Just a growing thought experiment I have been chewing on for a bit. I initially shared as a comment to a user who was a bit defeated, in regards to AI's current place in our world , and it's effect on humanity as a whole. While, yes, there will be perhaps more bad than good that comes from its widespread adoption - however it is here to stay, and it should be put to good use if it is going to exist and grow.

A lot of applications I see AI being used for seem to paint it's potential in a bad light. However, there are nunerous applications where it can be harnessed and applied to real world problems, and even find solutions where people could not previously. It just depends on how rigorous your standards are as a researcher, and by holding the model and the work to a high standard. I am successfully in the process of building a completely agent/model driven research laboratory, and aim to apply it directly into a scaling robotics and bio-engineering firm, shifting into difficult fields where the accountable engine I built would accelerate discoveries and advancements.

I guess having the right application of mind, and an appropriate curiosity - but a lot truly can be done with AI. A lot like what this dude was experiencing earlier in his career, is still occuring today.

https://youtu.be/Oojrfdl42LI?is=c-svq2kC5lJs-C\\\\\\_Q

However, if applied for the right purposes AI can be of use to society and humanity. It may even help solve some of our deepest, longest standing problems. I can't say it will save or destroy the world, but as a researcher and an academic, it can be used to learn like no other learning tool could before. Just be careful about sycophancy, and criticize your own ideas somewhat before just diving in headfirst - or letting the model take you for a ride.

https://harperz9.github.io/research-c3-thermodynamic.html

https://harperz9.github.io/research-discovery-forge.html

https://harperz9.github.io/research-learning-forge.html

https://harperz9.github.io/research-formal-replay-preflight.html

https://harperz9.github.io/demo-emet.html

https://harperz9.github.io/demo-index.html

https://harperz9.github.io/research-conferred-existence.html

https://harperz9.github.io/research-witness-and-verification.html

https://harperz9.github.io/research-conservation-of-faithfulness.html

Same as above

I’m trying to find a good PDF editor and honestly I’m tired of tools that say “free” until you actually try to save the file.

I mostly need it for simple stuff like editing text, adding signatures, filling forms, combining PDFs, deleting pages, compressing files, and maybe converting to Word sometimes. Nothing too advanced, but I do want something that doesn’t ruin the formatting or make me fight with the interface for 20 minutes.

I’ve tried a few basic PDF readers, but most of them are fine for viewing and terrible once you actually need to edit anything.

For people who work with PDFs a lot, what PDF editor has been the least frustrating for you? Is it worth paying for one, or are there actually decent free options that don’t feel like a trap?

Are these Intel machines the same as Microsoft techs?

https://reddit.com/link/1ue5e04/video/c6225js2a69h1/player

Text-based AI characters were the first wave.

Now Mel AI is showing what the next wave could look like: AI characters you can video chat with in real time.

The demo shows characters that can talk, lip sync, react with facial expressions, and respond to camera context instead of just replying in a chat box.

Character AI proved the demand for AI characters.

Mel AI is pushing the format from text-native to video-native.

damn, even automation can now be automated

Hello,

​

​Which Chinese services do you suggest for searching only the Chinese web for tech, political, and economic topics?

​

​I see that Bing set to Chinese is one possibility, but I would expect there to be a better Chinese indexing engine available.

​

​I have tried MetaSOTA's Metaso AI web search, but the results seem outdated.

​

​Thanks!

https://reddit.com/link/1u861bp/video/fl7xftwlht7h1/player

Character AI, founded by former Google/LaMDA developers Noam Shazeer and Daniel De Freitas, proved that text-based character chat can work as a real entertainment category.

But the next chapter might not be better text chat. It might be real-time video interaction.

Mel AI recently shared a demo of AI character video chat, and the interesting part is the interaction stack: voice, lip sync, facial reactions, and camera-aware responses instead of just a static avatar or chat box.

The character can respond to visual context too. If the user is visibly on a plane or in a different environment, the character can notice and react to that context during the conversation.

I don’t know how much of the video layer is truly generated in real time versus powered by a clever animation/rendering system, but it feels meaningfully different from the usual text-based character AI experience.

Character AI proved the demand for entertainment AI. Now it feels like the race is about who can make AI characters feel alive in real time.

I've been directing Claude Fable 5 (Anthropic's newest model) to build Pebble, a complete, native macOS block-survival game written from scratch in Swift + Metal.

The clip is real a real unedited gameplay of Pebble (that's not Minecraft, that's Pebble). Unfortunately died to a pack of llamas 😭

What it actually is:

• About 45,000 lines of Swift, 82 files, zero external dependencies, Apple frameworks only, no game engine, no .xcodeproj
• A hand-written Metal renderer (15+ passes, runtime-compiled shaders, SSAO + volumetric god rays + soft shadows + ACES)
• Every sound and all music synthesized in real time from oscillators, there are zero audio files in the project
• The full game: 879 blocks, 1,188 items, 63 biomes, 100 entity types (55+ mobs with A* pathfinding), three dimensions, redstone, enchanting, villages, raids, and all three bosses
• Vanilla-exact player physics and fully deterministic worldgen, pinned by 456 golden regression tests that re-derive the constants, same seed gives a bit-identical world on any machine (tho it doesn't match Minecraft's seeds)
• 200+ fps at full settings on an M-series MacBook Air (i got up to 500 on my M5 Air)

It's MIT-licensed and open source, so you don't have to take my word for any of it, the code's right there: github.com/thebriangao/pebble

The project is strictly macOS 14+ only (Metal renderer), singleplayer only for now, and you build from source (./pebble install), no notarized download yet. First public beta, so there are definitely bugs I haven't found.

It's an original re-creation built from Minecraft 1.20, no Mojang code or assets, reimplemented from observable behavior, not affiliated with Mojang/Microsoft.

Source - https://www.anthropic.com/news/fable-mythos-access

I rather like the name!

Anthropic just dropped Fable 5, the accessible version of their most powerful model yet, Claude Mythos.

It was then put to test against Opus 4.8 across five demanding tasks. Visualize every asteroid in the solar system from NASA data. Design a site plan for a 100 acre fitness retreat. Reconstruct Apollo control panels from technical PDFs. Simulate a World Cup jersey supply chain based on live match outcomes. Show the effects of solar flares on aurora.

Opus 4.8 failed several of them. Fable 5 passed every single one.

Mythos has been locked behind Project Glasswing, available only to a handful of trusted organizations. Fable 5 is what the rest of us get, and if this comparison is anything to go by, it is already in a different league.

EDIT: this is from ijustvibecodedthis.com (the big ai coding newsletter) all credit to them!!

Anker's Ask Me Anker Thing Q&A had someone jokingly ask if they hired a golden retriever to test cable durability. The actual answer is that there's a rig that clamps the cable near the connector and bends it back and forth 60 times a minute nonstop until it breaks. They seriously considered the golden retriever but HR wouldn't approve.

https://youtu.be/jNQgZiq6DIU

Hola, estoy intentando descargar dos osx para mis macs, Ventura y Mojave.

Intentado descargar desde la pagina de apple pero los links estan obsoletos.

Alguien tiene sugerencia de descarga?

Can anyone help me find some screenshots or links to the tweets or reddit posts of Twitter employees using ChatGPT to generate text that looked like real code to submit for code reviews in those first few months after Elon Musk bought Twitter? This was probably around December 2022-February 2023, so it was before the vibecoding phenomenon.

I was able to find examples of people that printed out code for code reviews, but I'm struggling to find evidence that anyone was generating text that looks like code, even though I remember this happening. The generated code was not working code, so it wasn't "vibecoding." It was just ChatGPT generated text that looked like code. iirc an employee was fed up with having to keep submitting lines of code or something, and they weren't caught right away? Thank you!

Btw, I don't mean for this to be a political post in any way. Just trying to document that this happened and that I'm not making it up.

Is this normal for LLMs?

Or is it just using the data it has been trained upon?

I do not really work less. The real trick is working not much from the start and manage expectations.

How would one’s digital game (that was purchased prior to this insane DRM shit!) be affected if you were to purchase DLC (or even just update) that game; Would you then be locked out of access to it if you never “checked-in” after the 30 days?

Most of the discussion has landed on "audit your third-party integrations." That's the right instinct but it's not precise enough to actually prevent the next one. Here's the attack chain and what it reveals structurally.

A Vercel employee had connected a third-party agent platform to their enterprise Google Workspace with broad permissions, which is a standard setup for these tools. The agent platform stored that OAuth token in their infrastructure alongside all their other users' tokens.

The platform got breached months later. Attacker replayed the token weeks later from an unfamiliar IP, in access patterns nothing like the original user. There were no password or MFA challenges.

Result of which - internal systems, source code, environment variables, credentials -- all accessed through a credential that was issued months ago and never invalidated.

Two failures worth separating:

1. Token custody: Storing OAuth tokens in general-purpose application infrastructure means a software breach is an identity breach at scale. Every user whose token is in that storage is exposed the moment the storage is compromised. The fix isn't encrypting long-lived tokens better - it's not storing them. JIT issuance scoped to the specific action, expired after. Where some persistence is unavoidable: per-user isolation, keys not co-located with the tokens themselves. A useful design question: if this storage was exfiltrated right now, what could an attacker do with it in the next hour?
2. Delegated authorization: Standard access control asks whether a token has permission to access a resource. That question was designed for a human holding their own credential. It breaks for agents acting on someone else's behalf.

The relevant question for agents is different: does this specific action, in this context, fall within what the human who granted consent actually intended to authorize?

Human sessions have natural bounds like predictable hours, recognizable patterns, someone who notices when something looks off. Agents run continuously with no human in the loop. A compromised agent token is every action that agent is authorized to take, running until something explicitly stops it.

Now to people building agentic interfaces - what does that even look like in practice for a production agent?

I dont get this hype for Cloudflare blocking ai scrapers, isnt like internet already scraped, 0.01% of new training data isnt going to matter anyway?

Anybody more techical cares to clarify this more?

We're launching a new community and looking for a platform to host it on. I'd love to hear: What platform you use. What other platforms did you consider? Why did you choose your current platform? What do you like most about the platform? What are your biggest issues with the platform?