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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
GitHub - jaquelinejaque/quorum-saas-starter: The only Nex...
jaquelinejaq · 2026-06-19 · via Hacker News - Newest: "LLM"

Multi-LLM SaaS Starter Kit

The only production-ready boilerplate that ships with 14 LLM providers in semantic consensus, EU AI Act audit-grade compliance, and 13 self-evolution loops out of the box. Built on the same code that powers api.quorum-ai.dev.

Why this exists

Every AI SaaS boilerplate on the market ships with one LLM provider — usually OpenAI, sometimes Anthropic. That's the easy part. The hard part is everything around it:

  • Routing queries across multiple providers without exploding costs
  • Detecting hallucination via semantic consensus instead of trusting a single model
  • Generating audit trails that pass EU AI Act Article 12/13 review
  • Building fail-closed gates for high-risk autonomous actions
  • Keeping per-user memory, RLHF feedback, and adaptive routing data isolated

This kit gives you all of that already wired, deployed, and battle-tested in production. You spend your engineering time on what makes your product different — not on rebuilding the orchestration layer everyone else stops at.

What's inside

Multi-LLM consensus engine

  • 14 provider integrations out of the box: OpenAI, Anthropic, Google Gemini, xAI Grok, Mistral, Cohere, NVIDIA, DeepSeek, Replicate, DashScope/Qwen, Zhipu/GLM, Moonshot/Kimi, Hermes (Nous) local, Llama local via Ollama
  • Semantic agreement scoring via cosine similarity on embeddings — not lexical overlap, not majority vote
  • Disagreement trace returned with every answer — your users see exactly which models agreed, which dissented, and why
  • Per-query cost log so you can show clients what each consensus cost in API tokens

Self-evolution loops (13 total)

Loop What it learns
RLHF tracker Per-user, per-query-class weight updates from thumbs-up/down
ELO competition Pairwise model wins → global ranking per query class
Hebbian co-activation Which models agree with each other (reduces redundant fan-out)
MoE router Picks the right subset of providers per query, not all every time
Hebbian memory Per-user vector memory with semantic recall
Genetic prompt evolution Mutates and selects system prompts that score highest on your eval set
Adversarial loop Red-team prompts to catch jailbreaks before users do
A/B testing Promotes new policies only when they beat the incumbent statistically
Distillation Promotes Llama checkpoints fine-tuned on your in-house data
Synthetic data Generates training pairs from real production traffic
Architecture search Evolves loop topology over generations
Federated learning Aggregates updates across tenants without sharing raw data
Web learner 4-source web ingestion (DDG + Wikipedia + HackerNews + arXiv) into a vector KB

Compliance & safety (all opt-in)

  • HSP fail-closed gate (opt-in) — wrap any high-risk function with requiresHspApproval() and it refuses unless a human-approved webhook signs off. Don't need compliance? Don't wrap. The kit doesn't force this on you. Toggle off globally with HSP_ENABLED=false even if a dependency does wrap it.
  • EU AI Act Article 12 audit trail (opt-in) — call writeAuditLine() from your business logic to log decisions; the audit cert generator reads this log
  • EU AI Act Article 13 audit certificate (opt-in) — SHA-256 hash-chained PDF generator, one call per audit period: generateAuditCert({...}). Useful when selling to regulated verticals (legal, fintech, health); ignorable otherwise.
  • Hosted vs local posture split — hosted deployment is fail-closed by infra-marker detection (Cloud Run, k8s, Lambda); local research mode opt-in via HSP_GATE_DEV_MODE=1
  • CUSTOMER_KEYS_ENCRYPTION_KEY — Fernet-encrypted BYOK provider keys, never logged, never leaked. Only relevant if you're letting your end users supply their own API keys.

Infrastructure

  • Auth — email magic links + OAuth-ready (Apple, Google), session cookies, JWT for API
  • Stripe billing — Pro tier subscription, Free tier metering, webhook handler with signature verification (Stripe Event SDK)
  • Resend email — transactional templates: welcome, billing, audit alerts
  • Firestore persistence — API keys, customer tiers, usage counters, encrypted BYOK keys
  • Cloud Run deploy — single command, autoscaling 0→N, regional failover ready
  • GitHub webhook receiver — HMAC SHA-256 signature verified, ready to dispatch audit jobs on push
  • Cloud Scheduler target — cron endpoint gated by shared secret, ready for nightly batch jobs

Deploy in one command

git clone https://github.com/jaquelinejaque/quorum-saas-starter
cd quorum-saas-starter
./scripts/setup.sh      # interactive: collects keys, creates Stripe products, configures Cloud Run
./scripts/deploy.sh     # gcloud run deploy + Firestore init + Stripe webhook registration

You ship to production in under 30 minutes.

What you build on top

The kit gives you the orchestration layer. You add the vertical:

  • AI Tax Assistant — your prompts + this kit's consensus = product
  • AI Code Reviewer — your evals + this kit's RLHF = product
  • AI Compliance Auditor — your domain knowledge + this kit's HSP gate = product
  • AI Legal Research — your case database + this kit's web learner = product

The hard infrastructure work is done. You focus on customers, prompts, and the niche-specific data only you have.

Licenses

Starter — £497

Single project deployment. Source code under modified Apache-2.0 (HSP commercial restriction). 3 months of updates. No support.

Pro — £997

  • 5 project deployments
  • Pre-trained MoE router weights (10,000+ production queries already shaped the policy table)
  • Genetic prompt evolution kit + 50 evolved seed prompts across common SaaS categories
  • 12 months of updates
  • Monthly recorded "office hours" video (no live calls — recordings only, accessible to all Pro+ buyers)
  • Read-only Discord access

Enterprise — £2,497

  • Unlimited project deployments
  • Commercial / white-label license — resell as your own product
  • EU AI Act Article 12/13 audit kit (forms, templates, cert generator)
  • 24 months of updates
  • Office hours archive (all past + future)

All tiers: source code only, no managed hosting, no live support. Buy what you can build on, not what you have to ask permission to use.

Why we charge this

The orchestration layer in this kit took 18 months and 60+ commits to get production-stable. It runs the Quorum API used in audit-grade compliance workflows. Pricing reflects what it would cost a senior engineer to rebuild from scratch: roughly 6 weeks of full-time work at standard contractor rates (~£25,000). At £497–£2,497 you're buying back that time.

Not for you if

  • You want managed hosting — go use Quorum hosted (£49/mo)
  • You want live support — this is code-only license
  • You want to vibe-code an AI app and ship in 2 hours — use Lovable or Vercel templates instead
  • You don't have a real product idea yet — this kit is for shipping, not learning

Refund policy

30 days, no questions asked, if the kit doesn't deploy successfully on your environment. After successful deployment, no refunds — you have the source code.


Built by Jaqueline Martins / Sovereign Chain Ltd. Patent Pending: HSP Protocol (PCT/US26/11908).