Prem Sankar G @premsankar

Today I'm publishing a new essay, Policy on the AI Exponential. AI is progressing extremely fast—much faster than the policy process was built to handle. The essay lays out where I think the technology is now, and the action needed to close the gap: darioamodei.com/post/policy-on…

I valued SpaceX for its IPO a few weeks ago, with minimal information and a promise to revisit the valuation, when the prospectus was made public. The prospectus is public, the offering price has been set and my update is up and running. bit.ly/4ocgzbV

Software horror: litellm PyPI supply chain attack. Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords. LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm. Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks. Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages. Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.

Daniel Hnyk @hnykda

3 months ago

LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8. It has been compromised, it contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to remote server + self-replicate. link below

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The Next Two Years of Software Engineering addyosmani.com/blog/next-two-… < these are five crazy-relevant questions to ask of yourself and your team. @addyosmani then offers strong recommendations to consider.

.@satyanadella gave me and @dylan522p an exclusive tour of Fairwater 2, the most powerful AI datacenter in the world. We then chatted through Satya's vision for Microsoft in a world with AGI. 0:00:00 - Fairwater 2 0:04:15 - Business models for AGI 0:13:42 - Copilot 0:20:56 - Whose margins will expand most? 0:37:12 - MAI 0:48:42 - The hyperscale business 1:03:39 - In-house chip & OpenAI partnership 1:10:30 - The CAPEX explosion 1:16:01 - Will the world trust US companies to lead AI? Look up Dwarkesh Podcast on Youtube, Apple Podcasts or Spotify to tune in.

A Survey of Context Engineering 160+ pages covering the most important research around context engineering for LLMs. This is a must-read! Here are my notes:

Announcing NVIDIA Project DIGITS, a personal AI supercomputer that’s powered by the NVIDIA GB10 Superchip and based on #NVIDIAGraceBlackwell architecture. nvda.ws/4fLyTm3 Preconfigured with the NVIDIA AI software stack, developers, researchers, data scientists and students can prototype, fine-tune and inference large AI models on their desktop and deploy them to the data center or cloud. #CES2025

Nvidia unveils Hymba - an approach to efficient NLP - infoq.com/news/2025/01/n…

🙏✨ A Year-Long Journey—Now Complete ✨🙏 After 1 year of devotion, I’m humbled to share 5 hours and 44 minutes of Sivavakkiyar’s divine verses. Hearing “ஓடி ஓடி / ஓம் நமசிவாய” in temples feels truly blessed. 🌸 This is more than music—it’s a prayer, a tribute to Lord Shiva. I hope it brings you peace and divine energy. 🔱✨ “நமசிவாய அஞ்செழுத்தும் நிற்குமே நிலைகளே” 🎧 Listen & feel the divine: 👉 youtu.be/wXpottBJzvE @GhibranVaibodha @GoldDevaraj @ThinkDivineOff #OdiOdi #OmNamahShivayaOm

@GhibranVaibodha Amazing work! Thanks for your divine contributions🙏

DataLab: A Unified Platform for LLM-Powered Business Intelligence Introduces DataLab, a unified BI platform that integrates an LLM-based agent framework with an augmented computational notebook interface. DataLab achieves state-of-the-art performance on various BI tasks across popular research benchmarks. It also achieves up to a 58.58% increase in accuracy and a 61.65% reduction in token cost on enterprise-specific BI tasks.

Reverse Thinking Makes LLMs Stronger Reasoners Shows that training LLMs to learn "reverse thinking" helps to improve performance in commonsense, math, and logical reasoning tasks. It claims to outperform a standard fine-tuning method trained on 10x more forward reasoning.

@spirosx @resolveai Thanks for the awesome goodies!

Introducing the Model Context Protocol (MCP) An open standard we've been working on at Anthropic that solves a core challenge with LLM apps - connecting them to your data. No more building custom integrations for every data source. MCP provides one protocol to connect them all:

A Thread of photos and videos featuring humans next to enormous things to illustrate their size 1. Old salt mine in Romania

Elon Musk's full speech at Trump's rally in Butler, Pennsylvania today.

The condensed @pmarca guide to career planning:

SQL injection-like attack on LLMs with special tokens The decision by LLM tokenizers to parse special tokens in the input string (, <|endoftext|>, etc.), while convenient looking, leads to footguns at best and LLM security vulnerabilities at worst, equivalent to SQL injection attacks. !!! User input strings are untrusted data !!! In SQL injection you can pwn bad code with e.g. the DROP TABLE attack. In LLMs we'll get the same issue, where bad code (very easy to mess up with current Tokenizer APIs and their defaults) will parse input string's special token descriptors as actual special tokens, mess up the input representations and drive the LLM out of distribution of chat templates. Example with the current huggingface Llama 3 tokenizer defaults: Two unintuitive things are happening at the same time: 1. The <|begin_of_text|> token (128000) was added to the front of the sequence. 2. The <|end_of_text|> token (128001) was parsed out of our string and the special token was inserted. Our text (which could have come from a user) is now possibly messing with the token protocol and taking the LLM out of distribution with undefined outcomes. I recommend always tokenizing with two additional flags, disabling (1) with add_special_tokens=False and (2) with split_special_tokens=True, and adding the special tokens yourself in code. Both of these options are I think a bit confusingly named. For the chat model, I think you can also use the Chat Templates apply_chat_template. With this we get something that looks more correct, and we see that <|end_of_text|> is now treated as any other string sequence, and is broken up by the underlying BPE tokenizer as any other string would be: TLDR imo calls to encode/decode should never handle special tokens by parsing strings, I would deprecate this functionality entirely and forever. These should only be added explicitly and programmatically by separate code paths. In tiktoken, e.g. always use encode_ordinary. In huggingface, be safer with the flags above. At the very least, be aware of the issue and always visualize your tokens and test your code. I feel like this stuff is so subtle and poorly documented that I'd expect somewhere around 50% of the code out there to have bugs related to this issue right now. Even ChatGPT does something weird here. At best it just deletes the tokens, at worst this is confusing the LLM in an undefined way, I don't really know happens under the hood, but ChatGPT can't repeat the string "<|endoftext|>" back to me: Be careful out there.

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