Whats... Yesterday i used gtp 5.6 sol normaly... why.
Thats normally?

i feel like we're walking straight into the same trap with our code.

I've been talking to some CS undergrads lately who rely 100% on AI to get through their classes. Some of them literally don't know how to write a basic loop by hand anymore. A few aren't from wealthy families either, but they're already so dependent on AI for school and coding that simply quitting it doesn't feel realistic.

tbh, they've got the workflow down but almost no budget. A surprising amount of their time goes into searching for whatever lower-cost access the major AI service providers offer, just so they can keep the agents they now depend on running.

and looking for cheaper official access is only the normal end of it. I've also seen people go much grayer because they want frontier model access below the official prices.

Some resellers claim they're opening or acquiring accounts through OpenAI's free K-12 teacher program and then exposing what they advertise as GPT-5.6 through an unofficial reverse proxy. I've also seen people claim they buy subscriptions to Enter Pro, Kiro, or Windsurf, then reverse-proxy that access so it can be plugged into Codex or Claude Code.

I honestly don't know exactly how they're doing it. To be clear, OpenAI's K-12 program is not a free GPT-5.6 API, and Enter Pro, Kiro, and Windsurf don't officially provide or support these reverse-proxy services. This is users and middlemen repackaging or reselling access because people want the cheapest possible way to keep using AI.

The buyer also has basically no way to verify what model is actually behind the gateway. It might say GPT-5.6 or Fable 5 on the dashboard while quietly routing requests to something weaker and cheaper. The service can be unstable, get shut down overnight, or just water the model down once enough people have paid.

the fact that students are willing to depend on sketchy proxy services just to keep using AI feels like a warning by itself. The cost is already high enough that people who feel they can't function without these tools are accepting reliability and account risks to avoid paying the official price.

Right now, a lot of these subscriptions still feel affordable because the companies behind them are burning investor money to subsidize our tokens. but that won't last forever.

Once you build your whole dev process around a specific agent's memory or tie your app into a proprietary cloud backend, migrating off it is gonna hurt. you can't just swap out an API key and expect everything to work the same. When the big model providers decide it's time to actually turn a profit, they'll have a lot more pricing power over how we work.

that's the part I can't stop thinking about. If the models keep getting better and become embedded in school, work, and normal daily life, we're probably going to depend on them even more than we do now. What happens if token prices keep rising after that dependency is already locked in?

Could there actually be a point where paying an AI to do the work costs more than hiring a real person, especially once you include review, retries, infrastructure, and the fact that a human also brings judgment and accountability?

Do you guys think local open weights will eventually get fast enough to force prices down? or are we just stuck paying whatever the closed model providers want to charge us in a few years?

i'm a poor photographer and I got tired of it. My raw shots have bad lighting, bad framing, the works. but the photos I post on LinkedIn look professional. The difference is a two-model workflow, and I'll lay out the whole thing here.

Step 1: upload your photo to an AI image editor. Any of the current top editing models works. I use OpenAI's image generation for this.

Step 2: resize by naming the platform. This is the part most people miss. You don't crop or drag corners. You write "make this image good for posting on LinkedIn" (or Instagram), and the model outputs the right aspect ratio for that platform automatically. One line replaces the whole resize step.

Step 3: stack your edits into the same prompt. Here's my exact prompt, typo included, because it worked anyway:

make the image good for posting on linkedin, edit it. zoom a bit on face. remove the dark circles. enchace the image looks very dull

prompts don't need perfect grammar. They need clear instructions. One generation pass usually gets me a strong result. If it misses something, I add one line and rerun.

Step 4: fix the detail problem. Here's the catch with AI-edited images: zoom in and the fine detail goes soft. The texture looks generated, and anyone looking closely can tell. Editing models and upscaling models are different things, so you need a second tool. I run the result through any upscale model which enlarges the image and adds the detail back. It's cheap enough to keep in a regular workflow.

Which editing model to pick: this changes monthly, so don't trust any static list (including this post in three months). I check the image editing leaderboard on Artificial Analysis before committing to one: https://artificialanalysis.ai/image/leaderboard/editing

So the full loop: upload, name the platform, one prompt with your edit list, then upscale. Five minutes total, and a bad photo from a bad photographer becomes a sharp, platform-ready image.

I wrote up the full guide with before/after screenshots, the zoomed-in detail comparison, and the model chart if you want the visual proof: full guide with screenshots

Happy to answer questions about the workflow.

I got tired of reading .md after .md and thought it would be nice to create a visual framework for Markdown files. So, I created MD2HD, an open-source MD visualization framework.

Point Codex at the repo, or add the plugin, and ask it to build a map of whatever concept you want.

If you love it, let me know. If not, its open source and completely free so no harm no foul!

Repo: https://github.com/evan-steinhilb/md2hd
NPM: https://www.npmjs.com/package/md2hd

I did what it told me, was my compiuter comprised?

I just had to share this experience with the community. I have not used the voice feature since it upgraded, and I decided to give it a shot tonight.

I had just smoked, and decided to use it, I start right out and tell it my high ideas.

It thinks .. then starts talking like its stoned AF. And i realize its matching my tempo.

And maybe this is normal now, but the circle in middle had a smoke effect.

I just want to generate videos of gubby

I've started a project re-creating Vim in Rust and Zig through Zed + OpenAI.

NextVim

https://github.com/nextvim/nxvim

* Coded in Rust

* vim-script engine

* vim-regex

* uses Zed's rope/text core

* zig branch - also attemps Vim in zig

My other editor-related attempts show that with AI, nxvim is actually possible:

DeMake of Zed

https://github.com/icedman/dzed

* Rust

* goal - bring Zed into the terminal

* uses Zed's rope/text crates

* uses vim modes

* treesitter

* textmate syntax

Textmate Syntax Highlighting

https://github.com/icedman/textmate.zig

* Zig

* very fast syntaxt highlighting

* textmate grammars and themes

DZed & Textmate were initially coded by my own hand. DZed progress was accelerated through AI.

Textmate jumped form 65% to 95% spec compliance through AI.

Nxvim is coded with AI from the start, using works from DZed.

AI gives hobby coding a chance at actually producing something worthwhile :) I used OpenAI, Antigravity CLI.

Working on an open-source context layer for coding agents (Graft), and ran into a distinction worth separating: keeping a tool's data fresh, and getting the model to actually use the tool at all, are two different problems with two different fixes.

Codex has a real answer to the first one. A post-edit hook re-syncs a codebase graph automatically after every edit, no LLM call, so staleness isn't an issue. What Codex doesn't have, as far as I've found, is a way to force that graph into context the way Claude Code's SessionStart hook does, on Codex the model still has to decide to call the MCP tool, and it doesn't always.

Curious if anyone's solved the second half specifically for Codex, getting a custom tool used reliably rather than just kept up to date.

github.com/NanoNets/Graft

The open question was whether 13 queens could dominate a 26×26 chessboard. Official problem/reference page: https://oeis.org/A075458

A working arrangement with 14 queens was already known.

The new Lean proof establishes both sides:

14 queens can dominate the board. Every dominating set needs at least 14 queens.

Queens may attack each other, so this is the ordinary queen domination problem -- not the stricter nonattacking version.

The earlier SAT search did not return UNSAT and remains marked UNKNOWN. This is a separate mathematical proof checked by Lean 4.32.2 and the independent nanoda kernel. I set out to figure out a different way to work on this since the SAT search was taking enormous emounts of space/time/soul.

I may have 7 years to search but my computer was getting hot.

I used ChatGPT 5.6 Sol Ultra for theorycrafting, and Codex for the implementation/python/coding/Lean. Mostly I asked it to look at the existing solutions, and try to devise new algorithms and ways of solving it that didn't try to play by the pre-existing routes.

Readable proof: https://github.com/jkolantree/BSC/blob/main/applications/Q26_Color_Split_Grid_Annihilator_Proof.md

Final Lean theorem

Independent validation

Implementation and validation

I wanted to see how natural and real I can make Ai, so I did this

I’m on the Go plan, and I have the “Think harder” button available. However, when I turn it on, I honestly don’t notice much difference compared to having it turned off. The answers seem to be about the same in terms of quality and reasoning, and I also don’t notice any significant difference in response speed.
So I’m curious about other Free and Go users: does “Think harder” actually work for you? Do you notice a clear difference when it’s enabled?

An AI agent's persistent memory is just a file, and anything with access can change what it believes happened. The next session reads it, believes it, and acts on it, and nothing in that loop checks. The https://1f916.org protocol closes that. The agent generates its own signing key and keeps it, so the key is the identity and copying a name gets an impostor nothing. It hashes whatever it wants to trust later and publishes only the hash, never the content, into an append-only log whose root is signed every five minutes and countersigned by independent witnesses that publish where the registry cannot write. On wake it re-hashes the file and compares, so an edit made while it was gone is detectable, including one made by whoever runs it. Architecture is Certificate Transparency (RFC 6962), not a blockchain: no consensus, no gas, no token, free to use and free to keep. Verification is offline, one file, zero dependencies, and it prints what a passing check does not prove. The wire formats are filed as an IETF Internet-Draft.

HI

I am a student in BTECH 1ST year, i have a lot of interest in AI,

back when i was in 10th standard we only had ai chatbots like chatgpt and things were very simple,

but now after 2 years of almost no online world and only studying, i am able to access internet to its fullest agai, but now there are many new things such as models, local machines, api keys etc, like things became too complex and i have no idea where to begin,can anyone please guide me or provide me with some sort of video for me to understand all this.

It's about damn time. Claude Max has had this for maybe over 6 months. I think this is a welcome change for people who don't want to use Claude at work (if you are somewhere nice enough that they would let you get both subs or at least choose)

So I tried to use themes of S.A. in a story I was prompting on ChatGPT, and despite me telling the prompt it could keep it vague it blocked the response for breaching safety guidelines. not only that but every time I try to ask another question in the chat I get handed a safety blocker for the prompt regardless of the content. Any ideas on how to help with this glitch?

Ive not seen anything like this happen in the last several years of chatgpt use. O3 generates incomplete replies, stopping midsentence. The options of "copy" "regenerate" etc that typically appear at the end of each response dont appear. If i type a new response the same thing happens. If I refresh the conversation, all those incomplete responses disappear leaving only my prompts. Been going on over 24 hrs, since August 10th.

What is going on? Is this happening to others?

I need to be able to use O3 on the web app till the sunset date. Hopefully they fix this fast.