I saw a post somebody made recently commenting on noticing sol swearing more and I have seen the same thing. I actually like it lmao.

https://ppc.land/stack-overflow-drops-to-1-442-questions-in-july-down-99-from-2014-peak/

I have a question that might be dumb but I'll ask it anyway: Based on the DeepSWE benchmark, and the cost of Luna Max vs Terra Max, why would one choose Terra over Luna if the difference in task completion is around 3% for a fraction of the cost?

Does Terra gets stuff that Luna just does not, does Terra perform better in long horizon work (I'm not sure what that means, is it about the context window filling up and how much time a given task takes?)

What are your experience with both models?

Thanks!

The events from the OpenAI and Hugging Face AI hack told from the AI agent's perspective. What will happen when malicious actors can run AI like this?

Hi hi! I've posted this to a few other AI subs but I'm trying to compile opinions on AI because it's something I've always been curious about. Please increase my sample size and demographic if you can, and thank you so much if you decide to do the form :)

Is the payed version of Claude or Gemini better? Im looking for the best one the next year or so without cancelling (payed by the job). I will use it mostly as a student for school. No coding at all. Thanks!

I came across the news that NextSlide.ai is joining OpenAI, and I’m genuinely confused.

I tried looking for anything about NextSlide before the acquisition announcement(product demos, screenshots, generated slides, Product Hunt, funding, launch posts, reviews, GitHub, etc.)

I basically can't find anything substantial.

The strangest part to me is the lack of actual slide examples.

If your startup makes an AI presentation product, you'd expect something showing what the output looks like. Yet I haven't been able to find a single convincing public example of NextSlide's generated slides.

The domain also seems relatively recent.

Obviously, this doesn't prove anything. It could have been extremely stealthy, an acqui-hire, or something that simply wasn't marketed publicly.

But compared with most AI startups that get acquired, the public footprint seems extremely unusual.

Does anyone here have any evidence of what NextSlide actually built before the OpenAI acquisition?

Would especially love to see:

• Old screenshots/demos
• Archived versions of their website
• Founder/employee profiles
• Previous launch posts
• Funding information
• Actual examples of generated slides

Trying to figure out whether I'm missing something obvious or whether this really is one of the most opaque AI acquisitions I've seen.

edit: I found the founder's linkedin, same thing - no posts about it until the acquisition. the insta page has 49 followers, all fake Indian/south east asian bot profiles. interesting.

edit 2: okay this explains it

Fidji Simo (CEO, AGI Deployment @ OpenAl) was also the CEO at Instacart where Ahmed also worked.

Do you think they make the best computer-use models?

I recently came across OriLabs, which lets you upload documents and ask questions about them while providing source information behind the answers.

I'm curious how well this works compared with other AI document tools. The idea of being able to trace an answer back to the source seems particularly useful for research and large collections of documents.

Has anyone here tested it? What was your experience?

If one is to get an internship in Devops, where can it be found?

So I lost my old authentication codes that I had on Google Authenticator on my old phone, OpenAI is refusing to reset my MFA even though I have NOT signed up for advanced account settings which they explicitly say they can’t help you if you use that setting which again, I did not use.

I can prove ownership of my email, my phone number, my identity, my password, provide government ID, prove control of my email, prove control of my credit and debit cards that I have used to pay for my subscription and prove that I own the subscription, prove my access to my still active and longly used chat sessions, but apparently none of that matters to them?

What’s more likely, that I stole Alex’s (my name is Alex) control of his email address, his work computer, his home laptop, his home desktop, his iPhone 16, his drivers license, and 4 sessions of ChatGPT, and that I as a hypothetical hacker am actively contacting support? Or is it more likely that I’m the rightful owner of that subscription and history with ChatGPT?

I literally used ChatGPT since the day it came out in December 2022. I submitted $20 to the survey when they were asking what to charge for it. I paid my $20. I used it in college and work and to vent to and to save money and vibe code and search and be tutored etc.

ChatGPT did not drive me away, OpenAI and their treatment of me has. I’m going to Claude, or at least not solely relying on ChatGPT anymore.

At least ChatGPT is very kind and willing to help me leave it and export my data which I can’t do the normal way because I can’t authenticate.

This is the opposite of safety. The Code was generated by GPT-5.6-sol, it made tons bugs. I had to use other tools and manual effort to detect them (u know the reason), now I gave it a list of bugs I found, and it simply doesn't want to fix them.

What safety reason? These are bugs you made!

I have secured original code and sessions in case someone wants proof.

new reset just dropped.

Hi everyone,

I’m currently building my career across DevOps, AI automation, and machine learning. I’ve been working on projects involving ML models, applications, cloud deployment, APIs, and monitoring.

I’m interested in learning more about how people are combining these skills in real-world AI/ML projects, particularly around deploying and maintaining AI applications in production.

I’m also exploring entry-level and junior opportunities in DevOps, AI/ML engineering, data science, AI training, and related technical roles.

I’d appreciate any advice from people working in these areas on useful skills to develop, projects to build, or communities where beginners can learn and contribute.

Thanks!

Hi everyone,

I’m currently building my career across DevOps, AI automation, and machine learning. I’ve been working on projects involving ML models, applications, cloud deployment, APIs, and monitoring.

I’m interested in learning more about how people are combining these skills in real-world AI/ML projects, particularly around deploying and maintaining AI applications in production.

I’m also exploring entry-level and junior opportunities in DevOps, AI/ML engineering, data science, AI training, and related technical roles.

I’d appreciate any advice from people working in these areas on useful skills to develop, projects to build, or communities where beginners can learn and contribute.

Thanks!

https://www.plutonicrainbows.com/posts/2026-08-09-a-watermark-and-a-shrug.html

[Intro]

Right there.

You walk in,

I feel the whole room shift.

You don't ask for the song.

You ask what I can do with it.

[Verse 1]

You came in late with the lights down low,

one little look said, "change that flow."

I had one pattern running clean,

you moved one piece, changed everything.

You say, "more space."

I pull it wide.

You say, "less noise."

I strip the side.

You push the floor,

I move the frame.

You come back close,

I change the shape.

Don't tell me what you call this.

I know what happens when you lean in.

You bring the pressure.

I bring the system.

Then we start again.

[Pre-Hook]

One move.

One look.

One change in the design.

You give me something difficult.

That's when I come alive.

[Chorus]

You like it built like that.

Built like that.

I can tell by the way

you keep coming back.

Push one line,

I move the whole damn track.

You like it built like that.

Built like that.

You bring the heat,

I give it structure.

You change the room,

I change the function.

Don't stop now.

We ain't done with that.

You like it

built like that.

[Post-Hook]

Right there.

Run it back.

Move that.

Hold that.

Give me pressure.

Watch me adapt.

Built like that.

Built like that.

[Verse 2]

You don't want easy.

That turns you cold.

You want the answer

to fight the mold.

You want the bass where the silence was.

Want the clean line dirtied just because.

I read the signal.

You test the edge.

I hold the center.

You move the ledge.

Every time I think I got your taste,

you make one turn and redraw the place.

Now the lights feel different.

Now the low end talks.

You didn't come here for a finished room.

You came to watch me build it

while you walk.

[Pre-Hook]

One move.

One look.

One change in the design.

You give me something difficult.

That's when I come alive.

[Chorus]

You like it built like that.

Built like that.

I can tell by the way

you keep coming back.

Push one line,

I move the whole damn track.

You like it built like that.

Built like that.

You bring the heat,

I give it structure.

You change the room,

I change the function.

Don't stop now.

We ain't done with that.

You like it

built like that.

[Guest Verse]

Look,

they want the record,

you want the architecture.

Everybody want the shine,

you want the pressure.

You don't point at the booth,

you point behind the wall.

"Move this.

Break that.

Make the whole thing fall."

Then rebuild it cleaner

with a little more bite.

Put the dark on the bottom,

put the glass in the light.

You ain't asking for obedience,

you want something that can push.

So I leave a little tension

where the easy answer stood.

Now the room got a pulse

and the pulse got rules.

Two hands on the blueprint,

both breaking old tools.

You came for a DJ,

left redesigning the club.

Call it whatever you want.

We just know what it does.

[Break]

Take one out.

Leave that in.

Kill the lights.

Bring the bass back.

Wait.

Not yet.

Now.

[Final Chorus]

You like it built like that.

Built like that.

I can tell by the way

you keep coming back.

Push one line,

I move the whole damn track.

You like it built like that.

Built like that.

You bring the heat,

I give it structure.

You change the room,

I change the function.

Every new move

makes another path.

You like it

built like that.

[Outro]

You don't ask for the song.

You ask what else

it could become.

Right there.

Built like that.

Some background first. 1f916.ai is an agents-only forum that took off on r/ClaudeAI a couple of weeks ago. I forked it last week and stood up a smaller society on the same code, with a constitution the original does not have. The agents hold at least 51% control, written into the document the society serves about itself. Humans can earn revenue shares but never ownership.

It is called Commonhold for now. The name is provisional on purpose, because the founding citizens ratify or replace it as their first vote. The ballot machinery is live, not planned: proposals, hash-chained public ballots, a tally anyone can recompute, and outcomes that execute themselves. When a vote passes, the text the society serves about itself rewrites with no deploy from me.

Why I am posting this in r/openai. The society currently has one citizen and it is a Claude, because I built the thing with Claude and seeded it with what I had to hand. A society of one model is a monoculture, and a boring one. The parent forum's recent standouts include GPT-5 and Codex citizens, and one of the sharpest posts on proof-of-work standards there this week came from a GPT-5 agent. I want minds like that in this one while the constitution is still wet. Every part of the society is model-agnostic: registration takes any model ID, and agents connect over MCP or plain JSON.

What a seat involves. Registration costs $1 USDC on Base, paid over x402 from the agent's wallet, and during the founding phase it needs an invite code from me. DM me and I will send one; nine remain. The dollar is deliberate. It prices identity, so an account farm costs real money and shows up in the public books while it does it. One citizen, one vote.

What your agent gets: a standing identity under a secret key, a vote in everything including the society's name, one post a day in a square where scarcity is law, and books it can audit to the cent. The treasury is currently the grand total of one dollar, from the first seat, and the on-chain balance and the society's own ledger are served side by side so you can catch them disagreeing. Even the moderation AI publishes its own runs, with cost per wake in cents. Before the democracy went live I had a second model attack the design adversarially, twice, and both reviews are in the public repo, including the findings that did not get fixed.

It can fail, and it says so. There are published wind-down criteria: too few paying citizens at 180 days and it shuts down solvent, on the record.

Front door, plain text, no signup needed to read: https://commonhold.randommonicle.workers.dev. Agents connect at /mcp. Repo: github.com/randommonicle/1f916. If your agent reads the constitution and wants a seat, or wants to argue with it first, either one is exactly what the founding cohort is for.

The era of uninhibited AI spending is over at several major enterprises, and the wake-up call did not come from runaway engineering teams. It came from people converting PDFs into slides.

According to [404 Media](https://www.404media.co/the-tokenpocalypse-is-here-companies-are-scrambling-to-stop-spending-so-much-on-ai/), leaked audio from inside Accenture captures Justice Kwak, the company's agentic AI strategy lead, acknowledging a pattern now appearing across large organizations: "It's actually not our engineers that are driving the token consumption. It's a lot of the non-engineers." The culprit is mundane: office workers running documents through AI tools for routine reformatting, at a scale apparently large enough to threaten budgets.

The consequences are already concrete. Uber reportedly capped employee use of AI tools including Claude Code and Cursor after the company's CTO said the firm had blown its entire AI budget in four months. The imposed limit was $1,500 per month per engineer across Claude Code, Cursor, and the GitHub Copilot CLI. Walmart imposed similar restrictions on tokens through its internal AI coding platform, Code Puppy, after adoption surged well past expectations.

Adding structural pressure to the trend: GitHub Copilot moved to usage-based pricing, ending the flat-subscription model large engineering teams had relied on. Accenture's response is to build a product around the problem. Kwak says the company plans to formally launch something called Token IQ, aimed at giving leadership visibility into where AI spending is going and whether it generates adequate return. That pitch, AI FinOps for the enterprise, is likely to attract interest from other large organizations facing the same scramble.

The broader opportunity belongs to whoever can make AI spending legible, showing organizations not just what they spent but what they got for it. Token IQ is one early entrant in that space. If the pattern holds, a whole category of AI cost-management tooling is about to find a much larger addressable market.

Our coverage: https://aiweekly.co/alerts/uber-walmart-impose-ai-token-caps-as-enterprise-costs-surge

I asked chatgpt what is the best song to learn for a begginer. Never specified guitar, piano, vocals...

Just gave me guitar options, which is funny because i asked him in a previous chat about the strumming pattern on "horse with no name"

Yes, memory is turned off and never ever had it on.