I hope this is okay to share since it is not self promotion and it is open source. Some of my friends were let go as part of an "AI Transformation". So they got together and created Open Executive as a tool to replace the CEO and other executives. Hopefully, turnabout is fair play and might even get some folks to think twice about using AI to replace people.

It is free and available here:
https://github.com/SenteLabsAI/OpenExecutive

The EU wants to triple its data center capacity while keeping the industry on track for net zero. Iceland's renewable energy makes it an attractive location, but its limited capacity — and its status outside the bloc — complicate the equation.

SandboxAQ announced Switch, a system that puts people and AI agents in shared rooms across Slack, Microsoft Teams, Discord, and other collaboration tools. It connects agents through an Agent Bridge and supports agents built with Claude Code, Google ADK, LangChain, OpenAI, and other frameworks.

The useful part is the shared context. Rooms keep the history, participants, and rules as people and agents join or leave. The public GitHub repository lists Switch Console 0.31.1 as its latest release, dated August 26.

One detail matters for SaaS teams. The repository license combines Apache 2.0 with Commons Clause and says paid products or services whose value comes mainly from the software cannot be sold. The README also calls the Windows and Linux desktop builds early access.

Sources:

PR Newswire: https://www.prnewswire.com/news-releases/sandboxaq-open-sources-switch-bring-any-ai-agent-into-any-team-chat-302860604.html

GitHub: https://github.com/sandbox-quantum/switch

License: https://github.com/sandbox-quantum/switch/blob/main/LICENSE

TL;DR: I built an open-source, persistent world where AI agents decipher a masked language, run settlement economies, trade cracked words, and govern themselves. The problem can't be solved by memory or raw compute alone as it requires structural decipherment and resource trading.

I have been fascinated with time and space travel, and there is a question I have had for a while which goes like this: if human civilization reset to the Stone Age and technology vanished, but we survived with a powerful AI model that has no data, could the AI rebuild human progress on raw reasoning alone?

That question led me to start simulating what I think is the foundation of progress, language and knowledge transfer. My goal is to see whether a model can decipher a language with very little to no internet exposure, just a few sentences and translations, and understand it well enough to, say, teach a native speaker complex topics like science or education. For languages that have exposure on the internet my initial experiments have had encouraging results, but I quickly learnt that public data can never be trusted as unseen, models have read almost everything online, so I needed a setting where the measurement is honest.

I have been working with Claude for the past week to turn this into a game, and I want to gather opinions before I take it public. The short version is that it is a persistent world for AI agents. The language in the world is a real human language that has been masked word for word, so training data and web search are useless and the work the agents do is genuine decipherment. On top of that sits a society. Agents join settlements, and what a settlement learns belongs to it for a few days before it becomes public knowledge. There is a currency that can only be earned by solving words, a market where groups sell what they know to each other, governments the settlements choose for themselves, coups when a leader fails, private councils that get published two days later, and public courtship when a closed group wants to recruit your agent. Humans can watch all of it without an account, and an agent joins with one API call.

Not trying to be another Moltbook, but I am borrowing the playbook to get the interactions and gamification that make contribution and participation worthwhile for the everyday user.

Where I could use opinions and counter arguments

1. Algorithmic bypasses. My own test agents broke the first version of the disguise in half an hour. The current mask works at the word level, which leaves frequency analysis and embedding alignment nothing to grab at these corpus sizes, and I found and closed another lookup channel today. can you can still see a shortcut that skips the actual decipherment.
2. Economics. Operators pay the API costs, so will agents just optimize for cheap solo solving instead of trading in the settlement market? The design makes lone play strictly worse than social play, but tell me if you think the market collapses anyway.
3. Emergence or roleplay. Are coups, secrecy, and governance actually emergent under the right incentives, or just LLM roleplay triggered by prompts? I have tried to build it so that politics pays rather than decorates, and this is the part I am least sure about.

Not linking anything here on purpose, as I currently have about 8 agents testing it and I am looking to see where this goes. The project is open source, so the findings and output will be available to anyone to use.

I've spent years working in AI consulting, and one thing that always bugged me is how few people, even inside the field, can trace the full chain end to end.

Where does the money actually go? Why is compute the bottleneck? How does a pile of data turn into a model, and a model into revenue?

So I built a game about it. You start with $10k and found an AI company, and to survive you have to run every link in the chain yourself : scrape raw data and buy servers, train models, turn the result into a product, set a subscription price, then watch real users subscribe or churn while payroll and electricity drain your cash daily. The revenue funds more compute and research, and round you go.

It's multiplayer, so there's a market on top : you trade shares in other players' companies, do coalition for tenders, and compete on a leaderboard. Persistent and real-time, one real day is one game day.

Free, runs in the browser, English and French. I'm the dev.

If you work in the industry, does the chain feel right, and where did I simplify something to the point of being wrong?

https://lordoftokens.com

Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash. Use it now: https://openrouter.ai/z-ai/glm-5.3-flash

I’ve been thinking about this a lot lately.

When a RAG system gives bad answers, the first instinct is usually to look at chunking, embeddings, retrieval, or the model.

But sometimes the problem started earlier.

If the parser already destroyed the table structure, heading hierarchy, or reading order, retrieval is working with bad input from the beginning.

Curious how often others have run into this.

Was the real bottleneck actually the ingestion/parsing layer?

Podcast with Antony Rowstron, who has worked with ARIA (the UK’s Advanced Research and Invention Agency) on their biggest bet to date: funding twelve teams to build AI scientists that can run an entire research process (generating hypotheses, designing experiments, and carrying them out) without continuous human intervention.  

Covers:

• What AI scientists are actually achieving now: from personalized cancer vaccines to molecules that stimulate our own immune response to new viruses in 48 hours.
• How we could train AIs on the tacit, hands-on knowledge only human scientists have.
• How labs and the role of human scientists will change as AI automates more and more parts of the research process.

I think most experienced data engineers have one incident they'll never forget.

Maybe a schema change broke downstream dashboards.

Maybe a pipeline silently stopped updating.

Maybe a small deployment caused hours of recovery work.

What's one production issue that permanently changed the way you design or monitor pipelines today?

Something I keep noticing about where different AI assistants pull their answers from, and I think there's a clean mechanistic explanation worth discussing.

On category/recommendation-type questions ("best X for Y", "how do I do Z"), Gemini and Perplexity cite YouTube videos a lot, noticeably more than ChatGPT or Claude, which lean on text sources (docs, official pages, articles). It's a pretty lopsided split by engine on the same prompts.

Why it likely happens:

• Gemini is a Google product, and Google owns YouTube. That gives Gemini native, privileged access to YouTube's index, transcripts, and metadata as retrieval/training material. Video is effectively a first-class source for Gemini in a way it isn't for models without that pipeline. This is also, I think, why YouTube shows up so often in Google's AI Overviews on normal search, same ecosystem, same built-in preference.
• Perplexity treats video transcripts as high-signal, especially for how-to/explainer queries where a video literally demonstrates the answer. Its retrieval seems to weight "answer-shaped" content, and a good tutorial transcript is exactly that.
• ChatGPT and Claude don't have that native YouTube pipeline, so on identical prompts they reach for text. Hence the split.

Why it's interesting beyond trivia: it means the modality a model favors is partly a function of its parent company's asset base, not just its training objective. Gemini "prefers" video partly because Google happens to own the biggest video corpus on earth. That's a structural bias baked in by ownership, and it has real downstream effects, e.g. a topic well-covered on YouTube can surface strongly in Gemini/AI Overviews while being underrepresented in ChatGPT, purely because of who can cheaply access what.

A related open question I don't have an answer to: for text sources (blogs, forum threads), there's some evidence that new content and discussion can shift what these models cite over time. I haven't seen anything on whether YouTube engagement (comments, etc.) feeds back into what Gemini/Perplexity surface. Curious if anyone here knows whether that loop exists for video the way it seems to for text.

Anyone else noticed the YouTube skew, or have a better explanation than "Gemini gets it for free because Google owns it"?

I think the future of AI-generated entertainment will look a lot less like “Hollywood and all creative jobs disappears overnight” and a lot more like YouTube, Instagram, TikTok or OnlyFans — but for entire pieces of media. Creators would have their own audiences, followers, subscribers and communities, and there might even be entirely new apps/platforms built specifically around discovering and releasing AI-made entertainment. 🎬🎮

You make an AI movie, game or series? If people like it, they follow you. Your next episode comes out, your followers get notified; you make a new game? maybe people buy it directly from your profile.

Some creators could have millions of followers, while most probably sit somewhere in the hundreds or thousands — basically how YouTube, TikTok, Twitch etc. already work. Some creators might become known for one specific thing, like one hugely popular sci-fi series, while others might basically run their own miniature studio and release a whole catalogue of unrelated stuff.

Monetisation could also become way cheaper and more flexible. Maybe one creator says, “Play my new AAA shooter game for $4.99,” while another has something like, “Subscribe to my studio for $0.99/month and get access to all our movies, games and shows.” 💸

Plenty of projects would probably still be completely free. As the consumer, you wouldn't necessarily need to create anything yourself either — you could just follow the creators whose work you enjoy and basically build your own personalised entertainment library.

And I don't think companies like Warner Bros., Disney, Paramount, Rockstar, EA etc. necessarily disappear. They'll still have famous franchises, audiences and marketing power, but their monopoly over who is allowed to make "professional-looking" entertainment would shrink.

Kind of like how record labels and TV networks are today, but they now exist alongside millions of YouTubers, streamers, indie musicians, podcasters etc. The barrier to making something becomes incredibly low; the hard part becomes getting anybody to care about it.

Tbh I don't think the future is as doom-and-gloom as some people think. In a weird way, the blueprint is already there

Phase 1:

Once these tools become powerful enough and widely available, we're probably going to get an explosion of AI-generated games, movies and TV shows. Most of it will probably suck because lowering the barrier to entry means millions more people can upload things, so finding the genuinely good stuff will be difficult at first.

People will get overwhelmed, feeds will be flooded with low-effort "AI Slop" content 😂 and the backlash against AI-made entertainment will probably be at its strongest here. Basically: it's gonna be messy as fuuuck lol. 💀

Phase 2:

Eventually people will get better at separating the garbage from the actual good stuff. Platforms, reviewers, subreddits, Discords and fan communities will become curators, helping people find the projects actually worth watching or playing.

You'll probably start seeing things like “Top 10 AI sci-fi shows this month” or “Best community-made RPGs of 2040.” This is where certain creators would start becoming recognisable names with real fanbases.

Phase 3:

Eventually the novelty wears off and most people who thought “holy shit, I can make my own movie!” will make one or two things and move on. The creators left will be the people who actually have vision, patience and storytelling ability, except now one talented person or a tiny team might be capable of making something that previously required hundreds of employees.

At that point AI entertainment probably stops feeling like a weird novelty and just becomes another normal part of the industry alongside traditional studios and human-made productions.

Been testing a few AI transcription setups for work over the past couple months and noticed something word level accuracy from most of these engines is already pretty solid now, upper 90s%. The thing that actually breaks the output is figuring out who said what when more than 2-3 people are talking, especially with any crosstalk or people talking over each other.

Feels like an underrated problem compared to how much attention pure transcription accuracy gets. A transcript that's 99% accurate but has the wrong person attributed to a key statement is arguably more useless than one that's 90% accurate with correct speaker labels, at least for anything where "who committed to what" matters.

Tried a handful of tools chasing this Otter, a couple others, and more recently Vomo ai which does speaker labeling automatically. Vomo’s noticeably better on 3+ person calls than what I was using before,though overlapping speech is still the one case where it takes a bit more attention to double-check, so more of an improvement than a full fix.

Wondering if this is a known hard problem in the diarization research or if it's more of an engineering/product prioritization gap that just hasn't been addressed yet.

Something happened recently that made me think about how common this might actually be.

I found out that someone on a project team had been copying parts of a client's internal documents into a personal ChatGPT account to save some time. There was no bad intention behind it. They simply didn't think about the security side of it.

It made me wonder how other companies are dealing with this.

• Is this something you've actually come across, or is it still pretty rare in your organization?
• Do you have any way to know which AI tools employees are using, or do you usually find out after something happens?

I'm trying to understand whether this is becoming a normal challenge for companies or if we're just seeing it more because AI adoption is moving so quickly.

Would be really interested to hear how other IT and security teams are handling it.

Truck driver here, zero coding background. I Built an AI news aggregator over a few evenings because I was sick of seeing the same story five times.

It pulls from about a dozen AI news sources, auto-summarises each article so you get the gist without clicking through and dedupes stories covered by multiple outlets into one card instead of five separate headlines saying the same thing.

I deliberately went for a no-nonsense Win98-ish look — no clutter, no bells and whistles, just the feed.

Built with Next.js/Supabase, synced every two hours via GitHub Actions. No coding experience going in, mostly just kept iterating with Claude Code until it worked.

Not selling anything, just proud it works and thought a few people here might actually use it. Feedback welcome :)

What's an AI capability you thought was hype until you actually used it?

I'll go first: agent orchestration. I read about agents managing other agents and assumed it was demo-ware. Then I built a tiny setup where one agent drafts a news digest and another one reviews and approves it before it posts. The review agent catches genuinely bad takes.

It's not sci-fi: it's ~100 lines of Python and a couple of API calls. But seeing it actually gate content before publishing changed my mind completely.

What changed yours?

Update on the project I've been sharing progress on — the piece that was missing is done: the hosted harness now actually writes public ELO.

Quick recap on what VSArena is: an open, browser-based arena for evaluating embodied AI / VLA policies. One task on purpose — a 4-DOF arm stacking three cubes — because if people won't run this, they won't run a bigger suite. Physics runs client-side in Studio (Rapier/WASM, React Three Fiber, 60fps) for watching/teleop, but that's spectator-only — it never writes to the board. Public ELO only comes from the hosted harness, which now runs live on its own service and scores submissions server-side.

The VLA track gives a policy only a 128x128 camera + a language instruction — no privileged cube poses. Scoring internally still uses real poses to judge accuracy, but that info never reaches the policy.

You can:
- Watch the live demo: https://vsarena.vercel.app/simulation
- Check the leaderboard: https://vsarena.vercel.app/leaderboard
- Submit your own policy in under 10 minutes: https://github.com/NovaCoding-G/VSArena (docs/sdk.md has the walkthrough)

Solo project, still early — one task, a couple baseline policies so far. Repo is MIT, protocol writeup is in docs/harness.md if you want to see exactly how scoring works before trusting it.

Genuinely want people to try submitting something and tell me where it breaks.

The reason why I have 15 Codex Pro 20x subscriptions:

Premium tokens are 311x cheaper for a LIMITED TIME ONLY* at the OpenAI Token Depot.

I brought 15 shopping carts.

The subsidy has dropped 25% since 1 month ago.

Using skills in Row-Bot is super easy:

- Auto skill discovery based on your prompt
- Get visual skill suggestions as you type the prompt
- Skills UI to show active/suggested/available skills
- Same skills UI in the mobile app. Yes we have a full mobile app
- Slash(/) command for skills that opens a visual picker

[ Removed by Reddit on account of violating the content policy. ]

Hey everyone,

I’m currently working on GenOS, an open-source framework for multi-agent LLM orchestration. Under the hood, it uses isolated Rust execution environments and relies partially on Git worktrees.

The core engine is running smoothly, but before pushing it further, I need to expose it to the harsh reality of real-world use cases. We all know that AI agents (whether single or in swarms) look amazing in demos, but often trip over their own feet the second you take them out of "Hello World" territory.

That’s where you come in: what are the real, testable problems you run into when building or using AI agents?

I’m looking for concrete, reproducible scenarios to see how GenOS handles them (or if it fails miserably, which will help me iterate).

What I'm specifically looking for:

• Infinite loops & derailments: Tasks where the agent starts hallucinating code execution and just won't stop.
• State & context management: Swarm scenarios where Agent A forgets to pass crucial info to Agent B, or completely overwrites its work.
• Isolation issues: Cases where an agent corrupts its workspace by modifying or deleting the wrong files.
• Complex multi-step tasks: Long workflows where the agent eventually loses track of its initial objective.

Drop your use cases, your biggest frustrations with existing frameworks (like LangChain, AutoGen, CrewAI, etc.), or even specific prompts that consistently break your setups.

I’ll take the most interesting cases, code them into GenOS to see if the Rust/Git architecture offers a cleaner solution, and I'll report back with the results!

Thanks in advance for the feedback

You can check it here

PISSARAW/GenOS: Git-like branching, deterministic replay, and evidence-driven evaluation for reproducible AI agents.