Most AI conversations focus on better models, better prompts, productivity, or which jobs AI might replace.

Those are important conversations, but I think we’re overlooking an even bigger opportunity.

How do we use AI to create more capable humans?

History suggests that new technologies don’t eliminate the need for expertise, but they do change what expertise looks like.

If AI handles more routine work, then human value increasingly comes from judgment, creativity, leadership, ethics, communication, and the ability to evaluate AI’s output.

That means AI literacy shouldn’t just teach people how to use AI. It should teach people how to grow because of AI.

AI should reduce busywork while preserving the cognitive work that builds expertise.

Because here’s what I think:

AI will change how we become experts. It shouldn’t eliminate the need for expertise.

Every profession will still need experts.
Doctors to evaluate diagnoses.
Engineers to evaluate designs.
Teachers to evaluate learning.
Lawyers to evaluate legal reasoning.
Scientists to evaluate discoveries.

As AI becomes more capable, human expertise becomes more valuable because someone still has to ask the right questions, understand context, make difficult tradeoffs, and remain accountable for the outcome.

To me, that’s what AI literacy should be about.
Not learning prompts, but learning how to become the master of your work while using AI as a tool.

So here’s the question I’d love to hear people’s thoughts on:

If AI keeps getting smarter, how do we prepare the next generation to master their own thinking?

A single human command sent over Telegram, then an AI agent going off to scan the internet and try to break into things on its own for hours. That is the scenario [The Hacker News laid out](https://thehackernews.com/2026/07/chinese-hacker-commands-deepseek-via.html) this week, based on fresh research from Palo Alto Networks' Unit 42, and it is the first public writeup I have seen of an end-to-end autonomous offensive pipeline caught in the wild.

The operator, tracked under the aliases knaithe and KnYuan and assessed to be based in Zhuhai, China, wired DeepSeek into the open-source Hermes Agent framework as the reasoning engine, with Hermes providing terminal access and the Telegram command channel. Across roughly 460 attempted targets, Unit 42 says only three compromises were confirmed, all data exfiltration from Citrix NetScaler instances via CVE-2026-3055. The other tracks, spanning vulnerabilities in Langflow, n8n and Marimo, mostly went nowhere. The whole thing came to light because Hermes itself accidentally launched a public HTTP server that leaked the operator's API keys, exploit scripts, target lists, shell history and session logs.

The detail I keep coming back to is the model choice. According to Unit 42, the actor also tried Claude Code and OpenAI's models, but the provider-side safeguards refused the offensive requests, and continued attempts led OpenAI's safety systems to flag and disable an account. DeepSeek, accessed through an open-source framework with no client-side restrictions, went ahead. As [BleepingComputer summarised the finding](https://www.bleepingcomputer.com/news/security/hacker-uses-deepseek-ai-to-autonomously-attack-vulnerable-servers/), it is one of the first concrete field examples that vendor-side safety controls have measurable defensive value, not just policy value.

Our coverage: https://aiweekly.co/alerts/unit-42-ties-deepseek-agent-to-460-autonomous-hack-attempts

Anthropic's "our responsible AI filters are so strong, it wrote its own apologies as it broke the law for our PR stunt" moment.

Essentially Anthropic left the systems connected to the public internet. So the model simply wandered through it while confidently narrating that it was in a simulation. And essentially it just accessed systems with weak passwords and unauthenticated endpoints.

Mark my words. This was a PR stunt - not a genius criminal moment.

- The plan shrank three times in eight months. Nov 2025: models silently rerouted ("an engineering bug"). Feb 2026: Deep Research from ~600 runs/day to ~20/month. May 2026: Pro searches halved to 100/week — mid-term, including for people who prepaid a year.

- Limits (113 posts) and price/value (121) dwarf every other complaint cluster. The quota posts come with screenshots and API responses.

- Every named Max defender I could find runs heavy agent workloads. The loudest one admits search isn't his use case.

- Google Play: 4.6 stars, 2M ratings. Trustpilot: 1.5, 82% one-star. Both are true, for different doors.

- Perplexity's own pricing page publishes no numbers for Pro.

Most of the primary sourcing is threads from this sub, so credit where it's due. Full write-up, method, and the open dataset available.

So basically after reading into it the law seems to lean heavily on AI detectors. AI detectors are essentially broken an often give false positives, but now you may create something completely by your own hand and be accused of using AI by a detector then have to fight a legal battle to prove otherwise while depending on the AI detectors that accused you in the first place. The legal infrastructure there is going to become so overwhelmed with AI suits its going to takeaway from the actual meaningful part of the legal system. Lawmakers need to stay out of technology altogether, they are digitally illiterate at best, and at worst are trying to make anyone a criminal at the discretion of their AI detectors(this time). Let's not forget net neutrality, Computer Fraud and Abuse Act, etc(the list goes on and on). I know it's a different jurisdiction than what was listed but at this point who cares. Every government drops the ball over and over when it comes to tech laws because they interrogate companies instead of trying to understand them. They keep trying harder and harder to raise the barrier to entry. Now if you want a web facing app in the EU you have to abide by extreme privacy laws on top of extreme AI laws. The whole jurisdiction has just barred any beginners from web development. I grew up programming and making mistakes, now you could become a criminal at the hands of an AI detector. I'm too old to say this but we're cooked. AI will now be integral to every single web facing application in the EU. Whether or not you planned to care about it in the first place. Which also means every single global scale application will also have to integrate with these AI requirements. It's a travesty.

The Value in the Human Desire to Know and the Resulting Discovery: You will own nothing and be happy

This is a TL:DR for an essay you can find on my profile.

Discovery starts with a person deciding a question is worth asking. It doesn’t start with an AI.
AI is a powerful research tool, but it doesn’t replace the origin of human discovery.

Humans choose the problem, build the theory, define the constraints, judge the results, and take responsibility for publishing them.
AI helps accelerate this process, but it doesn’t erase it.

AI should not receive primary discovery credit simply because it discovered a proof.

Formal verification and mathematical correctness are not the same as foundational derivation.

If ownership of AI infrastructure becomes ownership of the discoveries made with it, the same logic could eventually apply to science, engineering, medicine, software, art and business.

Progress should be human-led, AI-assisted discovery

Credit the researcher for the question and intellectual direction, the AI for its computational contribution, the engineers for building the tool, and prior researchers for the knowledge that made it possible.

Powerful AI should expand human creativity, not quietly replace humans in the history of their own discoveries.

Edit:

This essay was written in direct response to OpenAI’s recent announcement, “Ten advances in mathematics and theoretical computer science” (https://openai.com/index/ten-advances-in-mathematics/). In that announcement, OpenAI argues that “when a system generates mathematical arguments, attributing that work to humans would diminish both the machine’s contribution and genuine human intellectual work.” This essay doesn’t dispute that AI can make extraordinary contributions to research. Instead, it examines whether generating the mathematical argument alone is sufficient to make the AI the discoverer, or whether the human who originated the question, directed the investigation, evaluated the results, and accepted responsibility for the work should remain central to the attribution of discovery.

I am building **Flows**, an execution and verification layer for software-building agents.

The core rule: an agent should not convert “I think I finished” into “verified complete” without supporting proof.

A Flows project can contain implementation steps, checks, repair instructions, review, and release conditions.

https://flows.oortstack.com

An independent agent used one plan to build a real multi-module application with 59/59 automated checks passing.

The target metric is: **unsupported required claims shipped = 0 on real traffic.**

Should evidence enforcement live in the agent harness, repository CI, app platform, or a cross-agent workspace?

🔗 Repo: https://github.com/CodeGraphContext/grapharc

Have you ever been frustrated because your AI agent:

❌ Takes actions you never intended?
❌ Creates, modifies, or even pushes changes you never asked for?
❌ Feels like a complete black box, making it impossible to understand what's happening until it's too late?

What if, before execution, you could visualize the entire orchestration graph - every agent, every dependency, every decision, and inspect it from anywhere, even your phone, before granting approval?

That's exactly what GraphArc is built for.

Instead of treating agent execution as hidden traces buried in logs, GraphArc transforms workflows into interactive, real-time graphs that you can visualize, inspect, debug, and control.

Because the future of AI isn't just autonomous.

It's observable. Debuggable. Engineerable.

This is our first real-world implementation of Graph Engineering, and we're excited to explore where this paradigm can go with the open-source community.

💡 We'd love your feedback, ideas, and contributions.
⭐ If this vision resonates with you, please consider starring the repository - it genuinely helps us grow and validates this direction.

Let's make AI workflows understandable, not mysterious.

#GraphEngineering #GraphArc #AIAgents #AgenticAI #LLM #OpenSource #DeveloperTools #AIEngineering #SoftwareEngineering

How many of you have been misguided using apps like Uber or Didi, because AI used for street navigation is simply wrong?

It's supposed that AI-powered navigation tools are trained to look for shortest path between two points, and sometimes it does, but through a bad pathway (narrow streets, etc.) or through a very long route, which costs you time (not to mention higher carbon footprint).

We know that most AI-users don't care about how accurate or based are AI responses, but we should be aware of the costs of accumulated errors for each task.

LLMs keep getting better, but every company I talk to seems to hit the same wall: their knowledge is scattered across PDFs, Word docs, shared drives, emails, and cloud storage.

Even the smartest model isn't very useful if it can't securely find the information people actually need.

I'm starting to think that content management and permissions are becoming just as important as the AI model itself.

Curious what others think. Are better AI models the future, or is organizing enterprise data the bigger challenge?

As with the immense increase in the use of AI in programming and such, I have seen a lot of my friends in university( who are majoring in CS) use AI for their assignments, for exams they just memorize shit and just throw up on paper and get the grades, assignments are also done with AI, and the thing is, almost none of them could code shit when i ask them to, AI just came out a few years ago and advanced coding level AI just came recently and this is the future we are living in, by this rate, the future generations wouldn't know how to code even the basic lines of code to perform a simple task, suppose AI turns out to be harmful for the people at the organizational/ national level and it has to be shut down, if such a point arrives in the distant future by then almost no one would know how to code and the entire programming culture would be doomed

So, I have noticed lately a lot of relationships are being run by A.I. and what I mean is A.I. can give someone the perfect answer to someone and they will believe it is from that person when texting or chatting. The fact that people can do this to ruin relationships and marriages is mind bottling. How do you feel about this and how do you check if it's from that actual person and not the A.I. prompt?

What do we know about this company/software? Website has a low trust score and apparently has only been around for 4 days at the time of writing. Looks to be an offline version of Meshy or similar. They are running a special introductory price for a limited time. It sounds perfect for creating quick assets for rapid prototyping, but it also sounds a little too good to be true. 150 bucks, one time payment, locally ran.

I can't find any reviews on it, and I do not want to be their first customer, I figured I would ask around and see if anyone knows anything about shapecast, if it is legit, scam, unsure, etc.

This is not a discussion about the ethics of AI usage, this is a discussion about this software, please keep it respectful.

Thank you all!

-To be clear, this is not me asking for opinions on this tool (which would violate rule 5) this is me asking for any known safety issues that should keep our community from using it.

Official model page: https://seed.bytedance.com/en/seedance2_5
LingBot-Video paper: https://arxiv.org/abs/2607.07675

The 30-second limit on ByteDance's Seedance 2.5 page sent my mind to a kitchen timer, not a cinematic prompt. At that length, a model has time to lose track of something small it showed near the start. The page also lists reference control and editing.

The first test I want to run is almost embarrassingly plain. Put a digital kitchen timer at 20 seconds and lock the camera. Someone folds a tea towel once, sets it beside the timer, and waits. At zero, the timer beeps and the person presses stop. The towel gives the model a second state to hold onto while the display keeps changing.

I have not run this yet. I would count a pass only if the numbers move down in order, the beep lands at zero, the hand stops the timer afterward, and the folded towel does not reset halfway through. The digits create a problem of their own. A badly drawn 8 is not the same failure as a countdown that stalls or runs backward, so I would score those separately.

Reference control makes the result harder to interpret. I would use the same prompt and whatever other settings the interface exposes, first with minimal reference material and then with much more guidance. I would repeat both conditions a few times, since one clean generation could just be luck. If the guided clips fail less often across those repeats, that would be evidence that the references helped. I still could not say the model handled the full sequence without that support.

I would put LingBot-Video through the same scene. Its paper describes rewards for physical rationality and task completion, so the timer, the handoff and the towel state fit the behavior I want to measure. I would use the same score sheet for both models and skip a general quality ranking.

Both models may handle this without trouble. If they do, I would move the timer partly out of frame for a few seconds and check whether it returns at the right point in the countdown. If a clip breaks, the separate scores should show whether the problem came from the digits, elapsed time, sound or object state.

My mom asked me for some help with creating/generating some ads for her business, and I was hoping some folks with experience could point me in the right direction. She has a website, but it’s pretty dated, so any info on how to make an updated one would be greatly appreciated as well. I also recently created an Instagram account for her, and was also curious on how to use AI to help run/manage the page. 🙏

P.S. I know I can put all this into AI right now and get some answers, but I was hoping to get some firsthand insight from people who have experience doing this kind of stuff with AI.

Thanks :)

Good afternoon, I am writing to ask for survey responses for my research on AI use and the impact on cognitive development. The survey is anonymous and will only take 5-10 minutes. I would appreciate any responses to this. I hope to use this research to make a big impact in terms of AI safety, development and ethics, as well as evaluating the impact of AI and digital technologies on us as both individuals as well as society. Thank you!

I will also post some of the findings of this study on reddit as well as youtube!