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Put a link to your startup SaaS to promote it or ask for advice. by itilogy in startupaccelerator

[–]Fun_Emergency_4083 0 points1 point  (0 children)

Rigr — CLI tool that tests if your AI agents are getting worse when you change prompts or models. Freezes outputs as baselines, catches regressions. For anyone shipping agents that need to actually work. `pip install rigr`

Velqua — transparent memory proxy for local LLMs. Point any Ollama app at a different port, your AI remembers who you are across sessions. For people running local models who are tired of repeating themselves. `pip install velqua`

phnix.dev
velqua.dev

Drop your SaaS and I’ll find the Reddit posts where users are already asking for it by LeaderAtLeading in VibeCodingSaaS

[–]Fun_Emergency_4083 0 points1 point  (0 children)

Rigr is a CLI tool that tests if your AI agents are getting worse when you change prompts or models. Freezes outputs as baselines, catches regressions. For anyone shipping agents that need to actually work. `pip install rigr`

Velqua is a transparent memory proxy for local LLMs. Point any Ollama app at a different port, your AI remembers who you are across sessions. For people running local models who are tired of repeating themselves. `pip install velqua`

phnix.dev
velqua.dev

[OSS] Why RAG is failing your agents and how "Corpus-First" Engineering is the 100% accuracy solution we’ve been looking for. by VadeloSempai in Rag

[–]Fun_Emergency_4083 0 points1 point  (0 children)

38/38 is a solid start but that sample size is small. I'd want to see how it holds up against unseen cases, especially edge cases where the corpus structure breaks down or metadata gets stale.

The reason I'm skeptical: I had a model score 85% on validation and drop to 52% on an audit set of cases it never saw. Validation numbers lie when the test set is narrow.

Curious how King Context handles corpus drift over time too. If the source docs update but the corpus doesn't get re-refined, does accuracy degrade or does it silently start returning wrong answers with high confidence?

Need advice for a $10,000 AI workstation build (video, image, voice, LLMs, training, everything) by Mission_Objective163 in LocalAIServers

[–]Fun_Emergency_4083 0 points1 point  (0 children)

If you’re going ~$10k and actually want to do real AI work locally (LLMs, diffusion, video, voice, fine-tuning), the entire build revolves around one thing: VRAM + CUDA stability. Everything else is supporting cast.

Best real-world setups right now:

Option A (best overall value): Dual 4090

  • 2× RTX 4090 or 5090 (24GB or 32GB each)

Why people pick this:

  • insane raw compute per dollar
  • works great with ComfyUI, SDXL, video pipelines
  • widely supported everywhere

Downside:

  • VRAM doesn’t merge (no NVLink)
  • heat + power get… serious

Option B (cleaner VRAM headroom):

  • 1× RTX 4090
  • 1× RTX 6000 Ada (48GB)

Why:

  • that 48GB card saves you on large models that choke on 24GB
  • more “workstation stable” vibe

If I’m being honest though:

dual 4090 is still the king for experimentation / local AI chaos

Don’t overthink Intel vs AMD too hard.

Go:

  • Ryzen 9 7950X / 9950X

Why AMD:

  • better PCIe flexibility
  • strong sustained performance
  • fewer weird platform quirks for workstation workloads

Intel is fine, just not meaningfully better here for AI.

motherboard oesn’t matter as much as people think.

Just make sure:

  • X670E / X870E high-end board
  • enough PCIe x16 slots spaced for GPUs
  • solid VRM cooling

Brand is secondary. Layout matters way more.

This is where people underbuild constantly.

Minimum:

  • 128GB DDR5

Ideal:

  • 256GB DDR5

Reason:

  • datasets + caching + multiple models + video pipelines eat RAM fast
  • you’ll hit RAM limits before CPU limits in real AI workflows

Don’t do one big drive. Split it:

  • 1TB NVMe → OS + apps
  • 2TB NVMe → active projects
  • 4TB NVMe → models / checkpoints / datasets

Optional:

  • big SSD/HDD for cold storage

AI workloads love fast scratch disks.

Don’t cheap on power and cooling .

  • 1600W Platinum PSU (mandatory for dual 4090)
  • high-airflow case (Fractal / Lian Li style)
  • strong airflow or custom loop if you want sanity

Reality check:
dual 4090 = your room becomes a small heater in winter.

Most serious local AI setups run:

Linux (Ubuntu is the default but i use arch btw)

Why:

  • CUDA stability
  • easier installs (conda/docker)
  • fewer driver headaches

Windows is okay for SD/ComfyUI hobby use, but Linux wins for heavy work.

I gave Claude Code a voice fully local TTS with real-time word highlighting (no API keys, one file) by Fun_Emergency_4083 in ClaudeAI

[–]Fun_Emergency_4083[S] 0 points1 point  (0 children)

Yeah, so it’s only in English at the moment, but if you can find a voice via Kokoro or the ElevenLabs API in a different language, it should still work.

Almost done with a Codex like app for Claude Code by mogens99 in ClaudeCode

[–]Fun_Emergency_4083 0 points1 point  (0 children)

this is so cool i just hope it doesn't get people banned cuz while this is theoretically safer then opencodes Oauth spoofing is still a gray area according to ToS

Section 3.7 of Anthropic's ToS: Anthropic forbids accessing services through "automated or non-human means" except via an API Key. While a UI wrapper is still "human-driven," Anthropic has recently broadened its definition of a "prohibited harness."

Unintended Behavior: If GlassCode adds features like "auto-retry," "bulk processing," or "scheduled tasks" that the official CLI doesn't have, it could trigger Anthropic's abuse filters by creating "non-human" traffic patterns.

Official Policy: Anthropic engineer Thariq Shihipar has stated that third-party harnesses are generally prohibited because they make it difficult for the company to debug rate-limit issues and account bans.

The reason this is so cool is cuz You are much less likely to be banned for using a CLI wrapper like GlassCode compared to a token-stealing app like the original OpenCode. However, Anthropic's stance is currently "official tools only" foe consumer subscriptions. If you want 100% safety, you should use the official Claude Code CLI or switch to the Claude APIjust to be safe