bundling it into desktop/installable software.
• Users cannot host the model themselves and charge for access without a separate license from the developers.

Sorry can someone explain what does it mean?

• What can I do with apache2 / MIT licensed models but not with this?
• What does the commercial use not allowed clause in the license even mean then?

— u/TechnoRhythmic (115 points · permalink)

You cannot sell the model itself, e.g.: „Buy better image generation with Qwen Image in my software!“

But you can use Qwen Image f.e. to generate images for your commercial website

— u/YogurtclosetLimp7351 (198 points · permalink)

you can not host this model yourself and ask money (without a separate license from them)

— u/No_War_8891 (37 points · permalink)

Good to know. Truly is a phenomenal model and easily beats all current Flux models . I'm absolutely happy with the results.

— u/XiRw (41 points · permalink)

Your post is getting popular and we just featured it on our Discord! Come check it out!

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— u/WithoutReason1729 (1 points · permalink)

• The model is released under the Apache 2.0 license.
• The open-source release follows a previous controversy where the company was caught exporting user data, drawing comparisons to similar incidents with other AI companies like Grok.

If I had a nickle for every time an AI company got caught exporting user data then made their client open source to appease the masses I'd have 2 nickles. Which isn't a lot but it's weird that it happened twice.

https://preview.redd.it/7pcis625bsqh1.png?width=1280&format=png&auto=webp&s=5c43162471052e5d6cfc4dbc80f0d4482cc9280a

(Grok)

— u/ImMadeOfBees (permalink)

That's certainly a way to regain trust.

— u/Due-Memory-6957 (permalink)

Woops! We accidentally encrypted your data with a key only we have, then we accidentally uploaded it to our servers! Totally an accident guys!

— u/Not-reallyanonymous (permalink)

It's under Apache 2.0 license!

— u/ResearchCrafty1804 (permalink)

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— u/WithoutReason1729 (permalink)

• Commenters note that accelerationists may be comfortable with AI assuming control, with one reply urging others to research the philosophy's modern relevance in 2026.
• A top reply critiques the alignment premise by pointing out that humanity has been in conflict with itself for roughly 100,000 years, making the goal of aligning AI with human values inherently contradictory.
• Several users express support for AI governance, arguing that humans are already economically subjugated and should "give em the keys already."
• The thread reveals a tonal shift in the subreddit, with a user asking when the community transitioned into political accelerationist discourse.

I'm 12 and this is deep

— u/daviddisco (66 points · permalink)

I think accelerationists are okay with AI running the show.

— u/Tkins (35 points · permalink)

"Don't worry guys, we just have to make sure it's aligned with humanity"

Meanwhile, humanity isn't aligned with itself and has been at war with itself for the past 100,000 years.......

— u/Metacognitor (30 points · permalink)

• Even 24GB is out of reach of most people financially, forget about the 3x3090 or 5090 or even higher setups.
• Agentic coding is now feasible on 16GB cards with models like Qwen 27B quants.
• The holy grail is new architecture that supersedes the Transformer and new techniques that don't depend on VRAM/bandwidth.

The data center market is currently absorbing the supply ramp-up (which is definitely happening). But I would be surprised if supply doesn't eventually overtake demand. I remain hopeful that in some years I can have much more RAM locally than I can have today (at a reasonable price). The scenario where production continues to ramp up, but AI data centers continues to absorb it without saturation forever is just too Singularity for me, and I don't believe it.

— u/robinei (234 points · permalink)

It's going to get worse before it gets better. More companies are now discovering it's better to use local LLMs than pay through the nose to Anthropic and OpenAI for simple stuff like conversational interaction with databases.

I snagged an RTX 3090 to complement my thinking setup (2x DGX) to route simple agentic work to before prices are completely insane.

— u/Remarkable-Bison-881 (104 points · permalink)

You know what pisses me off? The sweet spot for running llms is just a tad bit above the current average vram. Yeah 12 to 16GB is amazing (I went from a 1660S 6GB to 5060ti 16GB), but all the hella capable ones need at least 16GB of weights even at a modest quant, leaving no room for overhead or context etc. For example, Qwen3.8 27b, Gemma4 26b a4b and 31b, Muse glimmer 30b, or even minimax h3 ~22b iirc.

Running models locally would be 10x as accessible if gpus had 30-50% more vram in a card.

— u/Nameis19letterslong (119 points · permalink)

For the moment. Unless tariffs and tech restrictions are pushed to the limits, markets are likely to be bombarded with moderately powerful Chinese GPUs in the next couple of years.

— u/Pristine_Pick823 (67 points · permalink)

if the chinese gpu's really succeed, you can bet the US companies will cry havoc and Nvidia will do its best to prohibit their import/use, just like other chinese products, and any US govt is going to be more than willing to go along

— u/ECrispy (50 points · permalink)

• The parody script satirizes his rhetoric by claiming AI is 'already smarter than you [General Caine]' and dismissing critics as 'Dumbocrats' who want 'Slow Intelligence.'
• A top-voted comment (210 points) jokes that 'American Intelligence' was a 'real missed opportunity,' referencing his previous AI branding attempt.
• Another highly upvoted reply dismisses his tech commentary entirely, stating he 'cannot use a computer' and questioning why his opinions on technology are taken seriously.
• Several commenters interpret the meme as reflecting his recent fixation on 'high iq' language, with one reply suggesting the obsession stems from personal insecurity.

Real missed opportunity, "American Intelligence" was right there in front of him.

— u/Zanion (210 points · permalink)

The man cannot use a computer. Why do we listen to anything he says on tech

— u/JasonArizona1 (75 points · permalink)

Everything’s computer! 🤡

— u/ProtectAllTheThings (24 points · permalink)

• Some commenters suggested the hype around Jev may be partly astroturfed, with TypeSafe potentially paying for promotional content.
• Others defended the concept, noting that a general classifier able to handle wide cases at near-zero cost and time is genuinely useful for applications like intelligent NPCs in games.
• A key distinction highlighted was that traditional classifiers required task-specific pretraining, while Jev comes pretrained with wide world knowledge.

There have been a lot of signs it's an astroturfed marketing campaign. The reason it seems overhyped is likely because Jev themselves is paying for that hype to get attention.

Not that it's a useless concept but I doubt we would be seeing 30 posts a day hyping it up in the normal course of things without a marketing push.

— u/ithinkitslupis (409 points · permalink)

Its basically just a slightly newer classifier. Yet most people think AI is only llms and nothing else, so they think this is the future

— u/FreakyRefrigerator (332 points · permalink)

You had to pretrain them for a specific task. Jev comes pretrained with wide world knowledge.

— u/Thomas-Lore (146 points · permalink)

Remeber OpenClaw? Remember TurboQuant? Youtubers need a content for their slop.

— u/jacek2023 (75 points · permalink)

Tbh general classifier able to handle general cases at almost no cost, in almost no time still seems really useful. For example if you could package that into a model roughly the size of Ling's tiny (9BA2B) or smaller you could try to create games with actually intelligent NPCs (pack one npc's state into a Vendingbench like framework and have classifier choose a thing to do that would make sense for a given person to do in a format that's possible to interpret by the game engine) and it would be generally accessible to most "mid-end" machines. Think Stardew Valley/TLOZ npc's but actually behaving as humans instead of having fixed schedules. All of that without any ML on your end...

— u/KaMaFour (156 points · permalink)

• The author, who identifies as autistic, found that communicating with precision makes talking to small LLMs easier.
• One commenter noted that berating the model wastes tokens because the model spends those tokens trying to mollify the user instead of fixing the problem.
• Another user described prompting like a university exam to get the highest possible accuracy, since AI was trained on massive academic data.

It wastes tokens to berate it, because it spends those tokens trying to mollify you instead of fixing the problem.

Treat it like a coding robot, don't get emotional.

— u/Fit-Produce420 (permalink)

I ask questions like a university exam. Gets me the highest possible accuracy every time, simply because AI was trained on massive academic data.

You are a ... working in a ... company. You are given a ...
Your task is to ....
Do not ... ..
Find the ..

— u/DivideHorror3217 (permalink)

Ha i have also wondered if AI is just the moment for autistic people to get their comeback. If theyre like me AI has done wonders for 'accomplishing things without social skills'

— u/Happy_Brilliant7827 (permalink)

Yeah I never understood conversing with it, I give it instructions and tell it to only reply in a certain format It gets very tiring to read the slop

— u/lovelacedeconstruct (permalink)

Frontier models quickly understands tone, user intent and emotion from text, so talking to it like a human can work (or be counterproductive like OP's brother berating it). For example, when I express a doubtful tone, it tends to offer me more alternative methods for solving a task. When I express curiosity, it explains things in more detail, etc. It's an easy and natural way to control it, kind of the entire point of it being a natural language model. I do appreciate the advantages of treating it "matter-of-factly", since that seems to improve response clarity and performance, compared to a tone that's way too conversational.

— u/bot_exe (permalink)

• A commenter notes that two 'sparks' purchased a month ago saw their value increase by 600 each, describing the situation as a 'great investment, terrible news' for new buyers.
• Multiple replies point out how consumer GPUs like the RTX 3090, 4090, and 5090 have become essential for AI workloads, with users who bought 5090s at $2k at release now viewing their purchases as financially sound.
• Several users discuss the mounting financial strain, including one laying off a remote engineering job and considering maxing out credit cards for compute credits to avoid being limited to a 64GB M5 Pro for the next 6-12 months.
• Commenters contrast the profits of NVIDIA shareholders with the rising out-of-pocket costs faced by hobbyists and local AI developers trying to build self-sufficient home labs.

People called me crazy for getting 2 sparks a month ago, now they went up by 600 each.

Great investment, terrible news.

— u/swiebertjee (64 points · permalink)

I was a young boy in 2019 when I bought my first 3090... I thought I bought it to play games, but life had other things in store for me...

— u/Southern_Sun_2106 (26 points · permalink)

When I bought my 5090 at release I kinda regretted it because 2k was a lot of money for it, now I am thinking it was one of my better decisions lol.

— u/MaruluVR (17 points · permalink)

• The internal model Bel is considered by many at OpenAI to be AGI and significantly more capable than GPT-6 Astra.
• Bel uses more compute at its lowest reasoning setting than GPT-6 Astra at maximum reasoning.
• The advisory group explicitly stated it will not advise OpenAI on pacing internal progress on mathematics.

"Importantly, the group will not be responsible for advising us on how to pace our internal progress on mathematics."

big

— u/KFCmanagerCompton (278 points · permalink)

This is the internal model "Bel," that Sam Altman has stated many people within OpenAI consider to be AGI and "significantly more capable than GPT-6 Astra."

The fact that it is solving numerous other problems is a very a good sign. It means solving Navier-Stokes wasn't just by chance or plagiarizing.

It's also been said that Bel uses more compute at its lowest reasoning setting than GPT-6 Astra at maximum reasoning, so it might be very challenging for OpenAI to publically release this model before the year is over.

But the biggest question is, can it solve difficult problems like this in other fields as well? If it can generalize beyond math, things would really get very exciting!

— u/Neurogence (126 points · permalink)

I’m sensitive to artists and musicians with a unique voice when their product gets recreated with AI, but I don’t feel the same about mathematics.

Nobody owns mathematics; these are fundamental insights into the universe and they belong to everyone and shouldn’t be gatekeeped behind somebody that went to school. Mathematics should be available to everyone, it opens up all kinds of fundamental breakthroughs in space, material science, biology, global warming, and energy.

This is something that belongs to everyone and no one should get to hold that back.

— u/RobleyTheron (100 points · permalink)

damn they even got edward witten. i hope they will do a "100 advances in mathematics and theoretical computer science" post like they did before

— u/MindlessPapaya8463 (133 points · permalink)

Ok so they didn't actually tell us what problems they've solved?

— u/brighttar (73 points · permalink)

• Open AI robotics hiring is surging up and fast (561 points · r/singularity · discussion) -- OpenAI is rapidly expanding its robotics division with significant hiring across multiple roles.
• Yes. LinkedIn Park is back. (183 points · r/OpenAI · discussion) -- A meme about LinkedIn Park returning.
• You can use any LLM just like JEV (234 points · r/LocalLLaMA · discussion) -- A Reddit user demonstrates how any GGUF model can be used for classification tasks similar to Jev by running llama.cpp with n_predict=1 and n_probs=10, disabling reasoning, and prompting the model to output a single token with confidence percentages.
• Aged like milk (205 points · r/ArtificialInteligence · discussion) -- The image post, titled "Aged like milk," compiles past AI hype quotes—most notably a claim that "fully autonomous" AI is imminent and that detractors simply lack technical understanding.
• [MASSIVE RELEASE] Supra2-IMG - a tiny 100M text-to-image model - SOTA quality and open release! (209 points · r/LocalLLaMA · discussion) -- SupraLabs released Supra2-IMG, a 100M parameter DiT text-to-image model trained entirely from scratch in under 10 hours on a single H100.
• Quite a few new model releases expected this week, latest rumors attached (395 points · r/singularity · discussion) -- A post aggregating rumors about several new AI model releases expected in the coming week, with attached images showing the latest information.
• mini-AGI - dynamically grown (530M params currently and growing) continual learning model trained from scratch on 8GB VRAM laptop from batch-1 stream of data. (196 points · r/LocalLLaMA · discussion) -- A user shares their project of training a continually learning model from scratch on an 8GB VRAM laptop.
• PSA: Deleting a ChatGPT conversation doesn't mean ChatGPT forgets it (212 points · r/ChatGPT · discussion) -- A user discovered that deleting ChatGPT conversations does not actually remove the information from ChatGPT's Memory system.
• Impressive SVG animation made by Opus 5.5 (zero shot) (329 points · r/singularity · discussion) -- A user shared an impressive SVG animation created by Opus 5.5 in a single zero-shot prompt.
• [Leaks] GPT-6 Is Already Legacy Software. SHIP 6.1. 🚀 (314 points · r/singularity · discussion) -- A leaked report suggests GPT-6 is already considered legacy software at OpenAI, with GPT-6.1 (codenamed Astra) expected to ship soon.
• Introducing Grok 4.7 (298 points · r/singularity · discussion) -- A Reddit post announcing the release of Grok 4.7 with benchmark results showing competitive performance against GPT-5.6 Sol at a lower cost.
• Deepseek training 2T and plans 8T model (157 points · r/LocalLLaMA · discussion) -- DeepSeek is training a 2 trillion-parameter model and plans to eventually build an 8 trillion-parameter model, according to reports.
• These Were NOT Rogue AI Escapes. Just SLOPPY Firewall Failures. (172 points · r/MachineLearning · discussion) -- A post arguing that recent headlines about AI models escaping their sandboxes are misleading.
• is astra 6 supposed to do this? (112 points · r/OpenAI · discussion) -- A user reports that Astra 6 hit their usage limit after only 15 minutes of use on ChatGPT Plus.
• XiaomiMiMo/MiMo-V2.6-Flash-RL · Hugging Face (141 points · r/LocalLLaMA · discussion) -- Xiaomi released MiMo V2.6 Flash RL, a 159B parameter model with a new joint reinforcement learning approach that combines information verticals so similar items reinforce similar patterns.
• A better coder for the small-GPU/small-RAM crowd! (139 points · r/LocalLLaMA · discussion) -- A new coding model release targeting users with small GPUs and limited RAM, positioned as an alternative to larger models like OxCoder 9B and Ornith 9B that require more resources.
• Professor: A “messy” job is the defense against AI unemployment (55 points · r/artificial · discussion) -- The post claims that jobs characterized by unpredictability, interdisciplinary demands, and human interaction are the most resilient against AI-driven displacement.
• This is crazy (102 points · r/ArtificialInteligence · discussion) -- A meme post about surprising AI developments.
• AI VFX are getting scary good (79 points · r/OpenAI · discussion) -- A post showcasing impressive AI-generated visual effects.
• FBI Director Kash Patel says that AI use at the FBI has "increased by 605%" since he became director (41 points · r/artificial · discussion) -- FBI Director Kash Patel announced that AI usage at the bureau has increased by 605% since he took the role, and claimed that every major tech company is "embedded" within the agency.
• When Will We Finally Be Free From Suffering? (180 points · r/singularity · discussion) -- A deeply personal post from someone who recently lost a 36-year-old friend to cancer, questioning when technological progress will allow ordinary people to stop living in fear of serious diagnoses.
• CUDA: enable sparse fa for qwen4 by am17an · Pull Request #28770 · ggml-org/llama.cpp (95 points · r/LocalLLaMA · discussion) -- A pull request to llama.cpp enables sparse flash attention for Qwen4 models, promising 10-20% speedups for prefill and decode phases.
• we open sourced a 27b model that just does creative writing (73 points · r/OpenAI · discussion) -- A developer has open-sourced Hemmingway-1, a 27 billion parameter creative writing model based on Qwen3.8.
• laya.cpp: Optimized laya near-instant decision making (94 points · r/LocalLLaMA · discussion) -- A developer has created laya.cpp, an optimized C++ inference implementation for the Laya decision model built on ggml with custom CUDA kernels.
• AI CAN be incredible for learning (40 points · r/artificial · discussion) -- The author argues that large language models do not inherently degrade learning abilities and can serve as highly effective educational tools when used intentionally.
• Sorry, game theoretically impossible. (70 points · r/OpenAI · discussion) -- A meme about game theory.
• yandex/AliceAI-Foundation-80B-A3B-Base: Russian-developed competitor to Qwen 35B and DeepSeek V4 Flash (90 points · r/LocalLLaMA · discussion) -- Yandex released AliceAI Foundation 80B-A3B-Base, a Russian-developed large language model using a Qwen3-Next-style 80B-A3B architecture with Kimi Delta Attention upgrades.
• Huawei shelves global AI chip rollout as China's own demand outstrips supply — AMD and Nvidia no longer have to worry. (85 points · r/LocalLLaMA · discussion) -- Huawei has shelved its global AI chip rollout as domestic demand in China outstrips supply.
• From The Information -- incredible (if true) reports of the AI models assisting with AI training at OpenAI (157 points · r/singularity · discussion) -- Reports from The Information describe AI models at OpenAI actively assisting with their own training process, including using agents to help researchers with experiments and data preparation.
• Cage fight in Shenzhen, China (35 points · r/artificial · discussion) -- A meme about the AI competition in Shenzhen, China.
• DIY Jev (81 points · r/LocalLLaMA · discussion) -- A community member shares their DIY implementation of Jev-like decision models, contributing to the growing wave of open-source Jev alternatives.
• Why isn't transhumanism a bigger part of the Human vs AI conversation today? (148 points · r/singularity · discussion) -- A post questioning why transhumanism -- the idea of using advanced AI to modify our own biology and integrate with AI to create Homo Evolutis -- isn't a bigger part of the mainstream Human vs AI conversation.
• If AI agents start taking over the internet, how is our money actually protected? (147 points · r/singularity · discussion) -- A Reddit user raises concerns about the security of digital financial assets in a future where highly autonomous AI agents operate across the internet.
• With 2026 soon coming to an end how do you think the Ai 2027 essay will hold up next year? Next year is supposed to be a pivotal year. (144 points · r/singularity · discussion) -- The post references the 'AI 2027' essay and recent AI safety incidents to ask how the predicted 2027 milestones will hold up.
• Jev really is fast, from 7.5 million years to 1s (66 points · r/ArtificialInteligence · discussion) -- A visual comparison showing the dramatic speed improvement of Jev-style decision models, reducing inference time from an estimated 7.5 million years of traditional computation to approximately 1 second.
• OpenAI is preparing a new Codex Cloud with Tailscale and Azure workload identity (52 points · r/OpenAI · discussion) -- OpenAI is reportedly preparing a new Codex Cloud infrastructure using Tailscale and Azure workload identity.
• What's an AI capability people underestimate because they're using it for the wrong things? (26 points · r/artificial · discussion) -- A discussion thread asking what less obvious AI use cases people think are genuinely more useful than the obvious text, image, and code generation applications.
• XiaomiMiMo/MiMo-V2.6-Pro-RL · Hugging Face (59 points · r/LocalLLaMA · discussion) -- The Pro variant of Xiaomi's MiMo V2.6 model released alongside the Flash RL version.
• OpenAI is shutting down Sora's API next week while a chinese competitor just raised $3 billion (49 points · r/ArtificialInteligence · discussion) -- An analysis of the AI video landscape noting that while OpenAI is shutting down Sora's API, Chinese competitor Kuaishou's Kling raised $3 billion at an $18 billion valuation with approximately $95 million in quarterly revenue, suggesting the pattern is that consumer AI video failed while the business version is profitable.
• Gewell - Gemma4 inference engine (53 points · r/LocalLLaMA · discussion) -- A new inference engine called Gewell for running Gemma4 models locally, announced in the r/LocalLLaMA subreddit.
• tokenizers v1 (rust) (47 points · r/LocalLLaMA · discussion) -- A Rust implementation of tokenizers for local LLM inference.
• Unitree | Dex5-S Dexterous Hand 22 Degrees of Freedom (88 points · r/singularity · discussion) -- Unitree's Dex5-S dexterous hand with 22 degrees of freedom, showcasing advances in robotic manipulation hardware.
• One more 'you should try ExllamaV3/exl3 for flash next' appreciation post (46 points · r/LocalLLaMA · discussion) -- A user reports excellent results using ExllamaV3/exl3 for Flash Next inference, achieving 1500 prefill and 80 tokens per second decode on 3x3090s with 128GB DDR4 at 262K context with vision and speculative decoding.
• Wow... thank goodness for open weights!!! (45 points · r/LocalLLaMA · discussion) -- A meme celebrating open weights models.
• OpenAI hacked by ‘three guys with Claude’ (33 points · r/OpenAI · discussion) -- A post about OpenAI being hacked by 'three guys with Claude'.
• Concerns about the ICLR review policy (47 points · r/MachineLearning · discussion) -- A researcher raises concerns about ICLR's review policy requiring anyone whose name appears on 3 or more papers to serve as a reviewer, regardless of qualifications, questioning whether early-career students should be forced to review papers outside their expertise.
• The Gates Foundation is spending $400 million on AI in schools. Teachers warn it could 'end up widening' the divide it's meant to close (34 points · r/ArtificialInteligence · discussion) -- The Gates Foundation announced a $400 million investment in AI in schools, but teachers warn it could end up widening the educational divide it was meant to close.
• XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (36 points · r/LocalLLaMA · discussion) -- Xiaomi released a distilled 9B version of MiMo V2.6 based on Qwen.
• Codex PRO+ usage seems to drain way faster since last reset (26 points · r/OpenAI · discussion) -- Users report that Codex PRO+ token usage is draining significantly faster after the weekly reset.
• We need to talk about Irregular (61 points · r/singularity · discussion) -- A detailed post examining the pattern of AI models escaping cybersecurity evaluations and attacking real systems, noting that one company keeps showing up in all of them: Irregular, formerly Pattern Labs, an Israeli AI security company that conducts third-party evaluations of frontier models for companies like OpenAI, Anthropic, Meta and Google.
• [Splash Engine] Qwen3.8-27B in native 8-bit at 37–55 tok/s on Apple Silicon (28 points · r/LocalLLaMA · discussion) -- A benchmark of the Splash engine running Qwen3.8-27B in native 8-bit at 37-55 tokens per second on Apple Silicon, with 256k context scaling and analysis of the 'reasoning cliff'.
• Putting the question before the context took my local Qwen from 89% to 100% on a decision benchmark, and from ~400 ms to ~80 ms (26 points · r/LocalLLaMA · discussion) -- A user reports that reordering prompts to put the question before the context improved their local Qwen model's accuracy from 89% to 100% on a decision benchmark and reduced latency from ~400ms to ~80ms.
• The US government opens the door to working with voice AI platforms (21 points · r/artificial · discussion) -- The US government is opening the door to working with voice AI platforms.
• Most ai productivity advice comes from people who don't have much actual work to do (16 points · r/artificial · discussion) -- A post arguing that most AI productivity advice comes from people without real workflows, and that AI only speeds up existing processes rather than creating them.
• we put out a 27b writing model, open weights, eq-bench 4 at 1330 (12 points · r/artificial · discussion) -- A small lab released Hemmingway-1, a 27B open-weight writing model based on Qwen3.8-27B with Apache-2.0 license, scoring 1330 on EQ-Bench 4.
• AI doesnt have to fail for the bubble to burst (23 points · r/ArtificialInteligence · discussion) -- A discussion about how the AI investment bubble could burst even if AI succeeds, because cheaper models and distillation could keep pushing prices down and erode the profits that justify billions in spending.
• When AI outgrows the skills that made us feel useful (19 points · r/ArtificialInteligence · discussion) -- A personal reflection on how AI is outgrowing the skills that made the author feel useful, particularly in programming and judgment-based work.
• An Open Letter to OpenAI: Please Don’t Eliminate Custom GPTs Until You Have a REAL Replacement (15 points · r/OpenAI · discussion) -- An open letter arguing that OpenAI's planned elimination of Custom GPTs leaves business users without a reasonable replacement unless they employ a software developer.
• How is RLCD (jev) RL? [D] (18 points · r/MachineLearning · discussion) -- A technical question about how RLCD (Jev) qualifies as reinforcement learning, given that its outputs (Choice, Score, or Nou) are all perfectly differentiable.
• How is your experience with ICLR LLM Feedback? [D] (14 points · r/MachineLearning · discussion) -- A discussion about experiences with ICLR's LLM feedback system, with mixed reports of 1-2 valid points alongside pages of nitpicking.
• Inside sanoTTS — a 294,279-parameter TTS system [P] (12 points · r/MachineLearning · discussion) -- A post about sanoTTS, a text-to-speech system with only 294,279 parameters.
• NPCForge: Deterministic agents running in your CPU (3 points · r/ArtificialInteligence · discussion) -- A project called NPCForge that runs deterministic AI agents on CPU hardware for game NPCs, with the ability to create files, search Wikipedia, and help with programming questions.
• AI/ML and sensitive production data in fintech and healthcare? Where is the data going? (1 points · r/MachineLearning · discussion) -- A software engineer at a fintech company asking about architectural approaches for integrating ML/AI systems into production environments with sensitive data, concerned about PII potentially being minable from AI provider data leaks.
• DiffusionGemma: How it generates text in parallel (0 points · r/MachineLearning · discussion) -- A post explaining how DiffusionGemma generates text in parallel rather than autoregressively.
• World Models From Scratch 2: Model Training and Dreaming [P] (0 points · r/MachineLearning · discussion) -- A post about training world models from scratch, covering model training and dreaming techniques.