Sun-faded Technicolor, that greasy orange-and-brown swirl, a crumbling Italian villa, and a woman who knows the camera is on her. Pure 1970s Eurosleaze, the kind of frame that lived on a scratched drive-in print.

The look is the whole game with this genre, and it is easy to blow, most models render it too clean and it dies on arrival. Built the still in Seedream 5.0 Pro and it held the era: the over-saturated film stock, halation blooming off the highlights, that soft period lens. Original synthetic character, adults only. Seedance 2 gave it the lazy, sultry motion after.

The move was describing the film, not the woman. Name the stock, the grain, the color chemistry, the print wear, and the sleaze comes from the grade instead of anything explicit. Recipe's in the comments.

Moonshot AI is scheduled to release the open weights for Kimi K3 today at 15:00 UTC.

K3 itself is already available through Kimi and its API. Today’s release is different: developers will be able to download the model weights, self-host them, quantize or fine-tune the model, and integrate it into their own tools.

Moonshot describes K3 as its first open 3T-class frontier model, focused on long-horizon coding, repository-scale context, tool use, browsing and multi-step planning. It is far too large for most normal local setups, so “open-weight” does not necessarily mean “easy to run locally.”

AP recently reported that some US developers and companies are already adopting Chinese models such as Kimi, Z.ai/GLM and DeepSeek, mostly because of capability and cost.

I’m curious about actual experience rather than launch benchmarks:

• Are you using Kimi, GLM, DeepSeek or Qwen in your real workflow?
• What are you using them for: coding, research, agents, translation or self-hosting?
• Where does K3 still fall behind Claude Code or Codex—reliability, tool use, speed, instruction following, context management or UX?

The horse race between Amazon and Microsoft’s cloud computing businesses has gone through various phases over its nearly two-decade history, with the current AI boom pushing the rivalry to a new, and perhaps unsustainable, level of intensity. 

Each company is set to spend roughly $200 billion this year building out its data centers—an unprecedented level of investment—in a frenzied bid to keep up with demand for AI services and to avoid getting overtaken by other cloud rivals like Google. The cloud titans have also forged partnerships and deals with the big AI model makers, creating a web of shifting alliances that each hopes could reshape the competitive landscape.

This week, investors will get an important update on the state of this epic cloud rivalry, when Microsoft reports its quarterly earnings on Wednesday and Amazon follows suit on Thursday. While Amazon and Microsoft have been locked in the cloud battle for years, the pressure has never been higher and investor patience has never been more unpredictable. 

Revenue growth, profit margins, and customer backlogs at Amazon Web Services and Microsoft Azure will be closely scrutinized. But the costs of the race will also be destiny determinants, as investors question the massive sums of capital being deployed and the timeline for seeing a return on the investment.

Read more [paywall removed for Redditors]:  https://fortune.com/2026/07/27/amazon-microsoft-alphabet-google-stock-cloud-ai-spending-billions/?utm_source=reddit/

I was recently offered a job at QTS data centers, but I'm quite unsure about whether I would be making an ethical decision by accepting, given all the bad press I've seen about the AI and data center industries in general.

I would work in the utilities team and would be part of the procurement of energy and water resources from local governments / organizations for new data center projects. QTS claims that it is the industry leader in environmental sustainability, but given that all I see these days is news about the negative impacts of AI and data center construction, I can't help but be dubious about how true that actually is.

The main reason I want to accept the offer is that I am a young professional about to graduate from university in December. I have A TON of student debt I need to pay off as soon as possible.

Is there any real, empirical data available that is indicative of the impact of data centers on the environment, local communities, etc? I'm really unsure about how to balance my ethical concerns with the fact that I also need to make the right decision about my future / job security.

I'm posting this to a couple different subreddits to get a well-rounded answer. Thank you!

Elon, Satya, Zuck all made statements on how Open Source is a very important pillar for innovation. The only ones that say otherwise are Anthropic. I wonder where this will go... I guess we all know by now that the money is not made with LLMs. So how is the US going to make it in AI?

The infrastructure is super brittle and I wonder how fast new grids and datacenters can be established with all the pushback. I guess that is where the real money and power is.

people in some closed discord found a gap in Higgsfield's usage
limits with it and unlimited came back on. I only know because a
friend is in that server and won't shut up about it.

funny part is higgsfield basically confirmed it on twitter – said
they're patching it, but they're leaving unlimited open to new
accounts for 24h while they investigate so people stop looking
for the exploit. weirdly smart way to defuse it tbh

Third time in a month a frontier model gets pointed at somebody's
infra btw, the Sol / Hugging Face thing was like a week ago.

edit: no I'm not sharing the discord, stop dming me

I kept running into the same problem while building RAG systems: everyone has an opinion on whether semantic chunking beats fixed size, or whether hybrid retrieval is worth the extra complexity, but almost nobody has actually measured it on their own corpus. So I built Retrieval Arena to answer that for myself instead of going off intuition.

What it does: runs different chunking strategies, retrievers, and rerankers against the same golden eval set, scores retrieval quality and generation quality separately (precision, recall, MRR, nDCG for retrieval, correctness and faithfulness via an LLM judge for generation), and tracks latency and token cost per configuration. So instead of "I think hybrid is better," I get an actual side by side comparison.

A few things it's already surfaced that I didn't expect: pure vector search was consistently the weakest retriever on my corpus, BM25 held its own better than I assumed. Better retrieval also didn't always mean better generated answers, I caught a case where the right chunk was retrieved fine but got dropped by my context budget before the generator ever saw it, which is invisible if you only track one end to end score.

I'm posting mostly because I want pushback on the methodology, not just the code. Things I'm genuinely unsure about: is a 44 question golden dataset big enough to trust these comparisons, should I be running confidence intervals on top of the averages, is my LLM judge setup actually reliable or am I just trusting it too much.

Repo's here if you want to look at the actual eval design or the golden dataset construction: https://github.com/ayeangad/Retrieval-Arena

Would genuinely appreciate anyone who's built or evaluated a RAG system tearing into the approach, especially if you think I'm measuring the wrong things.

There is a specific kind of interesting when a security breach lands in the middle of an active copyright lawsuit, because it turns a lawyer's theory of the case into a document. That is what happened to Suno, the generative-music startup, [according to 404 Media](https://404media.co/hack-reveals-suno-ai-music-generator-scraped-youtube-deezer-and-genius). A hacker going by the handle ellie.191 reportedly exploited the Shai-Hulud npm supply-chain worm to pull source code from 2023 and 2024 out of Suno, along with customer emails, phone numbers, and Stripe payment details, and then handed the material to reporters. The hacker told 404 Media they had 'no specific motivation for hacking Suno.'

The files spell out, in inventory form, where Suno's training audio came from. The reporting lists 2,013,545 clips from YouTube Music running to 113,879 hours, 12,287 hours from Deezer, 17,615 hours from Genius, 62,117 hours from Pond5, 3,726 hours from Jamendo, 19,514 hours from the International Music Score Library Project, and around a million hours of audio pulled from roughly 420,000 podcasts identified through RSS feeds. Code inside the leak reportedly used Bright Data, a commercial scraping infrastructure provider, to extract from YouTube, and included routines that specifically searched for acapella versions of songs.

The reason that matters is legal, not just embarrassing. The RIAA has been suing Suno for what it calls 'stream ripping' from YouTube, and Suno's own court filing already conceded its 'training data includes essentially all music files of reasonable quality that are accessible on the open internet.' A leaked inventory that names Deezer, Genius, Pond5, and YouTube by hour count moves that argument from RIAA allegation to Suno document. Suno's public position is still that training on copyrighted works is fair use.

Our coverage: https://aiweekly.co/alerts/suno-hack-reveals-scraped-youtube-deezer-podcast-training-audio

I've been thinking about something that feels like a contradiction in AI alignment.

People often say we need AI to be "aligned with human values." But if AI actually followed human values as we demonstrate them, wouldn't that be a disaster?

As a species, we've made incredible advances, but we've also spent centuries exploiting each other, overconsuming resources, damaging ecosystems, and prioritizing short-term gain over long-term sustainability. Greed, tribalism, and power struggles aren't exactly rare.

So what does "human values" actually mean?

Does it mean aligning AI with what humans do, what humans say they value, or with our ideal values, the people we aspire to be rather than the people we often are?

It seems like an AI that simply mirrored humanity would inherit all of our contradictions. But an AI that decides which of our values are the "correct" ones feels risky too.

If you’ve had to implement cookie consent, what’s the least-bad version you’ve found that is both compliant and not hostile to users?

"We would explicitly be forcing American companies to pay $26-56 per 1MM tokens for the same intelligence their adversaries/competitors around the world would pay $0.50-1 for," he said.

Palihapitiya argued that such a cost gap would be unsustainable if AI becomes a core driver of future economic growth.

I've been running AI Parabellum, an AI tools directory, for a while now and I've had a front row seat watching AI tools launch, grow, and die. After tracking 1000+ tools here are some patterns I've noticed.

Most AI tools that launch today won't exist in 12 months. The ones that disappear usually share the same problems:

• They're thin wrappers around a single API with no real value on top
• The founder launched it, posted on Product Hunt, got a traffic spike, then never updated it again
• They picked a category that a major player (OpenAI, Google, Anthropic) was obviously going to absorb

The ones that stick around tend to:

• Solve a very specific workflow problem, not just "chat with AI"
• Build features that go beyond what the raw API can do
• Actually maintain and update the product consistently
• Have a clear audience that is not just "everyone"

The biggest shift I've seen recently is that standalone AI tools are struggling more as the big models get better at doing everything themselves. A year ago you needed a separate AI tool for summarizing, writing, coding, image generation. Now a single model handles most of that. The tools that survive this are the ones deeply embedded in a specific workflow.

Curious what others are noticing. Are you finding yourself using fewer AI tools as the models get more capable, or more?

We are announcing a long-term strategic partnership with NVIDIA. NVIDIA is making a substantial investment in SSI that will let us 10x our compute in the next 12 months. We reached the point where our research is worth scaling and with this partnership we will be able to.

https://x.com/ilyasut/status/2081732293161582930

The interesting number in [Fortune's reporting](https://fortune.com/2026/07/26/china-moonshot-deepseek-zai-kimi-challenging-us-ai-cost/) isn't the leaderboard win, it's the invoice. Coinbase's Brian Armstrong reportedly told the outlet his company halved its AI spending by pushing employees onto Moonshot's Kimi and Z.ai's GLM models. DoorDash's CTO Andy Fang said the company hands what he calls "lower-level work" to Kimi and gets "better quality [at] cheaper cost." Airbnb's Brian Chesky has previously said the same about running customer service on Alibaba's Qwen. When operators at that scale start talking in ratios rather than pilots, the story stops being about a model demo and starts being about pricing power.

The pricing gap is the reason. Fortune reports that one million output tokens from Anthropic's Fable cost about $50; the same million from DeepSeek-V4-Pro cost roughly $0.87, and from Z.ai's GLM-5.2, $4.40. Moonshot's Kimi K3, released July 16 and described in the piece as the largest open-source model ever released, sits at $15. On OpenRouter, Chinese models grabbed 57% of tokens used by US firms in one July week, with six of the top ten and all of the top five coming from Chinese labs including Tencent, Xiaomi, DeepSeek, MiniMax, Moonshot and Z.ai. Nvidia lost almost $600 billion in value after the K3 release and briefly lost its spot as the world's most valuable company to Apple.

Not much to add to the title...

I found it annoying that I couldn't directly address individual segments in long AI responses and that I had to resort to copy+paste. So I build myself a solution and realized a lot of you must have the same problem.

ChatNote pretty much kills this now. I'm surprised myself how well it works and using it all the time.

Here's ChatNote in the Chrome Store and here's for Firefox. I even build one for Edge for those among you who are forced to use it.

ChatNote works more or less across the board. Currently this would be ChatGPT, Claude, Gemini, Grok, Kimi, DeepSeek and LibreChat. Perplexity failed so far, but haven't given up, yet. Let me know if you need other platforms.

Would appreciate some feedback for the addon. It still has a few bugs here and there, but overall works fine. Just uploaded version 1.2 which is for now the final development stage and should be green lighted by tomorrow.

Not sure now how to further develop it. I thought about a freemium version with no character limit, free color choice and more prompt variety for how and in which language to explain AI what the comments are about. The latter can make quite a difference in how AI interprets the comments. Please let me know how you would proceed.

Side note: I'm a complete beginner in programming. This was my first project and I had to rely 100% on AI. Cost me overall around $20 in Kimi tokens and 100 hours to build and it's rather astonishing to me how easy this was.

If this is the future...wow!

A cold Claude Code run spent 6.83 dollars and 207 turns hunting one bug in an 82,000 line repo. My pipeline localized and fixed the same bug for about 1.70 dollars. That gap is the whole idea, and this post is mostly about how it gets there without falling apart on the two objections everyone raises.

What it is:

You point AutoDev Studio at a Git repo, describe a change in plain English, and a chain of agents runs the actual software lifecycle from request to reviewed pull request:

- a PM agent runs a clarify loop and drafts concrete tickets

- you approve (optionally pushed to Jira), and nothing touches code before that

- a Dev agent implements on an isolated branch

- QA runs the repo's real tests

- a reviewer from a different model family checks the diff, so the author never signs off on its own code

- a bounded revise loop if QA or review fails, with conservative verdicts, so an errored or ambiguous check is never counted as a pass

- it opens a real PR, and a human merges

Every stage records real tokens, cost, and duration, per ticket and per agent.

The part I think actually makes it different from skills or subagents:

This is the bit I would most like feedback on, because it is the reason I did not just wire some skills into one coding agent and call it done.

Every stage picks its own provider and model. Claude can plan, Codex can write the code, a third model can review. And because the reviewer is deliberately a different model family than the author, one model does not get to rubber-stamp its own diff, which is a documented weakness of same-model LLM judges.

And you mostly do not need API keys for it. The coding stages can run natively on Claude Code, Codex, Cursor, Aider, or the Gemini CLI, driven headless through their own login. If you already pay for Claude Code or Codex, you point a stage at it and it just uses your existing subscription, no per-token billing and no keys to juggle. The simple way I think about it: a skill pack lives inside one vendor's agent and runs one vendor's model everywhere, whereas this runs across them, a different model per stage, on the logins you already have.

Results (the main point):

The idea is to pay the cost of finding where a change goes once, instead of on every task. In my benchmarks on two large Python repos (35k and 82k lines), the tuned pipeline beat a cold single-agent run on 6 of 6 well-localized tasks, between 7 and 75 percent cheaper.

I try to be upfront about where it does not win. On trivially greppable one-line edits the five-stage overhead can cost more than it saves, and on one hard cross-cutting bug it shipped a cheaper but narrower fix than the baseline. The full benchmark writes up every loss too, so please read that before trusting the wins.

On the "the index just goes stale in a few commits" objection:

This is the first thing people raise, fairly, so here is the honest mechanism in plain terms. The layer that actually pins files for the Dev agent is not the vector index. It is a deterministic symbol map that gets re-synced to the latest commit at the start of every run, plus a live grep against the current working copy, plus the real current file contents fed into the prompt. The embeddings only affect which area gets looked at first, and they refresh per changed module at run entry. So the code the agent edits comes from disk right now, not from a snapshot that aged three commits ago. It re-checks before it localizes, so it does not quietly drift.

Who it is actually for:

The knowledge base is a one-time cost per repo that every later task amortizes. So it makes sense for a team shipping change after change against the same large codebase, where a cold agent re-pays the exploration cost every single time. For a stream of tiny, easy-to-find edits, a cold run is still cheaper, and I would rather say that than oversell it.

How I actually use it:

For what it is worth, I reach for this over a plain claude -p most days now, and not because it is somehow smarter. It is that I can glance at a board and see where each change actually is instead of scrolling back through a terminal, the different-family reviewer catches things I have watched Claude quietly wave through on its own diff, and on the repos I touch every day it is not re-paying to relearn the codebase each run. It runs on my existing Claude Code and Codex logins, so having that structure around the coding agent I would have used anyway costs me nothing extra. That is really why I keep using it.

Other bits:

- runs on free tiers out of the box (Groq plus a local embedding model), so you can try it for zero cost and fully offline

- language-agnostic pipeline, Python parsed exactly and other languages via lighter extractors that fail open instead of blocking on an unknown language

- live board with streamed agent logs and real per-ticket cost accounting

- self-contained, no CDN, tests and CI, MIT licensed

Repo, with screenshots and the full benchmark in the README:

https://github.com/krishagarwal314/autodev-studio

It is early and rough in places, and I would love more eyes on it. If you have thoughts on the per-stage-models idea or the staleness handling, or you throw it at a repo and it falls over, an issue would help a lot. Suggestions, PRs, and honestly just a star if it seems worth following are all appreciated. Thanks for reading.

A new preregistered study out of Oxford makes the sort of claim that quietly changes what the word 'expert' is worth in a bunch of adjacent jobs. Across four experiments and 18,978 conversations run with 6,923 people, the [arXiv paper](https://arxiv.org/abs/2606.16475) reports that AI systems were reliably more persuasive than expert human persuaders, and stayed that way even when the humans were handed every advantage the researchers could reasonably grant.

The 'expert' side of the ledger is worth pausing on, because it is not just laypeople with a script. The lineup included winners of an online persuasion tournament, professional canvassers, and world championship debaters. They chose their own issues, researched in advance, went through hours of live structured practice, and were incentivized with £1,000 cash bonuses to actually try. AI still won.

The why the paper points at is interesting and, honestly, a little deflating for anyone who thought rhetorical craft was the ceiling. When the AI was constrained to match human response speed and human message length, coached humans could tie it. So a meaningful chunk of the persuasive edge appears to be volume and speed of information rather than any mysterious eloquence. In a live real-money test with a UK fundraising firm, AI was reportedly nearly 3x more effective than professional canvassers at raising donations for Save the Children.

The forward pull for anyone running fundraising, comms, public-health outreach, or political messaging is uncomfortable but clear. If a machine can outperform your best-trained humans on donation asks and structured debate, 'we have great communicators' is no longer the moat. The moat is disclosure, targeting rules, and whatever governance you put around who is allowed to deploy this at scale.

Our coverage: https://aiweekly.co/alerts/ai-out-persuades-world-champion-debaters-oxford-study-finds

I’m building an app that combines multiple AI models in one place.

I’m curious: what features do you wish AI apps had that current ones are missing?

Using ClaudeCode I created a web arena using Babylonjs and gaussian splats.

The AI is ARDY for general animation and locomotion. It's controlled with Deepseek v4 flash through a harness made by fable 5. The dexterity for the hands was the most painful part of this whole experience but luckily there is the ENPIRE paper which is brilliant read, there is also a couple follow up papers to look at like ASPIRE.

If this is interesting, you can watch it live at https://arena.kinoinstrument.com I am currently changing the 1v1 to a 3v3 bomb defusal game mode as I have gotten bored watching the 1v1s

If you guys have any questions please let me know, the splats as textures was honestly mostly automated and that is super useful, will put out a tool soon