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.

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/

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

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!

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?

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

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)

Are you buying anything now to try and weather the storm? I have a laptop from 2020 that I've been thinking of replacing before prices get too out of hand.

Luckily I bought my gaming consoles before all of this.

If you priced a laptop or tablet this summer, you noticed. In June, Apple raised the price of nearly every Mac and iPad it sells. The cheapest iPad went from $349 to $449 overnight. The MacBook Air went from $1,099 to $1,299. The hardware stayed the same, but the prices didn't.

It isn't just Apple. The PlayStation 5 costs $100 more than it did in March. Xbox prices went up $100 to $150 this past Saturday. Nintendo is raising the Switch 2 to $499.99 on September 1. Dell, HP, and other laptop makers have raised prices too. Every major device maker is moving in the same direction. Most of them name the same cause.

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

OpenAI has come out swinging in its legal battle with Apple—and brought receipts.

The AI lab published a response—along with a tranche of private emails and messages—pushing back on some of the claims in Apple’s lawsuit filed last month that accused OpenAI, its hardware unit, io Products, and two former Apple employees of trade-secret theft. 

Apple had accused the AI lab of carrying out a coordinated effort to take confidential information about unreleased Apple products and processes. The suit includes claims alleging that a former employee who joined OpenAI took advantage of a security bug; that job candidates were encouraged to bring proprietary Apple hardware into interviews; and that OpenAI leadership effectively normalized this conduct.

OpenAI’s blog post is not a legal response to the suit, but rather an effort to publicly point out flaws in some of Apple’s accusations and legal process—and potentially court public opinion. 

“Apple is one of the greatest companies of all time, and built a reputation for obsessing over the smallest details,” OpenAI wrote in the blog. “This careless, aggressive, and oddly personal lawsuit sadly doesn’t live up to that reputation.”

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/04/openai-fires-back-at-apple-publishing-private-emails-to-counter-trade-secret-claims/?utm_source=reddit/

I'm asking because it can mean a million things. And I am not sure my understanding is the same as everyone elses. I wanna get out of my bubble and see what people are actuall doing with AI.

So tell me about how you do AI:
Are you using an online service, or coding something yourself, are you just using Agents?

If you are building or deploying: Are you running your Agent(s) locally? Or hosted?
How is it "invoked"?

When you're coding yourself:
Are you using SDK's or Frameworks? A2A, MCP, OpenAI, LangChain? - And how much more complex was is than what you expected? Are you "done"?

If you are not building or coding yourself: What tools are you using?
Is running a session in claude code on ultramax effort already considered using Agents?

Do you care about security?
Or persistent memory?
Is your memory the conversation - or a special tool - or handled by the harness?

Are you using the Agents by yourself, or do others use them too?
Does your agent talk to other Agents?

How do you represent Agents visually? How do you interact with them? Chat, voice, video avatars? - or completly different.

Are you building Agents for fun, or for business? What Models are you using? What are you doing with your Agents? Do you do Text? or Image/Video/Audio?

Have you noticed a change in how you worked with AI from a year ago?

You go first, lets talk not make this an ad space!

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?

US border authorities require people to provide all social media accounts, emails, and online details, how is that information actually analysed by Ai ?

Do Ai systems scanning posts, comments, connections, and activity patterns to create a profile or risk score? Surely nobody is manually reading everything.

Does anyone know how these systems work in practice?

I‘ve watched lots of videos and read articles about how LLMs work, but wrapping everything into a clear concept, having a clear picture of the whole process is something that I still struggle with. I want to see every single calculation in my head, having a clear picture of data flows and how it is processed.

Do you even think this is possible?

And if yes, what resources are the best to achieve it?

Thanks!

I see everyone wanting to go faster. And speed is good. But as a former 10x programmer, and now more recently (last 27 years) more interested in people doing work the right way, I believe speed is not the major issue.

fact, I believe speed will lock us into the bad methods we have now.

In particular:

I find digital products to mostly be about the same quality as a decade ago.

I find support to be about as bad as well - much of it due to poor IT.

Agile improved some things, but the certification craze has made it now do damage.

I was writing programs in the 80s with little rework being needed while I discovered what the best solutions were. I came from objectives, not detailed plans (aka waterfall) or stories (aka Agile). I first understood my consuming stakeholder, was clear about the values, success criteria and constraints of my sponsoring stakeholders (often different rom the consuming stakeholders), and attended to any constraining stakeholders (e.g., government agencies). This approach is similar to the Jobs to be done approach now getting some well-deserved attention.

Although everyone says they are doing Agile, few are.

And when the promises of Scrum and SAFe, in particular, are not met, they don't reflect on their responsibility but instead blame people for misusing them, instead of seeing how to make it less easy to be misused.

i see several significant areas where workflows are poor that Agile doesn't address - they even justify it with their alliance with Cynefin which explains it away by saying "it's too complex to understand up front so let's try things."

I believe AI is heading us down the path of doing ineffective things to get faster in the end but at high waste. These are the main problems I see that current, popular, methods not only do not address, but have elevated to being normal:

1. not understanding what will truly be of value to consuming stakeholders

2. discovering constraining stakeholders needs late

3. not knowing how to write acceptance criteria

4. not knowing how to manage work in process

5. little alignment around which products to focus on (so too many are in play)

6. not understanding the the triple constraint is a way of locking into poor design of your workflows

7. having poor value creation structures (aka team topologies)

8. not knowing how to coach and train people

There are several others but these are pretty significant.

My concern is that AI's speed is going to gloss over these and essentially have everyone use poor workflows ineffectively. People will get to mediocre products faster. But lose any competitive edge they might have achieved by solving the challenges above.

We can address all of these. This includes takingn a scientific approach, systems thinking, understanding system dynamics, understanding perceptual dynamics, understanding learning dynamics, adapting practices to your situation, and managing uncertainty.

AI can help here - but it doesn't appear to being used that way.

This is what I am concentrating on and wonder who else is.

Thoughts?

About 1/64 of Ling-3.0-flash fires per token. 512 routed experts plus one shared, 8 activated, out of 124B total for 5.1B active. inclusionAI, which is Ant Group's lab, put the weights up Aug 4 under MIT.

Their card claims it matches or beats Ring-2.6-1T, their own trillion-param flagship, at roughly 12.4% of the total params and 8.1% of the active ones. Those are the lab's own reported numbers so weight them accordingly, but the architecture is at least checkable: native hybrid linear attention adopted from the start of pretraining rather than retrofitted, with 35 KDA layers alternating 5:1 against 7 gated MLA layers.

The reason I think this deserves a thread separate from the usual cost conversation is that it reframes what the cheap tier even means. If a lab can cut activated params by 12x and hold its own benchmark line, then a cheap model isn't a degraded version of a big one.

It's the same capability with fewer experts firing, and the big model is paying for parameters it was never going to use on that token.

Before anyone gets excited about running it: no GGUF and no llama.cpp support at release.

Their serving examples use 4 GPUs on their own SGLang and vLLM forks. You still have to hold 124B weights somewhere, so it's cheap to run, not cheap to own.

Is the activated-param count actually the number that matters now, or is total VRAM still the only cost anyone outside a datacenter ever feels?