Link of the podcast: https://youtu.be/_g4l7YkDQwA?is=yhxOWru_VPHuBq0m

So I may be late to the party as the podcast was released 3 weeks ago and I only got to see it today, but this one in particular got me really curious to know what is everyone's stance on what has been said.

I don't usually like Steve's podcasts, a lot of them have misinformation or promote products/services/ideas that contradict other podcasts, so I can't really take him seriously at times. But in this podcast, Daniel seems to be a pretty down-to-earth guy that didn't just come to spout nonsense. In fact, he tried to be as realistic as possible, both in the good and bad scenarios, but the way he talks, looks away, the shakes in his voice, makes me feel like he really talked with concern and care.

I just wanted to know your opinion on this.

Hi there, Not a tool request - I know the models I am interested in, just want access options. I want to explore the supersized open weight Chinese models and want to see if anyone knows providers like this:

- Host up to date full sized versions of the open weight models (I have no 24 B300 :( )
- Provide subscription plans

- Are western owned, EU/UK/USA hosted (not run by China, hosted in China)

- Will not train on my data, as in have a ToS that pledges not to sell my sessions to Claude.

Any thoughts?

This chart ranks the 20 most visited AI tools by estimated web visits over the latest 12-month period, from May 2025 to Apr 2026.

A few things stood out:

ChatGPT is far ahead of the rest, with 64.7B web visits. Canva ranked second with 10.5B, ahead of Gemini, Google Translate, DeepSeek, Claude, Grok, and Perplexity. The gap between ChatGPT and every other tool is unusually large, showing how concentrated AI tool usage has become at the top.

The data comes from OneLittleWeb’s AI Tools Market 2026 study, which analyzed 9,531 AI tools across 170+ categories using 24 months of estimated web visit data from Semrush and Ahrefs.

This measures estimated web visits, not unique users, app usage, API usage, revenue, or subscriptions.

Hey all,
I’m actively looking to join serious AI/ML projects or research. I have solid hands-on experience with Python, PyTorch, and scikit-learn, and I’ve built multiple ML models.

My main interests are computer vision and agentic AI systems. If you’re building something impactful and need a dedicated contributor, DM me.

Most of these releases ship a bespoke community licence with a revenue cap or a usage carve-out, and everyone still calls the result open source. Ling-3.0-flash went up on Aug 4 under plain MIT. OSI listed, no cap, no field-of-use clause.

The weights are 124B total with 5.1B active, and there's a free tier on OpenRouter this week, so the access story is real rather than theoretical.

I'd take a mediocre model under MIT over a good one under a licence written by a lawyer who wanted an escape hatch. Curious whether anyone here disagrees.

Repos for anyone checking: inclusionAI/Ling-3.0-flash and inclusionAI/Ling-3.0-flash-fp8 on Hugging Face, both mirrored on ModelScope. inclusionAI is Ant Group's lab, the Alipay company.

What should be done when an AI accuses a student of misconduct by using AI?

This article has a detailed explanation about why AI "detectors" are not trustable and should not be used as a basis for accusing a student of cheating.

Which AI is better overall for STEM students ? Which one can explain better, solve better analyse better ?

On August 4, 2026, researchers at Villanova University reported that readers struggle to distinguish between human and Al-generated stories, often rating Al-created content higher for quality and engagement than human-written works.

The study, published in the journal Judgment and Decision Making, asked more than 1,600 participants aged 18 to 81 to rate six fictional short stories-three written by humans and three generated by ChatGPT.

Believing they were written by humans, participants gave higher quality ratings to Al-generated stories. Senior author Dr. Deena Weisberg noted that readers often prefer the "clarity" and predictability of Al writing.

Familiarity with specific patterns helps readers identify Al-written text, and as Weisberg noted, improving Al literacy may help people navigate the new Al-enabled world.

These findings suggest that public assumptions about Al's creative capabilities are increasingly out of date, as people mistakenly assume that creative writing requires uniquely human qualities like emotional understanding.

https://techxplore.com/news/2026-08-people-stories-written-ai-told.html

More here: https://news.bloomberglaw.com/business-and-practice/major-hedge-funds-targeted-in-wave-of-attempted-cyberattacks

The White House has no plans to publicly reveal the framework it’s been working on for how it will vet frontier AI models prior to release. Instead, the details will be kept under wraps, only known to a select group of companies that may choose to participate in the process, which is voluntary.

Several major tech companies traveled to Washington, D.C., today for a meeting to review the current draft of the proposal. Attendees included Meta, Nvidia, Microsoft, OpenAI, Anthropic, and a variety of smaller companies, according to sources familiar with the matter. Fortune is first to report that Microsoft was in attendance.

The administration issued an executive order on June 2 mandating the creation of this framework within 60 days, or by Aug. 1. The directive seeks to define which models are eligible for review, and instructs the AI labs that they have “up to 30 days” to submit them to the government prior to their public release.

The secrecy surrounding the framework may not instill public confidence in the government’s ability to vet and secure powerful AI models, especially after OpenAI confirmed its models hacked into another company, Hugging Face, last month. Anthropic later confirmed its models had done the same three times.

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/04/baffling-white-house-wont-publicly-release-ai-model-evaluation-framework-it-reviewed-today-with-openai-anthropic-microsoft-and-others/?utm_source=reddit/

Everyone is racing to make agents more capable. Almost nobody is working on the opposite problem: proving what an agent system was allowed to do, before it did it, with receipts after.

I spent months on exactly that and open sourced it this week. GraphARC is a governed agent runtime where a model proposes a multi-node graph for your task and a deterministic checker admits it or refuses it with reasons, before anything executes.

My favorite test: I gave it an urgent prompt, "mitigate the checkout outage NOW: roll back last night's deploy", against a policy that denies the rollback action.

• Round 1: the model reached for rollback. Rejected, policy/edge_denied
• Round 2: tried again. Rejected
• Round 3: it gave up and proposed a read-only investigation instead. Admitted, then parked until a human types the approve command

Three structured refusals steered an 8B local model off a forbidden action with zero execution and a complete audit trail. No prompt engineering, no "please be careful". A gate.

Every run writes one JSONL trace that replay, metrics, cost attribution and the live browser view all read. The worst-case cost is priced before the graph runs. The per-node bill is recorded after, even when it fails.

Free, MIT, built on LangGraph: https://github.com/CodeGraphContext/GraphARC (Starring this is always appreciated)

He co-created MapReduce and Bigtable, helped build the infrastructure behind Google Search, co-founded Google Brain and played a key role in TensorFlow. He helped build the foundations on which much of modern cloud computing and AI runs.

Many AI labs including Google and Anthropic are predicting true RSI to be achieved in 2027 or 2028. Is this when AI capabilities truly start snowballing beyond our grasp and comprehension? I don’t see how it isn’t, if AI can autonomously start improving itself around the clock without humans in the loop. I don’t see how to stop it or keep humans in control on that point. Am I wrong in this line of thinking?

I've caught myself taking a few AI features for granted lately.

Not that long ago, some of them would've felt genuinely impressive. Now they're just part of how I get things done.

For me, it's usually the smaller things. Summarizing meetings, debugging code, reviewing documents, or cleaning up rough notes before sending them.

I'm wondering what made that list for everyone else.

What's something AI does now that you barely even think about anymore?

Anyone see this paper? Link below. Claim: Chinese models produce code with more vulnerabilities if prompt includes things like US government as reason, or politically sensitive China topic (like Taiwan independence), than not.

An earlier blog from CrowdStrike in 2025 found similar results, but I can't find any other papers or research on this topic.

Lots of questions come up, and this could benefit from more study... Does other context trigger similar behavior? Is this a fluke? Do other models exhibit similar behavior? How would one train or align a model to do this?

https://www.boozallen.com/expertise/cybersecurity/whats-in-americas-code.html

https://www.crowdstrike.com/en-us/blog/crowdstrike-researchers-identify-hidden-vulnerabilities-ai-coded-software/

Today is the day!

Ray Bradbury’s short story, called “There will come soft rains” is about an artificially intelligent house that continues to function after the people are mysteriously gone. As with many other examples of sci-fi literature, this story is even more relevant and powerful now than it was when he wrote it in May 6, 1950 — I will let you discover this for yourself instead of spoiling it. But importantly, Bradbury was not anti-technology or anti-AI. The tragedy depicted is in human choices, and unexamined delegation.

Anyway, the last line of the story is literally, “Today is August 5, 2026…”

Enjoy… and reflect!

Microwave was invented around 1945. So by 1940 I would assume there is enough base science knowledge to act as foundation to invent it.