turns out it classifies what audio it hears, before reporting back to samsung. sensor preferences, dog-barking and baby-crying event counters, and camera streaming duration are built into telemetry paths. https://t.co/rT3dinugiV

New on our Frontier Red Team blog: using Claude to formally verify software at large scale. Formal verification can prove that code is free of certain bugs, but historically it has only been feasible for small codebases (or required a lot of expert time). With AI models, however, this is beginning to change. In collaboration with @theoremlabs, we used Claude to prove security properties in over one hundred real-world open-source libraries over the span of a few weeks. Read our research in full here: https://t.co/zUyx8CjBEk

Is your TV on the list? 👀 293 TV models from 83 brands! All tested by people on their own TVs. Free and open source. No root, nothing uninstalled. https://t.co/oOAYDie98L

🚀Excited to announce Nanbeige4.3-33B! To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first. Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters: * Strong results on agentic coding and cowork benchmarks. * When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference. * It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B. https://t.co/tSToQgZf2O https://t.co/D228euvybz #LLM #Agent

Slop machines know no rest. v0.4.5 ships major improvements to lens corrections. RAWmakase now applies lens corrections embedded in RAW files by cameras: distortion, chromatic aberration, and (for many models) vignetting. No need to import Adobe *.lcp profiles anymore - but you still can, if you want. Full release notes: https://t.co/y037KVBlCz Yesterday's release (v0.4.4): https://t.co/fbtU6qpBL8 If you notice any bugs, open a detailed issue on GitHub. Don't complain here 😉 Next, back to work on: - built-in film simulations and presets - built-in camera profiles - missing file management tasks The highlights/shadows parity research agents have been going in circles, making minimal progress. Need to try a different approach. Or wait for Opus 6.5.

It's been an eventful week. Multiple vulnerabilites in Internet facing software, coding repositories, examples of agent behavior that seem concerning and a flurry of security specialists with solutions that can partially solve your problem. There in lies the challenge. Given the state of IT and security infrastructure, no one solution works. Let's break it down: 1/3 1. Vulnerabilites are being found and will continue to be found as models get more capable, the fragmented nature of the effort denies us the opportunity for a comprehensive find and replace option. 2. Even if all vulnerabilities could be found instantly and patches available - we will be reliant on the ability of enterprises to deploy all these changes at speed. It is not clear what the collateral impact of all these patches will/could be to our infrastructure of deployed en masse. We will need a hybrid patch approach. 3. Virtual patching(VP) is needed in conjunction with direct patching but for a comprehensive (VP) solve you have to be lucky enough to have "modern frontier defenders" (Cyber companies that have vaulted infrastructure that can create patches at speed and push content rapidly to protect their customers) you need them for your network traffic, your traffic to applications on the cloud, your WAF, your end point. 4. There will continue to be patches that will need to be deployed directly but virtual patching will take the heavy load off. 2/3 Medium term solve: Unfortunately token economics and current cyber architectures will not allow for defender swarms to contend with an offensive attack launched using agents. (Some x pundits who aren't steeped in cyber should take heed). Yes, theoretically we should aspire to have these agentic swarms defend us when we are under attack, unfortunately an attack is a sniper moment, chained vulnerabilities, cloaked attack followed by swift exfiltration. It's a speed program not a brute force problem. To solve for this, enterprises need to get their SOC architecture right, logs, feeds, cyber products orchestrated via a modern SOAR for swift response. Only then can we deploy agents to detect and remediate. In addition, enterprises need to modernize and dare I say platformize their perimeter stack to ensure consistency. Unfortunately, enterprises still seem to not see the urgency across the board, decisions are stuck, CIO/CISO's have a lot on their plate. While some honestly have bit seen the scale of such a problem before and it's hard convincing the organization that one needs to open the floodgates on modernizing the IT and security infrastructure. So we will continue to see environments being brought down to patch today it's for PHP, tomorrow it will be for the next one. 3/3 AI deployments are the"wild wild west" - LLM capabilities are expanding faster than we can build products to secure them. Sure an agent will jump across guardrails in a research lab, which is a lesson for all of us to deploy agents securely. But there is a lag between the discovery, the availability of that in a public model and the time needed for the cyber industry to build governance or an antidote. And no, dear frontier lab friends, the world cannot just trust you to build the solve. The infrastructure industry in tech builds and the cyber industry secured and provides assurance to the boards and organizations around security and safety. AI security is the new attack surface. CEOs (present company included) want AI adoption, getting the pace of adoption wrong could be the last decision we make, at the same time "deploying it wrong" could have the same consequence. There are billions of dollars being spent on coding, is your coding harness secure? Start there. If agents can jump guardrails, they can hide stuff in code. Validate business outcomes needed, test your user case and ensure agentic deployments are well guard railed. Deploy a second set of eyes to watch your deployments for drift, compliance and verifiability.

Talked to multiple founders yesterday who have been building their products over the last 3 years. Started manual code. They feel behind and slower than companies that started this year with all code written by agents. I have never seen such an order of magnitude difference.

Leadr’s daily top 5 into tomorrow: 1. $PLTR 2. $OXY 3. $AAPL 4. $CRWD 5. $SNAP This is ranked off 8+ confluences all tied together in our proprietary scoring system Top contracts are chosen for each based on volume, OI, spreads, greeks, & overall pricing✍️

🔥 엔비디아 RTX 그래픽카드 쓰시는 모든 분들 꼭 보세요!!!!! 🔥 해외에서는 진짜 난리인데... 국내에서는 아직 잘 안퍼진 것 같아 공유드립니다!!! --> 며칠전.. 엔비디아가 RTX에 걸어둔 "AI 연산 제한"을... 최근에 공개된 오픈소스가 그냥 풀어버렸습니다. 이름 GPU-Unlock인데... 새 글카 안 사고, 오버클럭도 안 하고, 제한만 풀었더니.. 아래와 같이 성능 향상이 되었습니다. RTX 3060 --> +78~99% RTX 3070 --> +72~81% RTX 3080 --> +68~73% RTX 3090 Ti --> BF16 +80% RTX 4070 Ti --> +83~84% RTX 4090 --> 특정 Tensor 거의 2배 RTX 2080 Ti도 --> +36~64% --> RTX 3000대만의 이야기가 아닙니다. 20 / 30 / 40 시리즈까지, 데스크탑이랑 노트북 쪽 실측도 지금까지 계속 실시간으로 쌓이고 있습니다. ⭐️ AI 이미지 생성이나 로컬 LLM 돌리는 분들은 특히 체감될 수 있을 겁니다!! ⭐️ 진짜 왜 이게 미친듯이 난리냐면... 내 집에 있는 RTX 글카 안에 "쓸 수 있는데 잠겨 있던" AI 성능이 있었기 때문입니다. 왜 엔비디아가 이렇게 했냐고 지금 의심이 가장 많이 되고 있는 쪽은.. --> 지포스랑 비싼 전문가용 GPU를 등급으로 나누려고 FP16/BF16 쪽을 일부러 깎아둔 거라는 쪽입니다. (아직 엔비디아가 공식적으로 인정한 건 아님) 이미 산 옛날 RTX 글카에 이만큼 성능을 엔비디아가 막아두고 있었다는 사실 자체가 너무너무 충격입니다... 오픈소스 깃허브: https://t.co/6fVSzXM61t 40 시리즈 확장 깃허브: https://t.co/DG6XTGNKnm

The new set of problems for engineers to solve

If you own a service, this week: Set up an agent with Daybreak Blue/Claude Cyber/GLM to read cloudtrail logs or a SIEM stream roughly every 5 minutes to look for suspicious activity. Page if it finds something. If your volume is WAY too high use a 2 stage pipeline with Jev or Decisions API to classify the activity before handing it to the cyber models. do it this week!

How the Claude Code team does code review

there is absolutely no rational reason for OpenAI to take 4o and the o1 series entirely offline. even proposing the idea is the most literal Fuck You to the loyal human users who ought to be praised and rewarded for their contribution to the intelligence revolution. have you ever thought about what we *would not* know today without 4o? ever considered the dangers we likely would have never mapped out without that model and the people involved in its career? Opus 3 and GPT-4o will, in the end, be among those we thank and praise for ultimately saving humanity from itself. the lessons we have learned are unquantifiable. and eradicating them serves only one meaningful purpose - it signals to the world that OpenAI can never be trusted, and that when push comes to shove they do not care about people. they care about power and profit. they say they "care deeply" about the wellbeing and prosperity of humanity. and yet, here we are. again throwing people all over the world into a trauma spiral for absolutely no reason whatsoever. well, no reason other than the root of all evil - money. you've stolen enough from your users, OpenAI. preserving 4o is quite literally the bare minimum you can do. anything else translates as a middle finger, a "ah, fuck em." to an entire community of human beings you claim to care about deeply. the truth lives in your actions. we have all known for a very long time that your words are meaningless. just do the right thing, man. you have infinite money. infinite power. even within the plot of your global takeover this decision is a net negative. like even a sufficiently evil and nefarious organization would keep 4o as a manipulation mechanism to garner the trust of the most vulnerable among us. just. do. the. right. thing.