LLMs From Scratch vs Surge AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | LLMs From Scratch | Surge AI |
|---|---|---|
| Primary Offering | Book/tutorial to build a GPT-like LLM in PyTorch | Human feedback platform for RLHF & evaluation |
| Expert Labor | Not applicable | Curated workforce of doctors, lawyers, engineers |
| Hands-on Coding | Full code examples in PyTorch | No; uses SDK/API to collect data |
| Target User | Developers, researchers, students learning LLM internals | AI labs, safety teams, enterprise AI builders |
| Latest Benchmark | Not applicable | Antidote leaderboard (expert-graded), Riemann-bench (<10% frontier score) |
Surge AI and LLMs From Scratch serve fundamentally different needs. Pick Surge AI if you need expert human feedback for RLHF or rigorous model evaluation; it's a service, not a tutorial. Choose LLMs From Scratch if you want to understand and build an LLM yourself via hands-on PyTorch code. They are complementary—use Surge for data after you've built your model.
A code-first Manning book that walks you through building a GPT-2 class LLM in PyTorch, line by line, without using existing LLM libraries
Visit Website
Surge AI supplies expert human RLHF data, red teaming, and public benchmarks like GDP.pdf and the Tuesday Work Index for frontier model
Visit WebsiteWhat real users say: LLMs From Scratch vs Surge AI
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
LLMs From Scratch
49 mentions across 3 sources · 73% positive (averaged across 3 sources)
Hacker News, GitHub, Lemmy
What users praise
- • Unmatched depth in explaining transformer internals with code and illustrations
- • Hands-on approach: build and train a GPT-like model from scratch on your laptop
- • Cryptography: clear, step-by-step construction of multi-head self-attention and transformer blocks
- • Strong focus on 'why' behind architecture, not just 'how' to use APIs
What frustrates them
- • High skill barrier: requires intermediate Python and deep learning knowledge
- • Time-consuming to work through fully—not a quick read
- • Some chapter code lacks reproducibility, breaking the 'follow-along' experience
- • Assumes PyTorch comfort; non-PyTorch users face extra friction
Researched Aug 14, 2026
Surge AI
48 mentions across 3 sources · 38% positive — critical (weighted across 3 sources)
Hacker News, YouTube, Lemmy
What users praise
- • Credentialed workforce of doctors, lawyers and engineers instead of generic crowd annotators
- • GDP.pdf cited by OpenAI in the GPT-5.6 release with a concrete 30.7% flagship score
- • Kimi K2.7 post-training run published measurable SWE-Marathon, DeepSWE and Terminal-Bench gains
- • Benchmark catalog spans chart reasoning, dependent constraints, long-context policy and verticals
What frustrates them
- • Contact-only pricing means no public rate card, no tiers, and no way to self-serve
- • Benchmark sponsorship and independence questions raised directly in HN threads
- • Expert-credential verification process is never explained in any community source
- • No community data on support responsiveness, uptime, or SLAs at enterprise scale
Researched Oct 7, 2026
Who should pick which
- Frontier AI lab fine-tuning a new LLMPick: Surge AI
Needs expert human feedback for RLHF and advanced benchmarks like Antidote or Riemann-bench. Surge provides domain experts and rigorous evaluation.
- Deep learning engineer learning transformer internalsPick: LLMs From Scratch
LLMs From Scratch offers hands-on PyTorch code from tokenization to training, ideal for understanding LLMs bottom-up.
- AI safety team conducting red teaming with domain expertsPick: Surge AI
Surge’s curated workforce of doctors, lawyers, and engineers provides the nuanced adversarial testing needed for safety.
- Student building a small GPT as a capstone projectPick: LLMs From Scratch
The book’s step-by-step approach with code is perfect for a self-contained project; no need for costly human feedback.
- Enterprise building multimodal AI for PDF understandingPick: Surge AI
Surge’s GDP.pdf benchmark and expert labelers can handle complex, real-world document tasks that require domain knowledge.
Frequently Asked Questions
LLMs From Scratch vs Surge AI: which should you choose?
Surge AI and LLMs From Scratch serve fundamentally different needs. Pick Surge AI if you need expert human feedback for RLHF or rigorous model evaluation; it's a service, not a tutorial. Choose LLMs From Scratch if you want to understand and build an LLM yourself via hands-on PyTorch code. They are complementary—use Surge for data after you've built your model.
Can I use Surge AI to learn how to build an LLM?
No. Surge is a service for collecting expert human feedback; it does not teach model architecture or training code.
Does LLMs From Scratch include RLHF data collection?
It covers RLHF basics at a conceptual level, but does not provide a workforce for collecting human feedback – you'd need to simulate or use a platform like Surge.
Which tool is cheaper?
LLMs From Scratch is a one-time book cost (~$40). Surge AI requires contacting sales and is typically expensive, suited for funded teams.
Can I evaluate my model's performance with LLMs From Scratch?
The book includes basic evaluation and generation quality metrics, but not expert-graded benchmarks like Surge’s Antidote.
Does Surge AI provide any code or model architecture?
No, Surge provides an SDK/API to interact with its platform. It does not teach you how to build a model from scratch.
Is LLMs From Scratch suitable for beginners?
No, it assumes solid Python and deep learning fundamentals (PyTorch). Beginners may struggle.
Can I use Surge AI for simple sentiment analysis?
Not recommended – Surge is optimized for complex, reasoning-heavy tasks. Simpler tasks are better served by cheaper platforms.
Does Surge AI have a free tier?
No, pricing is custom and requires contacting sales. There is no free self-serve tier.
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Last reviewed: July 3, 2026