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

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?

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...

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?

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

Hi! I'm Daniel and I'm building Briiq, an AI-native IDE for designers. It works like a "Cursor for designers".

For 5+ years I was the only designer at a startup. I was responsible for how the product should work and look, all in Figma. What always bothered me is that my work was just a drawing. A developer still had to interpret it and build the real thing in code.

Using Claude Code, I finally had the freedom to build with code myself. But only using prompts isn't the best way to design and build.

That's why I built Briiq. It looks like a design tool, but every element is real shadcn/React in your own repo. You edit by hand or prompting your coding agent, and everything you build is real code. Nothing to hand off, because there's no drawing to translate.

The first product is Briiq Slides. Next up are editors for UI and diagrams, and a lot more on this same base, so you can design in real production code without worrying about the code itself.

It's not out yet, but you can see how it works here: letsbriiq.it

I'd love to hear what you think ;)

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?

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?

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)

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.

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?

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?

To be fair, my company has a pretty open policy about AI tools so they let us experiment with different tools. I see a lot of the typical laziness and slop form my coworkers, but i’ve been obsessed with learning and skill development and think i’ve finally unlocked a new way to work. So bear with me cause it truly is crazy.

I was using chatgpt when it came out and was never that impressed. Used claude code a lot earlier this year for side projects and fun but couldn’t really see how it could help me at work much. But over the last few months i’ve just been experimenting with different tools and spending a lot of time developing my own process with AI and it’s been an insane change.

For my job, i do a lot of project management, writing briefs and proposals, drafting plans, etc. It’s super fast paced and stressful to manage all of that. I mainly avoided AI cause i thought it would add unnecessary complexity to my work that already had too much. But i started to learn that i could offload some minor tasks that just helped triage all of the information coming at me everyday.

I first did the typical “chief of staff” setup where my agent read my inbox and some slack channels to keep me updated and i found it just took off a lot of that ongoing anxiety in the back of my head that I was missing something important. I found perplexity to be the best with this with the tool connecters and i didn’t have to do any crazy manual setup.

I then started to experiment with it drafting briefs for me since that seemed lower stakes and wouldn’t break anything if it got something wrong (and i vetted everything btw). After giving it some of my references and building a voice style it started giving me really great briefs that met my own standards and even helped add additional clarity. This is where i setup my first automation.

Now when a project comes in, my agent will recognize that it needs a brief and go ahead and spin one up for me, send to me for approval, and then email it after i’ve edited and approved. This easily saves me a few hours each week.

The automations really unlocked things for me. But to be clear, I have spent a ton of time making sure that I organize tasks and works into folders with specific instruction (in Perplexity and Claude i think they are called Projects). This way,at agents and tasks start with full context and have the right templates and boundaries that i set. Claude is good but the automations and memory in Perplexity make the automations more reliable and i trust the output more.

I now have agents monitoring slack for upcoming projects, processing the information and giving me an overview with what is needed, and then drafting the initial briefs and plans for me. It’s gotten so good that i rarely have to edit anymore. And this happens in minutes of it picking up on a project. And will continue to update as the project changes or feedback comes in. This process is now probably 80-85% automated, just using me for the final writing and approvals.

I also have my agents drafting and posting project check ins to keep stakeholders on track and giving me updates on where things are falling behind or need my review.

Again, all of this is now automated. It’s truly crazy that all of this has been a majority of my job for so long and now I feel like I can actually think again and am not bogged down by endless low level tasks. My coworkers and execs just think i’m on top of everything and the outputs I own and still do manually have much higher quality because i can actually spend time on them.

Don’t worry, i still manually approve everything and vet for quality and errors (trust me o still see a lot of slop from others and and very wary about that). But maybe it’s just choosing to use Perplexity for this work, but the accuracy and ability to trace where it all comes from gives me a bit more confidence in the output. So i mainly use there agent tool, Computer, for that work and then still use Claude for anything coding or technical.

Yeah so now my job is like 70% automated and I feel like I actually enjoy work again cause i’m not drowning in stupid work anymore. It’s not a magic genie, but man it is so nice to see that some of these tools can actually help me in work now.

Researchers from New York University and the University of Texas at Austin analyzed language from 14 models across chat, coding and essay settings, including 980 model-written essays.

They compared model outputs with human reference text and with round-trip translations used to create alternative formulations for comparison.

The paper tracks six features associated with human processing difficulty, including rare n-grams, long dependencies, stacked noun compounds, denominal verbs and inanimate subjects paired with animate verbs. Most frontier models overused several of these features relative to the paraphrase comparison.

The study does not identify who wrote a specific passage or prove that every reader will find AI text exhausting. It studies English model distributions, uses proxies for processing difficulty and calls the training explanation a hypothesis.

Source: https://arxiv.org/html/2610.29162v1

Given Google’s massive resources, AI expertise, and the strategic importance of ChatGPT, why hasn’t Google attempted to acquire OpenAI?