Office apps are strictly based on written spec. You can if spend good amount of token could easily build something very close not but 100% with that spec. PS is a different animal.
GC absolutely has performance costs, even with minimal allocations, because tracing collectors must scan live objects. This is exactly what this post says. Don't know if its really improved over time in real world cases.
Days are over for building a business over trivial solutions. I think future is reserved highly complex piece of software that need heavy infra to run and constant maintainance that looks less attractive to maintain and build.
Looking back through history, there has never been a breakthrough like this, one that impacts virtually every known industry simultaneously with the potential for a 10x impact.
Wrong perception inmho. Ai is a new tool. people doesnt know how to efficiently use it. Iam saying after seeing countless of x and reddit feeds. We work on strict budget an dthat is where we iterates on maximum efficiency and it worked. Money may be yours, but compute consumes resources that belong to the world.
That is where benchmarks matters. Good developers may be terrible with AI. Companies should put efforts, pick up a small team, and iterates on ways to reduce token usage. In our case we arrived after lot of back and forth. You shoul dnever allow somebody to use AI on one fine day and expect them to deliver 10X.
There is a big gap currently in AI assisted development. You don't need to max out tokens to build products or develop with AI. From my experience with AI assisted coding, we still use just two Plus accounts each across a three person team for maintaining multiple repositories totaling around 700K lines of code.
Companies should handhold employees, establish clear SOPs, and train them on responsible and effective AI assisted coding.
I find very little reason to use a pure vector database for enterprise retrieval. We built an enterprise retrieval engine on top of a SQL database with native vector support, and the flexibility is something we cannot ignore. Vector similarity is just one query primitive alongside full text search, filters, joins, ordering and normal relational predicates. Tenant/app/collection isolation becomes part of the query itself. ACLs, document versions, categories, metadata constraints and temporal filters are ordinary predicates rather than something you have to bolt onto a vector store. SQL is already going to be part of almost any enterprise system. Adding a separate vector database introduces another moving part and syncing two system whenever you update your data is the most difficult thing to get right.
We use codex in vscode. Use agentic development but, agents only do things that We agree upon. Like We create impact study, proposals, feasibility and then implementation plan with full regresison, test coverage and end to end smoke test. A new fetaure will be given to front end only if it satisfy all our requirement in real API smoke test. It is not fast. but still we think we get 10X productivity gains and clean code, consistant quality and maintainability. And we dont use AI for front end.
We have also found C# is more effective for agentic development. With Roslyn APIs, strong typing, structured namespaces, and compiler level semantic information, agents can navigate and reason about the codebase with relatively little context. In our experience, C# is surprisingly token efficient for large codebases cmpared to Go or rust.
i was a heavy drinker. but stopped when i was at the rock bottom of my life. Now ocassionaly may be once in a month i use to drink just to get some cognitive rest.
I don't really see the breakthrough in Jev. Classification, scoring, routing and returning probabilities over predefined choices are all established problems. We implemented category routing in our own retrieval system in a slightly different way: embed the incoming query, compare it against category profiles and route to the highest cosine-similarity. Obviously Jev isn't similarity based, but the underlying task of making a constrained decision from predefined choices isn't novel. TypeSafe says Jev has a new architecture and RLCD training, but Jev's actual architecture, weights and training details aren't public. So we can't even claim Jev is specifically a BERT classifier, but also don't see enough public technical evidence yet to call the underlying idea a breakthrough. Atleast they should publish a technical paper to prove their idea is breakthrough.
I'm doing this. After getting started with LLM coding, I became super interested in learning to code, just out of passion. I walked out of engineering thinking physics was elite, but now I understand how passionate I am about building things, and how boring quantum mechanics was. Better late than never.