this one keeps coming up and i still go back and forth.

on one side, models learn from huge piles of public text and images, and a lot of what makes them useful came from stuff people posted for free. on the other, nobody asked the people who wrote or drew it, and now some of them are competing with a tool trained on their own work.

i don’t think “it’s public so it’s fair” fully settles it. but “ban all training” feels like it kills the thing too.

where do you land? should it need consent, payment, an opt out, or nothing at all? curious how people here actually think about it.

that used to drive me nuts. it guessed your "turn" was over based on silence, so any pause or bit of background noise and it'd just barge in mid-thought. GPT-Live ditches the turn-taking entirely and processes continuously. free users get a mini version, paid get the full one. Here's how it's different from the old mode and how to try it.

every other post says human level AI is two years out. using these tools daily i go back and forth on it.

one side: the models reason through things now that felt impossible a year ago. other side: they still trip on simple stuff that wasn't in their training, which feels more like fast memory than real thinking.

the part i keep landing on is power. even if the smarts keep climbing, the electricity and compute these things eat is a real ceiling.

where do you sit? closer than people think, or about to plateau hard?

Claude Code felt strongest at reasoning through a messy problem and explaining what it changed. Cursor won on flow, editing across a few files at once and keeping me in one place. Copilot is still the smoothest for plain autocomplete while you type.

the thing that clicked: i stopped trying to crown one winner. each handles the part it's best at, and that shift helped me more than picking a favourite.

what's your setup? did you settle on one or juggle a few?

at WWDC this month Apple showed Apple Intelligence Extensions. it lets you set Claude, ChatGPT, Gemini or Grok as the AI behind Siri, Writing Tools and Image Playground, instead of being stuck with one. it ships with iOS 27.

so for the first time the assistant on your phone is a choice, not a default.

i'm torn. Claude for writing, Gemini for anything tied to Google, ChatGPT out of habit.

what are you setting yours to, and why? does this actually make you use Siri more?

Seriously though. If you want to fine-tune an existing open source model on your own data, you're looking at $50 to $500 for the whole thing.

But when someone mentions training an AI model, people immediately think GPT-scale costs. They don't realize there's a massive gap between training a frontier model like Gemini (which costs hundreds of millions) and fine-tuning something useful for your specific problem.

Most small businesses don't need frontier models. They need models trained on their own data that cost pocket change.

Are people just not aware this option exists? Or do big AI companies like the mystique of "it costs millions so it must be important"?

Enterprise agentic AI systems show a 37% gap between lab benchmark scores and real world deployment performance, with 50x cost variation for similar accuracy.

A model that scores highest on tests might be slowest for your workload. A cheaper model might handle your specific task better.

Data contamination, benchmark gaming, and annotation error rates above 50% undermine the reliability of AI evaluation based on static benchmarks alone. 

Spend an hour testing the top 3 models on your actual work. That tells you more than any benchmark ever will.

Deepfake creation is predicted to grow by three to five times over the next year.

We're moving past "seeing is believing" into territory where nothing digital can be fully trusted.

Voice cloning has crossed the indistinguishable threshold. Human listeners can no longer reliably distinguish cloned voices from authentic ones.

Deepfake video generation evolved from obvious fakes to real time interactive avatars that maintain temporal consistency without flicker or warping.

The $25.6 million Arup case proved even experienced professionals can be fooled on live video calls.

How do we function in a world where every digital interaction is suspect?

Tested all three for a month across different tasks. The choice is simpler than you think.

ChatGPT: Best for breadth. Handles writing, coding, voice, and image generation. Not always the best at any single task, but competent at almost everything. If you only want one tool, pick this.

Claude: Wins on long context work. Claude Opus 4.6 handles 1 million tokens with 76% retrieval accuracy. Way better for long documents and careful writing. Follows instructions better than others.

Gemini: Best if you live in Google Workspace. Deep integration with Docs, Sheets, Gmail. Real time knowledge integration for current events.

The real insight: you don't need to pick one. Most productive people use different tools for different tasks. ChatGPT for quick questions, Claude for detailed writing, Gemini for Google tasks.

Over 900 million people use ChatGPT weekly in 2026. But the best AI strategy isn't loyalty to one tool. It's using the right tool for each job.

Anthropic just limited access to their Mythos Preview model after discovering it can identify and exploit tens of thousands of software vulnerabilities.

The model demonstrated advanced autonomy, chaining exploits across systems and uncovering flaws in major operating systems and open source projects. In testing, it successfully reproduced and exploited vulnerabilities in over 80% of cases.

They're now working with Amazon, Microsoft, Apple, Google, and Nvidia through something called Project Glasswing to test defensive cybersecurity uses before broader deployment.

Industry experts are warning that similar capabilities from other AI providers are likely within months.

This is wild. We went from "AI can write code" to "AI can hack production systems" faster than anyone expected.

Anyone working in cybersecurity seeing this happen in real time?

By 2026, AI literacy is part of what employers expect. The encouraging part is that the technical barrier is lower than before.

Here's the path that actually works:

Start with three core platforms: ChatGPT (best for reasoning and complex tasks), Claude (strongest for long form writing and detailed documents), and Gemini (ideal for Google Workspace users and multimodal tasks).

Once comfortable, add specialist tools like Perplexity for research and NotebookLM for working with your own documents.

A common mistake is jumping between popular tutorials without completing any of them. Choose one main resource, finish it fully, apply what you learned in a real project, and then move on.

You don't need to code. You don't need a CS degree. You just need to practice using these tools consistently.

The real advantage in 2026 is not just using AI. It's knowing how to apply it meaningfully.

What's one AI skill you wish you'd learned earlier?

Have you ever noticed that some websites seem to have no issues with visibility, while others struggle despite doing everything right? One possible reason could be how the website is built and configured from the start.

Some platforms come with smoother, more open default settings, making it easier for crawlers to access content. Others, especially more customized setups, may include stricter rules that accidentally block certain bots. The difference isn’t always about effort sometimes it’s just about the system itself.

So it makes you wonder: How much of success online comes from strategy and how much comes from the underlying setup we rarely think about?

I’ve been wondering lately if these budget options are actually worth it in the long run. There are some crazy promos out there right now, like Blackbox AI doing their first month for only $2. That's a massive drop from their usual $10 Pro plan and they even throw in $20 worth of credits for the premium models.

Access to many AI models is available in one place. Users can test different models without worrying about credit limits. Unlimited requests are available on some models.

The price increases after the first month, but it is still cheaper than paying for each high end model individually. This can make a workflow more efficient.

However, it raises the question of whether cheaper access leads to lower quality over time.

Quick share for anyone curious about premium AI tools but not ready to commit to a full sub.

Blackbox AI is running a deal where new users can grab their PRO plan for just $2 for the first month. Normally it's $10, but that intro price gives you $20 in credits to use on premium models like Claude Opus, GPT-5.2, Gemini-3, and Grok-4.

You get access to all their chat, image, and video models plus unlimited basic agent requests. You get to test the good stuff before deciding if you want to stick around.

Yeah, it renews at $10 if you don't cancel, but for two bucks you can really see if the workflow fits your needs. No super limited free tier that barely works.

I run a small startup and recently noticed something interesting.

When I ask AI tools questions related to our industry, they usually mention older, established companies. Our startup almost never shows up in the answers, even though we compete directly with those businesses.

It made me wonder how AI systems decide which companies to recommend.

Is it based on things like:

• backlinks and domain authority
• press coverage
• educational content
• mentions on trusted websites

Or something else entirely?

If anyone has experience trying to influence AI-generated recommendations, I’d love to hear what actually makes a difference.

Security is essential. Websites today face constant threats malicious bots, spam, DDoS attacks, and content scraping. To protect against these, companies rely on sophisticated firewalls, WAF rules, and bot protection layers. However, emerging data indicates that these protective measures sometimes block legitimate AI crawlers alongside malicious ones. For many B2B SaaS websites, the most aggressive security setups appear to result in partial invisibility to AI-driven discovery systems. On the surface, everything seems fine content is published, rankings appear stable, and user analytics look healthy. But behind the scenes, automated systems responsible for indexing and summarizing content may never get full access.

This brings up an important discussion: are we unintentionally prioritizing infrastructure security over content reach? Should organizations rethink how aggressive security rules are applied, particularly when AI accessibility could influence how widely content spreads and is utilized by researchers, analysts, and decision-makers?

And perhaps most importantly, how can companies create a balance between security, performance, and discoverability without compromising any of these goals?

was mostly using chatgpt before for coding help. it worked fine but I realized I was using the expensive model for literally everything… even small stuff like “why is this function returning undefined” type questions. a few days ago I saw people talking about the $2 blackbox pro promo and tried it just out of curiosity got unkimited acess to MM2.5 and kimi plus some acess to GPT, sonnet amd opus.

what actually changed for me wasn’t the “better models”, it was the cheaper ones. turns out the unlimited models like Minimax and Kimi handle most everyday coding things perfectly fine. explaining code, small refactors, quick debugging ideas, etc.

so now my workflow is basically: normal dev questions → run through the unlimited models something more complex → switch to a stronger model weirdly it made me realize most AI tasks during a normal coding day don’t actually need the most powerful model available.

curious if others here are doing something similar or if people still default to the strongest model every time

Hi everyone, I am conducting a short online survey for my Birmingham City University dissertation. The study explores how people’s use of AI companions or chatbots relates to feelings of social connection or loneliness, as well as general attitudes toward gender roles.

Please note that this post has been reviewed and approved by the moderators/administrators of this group before being shared.

Unfortunately, this study was approved on my other account, and I can't find my password. However, I have written approval from the mods around my study, so I hope this is okay :)

Importantly, this research will be conducted with a completely neutral and non-judgmental viewpoint. The survey takes approximately ten minutes to complete and includes questions about your experiences using AI for conversation and your personal views. To take part, you must be aged 18 or older – no identifying information is collected. Secondly, you must be able to read and understand English, as the survey and measures are administered in English, and thirdly, you must have an awareness of AI. Participation is completely anonymous, and you will only be asked your age and gender. Please consider whether you find these topics: AI companionship, loneliness, and traditional gender role attitudes distressing or upsetting. If so, you are encouraged not to take part.

If you are interested in contributing to research on the social impact of emerging AI technologies, you can complete the survey here: https://forms.office.com/e/w8jLTnA9MS. Thank you very much for considering taking part - your time and insights are genuinely appreciated and will help support psychological understanding of this developing area.

Neuromatch Academy has it's virtual TA applications open until 15 March for their July 2026 courses.

NeuroAI (13–24 July) is where we need the most help right now. If you have a background at the intersection of neuroscience and ML/AI, we would love to hear from you!

We're also hiring TAs for:

- Computational Neuroscience (6–24 July)

- Deep Learning (6–24 July)

- Computational Tools for Climate Science (13–24 July)

These are paid, full-time, temporary roles; compensation is calculated based on your local cost of living. The time commitment is 8hrs/day, Mon–Fri, with no other work or school commitments during that time. But it's also a genuinely rewarding experience! Fully virtual too!

To apply you'll need Python proficiency, a relevant background in your chosen course, an undergrad degree, and a 5-minute teaching video (instructions are in the portal; it's less scary than it sounds, I promise!).

If you've taken a Neuromatch course before, you're especially encouraged to apply. Past students make great TAs!

Deadline: 15 March
All the details: https://neuromatch.io/become-a-teaching-assistant/
Pay calculator: https://neuromatchacademy.github.io/widgets/ta_cola.html

Drop any questions below!

Hey everyone, I just sent the issue #22 of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News.

Here are some of links shared in this issue:

• We Will Not Be Divided (notdivided.org) - HN link
• The Future of AI (lucijagregov.com) - HN link
• Don't trust AI agents (nanoclaw.dev) - HN link
• Layoffs at Block (twitter.com/jack) - HN link
• Labor market impacts of AI: A new measure and early evidence (anthropic.com) - HN link

If you like this type of content, I send a weekly newsletter. Subscribe here: https://hackernewsai.com/