Johannes Gilger

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Johannes Gilger

Johannes Gilger

@heipei

CEO & Founder @urlscanio. I like building things that spark joy.

Germany Katılım Haziran 2009
584 Takip Edilen2.1K Takipçiler
Johannes Gilger retweetledi
urlscan.io
urlscan.io@urlscanio·
New TI report 📷 Chenlun (“Outsider”) is a feature-rich phishing kit using modern web frameworks, verification flows, and anti-bot techniques. A step up in sophistication across Chinese Phishing-as-a-Service ecosystems. Full analysis + detections 📷 urlscan.io/pricing/urlsca…
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critter
critter@BecomingCritter·
how it started how it's going
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vas
vas@vasuman·
POV: you’re Garry Tan attempting to write Hello World in Python
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Georgi Gerganov
Georgi Gerganov@ggerganov·
llama.cpp adds MTP for the Qwen3.6 family This is a significant milestone for the local AI ecosystem. The performance jump with these changes is massive and elevates local inference on commodity hardware further. Special thanks to Aman Gupta for leading this development! github.com/ggml-org/llama…
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urlscan.io
urlscan.io@urlscanio·
New TI report on urlscan Pro 📷 Flyfish is a lightweight phishing kit built around simple but effective API endpoints. Despite its simplicity, it’s actively used for large-scale victim interaction and data capture. Detection patterns included 📷 urlscan.io/pricing/urlsca…
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Mitchell Hashimoto
Mitchell Hashimoto@mitchellh·
I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really). It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely. The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying. I worry.
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The Vertex Project
The Vertex Project@vtxproject·
.@invisig0th reflects on the work The Vertex Project has accomplished in the past decade (and where things are heading!) Read the full post here: hubs.ly/Q04gcVdp0
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urlscan.io
urlscan.io@urlscanio·
Last week we hosted a hands-on workshop at @pivot_con in Málaga. Participants learned how to hunt and cluster web-based phishing activity using our urlscan Pro platform. If you did not manage to get in, just send us a message and we'll give you a private tour of the platform!
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urlscan.io
urlscan.io@urlscanio·
New report: Darcula (“Magic Cat”) is one of the most active phishing frameworks we’re tracking. From API-driven infra to socket-based comms and fake shop deployments, this kit continues to evolve rapidly. Breakdown, detections: urlscan.io/blog/2026/05/1… Full report on urlscan Pro
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signüll
signüll@signulll·
networking as activity is mostly cope. e.g. the conference circuit, the warm intros, the moving to sf discussions or whatever, oh & the “grabbing coffee” economy.. all of this is overwhelmingly negative selection esp with vc (lol). the ppl worth knowing are usually too busy doing the thing to be farmable, & the ppl available to be networked w/ are available cuz they have literally nothing better going on. do the work, then publish it loudly enough that the right ppl can find you w/o you having to chase. one way broadcast > two way schmoozing. this is why x matters a ton now more than ever before.
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urlscan.io
urlscan.io@urlscanio·
New urlscan report 🚨 We’re kicking off our Chinese phishing series with a deep dive into the Sailor framework. A modular kit leveraging client-side storage for session tracking and victim management at scale. Detection included 👇 urlscan.io/blog/2026/05/0…
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Brian Graham
Brian Graham@iroasmas·
me as i read 40% of what claude wrote back and type in “continue”
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urlscan.io
urlscan.io@urlscanio·
New research drop 🚨 We're diving deep into Chinese-language phishing-as-a-service ecosystems powering large-scale global campaigns. From infrastructure to operations, this series uncovers how these platforms scale and evade detection. Starting May 4th: urlscan.io/blog/2026/04/2…
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Mario Zechner
Mario Zechner@badlogicgames·
this is probably the most important piece of software of the decade next to vllm and sglang. i'm not joking.
Georgi Gerganov@ggerganov

llama.cpp at 100k stars now that 90% of the code worldwide is being written by AI agents, I predict that within 3-6 months, 90% of all AI agents will be running locally with llama.cpp 😄 Jokes aside, I am going to use this small milestone as an opportunity to reflect a bit on the project and the state of AI from the perspective of local applications. There is a lot to say and discuss and yet it feels less and less important to try to make a point. Opinions about viability of local LLMs are strongly polarized, details are overlooked, the scientific approach is lacking. Arguments are predominantly based on vibes and hype waves. One thing is clear though - local LLMs are used more and more. I expect this trend to continue and likely 2026 will end up being one of the most important years for the local AI movement. I admit that I didn't expect the agentic era to come so quickly to the local LLM space. One year ago, the available models were too computationally expensive for doing long-context tasks. There wasn't an obvious path towards meaningful agentic applications. The memory and compute requirements were huge. Last summer, with the release of gpt-oss, things started to change. It was the first time we saw a glimpse of tool calling that actually works well within the resource constraints of our daily devices. Later in the year, even better models were released and by now, useful local agentic workflows are a reality. Comparing local vs hosted capabilities at a given moment of time is pointless. To try put things into perspective: - We don't need frontier intelligence to automate searches and sending emails - We don't need trillion parameter models to be able to summarize articles or technical documents - We don't need massive GPU data centers to control our home appliances or turn the lights off in the garage I believe that there is a certain level of intelligence we as humans can comprehend and meaningfully utilize to improve our working process. Beyond that level, access to more intelligence becomes unnecessary at best and counterproductive at worst. I also believe that that level of useful artificial intelligence is completely within reach locally and it has always been just a matter of implementing the right software stack to bring it to the end user. With llama.cpp, I am confident that we continue to be on the right track of building that software stack! The llama.cpp project is going stronger than ever. With more than 1500 contributors, the project keeps growing steadily. From technical point of view, I think that llama.cpp + ggml is the only solution that actually makes sense. That is, the software stack must run efficiently on every possible device, hardware and operating system. The technology is too important to be vendor-locked. It has to be developed in the open, by the community, together with the independent hardware vendors. This is the only right way to build something that will truly make a difference in the long run. I won't try to convince you about what is currently and will be possible with local AI. We will just continue to build as usual. I am confident that after the smoke clears and we look objectively at what we have built together, the benefits will be obvious to everyone. Big shoutout to all llama.cpp maintainers. I feel extremely lucky to be able to work together with so many talented contributors. Every day I learn something new and I feel there is so much more cool stuff that we are going to build. Also, I am really thankful that the project continues to have reliable partners to support it! Cheers!

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Awni Hannun
Awni Hannun@awnihannun·
Adopting Claude speak in my regular life, episode 1: Partner: Did you do the dishes tonight? Me: Yes they're done. Partner: Why are they still dirty? Me: You're right to push back. I didn't actually do them.
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the Rich
the Rich@Duderichy·
walking into a system design interview like “cdn, sharding, consistent hashing” where’s my job
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Sudo su
Sudo su@sudoingX·
okay this is absolutely insane. my undisputed king qwen 3.5-27b dense on single RTX 3090 just got replaced by the same team today. qwen drops 3.6-27b dense just now and the chart says it beats its predecessor on every single benchmark, beats qwen 3.5-397b-a17b moe which is 15x larger, and matches claude 4.5 opus on terminal-bench 2.0 at 59.3 flat, while beating claude on skillsbench, gpqa diamond, mmmu, and realworldqa. a 27 billion parameter open weight model matching a frontier proprietary model on agentic coding. let that sit for a second. pulling weights right now. testing on my 3090 desktop first because that is where the crown lives, then 5090 mobile for the same 24gb class speed story. same quant, same hermes agent, head to head against 3.5-27b dense on same hardware. if this chart holds even half the gain in real agentic runs it's a gamechanger for every builder sitting on a single consumer card. thank you @alibaba_qwen, this is what open source looks like when a team is serious. the corporate salesmen telling you local ai is not ready yet are getting lapped every week by teams that actually ship. new 27b dense is here. open is winning. the best model for a single 24gb gpu just changed in the middle of my benchmark. data drops soon anon
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Qwen@Alibaba_Qwen

🚀 Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power! Yes, 27B, and Qwen3.6-27B punches way above its weight. 👇 What's new: 🧠 Outstanding agentic coding — surpasses Qwen3.5-397B-A17B across all major coding benchmarks 💡 Strong reasoning across text & multimodal tasks 🔄 Supports thinking & non-thinking modes ✅ Apache 2.0 — fully open, fully yours Smaller model. Bigger results. Community's favorite. ❤️ We can't wait to see what you build with Qwen3.6-27B! 👀 🔗👇 Blog: qwen.ai/blog?id=qwen3.… Qwen Studio: chat.qwen.ai/?models=qwen3.… Github: github.com/QwenLM/Qwen3.6 Hugging Face: huggingface.co/Qwen/Qwen3.6-2… huggingface.co/Qwen/Qwen3.6-2… ModelScope: modelscope.cn/models/Qwen/Qw… modelscope.cn/models/Qwen/Qw…

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