toplinesoftsys

Brilliant idea and fantastic project. New use for security cameras. I love it.

I did not try to play with it yet, but from what I read - it is a great idea.

I think this is a great startup idea and unique opportunity - create Google Maps competitor on promise to have Lake Ontario and Gulf of Mexico named correctly, along with a promise to ignore other possible renamings in future. :)

I like this project. Simple, open-source, nice UI.

Great article. What fascinate me about the current situation is how fast the old world died - literally within a couple of years. This is the best evidence that progress is not just moving forward - it accelerates.

Handling 911 calls is THE worst idea of AI usage, considering its current state of maturity.

PCB made in USA. Fantastic! I love this project.

I love it. No need to deal with windows just for the sake of one or two commands.

Great idea! I love it.

Like everything else, modifying code of open-source tools has its cons and pros. The biggest advantage is of course an ability to customize the tool exactly to your needs. The problem is that it makes patching and upgrading impossible. This is the reason plugins exist - they do not break ability to patch the host product. So, if customization cannot be avoided even at the cost of losing ability to patch/upgrade - that would definitely work. Otherwise, plugins is probably the best approach.

The biggest problem is not that bugs are migrated with COBOL, but that lots of new bugs are going to be introduced. AI is not deterministic, it will be making tons of mistakes. The only realistic low-error approach is incremental step-by-step migration using Cursor or similar tools. However, it requires much more time as each step must be prompted, tested and committed manually. Any hope that one-shot migraton of a large code base will not introduce enormous number of bugs is very naive. LLM is very bad on handling long context - it is their nature unfortunately. There is no answer to this problem yet.

Fantastic project! Sim-city over San Francisco.

Absolutely amazing project. I did something like that myself back in 2017. It was a desktop-like interface for iOS and then Android tables, well before windows were added to tablet OS. Here are demos:

- iOS: https://youtu.be/Wwf1TZEbFGE

- Android: https://youtu.be/_v3JiK6ZN3U

This is amazing project! I hope it will result in real miniaturization of AI - for example, edge LLM inside of glasses. That will be awesome.

I think one of key reasons why we have so many issues with LLMs is because there is no clear understanding of their limitations. This is how I see LLM limits:

1. AI is inconsistent and unpredictable. It can do what is expected today, but the same AI can do totally different, unpredictable, and maybe even dangerous thing tomorrow.

2. AI is bad on a long context - if you give AI instructions, it forgets/misses/misinterprets them. The longer the context, the more deviations are produced.

As a result, AI usage must follow these rules:

1. There should always be a human-in-the-loop to review every AI output - it solves problem #1. Yes, there will be a significant slowdown of the processes, but alternative is a quickly accumulating mess.

2. AI must be given only a short context to deal with - this is solution for problem #2. I predict this term will be popular soon - "AI microagent", meaning agent handling a short context. Yes, there will be a manual work integrating and orchestrating a huge number of these microagents, but it is still better than fixing all defects of AI-generated output after feeding it a single long context.

This is just a very rough basic game skeleton. Companies believing AI is smart enough to spit out almost ready product will learn a hard way that it will will give them just a starting point that still needs a lot of work. This is a nature of all modern LLMs - they easily lose context: the bigger the context the more losses and distortions are, especially in the middle. So, giving AI the complete book does not mean it will follow everything in it - quite the contrary.

I am not sure there is a good solution for filtering out a synthetic content. AI detectors became basically useless, giving false positives a lot. Even human verification does not help - nobody prevents verified account to start using bots.

This is a very familiar pattern: brilliant visionary bring new technology too early and it fails. The next guy does not need to be such a prodigy - he just need to be right on time to succeed.

Great project - clean and simple syntax, pure functional language, uses GRIN framework.

Skill is a document that AI agent ignores, distorts and forgets immediately after reading it :)

Banning competition is the fastest way to lose the race.

I love this approach. Simple ideas, specific problems, clean interface, right on point, took a week or one weekend to build. This is what software development industry was missing for sooooo long.

At the end of the day, the real threat of AI is degradation of businesses, eventually leading to extinction of those of them still absolutely convinced AI will replace everybody. The real AI adoption is extremely low, but many business leaders are (willfully?) blind. Eventually, the pendulum will swing in other direction and there will be enormous demand for human labour. It will happen when businesses finally start to realize that AI is ruining their businesses.

There is a third mode that requires combination of greenhouse and lens modes, and I did not see AI that is every remotely good working at that third mode. Unfortunately, many real applications are actually require the third mode, and I suspect this is one of key reasons why adoption of AI is so low.

This is a great food for thought next time I am waiting for elevator :)

I would not be overestimating this achievement, but not underestimating either. The truth is probably somewhere in the middle. China had access to the latest EUV tech for some time already, so they had abilities to reverse-engineer that tech. Eventually, the verdict will be clear only after we see actual chips produced using these machine, and when we learn their real yield.

We need books like that now more than ever. AI makes us lazy, forgetting how it works "under the hood". We should be curious and keep asking questions, or we will not be able to understand all this geneated AI code slope.

Yes, the dam has been broken. Major linux distibutions are getting polished for many decades already and they are totally ready for the mainstream adoption. No amount of marketing will be able to reverse this trend. Considering that Big Tech is spending all their earned money (and even more by borrowing from investors) on AI, nobody will be even trying.

Great project! Keep pushing, do not pay attention to anyone saying that AI will replace all software engineers.

One thing is absolutely clear - open-source models have already reached the level of top commercial ones. The last bottleneck is hardware, but that threshold is decrasing fast too. While it is still extremely expensive to run Kimi K3 model at home, there are already many very capable free models you can run on decent hardware. This trend will definitely continue.