sreekanth850

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.

And you think all this web AI crawlers will respect robots.txt. That era is gone.

Create bot detection and bot protection, then sell crawlers. Is this the peak of hypocrisy?

Yes, but we were looking for a distributed sql for massive government data scale.

I only use their vs code extension.

We use cratedb. but now, clickhouse, starrocks all hve vector.

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.

how on the eart they are able to give 1 Million Output context? is this due to their custom inhouse tensor processing units?

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.

build this prototype from end-to-end, is this how people build serious software with AI?

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.

Adieu, Ruby on Rails.

People on X will use JEV for self driving cars too. That is the height of stupidity. I’ve seen this in so many tweets.

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 wish this can be a extension of existing languages.

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.

This is best thing to use for decision making, evaluation, classification. If I'm not wrong.

i don't need all this shit, can i have a modern browser that run in 10 mb ram and doesnt multiply the memory with tabs. something like kmelon but works on all sites.

I’m using dpupek/roslyn-code-navigator (codenav-mcp) with Codex.

Yes. and i find model makes less errors and reasoning the codebase well, especially when you do a large refactor.

i don't know if such hacks works, but in C# if you use roslyn mcp, you save a lot.

Pretty happy with Luna. We use C# and add roslyn compiler MCP and Graft MCP, its super efficient and like infinite usage on plus plans. Maintaining 3 Rpeo with size of 360 K Loc. And a dozens of smaller repo collction together exceeds 500 K LOC in total. 3 team members 2 Luna account each. Product is piloting in a government use case with actual data. Nothing broke and has evaluated by state agencies on security aspects. Edit: but we have strict workflow where thinsg are implemented after plan, proposal, features, task ledgering and then test coverage.

The problem is when people ignore or dismiss developments in AI purely out of insecurity or a sense of human superiority. Machines don’t have to be superior to humans to dramatically amplify what humans can do.

Many will disagree and say that anything done by humans is by far superior to anything done by machines. You don't want to correct them, do you?

A fuzzer is a tool. An LLM can decide when to use the fuzzer, interpret the result, switch tools, change strategy and continue toward a high level objective.

D o you think a machine gun is marter than humans? or a car is smarter than Human brain? Human doesnt need to test, if the outcome can be tested deterministically by harness. The model tries. The harness checks whether the expected outcome happened. If not, retry.

I think people here still evaluating the model in isolation. It is the combination that matters, model + strong harness + tools + long running autonomy + memory + retries + parallel agents + code execution + credentials + access to real systems. The model does not need to be perfect. If it fails 30% of the time, the harness can retry, verify, branch, use another agent and keep going. I don't think we necessarily need some magical AGI breakthrough first. The dangerous part may come from combining models that are already good enough with an extremely capable harness and enough access.

Model doesnt need to. Human bran never do either. its the mix of Model + harness + tools that will become dangerous combo. See how coding chanegs when agentic harness released?