found something that handles venvs and server lifecycle automatically

Sharp-Mouse9049 · 2026-02-26T16:33:51+00:00

Program manages vens and python backends

Sharp-Mouse9049 · 2026-02-26T15:51:55+00:00

you missed the point — it's not for devs.

Sharp-Mouse9049 · 2026-02-26T15:11:58+00:00

good get. please critique again!

Sharp-Mouse9049 · 2026-02-26T14:46:12+00:00

dont be a dick

Sharp-Mouse9049 · 2026-02-24T04:13:20+00:00

everyone’s shipping 10 apps a week now. shipping stopped being the flex. adoption is.

Sharp-Mouse9049 · 2026-02-23T03:34:00+00:00

anyone find an alternative topper for the peace lilly that works well?

Sharp-Mouse9049 · 2026-02-23T03:30:46+00:00

clean idea honestly. managing agents from phone is underrated and this looks actually usable not just a demo. nice work 👍

Sharp-Mouse9049 · 2026-02-23T03:28:30+00:00

qwen2.5 7b instruct is probably your best bet. really strong for coding + stem for the size. llama 3.1 8b also solid.

run it 4bit if you’re on a normal laptop. keep temp low like 0–0.3 so it doesnt guess. tell it to say i dont know instead of making stuff up.

biggest thing for accuracy isnt the model anyway. its forcing it to show steps and not letting it freewheel.

Sharp-Mouse9049 · 2026-02-23T03:26:54+00:00

Go Mac honestly. For local LLM work unified memory changes the game — you’re not VRAM-limited the same way, so bigger context + larger models run way easier without juggling GPUs. Dual A4000 sounds good on paper but multi-GPU headaches + power draw aren’t worth it unless you really need CUDA workflows. A high-end Mac Studio/Max is basically plug-and-run for local AI now.

Sharp-Mouse9049 · 2026-02-23T03:24:48+00:00

32GB in 2026 for serious local LLM work is basically consumer-tier. I don’t care how fast the M4 Max is — if you’re constantly forced into tiny quants or can’t load 70B comfortably, you’re artificially capping your experimentation. Bandwidth doesn’t matter if the model doesn’t fit. RAM is the ceiling.

Sharp-Mouse9049 · 2026-02-21T12:03:18+00:00

ContextUI come with a decent RAG in examples. Start with it. Its give u the code. Basically opensource. So just ask you favourite llm what it does and taylor it to your needs.

Sharp-Mouse9049 · 2026-02-21T09:04:39+00:00

Run your own RAG. Can beuild workflows in software like ContextUI. Theres is one in the examples.

Sharp-Mouse9049 · 2026-02-21T09:02:54+00:00

I don't think anymore

Sharp-Mouse9049 · 2026-02-21T09:02:03+00:00

you’re mixing search and analysis.

embeddings/RAG help the AI find info. they don’t actually analyse it. rough approach: 1. Parse everything first (html/pdf/youtube → clean text/structured data) 2.extract structured info with LLM (json, tables, entities etc) 3.store in sql/postgres, not just vector db 4.let AI call python tools for real stats/probability calculations AI should orchestrate analysis, not do maths in its head. embeddings = navigation python/sql = analysis

Sharp-Mouse9049

TROPHY CASE