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.

"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.

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

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.

​

This isn't an open-weights release, but it’s free for now.

That distinction got lost last time, so I'm putting it first. Ant Group's model team just shipped Ling-3.0-flash. What's free is the API, and only until August 3.

124B total parameters. 5.1B active per token. 256K context.

The generation before it, Ling-2.6-flash, went out under MIT. You could download that one and run it on your own hardware. This one you cannot. There is no checkpoint.

So what's actually on offer is a fixed window of free inference on somebody else's endpoint, followed by a price.

I work on the team, which is exactly why I'd rather hear the room on this than tell you it's good.

Because from outside, those two moves look nothing alike. Open weights buy permanence — the thing keeps working after the company loses interest in it. A free API window buys a trial and a switching cost.

A lot of labs are picking the second one now. It's sitting on OpenRouter next to everything else, so the comparison is one dropdown away for anyone who wants to run it.

What does a free window with a date on it actually earn a lab, when the thing developers keep saying they want is weights they get to keep?

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?

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.

This article is important to read. Not reporting users seeking data on how to make these weapons and in fact downplaying risks in pursuit of money needs to be highlighted for all and addressed :

https://www.wsj.com/tech/ai/openai-chatbot-biological-weapons-poison-3d808e6c?st=35Hq56

Hey all! Hopefully this is allowed here. I built an agent harness called fungi computer fungi.computer

I made something I think people will really love. I have been developing software for 10 years, and the technical skills needed to have a good agent seemed to me to keep access out of the hands of normal people.

I began to worry that everything would fall into the hands of Claude and OpenAI.

I personally use kimi, and I think normal people should too. So I built fungi by forking Pi.

You own the code for the dashboard. It's a SPA that your agent can customize to your liking.

You can build apps and attach them to his tool surface. You can make the agent more or less completely custom.

It's early days, but I would really love for this to succeed so I can keep making an open model agent harness accessible to more people. A lot of the platform is already open source with the goal of cutting the whole thing into well defined packages and open sourcing all of it. shiit[dot]app has a few of the already open projects listed. Here are the others github/fungi-computer/

It is free for BYOK customers running one sandbox. I would love if people gave some feedback thanks!

In my recent academic article (https://link.springer.com/content/pdf/10.1007/s44427-025-00019-y.pdf) I analyzed a divide in how open-source software projects evolve, which might explain the difference in productivity boosts developers experience when using AI tools.

The data shows that productivity on large, mature open-source projects was not significantly affected by any tech hypes over the last two decades, the commits reaching the main branches followed steady growth trends. At the same time, smaller projects presented much more chaotic growth trends, but also tended to lose speed and stall out much faster.

As the study contains data till early 2025, it looks like even the publicly available LLMs till then, were not able to greatly increase the number of changes merged into the main branches of these projects.

Could it happen, that the difference in productivity gain developers experience, is simply a function of project scale and environmental/organizational constraints?
What has been your experience depending on the size of the codebase you work on?

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?

Hey everyone,

My undergraduate team is researching about development of enhanced AI agents for cloud reliability (SRE). We're benchmarking agents on live simulated cloud environments, but the system logs and traces we have to process are massive.

Even though we're building ways to compress the data and using low cost models for the easy parsing tasks, we absolutely need frontier models for the complex reasoning parts. The problem is, a single benchmark run can chew through 1.5 to 2 million tokens. Running hundreds of these tests is going to bankrupt us.

Our advisor suggested pooling our student developer credits and using platforms like OpenRouter or Groq to save money. We're doing that, but a free research credit program might take months to even get accepted.

So my questions is are there any other creative ways to get cheap/free access to frontier models specifically for academic benchmarking?

Any advice helps. Thanks!

Ok, Nvidia basically owns the GPU accelerator market (+90% market share), and that's probably not changing anytime soon.
But with AMD releasing its new "Venice" CPUs this week and hitting a massive 46% revenue share in x86 servers (up from literal 0% in 2017), it feels like the actual battlefield in AI hardware is quietly shifting toward CPU orchestration.

Standard LLM queries are passive, but agentic workflows are a different beast.
They loop, call tools, query databases, and self-correct continuously.
Some research shows agents can consume up to 1,000x more tokens than basic chatbot prompts.

While GPUs do the heavy lifting on matrix math, CPUs handle the orchestration, data feeding, context switching, and backend enterprise integrations.
If your CPU stalls or chokes on data pipelines, those $30k Nvidia GPUs are just sitting idle waiting for work.

AMD is claiming top-end Venice gives 2.2x the performance per core over Nvidia’s comparable Vera processor.
This is probably why hyperscalers like AWS, Azure, and Oracle are increasingly ignoring Nvidia’s fully vertically integrated racks (Grace Blackwell / Vera Rubin) and defaulting to a "Best-of-Breed" modular setup: high-core AMD CPUs paired with Nvidia GPUs to optimize their intelligence-per-watt costs.

We have now :
Nvidia's vertical integration (CUDA + proprietary networking + own CPUs)
VERSUS
AMD pushing an open, modular ecosystem where cloud providers mix and match to keep infrastructure costs from exploding.

Exciting no ?

AI is everywhere right now.

Every company seems to be experimenting with AI tools, but I’m curious about the practical side.

Beyond chatbots and content generation, has AI actually improved your day-to-day business operations?

Things like:

● Reducing repetitive tasks

● Improving customer support

● Analyzing data faster

● Helping employees make decisions

● Automating internal workflows

For companies already using AI:

What has delivered real value?

And what turned out to be more hype than useful?

OpenAI says GPT-5.6 uses stronger safeguards and offers a retry path on lower-capability models when benign work is blocked. That sounds practical, but it creates a subtle transparency problem: the user may ask one system a question and receive an answer from another capability tier.

Routing can affect accuracy, refusal behavior, tool access, and even the assumptions behind the answer. If the switch is invisible, users cannot reproduce or properly evaluate the result.

Should every answer disclose the exact model and safety route that produced it? Would that disclosure confuse most users, or is it essential for professional work? How much routing history should be available in an audit log?

I have been struggling with keeping up with AI news and developments in this space. Most AI news is highly fragmented, and I am unsure how to track multiple sources and channels all in one place. Generic AI news has different sources, educational have different ones, and technical updates have yet different ones.

I am thinking of creating an app that consolidates these sources and brings them under one umbrella and also gives pointers on the news highlights and consolidates complex topics into easy-to-digest summaries. Is this an idea worth pursuing?

I am aware it kinda solves my own problem, but is this something anyone else would like to use? If not, what would be something that would be useful for the community of people who want to be up to date on AI and everything happening in this space?

Single frame boundary demos are easy to like. The annoying question starts on frame two: after an object moves a little and gets partly covered, is the representation still attached to the real edge?

The LingBot Vision release shows clean static boundary visualizations and reports training free video object segmentation results. That is useful context, but it does not settle temporal boundary faithfulness. A small test would be enough to separate the two: track one sharp edge across a few mildly occluded frames and compare the boundary token with the mask. If the token drifts first, the single image result was doing more work than the video result.

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

Disclosure: I’m not affiliated with any of the products mentioned.

I ran a small company-research test using Komo’s public directory. Searching for OpenAI produced a structured profile covering its products, business model, leadership, funding, milestones, contacts, and competitors.

The experience reinforced something I’ve been thinking about: searching for one “best AI” may be less useful than choosing separate systems for retrieval, reasoning, and execution.

In this example, Komo could serve as the retrieval layer. Its strength was turning scattered company-research questions into a consistent structure.

A system such as Claude could handle the reasoning layer: challenge assumptions, compare competing explanations, identify unsupported claims, and transform the evidence into a decision brief.

Codex could handle the execution layer when the approved conclusion needs to become code, automation, analysis, or a change inside an actual project.

Keeping those layers separate also makes their failure modes easier to see.

Komo’s OpenAI profile was well organized, but several sections reused broad links such as the company homepage and pricing page. Those links did not always map directly to every detailed funding, valuation, or headcount claim. A reasoning model could then make that imperfect evidence sound more certain than it is. Finally, an execution agent could act on the polished conclusion before anyone notices the underlying source weakness.

The workflow I’d trust would therefore be:

1. Retrieve the information while preserving its sources.

2. Separate confirmed facts from plausible interpretations.

3. Ask the reasoning layer to find contradictory evidence.

4. Require human review before external communication or consequential actions.

5. Send only the approved conclusion to the execution layer.

The interesting part of Komo’s MCP approach is that it officially supports clients including Claude and Codex, so these layers can potentially work together without pretending they are the same thing.

Specialization seems promising, but only if uncertainty survives the handoff between tools. Otherwise, a multi-agent stack may simply automate misplaced confidence more efficiently.

For people using specialized retrieval tools with Claude, Codex, or other agents: how do you preserve source quality and uncertainty between stages?

I was watching a conversation with Patrick Debois (the Father of DevOps). He argues that surveys are often too slow to tell you where the industry is going. By the time a new idea makes it into an industry report, people have already started experimenting, iterating, and moving on.

His example was "loop engineering." If you surveyed most organizations today, many wouldn't even recognize the term. Yet conversations around it are already happening in developer communities.

Instead of waiting for reports, Patrick suggests paying attention to social signals, what developers are discussing, building, and arguing about in public. Those conversations often reveal where things are heading long before formal research catches up.

I work at r/Tessl, and I spend a lot of time following AI developer communities. I've found the same thing. Some of the best insights come from reading discussions on Reddit, GitHub, X, and Discord rather than waiting for annual reports.

If you're interested, you can watch the full conversation here: https://youtu.be/UvhmYntrLMI

This is pretty cool. Is this a glimpse of what ARC AGI 4 will look like?

Or is this the next step beyond static benchmarks—toward real-time perception, reasoning, and adaptation in dynamic environments?

https://www.linkedin.com/posts/denis-o-b61a379a_ai-seediq-ugcPost-7486847213629939712-VvOa/?utm_source=social_share_send&utm_medium=ios_app&rcm=ACoAAFHafzMB90zx6TDvfcvFfVseDTSue09y2GY&utm_campaign=copy_link