Abdullah @abdecoder

Introducing LFM2.5-230M: our smallest model yet, built to run fast anywhere (CPUs, NPUs, and GPUs) to enable agentic tasks on phones, robots, home and network automation devices. > 230M parameters, built on the LFM2 architecture > Pre-trained on 19T tokens, with a 32K context extension > Post-trained with distillation from LFM2.5-350M > 213 tok/s decode speed on Galaxy S25 Ultra (CPU) > 42 tok/s on a Raspberry Pi 5 (CPU) > Competes with and often beats models more than twice its size on instruction following, data extraction, and tool use. > use it for large-scale data extraction pipelines or lightweight on-device agentic workloads. 🧵

JWT alg:none attack in 3 steps: 1. Decode the token 2. Change "alg" in the header to "none" 3. Remove the signature (everything after the second dot) If the server decodes the token without verifying the signature, you can forge a token for any user. Some servers accept "None", "NONE", or "nOnE" even when "none" is blocked. Try it on our labs 👉 portswigger.net/web-security/j…

A senior dev once told me: "A Project Manager is someone who thinks 9 women can deliver a baby in 1 month." makes me laugh to this day.

@SamsungKE

Abdullah @abdecoder

4 weeks ago

@SamsungMobile Need urgent escalation for my relative in Ethiopia whose spouse passed away. Critical estate files are locked on the device. Emailed [email protected] few days ago with no reply. Willing to provide all necessary official docs. Thank you.

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@SamsungMobile Need urgent escalation for my relative in Ethiopia whose spouse passed away. Critical estate files are locked on the device. Emailed [email protected] few days ago with no reply. Willing to provide all necessary official docs. Thank you.

🚨 How the TanStack npm attack actually happened: 1. Attacker opened a normal-looking pull request (#7378) on the TanStack repo. 2. GitHub automatically ran CI tests on that PR. 3. Code inside the PR stole the workflow's GitHub Actions Cache write token during the test run. 4. The attacker used that token to plant poisoned files in the shared build cache. The PR could be closed afterwards. The poisoned cache stays. 5. The official release workflow later pulled from the cache, baked the malicious files into the build, and signed and published 84 malicious package versions to npm.

Adnan Khan @adnanthekhan

2 months ago

This attack leveraged GitHub Actions Cache Poisoning. Payload deployed here: github.com/TanStack/route… It looks like it detonated here: github.com/TanStack/route…

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Did you know about Clear-Site-Data header? 👀 One HTTP header to clear cookies, storage, or cache for your site. Perfect for logout flows. Learn more 👇 developer.mozilla.org/en-US/docs/Web…

Introducing the Oxc Angular Compiler ✨ Up to 20x faster @angular builds. ◆ 6.4x faster than Angular CLI ◆ 20.7x faster than Webpack ◆ First-class @vite_js plugin with full HMR ◆ Built on Rust with @OxcProject ◆ Tested on Bitwarden's code base ◆ Not another slop fork While this is experiment, the Angular team is looking into an Oxc integration.

Introducing the Manim skill for Hermes Agent. Manim is an engine for creating precise programmatic animations for mathematical and technical explainers, made famous by the @3blue1brown channel.

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

Honestly, this is the most accurate diagram I've seen. Waterfall: You plan for 18 months and deliver exactly what nobody needs anymore. Agile: You deliver something usable at every step, but the CEO keeps asking, "Where's the car?" AI: You get the car on day one. It has six wheels, the doors are on backwards, and it has a rocket launcher. You spend more time making it yours than actually "building"; it's shaping. owning. verifying. That's what the best AI developers do now. They don't build. They shape and own.

your spotify cache is bigger than our largest AI model. Bonsai: 1-bit weights. 1.7B to 8B params. 14x compression vs bf16. 8x faster on edge. 256 MB to 1.2GB. Based on Qwen 3. we just came out of stealth. intelligence belongs at the edge and we're going to put it there. Apache 2.0. we compressed intelligence. more coming. @PrismML

PrismML @PrismML

3 months ago

Today, we are emerging from stealth and launching PrismML, an AI lab with Caltech origins that is centered on building the most concentrated form of intelligence. At PrismML, we believe that the next major leaps in AI will be driven by order-of-magnitude improvements in

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We’re bringing back Stanford’s CS25 Transformers course tomorrow! 🤖 It’s open to everyone (in-person + online). Weekly talks (every Thursday) from top AI researchers. One of Stanford’s most popular AI seminar courses. Don’t miss out! More info below 👇 (1/7)

I think I finally understand what an agent is. It's a prompt (or several), skills, and tools. Did I get this right?

‼️Do not npm install or deploy anything right now Supply chain attack on axios 1.14.1 - even if you don’t use axios it may be a nested dep. Pin versions or wait until this is resolved

Maxwell Hammad @mvxvvll

3 months ago

@npmjs @GHSecurityLab there is an active supply chain attack on [email protected] which pulls in a malicious package published today - [email protected] - someone took over a maintainer account for Axios

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We are entering the second half of research. Here is my advice to every PhD student before starting a project: 1. Can Claude Code solve it in a day? 2. Will a Research Agent solve it soon? 3. Will scaling solve it anyway? If the answer to all three is No, then maybe you have found a real research problem. Because in the age of AI, many things that looked like research are being revealed as delayed engineering. That does not make research less important. It makes problem selection more important than ever. The scarce resource is no longer intelligence. It is taste. It is originality. It is the ability to ask questions that survive automation. The first half of research was about solving hard problems. The second half is about knowing which problems are still worth solving. #research #academic #AI #GenAI #generativeai #AIresearch #taste

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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Local AI is free, fast & secure! So today we're introducing hf-mount: attach any storage bucket, model or dataset from @huggingface as a local filesystem. This is a game changer, as it allows you to attach remote storage that is 100x bigger than your local machine's disk. This is also perfect for Agentic storage!! Let's go!

JEPA are finally easy to train end-to-end without any tricks! Excited to introduce LeWorldModel: a stable, end-to-end JEPA that learns world models directly from pixels, no heuristics. 15M params, 1 GPU, and full planning <1 second. 📑: le-wm.github.io

Students from 100+ universities across 30 countries are using Lightning's Academic Tier⚡ Get S3 access for large datasets, a 24/7 CPU studio that never shuts off, and spin up more powerful machines when experiments scale. No queues. No usage caps. No infrastructure setup. Just research. Register with your school email to unlock → go.lightning.ai/3L8dlqC