Korede Bishi @raymonbash

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HAPPY 4TH JULY AMERICA! 🦅🇺🇸

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@ylecun @kuchaev @andykonwinski I honestly don’t see how corporations don’t see this coming.

Exactly. I've been disseminating a similar message for years. The concentration of power in AI and the desire for control is by far the biggest danger of AI. It could lead to a few private companies and/or countries being in control of access to information, access to knowledge, and access to the tools of economic expansion. It's a kind of medieval obscurantism akin to the Ottoman empire banning the use of the printing press for 200 years, in part to keep control of the dogma, but also to protect the corporation of the calligraphers and scribes. Relevant historical bits about the Internet: 1. It took a deliberate decision by Al Gore and Bill Clinton to open up access of what was then ARPAnet to commercial entities and to the public, against the desires of the entrenched telecom industry. During a public roundtable about the "information superhighway" in 1993, the CEO of AT&T told Gore and Clinton "leave it to us". Gore said no. 2. In the late 1980s, setting up an Internet presence required buying proprietary hardware with proprietary OS and software stack from Sun Microsystems, HP, IBM, or Dell. By the 2000s, all of this was wiped out by commodity hardware, Linux, Apache, and an entirely free/open software stack. This migration to open platforms was the result of market forces. Infrastructure wants to be open. Foundation models are becoming an infrastructure and will inevitably become commoditized. Long term, the money is in the application layer, which is what I, Arthur Mensch, Alex Karp, and others have been saying.

I didn't realize this till recently, but the math section of the SAT is a de facto second English section. For whatever reason they can't give you problems that are mathematically difficult, so the only way to add difficulty is to make the problems hard to read.

@talkeyidah @ruffydfire Did you just say sympathy? Did you see how many of those bandits versus the police? On top of 50k salary that will come in 3 months after … lol. If na you, no hail them, make them go.

@arcane_bloom Commenting for reference.

Amazing blog by Alisa on her job search. Here are the resources she used to study ML/LLM stuffs: 1. Stanford's "Language Modelling from Scratch" course: cs336.stanford.edu/spring2025/ (To understand the breadth of the field and keep a coherent picture in the mind) 2. After getting the breadth, she deep dived into concepts ONE at a time using blogs, papers, chatting with ChatGPT and Claude and implementing things from scratch. 3. Implementing / debugging a transformer comes up so often in interviews. Turn it into muscle memory: github.com/stanford-cs336… 4. Ofc, do Leetcode🥲 leetcode.com/studyplan/leet… 5. Other Learning resources she shared: a. Self-Attention & Transformers: web.stanford.edu/class/cs224n/r… b. The Illustrated GPT-2: jalammar.github.io/illustrated-gp… c. Backpropagation cs231n.github.io/optimization-2/ d. Introduction to Policy Gradient for LMs ivison.id.au/2026/02/09/pol… e. Lightweight Guide to understanding GRPO and RL principles gitlostmurali.com/blog/grpo-intr… f. How to Scale Your Model jax-ml.github.io/scaling-book/

Alisa Liu @alisawuffles

4 weeks ago

I'm joining OpenAI next week!🥹 The job search turned out to be really challenging but also super rewarding, so I wrote a small blog to share what I learned along the way and hopefully make the process a little less mysterious for the next person. alisawuffles.github.io/blog/job-search

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I don't fear AI taking my job, like at all, but it's still very disorienting. I realized today that, because of AI, I don't feel like I know how to do things anymore. There are things I learned to do for myself that it now makes more sense to hand off to AI, but the only way to figure out which things those are is through trial and error. People exaggerate to a ridiculous degree, and the different models all have different pluses and minuses. It's all very confusing. It leads to this weird feeling of not necessarily knowing the best way to proceed with things I actually have years of experience with.

@OlawaleOlanir12 Ogbe pastor ni handicap lolz 😂

I’ve had a number of conversations with folks inside and outside government about the current situation with Anthropic, and here is what I believe to be true: — As we know, Anthropic publicly released its Mythos class models earlier this week under the commercial name Fable. — Fable is Mythos with guardrails. But if those guardrails fail, then you’ve exposed Mythos and its advanced cyber capabilities to people who shouldn’t have them. (Keep in mind that Anthropic itself widely promoted the idea that Mythos was a cyberweapon and needed to be regulated as such. They asked for government regulation of Mythos and championed the guardrails on Fable. If there is a vulnerability — big or small — it is Anthropic’s responsibility to patch.) — A highly credible trusted partner of both Anthropic and the USG who was testing Fable came forward with a jailbreak of those guardrails. The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused. — In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the USG believe; nor is that kind of minimizing language consistent with Anthropic’s brand as the AI safety company. It’s difficult to fathom how they could claim a jailbreak allowing operability of a cyber weapon could be defined as not “serious.” — In the past, Anthropic has always said that safety must be top priority and taken super seriously. In this case, Anthropic prioritized the continued offering of the consumer model over safety. — In reaction, the Admin issued the export control. The Admin did this reluctantly. It’s been very surprised that Anthropic hasn’t wanted to cooperate with a reasonable safety request (ie fixing the jailbreak issue). Anthropic’s reaction is very much at odds with their branding and ethos as a safe AI research community. — The Admin’s hope now is that Anthropic remediates the safety issue, the export control is lifted, and Fable goes back into general release. The Admin wants all of this to happen as soon as possible. It is frankly bewildered that Anthropic hasn’t wanted to comply with safety requests that it previously said were its highest priority. — Those trying to misdirect and tie this action to the prior DoW/Anthropic issues are wrong. The Admin values Anthropic’s technical capabilities and feels that this issue, while serious, should be easily resolved. The ball is in Anthropic’s court.

These engineering blogs have leveled up my tech skills more than any bootcamp, course, or conference. Here are the ones worth bookmarking:

@NigeriaStories The most annoying thing is they're hiding their faces. Why are you hiding these evil people's faces?

Top 26 Essential Papers for Mastering LLMs and Transformers Implement those and you’ve captured ~90% of the alpha behind modern LLMs. Everything else is garnish. 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!

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DSA prep can be demotivating if you are not doing the right kind of problems. I tell people to learn 10 patterns, then do 15–25 target questions per pattern until you can explain the invariant. 1. Two pointers: 125, 167, 15 2. Sliding window: 3, 76, 424 3. Prefix sum + hashmap: 560, 974, 525 4. Binary search: 33, 153, 875 5. Monotonic stack: 739, 84, 901 6. Heap / Top K: 215, 347, 295 7. Intervals: 56, 435, 57 8. BFS/DFS grid: 200, 994, 417 9. Trees: 102, 236, 543 10. DP starter pack: 70, 198, 322

📢 @CVPR 2026 Tutorial We’re turning our book 《The Principles of Diffusion Models》 into a live tutorial at #CVPR2026！ Part I · Continuous Diffusion — by me 👋 🎯 First-principles & intuitive view of diffusion 🎯 Fast generators & flow maps 🎯 Audio-visual content creation & protection — by @mittu1204 🔗 Link to our book: …nciples-of-diffusion-models.github.io Part II · Discrete Diffusion — by @ssahoo_ 🎯 The first tutorial crafted for discrete diffusion Co-organizers: @StefanoErmon @DrYangSong @gimdong58085414 Full schedule + details 👇

Chieh-Hsin (Jesse) Lai ✈️ ICML @JCJesseLai

9 months ago

Tired to go back to the original papers again and again? Our monograph: a systematic and fundamental recipe you can rely on! 📘 We’re excited to release 《The Principles of Diffusion Models》— with @DrYangSong, @gimdong58085414, @mittu1204, and @StefanoErmon. It traces the core

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Everything you need to know for coding interviews in one roadmap. Coding Interviews │ ├── 1. Data Structures │ ├── Arrays │ ├── Strings │ ├── Hashmaps │ ├── Linked Lists │ │ ├── Singly │ │ └── Doubly │ ├── Stacks │ ├── Queues │ ├── Deques │ ├── Heaps / Priority Queues │ ├── Trees │ │ ├── Binary Tree │ │ ├── Binary Search Tree │ │ ├── AVL Tree │ │ └── Red-Black Tree │ ├── Tries │ ├── Segment Trees │ └── Fenwick Trees │ ├── 2. Core Problem Patterns │ ├── Two Pointers │ ├── Sliding Window │ ├── Binary Search │ ├── Recursion │ ├── Backtracking │ ├── Greedy Algorithms │ ├── Divide & Conquer │ └── Sorting │ ├── Merge Sort │ ├── Quick Sort │ └── Heap Sort │ ├── 3. Dynamic Programming │ ├── Memoization │ └── Tabulation │ ├── 4. Graph Algorithms │ ├── BFS │ ├── DFS │ ├── Topological Sort │ ├── Dijkstra │ ├── Bellman-Ford │ ├── Floyd-Warshall │ ├── Kruskal │ ├── Prim │ └── Union Find │ ├── 5. Advanced Techniques │ ├── Bit Manipulation │ ├── XOR Tricks │ ├── Prefix Sums │ ├── Difference Arrays │ ├── Kadane’s Algorithm │ ├── KMP │ ├── Rabin-Karp │ ├── Z Algorithm │ └── Manacher’s Algorithm │ ├── 6. Object-Oriented Design │ ├── SOLID Principles │ └── Design Patterns │ ├── Singleton │ ├── Factory │ ├── Builder │ ├── Adapter │ ├── Observer │ ├── Strategy │ └── Visitor │ ├── 7. System Design │ ├── CAP Theorem │ ├── Load Balancing │ ├── Caching (LRU / LFU) │ ├── SQL vs NoSQL │ ├── Sharding │ ├── Replication │ ├── Message Queues │ │ ├── Kafka │ │ └── RabbitMQ │ ├── Microservices │ └── API Gateway │ ├── 8. Design Problems │ ├── URL Shortener │ ├── Twitter │ ├── WhatsApp │ ├── Netflix │ ├── Google Drive │ ├── Payment System │ ├── Notification System │ └── Live Streaming Platform │ └── 9. Interview Survival Skills ├── Thinking Out Loud ├── Recovering from Bugs ├── Explaining Tradeoffs └── Staying Calm Under Pressure Save this before your next interview.

Before writing your first lines of agent code, take the Agent Fundamentals course on Google Skills. Ideal for developers who want to understand AI systems through the lens of autonomous, goal-directed behavior (and not just text generation) → goo.gle/4rkShMK

If you want to improve your technical writing skills, this course is for you. It'll help you make your writing more concrete and practical while teaching you to communicate clearly. You'll learn about framing definitions, describing objects and processes, writing formal reports, and more. freecodecamp.org/news/improve-y…