ZERO OUT-OF-POCKET EXPENSE

Invest in the 0.1% Technical Advantage.

Frontier ML intelligence and verified implementations that save your engineering team 15–20 hours of research and prototyping time per paper.

Public Brief
$0/ forever

For curious engineers tracking high-level AI paper trends, public graveyard alerts, and morning executive digests.

  • ✓ Daily SOTA Pulse (Delivered to ALL users 7 days/wk)
  • ✓ Pillar 2 BS Detector preview card
  • ✓ Public Code Graveyard & Broken Promise Tracker
  • ✕ Verified runnable PyTorch & Triton code
  • ✕ Direct IDE Delivery via ARGMAX MCP Server
  • ✕ Pillar 1 Cryptographic Benchmark Receipts
FOUNDING COHORT • $29/MO (FIRST 500)
Architect Pro
$290/ year ($24.1/mo)
● Seats 1–500 locked at $29/moScales to $45/mo

For Staff & Principal Engineers who need production-verified PyTorch kernels, direct Cursor/Claude Code MCP tool delivery, and hardware execution receipts.

  • ✓ Direct IDE Integration via ARGMAX MCP (`uvx argmax-mcp`)
  • ✓ 15-Minute nanoGPT Porting Kits (<250 lines PyTorch)
  • ✓ Pillar 1 Cryptographic Benchmark Receipts (HMAC-SHA256)
  • ✓ Twice-Weekly Flagship Deep Teardowns (Tues & Thurs)
  • ✓ Friday "The Weekly Redaction" Contrarian Debunk
  • ✓ Non-expiring lifetime lock at $29/mo
Claim Founding Pro ($290/yr)→
100% Expensable via L&D • Instant Access • Cancel Anytime
AI Lab & Enterprise Team
$1,990/ year
Founding rate for first 50 AI Labs (Scales to $299/mo)

For AI startups, research labs, and platform teams needing shared MCP pools, real-time Slack research bots, and priority audit SLAs.

  • ✓ 5 to 10 Shared Team Seats Included
  • ✓ Shared Centralized MCP Server Pool for Cursor & Claude
  • ✓ Real-Time Slack & Discord Bot (`#ai-research` webhook)
  • ✓ Raw SQLite & DuckDB Telemetry Feed
  • ✓ 48-Hour Priority Paper Adversarial Audit SLA

Full Feature & Capability Matrix

Everything included across Public, Architect Pro, and Team Tiers.

Capability Public Brief ($0) Architect Pro ($29/mo) AI Lab Team ($199/mo)
1. Intelligence & Dispatch Cadence
Daily SOTA Pulse Email Notification YES (7 days/wk) YES (7 days/wk) YES (7 days/wk)
Twice-Weekly Flagship Deep Teardowns Preview only ALL Issues (Tues & Thurs) ALL Issues + Webhooks
The Weekly Redaction (Friday Contrarian Debunk) YES (Public) YES + Uncut Analysis YES + Raw Telemetry
Private Authenticated Pro RSS Feed Public RSS only YES (Token Auth) YES
2. Code, Tooling & Sandbox Artifacts
ARGMAX MCP IDE Delivery (`uvx argmax-mcp`) Index search only YES (Personal License Key) YES (Shared Team Pool)
15-Minute nanoGPT Porting Kits (<250 lines PyTorch) Snippet preview only FULL Verified Module + Test Suite FULL Verified Module + Test Suite
Pillar 1 Cryptographic Benchmark Receipts — YES (HMAC-SHA256 Signed) YES (Raw JSON / Parquet)
Pillar 2 Adversarial BS Detector HUD Preview Card Full Audit & Roofline Full Audit & Roofline
1-Click Google Colab Notebook with Pre-loaded Weights — YES (GPU Verified) YES (Shared Workspace)
Production vLLM & SGLang Configs — YES YES
3. Team Seats & Programmatic Feeds
Seats Included 1 Individual 1 Individual ($29/mo Lock) 5 to 10 Team Seats
Slack / Discord Webhook Dispatches — — YES (`#ai-research` bot)
Priority Paper Evaluation SLA — — 48-Hour SLA
87% EXPENSE APPROVAL RATECorporate L&D Budget

1-Click Corporate Expensing Pack

Engineers at Google, Meta, OpenAI, Anthropic, Stripe, and Datadog expense ARGMAX under their company's L&D or tools budget with zero out-of-pocket spend.

Customize Memo Fields
* Receipts generated by Polar Software Inc. comply with US and international expensing requirements.
Generated Email Memo PreviewReady to Paste
Subject: Professional Development: Request to expense ARGMAX AI Research ($190/yr)

Hi Alex,

I would like to request approval to expense an annual subscription to ARGMAX (https://argmaxai.dev) under our team's Learning & Development / Engineering Tools budget ($190/year, billed through Polar Software Inc.).

ARGMAX provides verified, production-grade technical teardowns of bleeding-edge AI models, inference optimization architectures, and GPU cluster efficiency techniques.

How this directly benefits our engineering initiatives on Inference & AI Platform Team:
1. Production Hardware Efficiency: Teardowns provide verified Triton attention kernels, vLLM/SGLang configurations, and VRAM memory profiling that can directly reduce our GPU cloud infrastructure costs.
2. Verified Implementations: Every paper analysis is sandbox-audited with runnable PyTorch code and Colab environments, saving tens of hours of exploratory engineering time.
3. Industry SOTA Tracking: Keeps our team ahead of algorithmic shifts in speculative decoding, quantization, and context-window scaling.

The cost is $190/year (or $19/month). If this helps our team implement even a single inference optimization or avoid one costly architectural dead-end, the ROI is easily 100x.

You can view their publication overview and compliance credentials here: https://argmaxai.dev

Let me know if you are comfortable with me putting this on my corporate card.

Thanks,
Staff Engineer

Frequently Asked Questions

Can I expense this subscription with my employer? ↓

Yes. Over 87% of our subscribers expense ARGMAX under their company's Learning & Development (L&D), Professional Education, or Developer Tools budget. Receipts are issued automatically by Polar Software Inc. and include full sales tax/VAT compliance. Use our 1-Click Expensing Pack above to generate a pre-formatted approval memo for your manager.

What happens after I subscribe via Polar.sh? ↓

You immediately receive instant VIP access to all locked technical teardowns, verified PyTorch modules, and one-click Google Colab notebooks. You are also sent your personal authenticated RSS feed token and private Discord invite.

Can I cancel anytime? ↓

Yes, anytime with a single click. There are zero long-term commitments or lock-ins. You manage your subscription directly through the Polar.sh customer portal.

How does ARGMAX verify PyTorch code snippets? ↓

Unlike newsletters that copy-paste unverified pseudo-code from paper PDFs, our engineering lab compiles and executes every PyTorch module inside an isolated GPU verification environment with realistic input tensor dimensions. We test forward-pass numerical stability, tensor shape boundaries, and backpropagation loss gradient norms before publishing.