Included in the search and seizure warrant for the raid on Natanson’s home is a section titled “Biometric Unlock,” which explicitly authorized law enforcement personnel to obtain Natanson’s phone and both hold the device in front of her face and to forcibly use her fingers to unlock it. In other words, a judge gave the FBI permission to attempt to bypass biometrics: the convenient shortcuts that let you unlock your phone by scanning your fingerprint or face.

"China reportedly coordinated the whole operation," the post reads. "The CIA oversaw it, the FBI covered it up, all to install Biden as a puppet."

OpenAI recently introduced their bespoke in-house AI data agent, a GPT-5.2-powered tool designed to help employees navigate and analyze over 600 petabytes of internal data across 70,000 datasets. By translating natural language questions into complex data insights in minutes, the agent enables teams across the company to bypass manual SQL debugging and quickly make data-driven decisions.

TikTok users in the US have reported being unable to write the word ‘Epstein’ in messages amid accusations that the social media platform is suppressing content critical of President Donald Trump.

Meanwhile, longtime government contractor Palantir was paid $30 million to extend a contract to build a system designed to locate people flagged for deportation. On Wednesday, the Trump administration disclosed it’s using Palantir’s AI models to sift through immigration enforcement tips submitted to its tip line.

This list bridges the Transformer foundations
with the reasoning, MoE, and agentic shift

Recommended Reading Order

1. Attention Is All You Need (Vaswani et al., 2017)

   The original Transformer paper. Covers self-attention,
   multi-head attention, and the encoder-decoder structure
   (even though most modern LLMs are decoder-only.)

2. The Illustrated Transformer (Jay Alammar, 2018)

   Great intuition builder for understanding
   attention and tensor flow before diving into implementations

3. BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2018)

   Encoder-side fundamentals, masked language modeling,
   and representation learning that still shape modern architectures

4. Language Models are Few-Shot Learners (GPT-3) (Brown et al., 2020)

   Established in-context learning as a real
   capability and shifted how prompting is understood

5. Scaling Laws for Neural Language Models (Kaplan et al., 2020)

   First clean empirical scaling framework for parameters, data, and compute
   Read alongside Chinchilla to understand why most models were undertrained

6. Training Compute-Optimal Large Language Models (Chinchilla) (Hoffmann et al., 2022)

   Demonstrated that token count matters more than
   parameter count for a fixed compute budget

7. LLaMA: Open and Efficient Foundation Language Models (Touvron et al., 2023)

   The paper that triggered the open-weight era
   Introduced architectural defaults like RMSNorm, SwiGLU
   and RoPE as standard practice

8. RoFormer: Rotary Position Embedding (Su et al., 2021)

   Positional encoding that became the modern default for long-context LLMs

9. FlashAttention (Dao et al., 2022)

   Memory-efficient attention that enabled long context windows
   and high-throughput inference by optimizing GPU memory access.

10. Retrieval-Augmented Generation (RAG) (Lewis et al., 2020)

    Combines parametric models with external knowledge sources
    Foundational for grounded and enterprise systems

11. Training Language Models to Follow Instructions with Human Feedback (InstructGPT) (Ouyang et al., 2022)

    The modern post-training and alignment blueprint
    that instruction-tuned models follow

12. Direct Preference Optimization (DPO) (Rafailov et al., 2023)

    A simpler and more stable alternative to PPO-based RLHF
    Preference alignment via the loss function

13. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)

    Demonstrated that reasoning can be elicited through prompting
    alone and laid the groundwork for later reasoning-focused training

14. ReAct: Reasoning and Acting (Yao et al., 2022 / ICLR 2023)

    The foundation of agentic systems
    Combines reasoning traces with tool use and environment interaction

15. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (Guo et al., 2025)

    The R1 paper. Proved that large-scale reinforcement learning without
    supervised data can induce self-verification and structured reasoning behavior

16. Qwen3 Technical Report (Yang et al., 2025)

    A modern architecture lightweight overview
    Introduced unified MoE with Thinking Mode and Non-Thinking
    Mode to dynamically trade off cost and reasoning depth

17. Outrageously Large Neural Networks: Sparsely-Gated Mixture of Experts (Shazeer et al., 2017)

    The modern MoE ignition point
    Conditional computation at scale

18. Switch Transformers (Fedus et al., 2021)

    Simplified MoE routing using single-expert activation
    Key to stabilizing trillion-parameter training

19. Mixtral of Experts (Mistral AI, 2024)

    Open-weight MoE that proved sparse models can match dense quality
    while running at small-model inference cost

20. Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints (Komatsuzaki et al., 2022 / ICLR 2023)

    Practical technique for converting dense checkpoints into MoE models
    Critical for compute reuse and iterative scaling

21. The Platonic Representation Hypothesis (Huh et al., 2024)

    Evidence that scaled models converge toward shared
    internal representations across modalities

22. Textbooks Are All You Need (Gunasekar et al., 2023)

    Demonstrated that high-quality synthetic data allows
    small models to outperform much larger ones

23. Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet (Templeton et al., 2024)

    The biggest leap in mechanistic interpretability
    Decomposes neural networks into millions of interpretable features

24. PaLM: Scaling Language Modeling with Pathways (Chowdhery et al., 2022)

    A masterclass in large-scale training
    orchestration across thousands of accelerators

25. GLaM: Generalist Language Model (Du et al., 2022)

    Validated MoE scaling economics with massive
    total parameters but small active parameter counts

26. The Smol Training Playbook (Hugging Face, 2025)

    Practical end-to-end handbook for efficiently training language models

Bonus Material

T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Raffel et al., 2019)
Toolformer (Schick et al., 2023)
GShard (Lepikhin et al., 2020)
Adaptive Mixtures of Local Experts (Jacobs et al., 1991)
Hierarchical Mixtures of Experts (Jordan and Jacobs, 1994)

If you deeply understand these fundamentals; Transformer core, scaling laws, FlashAttention, instruction tuning, R1-style reasoning, and MoE upcycling, you already understand LLMs better than most

Time to lock-in, good luck ;)