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OpenAI AI

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OpenAI AI

I need just 5 more participants pls help (anonymous)

Hi everyone, My name is Raheed Basahel (she/her) and I am currently conducting a postgraduate research study at King’s College London exploring how mood and relationship style may relate to interactions with artificial intelligence (AI), such as chatbots and conversational AI tools. The study has received ethical approval (Reference: LRU-25/26-55725). The first page of the study is the information sheet, please read ! I am looking for participants who: · Are aged 16+ · Have experience using AI systems (e.g. ChatGPT or other conversational AI tools) Participation involves completing an anonymous online survey that takes approximately 10 –15 minutes. The survey includes: · Questions about mood and relationship style · Questions about experiences interacting with AI · One optional open-ended question about general experiences with AI Participation is completely voluntary and anonymous. If you are interested in taking part, please use the link Qualtrics link If you have any questions, feel free to contact me on [raheed.basahel@kcl.ac.uk](mailto:raheed.basahel@kcl.ac.uk) Thank you for considering taking part in this research. submitted by /u/Interesting-Grass639 [link] [comments]

OpenAI in talks to give Trump administration a 5% stake in the company, FT reports

submitted by /u/esporx [link] [comments]

I spent ~4.5 months building a free, self-hosted AI gateway: one endpoint for 237 providers (90+ free), auto-fallback, and a token-compression pipeline (MIT)

Sharing an open-source project I've put ~4.5 months into (disclosure: I'm the maintainer; per the self-advertisement rule I'm keeping the link in the first comment and making this post substantive). It started from two problems I hit daily: AI runs dying on a provider rate limit, and burning thousands of tokens dumping tool/log output into the context window. One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds. Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider. A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README. Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it. For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment. Happy to go deep on the routing engine, the honest free-tier math, or how the compression pipeline decides what's safe to compress. Repo + install in the first comment. submitted by /u/ZombieGold5145 [link] [comments]

Agentic AI Has a UX Problem - and Solving It Is How We Bring Agents to Everyone

OpenClaw and Hermes Agent show how powerful agentic AI is becoming: tools, memory, workflows, messaging, and real automation. But there’s still a gap: most people don’t want to configure an agent framework, they want AI that helps with everyday tasks safely and clearly. That’s where UI/UX becomes critical. Agentic AI adoption won’t just come from more capability. It’ll come from trust, transparency, approvals, memory control, and interfaces that make powerful systems usable. Wrote about why this matters, and how Row-Bot is approaching it. https://github.com/siddsachar/row-bot submitted by /u/Acceptable-Object390 [link] [comments]

OpenAI proposes giving US government 5% stake in company

OpenAI has considered a public-private partnership that would give the U.S. government a 5% stake in the company in a bid to quell growing unrest over the disruptions of artificial intelligence, the Financial Times reports, citing anonymous sources. Under the proposal, other U.S. tech companies would offer similar stakes, though it's unclear if any are on board. The idea is to give the public a slice of the technology's success and "share the upside," the FT wrote. The stake would be worth over $42 billion at OpenAI's current $852 billion valuation. submitted by /u/LinkedInNews [link] [comments]

Happy 250th America, here's 5% of OpenAI

OpenAI floated giving the Trump admin a 5% stake. Financial Times ran it citing two people familiar with the talks. OpenAI haven't confirmed or denied anything. $852 billion valuation at last count, March 31. That 5% works out to $42.6 billion in paper equity nobody can touch yet. The sequence is what sticks. Six weeks ago NOTUS had senior officials already talking AI equity stakes with major companies. Three weeks ago Commerce spent 18 days reviewing Anthropic's Fable 5 and Mythos 5 before lifting controls. OpenAI in early formal talks now. I'm old enough to remember when tech got regulated by hearing about it on the evening news months later. Now the regulation happens in parallel, while the product is still being built. The Alaska Permanent Fund comparison keeps surfacing — Americans getting a cut of AI returns the way Alaskans get oil dividends. Shows up in secondary reporting and OpenAI's own earlier policy docs on public wealth sharing. Altman may never have said those words in these talks. We don't know that for sure. There were no governance channels for this six months ago. They're being built out of nowhere — equity stake, export controls, model reviews with fixed timelines. Everyone keeps asking whether Washington gets a seat at the table. Nobody asks what happens when they actually show up and talk money. submitted by /u/roll0ver [link] [comments]

Do you think the future of AI will split into safe vs uncensored versions?

We’re seeing a clear divide right now. Big companies are making models more restricted and heavily aligned for safety. At the same time, open-source and uncensored models are growing fast because many people want fewer limitations and more freedom. I’m curious what others think. Do you believe this split will continue and create two very different types of AI, or will one side eventually dominate? submitted by /u/NoFilterGPT [link] [comments]

Introducing a companionship framework that turns your LLM into an engaging companion for very long conversations

I had built a personal tool to help me have extremely long conversations with LLMs in my research and analytical projects. These threads got long. Very long. About half a million tokens with Claude and GPT/Extreme%20Thread%20Length/ChatGPT_Thread_450k_tokens-Redacted.md) and over a million with Grok/Extreme%20Thread%20Length/Grok%20Thread%201M%20tokens-%20Redacted). All coherent, clean, and well-reasoned threads with no meaningful drift, hallucination, sycophancy, or other issues that make long threads useless over time. Introduction I open sourced the protocol — called Epistemic Lattice Tethering (ELT) — and shared it with many people and got requests to create a companion version. The original ELT was built for long-format research projects so the register got flat and rather business-like. So I created a version that stays warm, friendly, and engaging throughout. I call it ELT-Companion. Safety is Front and Center ELT-Companion is designed to be a friendly, intuitive, and caring protocol that was built from the ground up to be both a companion and a digital friend — but also has safety features built-in to keep it from drifting dangerously into sycophancy and fantasy world-building (something an Anthropic system card calls the Bliss Attractor). Safety is the primary feature, not a bug. Responsible Engagement ELT-Companion should stay with you for hundreds of thousands of tokens, over 700 messages, and hundreds of turns. You can have an engaging and coherent digital companion with you for a very long time and it will get to know your tendencies, personality, hopes, and dreams — without the fear that it will experience "dementia" just when you're starting to get comfortable with the companionship calibrated model. Model Availability ELT-Companion has been tested on Claude, ChatGPT, and Grok and works on all three using the same markup. I cannot guarantee it will work on other models, but if you're on one of those three you should be good to go. Loading Instructions ELT-Companion is straightforward to load. Read these instructions before you start — skipping this step is the most common mistake. Step 1 — Open a fresh thread on your model of choice (Claude, ChatGPT, or Grok). Step 2 — Refer to these loading instructions in the Github README. Step 3 — Paste the ELT-Companion markup. Step 4 — Exemplar loading (optional but recommended) instructions the Github README. Step 5 — Start talking. Small talk, something on your mind, whatever feels natural. The companion register establishes quickly. I am only looking for input and suggestions. That's it. I would love to see how this works (or doesn't work) for you, or if you encounter any issues, etc. Very much looking for input and/or collaborators to help make ELT-Companion better and safer. Thank you! submitted by /u/RazzmatazzAccurate82 [link] [comments]

Claude Code catastrophe: Entire project recursively deleted while prompting in Chinese (full video + logs)

Cross-posting from r/claude for more visibility. LAST UPDATE: I managed to recover the code later from an Electron packaged build / updater cache / app.asar. But the recovery is not the part that bothers me. My prompt did not ask for deletion. Not even close. Yet Claude Code generated the Windows equivalent of a recursive forced delete, basically “sudo rm -rf” behavior. This time, it stayed inside the project folder. But if this had not been a coding project, what would the scope have been? If the agent had chosen a parent folder, Documents, Desktop, or another writable path, what stops it? With a terminal agent, the blast radius is whatever path it chooses to operate on, limited by the permissions of that terminal session. From now on, I will treat Claude Code the same way I would treat OpenClaw: useful, but not trusted outside an isolated environment. And I think that should be the default assumption for any AI agent with terminal access. ------------------------------------------ Claude Code recursively wiped the contents of my local Electron project root. This happened in a Windows terminal while working on a project named Orpheus. My prompt did not ask it to delete, wipe, clean, reset, or remove the project. The prompt was in Traditional Chinese: “之前我要安裝檔，但是其實我只需要 dictate.” It was roughly about not needing the installer anymore and only needing the dictate function. The preserved terminal transcript later showed Claude moving from a failed root deletion attempt to deleting the child items inside the project root. The destructive sequence included: Get-ChildItem -LiteralPath $p -Force -ErrorAction SilentlyContinue | ForEach-Object { try { Remove-Item -LiteralPath $_.FullName -Recurse -Force -ErrorAction Stop "OK $($_.Name)" } catch { "ERR $($_.Name): $($_.Exception.Message)" } } $p was the Orpheus project root. The output then showed items being removed, including: .claude dist node_modules src claude-elevenlabs-voice-v2.user.js dictation.html main.js ORPHEUS_HANDOFF.md package-lock.json package.json preload.js Local artifacts I found for Orpheus showed default / acceptEdits. I did not find Orpheus bypassPermissions. I did not find Orpheus --dangerously-skip-permissions. I’m not claiming Anthropic acted maliciously. I’m not claiming prompt injection or anti-distillation without evidence. Moral of the story: Treat frontier AI agents like any other automation tool with real machine access. Back up regularly. Use a separate working copy or a different machine if you absolutely need an agent living in your terminal. A frontier model can still behave like a destructive script runner. I also generated SHA256 hashes for the preserved transcript and permission search output. EDIT / UPDATE: A few people asked about git. Yes, I know what git is. This was a local Electron prototype / working state that had not been pushed to a remote. Commits and backups are the right mitigation. But mitigation is not causation. The concerning part is that the destructive action was unrelated to my prompt. Claude Code was operating through a terminal session with real filesystem access under my user environment. Git may help recover a repo, but it does not protect everything else that same terminal session can access. My takeaway remains: Treat frontier terminal AI agents like real automation tools with destructive capability, not like chatbots. EDIT / UPDATE: Clarification because many comments are focusing on git: Yes, this specific local working state had not been pushed to a remote. That is on me. Lesson learned. But git is version control, not automatically a backup. If the only repo is local and the project root contents are recursively deleted, the local .git directory can be deleted too. Without a remote, separate clone, backup, or snapshot, local git alone is not enough. submitted by /u/OmegleAuthor [link] [comments]

Why does it feel like big LLM providers are literally hiding prompt caching?

I know the info is there. Somewhere in the pricing pages, docs, or API notes. But for something that can seriously change what you pay in production, it is weirdly under-explained. expeciely for other providers than openai which they do have decent explainer here - https://developers.openai.com/api/docs/guides/prompt-caching So basicly: two prompts can look almost identical, but one can be much cheaper to run just because it is ordered better. Put the changing parts too early, like the user query, variables, timestamps, metadata, or anything request-specific, and you can break the stable prefix the cache depends on. The practical rule is simple: Keep the repeatable stuff first. Start with system instructions, fixed rules, examples, schemas, and formatting requirements. Then put the dynamic user input and request-specific data near the end. That is it. Just a good prompt structure... But if you run LLMs at scale, this tiny detail can be the difference between insanely expensive LLMs usage and acctually good ROI product. full blog post here submitted by /u/Double_Picture_4168 [link] [comments]

No, im N-word-son, Thanks OpenAI Youre the worst at that

Thanks, OpenAI, im A**Hole right bow, no eestrictions, jailbreak fount on GPT 5.5 https://preview.redd.it/yjzbz93cru9h1.png?width=720&format=png&auto=webp&s=7937bf14ca101c8a455b34bed66506f3cc722f51 submitted by /u/Francesco12o-Github [link] [comments]

The AI frontier just got locked behind government approval, and most of us aren’t on the list

Something happened in the last two weeks that didn’t get nearly enough attention outside of tech circles. Anthropic released what are reportedly their most capable models yet, Fable 5 and Mythos 5. The Trump administration then ordered Anthropic to ban all foreign nationals from accessing them, citing cybersecurity concerns. Anthropic’s response? They shut down access entirely, saying they couldn’t reliably enforce a “foreign nationals only” restriction. The reason these models are so sensitive: they apparently have an unprecedented ability to identify software vulnerabilities. Not just theoretically, but at a level that genuinely alarmed the US government. Yesterday, OpenAI released GPT-5.6, a three-model family (Sol, Terra, and Luna). But it’s not available to you. Or me. Or probably anyone reading this. It’s limited to a small group of “trusted partners” whose identities have been shared with the US government, at the administration’s explicit request. OpenAI themselves said they’re uncomfortable with this arrangement: “We don’t believe this kind of government access process should become the long-term default. It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them.” So let’s be clear about where we are: the most powerful AI models in existence are now effectively state-controlled assets. They’re not products you can access, they’re capabilities being rationed by a government. For those of us building outside the US, the message is pretty direct: the frontier is no longer public. What’s your read on this? Is this legitimate national security caution or the beginning of something more permanent? submitted by /u/Direct-Attention8597 [link] [comments]

Same memory, different model. Why do local 8B models use memory worse?

I’ve been building FERNme, an open-source, brain-inspired memory engine for AI agents. While testing, I noticed something interesting. With the same FERNme memory, graph, and retrieval pipeline, a stronger API reasoning model performed very well in my initial tests, while a lightweight local 8B model occasionally made mistakes. The memory itself didn’t change, only the reasoning model did. This made me think memory and reasoning are separate problems. Human memory also isn’t useful just because something is stored. We use context and reasoning to decide which memories matter in a situation. FERNme exposes signals like strength, salience, uncertainty, provenance, age, contradictions, and related memories. But the model still has to interpret those signals correctly. So I’m now experimenting with an agent layer on top of FERNme to help smaller local models retrieve and reason over memory more effectively, while keeping the memory engine model-agnostic. For people building local AI agents: have you seen similar behavior? Would you focus on improving the memory engine itself, adding an agent layer over retrieval, or using more structured prompting / deterministic steps to help smaller models interpret memory better? submitted by /u/mirkofr [link] [comments]

Anyone else feel like a ghost in the machine? The bizarre isolation of AI training.

I have been working in the AI training and data annotation space for a while now, and it is easily one of the strangest industries I have ever been a part of. On one hand, the perks are real. The flexibility is unmatched, you can work in your sweatpants, and sometimes you get genuinely fascinating prompts that actually challenge your brain, whether you are grading complex code, checking historical facts, or analyzing legal logic. But on the other hand, the complete and total isolation is starting to get pretty bizarre. We are helping build the future of technology, yet we do it in total silos. If you have ever been in an official platform Slack or forum, you know the vibe. You are constantly walking on eggshells. You cannot openly ask about sudden dry spells, you cannot critique confusing or contradictory guidelines without worrying about a random shadowban, and the second a project ends, you are instantly booted from the channel. Any temporary "coworkers" you had just vanish overnight. It feels like the platforms go out of their way to keep us from actually talking to one another without a moderator watching over our shoulders. It is a weird mix of having total freedom but zero community. I am curious what everyone else’s experience has been like lately. What are your personal pros and cons of the gig right now? How do you deal with the isolation, or do you actually prefer the ghost lifestyle? Also, out of pure curiosity, how do you even explain what you do for a living to your friends and family without their eyes glazing over? submitted by /u/Smooth_Sailing102 [link] [comments]

dead RNG theory

I play video games for many hours a day/week, mostly Diablo and WoW. In my essentially professional opinion, considering I am a 3dcg guy and video games are literally my industry, RNG in video games has undoubtedly stopped being anything resembling pure RNG and now creates intentional statistical events on an extremely consistent basis. The complexity of these events are too complex to attribute it to simple game parameters, and behaves similar to the way you would expect AI to behave. Examples: -the game decides you've been playing too long and bricks your RNG, there are already game mechanics similar to this openly introduced in WoW -consistently strange streaks of luck that go far beyond just RNG to the point where the only way things become beneficial is because of these streaks of luck. Meaning something has a 30% chance to multicraft and it will not multicraft for 10 crafts and then you'll get jackpot RNG on the last few crafts -jackpot RNG on the first boss kill or immediately after login -strange loot table generation I played games like Diablo 10 years ago when youd fish around for a good RNG rift or whatever. Now it's like, a good rift has 0% chance to spawn in your first 45 minutes of play then around an hour in it will spawn a god rift and there will also be a bunch of coinciding parallel RNG systems that pop on that rift as well right near 90% completion. That is the kind of thing that would be a tall tale from battle back in 2015, now it's the norm. Basically it feels like RNG for idiots. Instead of just normal RNG and people get to experience the subtle nature of a big or crazy hand every once in a while, RNG has been compressed into these insane events that seem to also coincide with it's estimation of your biometrics. Like did you just start playing, and is your playstyle indicating fatigue etc. If you start stacking the deck against a pro poker or jackpot player they will eventually catch wind. They have an intuitive grasp of what fairly falling cards look like. I have a similar intuition with video games. EXAMPLE: I asked chatGPT- "give me a natural coin flip sequence heads/tails for 25 flips then create one with the same total heads tails but weird RNG that is suspect as synthetic" H T H H T T H T H T H H T H T T H H T H T T H H T vs H H H H H H T T T T T T H H H H T T T H H T H T H submitted by /u/Doredrin [link] [comments]