lemonademan

I think this is a very useful feature if the users still have the option to exchange phone numbers if they want.

AI inference infrastructure is a solid direction. I don't know much about this field, but I believe building strong systems, distributed computing, and GPU optimization skills will put you in a great position. I hope this could be of help.

You could try ProductHunt, or you could show the Hacker News for feedback.

I don't know of any website that lists all employers who break contracts or miss payments. I might just create one. However, sometimes past contractors or clients leave reviews warning about unpaid freelance invoices or poor business ethics on review sites like Trustpilot.

This is an interesting question; very few people in here would be able to make a $1M in a year. However, for me personally, I would probably use the money I have now to buy whatever nice flashy thing I have to seem rich, and go around interviewing any millionaire I can find for the first 6 months. I may have to exaggerate a few things to avoid appearing too impoverished and secure at least a handful. I would get their contacts and tell them I connect rich people. The next 2 months will probably be spent creating and marketing an agency that links rich people together while still interviewing millionaires. The remaining months would be spent going hard on the interviews and selling the service of linking people to millionaires. I know this is easier said than done, and there is a high chance I fail, but this is my answer to the question. I got this idea from a YouTube channel I used to watch.

I believe the World Cup is a hot topic right now.

The part of the bomb is an interesting perspective, and I do believe you are right on this one. Basically, you could blow up a place with a bomb you invested more in and achieve your particular goal, or not achieve it at all, and still lose money. I yield

This is a really nice project. I would like to know your thought process while building this project, and how you got people to engage with it?

If you invested money into a trade where you bought and lost, there is someone else or others who sold and won. You may have lost that particular sum at that time, but someone else gained a part or some of that sum you lost. If you invested in building and opening a shop and eventually closed it because it wasn't making money. We can say the workers hired to construct the store made money from that investment, the manufacturers and wholesalers you bought from made revenue from selling you their products, which you intended to sell for higher prices to make a profit. You are right in your assessment that you could lose money, but that loss was someone else's gain, hence money moved from you to them. In this life, two seemingly opposing ideas could be correct at the same time.

More often than not, money is never truly lost; it is just passed from one person's hand to another. I believe that with the help of the big tech companies, governments have found faster and better ways to move money from the hands of the many with little into the hands of the few with a lot. The layoffs are an example of this as coders, assitance and other white-collar workers are replaced by AI for low prices so as to save money, hence increasing the revenue for the few at the top.

I believe there will be a massive shift from a lot of white-collar jobs to blue-collar jobs, as these jobs will be the ones most affected by AI. I believe Western countries will be poorer than they are now, and countries in Asia and Africa will become richer as more money will flow into their manufacturing and Agricultural industries.

You could try the Nimbalyst app. It features a visual Kanban board for parallel sessions, visual editors, and it is also an iOS companion app. This is my suggestion if you are looking for a straightforward GUI focused on single-session chats. I don't know if it offers the full features you are looking for, but you could always do a search on it to be sure.

Moving from generating code to mapping code is already a major leap. But to break through the "feed data and read Markdown" plateau, you must transition from passive reading to active, structured prompting.

You can achieve this by moving from summarize to deconstruct. For example, standard agents default to generic summaries. You must force the agent to act like a senior engineer conducting a code review or a research lead. You can do this by prompting for architectural boundaries and friction points, not just descriptions, by asking "why", not "what".

You could also use tactical prompts for Code onboarding. An example prompt would be, "trace a single user request from the entry point API down to the database. List every module it touches and where mutations happen."

This is how I would do it if I were in your shoes, but there are certainly other methods that could be better suited for you. Either way, I hope this was at least useful in some way.

If you ask everyone this question, I genuinely believe they would tell you that demos always have to oversell the product, but a professional will tell you that this is because AI demos are crafted in highly controlled environments with people who know the full potential of the AI. I genuinely believe they are both correct in most cases, or at least one is correct. The average user of most products like AI doesn't know the AI's full potential, or they know the AI's full potential but still don't use it to its full capacity, and I believe most users are the former rather than the latter. In some cases, some don't even know how to fully operate it when they get their hands on it, which could make it seem more like a disappointment. Either ways the reason depends on the AI, the person using it, and the environment it is used in. I hope this can be helpful, but also take my opinion with a grain of salt, as I am not an authority on this subject. However, I hope this was helpful either ways.

I believe Agents and the Model Context Protocol (MCP) will remain resilient, while rigid Agentic workflows will likely churn away. Hard-coded, step-by-step state machines—like rigid "if-this-then-that" prompt chains—are just temporary scaffolding. In my opinion, autonomous agents are likely going to be the most resilient piece here, but I also believe they harbor the most unaddressed "dark corners" of the AI ecosystem. A lot of people are rapidly deploying them to multitask like scheduling meetings, running and testing code, etc but there is also a massive lingering issue regarding how much power and execution authority we are giving them, and the unchecked decisions they could make could be problematic depending on the situation.

I personally believe once you get beyond a handful of GPUs, people probably end up using both levels of telemetry because they answer different questions. NVML is nice for per-request attribution and understanding model behavior, but I believe PDU/BMC measurements are better suited for actual power draw since they capture everything (CPUs, networking, PSU losses, fans, etc.).

For instance, people running 32+ GPU setups probably correlate timestamps rather than trying to preserve strict per-request attribution at the rack level. This will enable these individuals to have rack/PDU power sampled every second.

Either way, I haven't seen many people publish how they instrument this in practice so take what I wrote with a gran of salt. I simple wanted to share a little bit of what I understand and I hope it helps.

To answer the first question, yes! Web operators and security professionals actively track and categorize AI agents separately from traditional search crawlers because they serve fundamentally different purposes and impact site resources in distinct ways.

I built a database website a few months ago and submitted it to Google, Bing, and Yandex. 2 months later, according to my Cloudflare dashboard, I have 1.5 million unique visitors monthly. I found that human visitors only accounted for about 10% of the total, followed by search engine crawl bots and then AI crawl bots. I also discovered that AI bots (like GPTBot, ClaudeBot, or PerplexityBot) scraped a lot rapidly without adhering to traditional crawl limits or deeply checking robots.txt files, resulting in high server loads.

That should also answer the second question, which is that AI retrieval systems index semantic structure faster than they index page content. You have to understand that AI doesn't just index your website like regular crawl bots, which index mostly your content, schema, and so on. AI bots go deeper by trying to understand your website structure, as this will also help in training other AI models.

Their Prime Minister is right to ban the use of AI in elementary schools, as most people who use AI to answer questions don't focus on retaining a set answer but rather answering whatever question was asked. So yes, elementary school students will miss a lot of steps, but this is still just my own opinion. However, I am more interested in how AI was used in elementary schools in the first place.

Most people in big tech companies relying on AI to code already know how to code. AI is an addition to their skills, and it makes their work even faster. People who don't understand coding to some degree can't use AI to code because they lack basic skills like deploying the code AI writes. I understand you're worried that coding will no longer be a valuable skill in the future, and you are right to be worried because of all the layoffs and other AI created crisis, but you should still learn to code because tech will always need coders in the future. Plus, humans who can code are the ones who check the code AI writes most of the time.