Fascinated by Living Systems | Clin Micro Bioinfo @ Karolinska | PhD https://xitter.cf/t.co/AaTZ9rG1sY | https://xitter.cf/t.co/9NntCT2IJ5 | Married, Dad, Sinner Saved by Grace

Stockholm
Joined March 2010
📜New post📜 1,5 years ago I pivoted into genomics from pharmaceutical bioinformatics. Pulling off such a pivot besides a full-time job is not easy though so I'm here sharing my experiences and recommendations for anyone else wanting to do the switch! livesys.se/posts/learning-ge…
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Security called me at 6:12 AM today. "Patch every PHP server immediately." I asked what happened. "Two critical vulnerabilities. Remote code execution. No authentication required." Cool. How many PHP servers do we have? According to inventory: 12 According to reality: Nobody knows. We found one running WordPress. Three running old internal tools. One under someone's desk. Two inside vendor appliances. A forgotten employee directory from 2016. And a server named: PHP-OLD-DO-NOT-TOUCH Naturally, that one runs payroll exports. Then we found a PHP 5.6 box. I asked who owned it. The documentation said: BRIAN. Brian left in 2019. We called Brian. He said: "Oh wow, that's still running?" Excellent. By 8 AM, leadership joined the incident call. CEO: "Can hackers actually get in?" Security: "Potentially yes." CEO: "Without a password?" Security: "Yes." CEO: "Then why do WE need passwords?" Not the lesson. Finance asked if we could wait until the weekend to patch because downtime affects revenue. Security explained that the alternative was letting strangers execute code on our servers. Finance asked for a cost comparison. At 9:17, someone suggested temporarily taking the vulnerable websites offline. Marketing refused. "Our campaign is live." So for the next hour, the company officially decided maintaining a landing page for 20% off webinar registration was more important than preventing remote server takeover. We finally patched everything we could find. Then our scanner discovered another PHP server. Hostname: TEMP-PHP-2014 Uptime: 4,512 days. Nobody knows what it does. Nobody owns it. Nobody can shut it down. But every time we block network access, the warehouse stops printing shipping labels. Security has classified it as critical infrastructure. It is running PHP 5.4. The vulnerability has existed longer than half our employees. The emergency patch took four hours. Finding out what PHP actually runs took eleven. The internet isn't held together with code. It's held together with servers everyone is afraid to turn off.
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Samuel Lampa - Also: smllmp.bsky.social retweeted
Japonya Başbakanı PHP'yi güncelleyin diye uyarı Tweeti attı 🤯
皆様に、今夜に対応して頂きたい大至急のお願いです。   多くのウェブサイトで使用されているPHPに関し、PHPのセキュリティチームが、重大な2件の脆弱性に関する注意喚起を行っています。   公開しているウェブサイトでPHPを使用しているか確認し、今夜、10月21日午後11時(日本時間)に公開される修正パッチについて、可及的速やかに適用できるよう準備をお願いします。   修正パッチを速やかに適用することが困難な場合は、例えばウェブサイト等を一時的に停止させることも、リスクを低減させるための有効策の一つと考えられます。   国家サイバー統括室のXアカウントでも関係する情報を発信していますので、そちらも参考にして下さい。 国家サイバー統括室(注意・警戒情報)(@cyber_forecast) / X
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Samuel Lampa - Also: smllmp.bsky.social retweeted
PHP2Shell attack traffic is being observed, Cloudflare says Cloudflare reports attacks targeting PHP2Shell and says attackers are changing payloads to try to bypass its protections. The flaws, CVE-2026-109618 and CVE-2026-109619, can enable unauthenticated remote code execution. Observed attack attempts do not establish how many websites have been compromised. * Update PHP to 8.5.12, 8.4.27, 8.3.36 or 8.2.35, or your vendor’s verified fixed package. * Keep WAF protections enabled, but do not use them as a substitute for patching. blog.cloudflare.com/php-vuln… #PHP #CyberSecurity
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Samuel Lampa - Also: smllmp.bsky.social retweeted
We cannot let AGI get locked inside 1 or 2 corporations. We are people with common sense who honestly want good things for humanity. And this is simply not acceptable. Imagine the web was closed. In 1993 CERN put the World Wide Web into the public domain, and anyone could build a site, a browser or a company on top of it. Now imagine 1 company owned it instead. You pay every month to open a page, and they decide which pages you are allowed to see. Thats exactly what is on the table with AI right now. We are at the start of a revolution that will change everything. And a handful of executives with a lot of money want to keep it in their hands. This is power. This is influence. This is control. We cant let it happen, no matter what. We HAVE to resist. Very few people understand where all of this is really going. The more people understand it, and the more people run AI at home, where nobody can limit it, ban them or bill them every month, the better our chances to win this. Download a model tonight. Thats how it starts.
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Samuel Lampa - Also: smllmp.bsky.social retweeted
🚨 BREAKING: DeepSeek just open sourced the engine that serves their own production traffic. Apache 2.0. Not a demo, not a paper. The actual stack behind DeepSeek V4.1 Flash. OpenAI has never shown a single line of the code that serves ChatGPT. Anthropic and Google, same. They sell you tokens and tell you nothing about how they make them. DeepSeek gives you the model AND the code that runs it in production. DeepInfer has their kernels, distributed inference, speculative decoding, batch-invariant inference and deterministic sampling. They also released deepseek-recipe, so your own deployment behaves like their API. And the inference code for future DeepSeek models will land in the same repo. Last year they wrote that their internal engine was too tied to their own infrastructure to release, so they would push the work into vLLM and SGLang instead. Now they released it anyway. It runs on NVIDIA Blackwell and on Huawei Ascend 950. The same week, the US government banned DeepSeek from federal systems. It wont run on my 3090s, its SM100 and Ascend only, and the KV cache system and prefill/decode split are not in this release. I dont care. This is how you make AI cheaper for everyone. Who the fuck is "Open"AI again?
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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Samuel Lampa - Also: smllmp.bsky.social retweeted
Using AI to write for you - start to finish - is really just you robbing yourself of one of life’s greatest joys: to think through a problem and figure out what you think about it, and in the process becoming passionate enough to take on the new challenge of solving it.
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interesting: Moondream 3.1 - 0.56s Qwen 3.8 Flash Next - 39s Gemini 3.8 Flash - 11s
Every model suggestion I get pushes the frontier on my images! Moondream 3.1 kicked Rex-Omni and LocateAnything out of the frontier by being better AND cheaper (plus easily local and quite fast). It's quite close to the top models... now I'm really curious how much we can improve it with some prompt optimization or program
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Samuel Lampa - Also: smllmp.bsky.social retweeted
Sufficiently advanced technology is indistinguishable from biology
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How many of y'all are still here?
How dead is Sci-Twitter? Can it be saved?
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No-brainer. If Google was a spy, LLMs literally see through you.
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It's Monday and get ready best week of Local AI Labs, let the model releases begin ! 👀 With so many models expected over the coming weeks, this could be an exciting week for the local AI community. Which one do you think we will get first?
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Blog got a new look (Using LLMs this time, yes). 👉 livingsystems.cc
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Great advice
You will fail. Try anyway. Failing costs you a few weeks, and not trying costs you everything you could have learned. 72 days ago I wrote the post below. I was 34 and I spent a few hours trying to understand XOR. 694 people saw it. I didnt stop. Since then I ranked all 12,096 experts in GLM-5.3 Flash. I found 1 wrong number in a released model and fixed it. I measured the refusal direction inside a quantized 27B and removed it with a 1.3 MB file. I ported a model to llama.cpp. I failed a lot too. On September 7 I wrote "I failed" about fitting GLM-5.3 Flash on my rig. I called a model useless and I was the problem. 2 days ago I got a Community Note for being wrong in public. Every one of those failures taught me more than the things that worked. Im still slow. I still ask the same question 10 times. Thats fine. Ask the model the 11th time, it will answer. If you are 34, or 44, or 54, and it feels too late: start today. You will be bad at it. Keep going anyway. It still hurts. Im still not stopping.
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Samuel Lampa - Also: smllmp.bsky.social retweeted
BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification. The most important article to read today is not one of OpenAI’s 700 AI-generated math papers. It is this other paper, making a deep and worrying point: A Lean-verified proof does not automatically validate the proof written in natural language, nor does it mean that the formal statement captures the intended theorem. During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely. It can hallucinate another theorem. Lean correctly verifies the result. But the proved result may no longer be what the paper claims. This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution. They identify at least two mismatches between the written intermediate results and their Lean counterparts: One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result. A pressure-flux estimate is obtained through a different bound, and proved through a different argument. It is not clear whether these mismatches invalidate the entire proof. But they raise an important issue. OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving. Epistemia at scale. * Paper in the first reply
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Samuel Lampa - Also: smllmp.bsky.social retweeted
This is great. Now I look forward to the specifics. The devil is in the details. This is 90% an experimental design problem & 10% an AI problem. If u get the former right, the latter will just work like magic.
🧵 Biohub, @ENERGY, @NIH, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation and new measurement technology - largest coordinated commitment to map the biology of the cell to power development of digital models of biology. bit.ly/3UhjoxD
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Is this a controversial take now?: Coding approaches that require 1000s of $:s per months in token spending are not worthy to be touted as "the only way forward" for all coding projects.
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Samuel Lampa - Also: smllmp.bsky.social retweeted
Exclusive: “The instant I saw the picture my mouth fell open and my pulse began to race.” So wrote James Watson in The Double Helix: A Personal Account of the Discovery of the Structure of DNA, his controversial account of how he and Francis Crick deciphered the structure of DNA in 1953. The image, now known as photograph 51, showed an elegant, symmetrical pattern of spots formed by x-rays diffracted from strands of DNA—the fingerprint of the molecule's helical structure. Rosalind Franklin, an x-ray crystallographer, had produced the image in the spring of 1952. But Watson's 1968 book, amid other slights and sexist language directed at Franklin, implied the flash of insight was his alone. Popular accounts still routinely suggest Franklin missed the significance of the photo. She never had a chance to challenge Watson's dismissive narrative; she died of cancer in 1958, a decade before his memoir was released and became a bestseller. But a new analysis of her notes on another x-ray image, which she made just days earlier using the same DNA sample, offers a firm rebuttal. Learn more: scim.ag/4hNj7KB @NewsfromScience
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Samuel Lampa - Also: smllmp.bsky.social retweeted
The plot thickens. "We show that the formalised Lean proof does not correspond to the [written paper] proof of blow-up of solutions to the Navier-Stokes equations." arxiv.org/abs/2610.08144
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BREAKING: The U.S. government, Google, Meta & Zuckerberg-backed @biohub are launching a $1.8B push to build the data layer for AI biology. The goal is to create the largest open, AI-ready biological dataset ever assembled, giving models the data needed to better predict how biology works & ultimately help treat disease. The coalition is massive: › Biohub committed $500M › DOE is investing $500M+ across measurement, modeling & compute › NIH is bringing $500M+ of federally funded datasets & repositories › Meta, Google DeepMind & @IsomorphicLabs are jointly investing another $300M This is effectively an infrastructure buildout for AI biology. Instead of just building bigger models, they’re attacking one of the biggest bottlenecks in biology AI today, generating standardized, high-quality biological data at massive scale & making it openly available to researchers. $1.8B committed to turning biology into something AI can increasingly model, predict & engineer.
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Samuel Lampa - Also: smllmp.bsky.social retweeted
“Use of AI tools, when done responsibly, can support not just the initial generation of a solution, but all of the valuable followup activities that traditionally flow out of that solution. But at the current time, the opposite is often occurring.” - Terence Tao
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