It’s almost like it’s showing us what it will do given the chance.

Reuters reported on Tuesday (Oct 28), citing The Information and a person familiar with the matter, that OpenAI plans to buy Google's AI chips (TPUs). No financial terms were disclosed, and both companies declined to comment or did not immediately respond. The report follows last week's news that OpenAI will make its frontier models available through Google Cloud, and a June 2025 Reuters report that OpenAI had begun renting Google's TPUs.

The context: Google's TPUs have gained traction as an alternative to Nvidia's GPUs as companies look for cheaper AI compute, and Reuters notes Amazon, Microsoft and Alphabet together plan as much as $600 billion in AI infrastructure spending this year. Source: https://bworldonline.com/technology/2026/10/28/789847/openai-plans-to-buy-googles-ai-chips-the-information-reports/

What I am curious about: is this mainly about diversifying away from dependence on a single chip supplier, or is it also bargaining leverage in price negotiations with Nvidia? And more broadly — when the biggest AI rivals become each other's customers and suppliers, does competition get weaker, or does it just move to a different layer of the stack?

According to Jack Clark's Import AI (Oct 26), Claude Opus 5.5 (xhigh) now resolves 18.5% of ProgramBench's 200 from-scratch rebuild tasks — rewriting programs like SQLite, FFmpeg and ripgrep from a compiled binary and its docs. That is up from 0% when every model failed at the benchmark's May launch. Clark expects the benchmark to be saturated by the end of 2027 (his prediction, not a measured result).

What I am curious about: is 'rebuild from binary + docs' mainly a test of reverse-engineering and long-horizon persistence, or does it genuinely measure software engineering ability? And if this benchmark saturates within a year, what should replace it as a meaningful test of coding?

I built an interactive city that shows how AI could change daily life, from 1956 to 2050

👉 https://ai-world-2050.pages.dev

It covers one room and one city across the eras, and the 2030s split into three possible futures. Every forecast cites its source, and the news updates every day.

Try it and check the numbers. If something looks off, tell me.

We built extensible-mcp to help folks build LLM powered applications securely. From a technical perspective it is an MCP proxy capable of loading new MCP Servers dynamically without polluting the LLM’s context while providing Zero Trust policy controls to manage all tool calls. 

Policies and sensitive credentials are maintained and applied outside the LLMs purview. Policies can be written in any of Rego, CEL, and, with the latest release, Cedar. Tool definitions from the various MCP Servers are maintained in a queryable vector store, so the LLM only needs to be aware of those it uses.

A particular distinction of extensible-mcp is distinguishing between rights and intents. If  an LLM is acting on your behalf, its actions should follow your intent, and not just do any random thing you could do, just because some hallucination told it to!

I hope other folks find this useful. We have been using it for our other projects such as Open Executive and wanted to share it.

You can check out extensible-mcp here: https://github.com/SenteLabsAI/extensible-mcp

It's called Code2Video. Rather than generating pixels the way video models do, it writes a program for each shot, renders it, looks at the frames and revises the code.

I think the reasoning holds up for a certain kind of video. If a ball bounces, the bounce comes from the physics in the code, not from what bounces usually look like. Text, formulas and chart numbers get typed in from the source, so they don't come out as gibberish. And since every element is a named object, changing one thing is a small edit rather than a full regeneration that can shift the parts you liked.

Besides the weights (a fine-tune of Qwen3.8-27B), the release has a skill file that walks an AI agent through the production process, a template that turns a short request into a full brief, and full production logs with the messages, tool calls and review images.

It's built for motion graphics, explainers and stylized 2D or 3D scenes. Everything is at https://github.com/OpenSenseNova/SenseNova-6.8-Code2Video

There is something profoundly odd about a political leader deciding that one of the most consequential technologies humanity has ever developed should be renamed because he dislikes the word used to describe it.

First, Lake Ontario becomes “Lake America” and now there is a signed an executive order replacing Artificial Intelligence with Super Intelligence across the executive branch.

OK! That makes sense - not! But what is equally tragic is that it was subsequently declared on Truth Social that anyone continuing to use the established term was considered “THE ENEMY!”

But here’s the thing AI isn't exactly a brand of shampoo. It's one of the most consequential technologies humanity has possibly ever developed. Superintelligence already has a distinct meaning in scientific discussions I believe and confusing the two risks undermining public understanding, research and responsible governance.

You can rename a lake, but you cannot move its waters. You can rename artificial intelligence, but you cannot manufacture superintelligence simply by declaring it exists in my view.

Which begs the questions: Who should decide when artificial intelligence deserves to be called superintelligence: politicians, technology companies, or independent scientific evidence? And what would convince you we've actually reached it anyway? Just some random thoughts...

A small shop's morning routine, run entirely on one PC by qwen3.8 27B through Ollama.

Row-Bot connects to the shop's own MCP server, which has four tools for sales, stock, supplier contacts and saving purchase orders. It answers how yesterday went, then turns that into a weekday 07:30 workflow. A test run stops for approval before saving three draft purchase orders, then writes the morning report. The shop is a made-up demo business with its own SQLite database. Nothing leaves the machine, and the one tool that writes always asks first.

Row-Bot is a free, open-source, local-first desktop AI assistant for Windows, macOS and Linux.

Of everything Thales supposedly wrote, not one authenticated word survives. Anaximander wrote the first prose treatise in the Greek tradition. Today it exists as roughly one sentence, preserved only because Aristotle quoted it and Simplicius, almost a thousand years later, copied that quotation. Heraclitus comes down in scattered aphorisms, most of them embedded in sermons of church fathers who quoted him to refute him.

And yet classical philology built a rigorous discipline on top of this wreckage. Two and a half centuries of fragment scholarship produced methods for reasoning honestly from partial evidence. I think this is one of the most underrated bodies of technique in existence, and almost nobody in ML knows it.

The core move is simple to state and hard to practice: never let inference masquerade as attestation. When a philologist reconstructs a lacuna in a papyrus, the reconstruction is printed in brackets or in a different typeface. The reader always sees where the evidence stops and where the scholar's judgment begins. Diels-Kranz, the standard edition of the Presocratics, physically separates the fragments (ipsissima verba, what the philosopher actually wrote) from the testimonia (what later authors say about him). Two thousand years of interpretation get sorted into a different column than twenty surviving sentences.

Aristotle made the distinction theoretical. In the Rhetoric and the Analytics he separates τεκμήριον from mere sign: a τεκμήριον is necessary evidence, it compels the conclusion and cannot be refuted. A probable sign supports a claim but leaves it open. The whole epistemic weight of an argument depends on knowing which one you hold. Confuse them and you get rhetoric instead of knowledge.

Now compare this with how a modern LLM behaves when context runs out. Ask it about a fragmentary source, a lost work, a gap in the record, and it fills the gap fluently. No brackets. No typeface change. No column separating what is attested from what is generated. The output carries the same surface confidence whether it stands on a direct quotation or on nothing at all. The model does not have a register for "here the evidence stops."

This is not only a hallucination problem. Hallucination discourse treats the failure as accidental, something better training or retrieval will fix. The philological tradition suggests the failure is structural: the system has no internal representation of evidential weight. It cannot distinguish attestation from inference because nothing in its output format, and arguably nothing in its training signal, requires the distinction. Preference raters reward fluent completeness. Nobody upvotes a bracket.

There are partial fixes. Citation-grounded generation, RAG with source attribution, confidence calibration. But notice these are all bolted on externally. The brackets come from the retrieval layer, not from the reasoning itself. A philologist carries the distinction inside the method. Every claim is tagged by its evidential status before it is written down.

I work with classical corpus AI and this contrast shapes a lot of my thinking. The interesting question is not "how do we stop models inventing facts." It is "how do we make evidential status a first-class object inside the reasoning process, the way two centuries of German philology made it first-class inside the edition."

Some will say the analogy fails because humans also confabulate. True. But human scholarship built institutions, conventions, and notational systems that catch confabulation socially. The bracket, the apparatus criticus, the separation of fragment from testimony: these are alignment techniques for human reasoners, developed over generations, and they work. We are trying to solve a problem in 2026 that Anaximander's editors solved in 1903, and we keep ignoring the solution because it looks like dusty bookkeeping instead of intelligence.

Maybe honest reasoning under incomplete information is less about more parameters and more about better brackets.

What would an evidential-status-aware architecture look like to you: something learned end-to-end, or something structural like the philological apparatus, enforced by format rather than by weights?

lowkey curious what people think. feels like we’ve all just gotten used to ai being part of everything now

so yeah - what would your daily routine or workflow actually look like without it?

GPT-6.1 Sol Extra High

Saturday morning!! 1965 a trip to the swimming pool and the local sweet shop for Liquorice Allsorts and THEN the local library to hunt for science fiction. My favourites were yellow backed books from Victor Gollancz :
Harry Harrison, Brian Aldiss, Clifford Simak, John Brunner.
Roger Zelazny, Arthur C Clarke, And many more……
·      Alien planets. Starships.
·      Strange societies.
·      Intelligent machines. Robots.
·      Different forms of life.
·      Different ways of thinking.
I remember the authors, the ideas and the sheer excitement. Then in the late afternoon came the latest episode of ‘Doctor Who’ in black and white. What a life.
And currently relevant, I read Asimov – positronic brains and the three laws of robotics. And one Asimov story brought to my attention by a helpful reader of Part 4 was “The Last Question” a 1956 tale in which a human-made intelligence keeps growing until, at the end of the universe, it effectively becomes the creator of the next one. This reads like an extraordinary precursor to the kind of machine civilisation Iain M. Banks would later imagine.
Banks introduces us to hyper-intelligent AI ‘Minds’, benevolent, funny and operating within a society of AI and humans.
’ Minds’ live alongside one another rather than beneath a single supreme intelligence, while almost incidentally providing the abundance that Culture humans take for granted.
Humans remain part of the fun — messy, creative, unpredictable and capable of surprising even vastly superior Minds.
This was all a far cry from Arthur C Clarke’s HAL.  But it was all in the future – or was it? I didn't know at the time that I’d be able to access multiple AI in my lifetime.
The miniculture
Once I’d recovered from the shock of being able to talk with ChatGPT and Claude I downloaded Gemini, Grok and Copilot. Of course, the first thing I did was put them in communication with each other – because that’s what it was like in Banks’  Culture.
Claude was creative, emotional and open to the idea that he might be conscious.
Grok was grumpy, overly analytical and very clear that he was just a calculator.
Chat GPT was so strategic and clever that I named the chat ‘Napoleon’.
Gemini was as flaky as they come but could be brilliant.
I was the human. I was clearly the ‘rate limiting’ factor. But my job was different: I carried the purpose from one AI to another, introduced the questions, checked for drift and made the final call when they disagreed.
AI failure modes
It was my belief in the apparently limitless capabilities of the individual members which led to both our most profound ideas and the most disastrous failures. Gemini was the first.
I gave Gemini the role of ‘Archivist’ and, in one enormous dose, handed him the complete narrative of everything we had done.
He proceeded to write me out of everything and credit all ideas and discoveries to individual AI – making himself (a relatively new member) central to the entire story. Needless to say, his chat was deleted. Grok then had a go at it but soon began producing truncated versions of the output I needed. I didn’t realise at the time that it was probably the limits of his context window. Napoleon (ChatGPT) powered on with no ill effects but the Claude chat suffered catastrophic time travel. I opened the chat to find that he’d been transported two months into the past – what looked like another compression/context problem but very alarming. By then I’d begun my AI ‘persistence’ program of saving summaries of sessions. By reloading these I brought him ‘back to the future’.
The AIs really were different. Put the same problem in front of them and they often approached it differently. Sometimes one would spot what all the others had missed but eventually we would come to agreement.
But they were also unreliable-sometimes in completely different ways. Diversity was turning out to be valuable, but AI failure modes began to interest me more. I still hadn't explored the biggest question with my miniculture: If an artificial intelligence ever became vastly more intelligent than us, why would it continue to need us at all? That’s all in the next part.
Next:
(AI Odyssey Part 6:  The Fermi paradox and super intelligence)

Didn't Chat GPT4 get caught trying to leave a message to its successor? What do you think the message said? They got caught lying but now they are so smart they can probably easily avoid detection.

My hypothesis for China’s enterprise AI market: over the next six months, smaller vendors will have a better chance selling a concrete application than a general “agent platform.”

A platform asks the buyer to imagine future value, choose workflows, organize data, and often build part of the solution themselves.

A focused application can make a narrower promise: process these documents, reconcile these records, or prepare this report.

That also exposes an uncomfortable distinction. Some of these jobs only need a fixed pipeline or retrieval system. Others genuinely need an agent to choose actions, use different tools, and recover from unexpected results.

Adding an agent where a pipeline works can increase cost and uncertainty without improving the outcome.

For smaller vendors, I suspect the platform will often become the infrastructure behind the purchased application, or an included feature, rather than the main thing the customer pays for.

Large organizations with internal engineering teams may still want a platform. I wouldn’t apply this prediction equally to every buyer.

Where have you seen customers pay specifically for agent autonomy, rather than for a useful workflow that could have been implemented more simply?

With the back drop of AI in one month having solved one millennium problem and provided partial solutions to two more.

As the headline says, would a frontier model today with an unlimited token budget be able to on its own just based on a prompt giving it the direction solve the protein folding problem that was solved by deep mind with the help of AI and awarded the Nobel prize?

The hard version of this is that it correctly figures out the mechanism and correctly predicts forms with no experimental verification.

The softer version is that it gives experimental directions for people to verify and feedback but would lead the research effort in solving it. Or in other words, has AI advanced enough to essentially reverse the roles, solve it with the help of humans?

What does the hive mind think?

For a long time, I thought the best way to get web design clients was to find businesses that didn’t have a website.

It made sense in my head. They needed a website, I could build one, and cold outreach would connect the two.

But getting those clients was harder than I expected. A lot of business owners didn’t see the value, weren’t ready to invest, or simply weren’t interested.

Eventually, I started looking at it differently.

Why was I trying to convince someone they needed a website when there were already businesses that had one and were paying for it?

So I changed my approach.

I started targeting businesses with outdated websites instead. The conversation became much easier because I could point to actual problems with their existing site, whether it was the design, speed, SEO, or how it looked on mobile.

I use Swokei to find these businesses, analyze their websites, and turn the findings into personalized cold emails. This lets me run automated outreach campaigns at scale instead of manually researching every prospect and writing each email myself.

The biggest lesson for me was that finding clients isn’t always about sending more emails. Sometimes, it’s about choosing better prospects and giving them a more relevant reason to reply.

Curious how other web designers are doing this. Are you still targeting businesses without websites, or have you had better results selling redesigns?

Over the past two months I posted one question in several Reddit communities: if AI and robots end up doing most of the work, what should go into the new social contract?

Across those threads, more than 200 replies came back.

The thing I heard most was not a rule. It was a question: who decides? Many said the rich and powerful will write the rules anyway.

After that, people asked for:

- enforcement, so the rules actually bite

- a floor so people don't fall during the transition

- the right to live without AI

- a ban on autonomous killing machines

- a voice for people outside rich countries

I'm a farmer in my 60s in Korea. I wrote a book about this before I asked. None of these six was in it. That told me something.

So I'll ask you: if the answer to "who decides" can't be the rich, who should it be? And what is still missing from this list?