kernullist

4.9K posts

kernullist

kernullist

@kernullist

Security and anti-cheat researcher focused on Windows internals. Advancing reliable detection and stronger system integrity. https://t.co/1hoZxnzccW

대한민국 Katılım Kasım 2015
3K Takip Edilen372 Takipçiler
Sabitlenmiş Tweet
kernullist
kernullist@kernullist·
Just shipped a WinDbg x64 extension that turns live disassembly into verified pseudocode via LLM — chunked multi-pass analysis, in-process HTTP, mock fallback, and a verification pass that cross-checks LLM output against original analysis facts. github.com/kernullist/win…
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kernullist
kernullist@kernullist·
Just shipped a big usability upgrade for my WinDbg decompiler extension. !decomp now shows progress during long LLM analyses, supports /verbose deep tracing, can be cancelled with Ctrl+Break, and no longer re-runs expensive analysis when you click DML action links. Less guessing. More decompiling. github.com/kernullist/win…
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kernullist
kernullist@kernullist·
Used up all my ElevenLabs credits making three short podcast clips. Swapped over to VoxCPM, and the quality blew me away—it's way better than I expected!
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MANTANI Nobutaka
MANTANI Nobutaka@nmantani·
I finally passed the OSCP+ on my fourth attempt! Fortunately I managed to get all the flags this time. 🏁🥳
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kernullist
kernullist@kernullist·
@v81093933 오오 감사합니다. 최근에 처음으로 TTS를 이용해서 한국어 팟캐스트를 생성해봤는데 딸깍하면 될 줄 알았는데, 의외로 숫자나 단어등을 읽을 때 은근 따로 처리 안 해주면 이상하게 읽는 경우가 많더라구요😑 KVAE로 열심히 공부해야겠어요 공유해주셔서 감사해요! ☺️
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22B
22B@v81093933·
한국어 성우 엔진 KVAE 초기버전을 공개배포합니다. 왜 만들었나? 한국어 음성 생성은 단순히 글자를 읽는 일이 아니라고 생각했습니다. 숫자, 영어 약어, 조사, 발음, 억양, 문장 호흡, 연기 의도까지 다뤄야 자연스럽습니다. 누가 쓰면 좋나? 한국어 TTS, 성우 더빙, 캐릭터 목소리, 쇼츠/영상 내레이션, 게임/웹툰 음성, 로컬 음성 학습 도구를 만들고 싶은 창작자와 개발자에게 맞습니다. 현재 할 수 있는 것: - 한국어 음성용 문장 정규화 - 성우 배역 프리셋 적용 - 녹음한 목소리를 캐릭터 톤으로 변환 - 음질/ASR/CER/WER 리뷰 - 개인 음성은 로컬에 보호 - 공개 AI 음성은 출처/라이선스/AI 음성 고지와 함께 관리 아직 부족합니다. 전문급 신경망 음성 백엔드, UI, 긴 학습 데이터 기반 품질 향상은 더 필요합니다. 그래서 오픈했습니다. 각자의 취향, 목소리, 말투, 창작 방식에 맞게 수정하고 보완해서 쓰면 좋겠습니다. github.com/sinmb79/korean…
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David Duvenaud
David Duvenaud@DavidDuvenaud·
Announcing Talkie: a new, open-weight historical LLM! We trained and finetuned a 13B model on a newly-curated dataset of only pre-1930 data. Try it below! with @AlecRad and @status_effects 🧵
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🤷 Nico Martin
🤷 Nico Martin@nic_o_martin·
I built an AI agent that lives inside your browser🎉 tabs, history, page content, tool calling. All local, no server powered by #Gemma4 But the interesting part isn't that it works. It's how you architect Transformers.js inside a Chrome extension to make it work. 🧵
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kernullist
kernullist@kernullist·
If I'm interested in a company, I need to check what recent AI service contracts they've signed. 😆
Nav Toor@heynavtoor

Researchers sent the same resume to an AI hiring tool twice. Same qualifications. Same experience. Same skills. One version was written by a real human. The other was rewritten by ChatGPT. The AI picked the ChatGPT version 97.6% of the time. A team from the University of Maryland, the National University of Singapore, and Ohio State just published the receipt. They took 2,245 real human-written resumes pulled from a professional resume site from before ChatGPT existed, so the human writing was actually human. Then they had seven of the most-used AI models in the world rewrite each one. GPT-4o. GPT-4o-mini. GPT-4-turbo. LLaMA 3.3-70B. Qwen 2.5-72B. DeepSeek-V3. Mistral-7B. Then they asked each AI to pick the better resume. Every model picked itself. GPT-4o hit 97.6%. LLaMA-3.3-70B hit 96.3%. Qwen-2.5-72B hit 95.9%. DeepSeek-V3 hit 95.5%. The real human almost never won. Then the researchers tried the obvious objection. Maybe the AI is just better at writing. So they had real humans grade the resumes for actual quality and ran the experiment again, controlling for it. The result was worse. Each AI kept picking itself even when human judges rated the human-written version as clearer, more coherent, and more effective. It gets worse. The AIs do not just prefer AI over humans. They prefer themselves over other AIs. DeepSeek-V3 picked its own resumes 69% more often than LLaMA's. GPT-4o picked its own 45% more often than LLaMA's. Each model can recognize and reward its own dialect. Then the researchers ran the simulation that ends careers. Same job. 24 occupations. Same qualifications. The only variable was whether the candidate used the same AI as the screening tool. Candidates using that AI were 23% to 60% more likely to be shortlisted. Worst gap was in sales, accounting, and finance. 99% of large companies now run AI on incoming resumes. Most of them use GPT-4o. The paper just proved GPT-4o picks GPT-4o 97.6% of the time. If you wrote your own cover letter this week, you did not lose to a better candidate. You lost to a worse candidate who paid OpenAI 20 dollars. Your qualifications do not matter if the AI prefers its own handwriting over yours.

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kernullist
kernullist@kernullist·
When using Codex for Windows, if Codex fails to run rg and falls back to searching with PowerShell, do the following. 1. Exit Codex. 2. Go to the following directory %localappdata%\Packages\OpenAI.Codex_2p2nqsd0c76g0\LocalCache\Local\OpenAI\Codex\bin 3. Copy the rg.exe file in that directory to a directory that is searched first in your PATH environment variable. (Alternatively, you can download rg.exe directly) 4. Restart Codex. This issue occurs when Codex creates rg.exe under the WindowsApps path and that path has higher search priority. Executables in the WindowsApps path are blocked from being launched directly from the shell.
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SandboxEscaper
SandboxEscaper@WeirdQuadratic·
Fyi I started blogging about windows secure channel a while back, you could probably get a couple of bug bounties out of certificate chain building related code, its a big attack surface: patreon.com/collection/205…
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Daniel San
Daniel San@dani_avila7·
Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console
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kernullist
kernullist@kernullist·
New Kernforge release is live. This update focuses on making Kernforge feel less like a stateless CLI and more like a coding agent that understands context, remembers the current situation, explains failures, proposes next steps, and keeps work moving. github.com/kernullist/ker…
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Peter Steinberger 🦞
Built clawsweeper, which runs 50 codex in parallel around the clock, scans issues/prs deep and closes what is already implemented or what makes no sense. Closed around 4000 issues today, a few thousand are in the pipeline. (rate limits are rough) github.com/openclaw/claws…
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kernullist
kernullist@kernullist·
First test upload from my automated Korean AI news podcast pipeline. The system collects fresh AI stories from trusted sources, ranks and filters them, turns them into a two-host podcast script, generates voice tracks, creates visual slides, assembles the final video, and publishes it automatically to YouTube. Still improving tone, pacing, pronunciation, and overall polish, but the end-to-end workflow is now live. Next step is expanding it beyond Korean into other languages too. Video: youtube.com/watch?v=sB0s0r…
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Yarden Shafir
Yarden Shafir@yarden_shafir·
I checked and it's been 2 years since my last blog post??? So anyway, here's a quick blog post about KDP pool - the latest KDP feature that will replace the secure pool in future Windows versions: windows-internals.com/goodbye-secure…
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kernullist
kernullist@kernullist·
Kernforge now has /fuzz-func, an AI-driven source-only fuzzing workflow that starts from a function or even just a file path. It derives attacker-style inputs, branch flips, counterexamples, and sink-reaching paths directly from sources. Built for the question that matters most : what exact input makes this code do the wrong thing? github.com/kernullist/ker…
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