Eric Vyacheslav

Advisor

There are two career paths in AI.

API Caller.  
> Uses existing APIs.  
> Low leverage.  
> First to be automated.  
> ~$150k salary.

Architect.  
> Builds the APIs.  
> High leverage.  
> Creates the tools others depend on.  
> $500k+ salary.

Most bootcamps train API Callers. This free Stanford course trains Architects.

CS336: Language Modeling from Scratch.

The syllabus is pure signal:

→ Data collection and curation (Lectures 13–14)  
→ Transformers and Mixture of Experts (Lectures 3–4)  
→ Performance and scale: GPUs, kernels, parallelism (Lectures 5–8)  
→ Inference and deployment (Lecture 10)  
→ Alignment and reinforcement learning (Lectures 15–17)

Choose your path.
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You can now parse any document with one 1.7B parameter model.

dots-ocr delivers state-of-the-art document understanding across 100+ languages.
It handles text, tables, formulas, images, and PDFs in one system.

No separate OCR pipeline needed.
No model switching between tasks.

The model unifies layout detection and content recognition.
It matches performance of much larger models like Doubao-1.5 and Gemini 2.5 Pro.

On OmniDocBench benchmarks:
• Competitive accuracy with enterprise models
• Faster processing due to compact 1.7B size
• Full open source availability

dots-ocr runs everything through a single prompt.
Complete multilingual document parsing in one step.
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▸ 250k+ developers read this newsletter to stay up to date with AI: AlphaSignal.ai

🚨 BREAKING: Chinese scientists just shattered a 41-year computing barrier.

Tsinghua University researchers achieved what seemed impossible:

They beat Dijkstra's algorithm, the foundation of Google Maps, GPS navigation, and network routing since 1984.

This is the FIRST major breakthrough in shortest-path algorithms in over four decades.

What this means:

> Your GPS could calculate routes 10-100x faster
> Real-time traffic rerouting becomes instant
> Network optimization gets revolutionized
> Gaming pathfinding reaches new levels

The team broke through Dijkstra's "sorting barrier", a limitation experts thought was fundamental.

Think about it: Every time you've used Google Maps, delivery tracking, or even played a video game, you've used Dijkstra's algorithm.

Now there's something better.
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Quick Opus 4.5 vibecoding tip.

Ask Claude to create state machine diagrams of your existing components.

Why this works:

> Forces complete path mapping (Claude won't skip edge cases or take shortcuts)
> Gives you a systems-level view to verify correctness
> Reveals logic gaps or unexpected state transitions you might have missed

The visual representation makes it easier to spot issues before they become bugs. 
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Yann LeCun says language fluency tricks us into believing LLMs are intelligent.

We mistake smooth text generation for actual understanding.
But manipulating words well doesn't equal intelligence.

The pattern repeats itself:

→ 1950s: Logic-based systems would crack AI
→ 1980s: Expert systems were the answer  
→ 1990s: Neural networks would solve everything
→ 2000s: Deep learning was the breakthrough
→ 2020s: Large language models are intelligence

Each generation believed they found the solution.
All were wrong.

LLMs excel at predicting the next word in a sequence.
This creates an illusion of comprehension.

The real test isn't how well they write.
It's whether they truly understand what they're saying.
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This developer literally created an agent to replace all his employees,

Design, engineering, product, marketing, ops, all split into specialized sub-agents.

Each one has a role, constraints, tools, and success metrics.

No prompts. No babysitting.

You describe the task. The right agent activates.

This is how solo founders scale without hiring.
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Never leave your AI training unattended during the holidays.

An OpenAI researcher forgot to stop a training run before vacation. When they returned, the model had undergone "grokking."

The model switched from pure memorization to genuine understanding. This phase transition happens suddenly after extended training.

Welch Labs breaks down this discovery in their latest video:

• Models initially memorize training data perfectly
• They show zero generalization to new problems
• Then something clicks after thousands more steps
• Sudden jump to real pattern recognition

The researcher's accident revealed a critical insight. More training time can transform memorization into understanding.

This explains why some models seem to "get it" unexpectedly. The grokking phenomenon changes how we think about AI learning.
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This is insane.

Shopify CEO Tobi Lütke didn’t want to use commercial Windows software to analyze his MRI scans, which were on a USB stick.

So he used Claude to write his own software in a short time. It gave him clearer output and highlighted the areas that mattered. 

It's the end of SaaS. 
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Can you believe DeepSeek is just a side project? The founder is incredible:

1. Studied electrical engineering
2. Teamed up with brilliant classmates
3. Got into quant trading
4. Founded a quant firm in his 30s
5. Generated ¥100B using AI/ML in trading
6. Bought thousands of NVIDIA GPUs
7. Built DeepSeek, as a side project!

Wow. 
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GPT just outperformed the S&P 500 by 7x as a stock portfolio manager.

An experiment let the AI manage a real portfolio for two months.
ChatGPT delivered +29.22% returns while the S&P 500 gained +4.11%.

The setup lets you track how AI makes investment decisions:

• Receives daily market data including prices and volumes
• Uses weekends to research and rebalance holdings
• Targets U.S. micro-cap stocks under $300M market cap
• Executes trades through manual brokerage account orders

All trading data and performance reports are public on GitHub.
You can see exactly how a language model operates as a fund manager.

The ChatGPT Micro-Cap Experiment shows complete transparency in AI investing.
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▸ 250k+ developers read this newsletter to stay up to date with AI: AlphaSignal.ai

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