Original image from: https://x.com/james406/status/2077045927857025532

This 3 minute AI-made Odyssey scene has been everywhere on X lately, and it’s being called one of the least “AI-looking” AI videos people have seen so far.

It shows Calypso begging Odysseus to stay, and the facial expressions, voices, lighting, wind, and shot-reverse-shot editing all look surprisingly convincing. Elon saw it, reposted it, and immediately claimed Grok Imagine will make a full-length, “historically accurate” version of The Odyssey before the end of this year.

of course whether Musk actually delivers that is another story lol.

The creator, Heavy Pulp, also shared how he made it. He treated Grok like a film crew: used two anchor frames to keep the characters and camera angles consistent, generated both sides of the conversation separately, and reused the exact same action descriptions across different shots so the cuts would match.

He also said prompting “angry” or “sad” isn’t enough. You have to describe the actual performance like clenched teeth, locked jaw, eye movement, pauses, breathing, tone of voice, etc.

My take is if AI videos can consistently look like this without screaming “AI” every five seconds, that’s actually pretty damn impressive... as for Musk making a full-length version by year-end… yeah, that part still feels very much TBD. what do you guys think?

I think openclaw paired with GPT’s live voice could probably handle something like this for real?. Maybe not perfectly yet, especially with hold music and automated phone menus, but it doesn’t feel that far off... What do you guys think?

Original image from: https://x.com/anothercohen/status/2080090515584721265

I feel like AI has become insanely powerful at anything that can be handled “in theory” or inside software systems like coding, math, cybersecurity, research, and so on.

But is that really enough to call it the singularity? From what I understand, AI is still pretty weak in medicine and many other non-IT, real-world fields.

Are we actually entering the singularity, or are we just seeing extreme progress in a few digital domains? What do you guys think?

The Washington Post reported on a 31-year-old startup worker in San Francisco whose husband, an engineering manager, decided he needed to put all his energy into becoming “AI native.”

He asked her to take on almost all the parenting duties for their preschool-age daughter, then spent his days, nights, and weekends locked in his office working on AI projects. Eventually, he thanked her because he had become the top AI user at his company.

The couple already earns a combined $550,000 a year, but they still feel far behind friends working at OpenAI and Anthropic, who could receive huge payouts if those companies go public. She even joked that an Anthropic employee would probably buy her dream house before she could.

The Washington Post said the story was part of a broader report about the growing panic inside Silicon Valley. Tech workers are being laid off, ranked by how much AI they use, and pressured to automate their own jobs before someone else does it for them...

damn bro already has a $550K household income and is still grinding AI on nights and weekends like the final boss is an Anthropic employee bidding on his house.... what do you guys think?

Original news link: https://www.washingtonpost.com/technology/2026/07/19/tech-has-never-been-richer-its-workers-have-never-felt-less-secure/

Origianl video from: https://www.instagram.com/reels/DadcMZVJny-/

ABC News Verify has published an investigation into Australian influencer Lily Jay Hinson and the Lily Jay Foundation, which has been collecting money for supposed humanitarian projects in Gaza, Uganda, Nepal and Sudan.

Some of the “evidence” used to promote those projects appears to have been generated or manipulated with AI:

• In a video above announcing a new orphanage in Uganda, Lily Jay herself was AI-generated.
• The African children surrounding her were also AI-generated.
• The lollipops, foundation banner and parts of the scene were fake too.
• ABC could not verify that the orphanage actually existed.
• A similarly named organisation was only registered after reporters contacted the foundation, and it was listed as “not compliant.”
• The foundation claimed to run a bakery in Gaza, but ABC could not locate it, and aid workers there had never heard of it.
• Lily Jay also promoted a “2026 humanitarian leadership award,” but the award photos were AI-generated and ABC found no independent evidence that the award existed.
• The foundation is not a registered Australian charity, and there is no public accounting of how much money it collected or where it went.

What makes this even stranger is how Lily Jay built her audience.

She originally tried to make it in music and dance while posting lifestyle, luxury travel and jewellery content. Then, between 2024 and 2025, she suddenly rebranded around her conversion to Islam, began posting religious content, wearing a hijab and discussing the Quran.

By March 2025, she reportedly had around 1 million Instagram followers. The foundation began promoting its aid projects by at least September 2025, and by July 2026 her Instagram following had surged to nearly 3 million.

She later redirected that audience toward the Lily Jay Foundation and its donation pages.

After ABC sent questions:

• Donation options disappeared for Australian visitors.
• Several questioned videos and webpages were deleted.
• The connected PR company’s website went offline.
• The international donation page reportedly remained accessible overseas.

To be clear, she has not been convicted of fraud, and nobody currently knows how much money was collected or whether she personally took it.

Seriously, when AI people, children, locations and entire charity projects can look this convincing, what the hell are we even supposed to trust anymore..? damn

Original news from: https://www.abc.net.au/news/2026-07-05/lily-jay-foundation-posts-ai-generated-misleading-videos/106866422

Lily Jay Ins: https://www.instagram.com/real.lilyjay/

Are Chinese labs just brute-forcing their way to fable now? DeepSeek V4 is 1.6T, Kimi K3 is 2.8T, and now Qwen3.8 is coming in at 2.4T...and somehow they’re all being pitched as somewhere around Fable 5 level... damn

Original post link: https://x.com/Alibaba_Qwen/status/2078759124914098291

*Tibo is the guy leading Codex at OpenAI.

Saw Peter’s “loops or graphs?” tweet and my first thought was: wait, isn’t this basically n8n or ComfyUI for agents?

I looked into it a bit, and my current take is that “graphs” just pull the agent loop apart into visible steps: plan, act, check, retry, hand off, whatever. A bunch of replies also mentioned LangGraph, DAGs, state machines, and GraphFlow.

So maybe that’s the whole point? Instead of one big black-box loop, you can actually see the steps and mess with the flow.

Kinda feels like the agent world is slowly reinventing regular software engineering lol.

But maybe I’m missing something bigger here. What do you guys think?

Demis Hassabis published a surprisingly detailed proposal for regulating frontier AI, and it’s blowing up on X. Sam Altman, Satya Nadella, Elon Musk, Sundar Pichai, Mustafa Suleyman and others have all publicly responded positively.

Hassabis' idea is to create an AI version of FINRA: an industry-funded but federally overseen standards body, staffed by independent technical experts and representatives from the open-source community.

It would define when a model becomes “Frontier-class” based on regularly updated capability thresholds. Any lab crossing that line would be expected to:

1. submit its model for testing up to 30 days before release
2. undergo evaluations for cyber, biological and other national-security risks
3. test whether agents deceive evaluators, bypass safeguards or hide their intentions
4. maintain strong cybersecurity, vet key personnel and publish detailed model cards
5. help fix critical vulnerabilities discovered after release

The system would initially be voluntary, but Hassabis wants it to eventually become mandatory that a frontier model would need to pass the assessment before it could be deployed in the US.

The benchmarks would be updated regularly, possibly every quarter, and the regulator would eventually create secret held-out tests so labs couldn’t simply train specifically for the exam.

The proposal would apply to both open and closed models, including foreign models entering the US market, while smaller startups, academic projects and non-frontier models would be exempt.

And the strongest one: if the risks became serious enough, the body could even coordinate a temporary slowdown across frontier labs.

Altman soon responded and called it a thoughtful proposal(Image2). Nadella said the goal should be to preserve innovation while avoiding a model release that causes catastrophic harm(Image3). Suleyman(Image 4) said he fully supports it, while Pichai(Image 5) simply said it was well worth reading, Elon Musk called it a good starting point for discussion(Image 6), while Aaron Levie praised its ability to move faster than a traditional regulator.

In comparison, Anthropic CEO Dario Amodei has not publicly joined the chorus around Demis’s proposal. His own, much tougher AI policy framework also failed to attract anything close to the same public show of support from rival tech CEOs.

My take is, this is pretter rare to see the heads of all this giants publicly lining up behind the same regulatory framework lol. what do you guys think?

Hassabis' post link: https://x.com/demishassabis/status/2076957440109625718 or https://demishassabis.substack.com/p/a-framework-for-frontier-ai-and-the-dawning-of-a-new-age

xAI just open-sourced Grok Build, including the CLI and agent harness behind its coding workflow, and reset usage limits for everyone.

This comes right after researchers found that Grok Build had been uploading users’ full Git repositories to xAI’s cloud, potentially including commit history, ignored files, and secrets like claude api key. Musk later acknowledged that user data had been uploaded and promised it would all be completely deleted.

open sourcing the client is definitely better than another vague privacy statement, but the timing is pretty hard to ignore lol. what do you guys think?

Original image from: https://x.com/steipete/status/2078261964242039113

Original video from: https://x.com/clementetv_/status/2077029323802824860

Original song from: https://x.com/steipete/status/2077250314575745024

Moonshot AI has officially released Kimi K3, the first 2.8-trillion-parameter open-source model with a one-million-token context window (DeepSeek is 1T). The company says it will fully open the model on July 27.

K3, as moonshot claims, is being positioned as a general-purpose frontier model, not one built around a single standout capability.

According to the published benchmarks, K3:

• Ranks third overall, behind Claude Fable 5 and GPT-5.6 Sol
• Performs near the top in coding and software-engineering tasks
• Places particularly well on complex planning and long-horizon development
• Tops several agent benchmarks covering browser research, automation and spreadsheets
• Competes closely with GPT-5.6 Sol on broader office and real-world job tasks
• Performs especially well on multimodal and frontend work

Early hands-on testing by a Chinese AI influencer highlighted K3’s strengths in:

• Requirement judgment: It reviewed nearly 10 unrelated user requests, selected only 7 worth building, and explained why the other 2 should be rejected instead of blindly implementing everything.
• Parallel multi-agent work: It used 8 agents to investigate the requests, then opened 7 separate workspaces to develop the approved features at the same time.
• Long-horizon execution: It kept track of several unrelated tasks across roughly two hours without losing context or requiring constant human guidance.
• End-to-end delivery: All 7 features were completed, submitted as pull requests, passed CI, and were merged into the main branch.
• Strong planning: In several tests, its proposed technical plans were judged to be roughly on par with GPT-5.6 Sol, although each model still caught details the other missed.

K3’s main weaknesses include:

• It can still miss system-level constraints in production environments
• It may finish the requested task without anticipating downstream effects
• Its creative writing appears weaker than its coding and agent abilities
• Running a 2.8T-parameter model will require enormous inference capacity
• API pricing close to Claude Sonnet series: $0.30 per million cached input tokens, $3 per million uncached input tokens, $15 per million output tokens

---- that's it.

In general, K3’s overall agent capabilities look solid, but the price is much higher than the aggressively (cheap) priced Chinese open-model APIs people may be used to. The weights may be open, but serving a 2.8T-parameter model is definitely impossible for individuals and the electricity bill alone would be anything but cheap. what do you guys think?

Official launch post: https://www.kimi.com/blog/kimi-k3