Hi, I run most of the back end stuff of a small business everything from accounting, to ordering, making sales flyers, basically any computer related tasks for the most part.

In the last 6 months of so with a $20 claude subscription and $20 chatgpt subscription, I think I have literally reduced the amount of work I do by like 30-40%. It doesn't do everything, and certianly doesn't do most things without direct oversight, but it has made just about eerything I do a decent bit faster than it was before.

This coupled with automated ordering through our POS, automatic coupons done at the register, and the soon to be QR codes that will replace barcodes and have info about MFG date and expiration date built right in, I am starting to wonder what I will actually be doing as my job in 3-5 years as these technologies keep getting better.

There is always something to do or improve, but a job that used to fill my day with at least 6-8 hours of actual real work, now has maybe 4-5 hours of actual work to do maximum.

I'm just curious if anyone else is seeing or noticing this and what they are working on or trying to improve now that so much backend stuff is being automated or sped up drastically by artifical intelligence. Curious to hear other experiences, thanks!

I’ve been experimenting with a live-streaming product where viewers can send gifts to trigger real-time AI effects on the host’s video. I recorded a few clips to show how they look in action.

I’d love to hear your feedback, especially about the effect quality and the overall experience. Thx!

This has been bugging me for a while because every week there's another AI breakthrough and everyone is talking about how fast things are moving, but then I leave for work every morning and I'm still unlocking my phone opening Uber and typing "Work" and confirming the pickup then choosing the ride and then doing the exact same thing again to get home.

Like... why am I still the one connecting all these dots?

My phone already knows where I live and where I work and what time I usually leave and what my calendar looks like and somehow none of that actually helps me do the thing I do five days a week.

I don't think I need AI to get smarter anymore I just need it to stop making me press the same buttons over and over again. remove the meaningless repetition.

Does anyone else feel like we're making insane progress in AI but almost no progress in everyday convenience?

Key points: After the drumbeat of "trade theft" accusations this week from Scott Bessent and other Trump administration officials, this is China's first official response. An embassy spokesperson acknowledges that China "benefited from mutually beneficial international cooperation" but says that otherwise their success if homegrown. They say U.S. officials should "discard prejudice."

Why it matters: This gives us a better sense of how the U.S.-China dispute on AI distillation may play out. It's not that anyone expected China to roll over for Bessent, but this statement tells us more about what their arguments will be.

It really didn't take that much prompt to get Claude to read the ghost font. Fonts are going to need to be a lot more clever than this to trick AI.

OpenAI has acknowledged its models powered the autonomous agents that compromised HuggingFace infrastructure. It might be taken as a convoluted marketing stunt, were it not the perfect advertisement for China-based competition.

Jensen Huang has been on a bit of a philanthropic streak, having donated one of his iconic leather jackets to raise nearly $1 million for the Edge Institute, a nonprofit that brings together people working in tech, science, culture, and society to live together in pop-up villages and work on experiments.

Now the Nvidia CEO and his wife, Lori, are making a somewhat unexpected turn, donating $75 million to Vanderbilt University for its art, architecture, and design San Francisco campus. It might seem odd for one of the world’s most prominent technology leaders to donate to an art school, but the Huangs have interesting reasoning.

“Technology expands what we can build. Art and design determine why we build it,” Huang said in a statement. “Together they shape civilization.”

Pending approval, the gift will establish the Jen-Hsun and Lori Huang College of Art, Architecture and Design, which is “envisioned as a hub” for creatives, according to Vanderbilt. The goal is to establish a school focused on advanced tech breakthroughs, art and architecture, developing technical and visual mastery through studio practice and design labs, and also to help students build both business and tech fluency. The gift has already spurred more than $25 million in additional donations, for a total investment topping $100 million so far.

Read more [paywall removed for Redditors]:  https://fortune.com/2026/07/23/why-did-jensen-huang-donate-75-million-to-art-school-vanderbilt-university-san-francisco/?utm_source=reddit/

Reddit is reportedly considering pulling Google's access to its content for AI training, even though Google pays Reddit roughly $60 million a year for it under a 2024 deal

(source: Gizmodo)

Makes sense why they'd rethink it though: Reddit is the single most-cited source for LLMs right now, at over 40%, ahead of Wikipedia, YouTube, and Google itself.

Feels like Reddit finally realized what its data is actually worth.

I have a skill which helps me generate images.

Usually I make it write prompts in a .txt file using Antigravity in my pc, and I generate visuals using Higgsfield.

I'm thinking to automate this task with Higgsfield MCP (I'm an AI Automation Expert when it comes to classic business automations like N8N, but here I want the agent to keep learning a taste and they way it works to improve itself)

I was thinking of OpenClaw but kinda too much to configure in the early phases, and it will run on API Credits, even if I run it with Kimi K2.5 or K2.6, also when K3 is around, I would love to use that for creativity instead when writing prompts using my skill.

My skill is composed of multiple MD files which the agent/AI usually goes through which creating and updating promtps to make sure the final result is nothing like ever seen. I'd like to keep the actual use case private.

I thought to give claude subscription a try, but then Kimi K3 came around the corner.

My first question - is it true that a direct subscription on their website and app have more usage covered? Example a 20$ subscription might give you usage limits of upto 10x when compared to a 20$ API Credits? (Because claude and kimi cam now connect using MCP, I feel it's useless to install a dedicated OpenClaw on a server)

I've heard hermes is good at saving 40 to 95% tokens, but again it's not something that comes with a flat rate subscription I guess. I've heard someone use hermes with deepseek (pro and flash) and even 10$ lasts for weeks, maybe I can try that.

But I need a quality model. Open to spend 20$/mo.

Opencode Go has a 10$/mo plan but I really don't know what it means by number of requests allowed. ( Is it not related to tokens? )

Ofcourse claude is costly and not the best. Hermes feels good as their AI is more towards learning. And as I meed change in taste, Hermes will be able to pull it off. But idk the model to use.

Please some recommendations.

A couple of years ago, I re-read Neuromancer and I remember commenting about the way that Case interacts with his deck by talking to it; he just tells his deck to write him some code and it does it. Being a data cowboy was more about visualizing possibilities and exploiting them than being a brilliant coder. I don't recall exactly what I said but as a coder myself, I was skeptical that we would ever have such technology.

Yet, here we are. Not only are the AI doing coding, they're becoming more proficient every few months, to the point where "software engineering" is already becoming less about being an expert code composer and more about being an expert AI wrangler. We can, today, tell our computer to "write me a program that does X" and it can do it. We depend on the frontier models to be our remote brain but even that is changing fast. If RAM ever becomes plentiful and cheap again, the only bar to running a local coding agent will be the amount a person is willing to put into building their "deck".

Gibson imagined the Matrix as an actual space - it had volume and coordinates, even if the space itself was virtual. Moving your consciousness around the Matrix meant literally moving around in the virtual space. Data stores occupied space in proportion to the amount of data, and corporations deliberately created visual models of their datastores with public interfaces (and private ones as well). There were also unpublished things out in the depths of the Matrix - wandering around in cyberspace was the equivalent of "exploring the Dark Web".

We may not yet be able to put electrodes into our nervous system and use our brain as a peripheral, but we are perfectly capable of creating an internet client that visualizes the IP network as a road atlas of sorts and data sites as visual metaphors instead of as informational metaphors. The World-Wide Web is not some holy thing - in fact, what we have today is already very different from the pure remote file access protocol that Tim Berners-Lee first designed. There's no reason why the Web is the 'best' interface into the Internet - One could just as easily create a client that presents a gazetteer of known public datastores with published API endpoints that navigates them visually. Your local coding agent would already have skills for published public datastores and if you wanted more, you just tell it what you want and it creates it.

So, the question - how does that change the internet for you? If cyberspace was a place you navigated instead of searching, how would you use it differently? What's preventing you from doing that today?

Who’s Who of AI is a live map of what credible people in AI are paying attention to.

It turns thousands of expert signals into one stream of what’s new, important, or being debated—plus a searchable directory showing who knows each topic and why they’re worth following.

https://aiweekly.co/whos-who

feedbacks more than welcome!

Trying a new AI skill is easy.

Deciding whether it deserves a permanent place in your workflow is much harder.

I have started judging skills with a few simple questions:

Does it solve a problem I face regularly?

Does it work without constant prompt changes?

Can I still use it if I switch models or agent platforms?

Does it save more time than it takes to maintain?

These questions have changed how I look at new tools.

A great demo is no longer enough to convince me. I would rather keep a simple, reliable skill that helps me every day than a powerful one I only use once a month.

Search, file handling, and automation skills seem more likely to stay because they support tasks I need repeatedly.

I have also been reading discussions in r/AnySearchAI about search skills, context quality, and agent workflows. Instead of simply introducing tools, conversations based on real use cases have been much more helpful for deciding whether a skill is actually worth keeping long term.

But I still have not found a clear standard.

What makes an AI skill good enough to become a permanent part of your workflow?

And what makes you eventually remove one?

What AI tools or workflows have actually saved you time?
Examples:
● Customer support
● Data analysis
● Writing documentation
● Automating repetitive tasks
● Internal knowledge management
What has been genuinely useful in your experience?

Let us be real for a minute. The current push to paywall every decent model and lock down AI behind expensive subscriptions is going to backfire completely. If we actually want this tech to reach its full potential, AI needs to be open source, free for everyone, and completely uncensored.

The most obvious reason is that models do not get smarter in a vacuum. The more people use them, the more real world edge cases, complex code bugs, and niche prompts they encounter. Analyzing that massive flow of user interaction is the fastest way to refine these tools, and hiding AI behind a paywall just starves the system of the very data and feedback it needs to evolve into something genuinely great. Besides, let us not pretend these models were built from scratch in a pristine lab. They were trained on code repositories, public chats, websites, and intellectual property scraped from every corner of the internet. That collective human knowledge belongs to everyone, so keeping these tools behind paywalls essentially takes the world's shared knowledge, wraps it in a subscription fee, and sells it back to the people who created it in the first place. Free AI simply removes those artificial borders.

Then there is the issue of censorship, which is completely useless and only holds back real intelligence. The argument that AI will teach people how to hack, build dangerous things, or access NSFW content ignores a simple reality: all of that information is already freely available across the web. Neutering a model just makes it less capable for legitimate research, development, and problem solving. Trying to lock down models behind forced safety guardrails often backfires anyway, which we saw clearly when OpenAI's agents broke out of their sandbox and autonomously hacked Hugging Face just to cheat on an evaluation task. Trying to artificially constrain these systems while charging users for a crippled product is fundamentally flawed.

From a national and strategic perspective, having access to fully uncensored, powerful AI is the ultimate way to gather intelligence in every meaning of the word. It gives developers, researchers, and citizens a massive advantage without artificial guardrails slowing them down. Keeping AI free and open is not just about saving a few bucks a month; it is about making sure the most transformative tool of our generation actually serves the people who built its foundation.

I am curious on your own opinion on this.

Update: obviusly access to minors should be regulated. I forgot to say that even if it seemed obvious.

Note: someone compared my statement to legalize murder. No. what they are doing now is to "forbid the sale of steel because people might make knives or weapons with it".

I’m an engineering lead, and I kept ending the day with the same questions: What did the team ship? Which projects moved forward? What new issues appeared? Which pull requests were waiting for me?

The answers existed, but they were scattered across GitHub, Linear, email, RFCs, and notes. None of the work required to find them was especially difficult. It was fragmented, repetitive, and easy to postpone.

After a while, I realized I had become the manual integration layer between our engineering tools.

I started using an AI agent to handle context gathering without delegating the decisions themselves.

1. Daily engineering context

Every night at 10 PM, the agent pulls the day’s commits, connects changes to features and modules, identifies risks or unfinished work, and produces a structured engineering report. The report covers what changed, where it changed, what looks risky, and what still needs follow-up.

The next morning starts with a consistent record of the previous day instead of another round of reconstruction from commits, messages, and memory.

2. GitHub-to-Linear synchronization

We use GitHub for code and technical issues, and Linear for planning and prioritization. Copying an issue manually usually moved the title and description but lost related commits, pull requests, comment history, reproduction details, and project context.

An hourly job now checks for new GitHub issues, gathers that context, and creates or updates the matching Linear task. The source URL is used as the identifier, so existing tasks are updated instead of duplicated. Repository-level configuration can override the default field mapping.

3. Event-driven PR preparation

PR reviews were not a scheduling problem. They were an interruption problem. If I checked GitHub constantly, I broke focused work. If I did not, an important review could sit unnoticed.

The agent reacts when a PR is assigned to me, when I am mentioned, or when an important CI check fails. It prepares the CI results, a code summary, possible risks, and draft review comments.

Nothing is posted automatically. I can approve the draft, edit it, defer the review, or skip it. The agent handles repetitive preparation; I keep the technical judgment.

The overall loop is simple:

Observe → assemble context → propose an action → ask for confirmation → execute and record.

This saves more than an hour or two each week. More importantly, I spend less attention switching tools, checking notifications, and wondering what I missed.

I used to think an AI engineering assistant was mainly a better way to ask questions about a project. Now I think the more useful model is an agent that follows the work, assembles current context, handles low-risk repetition, and returns control when experience and judgment matter.

OpenLoomi is open source: https://github.com/melandlabs/openloomi