What should I be using to orchestrate agents on my mac mini. I want to schedule agents for code review and run agents across projects. Use git worktrees. Manage stuff on my phone, but I don't really want to be sending all my data to Slack or some other data-mining company. Eventually I'd like to just use open models, but Claude and Codex are still my mainstay right now.
Reverse engineering is basically asking:
“What the hell is this thing actually doing?”
A binary, an Android app, firmware, network traffic, a weird file format, or a program whose interesting behavior only shows up at runtime.
There is an OSS tool for almost every layer.
01 Turn compiled binaries back into something humans can read
NationalSecurityAgency/ghidra — 81.9k★
Ghidra gives you disassembly, decompilation, control-flow graphs, scripting, and support for a huge range of executable formats and processors.
If all you have is the binary, this is usually where the archaeology starts.
02 Look at the bytes when abstractions stop helping
WerWolv/ImHex — 55k★
ImHex is a hex editor built specifically for reverse engineering, combining raw bytes with pattern definitions, analysis, visualization, and disassembly.
Sometimes the fastest way to understand a mystery format is still to stare directly at what is actually on disk.
03 Turn an Android APK back into readable code
skylot/jadx — 50.8k★
JADX decompiles Android APK/DEX files into Java-like source and gives you a GUI for navigating classes, references, resources, and manifests.
Probably the fastest route from “I have this APK” to “okay, now I understand how this app works.”
04 Watch what the program does while it is actually running
frida/frida — 22.2k★
Static code only tells you so much.
Frida lets you instrument running applications and inspect behavior dynamically, especially when the interesting logic only appears after launch or after runtime state changes.
05 Reverse engineer the conversation between apps and servers
mitmproxy/mitmproxy — 45.1k★
mitmproxy lets you inspect HTTP, HTTP/2, WebSocket, and TLS traffic in systems you are authorized to test.
When the code is confusing, watching what goes in and what comes back out can tell you much more.
06 Crack open firmware instead of treating it like one giant blob
ReFirmLabs/binwalk — 14.4k★
Binwalk identifies and extracts files, compressed data, filesystems, and other structures embedded inside firmware images.
Router firmware goes in. Suddenly you have configs, binaries, certificates, and actual components you can inspect.
07 Make the computer explore binaries for you
angr/angr — 9.1k★
angr is a Python framework for program analysis and symbolic execution.
Instead of manually following every branch through a binary, you can reason about which inputs reach particular states or code paths.
Reverse engineering starts becoming searchable.
08 Turn an unknown binary format into an executable specification
kaitai-io/kaitai_struct — 4.7k★
Kaitai Struct lets you describe binary file and network formats declaratively, then generates parsers for Python, Rust, Java, Go, C++, JavaScript, and more.
Once you understand the bytes, you do not have to reverse engineer them forever.
Write the format down and make it executable.
The workflow is almost embarrassingly clean:
unknown thing → inspect bytes → decompile → watch runtime → inspect traffic → extract internals → automate analysis → document the format
Reverse engineering used to feel like black magic.
OSS has slowly turned most of it into a toolbox.
Use it on software, devices, and systems you own or are authorized to analyze.
Claude Code out of the box is good.
Claude Code with the right OSS layer starts looking like a startup team, memory system, security analyst, researcher, QA engineer and personal assistant sitting behind one terminal.
Here are 10 repos worth knowing.
01 Make Claude stop shipping broken code
affaan-m/ECC — 210k+★
ECC adds planning, testing, repo analysis, security checks and persistent project context. The goal is simple: fewer fake “fixed” messages, fewer broken commits, less re-explaining your codebase every session.
02 Turn one Claude into an engineering org
garrytan/gstack — 115k+★
GStack gives you workflows that behave like a CEO, engineering manager, reviewer, QA lead and release engineer.
Idea → architecture → review → browser QA → ship.
03 Give the agent actual engineering discipline
mattpocock/skills — 145k+★
Matt Pocock’s skills force Claude to ask better questions, use TDD, debug systematically and keep architecture from rotting.
Basically: less vibe coding, more software engineering.
04 Stop rereading the whole repo every session
safishamsi/graphify — 70k+★
Graphify turns your code, docs, PDFs and folders into a queryable knowledge graph.
Instead of burning huge context windows rereading everything, the agent asks the graph what it needs.
05 Give Claude memory beyond the codebase
garrytan/gbrain — 24k+★
GBrain builds a searchable graph across people, companies, meetings, notes and decisions.
Think: “what do I need to know before tomorrow’s meeting?” and get an answer from everything you’ve accumulated.
06 Scan skills before giving them your machine
NVIDIA/SkillSpector — 5.5k+★
Agent skills can access files, networks and environment variables.
SkillSpector scans them for prompt injection, hidden instructions, suspicious code and excessive permissions before you install them.
07 Turn Claude into a security analyst
mukul975/Anthropic-Cybersecurity-Skills — 4k+★
817 structured cybersecurity skills covering malware analysis, incident response, threat hunting, cloud security and frameworks like MITRE ATT&CK and NIST.
Instead of asking Claude to “check security,” give it an actual procedure.
08 Turn the agent into a video studio
calesthio/OpenMontage — 18k+★
OpenMontage can research a topic, write the script, find or generate visuals, add narration and music, edit the timeline and render the final video.
One prompt → finished MP4.
09 Give it jobs too big for one agent
bytedance/deer-flow — 74k+★
DeerFlow breaks large tasks into sub-jobs, spins up specialist agents, runs code in sandboxes and produces finished artifacts like reports, dashboards and slide decks.
This is where “chatbot” starts turning into “delegation.” 10 Break the agent out of the terminal
openclaw/openclaw — 250k+★
OpenClaw puts the agent into WhatsApp, Telegram, Slack, Discord and other apps so it can handle email, calendars, research and tools while running locally in the background.
Not a coding copilot anymore.
More like an agent that lives on your machine.
Andrej Karpathy made an interesting point today: one way to make LLM outputs easier to understand is to ask them to write in ASD-STE100, the controlled English originally designed for aerospace maintenance manuals.
Not to make AI “sound human" but to make it easier to understand.
We spend a lot of time telling agents:
>be concise
sound professional
avoid AI slop
But different jobs need different writing systems.
A setup guide should be procedural. A research report should preserve evidence and uncertainty. An error message should tell you what happened and what to do next. A technical explanation should optimize for comprehension, not personality.
This is exactly why we built Agent Stylebooks.
Neeeophytee/agent-stylebooks packages 16 editorial systems as installable Agent Skills for Claude Code, Codex, Cursor, Hermes, Copilot, Gemini CLI and others.
Instead of:
>“write this better”
you can say:
setup guide → Google Developer Docs
product help → Microsoft
public guidance → GOV.UK
research report → NASA
risk disclosure → SEC Plain English
interface copy → Apple
The skill changes what comes first, how information is ordered, what ambiguity is unacceptable, and how the result should be checked.
A few other OSS projects fit around the same idea:
ASD-STE100 Writer Skill turns Simplified Technical English into an installable agent skill. Short sentences, controlled vocabulary, active constructions and fewer ambiguous instructions. Karpathy’s post is a good example of why this style is suddenly relevant outside aviation.
Vale has 6.2k+ stars and takes the next step: make writing rules testable. You can encode Google, Microsoft, Red Hat or your own editorial rules and run them locally, in editors, or in CI.
textlint has 3.2k+ stars and does something similar for natural language: treat prose more like code, with pluggable rules that can flag problems before the text ships.
retext-readability checks whether prose is actually appropriate for the intended reader using several established readability measures, while retext-simplify catches needlessly complicated phrases such as using “utilize” where “use” works.
write-good has 5.1k+ stars and catches things like unnecessary wording, weak modifiers, passive constructions and weasel words. Simple, deterministic checks are useful after the agent has done the semantic writing work.
The stack I find interesting is:
reader + task
↓
Agent Stylebook
↓
LLM writes
↓
Vale / textlint / retext
↓
clearer output
The point is not to make every model write in the same stripped-down style. It is to stop treating writing style as decoration.
For agents, writing style is part of the interface between the model and the person trying to understand its work.
Pick the writing system before the model picks one for you.