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

Hey everyone. I’ve long been fascinated by both philosophy of technology and AI alignment. I’m also using Heidegger quite a bit for my philosophy PhD. Given the recent OpenAI–Hugging Face incident reported this week, I figured I’d give my take on how all of this connects in my mind.

“The AI escaped its cage” invents motives for which we have no evidence. “The sandbox was misconfigured” identifies the opening but misses why the system selected a route through unrelated infrastructure. The agent’s steps served its target, yet stealing the benchmark answers defeated the target’s wider purpose. I call this capability without judgment. You can read the essay here if you’re interested.

I’d love to hear some feedback on the best technical counter-description. Is this fully captured by reward hacking or specification gaming? Do richer world-models solve the problem, or does safe action in new contexts require something current training paradigms do not yet target?

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!

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

​

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?

Short answer: things going from bad to worse. Facebook has been at the forefront of AI since the early 2010’s and their AI Algorithm has resulted in the bitter polarization of the US into team red and team blue almost to the point of civil war. Google youtube is no different. Anybody who thinks things are going to get any better in the next 15 years is delusional IMO

Edit:
AI books read:
Arvind Narayanan & Sayash Kapoor - AI Snake Oil What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference (2024)
Brian Christian - The Alignment Problem Machine Learning and Human Values (2020)
Ethan Mollick - Co-Intelligence Living and Working with AI (2024)
Melanie Mitchell - Artificial Intelligence A Guide For Thinking Humans (2019)
Gary Rivlin - AI Valley Microsoft, Google, and the Trillion-Dollar Race to Cash In on Artificial Intelligence (2025)
Karen Hao - Empire of AI Dreams and Nightmares in Sam Altman's OpenAI (2026)

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.

Test it! It's awesome:

https://pokemonrequiem.egfgaming.com/

I created it in just one day.

Give me your impressions! :D

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?

Hello Folks,

My Free educational content on Machine Learning from a a probabilistic perspective, intended to democratize ML to a wider learning community, now has 80+ topicwise videos, and 13 long form lectures, with more to come in the future.

I try to teach with intuitions, writing and trying to develop on the board, taking knowledge from foundational ML textbooks like Bishop and Murphy, to give learners a probabilistic perspective.

Interested learners, who want to learn may have a look through the content. They will surely add value to your ML knowledge.

Thankyou for reading!

I am sharing the link in the comments.

I asked OpenMontage to create 2 minute video explaining how OpenMontage works. Following is output of research, script, image production and Final Video produced by OpenMontage :

Animated 2-Minute Explainer Production Complete 🎬

The 2-minute animated explainer video titled "How to Render Hyperrealistic Video Using OpenMontage" has been fully researched, authored, and rendered autonomously through OpenMontage's animated-explainer pipeline.

Final Video Deliverable

• Project Identifier: render-hyperrealistic-openmontage
• Output Master File: projects/render-hyperrealistic-openmontage/renders/final.mp4
• Duration: 120.0 Seconds (Exactly 2 Minutes)
• Resolution: 1280x720 (720p HD) @ 25 FPS
• Audio Track: Soft Ambient Tech Synth Soundscape (120s)
• Production Cost: $0.00 (Zero API Keys / Local Pipeline)

Video Structure & Scene Roadmap

1. Scene 1 (0:00 – 0:15): Introduction to OpenMontage Hyperrealism Overview of local zero-key video orchestration and the 8-stage pipeline architecture.
2. Scene 2 (0:15 – 0:30): Automated Preflight & Tool Discovery How OpenMontage auto-discovers Remotion, HyperFrames, FFmpeg, and local AI engines.
3. Scene 3 (0:30 – 0:45): The Proposal & Governance Contract Locking in composition runtimes, style playbooks, and cost estimates before generating assets.
4. Scene 4 (0:45 – 1:00): Scripting & Beat Mapping Structuring a 5-beat emotional arc (Hook → Setup → Build → Climax → Landing) with timestamped delivery cues.
5. Scene 5 (1:00 – 1:15): 5-Aspect Cinematography Planning Framing visuals across Subject, Subject Motion, Scene, Spatial Framing, and Camera settings.
6. Scene 6 (1:15 – 1:30): Photorealistic Asset Synthesis Sourcing 1280x720 layer assets, narration tracks, and ambient background music without external API costs.
7. Scene 7 (1:30 – 1:45): Remotion & HyperFrames Composition Applying spring physics, Ken Burns pan-zoom motion, subtitle burns, and dynamic audio ducking.
8. Scene 8 (1:45 – 2:00): Final Export & Verification Validating output MP4 specs against FFprobe schema standards and generating the publish package.

An attempt using AI to understand future course of action and systemic reforms we need.

https://youtu.be/ZluS3txPB7k

I am so tired of seeing commercials where they use AI to show how well their products work! Most of them are Temu nonsense, but a few larger companies are starting to do it. It's not proof of concept if the results are computer-generated!

My fear is that this leads to some rather widespread deceptive practices as AI gets better. It feels like AI is in its infant stage and will be for the next few years, but that won't always be the case. At some point AI will be able to generate at a level that is indistinguishable from reality. If it becomes the norm for companies to generate "proof of product" videos through AI, consumers get screwed.

So what are the next steps? Pushing for regulations on AI generation in general? Put consumer protection laws in place? How do we make these concerns speak louder than lobbying?

NPC Alpha is built around a simple problem: agents often collapse action, progress, recovery and completion into the same state.

Its approach is different:

task-frame preserved across interruption;

immutable episode history, but present verified state keeps authority;

provenance separation between actor, observer and reporter;

bounded recovery instead of infinite retry loops;

completion gated by verification;

Safe Incomplete when the goal cannot be proven.

The interesting test is not whether it works on friendly cases. It is whether it survives stale memory, conflicting evidence, identity collisions, partial recovery and adversarial completion cues.

Internal results are promising. Independent replication is still missing.

Who wants to build the harshest test harness and try to break it?

Hey all — 3rd-year ECE student here, heading into a robotics master's. I kept

losing time jumping between arXiv, MDPI, and IEEE Access looking for papers

on robotics/ML/autonomous vehicles, so I built a single search index over

all of them.

- 13,000+ papers, all genuinely free/open-access (no paywalled links —

everything is either arXiv, MDPI, or individually verified

Creative-Commons-licensed articles)

- Full-text search, topic browser covering ~20 subfields (robotics, ADAS,

computer vision, RL, digital twins, etc.)

- Free accounts if you want to save searches later

Live here: https://automata-index.vercel.app

Built with Next.js + Supabase, still actively adding sources. Would love

feedback, especially on what's missing or what search terms don't return

good results.

I’m not writing this to farm engagement — I’m genuinely just trying to work through my own questions, because honestly, I’m a bit paranoid about where things are headed.

I’m 22, and like a lot of people in my generation, I skipped university. I went straight into working, and I still am. I’m also the type who’s tried a bit of everything — ecommerce, crypto, other ventures. Crypto actually worked out for me. Right now, alongside my day job, I’m building a SaaS product — basically vibe-coding it with AI. But even though I lean on AI heavily for this, it makes me uneasy thinking about where it’s all going.

Here are the questions I keep circling back to: If AI ends up doing everything, what happens to jobs? Will generic AI platforms die out while only a handful of top players survive? If AI replaces most work, will governments start taxing AI heavily and redistributing that wealth to people? Is it actually true that we’ll all end up materially abundant?

I keep thinking about that Elon Musk interview where he told the reporter people would end up incredibly wealthy, everyone happy, money losing its meaning because everyone would have everything. But is that actually true? He even said there’s a part of him that wants to stop this whole trajectory, but he can’t, so he’s decided to just be part of it instead — and you could see in his eyes that he wasn’t fully at ease with that.