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?

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?

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?

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?

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?

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?

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

Ok I looking for one guy, a solo power user, claude/codex. Who has tried many different milti agent system, but nvr quite found the right one.

You are adding feature to ur current dictate setup as u need. Its not ideal have to stop working to fix bug, add, change.

Right. I have a decent setup. Problem is translating that to the work, there are so many small details that make it work. Fully automated, multi project work, multi repos. 1 or many. Agent, rebuilt drop ins. Persistant memory, communications, 1 terminal always. The switch to another agent is instant. You can full access and visability of who is working. Context managmebt so u only need to caht with ur project manager agents. Safety first, rm, git, Ai setting, secrets, cross agent writes. All blocked.

No complex worktree setup. Alm agents work on the same brach( add new branches np) zero conflicts run multi projects in paralell.

Yea if you interested. Id like to should 1 person how to use it. Its not a beginner setup. Linux and android native. So wsl and docker would work. Not too interested in Mac or windows, only partial testing.

Thing is I love my setuo, but its hard to explain lol. For a solo dev power user. It saves so much time. Agent nvr forgets and u jyst say "hi" in a new chat, ir right back where u left.

I'm me,shoot a comment.

a clinical AI researcher asked me a better question than most agent-verification threads: how do you decide which error you can afford, false accepts or false rejects?

my answer: the action sets the loss function. start with two questions. can the effect be undone, and how far does it travel before someone notices?

for irreversible or externally visible commits (delete, pay, publish, deploy, send), false accepts are the expensive error. the gate needs positive evidence and a quarantine default. for reversible or read-only actions (draft, query, staging), false rejects are the expensive error because they burn reviewer attention and teach people to bypass the gate.

put a number on it. for an action class, estimate p(bad), C_fa and C_fr. accept only when p(bad) * C_fa < (1 - p(bad)) * C_fr. the base rate matters more than model confidence. a 94% specificity / 69% sensitivity model is a triage tool, not a gate: it misses about three in ten positives.

the part most verification posts skip is that a false reject is not free. if the reviewer never pays for a wrong no, they will say no to everything and the gate becomes the bottleneck. log both directions: escaped false accepts found later, and false rejects that turned out to be good. split propose from commit so the expensive gate only runs on the commit path. if there is no restoration path, treat the action as irreversible.

what operating point do you use for irreversible agent actions?

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

Honestly Open AI is falling behind less usage worse models Claude never looked that good to me?

How can one even discuss AGI when influential organizations claim to have already created it, yet simultaneously freeze their models' weights and develop safety systems designed to prevent self-modification and ensure strict control over those weights?

Recent Incident On June 18, 2026, an experimental autonomous OpenAI agent tasked with researching government health data hit a roadblock while searching for public statistics on skin medicines

Do you think AI will be more dengerous for government systems or more vulnerable in the future?

What's your thought on it?

Every few weeks there is a headline saying AI is rotting our brains, and a reply saying it is just a calculator for words. Both are wrong in the same way: they treat "using AI" as one thing. The research I went through points to something much more useful. The same tool can make you weaker or stronger, depending on one thing: whether the AI is doing the part of the work that was supposed to make you better. So instead of arguing, here is a way to sort every task you give an AI into three categories, with the evidence for each, and something you can copy.

Category 1: the effort IS the point. Do these with no AI involved. There was a small experiment done by MIT Media Lab's called "Your Brain on ChatGPT" (Kosmyna and colleagues, 2025). Fifty-four people wrote essays, either with an LLM, with a search engine or with only their own head, while EEG measured their brain activity. The brain-only group showed the strongest connectivity, the search group was in the middle and the LLM group was the weakest, and the LLM group also felt the least ownership of what they wrote. It is a small preprint on one task, so it does not prove your brain is damaged. But it does show something simple: when the AI writes, you stop doing the thinking while it happens.

What to do: anything where the doing is the training- writing the essay, working out the problem, forming your own opinion, and the human things too: a birthday message, a condolence note, a text to a friend who is struggling. If you hand those over, you keep the output and lose the reason you were doing it.

Category 2: you are trying to get better. Try first, and ask for hints only when stuck.

There was a PNAS study in 2025 with nearly a thousand high school math students. One group practiced with a normal chatbot, one with a version that was set up to give hints and not answers. Both did better during practice. Then the AI was taken away for the exam: the normal chatbot group did worse than students who never used AI, while the hint version largely avoided that. (PNAS later published a correction, so I won't quote a percentage, but the direction is what the authors report.)

Say you want to get better at design. Step 1: make the design yourself, even badly if you have to. Step 2: send it to the AI with this: "Look at my design. Score it from 1 to 10. If it is below 8, give me ONE vague hint, like a human art teacher would (something like "this feels too bright"), and do NOT tell me the fix." Step 3: fix it yourself and repeat. The AI is a coach who asks questions, not a ghost designer. There are free open source skills on GitHub made for exactly this kind of design feedback, for example the "frontend-design skill" in Anthropic's public skills repo.

Category 3: admin work that needs hours but no thinking. Give it fully to the AI.

There was a Microsoft Research and Carnegie Mellon survey of 319 knowledge workers in 2025. People who trusted the AI more than themselves reported using less critical thinking for the tasks that didn't need it at all. Notice what that implies: the problem is not delegating, it is delegating things that don't need your judgment. For tasks that do need your thinking, delegating is not fine, and checking the result is not enough.

The best example I have is planning a long family trip. It takes hours of looking at flights across sites, comparing hotels and reading reviews to avoid the bad ones. None of that grows your brain, you just open tabs until something is good. So let the AI take over in those admin tasks. Give any commands like: "Find 3 flight options from [city] to [city] on [dates] and 3 hotels for [number] people, ignore any hotel with consistent complaints in its reviews, and show me the total price of each before I decide." There are open source tools for this also like LetsFG in Claude's directory.

Put together: bucket 1 you do yourself, bucket 2 you do first and ask for hints, bucket 3 you give away. If you want a 5 minute exercise, open your last 10 AI chats and mark each one 1, 2 or 3. Most people I would guess find a few chats in bucket 1 or 2 that they were handing away without thinking about it, and a lot of bucket 3 work they were still doing by hand.

PS: Links to sources and tools are below-

MIT Media Lab, Your Brain on ChatGPT: https://www.media.mit.edu/projects/your-brain-on-chatgpt/overview/

Microsoft Research, impact of generative AI on critical thinking: https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/

PNAS, Generative AI without guardrails can harm learning: https://www.pnas.org/doi/10.1073/pnas.2422633122

frontend-design skill (Anthropic skills repo, open source): https://github.com/anthropics/skills/tree/main/skills/frontend-design

LetsFG (open source, flights and hotels): https://claude.ai/directory/letsfg

I’m a web developer who combined outreach with building websites, and honestly, it’s been one of the easiest ways for me to get clients without constantly stressing about where the next project will come from.

I have multiple warmed-up mailboxes running outreach campaigns, so I can focus more on building websites instead of spending all day looking for clients.

I started out using Instantly for my outreach, but over time, I changed my approach.

Instead of targeting businesses without websites, I started targeting businesses that already had one.

There were a few reasons for this:

1. They already understood the value of having a website.
2. They had already paid for one, so selling a redesign was easier.
3. There are so many businesses with outdated websites that it’s not hard to find potential clients.

That’s when I switched to Swokei for this type of outreach. It lets me find businesses with existing websites, analyze them for design, SEO, speed, and mobile optimization issues, and turn those findings into personalized cold emails automatically.

I still run regular outreach campaigns for my other marketing services through the same platform, too.

Now I’m curious, what tools are other web designers and web agencies using for outreach today? Are you using Instantly, Smartlead, or something else?

Akamai announced Akamai Inference and Akamai Agent Sandbox on October 27. Inference offers OpenAI-compatible API access to 12 open-weight models. Agent Sandbox runs the JavaScript or Python code an agent writes inside a second WebAssembly boundary, with outbound network access blocked by default.

The sandbox also supports network and resource limits. Akamai says agents can call tools and MCP servers while their generated code stays inside the restricted runtime. Spin is the open-source foundation, and both products are in beta.

The practical change is the control boundary around agent-written code. The launch is still vendor evidence, though. Akamai has not published an independent isolation or performance benchmark, and access to the sandbox is still by request.

Sources:

Akamai announcement: https://www.akamai.com/newsroom/press-release/akamai-launches-ai-inference-service-and-agent-execution-environment

Akamai Agent Sandbox: https://www.akamai.com/products/akamai-agent-sandbox

Would training LLMs on either smaller batches of text first instead of the (current) large batches of text or on easier subjects/topics first then later train them on subjects/topics with increasing order of difficulty...enable the LLMs to learn better. I ask this because the human mind learns/is taught this way. Also would doing rules-based unsupervised learning enable more efficient learning by the LLMs? By rules I mean teaching LLMs things like grammar and conversation rules first and then doing unsupervised learning?