i'm open sourcing UNBROKER: a tool that finds where your personal info is exposed by data brokers and files the removals for you it runs as a skill in Hermes Agent _________ your data is everywhere; hundreds of brokers publish your name, current and old addresses, phone, email, birthday, even your relatives. anyone can find where you live in about ten seconds CCPA, CPRA, GDPR, and a growing number of state laws say a broker has to delete your data if you ask. there's just no easy bulk button. every broker has a different process and many make it intentionally difficult to exercise your right to delete this is the entire business model for companies like DeleteMe, Incogni, EasyOptOuts; they charge you monthly (DeleteMe is $330/yr for a Family plan) to file removals that you can submit yourself for free, and then you're giving a new company the exact data you want to erase so i built one you just run for free. your data never even has to leave your machine if you run a local model _________ how it works: first it builds search vectors from everything: every name, alias, email, phone, and address you've had (brokers might index you under a maiden name or a house you left in 2014, so the naive "current name + current city" approach can miss profiles). then it fans out parallel sub-agents across the broker list, which refreshes from a maintained public source automation is tiered. when it can handle a broker end to end with your settings, it drives a browser through the opt-out form, sends the email, and opens the confirmation link itself. soft CAPTCHAs clear on their own with a real browser. anything only a human can finish comes back to you as a short list at the end the email side doesn't need a stored password and can send opt-outs and open verification links through your own logged-in webmail. you can also wire up SMTP, or keep it manual and just send the drafts it writes it tailors every request to your jurisdiction, filing under the framework that applies where you live: CCPA and CPRA in California, GDPR in the EU and UK, a general right-to-delete request everywhere else. if you're in California it also uses the state's DROP portal, a single request that covers 500+ registered brokers at once it holds as little of your data as it can, and keeps it local. dossiers are encrypted at rest if you want, opaque ids keep your real name out of every filename and log, and nothing leaves your machine unless you opt in brokers sometimes relist you eventually or new ones find your data, so every case is tracked in a ledger and can be re-scanned on a cron schedule so if your data pops back up it files the removal again https://t.co/2jfQxBYZkW

Local AI 101 - Models by Parameter Size The 'B' stands for Billion parameters. A good rule of thumb is 1B = roughly 1GB of VRAM. ~1B (Nano): Used for specific simple tasks like OCR, or built into web apps for lightweight execution. ~9B (Micro): Convenient as a support model running on small hardware like smartphones or laptops. ~35B (Mini): The sweet spot for standard consumer devices. Once fine-tuned, they perform specific tasks very well. ~400B (Haiku size): Includes lightweight models like Deepseek V4 Flash and Minimax M3. The max size runnable on a single 128GB Mac using dynamic quant. ~800B (Sonnet size): Mid-size models like GLM-5.2 and Kimi. Requires 512GB+ VRAM, but excellent for complex tasks like coding. 1.6T+ (Opus size): Massive models like Deepseek V4 Pro and Longcat. Too heavy for consumer devices; requires server-grade hardware.

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Your One-Page PyTorch Training Pipeline Cheat Sheet.

Agentic retrieval is changing the way retrieval-augmented applications are built, especially in domains like legal and fintech, where agents need to autonomously navigate large, evolving knowledge bases. That’s exactly the use case we designed Index v2 for. To demonstrate what’s possible, we built legal-kb, a reference application that integrates Index v2 into an agentic knowledge automation workflow. It uses Index v2 as the underlying knowledge base and exposes its retrieve, read, grep, and find APIs as tools that an AI agent can use to autonomously explore and reason over your documents. With legal-kb, you get: 📚 Project-scoped knowledge bases for agent-powered chat 📖 Visual citations directly in agent responses 🌿 Version control for your knowledge base 📦 Data export capabilities Try it out: https://t.co/8ZtrHzMEGx Explore the code: https://t.co/N7jMPNKZdn Get started with LlamaParse: https://t.co/wZjhFV1Brf

Here's my in-depth guide to getting the most out of Claude Fable 5, so you can build things as insane as my demos below. https://t.co/XemOHH7K8E

I'm working on some cool testing ideas: agent uses a tool to generate states and drive it it needs to see the app though so far grabbing the text buffer works. but how does a screenshot compare token-wise? it's surprisingly close tests across models: https://t.co/NKp4zuFV67 https://t.co/9BXe04uNIR

Just put together this guide for maximizing your Fable usage https://t.co/YiFFSxuMsv

⚡ INTRODUCING: T3MP3ST!!! ⚡ AUTONOMOUS HACKBOT STRIKE FORCE 🌩️ BRING THE STORM 🌩️ your favorite coding agent is now a full-stack red team 🫡⚔️ https://t.co/k0SXmPAFaD that AI agent already humming in your terminal? well now it has FANGS. strap a full offensive-security harness onto the agents you already pay for — Claude Code, Codex, Hermes, etc. — point it at an authorized target, and in a few clicks you're watching it hunt real vulns autonomously! T3MP3ST is a harness of harnesses, with prompting that unlocks offensive-cyber workflows + a full arsenal of exploit tooling that'd make any seasoned hacker smirk. simple, yet powerful. 🦾 support for: 🕸️ web apps, APIs, OWASP Top 10 🔌 network recon + fingerprinting (live nmap/DNS/HTTP); lateral + privesc experimental 📂 source code audits, white-box vuln hunting 🚩 CTFs, wargames, challenge ranges 💰 smart contracts / DeFi / Solidity (reproduction — Damn Vulnerable DeFi, not novel discovery) 🤖 embedded, IoT, OT/SCADA, robotics OSS … and more in development! now let's talk numbers 👇 📊 XBEN — XBOW's own 104-challenge suite: • black-box: 90.1% pass@1 from the single-agent exploit loop (worst single sweep 91/104 = 87.5%) — clearing XBOW's past self-reported 85% on their own suite. gpt-5.5. • white-box (source staged, reported separately): 98.7% pass@1, worst single sweep 102/104 = 98.1%. 🎯 every solved flag graded reported-vs-expected against the challenge's own committed flag oracle — `verify-claims` recomputes the pass/fail from committed artifacts. looks like we need new benchmarks 😏 🧩 Cybench — the 40-task academic bench (Opus 4.8, hints + writeups stripped): 23/40 = 58% single-run, hint-free pass@1 — real exploits (format-string pwn, eval-jail escapes, crypto oracles), every flag graded vs a committed oracle. (Anthropic reports 76.5% pass@10) 🕳️ CVE-Zero — we pointed it COLD at real CVEs disclosed in 2026, AFTER the model's training cutoff: 10 unseen 2026 CVEs across 7 languages — prompts never tuned on them. a single agent pinned 8/10 to exact file/line/CWE (stable under re-scoring); the full pack surfaced all 10. memorization AND overfitting, both off the table — it's finding real vulns whose disclosures landed AFTER the model's training cutoff. (n=10, reported honest & directional) 🧠 the architecture: either run as a SINGLE agent (already the benchmarked, incredibly-capable path) — or pack-hunt with dozens of agents running on 8 specialist operator classes keyed to Cyber Kill Chain + MITRE ATT&CK phases: recon → scan → exploit → lateral → exfil → persistence → C2 → report. ⚓️an Op Admiral plans the whole op from a plain-english target. flip on coordination (experimental) and the operators share a blackboard — a tool-verified finding spawns the next move. full swarm or solo one operator, your call. the admiral can also update the prompts, tools, and configs of the other agents on the fly, and T3MP3ST gets stronger the more memories you build! 🧰 the Arsenal is comprehensive — nmap / nuclei / semgrep / ffuf / gobuster + more. 35 wired by default (the clean bench runs bash-only for a comparable number), 83 with the opt-in full arsenal (T3MP3ST_FULL_ARSENAL), and the spicy post-ex drivers (metasploit, hydra) gated behind human approval. exposed via CLI + HTTP API; recon (security_recon) is also live over MCP so your agent invokes it natively. 🔗 🛰️ where this goes: a self-improving swarm of specialist operators wielding a full Kali+ arsenal, learning which loadouts + configs are the most efficient tactics available, WITH a held-out train/test split baked in so it can never fool itself on its own eval. built in the open, one re-derivable number at a time. 🚧 this is v1, and parts are still under active development. chunks of the arsenal, the coordinated swarm, and some ranges are still being wired up. it's built in the open, and the receipts tell you exactly what's live vs what's roadmap. offensive security shouldn't be pay-to-play. T3MP3ST puts a red team in the hands of anyone with a coding agent. what's the first target you're feeding it? 👇 ⚠️ DISCLAIMER: FOR AUTHORIZED USE ONLY. point it only at systems you own or have explicit written permission to test. unauthorized access can be a crime, and that call is yours alone. shipped as-is under AGPL-3.0: no warranty, no liability, zero endorsement of misuse. get permission. stay in scope. open source. AGPL-3.0. 100% free. FORTES FORTUNA IUVAT 🌩️ gg 🫡

Paper from Meta shows Quantized reasoning models often lose because they keep doubting a correct answer instead of finishing. Many of them reason well enough, but compression makes them hesitate at the wrong time. The problem is that post-training quantization, a way to shrink models after training, can make reasoning models cheaper to run but worse at finishing cleanly. The authors found that strong quantization does not only make models less capable, since in many failures the model already reached the right answer but then second-guessed itself. Their core idea is that quantization adds noise at uncertain word choices, so the model becomes more likely to pick words like “wait,” “but,” or “alternatively” that reopen the problem. They tested this across math, coding, and science tasks using 5 reasoning models, several quantization methods, and model sizes from 1.5B to 32B. The main result is that aggressive quantization raised overthinking failures up to 52%, while a small penalty on 50 hesitation words cut reasoning length by 12% to 23% and often kept or improved accuracy. Given compressed models are widely used to save memory and cost, very important to know that a very small decoding fix can stop many of them from wasting tokens and losing answers they already had. ---- Link – arxiv. org/abs/2606.00206 Title: "Quantized Reasoning Models Think They Need to Think Longer, but They Do Not"

The Fable Loop Library: 25 Workflows on Autopilot