tegeek

Tried to add AI Ultra or Pro to my business workspace account and I wasn't able to do that a few months back, then tried again when Gemini 3.8 news came out, failed again, then finally upgraded my personal account to AI Pro, tried gemini 3.8 flash on my projects, it was worst model ever. The only benifit I got by having AI Pro is that you can use Youtube adsfree.

Back in the days we used to call these kind of demo cars as concept cars which rarely see the markete. But Kudos to Elon Musk who single handedly running entire Tesla's marketing compaign and keep its stocks and markete value higher than the next 10 car brands combined. Tesla Robots, Robotaxies and now Roadster, these are R&D and concept projects.

You are almost 100% right. I’ve been providing my consultancy services to an e-commerce shop for the last few years. It was a team of five people, including a permanent UX designer. I introduced an AI-driven process and gradually automated the entire workflow, and everyone was laid off around March this year. I’m now the only one running the entire site. Apart from 10–15 hours per month from a UX designer, all the developers have been laid off. The entire software development process has been automated.

If I weren’t doing this, another IT consulting firm would. It’s now just a matter of time before most people realize the current state of AI and what can be accomplished with it

So a stable company like JetBrains, which sells developer tools and has extensive experience in compilers, ended up with a net financial loss. Meanwhile, Cursor—a startup only a few years old, founded by students, built on a VS Code fork, and primarily powered by OpenAI's API—was valued at $60 billion.

It all comes down to where top management puts its best resources. Apple's leadership focused theirs on designing a more useful music device, one that could also serve as an internet navigator. Once they had the prototype, they moved to a higher-level vision: a truly personal computer, where music, the internet, and the phone were just apps.

Nokia, the giant at the time, was designing a better phone. And then a better phone. And another phone after that. Their design and engineering were in no way inferior to Apple's, but they were designing phones, not a personal computer.

I've fully automated the software development part in my company. With production apps, no code has been written by hands, no requirement written by hand. Everything is fully automated except the testing part. And testing means the human testable parts are still performed by hands. The resule, with less people we produce more, actually way more. Our cost has gone down, quality has improved litrally. The code is being produced with AI is of higher quality, we employ multiple agents during planning phase and then different agents during execution phase. Sometimes some tasks has to be redone because some agents are better than others. Our release cycle is reduced from 2 weeks to 2 days.

You need to learn the concepts not implementation detail. If you know what is relational algebra and AI teaches you this in hours then you dont need to know the implementation details any longer. If you can borrow intelligence to solve a problem, you should do that. We, the most IT industry is already working in high level languages which is not how the computer works but abstractions over abstractions that very very few people know the entire cycle of a program how it runs. So AI is not a future abstracton, it is already here and its "intelligent" as opposed to other abstractions. Writing code or documentation by hand is gone, its past. All programming languages, framworks are implementation details and AI has already mastered them, we need to now move on to high goals than learning how to write a loop.

Databases, programming languages, compilers, operating system internals, networks these things are pretty complicated in itself. With your day jobs you can't master any of this without spending years in your part time. But now at least you can start something of your interest and learn on the way. Learning a new concepts, implements it, test it and reproduce cycle is so fast that it is only possible because of AI. You need to find the process which works for you. AI already knows every programming language, its best practices, entire computer science knowledge is already mastered by current state of AI. There isn't any single database internals which Ai doesnt know. If you dont believe just start implementing JavaScript or C compiler yourself without even having knowledge of compilers and in a few weeks you will where you stand. You will not only having a functional compiler but you will know the internals of compiler constructions, all the concepts.

I didnt know what is relational algebra is? why query plannering execution done in certain ways. What are backend storage system available in open source or commercial products. What are the traitts of each backend storage and how does that effects the replication and failover scenarios. I know all of this, I implemented a small subset of sql's relational algebra in my project and its correct and concise. I expored different backend storage systems, first tried one, then move to next and then 3rd in line, all that in span of less than a month without ever looking what code the coding agents generates.

In the beginning of August 2026, I started a hobby project: building a MongoDB-like database. I have 20 years of industry experience and a master's degree in computer science, so I followed a disciplined, spec-driven development model using Claude, Kiro, Qwen Coder, and Cursor.

The first version was built in about two weeks of part time work. Then I started exploring. I learned relational algebra, researched almost every kind of database, reworked the internals, built a small relational algebra layer, a query planner, and an executor, covering everything from the backend storage to the query language. I learned more in those two months than in the previous 20 years.

Did I care what code the agents wrote? No. I read zero lines of generated code. What I cared about was correctness, verified through tests, and the high-level product features. For the first time in my career, I acted as a senior product manager, steering the project along the right roadmap. Without AI, I wouldn't have been able to do that.

When you have superpowers in your hands, you don't need to worry about the laundry. For the first time in my career, I can produce code in C, C++, Java, .NET, or any other language. Sometimes it takes me longer than a senior developer in that language, but does that really matter? Absolutely not. Writing documentation and code by hand in 2026 is like driving a horse and buggy. It doesn't matter how skilled you are with the reins; you'll never compete with a car. My hobby db project isnt opened source yet.

At the AtCoder World Tour Finals 2026, an OpenAI system crushed all human competitors in an exhibition match, solving all five problems in the Algorithm Division. Two of those problems were rated exceptionally difficult by observers.

I had been using Antigravity for about 4 months now. I used Gemini Pro 3.1 heavily for small to medium size projects, alongside I used AWS Kiro and Claude Code. Then I use Angtigravity but instead of Gemini I installed Claude code extention which was working great till Today with the new update, Antigravity removed all the vscode extentions. Not sure even how git will work here. This antigravity update isn't an update but a completely new product. All the investments we have made in order set it down with our dev process, integration is gone, wasted. This is a very bad move but actually its the permanent state of Google, launch a product and a bunch of similar kind of products and then pull the rug.

A good effort, but i failed to see any use case why someone will select Lumina over TypeScript. Infact Lumina itself is written in TypeScript.