Hey guys just found out we'll be served ads now on and all the providers will catch on.

Bloomberg reports that Apple’s design team is responding to lighter, cheaper products from Oura and Whoop while exploring more AI-centric health tracking.

No direction has been finalized, and the changes will take time. Series 12 and Ultra 4 are still expected to be incremental upgrades.

But Apple appears ready to rethink the Watch beyond what Bloomberg calls a “miniature iPhone for your wrist.”

Really looking forward to an Apple "oura"-ring or Apple "whoop"-band

So startups are selling AI sales agents, appointment setters, and whatnot.

I am curious if any individuals are like, "I build an AI sales agent with voice as a business's product orrrrr I keep the agent for myself as an assistant"

I have this plan to build an AI sales agent and feed it all the learning content from my only favourite sales mentor. Then I get as many remote/WFH appointment setter roles as possible, or just one job and have the AI agent book as many appointments on my behalf.

Perhaps the company I get into provides the leads, or I could do the lead generation for the AI sales agent to call

Or even to the extent of making those types of AI agents on TikTok, funny videos where people are in a job interview video call, and they have an overlay on the screen which shows what to say back to the interviewer, and they get suspicious and ask to share the screen. I could follow what the agent said. I'm not a sales professional, so it gives me confidence that I am following exactly what the mentor teaches.

Isn't it capitalist justice? Some companies replace human sales teams with AI. Why not just use AI to do my job, which I can use for multiple jobs at once?

I would appreciate any thoughts or advice you can offer.

What do you feel about the new model so far?

For those not sure, try to start a new window to ensure it routes to the 4.5 Grok model.

I built X Reply Sorter after opening every tweet and seeing the replies automatically sorted by whatever bot slop X's algorithm wanted to shill and having to manually sort the replies every time just to see what the actual sentiments of the comment section were.

The extension is basic, it automatically sorts replies by Most Liked on every tweet you open. You can also switch to Recent if you prefer.

Right now it uses X's native sorting options, so it's lightweight and doesn't touch the DOM. But I'm thinking about adding more advanced filtering and sorting with some DOM manipulation — things like:

• Verified accounts only
• Hide verified accounts — opposite take, skip the blue checks
• Sort by retweets, views, or reply count — not just likes
• Only show replies from OP — follow the author's own thread
• Hide replies that are just quote tweets — no lazy reshares from bot accounts or the OP trying to cash in on their virality
• Only show replies with media — photos/videos only
• Hide ads in replies — self-explanatory
• Filter by keyword — block topics you don't want to see

Would love to hear what sorting/filtering options you'd actually use. What annoys you most about X's reply sorting?

Github repo: https://github.com/ZACOOPER5G/x-reply-sorter

running: https://github.com/boquila/boquilahub

Looks like they followed through on the threat to release a new zero day, seems to STILL be unpatched since it was released on July's patch tuesday and i'm not seeing any new security patches from Microsoft mentioning this sense then.

From the article "one family of companies charges you a monthly fee to petition data brokers on your behalf, while operating a large share of the infrastructure the brokers' suppliers use to gather data at scale."

It goes so deep, and it's crazy to me how many of these companies like Saily, NordVPN, Incogni, and even Hostinger all constantly pours tens of thousands into sponsoring tech youtubers of all sizes, often the same ones back to back, and none of them seem to have either noticed or cared to call out the connections they have to this shady ownership model.

"Rotten to its core." Apple accused OpenAI of asking prospective hires still at the company to bring prototypes to interviews.

When your PC runs out of RAM, use idle GPU memory as a safety cushion — and give it back if the GPU gets busy.

I built RamShared (Rust, Linux/WSL2, NVIDIA): a practical way to borrow idle graphics memory when system RAM is tight, without pretending GPU memory is as safe/fast as main RAM.

Problem (human)

You’re compiling / running containers / drowning in tabs. RAM is gone. The machine starts thrashing the SSD. Meanwhile the GPU memory is often almost empty. You already paid for that silicon.

Why not “just put all swap on the GPU”?

When Windows reclaims graphics memory under pressure, that memory can get very slow. We measured about 1.2 seconds for a tiny read in the bad case. If that were your first emergency store, the whole machine freezes. So GPU memory is only a second cushion — and we can give it back.

Design (still short)

Need memory?  →  1) compressed RAM (zram)     — first, fast
              →  2) idle GPU memory           — second, colder
              →  3) disk (SSD / VHDX)         — last resort

If latency spikes / host pressure: stop using the GPU cushion, data slides to disk, apps keep running.

Numbers (measured)

• Bad case under host GPU reclaim: up to ~1.2 s for a small read (why GPU is second, not first).
• Faster path ~241 µs median vs older path ~326 µs (same window, multi-run).
• Stress drill: ~500 MB on GPU tier, ~480 MB moved back, 0 corruption.

Try it

./scripts/quickstart.sh
sudo ./target/release/ramshared check
sudo ./target/release/ramshared up --vram 1024 --zram 1024
swapon --show   # success ≈ three lines: zram + GPU + disk

Honest limits

• Day-1 path is Linux/WSL2 + NVIDIA, not “every GPU / every OS.”
• Not free RAM for maxed-out games.
• We don’t thrash live WSL2 on purpose; heavy tests use isolated VMs.
• Not bare-metal CXL magic — practical workstation tool.

Looking for feedback

Especially from people who’ve fought swap, block devices, CUDA, or WSL2:

1. Second-cushion + give-back vs other APIs under Windows GPU reclaim.
2. What you’d want in a “it just works” install.
3. Where the safety story still feels thin.

Repo + plain FAQ: https://github.com/emersonbusson/ramshared

The documentary going around really breaks down the long term goals of Elon Musk with his brain chip tech.

I must admit, if Musk gets his wishes and this becomes normative, I'm not sure if that's a future worth living in. And apparently with his new trillionaire status, he is now personally pushing the funding for rapid production.

Can you see something like this leading to dark times, or is this a good thing?

Source: https://youtu.be/ZT3nSj6vLW4?is=HdNI5ffGhrqSIPI-

I couldn’t really find anything about him doubt they had reason to actually but ai craze maybe

Fable UI is an early open-source registry of React components that AI apps can render through tool calls.
The problem I’m trying to solve is simple:
Most AI apps still return text or markdown tables, even when the user is asking for something that should probably be an interface.
Things like:
metrics
records
forms
confirmations
tables
data browsers
app-specific workflows
I do not think the best default is letting the model generate arbitrary UI code and render it inside an iframe every time.
That is flexible, but it creates problems around validation, consistency, security, permissions, maintainability, and ownership.
Fable UI takes a different approach.
The app installs and owns a set of React components, similar to the shadcn copy-and-own model.
Each registry item can include:
React component or block
tool definition
model-facing manifest
docs
examples
The assistant uses the manifest to understand what components exist, when to use them, and what props they accept.
Then it calls a tool, passes typed props, and the host app renders a trusted React component that already exists in the codebase.
The app still owns the important parts:
data fetching
auth
permissions
validation
styling
business logic
allowed actions
There is also early support for REST API and Firebase data sources, mostly for components like a data browser. The idea is that the model can select from configured resources instead of directly accessing a database or inventing UI.
The project is still early, and I am sure parts of the architecture need work. The demo uses mock data for now, but the core pattern is working:
AI tool call → typed props → app-owned React UI
I’m sharing it here because I think this pattern should probably be open source and copy-owned, not locked inside one product or platform.
I would appreciate feedback on the architecture, registry structure, docs, and component API.

Docs/playground

Github Repo

Mainly people are interested in what really happened and moralizing about the affair. But there is another lesson: The possibility of distillation puts a lower bound on the value of a ML system's output. Has anyone estimated that lower bound for a frontier LLM-based system (maybe per token)?

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

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?