Michael Sandborn, PhD

Going to try booting omarchy on old (2013/2015) intel MBPs learn.omacom.io/2/the-omarchy-…

Is meta back

@sebkrier Cc @davidasinclair

This is wild. theaustralian.com.au/business/techn…

@dwarkesh_sp About the applications of modular forms to physics

What should I ask Terence Tao?

@tqbf Which soc

I bought an ARM server, because I need it for work. That is all.

x.com/i/grok/share/2… thank you @grok and @thedawgyg

@jsuarez Switch to comet

Is wandb supposed to take a full minute to upload a 200 kb artifact? If so will just drop support for cloud model saves because that is ridiculous

@levelsio 🐐

This week I decided to just permanently switch to running Claude Code on the server mostly on bypass permissions mode: c() { IS_SANDBOX=1 claude --dangerously-skip-permissions "$@"; } And for the first time in my life I think I've actually managed to outrun my todo list What happened is I simply blasted through my to do list of features I had to build and bugs I had to fix I've never shipped so fast and Claude Code almost made no mistakes, and when it did it they were tiny that weren't fatal (important because I'm mostly working on the server in production now) Before I was always known to ship fast (also because I always work alone) but while I shipped new things would always build up on my features/bug board (my users can submit them there) But this is the first week where I've been fast enough to outrun them The board is actually empty! As other people have written on here the real bottleneck is becoming myself and my creativity, not how fast I can ship. Because I think I ship faster now than I can come up with new ideas, or maybe my brain will adjust to this new speed (probably) Also I feel another limit is becoming my own mental context window, as in how many things, features, bugs, projects, I can keep in my mind in parallel while building on all of them. It's a lot and I haven't reached that limit yet but I feel I might be close I also noticed that you start going really fast the more you let it just go loose, before I was slow because I didn't trust it and I was scared it would destroy my code, now I just let it go. As @karpathy wrote, things feel like they've changed a lot around December last year when models became good enough to really code with and I feel the same When I see other friends code with Claude Code I often notice they're slow because they still check everything, which is good of course, but I feel the better way would be to create some tests and just let it run freely and see if it can pass those For me the tests are mostly just me checking out if the new feature on the site works or not, and in 99% cases it just does, and then I ask it to improve it further Because I run Claude Code on the server in production, I don't have to wait for deployment anymore (although that took only 3 seconds anyway before, that still adds up), now it's wait for it to be done coding, I refresh the site and I test it, that feedback loop is how I work and it's made me WAY faster Anyway here's what I did this week and the majority of these things were requested by people on the bug board, I'd say this is about 10x my normal output: 📸 Photo AI - Built new image viewer and mobile image viewer - Added batch remix, multi-photo import, filtering by model in gallery - Security overhaul: phased out insecure ?hash= login, migrated to session tokens - Fixed Google login loop, multi-model selection, talking scripts - Added custom audio upload for talking videos - Created dynamic model selector from server endpoint 🏡 Interior AI - Revived [ Add furniture ] feature (started 6 months ago, image models now good enough) - Added custom style upload for redesigns - Built own Gaussian Splat viewer for 3D - Made /remove_bg endpoint for furniture backgrounds - Migrated 3D walkthrough to new World Labs API - Added .skp file support, paint color masking, empty room button 🎒 Nomads - Launched weekly AI-generated newsletter from chat - Built profile edit modal, moved profile editing from /settings to profile page - Added TikTok/YouTube links, status bar, server-side API tracking - Added hundreds of new profile tags and traits - Fixed timezone filters, broken links, user avatars 🗺️ Hoodmaps - Revived write mode (before was only read for last few years because db was rekt) - Built heatmap mode using sentiment-scored tags (50K+ tags) - Fixed root cause: tags not entering DB due to wrong PRAGMA (should be WAL) - Added good/bad area detection with admin grid controls - Set up Claude Code Telegram bot for live changes - Enabled CF cache, fixed health check, fixed Brussels 📕 MAKE book - Built auto ePub/PDF generator cron worker - Added dynamic generation with personal customer watermarks - Added image compression for file size 💾 Pieter .com - Added Wikipedia text-only reader for Kindle - Exploring Windows 3.11 emulator using v86 (to replace Em-DOSBox) - Added product recommendations on homepage - Installed Wall Street Raider (1986) 👩‍💻 Remote OK - Installed Chatbase AI customer support bot - Added "report not remote" link on job posts 🏨 Hotelist (3 todos) - Fixed hotel URLs and city range bugs - Added iron amenity

@businessbarista Foremost examples at each level? Feels like a lot of them

If you're worried you're behind on AI transformation...you probably are. I have studied 100+ companies AI-readiness & every company can be mapped to one of these 9 levels: Level 1 (Awareness): - leadership recognizes AI as strategically important, but it has not yet changed how the company operates. Level 2 (Shadow AI): - employees independently use AI tools to improve their own productivity, but usage is informal, inconsistent, and largely ungoverned. Level 3 (Tool Standardization): - the company formally approves and deploys AI tools through IT and security, reducing risk but not yet transforming workflows. Level 4 (Workflow Integration): - ai becomes embedded within defined processes and operating workflows, improving consistency and measurable output. Level 5 (Business-Aware Systems): - ai systems are grounded in internal data and company-specific definitions, enabling context-aware analysis and decision support. Level 6 (Supervised Autonomy): - ai systems begin executing tasks within guardrails and human oversight, shifting people from doing the work to supervising it. Level 7 (Role-Based AI Teammates): - ai collaborators are aligned to functional roles, and roles are redesigned assuming AI participation in daily operations. Level 8 (Unified Intelligence Platform): - the organization operates from a shared intelligence layer that provides a consistent, company-wide source of truth. Level 9 (Adaptive Organization): - the company continuously adjusts decisions and operations through closed-loop AI feedback systems, with humans guiding strategic intent.

@HackingDave Thank you 🔥🔥🔥

Introducing a new tool called "SideChannel". A secure alternative to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?". github.com/hackingdave/si…

@thedawgyg noice, are you using afl++?

New vuln for the first time all weekend <3

who is benchmarking this?

the acceleration of labor compression

@wifiadvies @karpathy Base model 16 mem 256 ssd connected with thunderbolt 4. For reverse engineering agents

@msxndborn @karpathy What HW configs and why?

Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :) I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level. Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. I also love their approach to configurability - it's not done via config files it's done via skills! For example, /add-telegram instructs your AI agent how to modify the actual code to integrate Telegram. I haven't come across this yet and it slightly blew my mind earlier today as a new, AI-enabled approach to preventing config mess and if-then-else monsters. Basically - the implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration. Very cool. Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). There are also cloud-hosted alternatives but tbh I don't love these because it feels much harder to tinker with. In particular, local setup allows easy connection to home automation gadgets on the local network. And I don't know, there is something aesthetically pleasing about there being a physical device 'possessed' by a little ghost of a personal digital house elf. Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.

@clattner_llvm @AnthropicAI You are THE compiler expert

The Claude C Compiler is the first AI-generated compiler that builds complex C code, built by @AnthropicAI. Reactions ranged from dismissal as "AI nonsense" to "SW is over": both takes miss the point. As a compiler🐉 expert and experienced SW leader, I see a lot to learn: 👇

are you kidding me

@XerzesX @lauriewired I was also inspired by ghidramcp to create angrmcp github.com/sandbornm/angr… which I am still experimenting with

@XerzesX @lauriewired Thanks Ben! The PR was a mistake as Claude was pointing to the wrong remote; I had forked ghidramcp and then got carried away with the gdb setup. For multi-binary, 1 Cursor tab per binary/analysis + ghidramcp / angr mgmt has worked well for me

2 unseen RE CTF challenges solved by claude code + ghidra in ~3 min

@__tinygrad__ Juiced rpi5

We are going to ship our first mass affordable product this year. Tentative price: $199. Who can guess what it is?

is this true